Improved ordinary measure and image entropy theory based intelligent copy detection method

Agency for Science, Technology and Research (A*STAR

AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints

Real-time and accurate information on fine-grained changes in crop cultivation is of great significance for crop growth monitoring, yield prediction and agricultural structure adjustment. Aiming at the problems of serious spectral confusion in visible high-resolution unmanned aerial vehicle (UAV) images of different phases, interference of large complex background and salt-and-pepper noise by existing semantic change detection (SCD) algorithms, in order to effectively extract deep image features of crops and meet the demand of agricultural practical engineering applications, this paper designs and proposes an agricultural geographic scene and parcel-scale constrained SCD framework for crops (AGSPNet). AGSPNet framework contains three parts: agricultural geographic scene (AGS) division module, parcel edge extraction module and crop SCD module. Meanwhile, we produce and introduce an UAV image SCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiple semantic variation types of crops in complex geographical scene. We conduct comparative experiments and accuracy evaluations in two test areas of this dataset, and the results show that the crop SCD results of AGSPNet consistently outperform other deep learning SCD models in terms of quantity and quality, with the evaluation metrics F1-score, kappa, OA, and mIoU obtaining improvements of 0.038, 0.021, 0.011 and 0.062, respectively, on average over the sub-optimal method. The method proposed in this paper can clearly detect the fine-grained change information of crop types in complex scenes, which can provide scientific and technical support for smart agriculture monitoring and management, food policy formulation and food security assurance

Aurora B Regulates Formin mDia3 in Achieving Metaphase Chromosome Alignment

SummaryProper bipolar attachment of sister kinetochores to the mitotic spindle is critical for accurate chromosome segregation in mitosis. Here we show an essential role of the formin mDia3 in achieving metaphase chromosome alignment. This function is independent of mDia3 actin nucleation activity, but is attributable to EB1-binding by mDia3. Furthermore, the microtubule binding FH2 domain of mDia3 is phosphorylated by Aurora B kinase in vitro, and cells expressing the nonphosphorylatable mDia3 mutant cannot position chromosomes at the metaphase plate. Purified recombinant mDia3 phosphorylated by Aurora B exhibits reduced ability to bind microtubules and stabilize microtubules against cold-induced disassembly in vitro. Cells expressing the phosphomimetic mDia3 mutant do not form stable kinetochore microtubule fibers; despite they are able to congress chromosomes to the metaphase plate. These findings reveal a key role for mDia3 and its regulation by Aurora B phosphorylation in achieving proper stable kinetochore microtubule attachment

Multi-criteria sustainability risk management for post-war residential re-construction: the case of Damascus

The Syrian conflict nine years of destruction have had catastrophic influence on the built environment. Post-war Residential Re-construction Projects (PRRP) have been one of the most challenging and controversial responsibilities. PRRP play vital role in building back to better level of sustainability, mitigating risks and resilience, providing housing for traumatised displaced people while coping with the war consequences. Hitherto, more sustainable PRRP are found to be riskier for construction professionals compared to traditional projects. Sustainability Risk Management (SRM) can be a challenging mission where multiple interrelated criteria exist. This research is set to identify and assess sustainability risks associated with more sustainable PRRP in Damascus and to understand how the Syrian construction professionals perceive these risks. The research study enhances a survey and interviews’ findings to develop a multi-criteria SRM framework that can be perceived as a decision-support tool to assess sustainability risks in Damascus PRRP. The survey revealed that while the sustainability risk categories weightings are 38%, 24%, 39% for economic, environmental and social risks respectively, the overall response categories weightings are 44%, 31%, 25% for economic, environmental and social responses respectively. The top five risks found are: expenses exceed anticipated, absence of sustainable technology, delays in planning for alternative social homes, unclear allocation of responsibilities and lack of qualified professionals. The interviews looked beyond the current prevailing approaches to sustainability risks while assessing the proposed multi-dimensional conceptual framework. The research framework enhances interrelatedness in management principles among: sustainability assessment, RM and multi criteria decision making in the post-war context. These findings are significant as this is the first-hand experience gathered from Damascus PRRP. It symbolises a turning point in Syrian construction; from traditional to sustainable housing, which will positively influence construction companies’ sustainability awareness in reconstruction process

Scaffold Structural Microenvironmental Cues to Guide Tissue Regeneration in Bone Tissue Applications

In the process of bone regeneration, new bone formation is largely affected by physico-chemical cues in the surrounding microenvironment. Tissue cells reside in a complex scaffold physiological microenvironment. The scaffold should provide certain circumstance full of structural cues to enhance multipotent mesenchymal stem cell (MSC) differentiation, osteoblast growth, extracellular matrix (ECM) deposition, and subsequent new bone formation. This article reviewed advances in fabrication technology that enable the creation of biomaterials with well-defined pore structure and surface topography, which can be sensed by host tissue cells (esp., stem cells) and subsequently determine cell fates during differentiation. Three important cues, including scaffold pore structure (i.e., porosity and pore size), grain size, and surface topography were studied. These findings improve our understanding of how the mechanism scaffold microenvironmental cues guide bone tissue regeneration

MetaSymNet: A Dynamic Symbolic Regression Network Capable of Evolving into Arbitrary Formulations

Mathematical formulas serve as the means of communication between humans and nature, encapsulating the operational laws governing natural phenomena. The concise formulation of these laws is a crucial objective in scientific research and an important challenge for artificial intelligence (AI). While traditional artificial neural networks (MLP) excel at data fitting, they often yield uninterpretable black box results that hinder our understanding of the relationship between variables x and predicted values y. Moreover, the fixed network architecture in MLP often gives rise to redundancy in both network structure and parameters. To address these issues, we propose MetaSymNet, a novel neural network that dynamically adjusts its structure in real-time, allowing for both expansion and contraction. This adaptive network employs the PANGU meta function as its activation function, which is a unique type capable of evolving into various basic functions during training to compose mathematical formulas tailored to specific needs. We then evolve the neural network into a concise, interpretable mathematical expression. To evaluate MetaSymNet's performance, we compare it with four state-of-the-art symbolic regression algorithms across more than 10 public datasets comprising 222 formulas. Our experimental results demonstrate that our algorithm outperforms others consistently regardless of noise presence or absence. Furthermore, we assess MetaSymNet against MLP and SVM regarding their fitting ability and extrapolation capability, these are two essential aspects of machine learning algorithms. The findings reveal that our algorithm excels in both areas. Finally, we compared MetaSymNet with MLP using iterative pruning in network structure complexity. The results show that MetaSymNet's network structure complexity is obviously less than MLP under the same goodness of fit.Comment: 16 page

Liuzijue training improves hypertension and modulates gut microbiota profile

BackgroundLiuzijue training (LZJ) is a traditional exercise integrating breathing meditation and physical exercise, which could prevent and improve hypertension symptoms.PurposeWe aimed to evaluate the therapeutic effect of LZJ on hypertensive patients from the perspectives of blood pressure (BP), vascular endothelial function, immune homeostasis, and gut microbiota.MethodsWe conducted a randomized, controlled, single-blind experiment to assess the effect of 12 weeks LZJ in hypertensive patients. We measured the blood pressure level, vascular endothelial function, serum inflammatory factor concentration, and fecal microbial composition of hypertension patients.ResultsCompared with aerobic training, LZJ has a more significant effect on serum inflammatory factors (IL-6 and IL-10) and gut microbiota. PCoA analysis showed that LZJ tended to transform the gut microbiota structure of hypertensive subjects into that of healthy people. This process involves significant changes in Bacteroides, Clostridium_sensu_stricto_1, Escherichia-Shigella, Haemophilus, Megamonas, and Parabacteroides. In particular, Bacteroides and Escherichia-Shigella, these bacteria were closely related to the improvement of BP in hypertensive patients.ConclusionIn conclusion, our results confirm that LZJ could be used as an adjuvant treatment for hypertensive patients, which could effectively reduce BP, improve the immune homeostasis and gut microbiota structure in patients, and provide a theoretical reference for the use of LZJ in the clinic.Clinical trial registrationhttp://www.chictr.org.cn/listbycreater.aspx, identifier: ChiCTR2200066269