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Multivariate variational autoencoder
(Cornell Univ, 2025-11-08) Yavuz, Mehmet Can
Learning latent representations that are simultaneously expressive, geometrically well-structured, and reliably calibrated remains a central challenge for Variational Autoencoders (VAEs). Standard VAEs typically assume a diagonal Gaussian posterior, which simplifies optimization but rules out correlated uncertainty and often yields entangled or redundant latent dimensions. We introduce the Multivariate Variational Autoencoder (MVAE), a tractable full-covariance extension of the VAE that augments the encoder with sample-specific diagonal scales and a global coupling matrix. This induces a multivariate Gaussian posterior of the form N (µϕ(x), C diag(σ2ϕ(x))C⊤), enabling correlated latent factors while preserving a closedform KL divergence and a simple reparameterization path. Beyond likelihood, we propose a multi-criterion evaluation protocol that jointly assesses reconstruction quality (MSE, ELBO), downstream discrimination (linear probes), probabilistic calibration (NLL, Brier, ECE), and unsupervised structure (NMI, ARI). Across Larochelle-style MNIST variants, Fashion-MNIST, and CIFAR-10/100, MVAE consistently matches or outperforms diagonal-covariance VAEs of comparable capacity, with particularly notable gains in calibration and clustering metrics at both low and high latent dimensions. Qualitative analyses further show smoother, more semantically coherent latent traversals and sharper reconstructions. All code, dataset splits, and evaluation utilities are released to facilitate reproducible comparison and future extensions of multivariate posterior models.
Early Alzheimer's disease detection from retinal OCT images: a UK Biobank study
(Cornell Univ, 2025-11-07) Turkan, Yasemin; Tek, Faik Boray; Nazlı, M. Serdar; Eren, Öykü
Alterations in retinal layer thickness, measurable using Optical Coherence Tomography (OCT), have been associated with neurodegenerative diseases such as Alzheimer's disease (AD). While previous studies have mainly focused on segmented layer thickness measurements, this study explored the direct classification of OCT B-scan images for the early detection of AD. To our knowledge, this is the first application of deep learning to raw OCT B-scans for AD prediction in the literature. Unlike conventional medical image classification tasks, early detection is more challenging than diagnosis because imaging precedes clinical diagnosis by several years. We fine-tuned and evaluated multiple pretrained models, including ImageNet-based networks and the OCT-specific RETFound transformer, using subject-level cross-validation datasets matched for age, sex, and imaging instances from the UK Biobank cohort. To reduce overfitting in this small, high-dimensional dataset, both standard and OCT-specific augmentation techniques were applied, along with a year-weighted loss function that prioritized cases diagnosed within four years of imaging. ResNet-34 produced the most stable results, achieving an AUC of 0.62 in the 4-year cohort. Although below the threshold for clinical application, our explainability analyses confirmed localized structural differences in the central macular subfield between the AD and control groups. These findings provide a baseline for OCT-based AD prediction, highlight the challenges of detecting subtle retinal biomarkers years before AD diagnosis, and point to the need for larger datasets and multimodal approaches.
Contributions of mindful parenting and parent–child relationships to children’s executive function: a structural equation model
(Springer, 2026-04-19) Acar, İbrahim Hakkı; Hamamcı, Beyza; Bostancı, Selen
Mindful parenting supports within-family functioning, including parent–child relationships, which may promote positive child outcomes. In the present study, we examined the contributions of mindful parenting and parent–child relationships (positive and negative aspects) to children’s executive function. The study sample consisted of 354 children (192 girls) between 31 and 101 months (M = 66.65, SD = 15.88) and their parents from relatively low socioeconomic backgrounds. Parents reported mindful parenting, parent–child relationships, and children’s executive function. Findings from the structural equation model indicated that higher levels of mindful parenting and positive parent–child relationships were associated with higher levels of children’s executive function. In contrast, a negative parent–child relationship was related to lower levels of executive function. Additionally, mindful parenting was indirectly related to children’s executive function through the parent–child relationship. Findings from the current study underlined the importance of mindful parenting for both parent–child relationships and children’s executive function. In detail, parents who are better at being present with their children and utilizing mindful discipline may exhibit more warmth and supportive relationships with their children. Consequently, positive relationships could lead children to display higher cognitive abilities, including executive function.
Experimental analysis and optimization of cutting strategies and parameters of thin-walled ABS thermoplastics
(Taylor and Francis Ltd., 2026-04-15) Keklik, Burak Taha; Kayıhan, Mete; Kuzu, Ali Taner; Bakkal, Mustafa
This study investigates the influence of tool path and milling parameters on the surface quality and dimensional accuracy of thin-walled structured thermoplastic polymer acrylonitrile butadiene styrene (ABS). Tests examined the effects of up-milling and down-milling on channels of two depths (10 and 20 mm) using three tool path strategies (zig-single direction, zigzag-double direction and follow periphery-peripheral cutting), five cutting speeds (25 to 125 m/min) and three feed rates (0.05, 0.1 and 0.2 mm/rev). The milling operation was carried out with an uncoated tungsten carbide end mill. Performance was evaluated through cutting forces (full and half immersion), surface roughness (base and side walls) and dimensional accuracy (channel width and 1 mm thin-wall deviations). A multi-objective optimization based on the chip removal rate (CRR), width deviation, wall deviations (left and right) and corresponding surface roughness values was conducted in order to determine the optimum cutting parameters. The results demonstrate that the Zig-Zag tool path combined with a cutting speed of 125 m/min yielded the optimal performance. Moreover, the optimization analysis revealed that the ideal feed rates for these conditions fall within the range of 0.05–0.09 mm/rev.
Drilling performance of novel lightweight carbon specialty graphites with different grain sizes
(Amer Chemical Soc, 2026-04) Kayıhan, Mete; Yıldız, Mustafa; Kuzu, Ali Taner; Bakkal, Mustafa
Because of their excellent machinability, low density, and great thermal stability, specialty graphite materials are being utilized more and more in precision engineering applications. The impact of the graphite particle size on drilling performance under various cutting conditions, however, has not been well studied. The drilling performance of three specialty graphite materials with various grain sizes was examined in this study. While MSG30 and MSG46 have micrometer-sized grains with greater mechanical strength, the graphite substance labeled MSG215 has a coarse grain structure (0.9 mm). High speed steel and tungsten carbide drills were used in drilling experiments at three distinct feed rates and cutting speeds. Using Taguchi experimental design and analysis of variance, the effects of cutting settings and particle size on the thrust force were examined. Workpiece temperatures were monitored during drilling using embedded thermocouples, and the correlation between the temperature evolution at four distinct depth points and cutting parameters was assessed. The dimensional accuracy, tool wear, and surface roughness were additionally evaluated. The findings show that the drilling performance is significantly impacted by graphite grain size. In comparison to fine-grained materials, coarse-grained graphite generated thrust forces that were about 30% lower and surface roughness levels that were about 20% greater. The temperature increase during drilling was minimal, owing to the strong thermal conductivity of graphite, resulting in an average workpiece temperature of 35 degrees C and a rise of about 15 degrees C under dry cutting conditions. Tool wear and thermal loads were also reduced compared with those commonly seen in conventional engineering materials. In addition to offering useful advice for choosing appropriate cutting parameters and tool materials in precision graphite machining applications, the findings provide light on the microstructure-dependent drilling behavior of specialty graphite.
LaSIPDE: Latent-Space Identification of Partial Differential Equations from indirect, high-dimensional measurements
(Frontiers Media SA, 2026-04-14) Koulali, Imane; Turan, Erhan; Eskil, Mustafa Taner
Discovering governing equations from data is a central challenge in scientific machine learning, particularly when observations are high-dimensional and the underlying state variables are not directly accessible. In this work, we introduce a framework for data-driven discovery of partial differential equations (PDEs) from indirect high-dimensional observations. The proposed approach combines nonlinear representation learning through an autoencoder with sparse identification of governing equations in the latent space, enabling simultaneous model reduction and PDE discovery while preserving spatial structure. Unlike existing methods that either operate on observable variables or discover latent ordinary differential equations, our framework identifies PDEs directly in the learned latent coordinates. We validate the approach on high-dimensional observations generated from Burgers and Korteweg-de Vries (KdV) systems, where the true state variables are intentionally hidden. In both cases, the method successfully recovers the correct dynamical operators, including diffusion, nonlinear advection, and dispersive terms. Although the recovered coefficients differ due to latent coordinate transformations, we show both theoretically and empirically that the discovered equations are dynamically equivalent to the ground-truth systems up to an affine transformation. These results demonstrate that governing PDEs can be recovered from indirect, high-dimensional data without access to the physical state variables, providing a foundation for interpretable model discovery in realistic measurement settings.
From solidarity to selective engagement: boundaries of feminist praxis and refugee women in Turkey
(Cambridge University Press, 2026-04-26) Bal, Sinem
This article investigates how feminist praxis in Turkey incorporates refugee women into their advocacy practices, and uncovers the extent to which these interactions expose the boundaries of solidarity. Anchored in Gramscian political theory, it asks whether feminist activism continues to operate as an inclusive counter-hegemonic political sphere, and the degree to which refugee women are incorporated within it. Drawing on interviews with feminist- and migrant-led non-governmental organizations in Turkey, the analysis demonstrates that interactions with refugee women frequently unfold through short-term, humanitarian-oriented, project-funded initiatives rather than collective practices of solidarity. These dynamics highlight tensions between the emancipatory claims of feminist politics and the selective solidarities that take shape under conditions of intersecting inequalities and governance frameworks. Rather than offering a definitive critique of feminist politics, the article treats the question of refugee women as an analytical lens through which the constraints of solidarity within contemporary feminist politics in Turkey become visible.
Adaptive incident escalation in SOCs via AI-driven skill-aware assignment and tier optimization
(Institute of Electrical and Electronics Engineers Inc., 2026-04-15) Abuaziz, Ahmed; Çeliktaş, Barış
Modern Security Operations Centers (SOCs) face significant operational bottlenecks driven by escalating alert volumes, increasingly sophisticated cyberattack vectors, and chronic imbalances in analyst workloads. Conventional rule-based escalation models frequently fail to account for the multi-dimensional nature of incident characteristics, the nuances of analyst expertise, and fluctuating operational demands. This study proposes a comprehensive AI-driven framework for intelligent incident assignment and workload optimization. The framework introduces five primary contributions: 1) a multi-factor scoring model that integrates severity and complexity metrics with dynamic workload balancing; 2) two novel optimization algorithms, Quantile-Targeted Normality-Regularized Optimization (QT-NRO) and Joint Optimization of Weights and Thresholds (JOWT), to calibrate scoring coefficients against target analyst utilization; 3) a Large Language Model (LLM) engine leveraging Retrieval-Augmented Generation (RAG) for semantic alignment between incident requirements and analyst expertise; 4) an Adaptive Capacity Zoning mechanism for dynamic workload management; and 5) a novel RAG Relevance Score metric—a pre-resolution, semantic alignment indicator that quantifies analyst-incident assignment quality independently of resolution time, addressing a fundamental limitation of traditional temporal metrics such as Mean Time to Resolution (MTTR) and providing a reusable benchmark applicable to any skill-aware assignment system. In addition, the framework incorporates a feedback-based continuous learning mechanism that utilizes historical resolution data to inform future assignments. An experimental evaluation using 10,021 real-world incidents from Microsoft Defender demonstrates that the JOWT algorithm achieves a tier distribution alignment within 0.8% of targets. LLM-enhanced semantic matching yields improvements between 26.7% and 126.8% in skill alignment across both normal-load and high-load evaluations, while simulations indicate a 31.8% reduction in MTTR. These results substantiate the efficacy of AI-driven methodologies in enhancing SOC operational efficiency and response precision.
Changes and continuities in opposition discourse: politicizing corruption ahead of 2023 elections
(Peter Lang Publishing, 2025) Demiralp, Seda; Kubicek, Paul
[No abstract available]
Future circular collider feasibility study report
(2025-12) Benedikt, Michael; Zimmermann, Frank; Auchmann, Bernhard; Bartmann, Wolfgang; Burnet, Jean Paul; Bayındır, Cihan
In response to the 2020 Update of the European Strategy for Particle Physics, the Future Circular Collider (FCC) Feasibility Study was launched as an international collaboration hosted by CERN. This report describes the FCC integrated programme, which consists of two stages: an electron-positron collider (FCC-ee) in the first phase, serving as a high-luminosity Higgs, top, and electroweak factory; followed by a proton-proton collider (FCC-hh) at the energy frontier in the second phase. The FCC-ee is designed to operate at four key centre-of-mass energies: the Z pole, the WW pair production threshold, the ZH production peak, and the top/anti-top production threshold-each delivering the highest possible luminosities to four experiments. Over 15 years of operation, FCC-ee will produce more than 6 trillion Z bosons, 200 million WW pairs, nearly 3 million Higgs bosons, and 2 million top anti-top pairs. Precise energy calibration at the Z pole and WW threshold will be achieved through frequent resonant depolarisation of pilot bunches. The sequence of operation modes between the Z, WW, and ZH substages remains flexible. The FCC-hh will operate at a centre-of-mass energy of approximately 85 TeV-nearly an order of magnitude higher than the LHC-and is designed to deliver 5 to 10 times the integrated luminosity of the upcoming High-Luminosity LHC. Its mass reach for direct discovery extends to several tens of TeV. In addition to proton-proton collisions, the FCC-hh is capable of supporting ion-ion, ion-proton, and lepton-hadron collision modes. This second volume of the Feasibility Study Report presents the complete design of the FCC-ee collider, its operation and staging strategy, the full-energy booster and injector complex, required accelerator technologies, safety concepts, and technical infrastructure. It also includes the design of the FCC-hh hadron collider, development of high-field magnets, hadron injector options, and key technical systems for FCC-hh.
Analysis and design of a resistor-less DC-bus active discharge and dynamic braking scheme using IGBTs in the active region
(Institute of Electrical and Electronics Engineers Inc., 2026-04) Sezer, Mustafa Murat; Deshmukh, Akshay Vijayrao; Hava, Ahmet Masum; Akın, Bilal
During shutdowns, emergency conditions, and dynamic braking, fully discharging the dc-bus capacitor or clamping the dc-bus voltage in industrial systems is typically managed using power resistors and additional switches. This conventional approach increases system cost, size, and complexity. This article introduces a compact, cost-effective, resistor-less method for two functions: 1) active discharge and 2) dynamic braking in low-power industrial systems. The proposed technique operates IGBTs in their active region with low gate-emitter voltages ($V_{\text {GE}}$ ), creating high impedance in the discharge path to limit current. For active discharge, a constant-power strategy is implemented using pulse frequency modulation (PFM), where the on-time (t_{\text {on}}$ ) of each pulse is fixed and the pulse frequency is ramped up to accelerate energy dissipation. This approach enables complete discharge of a 600-V dc-bus within 1 s, handled entirely by a single IGBT. The method is validated across three different IGBT vendors, showing consistent results and long-term reliability with no parameter degradation after over 200000 completed discharge cycles. For dynamic braking, the PFM method with fixed pulse frequency enables continuous power dissipation between 50 and 150 W for over 30 min. It effectively replaces conventional internal braking resistors typically rated from 20 to 200 W with resistance values of 5-$120~\Omega $. The system can also tolerate brief overloads up to 50% beyond IGBT current ratings for 10-20 s, providing sufficient time to complete braking without failure, as confirmed by test results. All these benefits are achieved through a simple gate driver modification that supplies partial $V_{\text {GE}}$ levels (3-10 V), eliminating bulky resistors, reducing cost by at least 50%, and saving space-making the solution ideal for high-volume industrial applications.
Improving the behaviour of sand with ceramic additives for sustainable ground engineering
(Emerald Group Publishing, 2026-04-14) Koçak Dinç, Beste; Dehghanian, Kaveh; Etminan, Ehsan
This study investigates the effectiveness of waste ceramic powder as a sustainable additive for improving the geotechnical behaviour of poorly graded sandy soils. Ceramic waste was mixed with sand at 5%, 10%, and 15% by dry weight, and its influence on density, permeability, and shear strength was evaluated through laboratory testing and numerical modelling. Results indicate a significant improvement in mechanical properties: the internal friction angle increased from 27 degrees for untreated sand to 40.6 degrees at 15% ceramic content, while apparent cohesion increased from 0 to 15.4 kPa. Permeability decreased markedly from 0.115 to 0.0356 m/s due to the micro-filler effect of fine ceramic particles. Maximum dry density increased from 1.553 to 1.904 g/cm & sup3;, indicating improved compaction behaviour. Finite element analyses using PLAXIS 8.6 confirmed the experimental findings, showing reduced static settlement (from 131 to 77 mm) and lower seismic-induced displacements under Kocaeli, Kobe, and Chi-Chi earthquake motions. The results demonstrate that waste ceramic powder is a low-carbon, cost-effective alternative for sandy soil stabilisation, contributing to sustainable ground engineering and circular material reuse.
Automating cyber risk assessment with public LLMs: an expert-validated framework and comparative analysis
(Institute of Electrical and Electronics Engineers Inc., 2026-03-26) Ünal, Nezih Mahmut; Çeliktaş, Barış
Traditional cyber risk assessment methodologies face a critical dilemma: they are either quantitative yet static and context-agnostic (e.g., CVSS), or context-aware yet highly labor-intensive and subjective (e.g., NIST SP 800-30). Consequently, organizations struggle to scale risk assessment to match the pace of evolving threats. This paper presents an automated, context-aware risk assessment framework that leverages the reasoning capabilities of publicly available Large Language Models (LLMs) to operationalize expert knowledge. Rather than positioning the LLM as the final decision-maker, the framework decouples semantic interpretation from risk scoring authority through a transparent, deterministic Dynamic Metric Engine. Unlike complex closed box machine learning models, our approach anchors the AI's reasoning to this expert-validated metric schema, with weights derived using the Rank Order Centroid (ROC) method from a survey of 101 cybersecurity professionals. We evaluated the framework through a comparative study involving 15 diverse real-world vulnerability scenarios (C1-C15) and three supplementary sensitivity stress tests (C16-C18). The validation scenarios were independently assessed by a cohort of ten senior human experts and two state-of-the-art LLM agents (GPT-4o and Gemini 2.0 Flash). The results show that the LLM-driven agents achieve scoring consistency closely aligned with the human median (Pearson r ranging from 0.9390 to 0.9717, Spearman ρ from 0.8472 to 0.9276) against a highly reliable expert baseline (Cronbach's α =0.996), while reducing the assessment cycle time by more than 100× (averaging under 4 seconds per case vs. a human average of 6 minutes). Furthermore, a dedicated context sensitivity analysis (C13-C15) indicates that the framework adapts risk scores based on organizational context (e.g., SME vs. Critical Infrastructure) for identical technical vulnerabilities. Importantly, the system is designed not merely to replicate expert intuition, but to enforce bounded, policy-consistent risk evaluation under predefined governance constraints. Overall, these findings suggest that commercially available LLMs, when constrained by expert-validated metric schemas, can support reproducible, transparent, and real-time risk assessments.
Effects of laurocerasus officinalis roem (Cherry Laurel) on cognitive function and neurobiochemical pathways in a streptozotocin-induced nontransgenic Alzheimer's disease model
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-03-08) Özsoy, Fulya; Yanar, Karolin; Sayılı, Uğurcan; Atukeren, Pınar; Uzun, Hafize
Background: This study investigated the effects of Laurocerasus officinalis Roem (cherry laurel; CL), a traditionally consumed fruit, on cognitive performance and selected neurobiochemical and metabolic pathways in a nontransgenic streptozotocin (STZ)-induced Alzheimer’s disease (i.c.v. STZ) model and an STZ-induced type 2 diabetes mellitus (T2DM; i.p. STZ) model. Method: Fifty-seven adult male Sprague–Dawley rats were allocated to control, T2DM, and Alzheimer (ALZ) model groups, with subgroup interventions including CL supplementation and, in the T2DM model, metformin as a comparator. Spatial learning and memory were assessed using the Morris Water Maze. Serum and brain tissue levels of GSK3-β, glutathione (GSH), interleukin-1 (IL-1), GLUT4, GLP-1, β-amyloid (Aβ), and acetylcholinesterase (AChE) were quantified. Results: Serum GSK3-β levels did not differ significantly between groups, whereas brain tissue GSK3-β showed significant between-group differences. CL increased GSH levels in both models, with significant elevations in serum and brain tissue GSH in the ALZ model following CL administration; in the T2DM model, GSH increased after both CL and metformin. In the ALZ model, CL was associated with decreased serum Aβ and AChE levels and improved Morris Water Maze performance, reflected by reduced escape latencies. Conclusions: CL supplementation was associated with antioxidant enhancement and modulation of amyloid- and cholinergic-related measures, alongside improved spatial learning performance in the STZ-induced nontransgenic ALZ model. In addition, CL reduced blood glucose in the T2DM model. Given the likely contribution of fruit phytochemicals (including total phenolics), further studies are warranted to better define the bioactive composition and mechanisms underlying these effects.
Development and validation of a short form of the mentalization scale (MentS-11)\: an evidence-based measure for Turkish adults
(Springer Science and Business Media Deutschland GmbH, 2026-03-03) Ünver, Buket
This study aimed to create a brief Turkish version of the Mentalization Scale (MentS-11) and to evaluate its reliability and validity in a large community sample. Turkish-speaking adults (N = 953) completed the original 25-item MentS, the Interactive Mentalization Questionnaire, and the Interpersonal Neurobiology–Based Prefrontal Cortex Functions Scale. Scale reduction combined exploratory and confirmatory factor analyses with graded-response item response theory. A three-factor solution—Self-related Mentalization (4 items), Other-related Mentalization (4 items), and Motivation to Mentalize (3 items)—displayed acceptable fit (CFI = 0.92, RMSEA = 0.08). Item-response analyses yielded strong discrimination (α = 0.93–2.07) and thresholds spanning the full latent range. Reliability was McDonald’s ωₜ = 0.84 for the total score, 0.81 for Other, 0.77 for Self, and 0.60 for Motivation. Scores on the MentS-11 were nearly identical to those on the 25-item form for the total scale (r =.92) and strongly aligned on their respective subscales (r =.72–0.81). Expected links with external measures confirmed convergent and criterion validity. The MentS-11 retains the theoretical scope and psychometric integrity of the original Turkish scale while halving administration time, making it a practical, time-efficient tool for assessing mentalization in both clinical practice and research.
The Killing magnetic curves in the anti-de Sitter space H31
(TUBITAK, 2026-03-10) Dursun, Uğur
In this paper, we study space-like, time-like and light-like Killing magnetic curves derived from Killing magnetic vector fields of the anti-de Sitter space H31 by using the half-space model H31 of H31 . We find the first integrals of the system of nonlinear differential equations that describe the Killing magnetic curves corresponding to some Killing vector fields of H31 , and then we give some particular solutions to obtain space-like, time-like and light-like magnetic curves of H31 . Also, we calculate the curvature and torsion of some space-like and time-like Killing magnetic curves, and the torsion of light-like Killing magnetic curves of H31.
Infrequent rebalancing, risk deferral, and equity returns at the turn of the month
(Elsevier Ltd, 2026-03-13) Kayaçetin, Nuri Volkan
We examine equity returns at the turn of the month using return data from thirty countries over the thirty-year period from January 1, 1994, to December 31, 2023. Our analysis reveals that the mean daily return on trading days surrounding the end of the month is significantly larger at 10 bps across the markets examined as compared to 0 bps on other days, with a narrow window bracketing month-ends accounting for all or nearly all positive mean return in each of the countries examined. Linking this pattern to the interaction between slow moving institutional capital and market frictions, we provide evidence in line with the idea that the observed pattern might be sustained by a dual-channel mechanism. First, the effect appears to be amplified hierarchically due to overlapping rebalancing mandates, peaking at lower frequencies due to the synchronization of a larger number of rebalancing schedules. Second, and more importantly, the effect also seems to be conditioned by the deferral of risky investments to structured rebalancing nodes during periods of market distress. Consistent with this mechanism, its magnitude is significantly larger after periods of market turbulence and during recessions, when investors are likely to store more cash in safe assets. Our findings thus provide a robust economic framework for understanding the enduring presence of the turn-of-the-month effect, suggesting that it may emerge as a joint consequence of infrequent rebalancing and risk deferral.
From policy to practice: a sector-agnostic operational framework for post-quantum cryptography transition
(Institute of Electrical and Electronics Engineers Inc., 2026-03-02) Birgin, Berat; Çeliktaş, Barış
The pace of quantum computing development necessitates not only the adoption of post-quantum cryptographic algorithms, but also the establishment of an executable and auditable institutional transition process. Although guidance documents published by the National Institute of Standards and Technology (NIST) and roadmaps proposed by the Post-Quantum Cryptography Coalition (PQCC) articulate strategic objectives, they largely remain procedural constructs lacking a concrete operational execution model. This paper presents an industry-neutral operational framework that translates policy-level post-quantum cryptography (PQC) guidance into deterministic, proof-producing process flows encompassing cryptographic asset discovery, classification, risk modeling, algorithm selection, deployment, monitoring, and governance enforcement. Central to the framework is a deterministic Quantum Risk Scoring (QRS) function, calibrated using the Analytical Hierarchy Process (AHP), which enables reproducible asset prioritization and policy-driven enforcement decisions. Framework executability is further strengthened through cryptography-aware continuous integration/continuous deployment (CI/CD) validation gates and downgrade protection mechanisms, ensuring the generation of verifiable and immutable audit artifacts. A scenario-based operational validation, implemented using open-source toolchains, demonstrates the framework’s operability, auditability, and governance alignment without relying on empirical cryptographic performance benchmarks, confirming that PQC transition can be operationalized as a verifiable lifecycle process bridging policy guidance with enforceable technical actions. Rather than introducing new cryptographic primitives, this work formalizes PQC transition as an operational systems-engineering problem centered on governance-enforced execution and lifecycle verifiability.
Future circular collider feasibility study report volume 3 civil engineering, implementation and sustainability (vol 234, pg 5113, 2025)
(Springer Science and Business Media Deutschland GmbH, 2025-12) Benedikt, Michael; Zimmermann, Frank; Auchmann, Bernhard; Bayındır, Cihan; Özaydın, Fatih
[No abstract available]
Posterior atrophy is a neuroimaging marker of mild cognitive impairment in Parkinson's disease
(Türk Nöropsikiyatri Derneği, 2026-02-02) Ay, Ulaş; Yıldırım, Zerrin; Kıcik, Ani; Erdoğdu, Emel; Bilgiç, Başar; Hanağası, Haşmet; Öztürk Işık, Esin; Demiralp, Tamer; Gürvit, Hakan
Introduction: Although there are several studies on the neuroanatomical mechanisms underlying Parkinson's disease (PD)-associated cognitive impairment, the clinical usefulness of the findings from these investigations is limited. In this study, we aimed to identify magnetic resonance imaging (MRI) markers that can be practically utilized for diagnosing PD-associated cognitive impairment using a visual rating scale (VRS). Methods: Anatomical MRIs of cognitively normal (PD-CN), and PD with mild cognitive impairment (PD-MCI) patients were visually evaluated for six bilateral cortical regions. Then, hypothesis-driven cortical thickness analysis (CTA) was performed in the regions obtained from VRS. Results: As a consequence of VRS, a significant difference was found between the two groups with regards to right posterior atrophy (PA) scores (pFDR-corr = 0.042, Cohen's d= 1.06). Hypothesis-driven CTA confirmed the result of VRS by revealing cortical thinning at the precuneus and parieto-occipital sulcus junction (Max. T= 6.171, P= 0.0006, MNIx, y,z = 11.0,-62.2, 25.4). The area under the curve was 0.75, showing a good association between the PD-MCI and the right PA score. The cut-off for maximum accuracy was >= 2, based on the highest sum of sensitivity (0.68) and specificity (0.72). Conclusions: Our findings indicate that right PA atrophy may be helpful for clinicians in the diagnosis of PD-associated cognitive impairment.

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