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This paper addresses the restrictions of Italian relative che 'what' to finite environments and direct case gaps. While the standard analysis takes these restrictions to follow from the C status of che, this paper argues for an alternative approach, according to which che is a DP, on a par with other interrogative and relative elements. Specifically, it is argued that relative che is identical to interrogative che and relative cui 'what.obl' in the narrow syntactic derivation, up to the point of transfer. Realization of relative che is then blocked at ext with oblique case gaps by the more specific cui, along the lines of the Elsewhere Principle. The status of cui as a specialized relative element is also discussed. The restriction to finiteness for relative che is treated as an instance of a more general phenomenon that precludes bare DPs from occurring at the edge of infinitival relatives. Here I adopt and extend (Richards, Norvin. 2010. Uttering trees. Cambridge, Massachusetts: MIT Press) Distinctness Theory to account for the facts of Italian. [ABSTRACT FROM AUTHOR]

Mercellis remarks that Baekeland "crossed boundaries between the spheres of science, technology, and industry - to the extent that they were separated" (p. 17), and his analysis reveals some of the divisions that Baekeland was keen to cross, overcome, or wilfully ignore when the occasion merited it. Mercellis dispels some myths, notably that Baekeland earned $750,000 by selling his invention of Velox photographic paper in 1899 to Eastman Kodak. Beyond Bakelite: Leo Baekeland and the Business of Science and Invention: By Joris Mercellis. [Extracted from the article]

This paper makes a first step towards a logic of learning from experiments. For this, we investigate formal frameworks for modeling the interaction of causal and (qualitative) epistemic reasoning. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment (Pearl, 2009, Causality. Models, Reasoning, and Inference , 2nd edn. Cambridge University Press, Cambridge; Woodward, 2003, Making Things Happen , vol. 114 of Oxford Studies in the Philosophy of Science. Oxford University Press). In a first step we extend a causal model (Briggs, 2012, Philosophical Studies , 160, 139–166; Galles and Pearl, 1998, An axiomatic characterisation of causal counterfactuals. Foundations of Science , 3, 151–182; Halpern, 2000, Axiomatizing causal reasoning. Journal of Artificial Intelligence Research , 12, 317–337; Pearl, 2009, Causality. Models, Reasoning, and Inference , 2nd edn. Cambridge University Press, Cambridge) with a simple Hintikka-style representation of the epistemic state of an agent. In the resulting setting, one can talk about the knowledge of an agent and information update. The resulting logic can model reasoning about thought experiments. However, it is unable to account for learning from experiments, which is clearly brought out by the fact that it validates the principle of no learning for interventions. Therefore, in a second step, we implement a more complex notion of knowledge (Nozick, 1981, Philosophical Explanations. Harvard University Press, Cambridge, Massachusetts) that allows an agent to observe (measure) certain variables when an experiment is carried out. This extended system does allow for learning from experiments. For all the proposed logics, we provide a sound and complete axiomatization. [ABSTRACT FROM AUTHOR]

• A data-informed building energy management (DiBEM) framework was proposed. • It was demonstrated in an office/laboratory building with two-years' experimental data. • The impacts of energy efficient interventions were quantified through data-driven models. • The energy use intensity was reduced from 54.1 kWh/m2 to 42.8 kWh/m2. To support building operations in reaching ultra-low energy targets, this paper proposes a data-informed building energy management (DiBEM) framework to improve energy efficiency systematically and continuously at the operation stage. Specifically, it has two key features including data-informed energy-saving potential identification and data-driven model-based energy savings evaluation. The paper demonstrates the proposed DiBEM with a detailed case study of an office and living laboratory building located in Cambridge, Massachusetts called HouseZero. It focuses on revealing the performance of the energy-efficient interventions from two-years' building performance monitoring data, as well as evaluating energy savings from the interventions based on the data-driven approach. With Year 1 as baseline, several interventions are proposed for Year 2 including improvements to controls and operation settings, encouragement of occupants' behavior for energy savings, and hardware retrofitting. These were deployed to heating/cooling, domestic hot water, lighting, plug and other loads, and photovoltaic (PV) systems. To quantify the impacts of different interventions on energy end uses, several data-driven models are developed. These models utilize linear regression, condition model, and machine learning techniques. Consequently, the heating/cooling energy consumption that was already ultra-low in Year 1 (12.8 kWh/m2) is further reduced to 9.7 kWh/m2 in Year 2, while the indoor thermal environment is well maintained. The domestic hot water energy is reduced from 2.3 kWh/m2 to 1.2 kWh/m2. The lighting energy is only increased from 0.3 kWh/m2 in pandemic operations without occupancy in Year 1 to 0.8 kWh/m2 in partial normal operations in Year 2, while the indoor illuminance level meets occupants' requirements. Combined with other relatively constant loads and the reduction of plug and other loads due to COVID building operation restrictions, the total energy use intensity is thereby reduced from 54.1 kWh/m2 to 42.8 kWh/m2, where 5.4 kWh/m2 of energy reduction for Year 2 is estimated to be contributed by the energy-efficient interventions. PV generation is 36.1 kWh/m2, with an increase of 1.4 kWh/m2 from a new inverter. In summary, this paper demonstrates the use of DiBEM through a detailed case study and long-term monitoring data as evidence to achieve ultra-low energy operations. [ABSTRACT FROM AUTHOR]

Public housing benefits are rationed through wait lists. Theoretical work on public housing allocation has debated how much choice applicants should have over units, identifying a possible trade-off between efficiency and redistribution. This paper empirically establishes the existence and economic importance of this trade-off using wait list data from Cambridge, Massachusetts. I estimate a model of public housing preferences in a setting where heterogeneous apartments are rationed through waiting time. Eliminating choice would improve targeting but reduce tenant welfare by more than 30 percent. Such a change is only justified on targeting grounds by a strong social preference for redistribution. [ABSTRACT FROM AUTHOR]

The LibGuides platform is a ubiquitous tool in academic libraries and is commonly used by librarians to compile and share lists of recommended social science numerical data resources with users. This study leverages the machine-accessible nature of the LibGuides platform to collect links to data and statistical resources from over 10,000 LibGuide pages at 123 R1 research institutions in the United States. After substantial data cleaning and normalization, an analysis of the most common resources on those guides provides a unique window into the data repositories, libraries, archives, statistical data platforms, and other machine-readable data sources that are most popular on academic library guides. Results show that freely available resources from U.S. government agencies are among the most common to be included on data and statistical resources guides across institutions. Resources requiring paid licenses or memberships for full access, such as Statistical Insight (ProQuest), Social Explorer, and ICPSR are linked to most frequently overall, regardless of the percentage of institutions that include them. Findings also suggest that libraries are more likely to share traditional licensed statistical resources (e.g., Cambridge's Historical Statistics of the United States) and collections of simple charts and graphs (e.g., Statista) than more robust and complex microdata resources (e.g., IPUMS). [ABSTRACT FROM AUTHOR]

• Control design for coupled natural ventilation and thermally active building system. • Assessment of operational performance using one-year high-fidelity measured data. • The annual heating and cooling energy consumption is 12.8 kWh/m2. • Issues identified and lessons learned for further improving the design and operation. The thermally active building system (TABS) has emerged as an energy-efficient measure for cooling and heating buildings since it facilitates the use of low-energy technologies, such as free cooling with geothermal sources and heating with low-temperature heat pumps. However, the control of TABS is challenging due to its high thermal inertia. Though a considerable amount of research has been done using simulations or small-scale experiments, few studies have published detailed long-term measured data of TABS operation. In addition, there has been little discussion on the control integration of slow-acting TABS with other fast-acting systems, such as natural ventilation. To fill these gaps, this paper presents a comprehensive assessment of the operational performance of the coupled NV and TABS using one-year high-fidelity measured data of an experimental small office building with an extensive sensor network located in Cambridge, Massachusetts. First, the coupled system and control design are described. Then, the thermal, energy and detailed control performance are assessed by comparing with design intents and simulated predictions. It is found that the coupled system demonstrates its effectiveness to control indoor thermal environment while achieving high energy efficiency. The annual energy consumptions are 11.5 kWh/m2 for heating and 1.3 kWh/m2 for cooling. The heating COP, including heat pump and two water pumps, is 3.5, and the cooling COP is 18.3 by utilizing free cooling from geothermal resources. The issues in operation are identified, including system/equipment faults, implementation of controls, and settings by building operators. Finally, lessons learned from this project are discussed and the strategies for improvement are summarized. [ABSTRACT FROM AUTHOR]