Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting

Multi-element fingerprinting of waters to evaluate connectivity among depressional wetlands

Establishing the connectivity among depressional wetlands is important for their proper management, conservation and restoration. In this study, the concentrations of 38 elements in surface water and porewater of depressional wetlands were investigated to determine chemical and hydrological connectivity of three hydrological types: recharge, flow-through, and discharge, in the Prairie Pothole Region of North America. Most element concentrations of porewater varied significantly by wetland hydrologic type (p \u3c 0.05), and increased along a recharge to discharge hydrologic gradient. Significant spatial variation of element concentrations in surface water was observed in discharge wetlands. Generally, higher element concentrations occurred in natural wetlands compared to wetlands with known disturbances (previous drainage and grazing). Electrical conductivity explained 42.3% and 30.5% of the variation of all element concentrations in porewater and surface water. Non-metric multidimensional scaling analysis showed that the similarity decreased from recharge to flowthrough to discharge wetland in each sampling site. Cluster analysis confirmed that element compositions in porewater of interconnected wetlands were more similar to each other than to those of wetlands located farther away. Porewater and surface water in a restored wetland showed similar multi-element characteristics to natural wetlands. In contrast, depressional wetlands connected by seeps along a deactivated drain-tile path and a grazed wetland showed distinctly different multi-element characteristics compared to other wetlands sampled. Our findings confirm that the multi-element fingerprinting method can be useful for assessing hydro-chemical connectivity across the landscape, and indicate that element concentrations are not only affected by land use, but also by hydrological characteristics

TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing

Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient's symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system -- syndrome differentiation (SD) -- and we introduce the first public large-scale dataset for SD, called TCM-SD. Our dataset contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZY-BERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.Comment: 10 main pages + 2 reference pages, to appear at CCL202

Scalable, Simulation-Guided Compliant Tactile Finger Design

Compliant grippers enable robots to work with humans in unstructured environments. In general, these grippers can improve with tactile sensing to estimate the state of objects around them to precisely manipulate objects. However, co-designing compliant structures with high-resolution tactile sensing is a challenging task. We propose a simulation framework for the end-to-end forward design of GelSight Fin Ray sensors. Our simulation framework consists of mechanical simulation using the finite element method (FEM) and optical simulation including physically based rendering (PBR). To simulate the fluorescent paint used in these GelSight Fin Rays, we propose an efficient method that can be directly integrated in PBR. Using the simulation framework, we investigate design choices available in the compliant grippers, namely gel pad shapes, illumination conditions, Fin Ray gripper sizes, and Fin Ray stiffness. This infrastructure enables faster design and prototype time frames of new Fin Ray sensors that have various sensing areas, ranging from 48 mm $\times$ \18 mm to 70 mm $\times$ 35 mm. Given the parameters we choose, we can thus optimize different Fin Ray designs and show their utility in grasping day-to-day objects.Comment: Yuxiang Ma, Arpit Agarwal, and Sandra Q. Liu contributed equally to this work. Project video: https://youtu.be/CnTUTA5cfMw . 7 pages, 11 figures, 2024 IEEE International Conference on Soft Robotics (RoboSoft

Track: Tracerouting in SDN networks with arbitrary network functions

The centralization of control plane in Software defined networking (SDN) creates a paramount challenge on troubleshooting the network as packets are ultimately forwarded by distributed data planes. Existing path tracing tools largely utilize packet tags to probe network paths among SDN-enabled switches. However, network functions (NFs) or middleboxes, whose presence is ubiquitous in today's networks, can drop packets or alter their tags - an action that can collapse the probing mechanism. In addition, sending probing packets through network functions could corrupt their internal states, risking of the correctness of servicing logic (e.g., incorrect load balancing decisions). In this paper, we present a novel troubleshooting tool, Track, for SDN-enabled network with arbitrary NFs. Track can discover the forwarding path including NFs taken by any packets, without changing the forwarding rules in switches and internal states of NFs. We have implemented Track on RYU controller. Our extensive experiment results show that Track can achieve 95.08% and 100% accuracy for discovering forwarding paths with and without NFs respectively, and can efficiently generate traces within 3 milliseconds per hop

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting

Positive correlation between the expression of hEag1 and HIF-1α in breast cancers: An observational study

Objectives: To explore the expression patterns of Eag1 (ether á go-go 1) and HIF-1α (hypoxia-inducible factor 1α) in a cohort of patients with breast cancer. Setting: Department of general surgery in an upper first-class hospital in Xi\u27an, China. Participants: A total of 112 female Han Chinese patients with a diagnosis of invasive ductal carcinoma were included. Patients with main internal diseases, such as cardiovascular, endocrine, gastroenterological, haematological, infectious diseases, etc, were excluded. Primary and secondary outcome measures: Expression profiles of Eag1 and HIF-1α. Results: Eag1 and HIF-1α were overexpressed in the tumour tissues compared with the pair-matched control tissues, p=0.002 and \u3c0.001, respectively. The expression of Eag1 and HIF-1α was negatively correlated with tumour size, p=0.032 and p=0.025, respectively, and lymph node status (p=0.040, p=0.032, respectively). The coexpression of Eag1 and HIF-1α was correlated with tumour size ( p=0.012), lymph node status (p=0.027) and tumour stage (p=0.036). HIF-1α has a strong correlation with hEag1 expression (κ=0.731, p\u3c0.001). Conclusions: HIF-1á expression has a strong correlation with hEag1 expression. We are the first to attempt to explore the correlation at the population level

A Lipoprotein Lipase–Promoting Agent, NO-1886, Improves Glucose and Lipid Metabolism in High Fat, High Sucrose–Fed New Zealand White Rabbits

The synthetic compound NO-1886 is a lipoprotein lipase activator that lowers plasma triglycerides and elevates high-density lipoprotein cholesterol (HDL-C). Recently, the authors found that NO-1886 also had an action of reducing plasma glucose in high-fat/high-sucrose diet–induced diabetic rabbits. In the current study, we investigated the effects of NO-1886 on insulin resistance and β-cell function in rabbits. Our results showed that high-fat/high-sucrose feeding increased plasma triglyceride, free fatty acid (FFA), and glucose levels and decreased HDL-C level. This diet also induced insulin resistance and impairment of acute insulin response to glucose loading. Supplementing 1% NO-1886 into the high-fat/high-sucrose diet resulted in decreased plasma triglyceride, FFA, and glucose levels and increased HDL-C level. The authors also found a clear increased glucose clearance and a protected acute insulin response to intravenous glucose loading by NO-1886 supplementation. These data suggest that NO-1886 suppresses the elevation of blood glucose in rabbits induced by feeding a high-fat/high-sucrose diet, probably through controlling lipid metabolism and improving insulin resistance

Absence of metallicity and bias-dependent resistivity in low-carrier-density EuCd2As2

EuCd2As2 was theoretically predicted to be a minimal model of Weyl semimetals with a single pair of Weyl points in the ferromagnet state. However, the heavily p-doped EuCd2As2 crystals in previous experiments prevent direct identification of the semimetal hypothesis. Here we present a comprehensive magneto-transport study of high-quality EuCd2As2 crystals with ultralow bulk carrier density (10^13 cm-3). In contrast to the general expectation of a Weyl semimetal phase, EuCd2As2 shows insulating behavior in both antiferromagnetic and ferromagnetic states as well as surface-dominated conduction from band bending. Moreover, the application of a dc bias current can dramatically modulate the resistance by over one order of magnitude, and induce a periodic resistance oscillation due to the geometric resonance. Such nonlinear transport results from the highly nonequilibrium state induced by electrical field near the band edge. Our results suggest an insulating phase in EuCd2As2 and put a strong constraint on the underlying mechanism of anomalous transport properties in this system.Comment: 13 pages, 4 figure