Trends in Emission Inventory of Marine Traffic for Port of Haifa

Located in the Middle East, Haifa Port serves both local and international trade interests (from Asia, Europe, America, Africa, etc.). Due to its strategic location, the port is part of the Belt and Road initiative. This research investigates Haifa Port’s emissions contribution to the existing daily emission inventory level in the area. This research is based on a developed full bottom-up model framework that looks at the single vessel daily voyage through its port call stages. The main data sources for vessel movements used in this research are the Israel Navy’s movements log and the Israel Administration of Shipping and Ports’ (ASP) operational vessel movements and cargo log. The Fuel Consumption (FC) data and Sulfur Content (SC) levels are based on official Israel ASP survey data. The observation years in this research are 2010–2018, with a focus on the Ocean-Going Vessel (OGV) type only. The results show that the vessel fleet calling at Israel ports mainly comprises vessels that have a lower engine tier grade (i.e., Tier 0 and 1), which is considered a heavy contributor to nitrogen oxide (NOx) pollution. The study recommends an additional cost charged (selective tariff) to reflect the external social cost linked to the single vessel air pollution combined with supportive technological infrastructure and economic incentive tools (e.g., electric subsidy) to attract or influence vessel owners to assign vessels equipped with new engine tier grades for calls at Israeli ports.

Adaptive and powerful microbiome multivariate association analysis via feature selection

ABSTRACT The important role of human microbiome is being increasingly recognized in health and disease conditions. Since microbiome data is typically high dimensional, one popular mode of statistical association analysis for microbiome data is to pool individual microbial features into a group, and then conduct group-based multivariate association analysis. A corresponding challenge within this approach is to achieve adequate power to detect an association signal between a group of microbial features and the outcome of interest across a wide range of scenarios. Recognizing some existing methods’ susceptibility to the adverse effects of noise accumulation, we introduce the Adaptive Microbiome Association Test (AMAT), a novel and powerful tool for multivariate microbiome association analysis, which unifies both blessings of feature selection in high-dimensional inference and robustness of adaptive statistical association testing. AMAT first alleviates the burden of noise accumulation via distance correlation learning, and then conducts a data-adaptive association test under the flexible generalized linear model framework. Extensive simulation studies and real data applications demonstrate that AMAT is highly robust and often more powerful than several existing methods, while preserving the correct type I error rate. A free implementation of AMAT in R computing environment is available at https://github.com/kzb193/AMAT.

Online student support: a framework for embedding support interventions into the online learning cycle

Support is one of the crucial elements of online students’ success. Although many support strategies have been documented in the past, less is known at what stages of the learning cycle suggested interventions can be embedded into the online learning curriculum. This paper offers a systematic review of the 28 empirical studies on effective support strategies and interventions that are indexed by the SCOPUS database between 2010 and 2020. Following an Inclusive Student Services Process Model framework, identified strategies are allocated across different phases of student learning to indicate where and when they can be delivered to online students. The analysis suggests that the effectiveness of the support provision depends on the time when support is offered. Furthermore, it was found that two areas support delivery, namely support at transitions and measurement of support interventions, remain under-researched. Finally, the analysis showed two emerging trends in online students support: an increasing role of technology and social network sites to design support interventions and a shift to a more personalised yet holistic approach to student support.

A Cost Model for 3D Woven Preforms

Lack of cost information is a barrier to acceptance of 3D woven preforms as reinforcements for composite materials, compared with 2D preforms. A parametric, resource-based technical cost model (TCM) was developed for 3D woven preforms based on a novel relationship equating manufacturing time and 3D preform complexity. Manufacturing time, and therefore cost, was found to scale with complexity for seventeen bespoke manufactured 3D preforms. Two sub-models were derived for a Weavebird loom and a Jacquard loom. For each loom, there was a strong correlation between preform complexity and manufacturing time. For a large, highly complex preform, the Jacquard loom is more efficient, so preform cost will be much lower than for the Weavebird. Provided production is continuous, learning, either by human agency or an autonomous loom control algorithm, can reduce preform cost for one or both looms to a commercially acceptable level. The TCM cost model framework could incorporate appropriate learning curves with digital twin/multi-variate analysis so that cost per preform of bespoke 3D woven fabrics for customised products with low production rates may be predicted with greater accuracy. A more accurate model could highlight resources such as tooling, labour and material for targeted cost reduction.

Performance Evaluation of a Maneuver Classification Algorithm Using Different Motion Models in a Multi-Model Framework

Environment perception is one of the major challenges in the vehicle industry nowadays, as acknowledging the intentions of the surrounding traffic participants can profoundly decrease the occurrence of accidents. Consequently, this paper focuses on comparing different motion models, acknowledging their role in the performance of maneuver classification. In particular, this paper proposes utilizing the Interacting Multiple Model framework complemented with constrained Kalman filtering in this domain that enables the comparisons of the different motions models’ accuracy. The performance of the proposed method with different motion models is thoroughly evaluated in a simulation environment, including an observer and observed vehicle.

A SEEC Model Based on the DPSIR Framework Approach for Watershed Ecological Security Risk Assessment: A Case Study in Northwest China

The DPSIR model is a conceptual model established by the European Environment Agency to solve environmental problems. It provides an overall framework for analysis of environmental problems from five aspects: driving force (D), pressure (P), state (S), impact (I), and response (R). Through use of the DPSIR model framework, this paper presents the SEEC model approach for evaluating watershed ecological security. The SEEC model considers four aspects: socioeconomic impact (S), ecological health (E), ecosystem services function (E), and control management (C). Through screening, 38 evaluation indicators of the SEEC model were determined. The evaluation results showed that the ecological security index of the study area was >80, indicating a generally safe level. The lowest score was mainly attributable to the low rate of treatment of rural domestic sewage. The water quality status was used to evaluate the applicability of the SEEC model, and the calculation results indicated that the higher the score of the ecological security evaluation results, the better the water quality status. The findings show that the SEEC model demonstrates satisfactory applicability to evaluation of watershed ecological security.

SDTM: A Novel Topic Model Framework for Syndrome Differentiation in Traditional Chinese Medicine

Syndrome differentiation is the most basic diagnostic method in traditional Chinese medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity, and vagueness. Recently, artificial intelligent methods have been introduced to discover the regularities of syndrome differentiation from TCM medical records, but the existing DM algorithms failed to consider how a syndrome is generated according to TCM theories. In this paper, we propose a novel topic model framework named syndrome differentiation topic model (SDTM) to dynamically characterize the process of syndrome differentiation. The SDTM framework utilizes latent Dirichlet allocation (LDA) to discover the latent semantic relationship between symptoms and syndromes in mass of Chinese medical records. We also use similarity measurement method to make the uninterpretable topics correspond with the labeled syndromes. Finally, Bayesian method is used in the final differentiated syndromes. Experimental results show the superiority of SDTM over existing topic models for the task of syndrome differentiation.

A Multi Model Framework for Grading of Human Emotion Using CNN and Computer Vision

Emotion analysis is an area which is been widely used in the forensic crime detection domain, a mentoring device for depressed students, psychologically affected patient treatment. The current system helps only in identifying the emotions but not in identifying the level of emotions like whether the individual is truly happy/sad or pretending to be happy /sad. In this proposed work a novel methodology has been introduced. We have rebuilt the Traditional Local Binary Pattern (LBP) feature operator to image the expression and combine the abstract characteristics of facial expression learned from the neural network of deep convolution with the modified features of the texture of the LBP facial expression in the full connection layer. These extracted features have been subjected as input for CNN Alex Net to classify the level of emotions. The results obtained in this phase are used in the confusion matrix for analysis of grading of emotions like Grade-1, Grade-2, and Grade-3 obtained an accuracy of 87.58% in the comparative analysis.

Linear Mixed Models

The linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.

Determining the economic impact of cryptocurrency adoption on international trade from a gravity model framework

As cryptocurrencies develop and circulate at greater rates, countries have appeared to consider the technology as an adoptable medium of exchange. By expanding the influence of cryptocurrencies through adoption, countries raise its impact on the global economy. This paper is the first to apply an augmented version of the gravity model to examine the effects of global cryptocurrency adoption on international trade. This empirical study involves aggregating datasets on U.S. bilateral trade flows, gravity variable statistics, and the adoption of cryptocurrencies. In application of the gravity model, regression analyses are used on the aggregated data to test the magnitude of cryptocurrencies’ impact on trade. Based on the overall findings, the variables for cryptocurrency adoption produce negative coefficients suggesting a negative correlation between the adoption of cryptocurrencies and international trade. The central tendency in the empirical evidence offers the interpretation that countries with weak institutions to promote trade are more likely to adopt cryptocurrencies resulting in a negative association between cryptocurrency adoption and trade.