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1. On-demand warehousing: main features and business models

   Tornese, F ; Unnu, K ; Gnoni, MG ; Pazour, J.A. ( July 2020 , XXV Summer School "Francsco Turco" - Industrial Systems Engineering)

   Logistics and distribution need to be more responsive and flexible to satisfy changing and demanding customer requirements due to e-commerce and customization trends. This work focuses in particular on warehousing, with the aim of understanding how emerging business models provide companies with additional ways to acquire warehouse space or fulfillment services. To do so, this work classifies and describes traditional warehouse models. Next, on-demand warehousing is analyzed as an emerging business-to-business (B2B) model that embraces the sharing economy principle of accessing resources rather than owning them. On-demand warehousing companies operate through online platforms connecting companies who have underutilized warehouses or fulfillment capacity to other ones searching for warehousing services. On-demand warehousing enables more flexible resource acquisition, as fixed cost investments are not necessary, and lengthy negotiations are eliminated through a standardized contract between the on-demand platform and the renter. This work contributes to the literature through an improved understanding and description of the main features of on-demand warehousing, representing a starting point for further research on this topic. Future developments are needed on the analysis of the main decisions a lender of space has to make when choosing an on-demand model. 

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2. Persistent, Global, Unique: The three key requirements for a trusted identifier system for physical samples

   https://doi.org/10.3897/biss.3.37334  

   Lehnert, Kerstin ; Klump, Jens ; Wyborn, Lesley ; Ramdeen, Sarah ( June 2019 , Biodiversity Information Science and Standards)

   There is growing recognition that unambiguous citation and tracking of physical samples allows previously impossible linking of samples to data and publications, linking and integration of sample-based observations across data systems, and paves the road towards advanced data mining of sample-based data. And in recent years, there has been an uptake in the use of Persistent Identifiers (PIDs) for physical samples to support such citation and tracking. The IGSN (International Geo Sample Number) is a PID for physical samples. It was originally developed for the solid earth sciences, and has evolved into an international PID system with members in five continents and a network of active allocating agents. It has been adopted by a growing number and range of stakeholders worldwide, including national geological surveys, research infrastructure providers, collection curators, researchers, and data managers, and by other disciplines that need to refer to physical samples. Nearly 6.9 million samples have been registered with IGSNs so far. The IGSN system uses the Handle System (Kahn and Wilensky 1995; see also Handle.Net ® ) and has an international organization, IGSN e.V., to manage its governance structure and the technical architecture. The recent expansion of the IGSN beyond the geosciences into other domains such as biodiversity, archeology, and material sciences confirms the power of its concept and implementation, but imposes substantial pressures on the existing capacity and capabilities of the IGSN architecture and its governing organization. Modifications to the IGSN organizational and technical architecture are necessary at this point to keep pace with the growing demand and expectations. These changes are also necessary to ensure trustworthy and sustainable services for PID registration and resolution in a maturing research data ecosystem. The essential criteria for a trustworthy system include an organizational foundation that ensures longevity, sustainability, proper governance, and regular quality assessment of registration services. It also includes a reliable and secure technical platform, based on open standards, which is sufficiently scalable and flexible to accommodate the growing diversity of specimen types, use cases, and stakeholder requirements. In 2018, a major planning project for the IGSN was funded by the Alfred P. Sloan Foundation. An international group of experts participates in re-designing and improving the existing organization and technical architecture of the IGSN system, revising the current business model of the IGSN e.V. and professionalizing its operations. The goal is for the IGSN system to be able to respond to, and support in a sustainable manner, the rapidly growing demands of a global and increasingly multi-disciplinary user community, and to ensure that the IGSN will be a trustworthy, stable, and adaptable persistent identifier system for material samples, both technically and organizationally. The end result should also satisfy and facilitate participation across research domains, and will be a reliable component of the evolving research data ecosystem. Finally, it will ensure that the IGSN is recognized as a trusted partner by data infrastructure providers and the science community alike. 

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3. On the Unique Features and Benefits of On-Demand Distribution Models

   Pazour, Jennifer A ; Unnu, Kaan ( January 2018 , Progress in material handling research)

   To close the gap between current distribution operations and today’s customer expectations, firms need to think differently about how resources are acquired, managed and allocated to fulfill customer requests. Rather than optimize planned resource capacity acquired through ownership or long- term partnerships, this work focuses on a specific supply-side innovation – on-demand distribution platforms. On-demand distribution systems move, store, and fulfill goods by matching autonomous suppliers' resources (warehouse space, fulfillment capacity, truck space, delivery services) to requests on-demand. On-demand warehousing systems can provide resource elasticity by allowing capacity decisions to be made at a finer granularity (at the pallet-level) and commitment (monthly versus yearly), than construct or lease options. However, such systems are inherently more complex than traditional systems, as well as have varying costs and operational structures (e.g., higher variable costs, but little or no fixed costs). New decision- supporting models are needed to capture these trade-offs. 

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4. Wake-up radio-based data forwarding for green wireless networks

   https://doi.org/10.1016/j.comcom.2020.05.046  

   Koutsandria, G. ; DiValerio, V. ; Spenza, D. ; Basagni, S. ; Petrioli, C. ( April 2020 , Computer communications)

   Green wireless networks Wake-up radio Energy harvesting Routing Markov decision process Reinforcement learning 1. Introduction With 14.2 billions of connected things in 2019, over 41.6 billions expected by 2025, and a total spending on endpoints and services that will reach well over $1.1 trillion by the end of 2026, the Internet of Things (IoT) is poised to have a transformative impact on the way we live and on the way we work [1–3]. The vision of this ‘‘connected continuum’’ of objects and people, however, comes with a wide variety of challenges, especially for those IoT networks whose devices rely on some forms of depletable energy support. This has prompted research on hardware and software solutions aimed at decreasing the depen- dence of devices from ‘‘pre-packaged’’ energy provision (e.g., batteries), leading to devices capable of harvesting energy from the environment, and to networks – often called green wireless networks – whose lifetime is virtually infinite. Despite the promising advances of energy harvesting technologies, IoT devices are still doomed to run out of energy due to their inherent constraints on resources such as storage, processing and communica- tion, whose energy requirements often exceed what harvesting can provide. The communication circuitry of prevailing radio technology, especially, consumes relevant amount of energy even when in idle state, i.e., even when no transmissions or receptions occur. Even duty cycling, namely, operating with the radio in low energy consumption ∗ Corresponding author. E-mail address: koutsandria@di.uniroma1.it (G. Koutsandria). https://doi.org/10.1016/j.comcom.2020.05.046 (sleep) mode for pre-set amounts of time, has been shown to only mildly alleviate the problem of making IoT devices durable [4]. An effective answer to eliminate all possible forms of energy consumption that are not directly related to communication (e.g., idle listening) is provided by ultra low power radio triggering techniques, also known as wake-up radios [5,6]. Wake-up radio-based networks allow devices to remain in sleep mode by turning off their main radio when no communication is taking place. Devices continuously listen for a trigger on their wake-up radio, namely, for a wake-up sequence, to activate their main radio and participate to communication tasks. Therefore, devices wake up and turn their main radio on only when data communication is requested by a neighboring device. Further energy savings can be obtained by restricting the number of neighboring devices that wake up when triggered. This is obtained by allowing devices to wake up only when they receive specific wake-up sequences, which correspond to particular protocol requirements, including distance from the destina- tion, current energy status, residual energy, etc. This form of selective awakenings is called semantic addressing [7]. Use of low-power wake-up radio with semantic addressing has been shown to remarkably reduce the dominating energy costs of communication and idle listening of traditional radio networking [7–12]. This paper contributes to the research on enabling green wireless networks for long lasting IoT applications. Specifically, we introduce a ABSTRACT This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively. 

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5. Site Resource Inventories – a Missing Link in the Circular City's Information Flow

   https://doi.org/10.5194/adgeo-54-23-2020  

   Baganz, Gösta ; Proksch, Gundula ; Kloas, Werner ; Lorleberg, Wolf ; Baganz, Daniela ; Staaks, Georg ; Lohrberg, Frank ( January 2020 , Advances in Geosciences)

   Abstract. A circular city builds upon the principles of circular economy, which keyconcepts of reduce, reuse, recycle, and recover lead to a coupling ofresources: products and by-products of one production process become theinput of another one, often in local vicinity. However, sources, types andavailable quantities of underutilised resources in cities are currently notwell documented. Therefore, there is a missing link in the information flowof the circular city between potential users and site-specific data. Toclose this gap, this study introduces the concept of a site resourceinventory in conjunction with a new information model that can manage thedata needed for advancing the circular city. A core taxonomy of terms isestablished as the foundation for the information model: the circulareconomy is defined as a network of circular economy entities which areregarded as black boxes and connected by their material and energy inputsand outputs. This study proposes a site resource inventory, which is acollection of infrastructural and building-specific parameters that assessthe suitability of urban sites for a specific circular economy entity. Aninformation model is developed to manage the data that allows the entitiesto effectively organise the allocation and use of resources within thecircular city and its material and energy flows. The application of thisinformation model was demonstrated by comparing the demand and availabilityof required alternative resources (e.g. greywater) at a hypothetical sitecomprising a commercial aquaponic facility (synergistic coupling of fish andvegetables production) and a residential building. For the implementation ofthe information model a proposal is made which uses the publicly availablegeodata infrastructure of OpenStreetMap and adopts its tag system tooperationalise the integration of circular economy data by introducing newtags. A site resource inventory has the potential to bring togetherinformation needs and it is thus intended to support companies when makingtheir business location decisions or to support local authorities in theplanning process. 

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