Ultrafast laser synthesis of zeolites

Research demonstrates that zeolite nucleation and growth can be controlled by fine-tuning chemical composition, temperature, and pressure, resulting in structures with diverse porosities and functionalities. Nevertheless, current energy delivery methods lack the finesse required to operate on the femto- and picosecond timescales of silica polymerization and depolymerization, limiting their ability to direct synthesis with high precision. To overcome this limitation, an ultrafast laser synthesis technique is introduced, capable of delivering energy at these timescales with unprecedented spatiotemporal precision. Unlike conventional or emerging approaches, this method bypasses the need for specific temperature and pressure settings, as nucleation and growth are governed by dynamic phenomena arising from nonlinear light-matter interactions, such as convective flows, cavitation bubbles, plasma formation, and shock waves. These processes can be initiated, paused, and resumed within fractions of a second, effectively "freezing" structures at any stage of self-assembly. Using this approach, the entire nucleation and growth pathway of laser-synthesized TPA-silicate-1 zeolites is traced, from early oligomer formation to fully developed crystals. The unprecedented spatiotemporal control of this technique unlocks new avenues for manipulating reaction pathways and exploring the vast configurational space of zeolites.European Research Council (ERC) ; TÜBİTAKPublisher versio

A Jungian psychosocial approach to entrepreneurship: The case of woman entrepreneurs in Turkey

Purpose - The relationship between feminine and masculine qualities is usually seen as a hierarchy and dichotomy, and what affects people's choices of these qualities remain largely unanswered. Through this study, we aim to elucidate variations in women entrepreneurs' entrepreneurial behaviour and definitions of success by using the Jungian psychosocial framework. Design/methodology/approach - Through 28 semi-structured in-depth interviews, we investigated female entrepreneurs' entrepreneurial behaviour in Turkey's unique, non-western, developing-economy context, surrounded by mixed cultural values melding East and West. Findings - In brief, we found that women entrepreneurs, being greatly affected by their psychosocial contexts, use different archetypal masculine and feminine qualities with various degrees during their entrepreneurial journeys. Thus, entrepreneurial behaviour results from deliberate choices of archetypal masculine and feminine qualities existing on a continuum and shaped by the specific psychosocial context of the individual entrepreneur. Moreover, the choices of different archetypal qualities play a significant motivational role in the construction of entrepreneurs' definitions of success, which seems to align with the individuation phenomenon Jung described as the life purpose of every individual. Originality/value - The Jungian framework we introduce to the field offers an alternative, yet comprehensive and enriching solution, to the problematic dichotomy of the qualities underlying entrepreneurship behaviour and offers a solid theoretical base in expanding the definition of success

Leveraging unlabeled data in federated learning: A review

In centralized machine learning, both the data and model to be trained reside on a single server, which may cause problems regarding data privacy as sensitive or personal data need to be transferred from clients to the server. Federated learning has been proposed to provide a solution to this problem by allowing the training of a model without the data leaving the clients. This training takes place between a coordinating server and the clients by continuously exchanging the model parameters instead of exchanging data. In real-life applications, the data on some of the clients or the server may be partially labeled or completely unlabeled, which poses a severe challenge to federated learning. In this paper, we present a survey of recently proposed methods that leverage unlabeled data in a federated learning setting to improve model performance. We also present a novel taxonomy of the methods that leverage unlabeled data based on whether the unlabeled data is assigned a pseudo-label during the process or not. We summarize the datasets, main data modalities, and application areas of federated learning with unlabeled data methods in the literature and highlight future research directions. We believe that this survey will be a useful guide for researchers planning to work on federated learning with partially labeled data.TÜBİTAKPublisher versio

Designing a preschool outdoor classroom in early childhood education: A case from istanbul

This study aims to define the spatial requirements and establish a design strategy for preschool outdoor classrooms (POCs), emphasizing their educational significance and promoting broader implementation. Additionally, through POC arrangements, the article aims to enhance children's interaction with nature, diversify and support their experiences, and foster environmental awareness by increasing their engagement with the natural environment at an early age. Integrating design-based research with qualitative methods, the study was conducted in four phases: research, design, implementation, and evaluation. The research phase involved an extensive review of literature, international design guidelines, and outdoor education organizations to identify pedagogical foundations and essential design components. In the design phase, the required spatial elements and tools were defined, and a strategic framework was developed. A participatory approach was applied during the implementation phase to create a POC in Nisantepe, Istanbul, Türkiye, referred to as N-POC. The final phase evaluated the N-POC design based on established POC criteria. The study identified core spaces and tools essential for POC development and validated the proposed, context and content-based design strategy through the N-POC case, demonstrating its applicability and effectiveness

Through-the-thickness investigation of grain structure and microhardness of ni-based and mg-based laminated composites for dental applications

Nowadays, metal-based composites are utilized in medical applications such as dental implant, oral surgery, and dentistry. This work looks into the evolution of grain structure and hardness of Ni and Mg layers from the surface to the center of Ni-based and Mg-based laminated composites processed by eight cycles of accumulative roll bonding (ARB) technique. The findings showed a gradual decrease in grain size from the surface to the center of composites. Moreover, the lowest grain sizes of Ni and Mg were obtained when they were in the matrix of composites that were closer to rollers. Also, the hardness of layers grew by increasing the cycles. The layers closer to the surface of composites showed higher hardness. Nonetheless, the variations of hardness values were also observed because of the slicing and re-stacking of composites prior to each cycle

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Test prioritization based on the coverage of recently modified source code: An industrial case study

Regression tests are re-executed to ensure quality and lack of side-effects after software changes to incorporate new/improved functionalities and/or bug fixes. Prioritizing these tests for detecting faults earlier can increase productivity especially when the testing duration increases. We conduct an industrial case study in the consumer electronics domain, where regression tests take several weeks to complete. We evaluate the effectiveness of a test prioritization approach in terms of the rate of early fault detection. We analyze test cases individually but apply prioritization at a higher granularity level, where we prioritize weekly test plans rather than individual test cases. Our approach gives higher priority to those test cases that cover the recently modified parts of the source code. We use 3 Digital TV projects as subject systems. We compare the effectiveness of the original execution order of test cases with the alternative ordering as suggested by our approach. Results show that the alternative ordering is more effective in finding faults earlier for all the 3 subject systems, where the rate of early fault detection can be increased by up to 38%

Agroecology and back-to-land migration in Turkey: Asset or obstacle?

Through semi-structured interviews with 83 back-to-landers, this paper examines how differences in preferred production practices shape relations between these newcomers and the locals. The paper shows that production practices are more than just production practices; they signal identity: For both locals and back-to-landers, if someone is an agroecological producer, they are more likely to be a back-to-lander; and if they engage in conventional agriculture, they are more likely to be a local. That said, because back-to-landers usually come from non-agricultural backgrounds, they tend to rely on locals for skill, experience, and traditional knowledges critical for agroecology. Tapping into locals' social networks, however, is challenging given that two groups have different preferred production practices - and identities. To bridge this gap, back-to-landers follow a variety of strategies, none of which are failsafe. In the long run, the differences in preferred production practices (and by proxy, identities) often lead to disagreements, sometimes escalating into conflicts. Switch to agroecology among locals as well as integration into social networks - of either side's and for either side - remain tenuous.TÜBİTA

Anomaly detection via graph contrastive learning

Graph contrastive learning (GCL) techniques have shown superior performance in many tasks, such as social networks and recommendation systems, which makes them good solution candidates for detecting anomalies more accurately. The existing noncontrastive learning-based approaches do not fully take into account dynamic agents that can camouflage themselves. These agents either establish frequent associations with regular objects or intentionally skip forming relationships with the remaining objects, which are called head and tail anomalies respectively. In terms of graph topology, such agent behavior makes the graph more imbalanced. To handle both types of anomalies, we come up with a novel ensemble graph contrastive learning-based GCAD (Graph Contrasted Anomaly Detection) which is an ensemble of two approaches: 1- We learn representations and embeddings by leveraging Siamese architecture, which learns to minimize/maximize similarity between graph pairs at different scales while capturing their hierarchical structure. 2- We integrate a self-supervised learning framework using graph augmentations (like node and edge dropout) and contrastive learning to learn robust graph embeddings. In this case, the main idea is to generate multiple views of a graph using augmentations and then maximize the agreement between these views using contrastive loss. We show our approach outperforms the competing approaches in detecting both tail and head anomalies across 6 different datasets from citation and finance domains. The ablation studies also show the importance of GCAD components as well as its robustness

A comparative analysis of large deformation behavior of thin flat and corrugated steel plates under static and blast loading

This paper investigates the large deformation behavior of thin flat and corrugated (crimp) steel plates used in prefabricated blast-resistant modular structures through finite element simulations. The study evaluates plate responses under static monotonic, cyclic static, and dynamic blast loads using pressure-impulse (P-I) curves. Closed-form models based on yield line theory are developed to predict deflection-pressure curves for both flat and corrugated plates, showing strong agreement with finite element analysis. The results indicate that the pressure-deformation behavior of flat plates changes significantly upon yielding. Under cyclic loading, their stiffness decreases substantially until membrane action mitigates the effects during unloading, which also results in a notable reduction in hysteretic energy dissipation capacity. In contrast, the cyclic analysis of corrugated plates reveals decreased load-carrying capacity due to buckling from their profile, yielding, and plastic deformations. However, these plates exhibit substantial energy dissipation and maintain consistent initial stiffness throughout hysteretic loops, with minimal deviation. The findings highlight the significant influence of aspect ratio on plate behavior. Flat plates show a highly sensitive pressure-displacement relationship based on their aspect ratio, while corrugated plates exhibit minimal sensitivity in both static and dynamic conditions. Corrugated plates display consistent one-way bending behavior, largely independent of their aspect ratio. Dynamic blast analysis reveals that corrugated plates perform better in impulse-sensitive regions across all response levels, while flat plates excel in pressure-sensitive regions, particularly at medium and high levels