Design of a Carburizing Treatment of Steel Base Gear in the Materials Science Course

Diffusion PDE Application to Carburizing Treatment of Steel Base Gear An introductory materials-science course is required in the mechanical engineering curriculum of many universities. This article describes an example effort to incorporate programming, diffusion transfer, heat treatment process and mechanical-property determination as an integral part of the materials-science course instruction. This effort was undertaken in order to give students additional experience in Fick’s 1st and 2nd laws and in-depth understanding of physics and mathematics involved in diffusion analysis. We chose to focus on Fick’s second law because its applications are not restricted to the materials-science field [1]. As a matter of fact, the same form of parabolic partial differential equation also finds applications in financial derivatives pressure, heat transfer, and soil mechanics consolidation [2,3]. For instance, the diffusion coefficients all share the units of m2/s [2]. From the perspective of materials science, diffusion refers to an observable net flux of atoms or other species [1,4,5]. It depends upon the concentration gradient and temperature. It is vital for the carburization process (Carbon diffusion into steel), determining the proper hardness values not only for surface hardness of gear teeth but also for carbon penetration into specified depths. Students will be required to write a MATLAB program with input parameters of diffusion couple to calculate the atomic flux on the basis of diffusivity and concentration gradient. They are able to predict heat furnace design temperature and time required to heat the metal using error function values and one-dimensional diffusion equation with the specified boundary conditions. This paper focuses on the application of diffusion to material science engineering and provides an example of how diffusion may be adopted in an integrated instruction of materials science instructions. Keywords: Materials Science, Diffusion, Carburizing, PDE Solutions, MATLAB Programmin

Application of Computational Tools to Spaghetti-Based Truss Bridge Design

Application of Computational Tools to Spaghetti-Based Truss Design Statics and Strength of Materials are two foundational courses for Mechanical/Civil Engineering. In order to assist students in better understanding and applying concepts to a meaningful design task, SolidWorks and theoretical calculation were used for a spaghetti-bridge design contest with the constraints of given maximum weight and allowable support-material weight. As the first step of this iterative designing process, both extrude feature and structural member were introduced to model planar bridge trusses. Then SolidWorks’ Statics module was used to run FEA analysis of the structural performance in efforts to optimize the load-carrying capacity of the structure. To make simulation possible, a universal material-response testing apparatus was used to measure the key mechanical properties of the bridge material, namely spaghetti bundles, and add it to SolidWorks’ material database. The building stage started upon completion of design refinement, and the project culminated with performance prediction (as to the weakest spots of the structure) and testing. The theoretical calculation went down two paths—A full truss analysis was performed based on the method of joints, along with more thorough FEA analysis through coding, before comparing the internal forces, displacements, etc., with the simulation results. Through the holistic design process, the course turned out more engaging and students gained experience of solving a typical real-life engineering problem involving trade-off between economy and quality

Flat electronic band structure and anisotropic optical, mechanical, and thermoelectric properties of two-dimensional fullerene networks

Nanoclusters like fullerenes as the unit to build intriguing two-dimensional topological structures is of great challenge. Here we propose three bridged fullerene monolayers and comprehensively investigate the novel fullerene monolayer as synthesized experimentally Zheng et al.,[Nature 606, 507-510 (2022)] by state of the art first principles calculations. Our results show that alpha-C60-2D has a direct bandgap of 1.49 eV owing to a flat conduction band bottom close to the experimental value, the optical linear dichroism with strong absorption in long-wave ultraviolet region, a small anisotropic Youngs modulus, the large hole mobility, and the ultrahigh Seebeck coefficient at middle low temperatures. Moreover, Li ions are found to migrate easily along the X path in alpha-C60-2D. It is unveiled that the anisotropic optical, mechanical, electrical, and thermoelectric properties of alpha-C60-2D originate from the asymmetric bridging arrangements between C60 clusters. Our study promises potential applications of monolayer fullerene networks in diverse fields

Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning

Federated learning (FL) enables multiple participants to train a global machine learning model without sharing their private training data. Peer-to-peer (P2P) FL advances existing centralized FL paradigms by eliminating the server that aggregates local models from participants and then updates the global model. However, P2P FL is vulnerable to (i) honest-but-curious participants whose objective is to infer private training data of other participants, and (ii) Byzantine participants who can transmit arbitrarily manipulated local models to corrupt the learning process. P2P FL schemes that simultaneously guarantee Byzantine resilience and preserve privacy have been less studied. In this paper, we develop Brave, a protocol that ensures Byzantine Resilience And privacy-preserving property for P2P FL in the presence of both types of adversaries. We show that Brave preserves privacy by establishing that any honest-but-curious adversary cannot infer other participants' private data by observing their models. We further prove that Brave is Byzantine-resilient, which guarantees that all benign participants converge to an identical model that deviates from a global model trained without Byzantine adversaries by a bounded distance. We evaluate Brave against three state-of-the-art adversaries on a P2P FL for image classification tasks on benchmark datasets CIFAR10 and MNIST. Our results show that the global model learned with Brave in the presence of adversaries achieves comparable classification accuracy to a global model trained in the absence of any adversary

Stability and Failure Mechanism Analyses of the Zhenggang Landslide in Southwestern China

The Zhenggang landslide is an ancient complex landslide located at southeastern Tibetan Plateau, China. Due to intensive rainfalls in 2008 and heavy snowfalls in 2009, the Zhenggang landslide exhibited a high probability of reactivation once again. In this study, geological structure, matter features, and macrodeformations of the Zhenggang landslide (including Zone I and Zone II) were investigated for uncovering its formation mechanism and evolution tendency first, and then the stability and failure mechanism analyses of the Zhenggang landslide were conducted in detail by a combined limit equilibrium and finite element analysis method. Results of geological investigations indicate that the Zhenggang landslide has undergone sliding several times and is in a metastable state now. The distribution of the activity of the landslide is a retrogressive landslide in Zone I but an advancing landslide in Zone II. Such conclusions are further proved by the numerical stability and failure analyses

Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications

Large language models (LLMs) are increasingly deployed as the service backend for LLM-integrated applications such as code completion and AI-powered search. LLM-integrated applications serve as middleware to refine users' queries with domain-specific knowledge to better inform LLMs and enhance the responses. Despite numerous opportunities and benefits, LLM-integrated applications also introduce new attack surfaces. Understanding, minimizing, and eliminating these emerging attack surfaces is a new area of research. In this work, we consider a setup where the user and LLM interact via an LLM-integrated application in the middle. We focus on the communication rounds that begin with user's queries and end with LLM-integrated application returning responses to the queries, powered by LLMs at the service backend. For this query-response protocol, we identify potential vulnerabilities that can originate from the malicious application developer or from an outsider threat initiator that is able to control the database access, manipulate and poison data that are high-risk for the user. Successful exploits of the identified vulnerabilities result in the users receiving responses tailored to the intent of a threat initiator. We assess such threats against LLM-integrated applications empowered by OpenAI GPT-3.5 and GPT-4. Our empirical results show that the threats can effectively bypass the restrictions and moderation policies of OpenAI, resulting in users receiving responses that contain bias, toxic content, privacy risk, and disinformation. To mitigate those threats, we identify and define four key properties, namely integrity, source identification, attack detectability, and utility preservation, that need to be satisfied by a safe LLM-integrated application. Based on these properties, we develop a lightweight, threat-agnostic defense that mitigates both insider and outsider threats

Systematically characterizing dysfunctional long intergenic noncoding RNAs in multiple brain regions of major psychosis

Schizophrenia (SZ) and bipolar disorder (BD) are severe neuropsychiatric disorders with serious impact on patients, together termed “major psychosis”. Recently, long intergenic non-coding RNAs (lincRNAs) were reported to play important roles in mental diseases. However, little was known about their molecular mechanism in pathogenesis of SZ and BD. Here, we performed RNA sequencing on 82 postmortem brain tissues from three brain regions (orbitofrontal cortex (BA11), anterior cingulate cortex (BA24) and dorsolateral prefrontal cortex (BA9)) of patients with SZ and BD and control subjects, generating over one billion reads. We characterized lincRNA transcriptome in the three brain regions and identified 20 differentially expressed lincRNAs (DELincRNAs) in BA11 for BD, 34 and 1 in BA24 and BA9 for SZ, respectively. Our results showed that these DELincRNAs exhibited brain region-specific patterns. Applying weighted gene co-expression network analysis, we revealed that DELincRNAs together with other genes can function as modules to perform different functions in different brain regions, such as immune system development in BA24 and oligodendrocyte differentiation in BA9. Additionally, we found that DNA methylation alteration could partly explain the dysregulation of lincRNAs, some of which could function as enhancers in the pathogenesis of major psychosis. Together, we performed systematical characterization of dysfunctional lincRNAs in multiple brain regions of major psychosis, which provided a valuable resource to understand their roles in SZ and BD pathology and helped to discover novel biomarkers

Biaxial strain modulated electronic structures of layered two-dimensional MoSiGeN4 Rashba systems

The two-dimensional (2D) MA2Z4 family has received extensive attention in manipulating its electronic structure and achieving intriguing physical properties. However, engineering the electronic properties remains a challenge. Herein, based on first-principles calculations, we systematically investigate the effect of biaxial strains on the electronic structures of 2D Rashba MoSiGeN4 (MSGN), and further explore how the interlayer interactions affect the Rashba spin splitting in such strained layered MSGNs. After applying biaxial strains, the band gap decreases monotonically with increasing tensile strains but increases when the compressive strains are applied. An indirect-direct-indirect band gap transition is induced by applying a moderate compressive strain (< 5%) in the MSGNs. Due to the symmetry breaking and moderate spin-orbit coupling (SOC), the monolayer MSGN possess an isolated Rashba spin splitting (R) near the Fermi level, which could be effectively regulated to the Lifshitz transition (L) by biaxial strain. For instance, a L-R-L transformation of Fermi surface is presented in monolayer and a more complex and changeable L-R-L-R evolution is observed in bilayer and trilayer MSGNs as the biaxial strain vary from -8% to 12%, which actually depend on the appearance, variation, and vanish of the Mexican hat band in the absence of SOC under different strains. The contribution of Mo-dz2 orbital hybridized with N-pz orbital in the highest valence band plays a dominant role on the band evolution under biaxial strains, where the R-L evolution corresponds to the decreased Mo-dz2 orbital contribution. Our study highlights the biaxial strain controllable Rashba spin splitting, in particular the introduction and even the evolution of Lifshitz transition near Fermi surface, which makes the strained MSGNs as promising candidates for future applications in spintronic devices.Comment: 21 pages, 7 figures, supplementary informatio

Elastically-Constrained Meta-Learner for Federated Learning

Federated learning is an approach to collaboratively training machine learning models for multiple parties that prohibit data sharing. One of the challenges in federated learning is non-IID data between clients, as a single model can not fit the data distribution for all clients. Meta-learning, such as Per-FedAvg, is introduced to cope with the challenge. Meta-learning learns shared initial parameters for all clients. Each client employs gradient descent to adapt the initialization to local data distributions quickly to realize model personalization. However, due to non-convex loss function and randomness of sampling update, meta-learning approaches have unstable goals in local adaptation for the same client. This fluctuation in different adaptation directions hinders the convergence in meta-learning. To overcome this challenge, we use the historical local adapted model to restrict the direction of the inner loop and propose an elastic-constrained method. As a result, the current round inner loop keeps historical goals and adapts to better solutions. Experiments show our method boosts meta-learning convergence and improves personalization without additional calculation and communication. Our method achieved SOTA on all metrics in three public datasets.Comment: FL-IJCAI'2