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Linear and Tree-Based Intelligent Investigation of Cross-Domain Housing Features to Enhance Energy Efficiency
No full textEnergy efficiency is a critical concern in built environment. Identifying key features that drive energy consumption is essential for optimizing building performance. Traditionally, studies have focused on single-domain datasets. These approaches overlook the potential insights gained from integrating data across different domains. This research addresses this gap using a cross-domain dataset that includes building characteristics, energy usage, and environmental factors. Feature selection techniques, including filter methods (correlation, mutual information), wrapper methods (RFE), embedded methods (Lasso, Random Forest, and gradient boosting), and dimensionality reduction are used to identify the most significant features contributing to the energy efficiency of residential properties. These techniques identify the most significant features influencing energy consumption. The findings show that cross-domain features like energy consumption, CO2 emissions, and heating cost play a key role in predicting energy performance. By integrating data from multiple domains, the feature selection process reveals areas for energy optimization that are previously overlooked in single-domain studies. The results provide valuable insights for energy consultants, building managers, and policymakers aiming to enhance energy efficiency in residential buildings. This research highlights the importance of cross-domain data integration and offers a robust framework for feature selection. Ultimately, it contributes to more effectiveenergy-saving strategies and sustainable building practices.</p
Counting Martingales for Measure and Dimension in Complexity Classes
No full textThis paper makes two primary contributions. First, we introduce the concept of counting martingales and use it to define counting measures and counting dimensions. Second, we apply these new tools to strengthen previous circuit lower bounds. Resource-bounded measure and dimension have traditionally focused on deterministic time and space bounds. We use counting complexity classes to develop resource-bounded counting measures and dimensions. Counting martingales are constructed using functions from the #𝖯, SpanP, and GapP complexity classes. We show that counting martingales capture many martingale constructions in complexity theory. The resulting counting measures and dimensions are intermediate in power between the standard time-bounded and space-bounded notions, enabling finer-grained analysis where space-bounded measures are known, but time-bounded measures remain open. For example, we show that BPP has #𝖯-dimension 0 and BQP has GapP-dimension 0, whereas the 𝖯-dimensions of these classes remain open. As our main application, we improve circuit-size lower bounds. Lutz (1992) strengthened Shannon’s classic (1-ε) 2ⁿ/n lower bound (1949) to PSPACE-measure, showing that almost all problems require circuits of size (2ⁿ/n)(1+(α log n)/n), for any α < 1. We extend this result to SpanP-measure, with a proof that uses a connection through the Minimum Circuit Size Problem (MCSP) to construct a counting martingale. Our results imply that the stronger lower bound holds within the third level of the exponential-time hierarchy, whereas previously, it was only known in ESPACE. Under a derandomization hypothesis, this lower bound holds within the second level of the exponential-time hierarchy, specifically in the class 𝖤^NP. We also study the #𝖯-dimension of classical circuit complexity classes and the GapP-dimension of quantum circuit complexity classes
Michael Gallope, The Musician as Philosopher:New York's Vernacular Avant-Garde, 1958–1978, Chicago: University of Chicago Press, 2024, £28
No full textA Psychoanalytical Profile of the Aeolian Mode and its Function in 21st Century Film and Television Soundtracks
Get PDFWater efficiency solutions for the oil refinery industry
No full textThe water-intensive oil refinery industry generates a high amount of wastewater that has the potential to be treated and reused for industrial and/or other purposes, with the aim of closing the water loop. A four-phase methodology was developed to identify fit-for-purpose technologies for treating wastewater derived from an oil refinery industry. The scope of this study is to simulate and assess the overall performance of five scenarios for the oil refinery wastewater (ORW) treatment in a real industrial oil refinery plant by utilising the existing industrial-scale conventional Ballast Water Treatment Plant (titled plate separator, mixing, coagulation/flocculation, dissolved air flotation) and implementing an advanced pilot-scale unit (aerobic granular sludge, ultrafiltration, reverse osmosis). To this end, process modelling, simulation and life cycle assessment tools were performed. Six performance indicators (Waste Reduction, Water-Eco, Water Sustainability, Improved Water Quality, Digitalisation and Environmental Protection) were defined to compare the performance of all scenarios compared to the existing status (scenario 1). According to the results, scenario 5 (only pilot-scale ORW treatment) proved to be the most efficient and sustainable approach to close the water loop in the oil refinery plant, enabling the reuse of reclaimed water as cooling water, firefighting water, or fed into a biological unit for further treatment
Accessing the artwork in COVID-19:loss, recovery and reimagination
No full textBeing ‘there’ and present within a physical space has been a long-accepted part of any art museum visitor experience. Once the sole access route, the emergence of digitisation and the Internet has since offered a new flexibility for visitors. Such online provision is often considered supplementary, ancillary, yet the year 2020 was witness to a seismic shift, when museums were compelled to unilaterally close their physical doors in response to Covid-19. This paper explores the sense of loss engendered by the abrupt removal of a direct interaction with physical artworks and examines the online initiatives that took its place. The theme of loss is explored through phenomenological experience of artworks/objects, an experience which cannot be recaptured virtually, in relation to the interchange between the physical and the virtual at both an individual artwork level and within a curated museum space. Advances in digital access, exciting new realities and future possibilities are examined, considering how this sense of loss might be accommodated in lieu, mitigated online. It is argued that a multiplicity of approaches that seek to redefine rather than replicate will engage new audiences experientially and offer a refreshed look at access between the physical and virtual art museum space
Heterogeneous price responses to trade policy uncertainty:Evidence from income-specific CPIs
No full textThis paper examines the heterogeneous effects of trade policy uncertainty (TPU) on consumer prices across the income distribution. Using income-specific CPI data and a TPU shock measure derived from monthly tariff rates, I estimate impulse responses via local projections. The results show that prices faced by top-income households rise more strongly in response to TPU shocks than those faced by low-income households. These findings suggest that uncertainty-induced demand effect is more pronounced among low-income households, highlighting the importance of accounting for heterogeneity when assessing the macroeconomic implications of TPU.</p
Optimal thermal comfort quantification in domestic indoor spaces under varying ambient and seasonal conditions – a numerical approach
No full textOne of the most crucial factors to consider in an indoor space is the ability to provide a comfortable and safe environment for its occupants. The right combination of relative humidity, temperature and ventilation is important not only for comfort but also for preventing the spread of respiratory diseases. In countries where there is wide ambient condition variation between seasons, it is not always straightforward to decide the right balance between heating/humidity settings and sufficient ventilation for a safe indoor environment. This study employs Computational Fluid Dynamics to assess the interaction between these parameters and determine the optimal radiator surface temperature for thermal comfort. Two seasons were considered (winter and summer) with three relative humidities and radiator surface temperatures. While relative humidity influences thermal comfort, the study reveals how it interacts with ventilation and heating under varying seasonal conditions. The effect of radiator surface temperature on thermal comfort was also examined, with five temperatures studied. Results show that an acceptable range of thermal comfort was achieved when the indoor space is heated with a radiator surface temperature of 49–53°C. Indoor spaces will be thermally uncomfortable with radiator surface temperatures below 43°C under all conditions
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