HOW TO SELECT PAIRED COMPARISONS IN AHP OF INCOMPLETE INFORMATION-STRONGLY REGULAR GRAPH DESIGN

Abstract It is said that paired comparison is the essence of AHP. But if there are N alternatives and M criteria in a standard AHP, we must compare pairs for each criterion and wC2 pairs for the set of criteria, and the total number of them becomes up tp C-) X M + C2. So for rather large M and N it takes much cost and time to get paired comparison data. But even if we have not the whole set Sn of nC2 pairs (let such a case be called incomplete information case), we can estimate the weights based on comparison data in an appropriate subset of Sn by Harker method or Two-stage method [4, 51. We can use LLS (logarithmic least square) method in AHP analysis, by which we can analyze AHP for incomplete information case. So we can reduce the number of paired comparisons by using incomplete information case. The problem is how to select pairs to be compared in Sn, that is, a design to get data. We propose the strongly regular (SR) design based on strongly regular graphs, and by numerical simulation show that the errors of the estimations by SR designs are smaller than any random designs for almost all cases. Since SR graphs are rather difficult to be constructed, we generalize them to quasi-strongly regular (quasi-SR) graphs, and propose quasi-SR design based on quasi-SR graphs. By simulation we show that quasi-SR designs also give the same good results as the SR designs. 1

Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs

In the field of document understanding, significant advances have been made in the fine-tuning of Multimodal Large Language Models (MLLMs) with instruction-following data. Nevertheless, the potential of text-grounding capability within text-rich scenarios remains underexplored. In this paper, we present a text-grounding document understanding model, termed TGDoc, which addresses this deficiency by enhancing MLLMs with the ability to discern the spatial positioning of text within images. Empirical evidence suggests that text-grounding improves the model's interpretation of textual content, thereby elevating its proficiency in comprehending text-rich images. Specifically, we compile a dataset containing 99K PowerPoint presentations sourced from the internet. We formulate instruction tuning tasks including text detection, recognition, and spotting to facilitate the cohesive alignment between the visual encoder and large language model. Moreover, we curate a collection of text-rich images and prompt the text-only GPT-4 to generate 12K high-quality conversations, featuring textual locations within text-rich scenarios. By integrating text location data into the instructions, TGDoc is adept at discerning text locations during the visual question process. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple text-rich benchmarks, validating the effectiveness of our method

ABatRe-Sim: A Comprehensive Framework for Automated Battery Recycling Simulation

With the rapid surge in the number of on-road Electric Vehicles (EVs), the amount of spent lithium-ion (Li-ion) batteries is also expected to explosively grow. The spent battery packs contain valuable metal and materials that should be recovered, recycled, and reused. However, only less than 5% of the Li-ion batteries are currently recycled, due to a multitude of challenges in technology, logistics and regulation. Existing battery recycling is performed manually, which can pose a series of risks to the human operator as a consequence of remaining high voltage and chemical hazards. Therefore, there is a critical need to develop an automated battery recycling system. In this paper, we present ABatRe-sim, an open-source robotic battery recycling simulator, to facilitate the research and development in efficient and effective battery recycling au-omation. Specifically, we develop a detailed CAD model of the battery pack (with screws, wires, and battery modules), which is imported into Gazebo to enable robot-object interaction in the robot operating system (ROS) environment. It also allows the simulation of battery packs of various aging conditions. Furthermore, perception, planning, and control algorithms are developed to establish the benchmark to demonstrate the interface and realize the basic functionalities for further user customization. Discussions on the utilization and future extensions of the simulator are also presented

Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine

Ti₃C₂ MXene-based Schottky Photocathode for Enhanced Photoelectrochemical Sensing

Nanomaterials are vital to the realization of photoelectrochemical (PEC) sensing platfrom that provides the sensitive detection and quantification of low-abundance biological samples. Here, this work reports a Schottky junction-based BiOI/Ti₃C₂ heterostructure, used as a photocathode for PEC bioanalysis. Specially, we realize in situ growth of flower-like BiOI on 2D intrinsically negatively charged Ti₃C₂ MXene nanosheet that endows BiOI/Ti₃C₂ heterostructure with admirably combined merits, noting in particular the generation of built-in electric field and the decrease of contact resistance between BiOI and Ti₃C₂. Under the visible light irradiation, the BiOI/Ti₃C₂ heterostructure-modified PEC platform displays superior cathodic photocurrent signal, while PEC response cuts down with the presence of L-Cysteine (L-Cys) as a representative analyte owing to the metal-S bond formation. The “signal-off” PEC sensing strategy shows good performance in terms of sensitivity, limit of detection (LOD, 0.005 nM) and stability. This research reveals the great potentials of MXene-based heterostructure in the application field of PEC sensor establishment

The Protecting Effects and Mechanisms of Baicalin and Octreotide on Heart Injury in Rats with SAP

Purpose. To observe the protecting effects and mechanisms of Baicalin and Octreotide on heart injury in rats with severe acute pancreatitis (SAP). Methods. The SAP rat models were randomly divided into the model group, Baicalin-treated group, Octreotide treated group, and sham operation group. The contents of some inflammatory indexes in blood were determined. The rat mortality, pathological changes of heart, the changes of NF-κB, P-Selectin, Bax, Bcl-2, and Caspase-3 protein expression levels as well as apoptotic index were observed in all groups, respectively, at 3 hours, 6 hours, and 12 hours after operation. Results. The survival rate of model group was less than treated groups at 12 hours, difference was significant. The contents of some inflammatory indexes of the treated groups were lower than those of the model group to various degrees at different time points. The pathological myocardial changes under light microscope were milder in treated groups than in model group. The changes of NF-κB, P-Selectin, Bax, Bcl-2, and Caspase-3 protein expression levels in all groups were different. There was only a case of myocardial cell apoptosis in an Octreotide-treated group at 6 hours. Conclusion. Baicalin and Octreotide have protecting effects on heart injury of rats with SAP

Cuproptosis-related MTF1 inhibits kidney renal clear cell carcinoma progression by suppressing proliferation and regulating immune cell infiltration

Cuproptosis is a newly identified specific form of programmed cell death. Our study aimed to identify cuproptosis-related genes (CRGs) in patients with kidney renal clear cell carcinoma (KIRC) from the The Cancer Genome Atlas database and to evaluate CRG biological functions. Using lasso regression, we identified four KIRC prognosis-associated CRGs and constructed an associated prognostic risk signature. Kaplan-Meier curves showed that patients with high-risk scores had significantly lower survival times than patients with low-risk scores. Multivariate Cox analysis identified MTF1 and FDX1 as two independent overall survival CRGs. Moreover, qRT-PCR showed that MTF1 and FDX1 expression was downregulated in KIRC and knockdown of MTF1 and FDX1 significantly promoted KIRC cell proliferation and migration ability. In addition, the MTF1 level was positively correlated with immune cell infiltration and knockdown of MTF1 promoted tumor growth in vivo. We developed a signature of prognostic risk-associated CRGs that accurately predicted the prognostic status of KIRC patients. MTF1 and FDX1 were shown to be key CRGs. MTF1 acts as a tumor suppressor, and may be involved in the progression of KIRC by inhibiting proliferation and regulating immune cell infiltration

Large-Scale Flow in Micro Electrokinetic Turbulent Mixer

In the present work, we studied the three-dimensional (3D) mean flow field in a micro electrokinetic (μEK) turbulence based micromixer by micro particle imaging velocimetry (μPIV) with stereoscopic method. A large-scale solenoid-type 3D mean flow field has been observed. The extraordinarily fast mixing process of the μEK turbulent mixer can be primarily attributed to two steps. First, under the strong velocity fluctuations generated by μEK mechanism, the two fluids with different conductivity are highly mixed near the entrance, primarily at the low electric conductivity sides and bias to the bottom wall. Then, the well-mixed fluid in the local region convects to the rest regions of the micromixer by the large-scale solenoid-type 3D mean flow. The mechanism of the large-scale 3D mean flow could be attributed to the unbalanced electroosmotic flows (EOFs) due to the high and low electric conductivity on both the bottom and top surface

Quantum-Inspired Distributed Memetic Algorithm

This paper proposed a novel distributed memetic evolutionary model, where four modules distributed exploration, intensified exploitation, knowledge transfer, and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches. Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents. Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably. Quantum computation is a newly emerging technique, which has powerful computing power and parallelized ability. Therefore, this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm, referred to as quantum-inspired distributed memetic algorithm (QDMA). In QDMA, individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace. The QDMA integrates the superiorities of distributed, memetic, and quantum evolution. Computational experiments are carried out to evaluate the superior performance of QDMA. The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test. The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model, but also to superior designs of each special component