Challenges in the CO2 emissions of the Turkish power sector: Evidence from a two-level decomposition approach

Decarbonization of the energy system is urgent to avert the disruptions in the climate. Considering its share, the low carbon transition of the power sector is pivotal. Growing electricity demand poses unique challenges for Turkey to enact deep decarbonization. It is vital to uncover the contributing causes of emissions to provide strategic oversight for carbon management activities. This study investigates key drivers of CO₂ emissions from the power sector using the Logarithmic Mean Divisia Index decomposition method. While efficiency improvement contributes to sustainable yet minor mitigation, changes in the fossil-fuel share indicate a cycling but significant overall impact.     

Enhanced transduction coefficient in piezoelectric PZT ceramics by mixing powders calcined at different temperatures

Large transduction coefficient ($d_{33}\times g_{33}$) is difficult to obtain in piezoelectric ceramics because these two parameters show opposite trends with compositional modifications. Herein, the Pb(Zr_0.53Ti_0.47)O₃ ceramic powders were calcinated under different temperatures (A:830 °C, B:860 °C, and C:890 °C), and then mixed together according to different weight ratios (1A:1B:1C, 1A:2B:1C, 1A:2B:3C and 3A:2B:1C) for ceramics preparation. Both d₃₃ and g₃₃ are improved successfully, and the transduction coefficient with the weight ratio of 1A:2B:3C reaches up to 17,500 × 10⁻¹⁵ m²/N, which is 60 % higher than that with the powders calcinated under 830 °C, and at least twice those of commercial PZT-4, PZT-5A and PZT-8 ceramics. The improved transduction coefficient is owing to the enhanced piezoelectric constant and spontaneous polarization resulted from the increased grain size, relative density and the fraction of tetragonal phase. These results indicate that this is a simple but effective way to tailor the transduction coefficient in piezoelectric ceramics.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

Theoretical and practical implications of creep curve shape analyses for 7010 and 7075

Over tensile stress ranges giving creep lives up to 2000 hours at 373 to 463 K, the creep and creep fracture properties of 7010 are compared with results recorded for 7075 and other precipitation-hardened aluminum alloys. Using the ϕ methodology to quantify the systematic variations in creep curve shape with changing test conditions, the behavior patterns displayed by the different alloys are shown to depend on the extent to which precipitate coarsening causes a progressive loss of creep strength with increasing test duration and temperature. The ϕ relationships also introduce new approaches for rationalization and interpretation of creep data sets, based on the activation energy for lattice diffusion in the alloy matrices and the yield stress or UTS at the creep temperatures.     

An element-based displacement preconditioner for linear elasticity problems

Finite element analysis of problems in structural and geotechnical engineering results in linear systems where the unknowns are displacements and rotations at nodes. Although the solution of these systems can be carried out using either direct or iterative methods, in practice the matrices involved are usually very large and sparse (particularly for 3D problems) so an iterative approach is often advantageous in terms of both computational time and memory requirements. This memory saving can be further enhanced if the method used does not require assembly of the full coefficient matrix during the solution procedure. One disadvantage of iterative methods is the need to apply preconditioning to improve convergence. In this paper, we review a range of established element-based preconditioning methods for linear elastic problems and compare their performance with a new method based on preconditioning with element displacement components. This new method appears to offer a significant improvement in performance.     

HACCP certification of food services in Philippine inter-island passenger vessels

This paper presents the efforts of the Bureau of Quarantine and International Health Surveillance (BQIHS), Department of Health (DOH), Philippines to launch a Good Manufacturing Procedure (GMP)–Hazard Analysis Critical Control Point (HACCP) Certification Program for food services operating in Philippine inter-island water vessels. The BQIHS improved its food sanitation audit system for inter-island ships by including in its traditional walk-through evaluation of GMP the review of documents for both GMP and HACCP, and the verification of HACCP implementation. The BQIHS is now identified as a GMP–HACCP certification provider to specific sectors of the local food service industry on a national level.     

Robot learning through task identification

The operation of an autonomous mobile robot in a semi-structured environment is a complex, usually non-linear and partly unpredictable process. Lacking a theory of robot–environment interaction that allows the design of robot control code based on theoretical analysis, roboticists still have to resort to trial-and-error methods in mobile robotics.The RobotMODIC project aims to develop a theoretical understanding of a robot’s interaction with its environment, and uses system identification techniques to identify the system robot–task–environment. In this paper, we present two practical examples of the RobotMODIC process: mobile robot self-localisation and mobile robot training to achieve door traversal.In both examples, a transparent mathematical function is obtained that maps inputs–sensory perception in both cases–to output — location and steering velocity respectively. Analysis of the obtained models reveals further information about the way in which a task is achieved, the relevance of individual sensors, possible ways of obtaining more parsimonious models, etc.