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A sensitive and simple method for detecting Cu2+ in the water source was proposed by using surface-enhanced Raman scattering spectroscopy (SERS) based on the Ag@SiO2/Au core–shell composite. The Ag@SiO2 SERS tag was synthesized by a simple approach, in which Ag nanoparticles were first embedded with Raman reporter PATP and next coated with a SiO2 shell. The Ag@SiO2 nanoparticles had strong stability even in a high-concentration salty solution, and there were no changes to their properties and appearance within one month. The Ag@SiO2/Au composite was fabricated through a controllable self-assemble process. L-cysteine was decorated on the surface of a functionalized Ag@SiO2/Au composite, as the amino and carboxyl groups of it can form coordinate covalent bond with Cu2+, which shows that the Ag@SiO2/Au composite labelled with L-cysteine has excellent performance for the detection of Cu2+ in aqueous media. In this study, the SERS detection of Cu2+ was carried out using Ag@SiO2 nanoparticles, and the limit of detection (LOD) as low as 0.1 mg/L was achieved. [ABSTRACT FROM AUTHOR]

Background: Spinal muscular atrophy (SMA) has a remarkable impact on function and participation. Subsequently, the caregivers of individuals with SMA are impacted as well. Providers and the SMA community should be aware of the presence of and likely expectations for the existence of caregiver burden. Methods: The Assessment of Caregiver Experience with Neuromuscular Disease (ACEND) quantifies caregivers' perceptions of function and quality of life pertaining to time, finance and emotion. Analyses were conducted among SMA types and ambulatory and ventilatory status. Participants with SMA had varying ranges of function and were on pharmaceutical treatment. Total ACEND score, longitudinal change in total ACEND score, total quality of life (QOL) score, change in total QOL score and subdomains for QOL, including time, emotion and finance, were all explored. Results: Overall, the ACEND demonstrated discriminant validity and some observed trends. Total ACEND scores improved for caregivers of those with SMA 2, remained stable longitudinally for caregivers of those with SMA 1 and 3 and were not influenced by ventilation status. The caregivers of individuals with SMA 1 had the lowest total quality of life (QOL) score, as did the caregivers of non-ambulatory individuals and those requiring assisted ventilation. Longitudinally, there were no changes in total QOL between caregivers of individuals with different SMA types or ambulatory or ventilation status. There were some differences in emotional needs, but no differences in financial impact between the caregivers of individuals with different types of SMA or ambulatory and ventilatory status. Conclusions: With this information enlightening the presence of caregiver burden and expected changes in burden with pharmaceutical treatment, providers, third party payors and the SMA community at large can better assist, equip and empower those providing the necessary assistance to enable the lives of those with SMA. [ABSTRACT FROM AUTHOR]

In the modern era, patients often resort to the internet for answers to their health-related concerns, and clinics face challenges to providing timely response to patient concerns. This has led to a need to investigate the capabilities of AI chatbots for ophthalmic diagnosis and triage. In this in silico study, 80 simulated patient complaints in ophthalmology with varying urgency levels and clinical descriptors were entered into both ChatGPT and Bard in a systematic 3-step submission process asking chatbots to triage, diagnose, and evaluate urgency. Three ophthalmologists graded chatbot responses. Chatbots were significantly better at ophthalmic triage than diagnosis (90.0% appropriate triage vs. 48.8% correct leading diagnosis; p < 0.001), and GPT-4 performed better than Bard for appropriate triage recommendations (96.3% vs. 83.8%; p = 0.008), grader satisfaction for patient use (81.3% vs. 55.0%; p < 0.001), and lower potential harm rates (6.3% vs. 20.0%; p = 0.010). More descriptors improved the accuracy of diagnosis for both GPT-4 and Bard. These results indicate that chatbots may not need to recognize the correct diagnosis to provide appropriate ophthalmic triage, and there is a potential utility of these tools in aiding patients or triage staff; however, they are not a replacement for professional ophthalmic evaluation or advice. [ABSTRACT FROM AUTHOR]

To enhance the safety management of steel-truss-bridge construction, an evaluation method based on the improved DEMATEL–ISM was proposed to analyze the risk factors involved in such construction. Decision Making Trial and Evaluation Laboratory (DEMATEL) is a method for systematic factor analysis that utilizes graph-theory and -matrix tools, allowing for the assessment of the existence and strength of relationships between elements by analyzing the logical and direct impact relationships among various elements in a system. The distinctive feature of Interpretative Structural Modeling (ISM) is the decomposing of complex systems into several subsystems (elements) and constructing the system into a multi-level hierarchical structural model through algebraic operations. Specifically, triangular fuzzy numbers are introduced initially to improve the direct influence matrix in the DEMATEL method, thereby reducing the subjectivity of expert evaluations. The degree of influence, influenced degree, centrality degree, and causality degree of each influencing factor are determined and ranked based on the above analysis. In response to the characteristics of top-push construction, 20 key factors were selected from four aspects: "human, material, environment, and management". The top five identified influencing factors are displacement during pushing (X10), safety-management qualification (X18), local buckling (X14), overturning of steel beams (X13), and collision with bridge piers during guide beam installation (X7). Subsequently, corresponding solutions were proposed for different influencing factors. The results of the study offer targeted measures to enhance the safety management of steel truss bridge construction and provide a reference for accident prevention. [ABSTRACT FROM AUTHOR]

This paper aims to solve the limitations of traditional gravity physical property inversion methods such as insufficient depth resolution and difficulties in parameter selection, by proposing an improved 3D gravity inversion method based on deep learning. The deep learning network model is established using the fully convolutional U-net network. To enhance the generalization ability of the sample set, the large-scale training set and test set are generated by the random walk, based on the forward theory. Founded on the traditional loss function's definition, this paper introduces an improvement incorporating a physical constraint to measure the degree of data fitting between the predicted and the real gravity data. This improvement significantly boosted the accuracy of the deep learning inversion method, as verified through both a single model and an intricate combination model. Finally, we applied this improved inversion method to the gravity data from the Gamburtsev Subglacial Mountains in the interior of East Antarctica, obtaining a comprehensive 3D crustal density structure. The results provide new evidence for the presence of a dense crustal root situated beneath the central Gamburtsev Province near the Gamburtsev Suture. [ABSTRACT FROM AUTHOR]

A transparent polyamide, poly(4,4′-aminocyclohexyl methylene dodecanedicarboxylamide) (PAPACM12), was studied and characterized by in situ wide-angle X-ray diffraction (WAXD) to establish the relationship between its crystallization behavior, crystalline form transition under external fields, and macroscopic properties. During the heating process, cold crystallization occurred and increased, and there was no form transition below the melting point. During the isothermal process, PAPACM12 exhibited the same crystalline structure as that during the heating process. The crystalline structure of PAPACM12 was attributed to α-form crystal, which is the stable form, according to the WAXD diffraction peaks of the conventional AABB-type polyamides. During stretching deformation, the crystal transition from α-form to γ-form and strain-induced crystallization were observed to contribute to the PAPACM12 with higher breaking strength and elongation. This study firstly determine the crystalline structure of transparent polyamides, and then the controlled strain-induced crystallization and transformation are demonstrated to be effective preparation methods for polyamides with high properties. [ABSTRACT FROM AUTHOR]

Tactile sensing is an important perception mode for robots, but the existing tactile technologies have multiple limitations. What kind of tactile information robots need, and how to use the information, remain open questions. We believe a soft sensor surface and high-resolution sensing of geometry should be important components of a competent tactile sensor. In this paper, we discuss the development of a vision-based optical tactile sensor, GelSight. Unlike the traditional tactile sensors which measure contact force, GelSight basically measures geometry, with very high spatial resolution. The sensor has a contact surface of soft elastomer, and it directly measures its deformation, both vertical and lateral, which corresponds to the exact object shape and the tension on the contact surface. The contact force, and slip can be inferred from the sensor's deformation as well. Particularly, we focus on the hardware and software that support GelSight's application on robot hands. This paper reviews the development of GelSight, with the emphasis in the sensing principle and sensor design. We introduce the design of the sensor's optical system, the algorithm for shape, force and slip measurement, and the hardware designs and fabrication of different sensor versions. We also show the experimental evaluation on the GelSight's performance on geometry and force measurement. With the high-resolution measurement of shape and contact force, the sensor has successfully assisted multiple robotic tasks, including material perception or recognition and in-hand localization for robot manipulation., Competing Interests: E. Adelson holds shares of GelSight Inc. (Waltham, MA, USA).