Developing countries face a difficult challenge in meeting the growing demands for food, water, and energy, which is further compounded by climate change. Effective adaptation to change requires the efficient use of land, water, energy, and other vital resources, and coordinated efforts to minimize trade-offs and maximize synergies. However, as in many developing countries, the policy process in South Asia generally follows a sectoral approach that does not take into account the interconnections and interdependence among the three sectors. Although the concept of a water-energy-food nexus is gaining currency, and adaptation to climate change has become an urgent need, little effort has been made so far to understand the linkages between the nexus perspective and adaptation to climate change. Using the Hindu Kush Himalayan region as an example, this article seeks to increase understanding of the interlinkages in the water, energy, and food nexus, explains why it is important to consider this nexus in the context of adaptation responses, and argues that focusing on trade-offs and synergies using a nexus approach could facilitate greater climate change adaptation and help ensure food, water, and energy security by enhancing resource use efficiency and encouraging greater policy coherence. It concludes that a nexus-based adaption approach-which integrates a nexus perspective into climate change adaptation plans and an adaptation perspective into development plans-is crucial for effective adaptation. The article provides a conceptual framework for considering the nexus approach in relation to climate change adaptation, discusses the potential synergies, trade-offs, and offers a broader framework for making adaptation responses more effective. Policy relevance This article draws attention to the importance of the interlinkages in the water, energy, and food nexus, and the implications for sustainable development and adaptation. The potential synergies and complementarities among the sectors should be used to guide formulation of effective adaptation options. The issues highlight the need for a shift in policy approaches from a sectoral focus, which can result in competing and counterproductive actions, to an integrated approach with policy coherence among the sectors that uses knowledge of the interlinkages to maximize gain, optimize trade-offs, and avoid negative impacts.

Abstract-Memory devices represent a key component of datacenter total cost of ownership (TCO), and techniques used to reduce errors that occur on these devices increase this cost. Existing approaches to providing reliability for memory devices pessimistically treat all data as equally vulnerable to memory errors. Our key insight is that there exists a diverse spectrum of tolerance to memory errors in new data-intensive applications, and that traditional one-size-fits-all memory reliability techniques are inefficient in terms of cost. For example, we found that while traditional error protection increases memory system cost by 12.5%, some applications can achieve 99.00% availability on a single server with a large number of memory errors without any error protection. This presents an opportunity to greatly reduce server hardware cost by provisioning the right amount of memory reliability for different applications.Toward this end, in this paper, we make three main contributions to enable highly-reliable servers at low datacenter cost. First, we develop a new methodology to quantify the tolerance of applications to memory errors. Second, using our methodology, we perform a case study of three new dataintensive workloads (an interactive web search application, an in-memory key-value store, and a graph mining framework) to identify new insights into the nature of application memory error vulnerability. Third, based on our insights, we propose several new hardware/software heterogeneous-reliability memory system designs to lower datacenter cost while achieving high reliability and discuss their trade-offs. We show that our new techniques can reduce server hardware cost by 4.7% while achieving 99.90% single server availability.

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Evaluating the performance of large compute clusters requires benchmarks with representative workloads. At Google, performance benchmarks are used to obtain performance metrics such as task scheduling delays and machine resource utilizations to assess changes in application codes, machine configurations, and scheduling algorithms. Existing approaches to workload characterization for high performance computing and grids focus on task resource requirements for CPU, memory, disk, I/O, network, etc. Such resource requirements address how much resource is consumed by a task. However, in addition to resource requirements, Google workloads commonly include task placement constraints that determine which machine resources are consumed by tasks. Task placement constraints arise because of task dependencies such as those related to hardware architecture and kernel version. This paper develops methodologies for incorporating task placement constraints and machine properties into performance benchmarks of large compute clusters. Our studies of Google compute clusters show that constraints increase average task scheduling delays by a factor of 2 to 6, which often results in tens of minutes of additional task wait time. To understand why, we extend the concept of resource utilization to include constraints by introducing a new metric, the Utilization Multiplier (UM). UM is the ratio of the resource utilization seen by tasks with a constraint to the average utilization of the resource. UM provides a simple model of the performance impact of constraints in that task scheduling delays increase with UM. Last, we describe how to synthesize representative task constraints and machine properties, and how to incorporate this synthesis into existing performance benchmarks. Using synthetic task constraints and machine properties generated by our methodology, we accurately reproduce performance metrics for benchmarks of Google compute clusters with a discrepancy of only 13% in task scheduling delay and 5% in resource utilization.

Opioid use and addiction in adolescents and young adults, including heroin and non-medical use of prescription opioids, is a serious and growing health problem of epidemic proportions. Opioid use has devastating consequences for youth and their families, including: progression to full addiction, severe psychosocial impairment, HCV and HIV transmission with injection use, exacerbation of co-occurring psychiatric disorders, overdose, and death. This chapter will provide an overview of opioid use disorders (OUDs) in youth, including: etiologic factors, epidemiology, consequences, clinical presentation and course, assessment and diagnosis, overdose, detoxification, and treatment. Opioid overdose is a life-threatening emergency. Respiratory depression should be treated with naloxone, and respiratory support if necessary. Overdose should always be utilized as an opportunity to initiate addiction treatment. Opioid withdrawal management (detoxification) is often a necessary, but never sufficient, component of treatment for OUDs. Medications used in the treatment of withdrawal may include buprenorphine, clonidine and others for relief of symptoms. Treatment for OUDs is effective but treatment capacity is alarmingly limited and under-developed. Although there is a limited evidence base for youth specific treatment, emerging consensus supports the incorporation of relapse prevention medications such as buprenorphine and extended release naltrexone into comprehensive psychosocial treatment including counseling and family involvement.