Collaborative Workplace Design: A Knowledge-Based Approach to Promote Human–Robot Collaboration and Multi-Objective Layout Optimization

The innovation-driven Industry 5.0 leads us to consider humanity in a prominent position as the center of the manufacturing field even more than Industry 4.0. This pushes us towards the hybridization of manufacturing plants promoting a full collaboration between humans and robots. However, there are currently very few workplaces where effective Human–Robot Collaboration takes place. Layout designing plays a key role in assuring safe and efficient Human–Robot Collaboration. The layout design, especially in the context of collaborative robotics, is a complex problem to face, since it is related to safety, ergonomics, and productivity aspects. In the current work, a Knowledge-Based Approach (KBA) is adopted to face the complexity of the layout design problem. The framework resulting from the KBA allows for developing a modeling paradigm that enables us to define a streamlined approach for the layout design. The proposed approach allows for placing resource within the workplace according to a defined optimization criterion, and also ensures compliance with various standards. This approach is applied to an industrial case study in order to prove its feasibility. A what-if analysis is performed by applying the proposed approach. Changing three control factors (i.e., minimum distance, robot speed, logistic space configuration) on three levels, in a Design of Experiments, 27 layout configurations of the same workplace are generated. Consequently, the inputs that most affect the layout design are identified by means of an Analysis of Variance (ANOVA). The results show that only one layout is eligible to be the best configuration, and only two out of three control factors are very significant for the designing of the HRC workplace layout. Hence, the proposed approach enables the designing of standard compliant and optimized HRC workplace layouts. Therefore, several alternatives of the layout for the same workplace can be easily generated and investigated in a systematic manner.

PERFICT: a Re-imagined Foundation for Predictive Ecology

Making predictions from ecological models – and comparing these predictions to data – offers a coherent approach to objectively evaluate model quality, regardless of model complexity or modeling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies and the public has been hampered by disparate perspectives on prediction and inadequate integrated approaches. We present an updated foundation for Predictive Ecology that is based on 7 principles applied to ecological models: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows, that are routinely Tested (PERFICT). We outline some benefits of working with these principles: 1) accelerating science; 2) bridging to data science; and 3) improving science-policy integration.

Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic

Background: Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially challenging to the commonly used mechanistic model when our understanding of this pandemic rapidly refreshes.Objective: We aim to develop a data-driven workflow to extract, process, and develop deep learning (DL) methods to model the COVID-19 epidemic. We provide an alternative modeling approach to complement the current mechanistic modeling paradigm.Method: We extensively searched, extracted, and annotated relevant datasets from over 60 official press releases in Hubei, China, in 2020. Multivariate long short-term memory (LSTM) models were developed with different architectures to track and predict multivariate COVID-19 time series for 1, 2, and 3 days ahead. As a comparison, univariate LSTMs were also developed to track new cases, total cases, and new deaths.Results: A comprehensive dataset with 10 variables was retrieved and processed for 125 days in Hubei. Multivariate LSTM had reasonably good predictability on new deaths, hospitalization of both severe and critical patients, total discharges, and total monitored in hospital. Multivariate LSTM showed better results for new and total cases, and new deaths for 1-day-ahead prediction than univariate counterparts, but not for 2-day and 3-day-ahead predictions. Besides, more complex LSTM architecture seemed not to increase overall predictability in this study.Conclusion: This study demonstrates the feasibility of DL models to complement current mechanistic approaches when the exact epidemiological mechanisms are still under investigation.

Generative hypergraph clustering: From blockmodels to modularity

Hypergraphs are a natural modeling paradigm for networked systems with multiway interactions. A standard task in network analysis is the identification of closely related or densely interconnected nodes. We propose a probabilistic generative model of clustered hypergraphs with heterogeneous node degrees and edge sizes. Approximate maximum likelihood inference in this model leads to a clustering objective that generalizes the popular modularity objective for graphs. From this, we derive an inference algorithm that generalizes the Louvain graph community detection method, and a faster, specialized variant in which edges are expected to lie fully within clusters. Using synthetic and empirical data, we demonstrate that the specialized method is highly scalable and can detect clusters where graph-based methods fail. We also use our model to find interpretable higher-order structure in school contact networks, U.S. congressional bill cosponsorship and committees, product categories in copurchasing behavior, and hotel locations from web browsing sessions.

IDENTIFICATION OF DAMAGE GEAR LANDING SYSTEM OF CESSNA AIRCRAFT

Designing a system to identify damage based on damage symptom data, so as to get repair handling in accordance with the procedure using a forward chaining expert system, where the expert system is one of the computer systems that aims to solve problems by imitating the human mindset as an expert applied to one of them. Aircraft components that are frequently maintained and damaged are found, namely the landing gear of the Cessna Grand Caravan C208-B aircraft. In the process of making the damage identification system in this final project, the UML (Unified Modeling Language) modeling paradigm and the PHP programming language are used. From the research results, the landing gear damage identification system of the Cessna Grand Caravan C208-B aircraft using a forward chaining expert system can facilitate the search for damage and handling for users or technicians in dealing with a symptom that occurs in the landing gear of the Cessna Grand Caravan C208-B aircraft

On the modeling paradigm of plant root nutrient acquisition

Plant root nutrient acquisition, and to a lesser extent foliar nutrient uptake, maintain plant metabolism and strongly regulate terrestrial biogeochemistry and carbon-climate feedbacks. However, terrestrial biogeochemical models differ in their representations of plant root nutrient acquisition, leading to significantly different, and uncertain, carbon cycle and future climate projections. Here we first review biogeochemical principles and observations relevant to three essential plant root nutrient acquisition mechanisms: activity of nutrient acquiring proteins, maintenance of nutrient stoichiometry, and energy expenditure for these processes. We next examine how these mechanisms are considered in three existing modeling paradigms, and conclude by recommending the capacity-based approach, the need for observations, and necessary modeling developments of plant root nutrient acquisition to improve carbon-climate feedback projections.

The Dynamics of Sustainability Risks in the Global Coffee Supply Chain: A Case of Indonesia–UK

Indonesia is one of the leading global coffee producers, and the sustainability of its coffee supply chains is therefore of crucial importance, not only for the coffee sector, but also for the thousands of livelihoods involved. Recognising sustainability risks within supply chains is an important component of understanding logistics. This research investigated the sustainability risks in the Indonesia–UK coffee supply chain by using System Dynamics (SD), a simulation modeling paradigm commonly used to assess complex systems. The model parameters and other components of the dynamic model were extracted through interviews with key stakeholders in the coffee supply chain, supported by evidence from a literature review. The model was then verified and validated in different stages, before being used to investigate five different what-if scenarios to consider changes to parameters in the system. The results of this investigation demonstrate the importance of improving agricultural productivity to support a sustainable coffee supply chain. This research also confirms that by combining the SD model and the multiple criteria decision-making technique, it is possible to achieve a more practical and accurate solution than by the individual tool alone, thus ensuring a better understanding of the whole issues affecting the coffee supply chain.