5916 research outputs found
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Purchasing and supply management in humanitarian settings
No full textPurchasing and Supply Management (PSM) plays a critical role in the work of Humanitarian Organizations (HOs). This position paper reviews the existing literature and analyzes two contemporary cases from practice to provide an overview of the key characteristics of humanitarian PSM. It highlights the unique objectives of humanitarian operations in contrast to profit-seeking firms. It also notes a convergence of boundary conditions, as uncertainty and disruptions become increasingly prevalent across all contexts. Despite sectoral differences, commercial PSM can significantly benefit from the strategies and practices developed in humanitarian settings. Using the Disaster Management Lifecycle to differentiate segments of humanitarian work, PSM is positioned not as a mere transactional function, but as a strategic enabler of humanitarian missions. To stimulate further research in humanitarian PSM, five key themes in the research landscape are summarized: make-or-buy decisions, global or local sourcing, collaboration and alliances, sustainable sourcing, and digitalization.Publisher versio
Harnessing pore size in COF membranes: A concentration gradient-driven molecular dynamics study on enhanced H2/CH4 separation
No full textThis work presents a novel approach for accurately predicting the gas transport properties of covalent organic framework (COF) membranes using a nonequilibrium molecular dynamics (NEMD) methodology called concentration gradient-driven molecular dynamics (CGD-MD). We first simulated the flux of hydrogen (H2) and methane (CH4) across two distinct COF membranes, COF-300 and COF-320, for which experimental data are available in the literature. Our CGD-MD simulation results aligned closely with the experimentally measured gas permeability and selectivity of these COF membranes. Leveraging the same methodology, we discovered promising COF candidates for H2/CH4 separation, including NPN-1, NPN-2, NPN-3, TPE-COF-I, COF-303, DMTA-TPB2, 3D-Por-COF, COF-921, COF-IM AA, TfpBDH, and PCOF-2. We then compared our findings with simulations utilizing the well-known approach that merges grand canonical Monte Carlo (GCMC) and equilibrium molecular dynamics (EMD) to predict gas adsorption and diffusion parameters in COFs. Our results showed that when the pore sizes of COF membranes are below 10 & Aring;, the choice of the method plays a significant role in determining the performance of the membranes. The GCMC+EMD approach suggested that COFs tend to exhibit CH4 selectivity when their pore limiting diameters are below 10 & Aring;, whereas the CGD-MD results reveal a preference for H2. Density functional theory calculations indicate that H2 has a lower affinity for three promising COFs, NPN-1, NPN-2, and NPN-3, compared to CH4, which results in H2 remaining unbound, while CH4 occupies all of the adsorption sites, thereby facilitating the selective recovery of H2 at the end of the separation process. We proposed a relationship between adsorption time and diffusion time, highlighting the critical role of selecting an appropriate simulation method. This relationship underscores how adsorption and diffusion processes interplay, impacting material performance. Overall, these insights not only improve the accuracy of predictive models but also guide the development of more efficient COF-based membrane applications for future research and industrial applications.TÜBİTA
Domesticity and dwelling in displacement: Home-making practices of syrian women in istanbul houses
No full textCombining architectural and cultural anthropological approaches, this study explores the domestic spaces of Syrian women in Istanbul in order to understand how they perform ‘home-making’ in a new social and architectural setting. Scholars who study migration and gender are increasingly interested in studying ‘home,’ but few studies examine migrant women’s spatial agency and how space and time are materialized by looking at past and present homes. Methodologically, we add to standard semi-structured interviews and photographic analysis, the method of mental map drawings of houses in Istanbul and reminisced houses from Syria. These methods allow us to examine interrelated spatio-temporal practices of material culture decorations of the residential interiors and (re)creating of daily routines from Syria within the residential interior. Each of these home-making practices is a form of personalisation, control of space and manner of performing gender roles while increasing contentment and belonging. By decorating with objects from Syria, plants, photos, carefully selected furniture; repurposing guestrooms into spaces of religious practice; and cooking, nurturing family members and hosting friends, women create domestic spaces of comfort. Ultimately, this research showcases how migrant women create homes out of new dwellings, even when they are not able to fully revive what has been lost.TÜBİTAK ; European Union’s Horizon 202
Advancing white balance correction through deep feature statistics and feature distribution matching
No full textAuto-white balance (AWB) correction is a crucial process in digital imaging, ensuring accurate and consistent color correction across varying lighting conditions. This study presents an innovative AWB correction method that conceptualizes lighting conditions as the style factor, allowing for more adaptable and precise color correction. Previous studies predominantly relied on Gaussian distribution assumptions for feature distribution alignment, which can limit the ability to fully exploit the style information as a modifying factor. To address this limitation, we propose a U-shaped Transformer-based architecture, where the learning objective of style factor enforces matching deep feature statistics using the Exact Feature Distribution Matching algorithm. Our proposed method consistently outperforms existing AWB correction techniques, as evidenced by both extensive quantitative and qualitative analyses conducted on the Cube+ and a synthetic mixed-illuminant dataset. Furthermore, a systematic component-wise analysis provides deeper insights into the contributions of each element, further validating the robustness of the proposed approach
Let's not tempt fate: The influence of future time-orientation, fatalism, and superstition on willingness to report expectations about future health
No full textThis article examines individuals' likelihood of engaging in future health prediction as a function of their fatalism, future time orientation, superstition, and history of chronic disease. Using a multistage cluster sample of 33 urban cities in Turkey, we asked respondents (N = 1,467), to report their past and current health and predict their future (expected) health status (i.e., future self-rated health). While less than 1% failed to report past or current health, 23% of respondents provided no prediction for their future health status. We employed a moderated-mediation analysis to identify the predictors of this avoidance of reporting future health status expectations. Our analyses point to two potentially distinct mechanisms influencing individuals' likelihood of providing future self-rated health. First, individuals suffering from a chronic disease were more likely to have higher fatalism, which, in turn, decreased their likelihood of providing a rating for their future health. Second, more superstitious individuals were less likely to report expectations about future health. This association was moderated by future time orientation such that for individuals with higher future time orientation (vs. present time orientation), higher superstition was associated with a steeper increase in the probability of avoidance of future health predictions. This finding suggests that some individuals might avoid sharing predictions about their future health because they fear talking about future outcomes can invite negative outcomes by "tempting fate."TÜBİTAKPublisher versio
Test prioritization based on the coverage of recently modified source code: An industrial case study
No full textRegression 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%
ZETROS: A zero-trust IoT network security framework using distributed blacklisting, trust scoring and smart contracts
No full textThe purpose of Internet of Things (IoT) security is to ensure the availability, confidentiality, and integrity of IoT networks. However, due to the heterogeneity of IoT devices and the possibility of attacks of various kinds from both inside and outside the network, securing an IoT network is a difficult task. Handshake protocols are useful for achieving mutual authentication, which allows secure inclusion of devices into the network. By verifying that the information they receive is accurate and from a trusted source, mutual authentication minimizes the possibility that a malicious actor will compromise their connections. However, handshake protocols do not protect devices from attackers in the network. Use of autonomous anomaly detection and blacklisting prevents nodes with anomalous behavior from joining, re-joining, or remaining in the network. Similarly, trust scoring is another popular method that can be used to increase the resilience of the network against trust based system attacks. In view of the above, the contributions of this paper are three-fold. First, to ensure the security of the IoT network from outsider attacks in a zero-trust environment, we propose a new handshake protocol based on Physical Unclonable Functions that can be used in IoT device discovery and mutual authentication between the IoT device and the server. The proposed protocol is resilient to Man-in-the-Middle, replay and forgery attacks, as proven in our security analysis. Secondly, we propose a real-time intrusion and anomaly detection framework based on machine learning to prevent network-based attacks from insiders. Finally, we propose a trust system which utilizes feedback mechanisms based on smart contracts for managing the trust of a dynamic IoT network to increase resilience against behavioral attacks. Simulation results show that by using blacklisting, our trust management model provides greater resilience against trust-based attacks compared to similar blockchain-based trust models in the literature, and the proposed distributed IoT network security framework can secure an IoT network from both internal and external attacks, even in an environment where half of the devices in the network are compromised.Business FinlandPublisher versio
A column generation heuristic for simultaneous lot-sizing and scheduling problems with secondary resources and setup carryovers
No full textThis study introduces an innovative approach to address the Capacitated Lot-Sizing and Scheduling Problem with Sequence-Dependent Setups (CLSD), considering both the sequence-dependent setups and costs. Facing the challenge of large-scale instances, a Column Generation-based Neighbourhood Search (CGNS) algorithm is proposed, efficiently handling real-life CLSD scenarios with extensions like secondary resources and setup carryover and crossovers. The algorithm demonstrates superior performance compared to commercial solvers and fix and relax-based benchmark algorithms, producing high-quality solutions within specified time limits on large data sets. The study's contributions include a distinctive pattern and column structure in the proposed formulation, effectively managing the exponential increase in decision variables. Test instances and a real- life case study validate the algorithm's applicability to production systems under the CLSD and Capacitated Lot-Sizing Problem (CLSP) frameworks, making it a valuable tool for optimising simultaneous lot-sizing and scheduling challenges in practical settings.TÜBİTA
Liquid synthetic jets for high flux electronics cooling
No full textThis study presents an approach to high-efficiency, low-energy liquid cooling using liquid synthetic jet devices. These devices generate dynamic pressure exactly where needed, addressing the inefficiencies of conventional liquid cooling systems. Powered by a piezoelectric actuator, localized, high-velocity jet impingement is achieved with minimal power consumption as low as 50 mW. With a dielectric working fluid as deionized water, liquid synthetic jet impingement showed a heat transfer coefficient of up to 1.52 W/(cm2·K). Compared to existing methods, superior heat removal per unit of consumed power is achieved. This work presents an advancement in sustainable thermal management, with broad potential applications, including immersion cooling in data centers.Auburn University Samuel Ginn College of Engineerin
MYFED: A dataset of affective face videos for investigation of emotional facial dynamics as a soft biometric for person identification
No full textPsychological studies have demonstrated that the facial dynamics play a significant role in recognizing an individual's identity. This study introduces a novel database (MYFED) and approach for person identification based on facial dynamics, to extract the identity-related information associated with the facial expressions of the six basic emotions (happiness, sadness, surprise, anger, disgust, and fear). Our contribution includes the collection of the MYFED database, featuring facial videos capturing both spontaneous and deliberate expressions of the six basic emotions. The database is uniquely tailored for person identification using facial dynamics of emotional expressions, ensuring an average of ten repetitions for each emotional expression per subject-a characteristic often absent in existing facial expression databases. Additionally, we present a novel person identification method leveraging dynamic features extracted from videos depicting the six basic emotions. Experimental results confirm that dynamic features of all emotional expressions contain identity-related information. Notably, surprise, happiness, and sadness expressions exhibit the highest levels of identity-related data in descending order. To our knowledge, this is the first research that comprehensively analyzes facial expressions of all six basic emotions for person identification. For further research and exploration, the MYFED database is made accessible to researchers via the MYFED database website.NVIDIA ; TÜBİTA