The general topic of this chapter is the relation of the society outside organizations to the internal life of organizations. Part of the specific topics have to do with the effect of society on organizations, and part of them concern the effects of organizational variables on the surrounding social environment. I intend to interpret the term “social structure” in the title in a very general sense, to include groups, institutions, laws, population characteristics, and sets of social relations that form the environment of the organization. That is, I interpret “social structure” to mean any variables which are stable characteristics of the society outside the organization. By an “organization” I mean a set of stable social relations deliberately created, with the explicit intention of continuously accomplishing some specific goals or purposes. These goals or purposes are generally functions performed for some larger structure. For example, armies have the goal of winning possible military engagements. The fulfillment of this goal is a function performed for the larger political structure, which has functional requirements of defense and conquest. I exclude from organizations many types of groups which have multiple purposes (or which perform multiple functions for larger systems, whether these are anyone's purposes or not), such as families, geographical communities, ethnic groups, or total societies. 1 also exclude social arrangements built up on the spur of the moment to achieve some specific short-run purpose. For instance, I will not consider a campaign committee for some political candidate as an “organization,” although a political party would definitely meet the criterion of continuous functioning and relatively specific purposes.
Is it possible to determine the location of a photo from just its pixels? While the general problem seems exceptionally difficult, photos often contain cues such as landmarks, weather patterns, vegetation, road markings, or architectural details, which in combination allow to infer where the photo was taken. Previously, this problem has been approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, this model achieves a 50 % performance improvement over the single-image model.
Abstract Introduction Although rhinitis is among the most common diseases worldwide, rhinitis prevalence in the general adult population is unclear and definitions differ widely. Objective To summarize the literature on rhinitis prevalence in the general adult population and to assess: (1) the prevalence according to different rhinitis definitions overall and in different regions of the world, and (2) the evolution of rhinitis prevalence over time. Methods We conducted an extensive literature review of publications including rhinitis prevalence using Pubmed and Scopus databases up to October 2020. We classified the definitions into three categories: unspecified rhinitis, allergic rhinitis (AR), and nonallergic rhinitis (NAR). Results Among 5878 articles screened, 184 articles were included, presenting 156 different definitions of rhinitis. Rhinitis prevalence ranged from 1% to 63%. The overall median prevalences of unspecified rhinitis, AR and NAR were 29.4%, 18.1% and 12.0%, and they varied according to the geographical location. Rhinitis prevalence tended to increase over time. Conclusions This review highlights the great heterogeneity of the definitions. The majority of studies had focused on AR, while only a few epidemiological data exist on NAR. We found geographical variability in rhinitis prevalence. Most of studies reported an increase of rhinitis prevalence over the last decades.
ABSTRACT Maps are an essential tool to inform fire governance and management. For instance, they can highlight which areas are most vulnerable to adverse fire impacts or be used to plan interventions for risk reduction and prevention. In recent years, several studies have mapped the fire management activities and the networks between the multitude of involved actors. They build upon previous advances to combine quantitative and qualitative social network analysis with geographical analysis and cartography, aiming to highlight areas of opportunity to enhance fire governance. This paper continues this line of research, examining cooperation in fire management within the south‐eastern part of the state of Chiapas. This area is the main fire risk area in Southern Mexico, characterised by the involvement of many different fire management actors. The paper proposes two advances to better visualise the networks between these actors—integration with modularity clustering and a thematic map integrating different spatial scales—and discusses the implications of these fire network maps for governance. The paper's main results are, first, the confirmation of the considerable influence of spatial distance and aspects of human and physical geography on network formation. Second, it shows the capacity of mapping to inform regional fire management arrangements.
AI agents are rapidly expanding in both capability and population: they now write code, operate computers across platforms, manage cloud infrastructure, and make purchasing decisions, while open-source frameworks such as OpenClaw are putting personal agents in the hands of millions and embodied agents are spreading across smartphones, vehicles, and robots. As the internet prepares to host billions of such entities, it is shifting toward what we call Open Agentic Web, a decentralized digital ecosystem in which agents from different users, organizations, and runtimes can discover one another, negotiate task boundaries, and delegate work across open technical and social surfaces at scale. Yet most of today's agents remain isolated tools or closed-ecosystem orchestrators rather than socially integrated participants in open networks. We argue that the next generation of agents must become Agentic Citizens, defined by three requirements: Agentic-Web-Native Collaboration, participation in open collaboration networks rather than only closed internal orchestration; Agent Identity and Personhood, continuity as a social entity rather than a resettable function call; and Lifelong Evolution, improvement across task performance, communication, and collaboration over time. We present Synergy, a general-purpose agent architecture and runtime harness for persistent, collaborative, and evolving agents on Open Agentic Web, grounding collaboration in session-native orchestration, repository-backed workspaces, and social communication; identity in typed memory, notes, agenda, skills, and persistent social relationships; and evolution in an experience-centered learning mechanism that proactively recalls rewarded trajectories at inference time.
In the day-ahead energy market, the offering strategy of distributed energy resource (DER) aggregators must be submitted before the uncertainty realization in the form of price-quantity pairs. This work addresses the day-ahead offering problem through a two-stage adaptive robust stochastic optimization model, wherein the first-stage price-quantity pairs and second-stage operational commitment decisions are made before and after DER uncertainty is realized, respectively. Uncertainty in day-ahead price is addressed using a stochastic programming-based approach, while uncertainty of DER generation is handled through robust optimization. To address the max-min structure of the second-stage problem, a neural network-accelerated column-and-constraint generation method is developed. A dedicated neural network is trained to approximate the value function, while optimality is maintained by the design of the network architecture. Numerical studies indicate that the proposed method yields high-quality solutions and is up to 100 times faster than Gurobi and 33 times faster than classical column-and-constraint generation on the same 1028-node synthetic distribution network.
This paper addresses the inverse problem of qualitatively recovering a clamped cavity in a thin elastic plate using far-field measurements. We present a strengthened analysis of the linear sampling method by carefully examining the range of the far-field operator and employing the reciprocity relation of the biharmonic far-field pattern. In addition, we implement both the linear sampling method for reconstructing the cavity and the extended sampling method for localizing the cavity under limited-aperture data. Numerical experiments demonstrate the effectiveness and robustness of both methods.
Joel Segarra, Andrea González-Fernández, Javier Osorno-Covarrubias
et al.
Since the 1970s, remote sensing has been used to monitor the environment, address national security concerns, and manage Earth resources within a market framework. However, environmental organizations can also utilize remote sensing data infrastructure to support oppositional narratives, legal processes, and direct action. We present a framework for the socio-technical practice of remote sensing in alliance with those communities and organizations that are struggling for environmental justice on the global commodity frontiers. Positioned at the intersection of critical geography and political ecology, we examine the ways that critical remote sensing has been adopted in five major types of environmental conflict: struggles opposing fossil fuel exploitation, timber extraction, intensive food production, water management practices, and the effects of mining. We present a baseline inventory of remote sensing resources that are useful to the five conflict types and are freely accessible online. A global perspective on the planetary environmental crisis is essential, and we suggest that remote sensing practitioners can, through workshops or online tutorials, help environmental justice organizations towards independent use of remote sensing data. The local communities should then determine whether remote sensing products can contribute to their struggles.