Sculptured Surface Oriented Machining Error Synthesis Modeling for five-axis Machine Tool Accuracy Design Optimization

Customer-oriented design is very important for machine tool manufacturers to win competition in the market. Mechanical parts with complicated sculptured surface are widely utilized in mechanical systems such as automobiles, aircrafts and wind turbines, and they are often machined by five-axis machine tools with high precision requirements. However, traditional machine tool design has not accounted for the varied machining errors in producing complex sculptured surface, which leads to inferior performance. To address this challenge, a novel machining error synthesis model is proposed in this paper for accuracy optimization in designing general five-axis machine tools used for making various sculptured surfaces. The new synthesis model is constructed by integrating a generic machine tool volumetric error model and two new surface machining error production models, and it bridges between the surface machining profile error and the machine tool accuracy. The synthesis model is then applied as a constraint in machine tool accuracy design optimization. A cost-tolerance function is formulated to construct the objective function, and a heuristic algorithm is developed to implement the optimization. These modeling and optimization methods are validated by one case study

A Shaft Pillar Mining Subsidence Calculation Using Both Probability Integral Method and Numerical Simulation

In order to prolong the life cycle of the coal mine, Jinggezhuang (‘JGZ’) coal mine decided to excavate the shaft pillar. The first panel 0091 was designed near the pillar boundary as an experiment in shaft pillar mining. Both probability integral method (PIM) and FLAC3D were used to evaluate the influence on the shaft safety. PIM parameters were obtained from previous surface subsidence station. The rock property is based on the lab mechanical test. A simulated FLAC3D model containing shafts and a panel was built based on stratigraphic information. Surface subsidence results of PIM show that the 0091-panel excavation has no influence on the shafts. The simulated results show that the subsidence of the main shaft and air shaft is small and can be ignored, but it could cause the auxiliary shaft 220 mm horizontal displacement. So, the stress and displacement of the underground part of shaft were analyzed, it shows that the stress changes, subsidence and displacement are mainly located at the top part of the shafts. According to the stress and movement of the simulated shafts, 0091 was decided to be excavated and a surface monitor line was built and measured. In comparison of PIM, FLAC3D, and measured data, the PIM results fit the surface subsidence better. And the FLAC3D results have smaller maximum subsidence and greater influence area than measured. But FLAC3D can provide more details such as displacement, subsidence, stress and strain of both surface and underground. So, for a planned mining excavation, both methods should be used especially for the evaluation of deformation of underground constructions. In the future, with the development of the rock numerical computation technology, the numerical simulation method will be recommended first. The research shows compare of two methods of the coal mine subsidence calculation and provides a solution method for shaft pillar mining

Fish-T1K (Transcriptomes of 1,000 Fishes) Project: Large-Scale Transcriptome Data for Fish Evolution Studies

Ray-finned fishes (Actinopterygii) represent more than 50 % of extant vertebrates and are of great evolutionary, ecologic and economic significance, but they are relatively underrepresented in ‘omics studies. Increased availability of transcriptome data for these species will allow researchers to better understand changes in gene expression, and to carry out functional analyses. An international project known as the “Transcriptomes of 1,000 Fishes” (Fish-T1K) project has been established to generate RNA-seq transcriptome sequences for 1,000 diverse species of ray-finned fishes. The first phase of this project has produced transcriptomes from more than 180 ray-finned fishes, representing 142 species and covering 51 orders and 109 families. Here we provide an overview of the goals of this project and the work done so far

Independent optical excitation of distinct neural populations

Optogenetic tools enable examination of how specific cell types contribute to brain circuit functions. A long-standing question is whether it is possible to independently activate two distinct neural populations in mammalian brain tissue. Such a capability would enable the study of how different synapses or pathways interact to encode information in the brain. Here we describe two channelrhodopsins, Chronos and Chrimson, discovered through sequencing and physiological characterization of opsins from over 100 species of alga. Chrimson's excitation spectrum is red shifted by 45 nm relative to previous channelrhodopsins and can enable experiments in which red light is preferred. We show minimal visual system–mediated behavioral interference when using Chrimson in neurobehavioral studies in Drosophila melanogaster. Chronos has faster kinetics than previous channelrhodopsins yet is effectively more light sensitive. Together these two reagents enable two-color activation of neural spiking and downstream synaptic transmission in independent neural populations without detectable cross-talk in mouse brain slice.PostprintPeer reviewe

The Causal Learning of Retail Delinquency

This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.Comment: This paper was accepted and will be published in the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21

Spin waves in Dirac semimetal Ca0.6_{0.6}Sr0.4_{0.4}MnSb2_2 investigated with neutrons by the diffraction method

We report neutron diffraction measurements of Ca$_{0.6}$Sr$_{0.4}$MnSb$_2$, a low-carrier-density Dirac semimetal in which the antiferromagnetic Mn layers are interleaved with Sb layers that host Dirac fermions. We have discovered that we can detect a good quality inelastic spin wave signal from a small (m ~ 0.28 g) single crystal sample by the diffraction method, without energy analysis, using a neutron diffractometer with a position-sensitive area detector; the spin-waves appear as diffuse scattering that is shaped by energy-momentum conservation. By fitting this characteristic magnetic scattering to a spin-wave model, we refine all parameters of the model spin Hamiltonian, including the inter-plane interaction, through use of a three-dimensional measurement in reciprocal space. We also measure the temperature dependence of the spin waves, including the softening of the spin gap on approaching the Neel temperature, $T_N$. Not only do our results provide important new insights into an interplay of magnetism and Dirac electrons, they also establish a new, high-throughput approach to characterizing magnetic excitations on a modern diffractometer without direct energy analysis. Our work opens exciting new opportunities for the follow-up parametric and compositional studies on small, ~0.1 g crystals.Comment: 6 pages including 4 figures and bibliography plus 13-page supplementary with figures S1-S1

The joint role of the immune microenvironment and N7-methylguanosine for prognosis prediction and targeted therapy in acute myeloid leukemia

BackgroundThe tumor immune microenvironment (TIME) and N7-methylguanosine (m7G) modification play crucial roles in the progression of acute myeloid leukemia (AML). This study aims to establish an IME-related and m7G-related prognostic model for improved risk stratification and personalized treatment in AML.MethodsImmune score for the Cancer Genome Atlas acute myeloid leukemia (AML) patients were calculated using the ESTIMATE algorithm, followed by identification of immune score-associated differentially expressed genes Non-negative matrix factorization (NMF) clustering was performed to stratify AML subtypes based on immune microenvironment (immune microenvironment)-related DEGs and 29 m7G regulatory genes. Intersecting DEGs co-linked to IME and m7G features were analyzed through weighted gene co-expression network analysis Weighted correlation network analysis combined with univariate Cox, LASSO, and multivariate Cox regression to establish a prognostic signature. Biological pathway disparities between risk subgroups were analyzed via Gene Set Enrichment Analysis, Gene Set Variation Analysis, and ssGSEA. A clinical nomogram integrating the signature with prognostic indicators was developed. The expression of the 12 prognostic genes were tested and compared in AML and healthy donors. Drug sensitivity predictions for high-risk patients were generated using oncoPredict, supported by molecular docking simulations of ligand-target interactions and in vitro validation of candidate compounds in AML cell models.ResultsWe constructed an IMEm7G prognostic signature comprising 12 genes (MPZL3, TREML2, PTP4A3, AHCYL1, CBR1, REEP5, PPM1H, WDFY3, LAMC3, KCTD1, DDIT4, KBTBD8) that robustly stratified AML risk and predicted survival in multiple cohorts. The high- and low-risk subgroups exhibited divergent biological pathways, mutational landscapes, immune infiltration patterns, immune checkpoint expression, and HLA profiles. This signature further guided therapeutic selection, with dactolisib identified as a high-risk-specific candidate. The quantitative real-time PCR (qPCR) analysis demonstrated that in comparison with healthy donors, the expression of WDFY3, PPM1H, and REEP5 was significantly lower, while that of PTP4A3, AHCYL1, CBR1, MPZL3, TREML2, and KBTBD8 was higher in AML patients. In vitro CCK-8 assays validated dactolisib’s monotherapy efficacy and synergistic cytotoxicity when combined with doxorubicin in AML cells.ConclusionThe IMEm7G gene signature established in our study effectively optimized the risk classification and predicted immunotherapy response in AML. Moreover, dactolisib was identified and demonstrated cytostatic activity alone and synergistic effects with doxorubicin in AML cells

Research on Comprehensive Evaluation of Electricity Market Risk Based on Subjective and Objective Weighting

[Introduction] With the emphasis and promotion of the electricity market system construction by the government, the electricity market is constantly growing towards a deeper and more unified direction. In order to promote the electricity market construction, the influence factors of the electricity market risk and its evaluation remain to be studied further. [Method] Based on the consideration of the whole cycle of electricity market trading, this paper took pre-trade risk, during-trade risk, and post-trade riskas the entry points, integrated the existing risks in each stage of electricity market, and established the risk evaluation index system for electricity market. Based on the thought of subjective and objective weighting, analytic hierarchy process (AHP) and entropy weight method were used to assign weights to the index system respectively, and fuzzy comprehensive evaluation (FCE) was adopted to evaluate the comprehensive risk level of the electricity market. [Result] The rationality, comprehensiveness and validity of the proposed model are verified through the analysis of the calculation examples of different electricity markets. [Conclusion] The model eatablished in this paper can conduct a comprehensive risk evaluation for the electricity market, and provide a theoretical reference for the construction of the risk system of the electricity market and its future development direction

Enhanced Generative Recommendation via Content and Collaboration Integration

Generative recommendation has emerged as a promising paradigm aimed at augmenting recommender systems with recent advancements in generative artificial intelligence. This task has been formulated as a sequence-to-sequence generation process, wherein the input sequence encompasses data pertaining to the user's previously interacted items, and the output sequence denotes the generative identifier for the suggested item. However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals. In this paper, we introduce content-based collaborative generation for recommender systems, denoted as ColaRec. To capture collaborative signals, the generative item identifiers are derived from a pretrained collaborative filtering model, while the user is represented through the aggregation of interacted items' content. Subsequently, the aggregated textual description of items is fed into a language model to encapsulate content information. This integration enables ColaRec to amalgamate collaborative signals and content information within an end-to-end framework. Regarding the alignment, we propose an item indexing task to facilitate the mapping between the content-based semantic space and the interaction-based collaborative space. Additionally, a contrastive loss is introduced to ensure that items with similar collaborative GIDs possess comparable content representations, thereby enhancing alignment. To validate the efficacy of ColaRec, we conduct experiments on three benchmark datasets. Empirical results substantiate the superior performance of ColaRec