Design and development of an intelligent system based on the Internet of Things for the early detection of forest fires

Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.

Exploiting Positional Information for Session-Based Recommendation

For present e-commerce platforms, it is important to accurately predict users’ preference for a timely next-item recommendation. To achieve this goal, session-based recommender systems are developed, which are based on a sequence of the most recent user-item interactions to avoid the influence raised from outdated historical records. Although a session can usually reflect a user’s current preference, a local shift of the user’s intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user’s initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness . Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.

Location-Centered House Price Prediction: A Multi-Task Learning Approach

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, and investors. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we make an important observation as follows – besides the in-house features such as floor area, the location plays a critical role in house price prediction. Unfortunately, existing work either overlooked it or had a coarse grained measurement of locations. Thereby, we define and capture a fine-grained location profile powered by a diverse range of location data sources, including transportation profile, education profile, suburb profile based on census data, and facility profile. Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire data for modeling, or split the entire house data and model each partition independently. However, such modeling ignores the relatedness among partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and select specific MTL-based methods with different regularization terms to capture and exploit the relatedness among tasks. Based on real-world house transaction data collected in Melbourne, Australia, we design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.

Computing Graph Neural Networks: A Survey from Algorithms to Accelerators

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data are inherently relational, for which conventional neural networks do not perform well. Indeed, as recent reviews can attest, research in the area of GNNs has grown rapidly and has lead to the development of a variety of GNN algorithm variants as well as to the exploration of ground-breaking applications in chemistry, neurology, electronics, or communication networks, among others. At the current stage research, however, the efficient processing of GNNs is still an open challenge for several reasons. Besides of their novelty, GNNs are hard to compute due to their dependence on the input graph, their combination of dense and very sparse operations, or the need to scale to huge graphs in some applications. In this context, this article aims to make two main contributions. On the one hand, a review of the field of GNNs is presented from the perspective of computing. This includes a brief tutorial on the GNN fundamentals, an overview of the evolution of the field in the last decade, and a summary of operations carried out in the multiple phases of different GNN algorithm variants. On the other hand, an in-depth analysis of current software and hardware acceleration schemes is provided, from which a hardware-software, graph-aware, and communication-centric vision for GNN accelerators is distilled.

Design Automation and Test Solutions for Monolithic 3D ICs

Monolithic 3D (M3D) is an emerging heterogeneous integration technology that overcomes the limitations of the conventional through-silicon-via (TSV) and provides significant performance uplift and power reduction. However, the ultra-dense 3D interconnects impose significant challenges during physical design on how to best utilize them. Besides, the unique low-temperature fabrication process of M3D requires dedicated design-for-test mechanisms to verify the reliability of the chip. In this article, we provide an in-depth analysis on these design and test challenges in M3D. We also provide a comprehensive survey of the state-of-the-art solutions presented in the literature. This article encompasses all key steps on M3D physical design, including partitioning, placement, clock routing, and thermal analysis and optimization. In addition, we provide an in-depth analysis of various fault mechanisms, including M3D manufacturing defects, delay faults, and MIV (monolithic inter-tier via) faults. Our design-for-test solutions include test pattern generation for pre/post-bond testing, built-in-self-test, and test access architectures targeting M3D.

Does the Carbon Emission Trading Policy Promote Foreign Direct Investment?: A Quasi-Experiment From China

As an important policy to promote global energy transition and carbon emission reduction, does the carbon emission trading policy help promote foreign direct investment inflows, thus alleviating the contradiction between environment and economic development? Based on the “OLI paradigm,” by using the data of China’s 30 provinces from 2007 to 2016 and taking China’s pilot implementation carbon emission transaction policy in 2013 as the natural experiment, so as to construct a differences-in-differences model, this study empirically analyzed the impact of carbon emission transaction policies on foreign direct investment and conducted an in-depth analysis and discussion on related heterogeneity. The empirical results show that 1) there is a positive correlation between the carbon emission trading policy and foreign direct investment; 2) the results of heterogeneity analysis show that the effect of carbon emission trading policy on the increase in FDI is more significant in the areas with a stronger environmental regulation, a higher degree of marketization, and low energy consumption. The conclusions of this study enrich the analysis of the effectiveness of government environmental policies from the perspective of both environment and economic development and provide relevant policy enlightenment for developing countries in environmental regulation and attracting foreign direct investment.Systematic Review Registration: [website], identifier [registration number].

Coordinated Development of Population, Resources, Environment, Economy, and Society under Engineering Management Combined with Bilevel Optimization Model

The in-depth analysis of the strategies for the coordinated and continuous development of population, resources, environment, economy, and society based on the engineering management model is highly important for the sustainable development of the regional economy and society. In this article, a population-economy-resources-environment bilevel optimization model is established based on the economic and social development in a provincial region. The method of bilevel optimization is adopted to introduce the specific bilevel optimization model. The concept and objectives of the bilevel optimization are explained, and its corresponding technical applications are described. In this article, the development in coordinated economic and social development of population, resources, and environment is analyzed and compared based on the bilevel optimization model. In particular, the evolution and changes before and after the implementation of engineering management are studied. Through the results, it can be observed that after the implementation of project management, the coefficient of industry location has presented a downward trend, and the coordinated development of population, resources, environment, economy, and society has become more coordinated.

Beyond Reassurance: The Reputational Effect of Cultural Reforms in Peace Agreements

We explore whether including cultural reforms in an intra-state peace accord facilitates its success. We distinguish between accommodationist and integrationist cultural provisions and employ a mixed research method combining negative binomial regression on a data set of all intra-state political agreements concluded between 1989 and 2017, and an in-depth analysis of the 1998 Good Friday Agreement for Northern Ireland. We recognize the important reassuring effect of accommodationist cultural reforms in separatist conflicts. However, we also find that they have an important and hitherto overlooked reputational effect across all conflict types. By enhancing the reputation of negotiating leaders, accommodationist cultural provisions contribute to ending violence by preventing leadership challenges, rebel fragmentation and remobilization across all civil conflicts. By the same logic, and despite the overwhelming emphasis of peace agreements on integrationist cultural initiatives, integrationist cultural reforms problematize leaders' ability to commit to pacts and to ensure compliance among their rank and file.

Sequelae and Comorbidities of COVID-19 Manifestations on the Cardiac and the Vascular Systems

COVID-19 patients with pre-existing cardiovascular conditions are at greater risk of severe illness due to the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus. This review evaluates the highest risk factors for these patients, not limited to pre-existing hypertension, cardiac arrhythmias, hypercoagulation, ischemic heart disease, and a history of underlying heart conditions. SARS-CoV-2 may also precipitate de novo cardiac complications. The interplay between existing cardiac conditions and de novo cardiac complications is the focus of this review. In particular, SARS-CoV-2 patients present with hypercoagulation conditions, cardiac arrhythmias, as significant complications. Also, cardiac arrhythmias are another well-known cardiovascular-related complication seen in COVID-19 infections and merit discussion in this review. Amid the pandemic, myocardial infarction (MI) has been reported to a high degree in SARS-CoV-2 patients. Currently, the specific causative mechanism of the increased incidence of MI is unclear. However, studies suggest several links to high angiotensin-converting enzyme 2 (ACE2) expression in myocardial and endothelial cells, systemic hyper-inflammation, an imbalance between myocardial oxygen supply and demand, and loss of ACE2-mediated cardio-protection. Furthermore, hypertension and SARS-CoV-2 infection patients’ prognosis has shown mixed results across current studies. For this reason, an in-depth analysis of the interactions between SARS-CoV2 and the ACE2 cardio-protective mechanism is warranted. Similarly, ACE2 receptors are also expressed in the cerebral cortex tissue, both in neurons and glia. Therefore, it seems very possible for both cardiovascular and cerebrovascular systems to be damaged leading to further dysregulation and increased risk of mortality risk. This review aims to discuss the current literature related to potential complications of COVID-19 infection with hypertension and the vasculature, including the cervical one. Finally, age is a significant prognostic indicator among COVID-19 patients. For a mean age group of 70 years, the main presenting symptoms include fever, shortness of breath, and a persistent cough. Elderly patients with cardiovascular comorbidities, particularly hypertension and diabetes, represent a significant group of critical cases with increased case fatality rates. With the current understanding of COVID-19, it is essential to explore the mechanisms by which SARS-CoV-2 operates to improve clinical outcomes for patients suffering from underlying cardiovascular diseases and reduce the risk of such conditions de novo.