High Quality Test Generation Targeting Power Supply Noise

Delay test is an essential structural manufacturing test used to determine the maximal frequency at which a chip can run without incurring any functional failures. The central unsolved challenge is achieving high delay correlation with the functional test, which is dominated by power supply noise (PSN). Differences in PSN between functional and structural tests can lead to differences in chip operating frequencies of 30% or more. Pseudo functional test (PFT), based on a multiple-cycle clocking scheme, has better PSN correlation with functional test compared with traditional two-cycle at-speed test. However, PFT is vulnerable to under-testing when applied to delay test. This work aims to generate high quality PFT patterns, achieving high PSN correlation with functional test. First, a simulation-based don’t-care filling algorithm, Bit-Flip, is proposed to improve the PSN for PFT. It relies on randomly flipping a group of bits in the test pattern to explore the search space and find patterns that stress the circuits with the worst-case, but close to functional PSN. Experimental results on un-compacted patterns show Bit-Flip is able to improve PSN as much as 38.7% compared with the best random fill. Second, techniques are developed to improve the efficiency of Bit-Flip. A set of partial patterns, which sensitize transitions on critical cells, are pre-computed and later used to guide the selection of bits to flip. Combining random and deterministic flipping, we achieve similar PSN control as Bit-Flip but with much less simulation time. Third, we address the problem of automatic test pattern generation for extracting circuit timing sensitivity to power supply noise during post-silicon validation. A layout-aware path selection algorithm selects long paths to fully span the power delivery network. The selected patterns are intelligently filled to bring the PSN to a desired level. These patterns can be used to understand timing sensitivity in post-silicon validation by repeatedly applying the path delay test while sweeping the PSN experienced by the path from low to high. Finally, the impacts of compression on power supply noise control are studied. Illinois Scan and embedded deterministic test (EDT) patterns are generated. Then Bit-Flip is extended to incorporate the compression constraints and applied to compressible patterns. The experimental results show that EDT lowers the maximal PSN by 24.15% and Illinois Scan lowers it by 2.77% on un-compacted patterns

Design criteria and applications of multi-channel parallel microfluidic module

The microfluidic technology for function microsphere synthesis has high control precision. However, the throughput is too low for industrial scale-up applications. Current scale-up design focuses on a multi-channel in 2D, in which the distribution uniformity parameter δ increases linearly, resulting in the deterioration of the flow distribution performance. The 3D modular scale-up strategy could greatly alleviate this problem, but no design principles have been developed yet. For the first time, this paper establishes the microfluidic 3D scale-up design criteria. Based on the modular design concept, the design method of 2D and 3D throughput scale-up parameters N and M, distribution uniformity parameters δ and β, and microchannel design parameter KRwere proposed. The equivalent resistance coefficient was defined, and the influence of different parameters on a 2D array and 3D stack was analyzed. Furthermore, the error correction method was studied. It was found that the two-stage scale-up process contradicted each other. A good scale-up performance of one stage led to the limitation of another stage. Increasing the resistance of each channel Rucould both increase the two-stage scale-up performance, which was an important factor. A single-module scale-up system with 8 channels in a single array and 10 arrays in a vertical stack, which had 80 channels in total, was designed and fabricated based on the proposed design criteria for generating Chitosan/TiO2composite microspheres. The average particle size was 539.65 μm and CV value was about 3.59%. The throughput was 480 ml h-1, which effectively increased the throughput scale and the product quality

Ophiostomatoid species associated with pine trees (Pinus spp.) infested by Cryphalus piceae from eastern China, including five new species

Cryphalus piceae attacks various economically important conifers. Similar to other bark beetles, Cr. piceae plays a role as a vector for an assortment of fungi and nematodes. Previously, several ophiostomatoid fungi were isolated from Cr. piceae in Poland and Japan. In the present study, we explored the diversity of ophiostomatoid fungi associated with Cr. piceae infesting pines in the Shandong Province of China. We isolated ophiostomatoid fungi from both galleries and beetles collected from our study sites. These fungal isolates were identified using both molecular and morphological data. In this study, we recovered 175 isolates of ophiostomatoid fungi representing seven species. Ophiostoma ips was the most frequently isolated species. Molecular and morphological data indicated that five ophiostomatoid fungal species recovered were previously undescribed. Thus, we proposed these five novel species as Ceratocystiopsis yantaiensis, C. weihaiensis, Graphilbum translucens, Gr. niveum, and Sporothrix villosa. These new ophiostomatoid fungi add to the increasing number of fungi known from China, and this evidence suggests that numerous novel taxa are awaiting discovery in other forests of China.Shandong Normal University.https://mycokeys.pensoft.netam2022BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant Patholog

Entering the Era of Earth Observation-Based Landslide Warning Systems: A novel and exciting framework

Landslide early warning remains a grand challenge due to the high human cost of catastrophic landslides globally and the difficulty of identifying a diverse range of landslide triggering factors. There have been only a very limited number of success stories to date. However, recent advances in earth observation (EO) from ground, aircraft and space have dramatically improved our ability to detect and monitor active landslides and a growing body of geotechnical theory suggests that prefailure behavior can provide clues to the location and timing of impending catastrophic failures. In this paper, we use two recent landslides in China as case studies, to demonstrate that (i) satellite radar observations can be used to detect deformation precursors to catastrophic landslide occurrence, and (ii) early warning can be achieved with real-time in-situ observations. A novel and exciting framework is then proposed to employ EO technologies to build an operational landslide early warning system.This work was supported by the National Natural Science Foundation of China under grants 41801391, 41874005, and 41929001; the National Science Fund for Outstanding Young Scholars of China under grant 41622206; the Fund for International Cooperation under grant NSFCRCUK_NERC; Resilience to Earthquake-Induced Landslide Risk in China under grant 41661134010; the open fund of State Key Laboratory of Geodesy and Earth’s Dynamics (SKLGED2018-5-3-E); Sichuan Science and Technology Plan Project under grant 2019YJ0404; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project under grant SKLGP2018Z019; the Spanish Ministry of Economy and Competitiveness, the State Agency of Research, and the European Funds for Regional Development under projects TEC2017-85244-C2-1-P and TIN2014-55413-C2-2-P; and the Spanish Ministry of Education, Culture, and Sport under project PRX17/00439. This work was also partially supported by the U.K. Natural Environment Research Council through the Center for the Observation and Modeling of Earthquakes, Volcanoes, and Tectonics under come30001 and the Looking Inside the Continents From Space and Community Earthquake Disaster Risk Reduction in China projects under NE/K010794/1 and NE/N012151/1, respectively, and by the European Space Agency through the ESA-MOST DRAGON-4 project (32244 [4]). Roland Bürgmann acknowledges support by the NASA Earth Surface and Interior focus area

The mediating role of negative symptoms in “secondary factors” determining social functioning in chronic schizophrenia

BackgroundChronic schizophrenia is significantly influenced by negative symptoms, with several known contributors to secondary negative symptoms. However, the impact of these factors and negative symptoms on social functioning warrants further exploration.MethodsWe assessed the clinical symptoms, antipsychotic adverse reactions, and social functioning of 283 hospitalized patients with chronic schizophrenia using various standardized interviews and scales. We conducted multiple regression and mediation analyses to elucidate the impact of secondary factors on negative symptoms, and the relationship among these “secondary factors,” negative symptoms, and social functioning.ResultsOur findings identified depressive symptoms, extrapyramidal symptoms, and positive symptoms as significant contributors to secondary negative symptoms. We found that negative symptoms play a notable mediating role in the effect of depressive and positive symptoms on social functioning. However, the relationship between positive symptoms, negative symptoms, and social functioning proved to be intricate.ConclusionOur findings propose that negative symptoms act as pivotal mediators in the correlation between “secondary factors” (including the depressive symptoms and positive symptoms) and social functioning. The treatment of chronic schizophrenia necessitates focusing on key factors such as depressive and positive symptoms, which might significantly contribute to the development of secondary negative symptoms. Further research is essential to clarify the complex relationship among positive symptoms, negative symptoms, and social functioning in schizophrenia

FGF18 Enhances Migration and the Epithelial-Mesenchymal Transition in Breast Cancer by Regulating Akt/GSK3β/Β-Catenin Signaling

Background/Aims: Fibroblast growth factors (FGFs) and their high-affinity receptors contribute to autocrine and paracrine growth stimulation in several human malignant tumors, including breast cancer. However, the mechanisms underlying the carcinogenic actions of FGF18 remain unclear. Methods: The transcription level of FGF18 under the hypoxic condition was detected with quantitative PCR (qPCR). A wound-healing assay was performed to assess the role of FGF18 in cell migration. A clonogenicity assay was used to determine whether FGF18 silencing affected cell clonogenicity. Western blotting was performed to investigate Akt/GSK3β/β-catenin pathway protein expression. Binding of β-catenin to the target gene promoter was determined by chromatin immunoprecipitation (ChIP) assays. Results: FGF18 promoted the epithelial-mesenchymal transition (EMT) and migration in breast cancer cells through activation of the Akt/GSK3β/β-catenin pathway. FGF18 increased Akt-Ser473 and -Thr308 phosphorylation, as well as that of GSK3β-Ser9. FGF18 also enhanced the transcription of proliferation-related genes (CDK2, CCND2, Ki67), metastasis-related genes (TGF-β, MMP-2, MMP-9), and EMT markers (Snail-1, Snail-2, N-cadherin, vimentin, TIMP1). β-catenin bound to the target gene promoter on the ChIP assay. Conclusion: FGF18 contributes to the migration and EMT of breast cancer cells following activation of the Akt/GSK3β/β-catenin pathway. FGF18 expression may be a potential prognostic therapeutic marker for breast cancer

Stacking Model for Photovoltaic-Power-Generation Prediction

Despite the clean and renewable advantages of solar energy, the instability of photovoltaic power generation limits its wide applicability. In order to ensure stable power-grid operations and the safe dispatching of the power grid, it is necessary to develop a model that can accurately predict the photovoltaic power generation. As a widely used prediction method, the stacking model has been applied in many fields. However, few studies have used stacking models to predict photovoltaic power generation. In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting decision tree to predict photovoltaic power generation, by using two datasets. The results show that the prediction accuracy of the stacking model is higher than that of the single ensemble-learning model, and that the prediction accuracy of the Stacking-GBDT model is higher than the other stacking models. The stacking model that is proposed in this research provides a reference for the accurate prediction of photovoltaic power generation