Desirable Properties Research Articles

Electrochemical Performance of Coal-Based Needle Coke Anode for Lithium Ion Batteries

Lithium-ion capacitors (LICs) with the capability of high energy and high power are considered to be attractive for advanced energy storage applications. However, the design and fabrication of suitable electrode materials with desirab-le properties by a facile approach using cost-effective precursors are still a great challenge. In this work, we have utilized needle cokes, an commercial carbon material with high carbon content and soft carbon structure, as a single carbon source for anode material. A lithium-ion battery fabricated using four kinds of needle cokes exhibits a maximum high energy density of 1357mAhg-1. Systematic characterization analysis demonstrates that unique characteristics of the green coke including large interlayer spacing, turbostratic and disordered microstructures that are composed of surface defects, nanopores or voids, and graphitic domains. That contribute synergistically to the outstanding performance of the needle coke-based LIC. More importantly, anode materials from a single source is an effective way for high value-added utilization of needle coke at the commercial level.

A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-Time Systems.

Deep learning inference that needs to largely take place on the "edge" is a highly computational and memory intensive workload, making it intractable for low-power, embedded platforms such as mobile nodes and remote security applications. To address this challenge, this article proposes a real-time, hybrid neuromorphic framework for object tracking and classification using event-based cameras that possess desirable properties such as low-power consumption (5-14 mW) and high dynamic range (120 dB). Nonetheless, unlike traditional approaches of using event-by-event processing, this work uses a mixed frame and event approach to get energy savings with high performance. Using a frame-based region proposal method based on the density of foreground events, a hardware-friendly object tracking scheme is implemented using the apparent object velocity while tackling occlusion scenarios. The frame-based object track input is converted back to spikes for TrueNorth (TN) classification via the energy-efficient deep network (EEDN) pipeline. Using originally collected datasets, we train the TN model on the hardware track outputs, instead of using ground truth object locations as commonly done, and demonstrate the ability of our system to handle practical surveillance scenarios. As an alternative tracker paradigm, we also propose a continuous-time tracker with C++ implementation where each event is processed individually, which better exploits the low latency and asynchronous nature of neuromorphic vision sensors. Subsequently, we extensively compare the proposed methodologies to state-of-the-art event-based and frame-based methods for object tracking and classification, and demonstrate the use case of our neuromorphic approach for real-time and embedded applications without sacrificing performance. Finally, we also showcase the efficacy of the proposed neuromorphic system to a standard RGB camera setup when simultaneously evaluated over several hours of traffic recordings.

On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem.

We contribute to the efficient approximation of the Pareto-set for the classical NP-hard multiobjective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyze the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal's single-objective MST algorithm to a weighted sum scalarization of a sub-graph. We prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator's practical suitability. Our results confirm that the sub-graph-based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front.

MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model.

Human motion modeling is important for many modern graphics applications, which typically require professional skills. In order to remove the skill barriers for laymen, recent motion generation methods can directly generate human motions conditioned on natural languages. However, it remains challenging to achieve diverse and fine-grained motion generation with various text inputs. To address this problem, we propose MotionDiffuse, one of the first diffusion model-based text-driven motion generation frameworks, which demonstrates several desired properties over existing methods. 1) Probabilistic Mapping. Instead of a deterministic language-motion mapping, MotionDiffuse generates motions through a series of denoising steps in which variations are injected. 2) Realistic Synthesis. MotionDiffuse excels at modeling complicated data distribution and generating vivid motion sequences. 3) Multi-Level Manipulation. MotionDiffuse responds to fine-grained instructions on body parts, and arbitrary-length motion synthesis with time-varied text prompts. Our experiments show MotionDiffuse outperforms existing SoTA methods by convincing margins on text-driven motion generation and action-conditioned motion generation. A qualitative analysis further demonstrates MotionDiffuse's controllability for comprehensive motion generation.

A Comprehensive Review of the Benzimidazole Scaffold as a Potential Nucleus for Anti-Ulcer Activity

Abstract: The benzimidazole scaffold is a promising nucleus for developing novel therapeutic agents for ulcer treatment. Its unique chemical structure provides desirable pharmacological properties, such as excellent bioavailability, metabolic stability, and low toxicity, making it an attractive candidate for ulcer treatment. Several benzimidazole derivatives have shown significant anti-ulcer activity in preclinical and clinical studies, acting through multiple pathways, including inhibition of gastric acid secretion, suppression of gastric inflammation, and promotion of mucosal protection. Some benzimidazole derivatives have also demonstrated anti-Helicobacter pylori activity, suggesting their potential for eradicating bacteria associated with ulcer formation. However, challenges such as poor solubility and limited selectivity remain. Various approaches, such as prodrug design and formulation optimization, have been explored to overcome these issues and improve the therapeutic profile of benzimidazole derivatives. Overall, the benzimidazole scaffold holds great promise as a nucleus for developing novel anti-ulcer agents. Further research and optimization efforts are needed to harness its full potential and translate it into effective treatments for ulcers. With continued advancements in medicinal chemistry and drug design, benzimidazole-based compounds may offer new therapeutic options for patients suffering from ulcers and related gastrointestinal disorders. Hence, this review highlights the knowledge about benzimidazole scaffold, the mechanism of ulcer formation, and various benzimidazole derivatives with anti-ulcer activity, which can be further studied in pre-clinical and clinical trials.

Effect of solution temperature on the microstructure and properties of ceramic coating on the surface of 2024 aluminum alloy

This study investigates the influence of the 2024 aluminum alloy was treated with solution before ceramic treatment on the microstructure and characteristics of ceramic coatings applied to 2024 aluminum alloy substrates. The microhardness, corrosion resistance, and microstructural properties of these ceramic coatings were assessed using a microhardness tester, an electrochemical workstation, and a scanning electron microscope. The findings indicate that pre-treatment involving solution treatment significantly enhances the hardness and corrosion resistance of 2024 aluminum alloy ceramic coatings. Notably, when the solution temperature was maintained at 460 °C, the most rapid decrease in current density was observed during the ceramization process, resulting in the attainment of the lowest final stable current density. This particular condition yielded ceramic coatings with optimal hardness and corrosion resistance, with hardness exhibiting a remarkable increase of 48.9 HV, self-corrosion potential rising by 0.207 V, and polarization resistance surging by 5310.7 Ω. Moreover, the surface of the ceramic coating displayed remarkable smoothness and was devoid of discernible defects such as cracks or looseness. In light of these findings, it can be concluded that the optimum solution temperature for achieving these desirable properties is 460 °C. This conclusion is derived from a comprehensive analysis encompassing both the morphology and corrosion resistance of ceramic coatings.

Identification of Selisistat Derivatives as SIRT1-3 Inhibitors by in Silico Virtual Screening

Sirtuins family are a Nicotinamide Adenine Dinucleotide (NAD+) dependent histone deacetylase enzyme. Sirtuins have been implicated in the pathogenesis of various diseases including cancer, neurological disorders and metabolic syndromes, hence sirtuins appointed as a promising therapeutic target for diseases, by regulating of its activity by small molecules modulators. The indole containing selisistat (EX-527) and its derivatives set as the most potent and selective SIRT1 inhibitors. Selisistat showed an effective sirtuin inhibition on various cancer cell line, and has reached the clinical trials for endometriosis and Huntington’s disease. In this study a set of selisistat derivatives were designed and virtually studied by means of molecular docking, ADMET, and molecular dynamics (MD) simulations. Two molecules were showed promising virtual binding affinity on the SIRT1-3 proteins. Compound 1 exhibits stronger in silico SIRT1 and SIRT2 affinities than EX-527, whereas compound 8 prefers SIRT3 binding. The ADMET analysis of the virtually active molecules demonstrated an acceptable drug-like profile and desirable pharmacokinetics properties. The MD simulation analysis revealed that compound 1 had significantly better alignment with SIRT1 and SIRT2 proteins than EX-527 according to Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) data, while compound 8 had a perfect alignment and fitting with SIRT3 protein than EX-527.

CHARACTERIZATION OF 3D PRINTED CONDUCTIVE FLEXIBLE MATERIALS FOR SOFT ROBOTIC APPLICATIONS

Soft robots composed of compliant and flexible materials can safely interact with humans and adapt to unstructured environments. However, integrating sensors, actuators, and control circuits into soft structures remains challenging. Additive manufacturing shows promise for fabricating soft robots with embedded electronics using conductive flexible composites. Nevertheless, there is still a limited understanding of the electromechanical behavior of 3D-printed conductive structures when subjected to the types of strains relevant to soft robotics applications. Optimized design requires characterizing the interplay between a soft component's changing shape and electrical properties during deformation. This study investigates the application of 3D printing technology to fabricate various geometries using a conductive, flexible material for soft robotic applications. The primary objective is to understand and characterize the behavior of differently shaped 3D-printed conductive materials under various mechanical stresses. Two distinct test setups are designed for conducting bending and tensile tests on the produced materials. Diverse geometries are printed using the conductive flexible material with desirable mechanical and electrical properties to employ tensile and bending tests. The experiments reveal a direct correlation between shape change and electrical resistance of the 3D printed materials, providing valuable insights into their adaptability for soft robotics. According to numerical results, honeycomb profiles are found to be the most linear and stable profile type. This research not only contributes to the field of flexible conductive materials but also lays the foundation for integrating these materials into future engineering designs, potentially enabling the development of highly responsive and adaptable devices for various industries.

Controllable Data Generation by Deep Learning: A Review

Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation. Recently, the advancement of deep learning has created the opportunity for expressive methods to learn the underlying representation and properties of data. Such capability provides new ways of determining the mutual relationship between the structural patterns and functional properties of the data and leveraging such relationships to generate structural data, given the desired properties. This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation. First, the article raises the potential challenges and provides preliminaries. Then the article formally defines controllable deep data generation, proposes a taxonomy on various techniques and summarizes the evaluation metrics in this specific domain. After that, the article introduces exciting applications of controllable deep data generation, experimentally analyzes and compares existing works. Finally, this article highlights the promising future directions of controllable deep data generation and identifies five potential challenges.

Endowing Metal-Organic Coordination Materials with Chiroptical Activity by a Chiral Anion Strategy.

Recently, chiral metal-organic coordination materials have emerged as promising candidates for a wide range of applications in chiroptoelectronics, chiral catalysis, and information encryption, etc. Notably, the chiroptical effect of coordination chromophores makes them appealing for applications such as photodetectors, OLEDs, 3D displays, and bioimaging. The direct synthesis of chiral coordination materials using chiral organic ligands or complexes with metal-centered chirality is very often tedious and costly. In the case of ionic coordination materials, the combination of chiral anions with cationic, achiral coordination compounds through noncovalent interactions may endow molecular materials with desirable chiroptical properties. The use of such a simple chiral strategy has been proven effective in inducing promising circular dichroism and/or circularly polarized luminescence signals. This concept article mainly delves into the latest advances in exploring the efficacy of such a chiral anion strategy for transforming achiral coordination materials into chromophores with superb photo- or electro-chiroptical properties. In particular, ionic small-molecular metal complexes, metal clusters, coordination supramolecular assemblies, and metal-organic frameworks containing chiral anions are discussed. A perspective on the future opportunities on the preparation of chiroptical materials with the chiral anion strategy is also presented.

Knowledge-enhanced Graph Topic Transformer for Explainable Biomedical Text Summarization.

Given the overwhelming and rapidly increasing volumes of the published biomedical literature, automatic biomedical text summarization has long been a highly important task. Recently, great advances in the performance of biomedical text summarization have been facilitated by pre-trained language models (PLMs) based on fine-tuning. However, existing summarization methods based on PLMs do not capture domain-specific knowledge. This can result in generated summaries with low coherence, including redundant sentences, or excluding important domain knowledge conveyed in the full-text document. Furthermore, the black-box nature of the transformers means that they lack explainability, i.e. it is not clear to users how and why the summary was generated. The domain-specific knowledge and explainability are crucial for the accuracy and transparency of biomedical text summarization methods. In this article, we aim to address these issues by proposing a novel domain knowledge-enhanced graph topic transformer (DORIS) for explainable biomedical text summarization. The model integrates the graph neural topic model and the domain-specific knowledge from the Unified Medical Language System (UMLS) into the transformer-based PLM, to improve the explainability and accuracy. Experimental results on four biomedical literature datasets show that our model outperforms existing state-of-the-art (SOTA) PLM-based summarization methods on biomedical extractive summarization. Furthermore, our use of graph neural topic modeling means that our model possesses the desirable property of being explainable, i.e. it is straightforward for users to understand how and why the model selects particular sentences for inclusion in the summary. The domain-specific knowledge helps our model to learn more coherent topics, to better explain the performance.

The Experimental Analysis Of The Effect Of Geotextile Reinforcement Of Cohesive Soil On The Settlement And Bearing Capacity

The properties of the soils in terms of engineering must be identified for the design of buildings and foundations to be constructed in areas where soft clay or loose sand ground conditions predominate. The engineering properties of different soils diversify in a broad range based on the sort of the ground in addition to the terrain conditions like the degree of compaction, water content, consolidation pressure, loading, and drainage conditions. Grounds may not perpetually hold the desired properties. Thanks to the structures to be built on the grounds, excessive settlements or collapses can happen because of inadequate ground bearing capacity. In order to reinforce the weaknesses of the soils, either the application of a deep foundation should be employed, or a proper improvement method should be applied to the foundation ground.In the study, the effect of the reinforcement of the geotextile, which has become widespread, on the clay ground was examined. Experiments have been conducted to identify the index properties, settlement, and sliding resistance parameters of soils on Süleyman Demirel University Soil Mechanics Laboratory on clay samples from the provinces of İzmir and İstanbul. Following the identification of the settlement and sliding values, the geotextile was added, and its effect on the settlement and bearing strength values was analyzed. Experiments were conducted by placing single-layered and double-layered geotextile in the samples with the values of optimum water content, the water content 10% more than the optimum value, and the water content 10% less than the optimum value.The results achieved after laboratory experiments are displayed with graphics and compared with each other.

Rheology of Superabsorbent Polymer-Modified and Basalt Fiber-Reinforced Cement Paste with Silica Fume: Response Surface Methodology

A composite's rheology can be changed by adding superabsorbent polymer (SAP) and basalt fibers and using silica fume. This study aimed to investigate the effects of these components on the viscosity and shear stress parameters of the paste. The proportions of the components were varied, with SAP content ranging from 0.01% to 0.03%, basalt fiber from 0% to 0.50%, silica fume (micro silica) at 15%, and water content from 0.40 to 0.50. Response surface methodology was used to optimize the mixture proportions, and the rheological properties of the resulting pastes were characterized using a rheometer. Results showed that the addition of SAP and basalt fiber had a significant impact on the rheological properties of the paste, with increasing amounts of both resulting in increased viscosity and shear stress. Overall, this study highlights the potential of SAP and basalt fiber in advances of the rheology of cement paste and provides insight into the optimal proportions of these components for achieving desired rheological properties. The findings of this study could be useful in developing high-performance concrete with enhanced rheological properties, which could have a wide range of applications in the construction industry. In addition, 0.50% BF, 0.01% SAP, and 0.445 water-to-cement were found as optimum proportions regarding the rheology of the cement paste.

Tuning a green carboxymethyl cellulose-based pre-tanning agent via peroxide oxidation for high chrome exhaustion in leather industry

The low chrome uptake by collagen in the conventional tanning process leads to the pollution of the wastewater. Due to environmental concerns, leather scientists are already searching for innovative ways to produce pre-tanning agents as a high exhaustion chrome tanning auxiliary. Herein, a novel kind of pre-tanning agent is engineered by converting carboxymethyl cellulose (CMC) to oxidized carboxymethyl cellulose (OCMC) via the hydrogen peroxide process. FT-IR and carboxyl content analysis demonstrated the increase in carboxyl content after oxidation. After that, the obtained OCMC was utilized as a pre-tanning agent, resulting in a high exhaustion of chrome (92.76 %) which is 27.76 % more than conventional chrome tanning (65 %), and the amount of chrome in wastewater reduced to 7.24 %. The hydrothermal stability of wet-blue increased by increasing the uptake of chrome (Ts = 118 °C). The obtained crust leather represented excellent mechanical properties (Tensile strength: 305.68 kg/cm2; tear strength: 50 kg/cm) and desirable organoleptic properties. The environmental analysis signifies a significant step towards a cleaner and sustainable tanning process (COD = 1600, BOD5 = 560 mg/L) compared to the conventional chrome tanning process. Consequently, the obtained results offer a green pre-tanning agent to meet the requirements of the sustainable development of the leather industry.

Node‐Reinforced Design and Selective Remanufacturing of Auxetic Lattice Structures

Auxetic structures have many potential applications due to their counterintuitive deformation behavior and desirable mechanical properties. However, auxetic structures have some drawbacks, such as bending or rotational deformation of ligaments, leading to relatively low energy absorption (EA) performance and stability. In this article, designing nodal reinforcement of the sinusoidal lattice is proposed, and silicone rubber is selectively 3D‐printed inside the structure. As a result, under quasi‐static loading, the nodes of the structure improve the EA performance by 214%; compared to resin reinforcement, silicone‐rubber gradient reinforcement not only continues to provide load‐bearing capacity to the structure but also improves EA by a significant 112%. The assignment of silicone‐rubber nodes is found to effectively prevent the lateral slip of the structure after ligament rupture, resulting in a more stable and controllable deformation of the sinusoidally auxetic structure. Under low‐velocity dynamic loading, silicone rubber's designed nodes improve the structure's deformation stability more significantly and enhance the EA performance, which increases with the strain rate. The design and fabrication techniques have the potential for the integrated fabrication of multi‐material lightweight structures with complex shapes, superior mechanical properties, and multifunctional attributes.

Abstract 607: Development of a novel, selective, and potent inhibitor of RON kinase

Abstract Purpose: Receptor d’origine nantais (RON) is a highly conserved transmembrane protein expressed at low levels in healthy adult tissues of epithelial origin and at various levels in immune cells such as macrophages. Aberrant activation of RON has been described in many solid tumors, and it contributes to tumorigenesis by modulating the immune tumor microenvironment, activating numerous oncogenic signaling pathways, and protecting tumor cells under stress. The overexpression of RON protein and the subsequent generation of oncogenic variants are mainly responsible for the persistent activation of downstream signaling pathways. In breast cancer, more than 80% of primary cancer samples are positive for RON expression, with overexpression reported in approximately 50% of cases. Importantly, tumoral RON expression correlates with increased breast cancer progression, metastasis, and poor prognosis independent of molecular subtype. We hypothesize that a small molecule inhibitor of RON that can target both full-length and truncated isoforms will promote anti-tumor immunity within the tumor microenvironment and block pro-metastatic signaling pathways. Herein, we report the development of a novel, orally bioavailable, selective, and potent inhibitor of RON kinase. Methods: With structure-guided and iterative medicinal chemistry approaches, we identified a novel chemical entity that potently inhibited RON kinase activity. Further optimization resulted in the identification of ZB-60 with improved potency, selectivity, and desirable drug-like properties. In vitro kinase assays were performed using ADP-Glo RON kinase assay kit (Promega). Changes in phosphorylation of RON was demonstrated by Western blotting. Plasma pharmacokinetics (PK) was conducted in mice to measure exposures and to determine the oral bioavailability of ZB-60. In vivo efficacy of ZB-60 was evaluated in syngeneic mouse models of breast (EMT6) and colorectal (CT26) cancer. Results: ZB-60 inhibited RON kinase activity with an IC50 of 56 nM and showed greater than 40-fold selectivity against MET and other closest members of the tyrosine kinase family. In ZB-60 treated breast cancer cells, there was a dose dependent reduction of RON phosphorylation and downstream signaling. PK analysis showed a high oral bioavailability with low clearance. In vivo evaluation of ZB-60 showed inhibition of tumor growth in EMT6 and CT26 models. Ongoing studies are focused on testing ZB-60 in combination with checkpoint inhibitors. The results from these studies along with ADME-TOX and pharmacokinetics will be presented. Conclusion: ZB-60 has target specific activity and exhibits favorable drug-like properties. Citation Format: Mohan Rao Kaadige, Trason Thode, Jaime Fornetti, Alexis Weston, Serina Ng, Tithi Ghosh-Halder, Taylor Bargenquast, Brian Durbin, Srinivas Kasibhatla, Raffaella Soldi, Alana Welm, Sunil Sharma. Development of a novel, selective, and potent inhibitor of RON kinase [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 607.

Abstract 7583: Discovery of a potent small molecule MALT1 protease inhibitor for the treatment of cancer

Abstract The paracaspase mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1), as the core component of CBM complex (CARD9/10/11/14-BCL10-MALT1), plays an essential role in activating the NF-κB pathway and regulating the cell proliferation, differentiation and immunity. Inhibiting of MALT1 is considered a promising therapeutic approach for the treatment of several non-Hodgkin B-cell lymphomas, chronic lymphocytic leukemia (CLL), solid tumors and autoimmune diseases. Herein, we report the discovery of a novel potent MALT1 allosteric inhibitor, BPI-530616, which strongly inhibited MALT1 protease activity with subnanomolar IC50, and low off-target activity on other proteases. BPI-530616 can inhibit the proliferation of B-cell diffuse large B-cell lymphoma (ABC-DLBCL) cell line, OCI-ly3, bearing CD79b and CARD11 mutations. The cell line is intrinsically resistant to covalent and non-covalent Bruton's Tyrosine Kinase (BTK) inhibitors. Meanwhile, BPI-530616 suppressed the cellular cytokine secretion (e.g.IL-10, IL-2) or MALT1 direct substrate cleavage (e.g. CYLD). In vivo, BPI-530616 dose-dependently suppressed tumor growth and inhibited the cleavage of CYLD in OCI-ly3 xenograft mouse model. To address the role of MALT1 inhibitor in the contest of immune-oncology, a CT26 syngeneic mouse model was set up. Oral administration of BPI-530616 exhibited a good antitumor effect comparable to that of the anti-PD1 antibody, with no observable adverse effects. Additionally, BPI-530616 displayed desirable drug-like properties, including low clearance and good oral bioavailability across multiple pre-clinical species. Taken together, our research has led to the discovery of a novel, potent, selective, and orally bioavailable small molecule MALT1 inhibitor, BPI-530616. It demonstrated anti-tumor efficacy in the DLBCL animal model and showed promising effects in an immune-oncology animal model. Citation Format: Yuzhen Zhou, Bang Fu, Jie Chen, Weidong Cai, Xiao Sun, Yinlong Li, Wei Ren, Qichao Shen, Guolong Du, Xiaojun Zhou, Zhengyao Zou, Haibo Chen, Xiaoyun Liu, Cheng Yang, Xiangyong Liu, Jing Guo, Hao Wu, Lieming Ding, Jiabing Wang, Hong Lan. Discovery of a potent small molecule MALT1 protease inhibitor for the treatment of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7583.

How Valid Are Trust Survey Measures? New Insights From Open-Ended Probing Data and Supervised Machine Learning

Trust is a foundational concept of contemporary sociological theory. Still, empirical research on trust relies on a relatively small set of measures. These are increasingly debated, potentially undermining large swathes of empirical evidence. Drawing on a combination of open-ended probing data, supervised machine learning, and a U.S. representative quota sample, our study compares the validity of standard measures of generalized social trust with more recent, situation-specific measures of trust. We find that survey measures that refer to “strangers” in their question wording best reflect the concept of generalized trust, also known as trust in unknown others. While situation-specific measures should have the desirable property of further reducing variation in associations, that is, producing more similar frames of reference across respondents, they also seem to increase associations with known others, which is undesirable. In addition, we explore to what extent trust survey questions may evoke negative associations. We find that there is indeed variation across measures, which calls for more research.

State of the Art Review for Titanium Fluorine Glasses and Glass Ceramics.

Titanium (Ti) and fluorine (F) have the potential to provide a variety of desirable physical, chemical, mechanical, and biological properties applicable to a broad range of indications. Consequently, Ti- and F-containing glasses and glass ceramics are currently under investigation for use in nuclear, optical, electrochemical, dental, and industrial fields. Accordingly, significant interest exists with respect to understanding the individual and interaction effects that these elements have on material structure and properties to support the accelerated design, development, and deployment of these materials. This review aims to serve as a foundational reference across multiple disciplines, highlighting the fundamental properties and versatility of Ti- and F-containing glasses and glass ceramics. By consolidating our current knowledge of these materials, this broad overview will identify areas in which we can further our understanding to support the a priori prediction and effective design of these systems. Finally, this paper will introduce the potential to improve material design by integrating experimentation, modelling, and computational approaches in a manner commensurate with the principles of the Materials Genome Initiative.

Optimizing nanoporous metallic actuators through multiscale calculations and machine learning

Nanoporous materials (NMs) immersed in electrolytes can achieve approximately 1 % deformation at a low operating voltage of about 1 V. The actuation renders them promising artificial muscles. The actuation performance significantly hinges on the structure and size of nanopores and ligaments in NMs. Consequently, designing an optimal configuration is imperative for excellent performance. The actuation mechanism of NMs involves the coupling of multiple fields at various length scales, posing a formidable challenge to conventional simulation and design approaches. To surmount this challenge, we have developed a computational framework capable of conducting concurrent and sequential multiscale calculations. By utilizing artificial neural network (ANN) surrogate models trained on data obtained through the finite element method (FEM), the framework achieves optimized values for both actuation strain and effective Young's modulus within a designated design space. The constitutive model, which establishes the relationship between surface stress and charges in FEM, is derived from the surface eigenstress model and symbolic regression. This involves utilizing data calculated through joint density functional theory. This framework not only ensures the desired properties but also demonstrates its potential for effectively addressing other multiscale optimization problems.