Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkout

Designing an automatic checkout system for retail stores at the human level accuracy is challenging due to similar appearance products and their various poses. This paper addresses the problem by proposing a method with a two-stage pipeline. The first stage detects class-agnostic items, and the second one is dedicated to classify product categories. We also track the objects across video frames to avoid duplicated counting. One major challenge is the domain gap because the models are trained on synthetic data but tested on the real images. To reduce the error gap, we adopt domain generalization methods for the first-stage detector. In addition, model ensemble is used to enhance the robustness of the 2nd-stage classifier. The method is evaluated on the AI City challenge 2022 -- Track 4 and gets the F1 score $40\%$ on the test A set. Code is released at the link https://github.com/cybercore-co-ltd/aicity22-track4

Adrenomedullin gene expression is developmentally regulated and induced by hypoxia in rat ventricular cardiac myocytes

Adrenomedullin is a recently discovered hypotensive peptide that is expressed in a variety of cell and tissue types. Using the technique of differential display, the adrenomedullin gene was observed to be differentially expressed in developing rat heart. Reverse transcription- polymerase chain reaction analysis revealed that the level of adrenomedullin mRNA was significantly higher in adult ventricular cardiac muscle as compared with embryonic day 17 ventricular cardiac muscle. Adrenomedullin receptor mRNA was constitutively expressed throughout development of the ventricular heart. Two potential hypoxia-inducible factor-1 (HIF-1) consensus binding sites were identified in the mouse adrenomedullin promoter at -1095 and -770 nucleotides from the transcription start site. Exposure of cultured adult rat ventricular cardiac myocytes to hypoxia (1% O2) resulted in a significant, time-dependent increase in adrenomedullin mRNA levels. Transfection studies revealed that the 5\u27-flanking sequence of adrenomedullin was capable of mediating a hypoxia-inducible increase in transcription. Mutation of the putative HIF-1 consensus binding sites revealed that the major regulatory sequence that mediates the hypoxia-inducible transcriptional response is located at -1095. These data demonstrate that the adrenomedullin gene is developmentally regulated in ventricular cardiomyocytes, that adrenomedullin transcription can be induced by hypoxia, and that this response is primarily mediated by HIF-1 consensus sites in the adrenomedullin promoter

Lignin biosynthesis in wheat (Triticum aestivum L.): its response to waterlogging and association with hormonal levels

Phylogenetic relationships of wheat C3H and CCoAOMT genes with the homologs from other species. Phylogenetic trees of C3H (A) and CCoAOMT (B) were generated based on nucleic acid sequence similarity of wheat genes with 15 C3H and 19 CCoAOMT genes, respectively, of other monocot and dicot species identified from the NCBI nucleotide database [39] using MEGA program [41], and the trees were inferred using Maximum Likelihood method based on the Tamura-nei model. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test of 500 replicates is shown next to the branches. ●, wheat candidate gene; ▲, genes from dicot species other than Arabidopsis; *, wheat sequence used for the analysis. (PDF 175 kb

Interaction Model of the Interface Nasicon with Aqueous Solution

Complex impedance spectroscopy (CIS) study was carried out using the 4-electrodes cell arrangement with NASICON membrane. The physical model developed in this work was based on own CIS characteristic of the system NASICON/solution, and it accurately described the experimental diagrams. The electronic parameters could be determined from the fittings with the designed model

Structural assessment based on vibration measurement test combined with an artificial neural network for the steel truss bridge

Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network’s input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach’s feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents.This research was funded by FCT/MCTES through national funds (PIDDAC) from the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the reference UIDB/04029/2020, and from the Associate Laboratory Advanced Production and Intelligent Systems ARISE, under the reference LA/P/0112/2020, as well as financial support of the project research “B2022-GHA-03” from the Ministry of Education and Training. And The APC was funded by ANI (“Agência Nacional de Inovação”) through the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference POCI-01-0247-FEDER069642, which was cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalisation Program (POCI)

Developing a comprehensive quality control framework for roadway bridge management: a case study approach using key performance indicators

Transportation infrastructures, especially roadway bridges, play a pivotal role in socioeconomic development. Recently, bridge engineers are increasingly facing the challenge in terms of shifting their strategy from building new facilities to maintaining the existing aging infrastructures, to preserve their service performance during the operational stage. In fact, the infrastructure administrators lack a quality control (QC) strategy for the existing roadway bridges, which leads to the decision-making application and tool being still minor. To overcome those challenging issues, this paper proposes a quality control framework for roadway bridge management using key performance indicators (KPIs). The case study methodology is suggested to be used and then conducted for several bridges, mostly in European countries. In which the performance indicators (PIs) and goals (PGs) are defined, after assessing the bridges and vulnerable zones, the derivation KPIs from those PIs are introduced and developed considering time functions and different maintenance scenarios. Eventually, a two-stage quality control framework will be proposed in which the static stage includes preparatory works, inspection responsibilities, and a quick assessment of KPIs; while the dynamic stage helps the decision maker in estimating the time remaining of the bridge service life, managing the evolution of KPIs as well as planning the best possible maintenance strategy. The selected two case studies are present and curated, which show the excellent potential to develop a long-term strategy for roadway bridge management on a lifecycle level.This research was funded by FCT/MCTES through national funds (PIDDAC) from the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the reference UIDB/04029/2020, and from the Associate Laboratory Advanced Production and Intelligent Systems ARISE, under the reference LA/P/0112/2020, as well as financial support of the project re-search “B2022-GHA-03” from the Ministry of Education and Training. And The APC was funded by ANI (“Agência Nacional de Inovação”) through the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference PO-CI-01-0247- FEDER-069642, which was cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalisation Program (POCI).Minh Q. Tran acknowledges the support by the doctoral grant reference PRT/BD/154268/2022, financed by Portuguese Foundation for Science and Technology (FCT), under the MIT Portugal Program (2022 MPP2030-FCT)

Finite element model updating for composite plate structures using particle swarm optimization algorithm

In the Architecture, Engineering, and Construction (AEC) industry, particularly civil engineering, the Finite Element Method (FEM) is a widely applied method for computational designs. In this regard, computational simulation has increasingly become challenging due to uncertain parameters, significantly affecting structural analysis and evaluation results, especially for composite and complex structures. Therefore, determining the exact computational parameters is crucial since the structures involve many components with different material properties, even removing some additional components affects the calculation results. This study presents a solution to increase the accuracy of the finite element (FE) model using a swarm intelligence-based approach called the particle swarm optimization (PSO) algorithm. The FE model is created based on the structure’s easily observable characteristics, in which uncertainty parameters are assumed empirically and will be updated via PSO using dynamic experimental results. The results show that the finite element model achieves high accuracy, significantly improved after updating (shown by the evaluation parameters presented in the article). In this way, a precise and reliable model can be applied to reliability analysis and structural design optimization tasks. During this research project, the FE model considering the PSO algorithm was integrated into an actual bridge’s structural health monitoring (SHM) system, which was the premise for creating the initial digital twin model for the advanced digital twinning technologyThis work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020. The authors also acknowledge ANI (“Agência Nacional de Inovação”) for the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference POCI-01-0247-FEDER-069642, cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalization Program (POCI).Minh Q. Tran was supported by the doctoral grant reference PRT/BD/154268/2022 financed by the Portuguese Foundation for Science and Technology (FCT), under the MIT Portugal Program (2022 MPP2030-FCT). Minh Q. Tran acknowledges Huan X. Nguyen (Faculty of Science and Technology, Middlesex University, London NW4 4BT, UK) and Thuc V. Ngo (Mien Tay Construction University, Institute of Science and International Cooperation, 85100 Vĩnh Long, Vietnam) for their support as cosupervisors as well as specific suggestions in terms of the “conceptualization” and “methodology” of this paper. Helder S. Sousa acknowledges the funding by FCT through the Scientific Employment Stimulus—4th Editio

Role of Process Control in Improving Space Vehicle Safety A Space Shuttle External Tank Example

Developing a safe and reliable space vehicle requires good design and good manufacturing, or in other words "design it right and build it right". A great design can be hard to build or manufacture mainly due to difficulties related to quality. Specifically, process control can be a challenge. As a result, the system suffers from low quality which leads to low reliability and high system risk. The Space Shuttle has experienced some of those cases, but has overcome these difficulties through extensive redesign efforts and process enhancements. One example is the design of the hot gas temperature sensor on the Space Shuttle Main Engine (SSME), which resulted in failure of the sensor in flight and led to a redesign of the sensor. The most recent example is the Space Shuttle External Tank (ET) Thermal Protection System (TPS) reliability issues that contributed to the Columbia accident. As a result, extensive redesign and process enhancement activities have been performed over the last two years to minimize the sensitivities and difficulties of the manual TPS application process

Human DNA ligases I and III, but not ligase IV, are required for microhomology-mediated end joining of DNA double-strand breaks

DNA nonhomologous end-joining (NHEJ) and homologous recombination are two distinct pathways of DNA double-strand break repair in mammalian cells. Biochemical and genetic studies showed that DNA ends can also be joined via microhomology-mediated end joining (MHEJ), especially when proteins responsible for NHEJ, such as Ku, are reduced or absent. While it has been known that Ku-dependent NHEJ requires DNA ligase IV, it is unclear which DNA ligase(s) is required for Ku-independent MHEJ. In this study, we used a cell-free assay to determine the roles of DNA ligases I, III and IV in MHEJ and NHEJ. We found that siRNA mediated down-regulation of DNA ligase I or ligase III in human HTD114 cells led to impaired end joining that was mediated by 2-, 3- or 10-bp microhomology. In addition, nuclear extract from human fibroblasts harboring a mutation in DNA ligase I displayed reduced MHEJ activity. Furthermore, treatment of HTD114 nuclear extracts with an antibody against DNA ligase I or III also significantly reduced MHEJ. These data indicate that DNA ligases I and III are required in MHEJ. DNA ligase IV, on the contrary, is not required in MHEJ but facilitates Ku-dependent NHEJ. Therefore, MHEJ and NHEJ require different DNA ligases