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Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates 1 . Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems 2 . Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models 3 . Bayesian optimization has also been recently applied in chemistry 4 – 9 ; however, its application and assessment for reaction optimization in synthetic chemistry has not been investigated. Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices. We collect a large benchmark dataset for a palladium-catalysed direct arylation reaction, perform a systematic study of Bayesian optimization compared to human decision-making in reaction optimization, and apply Bayesian optimization to two real-world optimization efforts (Mitsunobu and deoxyfluorination reactions). Benchmarking is accomplished via an online game that links the decisions made by expert chemists and engineers to real experiments run in the laboratory. Our findings demonstrate that Bayesian optimization outperforms human decisionmaking in both average optimization efficiency (number of experiments) and consistency (variance of outcome against initially available data). Overall, our studies suggest that adopting Bayesian optimization methods into everyday laboratory practices could facilitate more efficient synthesis of functional chemicals by enabling better-informed, data-driven decisions about which experiments to run. Bayesian optimization is applied in chemical synthesis towards the optimization of various organic reactions and is found to outperform scientists in both average optimization efficiency and consistency.

Geo-ecological security can be understood as the geo-ecological environment (natural-social-production system) in a certain area is not subject to the threat of geological disasters caused by active technogenesis, such as engineering construction and agricultural production. This article elaborates on the relationship between the geo-ecological security and related concepts, reviews and summarizes the current index system and methods of the geo-ecological security evaluation, and puts forward suggestions for using 3S technology in combination with landscape ecological theory to study geo-ecological security.

The current trajectory for crop yields is insufficient to nourish the world’s population by 2050 1 . Greater and more consistent crop production must be achieved against a backdrop of climatic stress that limits yields, owing to shifts in pests and pathogens, precipitation, heat-waves and other weather extremes. Here we consider the potential of plant sciences to address post-Green Revolution challenges in agriculture and explore emerging strategies for enhancing sustainable crop production and resilience in a changing climate. Accelerated crop improvement must leverage naturally evolved traits and transformative engineering driven by mechanistic understanding, to yield the resilient production systems that are needed to ensure future harvests. Genetic strategies for improving the yield and sustainability of agricultural crops, and the resilience of crops in the face of biotic and abiotic stresses contingent on projected climate change, are evaluated.

Advances in information and communication technology have created innovator technologies such as cloud computing, Internet of Things, big data analysis and artificial intelligence. These technologies have penetrated production systems and converted them smart. However, this transformation did not only affect production systems, but also differentiated supplier selection processes. In the supplier selection process, the usage of new technologies along with traditional and green criteria extensively has been investigated in recent years. This paper aims to develop a new group decision-making approach based on Industry 4.0 components for selecting the best green supplier by integrating AHP and TOPSIS methods under the Pythagorean fuzzy environment. In the proposed approach, judgments of different experts are expressed by linguistic terms based on Pythagorean fuzzy numbers. The interval-valued Pythagorean Fuzzy AHP method is utilized to determine the criteria weights. The Pythagorean Fuzzy TOPSIS method based on the distances of suppliers is applied to obtain the ranking of the suppliers and determine the most suitable one. Finally, a real case study on an agricultural tools and machinery company is presented to indicate the effectiveness and accuracy of the proposed selection approach.

Global agricultural feeds over 7 billion people, but is also a leading cause of environmental degradation. Understanding how alternative agricultural production systems, agricultural input efficiency, and food choice drive environmental degradation is necessary for reducing agriculture's environmental impacts. A meta-analysis of life cycle assessments that includes 742 agricultural systems and over 90 unique foods produced primarily in high-input systems shows that, per unit of food, organic systems require more land, cause more eutrophication, use less energy, but emit similar greenhouse gas emissions (GHGs) as conventional systems; that grass-fed beef requires more land and emits similar GHG emissions as grain-feed beef; and that low-input aquaculture and non-trawling fisheries have much lower GHG emissions than trawling fisheries. In addition, our analyses show that increasing agricultural input efficiency (the amount of food produced per input of fertilizer or feed) would have environmental benefits for both crop and livestock systems. Further, for all environmental indicators and nutritional units examined, plant-based foods have the lowest environmental impacts; eggs, dairy, pork, poultry, non-trawling fisheries, and non-recirculating aquaculture have intermediate impacts; and ruminant meat has impacts ∼100 times those of plant-based foods. Our analyses show that dietary shifts towards low-impact foods and increases in agricultural input use efficiency would offer larger environmental benefits than would switches from conventional agricultural systems to alternatives such as organic agriculture or grass-fed beef.

Agriculture in South Asia is vulnerable to climate change. Therefore, adaptation measures are required to sustain agricultural productivity, to reduce vulnerability, and to enhance the resilience of the agricultural system to climate change. There are many adaptation practices in the production systems that have been proposed and tested for minimizing the effects of climate change. Some socioeconomic and political setup contributes to adaptation, while others may inhibit it. This paper presents a systematic review of the impacts of climate change on crop production and also the major options in the agricultural sector that are available for adaptation to climate change. One of the key conclusions is that agricultural practices that help climate change adaptation in agriculture are available, while the institutional setup to implement and disseminate those technical solutions is yet to be strengthened. Thus, it is important to examine how to bring the required institutional change, generate fund to invest on these changes, and design dynamic policies for long-term climate change adaptation in agriculture rather than a mere focus on agricultural technology. This is one of the areas where South Asian climate policies require reconsidering to avoid possible maladaptation in the long run.

Modern production systems face enormous challenges due to rising customer requirements resulting in complex production systems. The operational efficiency in the competitive industry is ensured by an adequate production control system that manages all operations in order to optimize key performance indicators. Currently, control systems are mostly based on static and model-based heuristics, requiring significant human domain knowledge and, hence, do not match the dynamic environment of manufacturing companies. Data-driven reinforcement learning (RL) showed compelling results in applications such as board and computer games as well as first production applications. This paper addresses the design of RL to create an adaptive production control system by the real-world example of order dispatching in a complex job shop. As RL algorithms are “black box” approaches, they inherently prohibit a comprehensive understanding. Furthermore, the experience with advanced RL algorithms is still limited to single successful applications, which limits the transferability of results. In this paper, we examine the performance of the state, action, and reward function RL design. When analyzing the results, we identify robust RL designs. This makes RL an advantageous control system for highly dynamic and complex production systems, mainly when domain knowledge is limited.

PurposeThe purpose of this paper is to explore how rising technologies from Industry 4.0 can be integrated with circular economy (CE) practices to establish a business model that reuses and recycles wasted material such as scrap metal or e-waste.Design/methodology/approachThe qualitative research method was deployed in three stages. Stage 1 was a literature review of concepts, successful factors and barriers related to the transition towards a CE along with sustainable supply chain management, smart production systems and additive manufacturing (AM). Stage 2 comprised a conceptual framework to integrate and evaluate the synergistic potential among these concepts. Finally, stage 3 validated the proposed model by collecting rich qualitative data based on semi-structured interviews with managers, researchers and professors of operations management to gather insightful and relevant information.FindingsThe outcome of the study is the recommendation of a circular model to reuse scrap electronic devices, integrating web technologies, reverse logistics and AM to support CE practices. Results suggest a positive influence from improving business sustainability by reinserting waste into the supply chain to manufacture products on demand.Research limitations/implicationsThe impact of reusing wasted materials to manufacture new products is relevant to minimising resource consumption and negative environmental impacts. Furthermore, it avoids hazardous materials ending up in landfills or in the oceans, seriously threatening life in ecosystems. In addition, reuse of wasted material enables the development of local business networks that generate jobs and improve economic performance.Practical implicationsFirst, the impact of reusing materials to manufacture new products minimises resource consumption and negative environmental impacts. The circular model also encourages keeping hazardous materials that seriously threaten life in ecosystems out of landfills and oceans. For this study, it was found that most urban waste is plastic and cast iron, leaving room for improvement in increasing recycling of scrap metal and similar materials. Second, the circular business model promotes a culture of reusing and recycling and motivates the development of collection and processing techniques for urban waste through the use of three-dimensional (3D) printing technologies and Industry 4.0. In this way, the involved stakeholders are focused on the technical parts of recycling and can be better dedicated to research, development and innovation because many of the processes will be automated.Social implicationsThe purpose of this study was to explore how Industry 4.0 technologies are integrated with CE practices. This allows for the proposal of a circular business model for recycling waste and delivering new products, significantly reducing resource consumption and optimising natural resources. In a first stage, the circular business model can be used to recycle electronic scrap, with the proposed integration of web technologies, reverse logistics and AM as a technological platform to support the model. These have several environmental, sociotechnical and economic implications for society.Originality/valueThe sociotechnical aspects are directly impacted by the circular smart production system (CSPS) management model, since it creates a new culture of reuse and recycling techniques for urban waste using 3D printing technologies, as well as Industry 4.0 concepts to increase production on demand and automate manufacturing processes. The tendency of the CSPS model is to contribute to deployment CE in the manufacture of new products or parts with AM approaches, generating a new path of supply and demand for society.

Pichia pastoris is an established protein expression host mainly applied for the production of biopharmaceuticals and industrial enzymes. This methylotrophic yeast is a distinguished production system for its growth to very high cell densities, for the available strong and tightly regulated promoters, and for the options to produce gram amounts of recombinant protein per litre of culture both intracellularly and in secretory fashion. However, not every protein of interest is produced in or secreted by P. pastoris to such high titres. Frequently, protein yields are clearly lower, particularly if complex proteins are expressed that are hetero-oligomers, membrane-attached or prone to proteolytic degradation. The last few years have been particularly fruitful because of numerous activities in improving the expression of such complex proteins with a focus on either protein engineering or on engineering the protein expression host P. pastoris. This review refers to established tools in protein expression in P. pastoris and highlights novel developments in the areas of expression vector design, host strain engineering and screening for high-level expression strains. Breakthroughs in membrane protein expression are discussed alongside numerous commercial applications of P. pastoris derived proteins.

Dairy production systems have rapidly intensified over the past several decades. Dairy farms in many world regions are larger and concentrated in fewer hands. Higher productivity can increase overall economic gains but also incurs site-specific social and environmental costs. In this paper, we review the drivers and impacts of dairy intensification. We identify in the literature four prominent concerns about dairy intensification: the environment, animal welfare, socioeconomic well-being, and human health. We then critically assess three frameworks—sustainable intensification, multifunctionality, and agroecology—which promise win–win solutions to these concerns. We call for research and policy approaches that can better account for synergies and trade-offs among the multiple dimensions of dairy impacts. Specifically, we suggest the need to (1) consider dairy system transitions within broader processes of social-environmental change and (2) investigate how certain framings and metrics may lead to uneven social-environmental outcomes. Such work can help visualize transformations towards more equitable, ethical, and sustainable food systems.