Existence of r-self-orthogonal Latin squares

AbstractTwo Latin squares of order v are r-orthogonal if their superposition produces exactly r distinct ordered pairs. If the second square is the transpose of the first one, we say that the first square is r-self-orthogonal, denoted by r-SOLS(v). It has been proved that for any integer v⩾28, there exists an r-SOLS(v) if and only if v⩽r⩽v2 and r∉{v+1,v2-1}. In this paper, we give an almost complete solution for the existence of r-self-orthogonal Latin squares

Commonsense knowledge enhanced memory network for stance classification

Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets

When Web 3.0 Meets Reality: A Hyperdimensional Fractal Polytope P2P Ecosystems

Web 3.0 opens the world of new existence of the crypto-network-entity, which is independently defined by the public key pairs for entities and the connection to the Web 3.0 cyberspace. In this paper, we first discover a spacetime coordinate system based on fractal polytope in any dimensions with discrete time offered by blockchain and consensus. Second, the novel network entities and functions are defined to make use of hyperdimensional deterministic switching and routing protocols and blockchain-enabled mutual authentication. In addition to spacetime network architecture, we also define a multi-tier identity scheme which extends the native Web 3.0 crypto-network-entity to outer cyber and physical world, offering legal-compliant anonymity and linkability to all derived identifiers of entities. In this way, we unify the holistic Web 3.0 network based on persistent spacetime and its entity extension to our cyber and physical world

Urban sustainability indicators re-visited: Lessons from property-led urban development in China

This paper proposes a bespoke urban sustainability indicator framework in the context of China's prevalent property-led urban development. Emphasising local characteristics and incorporating underlying institutions, it advocates a more nuanced, holistic and dynamic approach when addressing sustainability issues. Selection of indicators were based on extensive literature reviews and tested through an international expert survey comprising both China-based and overseas-based experts. The two groups of experts have shown divergent views, with the former prioritizing economic and institutional aspects over environmental and social factors. It also provides transferable policy insights to developing countries more generally, given many similarities in broader development challenges. Discussion on recent literature and urban development reinforces the applicability of these tailor-made indicators to not only monitoring but also explaining and predicting urban changes. We argue it is necessary to recognize the centrality of property-led urban development in urban sustainable development, and the need for examining the complex relations between the property sector and urban sustainability via inclusion of institutional analysis and a multi-method approach combining quantitative and qualitative evaluations

Ferroptosis in COVID-19-related liver injury: A potential mechanism and therapeutic target

The outbreak and worldwide spread of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a threat to global public health. SARS-CoV-2 infection not only impacts the respiratory system but also causes hepatic injury. Ferroptosis, a distinct iron-dependent form of non-apoptotic cell death, has been investigated in various pathological conditions, such as cancer, ischemia/reperfusion injury, and liver diseases. However, whether ferroptosis takes part in the pathophysiological process of COVID-19-related liver injury has not been evaluated yet. This review highlights the pathological changes in COVID-19-related liver injury and presents ferroptosis as a potential mechanism in the pathological process. Ferroptosis, as a therapeutic target for COVID-19-related liver injury, is also discussed. Discoveries in these areas will improve our understanding of strategies to prevent and treat hepatic injuries caused by COVID-19

Effects of aerobic exercise on serum adiponectin concentrations in children and adolescents with obesity: a systematic review and meta-analysis

Serum adiponectin plays a vital role in various physiological processes, such as anti-inflammatory, anti-atherosclerotic, anti-apoptotic and pro-angiogenic activities. Any abnormalities in its concentration can lead to adverse health outcomes, particularly in children and adolescents. Therefore, it is crucial to investigate factors influencing serum adiponectin concentrations in this population. The primary objective of this study was to systematically evaluate the impact of aerobic exercise on serum adiponectin concentrations in children and adolescents with obesity. To achieve this, a comprehensive literature search was conducted up to January 2023, utilising five databases: PubMed, Web of Science, Embase, Cochrane Library and Clinicaltrial.gov. The inclusion criteria involved studies that focused solely on aerobic exercise as an intervention for children and adolescents with obesity. Only studies that reported outcome indicators related to serum adiponectin were considered for analysis. The quality of the included studies was assessed using the Cochrane Risk of Bias (ROB) assessment tool, and statistical analysis was performed using RevMan 5.4.1 analysis software. This meta-analysis incorporated data from eight trials, involving a total of 272 subjects. The results demonstrated that aerobic training significantly increased serum adiponectin concentrations [standardized mean difference (SMD) = 0.85; 95% confidence interval (CI) = 0.33 to 1.37; I2 = 0%; p = 0.001] in children and adolescents with obesity when compared to non-exercise controls. Furthermore, the magnitude of this effect appears to be influenced by the intensity of aerobic exercise, with higher-intensity aerobic exercise resulting in greater increases in serum adiponectin concentrations