Methodological issues in life cycle assessment for remanufactured products:a critical review of existing studies and an illustrative case study

Remanufacturing is an important strategy in the manufacturing industry. A life cycle assessment (LCA) is often used to measure whether, and to what extent, a remanufactured product is ‘better’ for the environment than a newly produced equivalent. In order to obtain valid and meaningful outcomes, LCA standards and guidelines need to be followed. However, for the system boundaries selection in the LCA for remanufacturing the standards and guidelines offer insufficient guidance to practitioners. This paper reports on a literature review conducted to analyze how the first step in the LCA, i.e., the goal and scope definition stage, is shaped in prior LCAs for remanufactured products. The review suggests that the goal and scope definitions are often shrouded in obscurity in prior LCAs for remanufactured products. Moreover, different perspectives that shape the goal and scope definitions are identified and their meanings and assumptions analyzed. An illustrative case study of a real-life remanufactured product demonstrates how different perspectives in the goal and scope definition stage lead to different LCA models and different LCA outcomes. The paper concludes with several recommendations on how to shape the LCA for remanufactured products

Strategic ambidexterity in green product innovation:Obstacles and implications

Scholars and managers routinely point to various uncertainties in explaining why manufacturing firms struggle with green product innovation and why green product innovation is different from conventional product innovation. This explanation is yet unsatisfactory as a thorough understanding of how a firm deals with uncertainty exists in conventional innovation literature. At the same time, there is a lack of agreement and understanding how a firm's capabilities shape its green product innovation practices, which could contribute to this gap. Based on a case study at five multinational manufacturers, this paper sets out to contribute to the capability perspective on green product innovation by understanding how manufacturing firms learn and innovate in order to make and sell greener products. A powerful and favored way for firms to learn and innovate under uncertainty is through strategic ambidexterity. With this learning strategy, firms rely on existing competences in one area (exploitation) while they simultaneously explore new competences in another area (exploration). However, our results show that strategic ambidexterity is oftentimes unachievable due to several factors, and as result, firms are forced to choose between a highly uncertain and risky alternative strategy and a more conservative but also less green strategy based on exploitation only, which is often the preferred option. In addition, our findings shed a new light on the role of uncertainty in green product innovation as we conclude that uncertainties firms face in green product innovation are indeed abundant, but are fundamentally not new nor caused by external sources only

Important maize weeds profit in growth and reproduction from climate change conditions represented by higher temperatures and reduced humidity

Climate change is predicted to result in rising temperatures and reduced precipitation during spring and summer in Central Europe. As a consequence, crops and weeds will be affected. Our study focuses on the three weed species in maize Amaranthus retroflexus, Echinochloa crus-galli and Setaria viridis. These weeds occur numerously in European maize fields and populations are likely to further increase. Yet, there is a lack of knowledge about particular biological strategies of the weeds. Our study focuses on how the weed species respond biologically to the climate change conditions. Experiments were conducted in two climate chambers with a 2°C difference in temperature and the warmer one with 13% less humidity. Emergence, development, biomass and seed production were determined of the weeds grown individually in pots and grown within maize. All tested weed species were taller during the first weeks under the climate change scenario. At later growth phases there was a trade-off between traits measured during vegetative growth and at the time when seeds were produced. To summarize the results, the weed species profited in the order E. crus-galli, S. viridis and A. retroflexus from the climate change conditions. Knowledge of the weeds biological responses to the predicted conditions helps to reduce their long-term population development by targeting crop protection measures at specific growth phases of the weeds. To ensure control of the tested weed species under climate change conditions various weed management strategies are necessary

Wie könnte der Klimawandel das Zusammenspiel von Unkräutern und Feldfrüchten verändern?

Der Klimawandel führt zu vielschichtigen Veränderungen im Agrarökosystem und verändert das Zusammenspiel von Unkräutern und Kulturpflanzen. Unkräuter reagieren individuell auf die Veränderungen. Durch Zu- und Abnahmen, dem Verschwinden und Neuauftreten von Unkrautarten, sowie Veränderungen von Merkmalen und Konkurrenzbeziehungen ergeben sich Umschichtungen im Unkrautartenspektrum, den Abundanzen einzelner Arten und veränderte Schadwirkungen. Doch auch agrarische Reaktionen auf den Klimawandel und Landnutzungswandel beeinflussen dieses Zusammenspiel. Aus den unterschiedlichen Zusammenhängen ergeben sich verschiedene Handlungsmöglichkeiten.Stichwörter: Feldfrüchte, Klimawandel, Unkräuter, Verschiebungen, Wechselwirkung, ZusammenspielHow might climate change alter the interactions between weeds and crops?Climate change will affect the agricultural ecosystem on different levels. It also alters interactions of weeds and crops. Each weed reacts on an individual basis to changes. Weeds can show an increase or decrease in abundance, can invade new areas or can completely vanish. They also can change traits to be more competitive. Overall, this will result in a different abundance and composition of weeds, as well as a different damaging potential. Land-use changes and agricultural adoption strategies to climate change will have dramatic influence on the interactions as well. Based on these interactions, there will be various options for dealing with climate change.Keywords: Climate change, crops, interactions, shifts, weed

ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition

The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot types and various expressions. To this end, we introduce ExGenNet, a novel deep generative approach for facial expressions on humanoid robots. ExGenNets connect a generator network to reconstruct simplified facial images from robot joint configurations with a classifier network for state-of-the-art facial expression recognition. The robots' joint configurations are optimized for various expressions by backpropagating the loss between the predicted expression and intended expression through the classification network and the generator network. To improve the transfer between human training images and images of different robots, we propose to use extracted features in the classifier as well as in the generator network. Unlike most studies on facial expression generation, ExGenNets can produce multiple configurations for each facial expression and be transferred between robots. Experimental evaluations on two robots with highly human-like faces, Alfie (Furhat Robot) and the android robot Elenoide, show that ExGenNet can successfully generate sets of joint configurations for predefined facial expressions on both robots. This ability of ExGenNet to generate realistic facial expressions was further validated in a pilot study where the majority of human subjects could accurately recognize most of the generated facial expressions on both the robots