5916 research outputs found
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Enhancing school buildings energy efficiency under climate change: A comprehensive analysis of energy, cost, and comfort factors
No full textIncorporating future weather predictions into building assessments is essential for enhancing resilience, energy efficiency, cost savings, comfort, and sustainable infrastructure development in response to climate change. This study investigates the interplay between climate change and building performance, primarily focusing on energy usage, cost implications, and occupant comfort. It examines how future weather conditions impact school buildings in different climates, analyzing energy, cost, and comfort aspects. The research underscores the significance of tailored climate adaptation strategies for various regions and emphasizes considering future performance, even for highly energy-efficient buildings. Employing a comprehensive simulation-based approach, the study implements and validates future weather data in a Turkish school building, incorporating envelope improvements and photovoltaic applications to boost energy efficiency. A distinctive feature is the rigorous validation of future weather predictions against current measured data, facilitating a regional-level assessment of climate change effects on building energy consumption. The study's novelty lies in its detailed evaluation of climate change's multifaceted impacts on buildings, innovative future climate data validation, and contribution to a more localized and climate-specific approach to addressing building energy-cost-comfort performance. Findings reveal that in hot climates, there is a potential for nearly doubling primary energy consumption, global costs, and CO2 emissions in the future for both cost-optimal and nearly zero-energy scenarios. Consequently, the savings would decrease from 53-63 % to 13–30 %. In contrast, in cold climates, the impact on these parameters differs slightly, with reduced primary energy consumption and CO2 emissions but higher global costs. Notably, a building retrofitted to a high energy efficiency level may experience a substantial increase in future energy consumption and global costs, approaching the levels of currently inefficient buildings.Publisher versio
Temperament and behaviour problems in children: A multilevel analysis of cross-cultural differences
No full textEarly temperament attributes have been linked to emerging behaviour problems and significant long-term consequences; however, these relations are rarely examined cross-culturally. The present study addresses this gap, employing multilevel modelling to explain within- and between-culture variances with respect to temperament predicting a spectrum of behaviour problems across 14 nations from the Joint Effort Toddler Temperament Consortium (JETTC). A total of 865 children between 17 and 40 months, with approximately equal age distribution across this developmental period and about equivalent representation of genders, were recruited from 14 nations. Greater negative emotionality was associated with more internalizing problems, whereas higher surgency and effortful control predicted fewer internalizing difficulties. Controlling for age and gender, temperament explained significant within- and between-culture variances in internalizing and externalizing problems (at the broad-band and fine-grained levels), as well as sleep problems. For internalizing difficulties, temperament accounted for more between-culture variance. In contrast, for externalizing difficulties, temperament accounted more for how individuals within the same culture differed from their same-culture counterparts. The within-culture findings suggest universal patterns of temperament-problem relations, informing cultural adaptation of interventions; between-culture findings enhance understanding of the implications of the cultural niche for normative behaviour and adjustment.Publisher versio
Populist attitudes and challenges towards liberal democracy: An empirical assessment of the Turkish case
No full textThe rise of populism presents a challenge to liberal democracy in various countries. This article questions how populist attitudes affect the democratic preferences of the electorate. Using representative survey data fielded from Turkey in 2019, we first tested the effect of populist attitudes on illiberal democratic attitudes. The results show the negative impact of populism on support for illiberal democratic attitudes. Next, we analyzed which dimension of populism correlates with illiberal democratic preferences. Our results pointed to the negative influence of the Manichean outlook on preferences concerning democracy. Contrary to expectations, as anti-elitist and people-centric attitudes increase, support for illiberal democracy decreases. Hence, the relative emphasis on different dimensions of populism is likely to shape the net balance of its influence on democracy. Electoral alliance preferences also shape democracy preferences. The ruling People’s Alliance voters are more supportive of illiberal democracy than the opposition blocs and parties.TÜBİTA
Trust in robot–robot scaffolding
No full textThe study of robot trust in humans and other agents is not explored widely despite its importance for the near future human-robot symbiotic societies. Here, we propose that robots should trust partners that tend to reduce their computational load, which is analogous to human cognitive load. We test this idea by adopting an interactive visual recalling task. In the first set of experiments, the robot can get help from online instructors with different guiding strategies to decide which one it should trust based on the computational load it experiences during the experiments. The second set of experiments involves robot-robot interactions. Akin to the robot-online instructor case, the Pepper robot is asked to scaffold the learning of a less capable 'infant' robot (Nao) with or without being equipped with the cognitive abilities of theory of mind and task experience memory to assess the contribution of these cognitive abilities to scaffolding performance. Overall, the results show that robot trust based on computational/cognitive load within a sequential decision-making framework leads to effective partner selection and robot-robot scaffolding. Thus, using the computational load incurred by the cognitive processing of a robot may serve as an internal signal for assessing the trustworthiness of interaction partners.Deutsche Forschungsgemeinschaft ; Japan Society for the Promotion of Science ; Osaka UniversityPublisher versio
A comparison of solutions of two convolution-type unidirectional wave equations
No full textIn this work, we prove a comparison result for a general class of nonlinear dispersive unidirectional wave equations. The dispersive nature of one-dimensional waves occurs because of a convolution integral in space. For two specific choices of the kernel function, the Benjamin–Bona–Mahony equation and the Rosenau equation that are particularly suitable to model water waves and elastic waves, respectively, are two members of the class. We first prove an energy estimate for the Cauchy problem of the non-local unidirectional wave equation. Then, for the same initial data, we consider two distinct solutions corresponding to two different kernel functions. Our main result is that the difference between the solutions remains small in a suitable Sobolev norm if the two kernel functions have similar dispersive characteristics in the long-wave limit. As a sample case of this comparison result, we provide the approximations of the hyperbolic conservation law
A suite of broadband physics-based ground motion simulations for the Istanbul region
No full textPhysics-based earthquake ground motion simulations (GMS) have acquired significant growth over the last two decades, mainly due to the explosive developments of high-performance computing techniques and resources. These techniques benefit high/medium seismicity regions such as the city of Istanbul, which presents insufficient historical ground motion data to properly estimate seismic hazard and risk. We circumvent this reality with the aid of the Texas Advanced Computing Center (TACC) facilities to perform a suite of 57 high-fidelity broadband (8–12 Hz) large-scale physics-based GMS for a region in Istanbul, Turkey. This paper focuses on the details of simulated GMS: (i) validation of the GMS approach against recorded ground motions produced by the 2019 (Formula presented.) Silivri earthquake; (ii) characteristics of 57 different source models, which aim to consider the uncertainties of many fault rupture features, including the length and width, dip, strike, and rake angles of considered fault planes, as well as hypocenter locations and earthquake magnitudes ranging between (Formula presented.) 6.5 and 7.2; (iii) high-resolution topography and bathymetry and seismic data that are incorporated into all GMS; (iv) simulation results, such as PGAs and PGVs versus (Formula presented.) and distances to fault ruptures ((Formula presented.)), of 2912 surface stations for all 57 GMS. More importantly, this research provides a massive database of displacement, velocity and acceleration time histories in all three directions over more than 20,000 stations at both surface and bedrock levels. Such site-specific high-density and -frequency simulated ground motions can notably contribute to the seismic risk assessment of this region and many other applications.TÜBİTA
A novel design framework for generating functionally graded multi-morphology lattices via hybrid optimization and blending methods
No full textOwing to its excellent mechanical properties, triply periodic minimum surfaces (TPMS) lattice structures have recently gained more interest in engineering applications. The superior properties of these structures make it easier to achieve engineering design goals such as strength and weight. However, technological advancements compel the designer to enhance the traditional TPMS design qualities. Hybridization of different lattice types emerges as a strong candidate for enhancing overall design performance. Therefore, a hybrid optimization scheme based on genetic algorithms (GA) and anisotropic homogenization-based topology optimization is considered to generate a functionally graded multi-morphology for a Messerschmitt–Bölkow–Blohm (MBB) beam design in this paper. The GA is performed to identify the best lattice morphology, including Diamond (D), Gyroid (G), I-WP, and Primitive (P), and their relative densities prior to topology optimization (TO). Once the best lattice morphology of the design domain is obtained via the GA, the homogenization-based topology optimization is applied to grade the multi-morphology lattice to improve the design performance further. The final step is the reconstruction of the graded multi-morphology using a novel blending algorithm. The reconstructed MBB beams are made of cobalt-chromium (CoCr) alloy and are then manufactured using the laser sintering method, direct metal laser melting (DMLM) technique. Destructive metallographic and non-destructive metrological techniques are utilized to assure manufacturing quality. An impact hammer test is conducted on the fabricated beams to validate and compare the proposed graded multi-morphology geometry with graded and uniform single lattice morphologies. Experimental results show that the stiffness of the graded multi-morphology structure designed by the proposed hybrid optimization is 4.5 % and 13.0 % higher than the graded form of D and P-type single lattice morphologies, respectively. Also, it is observed that the graded form single lattice morphologies deliver superior performance than their uniform encounters namely D and P-type lattice structures.TÜBİTA
Bimanual rope manipulation skill synthesis through context dependent correction policy learning from human demonstration
No full textLearning from demonstration (LfD) with behavior cloning is attractive for its simplicity; however, compounding errors in long and complex skills can be a hindrance. Considering a target skill as a sequence of motor primitives is helpful in this respect. Then the requirement that a motor primitive ends in a state that allows the successful execution of the subsequent primitive must be met. In this study, we focus on this problem by proposing to learn an explicit correction policy when the expected transition state between primitives is not achieved. The correction policy is learned via behavior cloning by the use of Conditional Neural Motor Primitives (CNMPs) that can generate correction trajectories in a context-dependent way. The advantage of the proposed system over learning the complete task as a single action is shown with a table-top setup in simulation, where an object has to be pushed through a corridor in two steps. Then, the applicability of the proposed method to bi-manual knotting in the real world is shown by equipping an upper-body humanoid robot with the skill of making knots over a bar in 3D space.Japan Society for the Promotion of Science ; New Energy and Industrial Technology Development Organization ; Osaka University ; Bilim Akademis
Sustainable future technology evaluation with digital transformation perspective in air cargo industry using IF ANP method
No full textSustainability is one of the most critical issues of recent years. Many companies that contribute to the global economy focus on sustainability studies more than ever. On the other hand, technological developments lead companies to digital transformation. This situation necessitated the adoption of a sustainability approach that includes digital transformation and digital technologies. Therefore, it is critical for companies embarking on a transformation journey to invest in technologies that support sustainability. Advances in technology and digital transformation have contributed to the growth of e-commerce volume globally. However, this has led to the growth and complexity of supply chain networks. One of the biggest players in this network structure is undoubtedly the air cargo industry. The air cargo industry plays a critical role in the sustainability of the global economy, employment, social development, and the environment. Therefore, this study proposes a new model for the evaluation of sustainable future technologies in the air cargo industry from the perspective of digital transformation. Multi-criteria decision-making (MCDM) approach was adopted in the study. The intuitionistic fuzzy analytical network process (IF ANP) is used to validate the proposed model in a real case conducted in the Turkish air cargo industry.Galatasaray Üniversites
Lane type classification & distance measurement system for autonomous vehicle
No full textIn this paper, lane type classification and lane distance measurement system are proposed for autonomous vehicles. In the proposed system, the perspective transformation method is applied to the image taken from the vehicle camera, so that a bird's-eye view is obtained and the parts with stripes are cropped from the image, and then a multi-class classification model is implemented using neural network-based architectures to determine the stripe type. In addition, with the proposed distance measurement system, the distance of the vehicle to the right and left lanes is calculated during autonomous driving, thus ensuring that the vehicle can drive autonomously on the lane. For this study, our own data set has been created