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End of day process optimization through multi-mode resource constrained project scheduling - a banking case study
No full textAn End-of-Day process is a batch job that includes a sequence of programs, wherein tasks are completed automatically at times specified by a scheduler. The efficient allocation of resources for the timely execution of tasks allows a company to reduce the overall time needed for the completion of the work and improve customer satisfaction by delivering orders on time. This paper presents a case study of a Turkish bank with the objective of minimizing the duration of the end-of-day process through the optimization of work scheduling and resource allocation. This problem is modeled as a multi-mode resource-constrained project scheduling problem, optimally resolved by mixed-integer programming, and approximated via simulated annealing heuristic. The scheduler in this paper can also be used to assess the importance of scheduling and crashing tasks, along with the sufficiency of the infrastructure to optimize the End-of-Day process
Distinct rules for perceptual grouping in position-based and velocity-based motion systems
No full textMotion perception relies on at least two distinct systems: a velocity-based motion system driven by early direction-selective cells and a position-based motion system that tracks objects over space and time. However, how these systems interact when operating in parallel remains unclear. We explored their respective contributions to the perceptual organization of motion using a bistable stimulus of eight moving dots, perceived either as rotating in local pairs (local motion percept) or as forming two illusory squares translating around fixation (global motion percept). To disrupt the velocity-based motion system, we varied interstimulus intervals (ISIs) stroboscopically from 0 to 116.6 ms – selectively impairing early direction-selective cells with short temporal integration windows (<100 ms). Additionally, we manipulated contrast polarity to bias perceptual grouping (local-group, global-group, or no-group). We found that the pattern of perceptual bistability shifted markedly at ISIs of 33 ms. For ISIs ≥33 ms, contrast- and proximity-based grouping strongly influenced perception. For ISIs <33 ms, the global motion percept dominated even in the presence of strong static grouping cues (i.e., contrast and proximity), suggesting that the velocity-based motion system introduces a perceptual bias that can override or counteract static grouping cues. These findings reveal distinct, and at times opposing, contributions of velocity- and position-based motion systems to the perceptual organization of motion
Additively manufacturing of functionally graded multi-material parts using directed energy deposition
No full textManufacturing of functionally graded (FG) components is desirable for many applications because of the capability of providing multiple functionalities and higher performance on the same component. Complex multi-material components cannot be produced using conventional manufacturing processes; therefore, additive manufacturing (AM) processes have recently been used for manufacturing multi-material parts. The laser powder directed energy deposition (LP-DED) process is a metal-based AM technique with multi-material production capabilities; however, the link between a complex multi-material design and the LP-DED machine is still missing. This paper aims to develop a unified algorithm to carry out design, process planning and manufacturing of complex multi-material parts, and generate the computer numerical control (CNC) program. The proposed methodology is implemented to produce functionally-graded multi-material samples using two different materials and functionalities namely, CuCrZr and Inconel 718 to demonstrate its capabilities. The energy dispersive spectrometry (EDS) and microhardness analyses are carried out on one of the case studies to evaluate the precision of material composition throughout the gradient direction. Overall, the developed algorithms are well-integrated with LP-DED systems and proved to be capable of producing spatially and materially complex FG structures directly from designs
The price of parking and everything else in cities
No full textIn urban centers, parking emerges not merely as a commodity, but as a vital conduit, facilitating economic exchanges by connecting people to city offerings. The need for parking consumes significant portions of land, transforming these areas into guardians of mobility. As an important intermediate good and a voracious consumer of land, parking stands as a linchpin in the city's economic machinery. Failure to accurately gauge its value risks distorting pricing across all sectors, casting a shadow of inefficiency over the entire urban economy. This essay explores illustrative examples from the author's “Shoupista” work, examining how parking mispricing echoes through the urban landscape, distorting transportation costs, shaping retail price dynamics, and influencing the delicate price balance of housing markets
Brokered bureaucracy: commodified integration in arrival spaces in Istanbul
No full textThis article examines the important role of arrival spaces and commercial actors in facilitating migrant integration within contexts characterised by increasingly complex bureaucratic requirements. It draws on ethnographic research conducted between 2021 and 2022 in a key arrival hub for diverse migrant populations in Istanbul, which emerges as a critical space where commercial actors enable migrants to navigate bureaucratic systems and access essential services. By integrating the frameworks of ‘arrival infrastructures’ and the ‘migration industry,’ the analysis sheds light on how commercial businesses mitigate barriers to integration while simultaneously commodifying the bureaucratic process. Such reliance on intermediaries and local businesses, we argue, reveals the paradoxical dynamics of migration management, where the state’s regulatory framework reinforces dependence on private actors to mitigate the complexities and bridge systemic gaps. A central arrival hub in Istanbul exemplifies how urban spaces adapt to the dynamic needs of migrants, offering pathways to legal status, employment, and integration. Thus, the study reveals how commercial infrastructures facilitate migrants’ longer-term settlement and mobility trajectories. By situating commercial infrastructures and document brokerage at the centre of migrant integration processes, this research contributes to migration studies by emphasising their indispensable yet understudied role in contexts of heightened regulatory constraints
From imposition to concession, from compliance to resistance: creating a Harvard Business School clone in a Turkish university, 1954-1965
No full textDrawing on archival sources, this study traces the early history of the ‘Turkish Institute of Business Administration’, established in 1954 within the Faculty of Economics at Istanbul University with Ford Foundation (FF) funding and Harvard Business School (HBS) guidance. The historical narrative describes what transpired on both the American and Turkish sides during this FF-HBS-initiated and directed transfer process. Through this account, the article contributes to the literature on post-World War II American influence on business education in three ways: First, it highlights how direct American interventions were shaped by who in the United States was involved, and when. Second, it demonstrates how tensions and power relations between the FF and HBS influenced both the direction and the outcome of the transfer. Finally, the process-based account shows how and why the reception of an imposed American model involved both full adoption and some significant deviations
Shape sensing and damage detection of composite pressure vessels using inverse finite element method coupled with physics-based strain pre-extrapolation
Get PDFThis study presents an advanced strategy for shape sensing and damage detection of composite Type IV pressure vessels using the inverse finite element method (iFEM) coupled with a novel physics-based strain pre-extrapolation approach. The pre-extrapolation methodology, developed based on Kirchhoff plate bending theory, enhances the accuracy of full-field displacement and strain reconstruction by addressing the need for strain input across all structural regions. By incorporating discrete experimental measurements, this framework enables precise residual strain estimation, facilitating damage localization in composite structures. The proposed inverse model is validated through both numerical and experimental investigations, leveraging fiber optic sensor networks strategically placed along axial and circumferential segments of the pressure vessel. Quasi-static compression and low-velocity impact (LVI) tests are conducted to evaluate the model's performance under complex loading conditions. The reconstructed displacement and strain fields demonstrate the exceptional capability of iFEM in accurately capturing structural deformations and detecting damage initiation and progression. Notably, the method effectively identifies damage induced by LVI by analyzing residual strain distributions at critical post-impact time instances. Overall, the results underscore the robustness of the iFEM framework in capturing complex shape deformations and damage patterns that might otherwise remain undetected, highlighting its potential for real-time structural health monitoring of composite pressure vessels
Synthetic data generator evaluative visualization tool
No full textThe increasing use of machine learning in sensitive domains has necessitated synthetic data generation to ensure privacy protection and data balance. This study introduces an innovative Synthetic Data Generator Evaluation Visualization Tool to address the complexities of creating and evaluating synthetic data. Our tool visualizes GAN model losses and performance metrics in real-time, enabling users to actively monitor and evaluate the training process. By progressively expanding GAN training with dynamic sampling of the dataset, our tool allows users to iteratively improve data convergence and efficiently adjust hyperparameters without using the entire dataset. The study validates the tool's effectiveness through rigorous testing and highlights potential improvements to extend its applicability. This research advances synthetic data studies by providing a scalable solution that simplifies the generation process, making a significant contribution to data-driven fields requiring synthetic datasets
Bir kış Godot'sunu beklerken: Yer Demir Gök Bakır'da korku, inanç ve direniş
Get PDFThis article examines the ontological and thematic divergences between the “village novels” of Village Institute authors and Yaşar Kemal’s “Dağın Öte Yüzü” trilogy, focusing particularly on Yer Demir Gök Bakır. While Village Institute writers—shaped by Enlightenment ideals—tended to portray villages as static sociological case studies, often reducing religion and traditional customs to mere symbols of backwardness, Kemal’s narrative transcends such limitations by embedding rural life within broader human struggles. Through a close reading of Yer Demir Gök Bakır, the article highlights Yaşar Kemal’s nuanced exploration of fear, collective mythmaking, and resilience. The villagers of Yalak, paralyzed by debt and the looming threat of the merchant Adil, collectively elevate Taşbaşoğlu to sainthood—a desperate act of resistance against existential despair. Kemal’s narrative, however, gradually subverts this myth in response to shifting material conditions, evoking Beckett’s Waiting for Godot in its meditation on the fragility of hope.
Unlike the typified figures in village novels, Yaşar Kemal’s characters exhibit psychological complexity and agency. The elderly Meryemce and Koca Halil, once revered as wise elders, are rendered powerless, reflecting the erosion of traditional hierarchies. Religion, rather than being rigidly vilified, is reimagined as a flexible, functional strategy for survival. The article underscores Yaşar Kemal’s ability to transform local narratives into universal allegories, rendering the village not as a static repository of tradition but as a microcosm of human vulnerability and ingenuity. By contrasting Yaşar Kemal’s literary strategies with the didacticism of Village Institute authors, the study positions Yer Demir Gök Bakır as a seminal text bridging social realism with existential inquiry, affirming Yaşar Kemal’s legacy as a novelist of both Anatolian specificity and global relevance
In-situ defect detection in directed energy deposition using thermal imaging and machine learning
No full textDirected energy deposition (DED) is a pivotal additive manufacturing technology, offering substantial advantages for complex and large-scale part fabrication. However, its widespread industrial adoption is hindered by the persistent challenge of in-situ defect formation, particularly porosities, which compromise the quality and reliability of manufactured components. This work addresses the critical challenge of defect detection in DED processes by formulating it as a supervised classification problem. Leveraging a nearly balanced data set and focusing on a single defect type (porosity), a detection framework based on meltpool thermal imaging is developed. The methodology begins with essential preprocessing steps—including region-of-interest extraction and segmentation to isolate meltpool regions—followed by domain expertise-based feature extraction. Two machine learning classifiers—random forest (RF) and support vector machine (SVM)—are employed to identify defect-free and defective thermal images based on the extracted features. Notably, the proposed methodology incorporates the spatio-temporal relationship among thermal images into the modeling, which is normally overlooked in existing literature. The defects of the samples were characterized using computer tomography (CT) imaging to assess the efficacy of the proposed methodology. The experimental results demonstrated the high effectiveness of both classifiers, achieving a high accuracy of detecting defects. However, RF consistently outperformed SVM, attaining an accuracy above 84%. RF also demonstrated superior robustness by reducing false positives, underscoring its reliability for porosity detection in DED processes. This study highlights the advantage of integrating machine learning-based frameworks with domain-specific preprocessing and feature engineering for in-situ defect detection in DED, without relying on expensive and time-consuming characterization methods