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Modeling a humanitarian-aid covering tour problem with location selection and vehicle assignment decisions
No full textIn post-disaster situations, time-critical decision-making is essential. Due to high demand and limited resources, visiting all affected locations is often infeasible. This study presents a novel variant of the covering tour problem that addresses these constraints by integrating location selection and vehicle assignment decisions. In the proposed problem, vehicles depart from selected relief centers, visit a subset of victim locations, and are allowed to complete their tours at any center. The demands of unvisited locations are satisfied through demand transfers from nearby visited nodes, with associated transfer times included in the total operation time. A mixed integer linear programming (MILP) model is formulated to minimize total operation time, incorporating both travel and demand transfer times. Scenario-based analyses are performed to evaluate the model's performance under various operational conditions, including transfer time sensitivity, route flexibility, demand coverage constraints, time-based covering radius, and partial fulfillment policies. To address scalability, a two-stage clustering-based heuristic is developed, offering a practical and computationally efficient solution method. From a humanitarian logistics perspective, the findings emphasize the importance of flexible routing, strategic placement of relief centers, and careful management of coverage thresholds. Additionally, the simplicity and adaptability of the proposed heuristic make it well suited for real-time decision-making in post-disaster response operations
Intelligent fault detection in wind turbine gearboxes: A machine learning approach using SCADA temperature data
No full textWind energy plays a pivotal role in the global transition to renewable energy, offering a sustainable solution to reducing greenhouse gas emissions. Gearbox (GB) failures remain one of the most critical issues, often leading to significant downtime and expensive repairs. This study presents a robust methodology for predicting wind turbine GB failures using Supervisory Control and Data Acquisition (SCADA) data. A novel Bagging Ensemble Temperature Prediction (BETP) model is proposed and evaluated alongside other ML models to forecast GB temperature and detect early signs of overheating. The BETP model demonstrated superior performance, achieving the lowest mean squared error (3.0259) and the highest R2 score (0.9527) among all models. By using its predictive capabilities, the model successfully detected early GB anomalies 59 days before failure and identified critical irregularities 9 days before a complete breakdown. These findings emphasize the effectiveness of the BETP model in enabling predictive maintenance, reducing unplanned downtime, and optimizing O&M costs. The proposed approach not only enhances the reliability and operational efficiency of WTs but also contributes to the long-term sustainability of wind energy systems
Electrochemical multi-drug sensors: Current advances, challenges, and future perspectives
No full textThe rapid growth of the pharmaceutical industry has highlighted the importance of controlling both the quality control process and appropriate dosing in drug purchasing to a critical level. In addition to this, pharmaceutical pollutants can harm the environment, especially water resources, creating a need for low-cost, sensitive, selective, and portable technologies to monitor personal health and environmental applications. Because monitoring more than one drug simultaneously provides convenience in terms of diagnosis and treatment, it increases the drug monitoring capacity and features. Therefore, various electrochemical methods and micro/nanomaterials integration have been employed to achieve well-separated peaks for drug molecules and enhance sensitivity. However, detecting more than one drug using an electrochemical method remains a challenging research topic. This review discusses electrochemical multi-drug (bio)sensing techniques, focusing on future trends. For this purpose, drugs were categorized as anticancer, anti-inflammatory, antidepressant, antibacterial, antiviral, and antifungal according to the area they act on. Moreover, integration of herbal drugs, which are highlighted by their rising therapeutic importance and intricate phytochemical compositions, has found place in electrochemical sensing. Current electrochemical (bio)sensors that detect multiple drugs for each group were discussed. In addition, mixed-type multi-drugs containing different drug groups were also discussed in a separate section
Skill-biased wage effects of domestic outsourcing
No full textThis study examines the impact of domestic outsourcing on the wages of workers performing outsourced tasks in Türkiye during the period 2012-2022, using an administrative employee-employer linked dataset. Outsourcing events are identified by tracking worker flows across firms with specific properties. Unlike existing studies, our dataset incorporates buyer-supplier transactions, enabling us to confirm that a relationship between the predecessor and successor firm b egins following the outsourcing event. This improves our ability to identify outsourcing events, which we use to explore wage effects of both high-skilled and low-skilled outsourcing. Our findings indicate that low-skilled workers experience wage losses from domestic outsourcing, while high-skilled, professional workers benefit, suggesting that domestic outsourcing may be one of the factors contributing to rising wage inequality
A multidimensional framework of bi- and multilingual disciplinary literacies
No full textThis paper presents a conceptualisation of bi- and multilingual disciplinary literacies (BMDLs) designed as a dynamic and versatile thinking tool for researchers and practitioners in bi- and multilingual educational settings. It builds upon established theoretical foundations and the work conducted within the COST network CLILNetLE, moving beyond traditional perspectives of literacy development that are often viewed as linear or narrowly confined to reading and writing. Instead, this framework conceptualises disciplinary literacies as situated and socially constructed processes that involve deeply intertwined aspects of knowledge-building, communication, and identity formation. These processes encompass diverse modes of meaning-making resources, manifesting differently across educational levels and disciplinary areas. The conceptualisation outlines several dimensions of bi- and multilingual disciplinary literacies: the bi-, multi- and translingual; multi- and transsemiotic; functional-textual; critical; and technological-digital dimensions. It acknowledges the inherently multifaceted nature of disciplinary literacies, which allows the framework to remain responsive to evolving needs and practices. The proposed flexible and adaptable framework aims at enhancing instructional practices and fostering collaborative approaches across language and content education. This approach ultimately seeks to equip learners with the skills and agency necessary to effectively participate, navigate, and contribute within increasingly complex and multilingual academic, professional, and civic domains
Revisiting critical regionalism from an urbanistic perspective: A generative typomorphology for contextual urban design
No full textAs self-organized systems, traditional settlements emerge from local codes that guide discrete building acts over time. These rule-based systems’ ability to shape the intricate spatial matrix and fabric of cities continues to inspire contemporary urban design, particularly in the search for alternative methodologies to create contextually distinctive and spatially coherent settlements for the new century. However, despite the growing interest in traditional urbanism, there is still a gap in understanding the morphology of historical fabrics and their generative logic for design application. This paper revisits Critical Regionalism, advocating for a nuanced integration of contemporary styles and techniques with local culture and identity. It proposes a systematic framework to decode the morphology of traditional urban fabrics, using their rules to inform the development of new settlements. The study examines Uçhisar, Cappadocia as one of the oldest traditional settlements in Türkiye, and explores the potential of a typomorphological perspective in contextual urban design
Haber Güvenilirliği Ölçeğinin Türkçeye Uyarlanması ve Medya Türlerine Göre Haberlere Güven Algısının İncelenmesi
No full textDownlink channel covariance matrix estimation via representation learning with graph regularization
No full textIn this paper, we propose an algorithm for downlink (DL) channel covariance matrix (CCM) estimation for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) communication systems with base stations (BS) possessing a uniform linear array (ULA) antenna structure. We consider a setting where the UL CCM is mapped to the DL CCM by an interpolator function. We first present a theoretical error analysis of learning a nonlinear embedding by constructing an analytical mapping, which points to the importance of the Lipschitz regularity of the mapping for achieving high estimation performance. Then, based on the theoretical ground, we propose a representation learning algorithm as a solution for the estimation problem, where Gaussian RBF kernel interpolators are chosen to map UL CCMs to their DL counterparts. The proposed algorithm is based on the optimization of an objective function that fits a regression model between the DL CCM and the UL CCM samples in the training dataset and preserves the local geometric structure of the data in the UL CCM space, while explicitly regulating the Lipschitz continuity of the mapping function in light of our theoretical findings. Simulation results show that the proposed algorithm surpasses benchmark methods with respect to three different error metrics
A Recursive Formula for the Height of a Random Walk
No full textThe height of a binary sequence serves as a metric that captures the randomness of a sequence. In this work, the height of a finite binary sequence, which was a missing random variable in the recent work published at SETA 2024, is defined, and an explicit formula is derived for the probability values of a sequence of length n, to have a height equal to t, in terms of binomial coefficients. While this formula yields useful insights, computing exact values of binomial coefficients and those probability values becomes infeasible as n gets larger. A method to overcome this problem is to use asymptotic approaches, which cause some errors when used in randomness tests. In this work, a recursive formula is derived for the same probability values, and the exact probability values for sequences of length up to at least 4096 is obtained. Using these exact values in a random walk randomness test, one can obtain more accurate results. In addition, formulas for the expected value and variance of the random variable that assigns the number of balanced points in a binary sequence of length n were derived, which had not been addressed in the previous work published at SETA 2024. A new randomness test is proposed based on random walk height
