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Protecting Whereabouts and Whatabouts for Check-In Based Location-Based Services
No full textUsing diverse location-based services (LBSs), especially through mobile smart phones, have become a daily routine for many people. Location privacy is an important concern with each and every LBS request. Besides whereabouts, location disclosure provides attackers with whatabouts as well. Since each individual's location privacy needs are not the same, most solutions enable individualized location privacy profiles. In this work, as attack models and trustfulness of LBS providers are different, in the context of location check-ins, we provide a framework offering a palette of location privacy protection methods to be picked for each LBS provider/attacker. Depending on what to protect per LBS provider, i.e., whereabouts and/or whatabouts, and attack model, i.e., weak or strong, we develop six privacy protection methods. A top-down location cloaking algorithm which is able to enforce the six protection methods is presented. An extensive experimental evaluation on two real datasets are performed.TUBITAK [114E132]This work has been supported by TUBITAK under the grant number 114E132
The Initial Experience of Turkish Neurosurgical Stroke Centers: A National Study
No full textAIM: To evaluate the clinical and radiological outcomes of newly established Turkish neurosurgical stroke centers, and to assess their competency in managing acute ischemic stroke from June 2023 to June 2024. MATERIAL and METHODS: We retrospectively analyzed data from 69 patients (mean age = 69.06 ± 13.48 years) from three stroke centers in Türkiye by reviewing hospital records and patient interviews, focusing on demographic variables, comorbidities, treatment methodologies, outcomes (using the Modified Rankin Scale (mRS)), stroke severity (using the National Institutes of Health Stroke Scale [NIHSS]), Alberta Stroke Program Early CT (ASPECT) scores, reperfusion status (using the modified Thrombolysis in Cerebral Ischemia (mTICI) score), complications, blood glucose levels, and creatinine levels. RESULTS: Of 392 acute ischemic stroke patients, 280 (71.4%) had no identifiable occlusion, 43 (11%) were out of the MT time window, and 69 (17.6%) underwent MT, with 57 (14.5%) having LVO and 12 (3%) MVO. Final reperfusion (mTICI ≥2b) was achieved in 78.3% of MT patients, and 29% achieved favorable outcomes (mRS ≤2) at three months. Younger age, lower baseline NIHSS, and higher ASPECT scores correlated with better outcomes, while elevated blood glucose (>127.50 mg/dL) and creatinine (>0.80 mg/dL) were linked to worse mRS scores. Complications occurred in 21.7%, including symptomatic intracranial hemorrhage in six patients. CONCLUSION: While Turkish neurosurgical stroke centers have made significant strides in managing acute ischemic stroke, challenges remain in optimizing patient outcomes. This initial experience underscores the need for further research, continued training, and educational standardization for neurosurgeons in endovascular techniques to improve patient care. © 2025 Elsevier B.V., All rights reserved
Diffusion Model-Based Augmentation Using Asymmetric Attention Mechanisms for Cardiac MRI Images
No full textBackground: The limited availability of cardiac MRI data significantly constrains deep learning applications in cardiovascular imaging, necessitating innovative approaches to address data scarcity while preserving critical cardiac anatomical features. Methods: We developed a specialized denoising diffusion probabilistic model incorporating an attention-enhanced UNet architecture with strategically placed attention blocks across five hierarchical levels. The model was trained and evaluated on the OCMR dataset and compared against state-of-the-art generative approaches including StyleGAN2-ADA, WGAN-GP, and VAE baselines. Results: Our approach achieved superior image quality with a Fr ; eacute;chet Inception Distance of 77.78, significantly outperforming StyleGAN2-ADA (117.70), WGAN-GP (227.98), and VAE (325.26). Structural similarity metrics demonstrated excellent performance (SSIM: 0.720 +/- 0.143; MS-SSIM: 0.925 +/- 0.069). Clinical validation by cardiac radiologists yielded discrimination accuracy of only 60.0%, indicating near-realistic image quality that is challenging for experts to distinguish from real images. Comprehensive anatomical analysis revealed that 13 of 20 cardiac metrics showed no significant differences between real and synthetic images, with particularly strong preservation of left ventricular features. Discussion: The generated synthetic images demonstrate high anatomical fidelity with expert-level quality, as evidenced by the difficulty radiologists experienced in distinguishing synthetic from real images. The strong preservation of cardiac anatomical features, particularly left ventricular characteristics, indicates the model's potential for medical image analysis applications. Conclusions: This work establishes diffusion models as a robust solution for cardiac MRI data augmentation, successfully generating anatomically accurate synthetic images that enhance downstream clinical applications while maintaining diagnostic fidelity
Future Workscapes: Strategic Insights and Innovations in Human Resources and Organizational Development Preface
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Equitable Generation Expansion Planning: A Data-Driven Approach to Energy Justice in Hydroelectric Power
No full textThis study aims to develop a structured framework for segmenting regions and designing energy planning models that promote both cost efficiency and environmental sustainability in hydro-dominated energy systems. Focusing on the United States-with a detailed case analysis of Texas-this research investigates the role of hydroelectric dams in energy landscape, emphasizing challenges encountered across various regions. Utilizing a comprehensive approach that integrates demographic, geographic, and dam property data, the research introduces a twodimensional population segmentation methodology. By employing population segmentation, the study utilizes supervised learning methods and data analysis to categorize regions within the U.S., with specific attention given to Texas counties. Subsequently, stylized optimization models for generation expansion planning are developed to address the unique needs and requirements of the segmented regions. Results show that minimizing costs led to a 63% reduction in total expenditures but results in higher CO2 emissions. In contrast, an approach that prioritizes emissions reduction achieves a 98% decrease in CO2 emissions, though at a higher cost. Leveraging these models, the study offers significant insights into strategic energy planning, underlining the imperative of transitioning to sustainable energy solutions. Furthermore, this research discusses broader implications for advancing energy justice and suggests a roadmap for extending similar analyses to other regions with significant hydroelectric potential
Devlet Yanlısı Milislerin Ordu Üzerindeki Sivil Kontrolüne Etkisi: Nicel Nedensel Bir Analiz
No full textBu doktora tezi devlet yanlısı milis grup (DYMG) varlığının ordu üzerindeki sivil kontrol üzerindeki nedensel etkisini test etmeyi amaçlamaktadır. Tez, vekalet kuramına dayanarak DYMG varlığının sivil kontrolünü azalttığını savlamaktadır. Bu hipotezi nedenselliğin üç ampirik koşulu olan sağlam istatistiksel ilişki, başka etkenlerce açıklanmama ve nedenin sonuçtan önce gerçekleşmesi açılarından test etmek için üç farklı veri tabanı birleştirilmiştir. Bağımlı değişken olan sivil kontrolü için Civilian Control Scores Dataset, bağımsız değişken olan DYMG varlığı için Pro-Government Militias Database ve kontrol değişkenleri olan demokrasi, devlet kapasitesi, ekonomik durum ve çatışma göstergeleri için Varieties of Democracy Database kullanılarak 155 ülke hakkında 1981 ila 2010 yılları arası döneme ilişkin veri toplanmıştır. Bağımlı değişken 1 ila 5 yıl ileri kaydırılarak zamansal fark yaratıldıktan sonra bağımlı değişken ve bağımız değişken ile kontrol değişkenlerinin tüm birli, ikili, üçlü, dörtlü…on dörtlü kombinasyonları Rastsal Etkiler regresyon analizleri ile test edilmiştir. Bu 98304 analizden elde edilen bulgular DYMG varlığının sivil kontrolü üzerinde güçlü olmasa da istatistiksel olarak anlamlı negatif etkisi olduğunu göstermiştir. Dolayısıyla bağımlı ile bağımsız değişken arasında ilişki olmadığı, olsa bile başka bir değişkenin sonucu olduğu ve nedenin sonuçtan önce gelmediği şeklindeki sıfır hipotezi reddedilmiştir. Anahtar Kelimeler: Milisler, Sivil Kontrolü, Nedensellik, Vekalet KuramıThis dissertation aims to test the causal effect of pro-government militia (PGM) presence on civilian control over the military. Drawing upon agency theory, it argues that PGM presence causes civilian control to diminish. To test this hypothesis on three empirical criteria of causality: robust statistical association, non-spuriousness, and temporal precedence of the cause over the effect, it incorporates data on 155 countries between 1981 and 2010 from three datasets: Civilian Control Scores Dataset for the dependent variable¬ (dynamic civilian control score), Pro-Government Militias Database for the independent variable (PGM presence), and Varieties of Democracy Database for control variables (democracy, economics, state capacity, and conflict indicators). After shifting the dependent variable one to five years to the future, Statistical association, non-spuriousness, and temporal precedence were tested using Random Effects regression models with each version of the dependent variable, the independent variable, and all the combinations (sized one, two, three, four, …fourteen) of fourteen control variables. In all these 98304 analyses, the coefficient of PGM presence remained negative and statistically significant, enabling the rejection of the null hypothesis on no-association, spuriousness, and no temporal precedence dimensions. Keywords: Militias, Civilian Control, Causality, Agency Theor
Mitigating Cavitation Effects on Francis Turbine Performance: a Two-Phase Flow Analysis
No full textDue to their ability to operate over a wide range of flow rates and generate high power, Francis turbines are the most widely used of hydroturbine type. Hydraulic turbines, are designed for specific flow and head conditions tailored to site conditions. However, Francis turbines can also be operated outside of design conditions due to varying flow and head values. Operation outside of design conditions can lead to cavitation. In this study, singlephase steady-state an alyses were conducted initially to examine cavitation in detail, followed by two-phase transient analyses. The results obtained from these analyses were compared to determine the cavitation characteristics of the designed turbine. The steady-state simulation results indicate the occurrence of cavitation, including traveling bubble and draft tube cavitation, under overload operating conditions. However, these cavitation characteristics are not observed in the two-phase transient simulation results under the same operating conditions. Additionally, the turbine efficiency is predicted to be higher in the transient simulation results. This is attributed to the frozen rotor interface used in the steady-state simulations, which over predicts flow irregularities. The reduced flow irregularities in the transient results have resulted in lower cavitation and losses, leading to higher predicted turbine efficiency.This work is financially supported by Scientific and Technological Research Council of Turkey (TUBITAK) under grant 113G109. The computational and experimental facilities of TOBB ETU Hydro Energy Research Center (ETU Hydro) financially supported by Turkish Ministry of Development, are utilized.Scientific and Technological Research Council of Turkey (TUBITAK) [113G109]; Turkish Ministry of Developmen
Search for Boosted Low-Mass Resonances Decaying Into Hadrons Produced in Association With a Photon in pp Collisions at S = 13 TeV With the ATLAS Detector
No full textMany extensions of the Standard Model, including those with dark matter particles, propose new mediator particles that decay into hadrons. This paper presents a search for such low mass narrow resonances decaying into hadrons using 140 fb−1 of proton-proton collision data recorded with the ATLAS detector at a centre-of-mass energy of 13 TeV. The resonances are searched for in the invariant mass spectrum of large-radius jets with two-pronged substructure that are recoiling against an energetic photon from initial state radiation, which is used as a trigger to circumvent limitations on the maximum data recording rate. This technique enables the search for boosted hadronically decaying resonances in the mass range 20–100 GeV hitherto unprobed by the ATLAS Collaboration. The observed data are found to agree with Standard Model predictions and 95% confidence level upper limits are set on the coupling of a hypothetical new spin-1 Z′ resonance with Standard Model quarks as a function of the assumed Z′-boson mass in the range between 20 and 200 GeV. © The Author(s) 2025.Ministerio de Ciencia, Innovación y Universidades, MCIU; Agencia Nacional de Investigación y Desarrollo; BSF-NSF; Australian Research Council, ARC; DRAC; La Caixa Banking Foundation; Centre National pour la Recherche Scientifique et Technique, CNRST; NAWA; Center for African Studies, CAS; Fundação para a Ciência e a Tecnologia, FCT; European Union, Future Artificial Intelligence Research; European Organization for Nuclear Research; Polish National Science Centre; Georgia Health Initiative, HGF; National Science Foundation, NSF; Baden-Württemberg Stiftung; Science and Technology Facilities Council, STFC; Horizon 2020, ICSC-NextGenerationEU; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministry of Science and Innovation; Istituto Nazionale di Fisica Nucleare; Ministry of Science and Higher Education; Japan Society for the Promotion of Science; MVZI; PROMETEO; Spine Education and Research Institute, SERI; IDUB AGH; Ministry of Education Youth and Sports; Neubauer Family Foundation; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Austrian Science Fund, FWF; BCKDF; Yerevan Physics Institute; ERDF; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Slovenian Research Agency; Canada Foundation for Innovation, CFI; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Göran Gustafssons Stiftelse; Generalitat de Catalunya; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; U.S. Department of Energy, FEA; EU-ESF; COST; CRC; Generalitat Valenciana; RGC; Duchenne Research Fund, DRF; Netherlands Organisation for Scientific Research; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; PRIMUS; Agencia Estatal de Investigación, AEI; Islamic Scholarship Fund, ISF; ICSC; ANR; Institutul de Fizică Atomică, IFA; Ministry of Science and Technology of the People's Republic of China, MOST; Natural Sciences and Engineering Research Council of Canada, NSERC; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; GenT Programmes Generalitat Valenciana, Spain; Marie Skłodowska-Curie Actions; National Science and Technology Council, NSTC; EU; MINERVA, Israel; Irish Rugby Football Union, IRFU; Cantons of Bern and Geneva; Defence Science Institute, DSI; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, FNS; Horizon 2020 Framework Programme; MNE; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Marcus och Amalia Wallenbergs minnesfond, MMW; CERN-CZ; National Research Foundation, NRF; Ministerstwo Edukacji i Nauki, MNiSW; FAPERJ; European Research Council; CERN, CERN; Ministerstvo Školství, Mládeže a Tělovýchovy, MEYS; European Union; National Research Council Canada, NRC; Multiple Sclerosis Scientific Research Foundation, MSSRF; DFG; AvH Foundation; Istituto Nazionale di Fisica Nucleare, INFN; CANARIE; Ministry of Education, Culture, Sports, Science and Technology, MEXT; UK Research and Innovation, UKRI; Center for Advancing Research Impact in Society, ARIS, (J1-3010); Center for Advancing Research Impact in Society, ARIS; Agence Nationale de la Recherche, (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-22-EDIR-0002, ANR-21-CE31-0022); Ministero dell’Università e della Ricerca, (PRIN — 20223N7F8K — PNRR M4.C2.1.1); FONDECYT, (1240864, 1230987, 1230812); FAIR-NextGenerationEU, (PE00000013); Czech Science Foundation, (GACR — 24-11373S); ERC, (948254, 101089007); Swiss National Science Foundation, (SNSF — PCEFP2_194658); MCIN, (RYC2019-028510-I, PCI2022-135018-2, RYC2021-031273-I, RYC2022-038164-I, PID2021-125273NB, RYC2020-030254-I); Investissements d’Avenir Labex, (ANR-11-LABX-0012); Polish National Agency for Academic Exchange, (PPN/PPO/2020/1/00002/U/00001); Leverhulme Trust, (RPG-2020-004); H2020 European Research Council, (ERC — 101002463); DNSRC, (IN2P3-CNRS); Knut and Alice Wallenberg Foundation, (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); GenT Programmes Generalitat Valenciana, (CIDEGENT/2019/027); Japan Society for the Promotion of Science, JSPS, (JP22KK0227, JP22H04944, JP23KK0245, JP22H01227); Japan Society for the Promotion of Science, JSPS; Swedish Research Council, (VR 2022-03845, 2023-04654, VR 2023-03403, VR 2022-04683, 2021-03651, VR 2018-00482); National Natural Science Foundation of China, NNSF, (12275265, NSFC-12075060); National Natural Science Foundation of China, NNSF; Research Council of Norway, (RCN-314472); Carl Trygger Foundation, (CTS 22:2312); Deutsche Forschungsgemeinschaft, (DFG — CR 312/5-2, DFG — 469666862); FEDER, (IDIFEDER/2018/048); Chinese Ministry of Science and Technology, (MOST-2023YFA1605700); National Natural Science Foundation of China, (NSFC — 12175119); U.S. Department of Energy, (ECA DE-AC02-76SF00515); FORTE, (CZ.02.01.01/00/22_008/0004632, PRIMUS/21/SCI/017); NCN, (UMO-2023/49/B/ST2/04085, 2021/42/E/ST2/00350, H2020 MSCA 945339, 2022/47/B/ST2/03059, UMO-2022/47/O/ST2/00148, UMO-2019/34/E/ST2/00393, UMO-2021/40/C/ST2/00187, UMO-2020/37/B/ST2/01043); MUCCA, (CHIST-ERA-19-XAI-00); Royal Society, (NIF-R1-231091
A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges
No full textBackground/Objectives: Magnetic resonance imaging (MRI) is crucial in detecting suspicious lesions and diagnosing clinically significant prostate cancer (csPCa). However, variability in MRI-targeted diagnostic pathways arises due to factors such as patient characteristics, imaging protocols, and radiologist expertise. Artificial intelligence (AI) offers potential solutions to these challenges by enhancing diagnostic accuracy and efficiency. Methods: This narrative review explores AI techniques, particularly machine learning and deep learning, in the context of prostate cancer diagnosis. It examines their application in improving MRI scan quality, detecting artifacts, and assisting radiologists in lesion detection and interpretation. It also considers how AI helps to reduce reading time and inter-reader variability. Results: AI has demonstrated sensitivity that is generally comparable to experienced radiologists, although specificity tends to be lower, potentially increasing false-positive rates. The clinical impact of these results requires validation in larger, prospective multicenter studies. AI is effective in identifying artifacts, assessing MRI quality, and assisting in diagnostic efficiency by providing second opinions and automating lesion detection. However, variability in study methodologies, datasets, and imaging protocols can impact AI's generalizability, limiting its broader clinical application. Conclusions: While AI shows significant promise in enhancing diagnostic accuracy and efficiency for csPCa detection, challenges remain, particularly with the generalizability of AI models. To improve AI robustness and integration into clinical practice, multicenter datasets and transparent reporting are essential. Further development, validation, and standardization are required for AI's widespread clinical adoption