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Search for rare decays of the Z and Higgs bosons to a J/? or ?(2S) meson and a photon in proton-proton collisions at ?s=13 TeV
No full textA search is presented for rare decays of the Z and Higgs bosons to a photon and a J/psi or a psi(2S) meson, with the charmonium state subsequentially decaying to a pair of muons. The data set corresponds to an integrated luminosity of 123 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV collected with the CMS detector at the LHC. No evidence for branching fractions of these rare decay channels larger than predicted in the standard model is observed. Upper limits at 95% confidence level are set: B(H -> J/psi gamma) psi(2S)gamma) J/psi gamma) psi(2S)gamma) < 1.3 x 10(-6). The ratio of the Higgs boson coupling modifiers kappa(c)/kappa(gamma) is constrained to be in the interval (-157, +199) at 95% confidence level. Assuming kappa(gamma) = 1, this interval becomes (-166, +208).FWF; FNRS; FWO (Belgium); CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); BNSF (Bulgaria); MOST; NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG [MoER TK202]; Academy of Finland; MEC; CEA; CNRS/IN2P3 (France); SRNSF; BMBF; DFG; HGF (Germany); NKFIH (Hungary); DAE; DST; IPM; SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE; UM (Malaysia); BUAP; CONACYT; UASLP-FAI (Mexico); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); Swiss Funding Agencies (Switzerland); NSTDA; TUBITAK; DOE; NSF (USA); Marie-Curie program; European Research Council; Horizon 2020 Grant [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee [22rl-037]; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); FWO (Belgium) under the Excellence of Science - EOS [30820817]; Beijing Municipal Science & Technology Commission [Z191100007219010]; Fundamental Research Funds for the Central Universities (China); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Shota Rustaveli National Science Foundation [FR-22-985]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 400140256 -GRK2497]; Hellenic Foundation for Research and Innovation (HFRI) [2288]; Hungarian Academy of Sciences [K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA64]; Council of Science and Industrial Research, India; ICSC -National Research Center for High Performance Computing - NextGenerationEU program (Italy); Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; Fundacao para a Ciencia e a Tecnologia [CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund; ERDF a way of making Europe [MDM-2017-0765]; Programa Severo Ochoa del Principado de Asturias (Spain); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation [B39G670016]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA)We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MOST, and NSFC (China); Minciencias (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MoSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and The European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; The Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science - EOS'' - be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); The Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), among others, under Germany's Excellence Strategy -EXC 2121 Quantum Universe'' -390833306, and under project number 400140256 -GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program - UNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA64 (Hungary); the Council of Science and Industrial Research, India; ICSC -National Research Center for High Performance Computing, Big Data and Quantum Computing and FAIR -Future Artificial Intelligence Research, funded by the NextGenerationEU program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF a way of making Europe'', and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B39G670016 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA)
A polynomial-time algorithm for critical node detection in large-scale networks|Büyük ölçekli ağlar için polinom zamanlı kritik düğüm tespiti algoritması
No full textNetworks play a crucial role in modeling and analyzing complex systems, providing valuable data in both social and technological systems. This study addresses the Critical Node Problem (CNP), which aims to identify critical nodes within networks. CNP seeks to find the nodes whose removal minimizes pairwise connectivity in the network. In other words, CNP aims to identify a set of nodes whose deletion fragments the network into several segments of similar size. This problem has significant applications in various fields, ranging from social networks to biological systems, from telecommunication networks to wireless multi-hop networks. As CNP is an NP-Hard problem, developing polynomial-time algorithms is essential to find efficient solutions for large-scale networks. The primary motivation of this study is to develop a polynomial-time algorithm capable of finding near-optimal solutions, particularly for large-scale networks. The proposed TrimCut algorithm employs a three-stage process to identify critical nodes that reduce network resilience. In the first stage, the network is sparsified by removing high-degree nodes. Then, in the second stage, a Depth-First Search (DFS) tree is used to detect cut nodes. Finally, in the last stage, the critical nodes in the network are identified. The performance of the proposed algorithm has been compared with existing algorithms in the literature. Computational experiments demonstrate that the TrimCut algorithm achieves an error rate below 6% on standard benchmark networks and provides high-quality solutions for large-scale networks. © 2025 Elsevier B.V., All rights reserved
Pupil adjustments to illusory perceptions of the light intensity of object surfaces
No full textUsing infrared eye tracking, we show that when gaze is maintained at the center of one of two equiluminant surfaces of a Cornsweet stimulus, designed by Lotto and Purves, that illusorily appear to be lighter or darker than the other, the eye pupils constrict or dilate, respectively. That is, pupil sizes mirror the subjective experience of differential brightness. Previous studies of pupil responses to illusions of light had focused on illusions of unveridical light sources (e.g., patterns resembling the sun), whereas in the present study, we show pupil adjustments to the illusory brightness of object surfaces within images of realistic scenes. In two control experiments, we also showed that when the edge gradients of the Cornsweet stimulus, which do differ in luminance, were either occluded or presented alone in a black field, there were no differences in pupil diameters. We also conclude that adjustments to the perception of surface reflectance are unlikely to represent anticipatory responses to probable risks of temporary visual impairment (i.e., dazzle to sunlight) and, instead, indicate that a gradual process of disambiguation of the visual scene is sufficient to elicit adjustments to the apparent light intensity of an object's surface
Search for heavy long-lived charged particles with large ionization energy loss in proton-proton collisions at ?s=13 TeV
No full textA search for heavy, long-lived, charged particles with large ionization energy loss within the silicon tracker of the CMS experiment is presented. A data set of proton-proton collisions at a center of mass energy at root s = 13 TeV, collected in 2017 and 2018 at the CERN LHC, corresponding to an integrated luminosity of 101 fb(-1), is used in this analysis. Two different approaches for the search are taken. A new method exploits the independence of the silicon pixel and strips measurements, while the second method improves on previous techniques using ionization to determine a mass selection. No significant excess of events above the background expectation is observed. The results are interpreted in the context of the pair production of supersymmetric particles, namely gluinos, top squarks, and tau sleptons, and of the Drell-Yan pair production of fourth generation (tau ') leptons with an electric charge equal to or twice the absolute value of the electron charge (e). An interpretation of a Z' boson decaying to two tau ' leptons with an electric charge equal to 2e is presented for the first time. The 95% confidence upper limits on the production cross section are extracted for each of these hypothetical particles.FWF; FNRS; FWO (Belgium); CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); BNSF (Bulgaria); MoST; NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG [MoER TK202]; Academy of Finland; MEC; CEA; CNRS/IN2P3 (France); SRNSF; BMBF; DFG; HGF (Germany); NKFIH (Hungary); DAE; DST; IPM; SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE; UM (Malaysia); BUAP; CONACYT; UASLP-FAI (Mexico); PAEC (Pakistan); FCT (Portugal); J MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA; TUBITAK; NASU (Ukraine); DOE; NSF; Marie-Curie program; European Research Council; Horizon 2020 Grant [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee [22rl-037]; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); FWO (Belgium) under the Excellence of Science - EOS [30820817]; Beijing Municipal Science & Technology Commission [Z191100007219010]; Fundamental Research Funds for the Central Universities (China); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Shota Rustaveli National Science Foundation [FR-22-985]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 390833306, 400140256 - GRK2497]; Hellenic Foundation for Research and Innovation (HFRI) [2288]; Hungarian Academy of Sciences [K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Council of Science and Industrial Research, India; ICSC - National Research Center for High Performance Computing, Big Data and Quantum Computing - NextGenerationEU program (Italy); Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552, CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund; ERDF a way of making Europe [MDM-2017-0765]; Programa Severo Ochoa del Principado de Asturias (Spain); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation [B39G670016]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.)We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); J MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science - EOS - be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), among others, under Germany's Excellence Strategy - EXC 2121 Quantum Universe - 390833306, and under project number 400140256 - GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program - uNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC - National Research Center for High Performance Computing, Big Data and Quantum Computing and FAIR - Future Artificial Intelligence Research, funded by the NextGenerationEU program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the FundacAo para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF a way of making Europe, and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B39G670016 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.)
Task Scheduling of Multiple Humanoid Robot Manipulators by Using Symbolic Control
No full textTask scheduling for multiple humanoid robot manipulators in industrial and collaborative settings remains a significant challenge due to the complexity of coordination, resource sharing, and real-time decision-making. In this study, we propose a framework for modeling task scheduling for multiple humanoid robot manipulators by using the symbolic discrete controller synthesis technique. We encode the task scheduling problem as discrete events using parallel synchronous dataflow equations and apply our synthesis algorithms to manage the task scheduling of multiple humanoid robots via the resulting controller. The control objectives encompass the fundamental behaviors of the system, strict rules, and mutual exclusions over shared resources, categorized as the safety property, whereas the optimization objectives are directed toward maximizing the throughput of robot-processed products with optimal efficiency. The humanoid robots considered in this study consist of two pairs of six-degree-of-freedom (6-DOF) robot manipulators, and the inverse kinematics problem of the 6-DOF arms is addressed using metaheuristic approaches inspired by biomimetic principles. Our approach is experimentally validated, and the results demonstrate high accuracy and performance compared to other approaches reported in the literature. Our approach achieved an average efficiency improvement of 40% in 70-robot systems, 20% in 30-robot systems, and 10% in 10-robot systems in terms of production throughput compared to systems without a controller
The effect of supervised aerobic exercise on adipokine and myokine biomarkers in patients with cancer during systemic chemotherapy: a single-blinded prospective controlled trial
No full textPurposeThis prospective study is aimed at assessing the effect of aerobic exercise on adipokine and myokine biomarkers of breast or colon cancer patients during chemotherapy.MethodsThirty-two patients (15 exercise, 17 control) were included in this study. In total, 27 out of 32 and 26 out of 32 patients were female and diagnosed with breast cancer, respectively. The exercise protocol was performed via stationary resistive cycle ergometer during chemotherapy 2 times each week for at least 12 weeks. Circulating serum biomarkers of IL-6, irisin, leptin, and adiponectin levels were measured before chemotherapy and after the last chemotherapy cycle. The intensity of exercise was set as submaximal (50-70% of heart rate reserve (HRR)). Each patient was started with the intensity of exercise at the rate of 50% of their maximal HR. Each supervised exercise session lasted for 35 min.ResultsThe mean exercise duration (weeks) and percent adherence rates in the EG were 19.20 +/- 3.63 weeks and 85.13 +/- 11.87, respectively. In EG, there were significant increases in IL-6 (t = - 2.985, p = 0.011) and adiponectin (z = - 2.229, p = 0.026). Although a nearly 10% increase was observed in Irisin levels (0.83 vs. 0.91), it did not reach statistical significance (t = 0.840, p = 0.416). In EG, the correlation between Delta of leptin and adiponectin (r = - 0.635, p = 0.015) and the correlation between Delta of leptin and irisin (r = 0.802, p = 0.001) were found significant. No significant change was observed in each biomarker for CG.ConclusionThis study demonstrated that aerobic exercise can be safely implementable and beneficial during chemotherapy with significant effects on anti-inflammatory biomarkers via increased levels of IL-6 and adiponectin.Trial registration.NCT07048847, date of registration: 02.07.2025, retrospectively registered.ConclusionThis study demonstrated that aerobic exercise can be safely implementable and beneficial during chemotherapy with significant effects on anti-inflammatory biomarkers via increased levels of IL-6 and adiponectin.Trial registration.NCT07048847, date of registration: 02.07.2025, retrospectively registered.ConclusionThis study demonstrated that aerobic exercise can be safely implementable and beneficial during chemotherapy with significant effects on anti-inflammatory biomarkers via increased levels of IL-6 and adiponectin.Trial registration.NCT07048847, date of registration: 02.07.2025, retrospectively registered.Idot;zmir Bakimath;ray University Scientific Research Projects Coordination UnitThe authors would like to thank patients who voluntarily participated in this study
Machine Learning-Driven Lung Sound Analysis: Novel Methodology for Asthma Diagnosis
No full textIntroduction: Asthma is a chronic airway inflammatory disease characterized by variable airflow limitation and intermittent symptoms. In well-controlled asthma, auscultation and spirometry often appear normal, making diagnosis challenging. Moreover, bronchial provocation tests carry a risk of inducing acute bronchoconstriction. This study aimed to develop a non-invasive, objective, and reproducible diagnostic method using machine learning-based lung sound analysis for the early detection of asthma, even during stable periods. Methods: We designed a machine learning algorithm to classify controlled asthma patients and healthy individuals using respiratory sounds recorded with a digital stethoscope. We enrolled 120 participants (60 asthmatic, 60 healthy). Controlled asthma was defined according to Global Initiative for Asthma (GINA) criteria and was supported by normal spirometry, no pathological auscultation findings, and no exacerbations in the past three months. A total of 3600 respiratory sound segments (each 3 s long) were obtained by dividing 90 s recordings from 120 participants (60 asthmatic, 60 healthy) into non-overlapping clips. The samples were analyzed using Mel-Frequency Cepstral Coefficients (MFCCs) and Tunable Q-Factor Wavelet Transform (TQWT). Significant features selected with ReliefF were used to train Quadratic Support Vector Machine (SVM) and Narrow Neural Network (NNN) models. Results: In 120 participants, pulmonary function test (PFT) results in the asthma group showed lower FEV1 (86.9 +/- 5.7%) and FEV1/FVC ratios (86.1 +/- 8.8%) compared to controls, but remained within normal ranges. Quadratic SVM achieved 99.86% accuracy, correctly classifying 99.44% of controls and 99.89% of asthma cases. Narrow Neural Network achieved 99.63% accuracy. Sensitivity, specificity, and F1-scores exceeded 99%. Conclusion: This machine learning-based algorithm provides accurate asthma diagnosis, even in patients with normal spirometry and clinical findings, offering a non-invasive and efficient diagnostic tool
ON BEŞ YIL SAVAŞLARI DÖNEMİNİN EN KRİTİK SAFHASI: HABSBURGLARIN BUDİN’İ ELE GEÇİRME ÇABALARI (1598 ve 1602/1603)
No full textKanuni Sultan Süleyman, 1520 yılında tahta çıktığında hedeflerinden birisi Balkan coğrafyasından Orta Avrupa’ya açılmak için jeopolitik bir noktada bulunan Macar topraklarını ele geçirmekti. Bu amaçla 1521 yılında çıkılan seferde Belgrad Kalesi’ni fethederek Orta Avrupa’nın merkezi olan Budin’e giden yol kolaylaştırdı. 1526 yılında Mohaç zaferiyle birlikte Macar Krallığı’nın çökmesinin ardından Budin, Osmanlı’ya bağlı yerel vasallar aracılığı ile yönetildi. Ancak Avusturya tehlikesi üzerine 1541 yılında Budin, ilhak edilerek doğrudan imparatorluk topraklarına katıldı. Hatta Budin savunmasını güçlendirmek için etrafta olan kaleler tek tek alınarak bir savunma hattı oluşturulmuştur. Budin, XVI. yüzyılın son çeyreğine kadar sükûnet içinde kaldı. Bu dönemde Habsburg İmparatoru II. Rudolf’un amacı Budin’i ele geçirerek Macaristan’da üstünlüğü ele geçirmekti. Bu amaçla Budin’e giden yolu kolaylaştırmak için Estergon, Vác ve Hatvan’ı ele geçirdi. Özellikle 1598 yılında Osmanlı kuvvetlerinin bir kısmının Oradea’da bulunması Budin için iyi bir fırsat olmuştur. Yaklaşık bir ay süren kuşatma başarısızlıkla sonuçlanmıştır. Uzun Savaşlar esnasında Habsburg güçleri, tam iki kez daha Budin’i kuşatma cesareti göstermişler ancak müdafilerin Budin’i iyi savunmaları ve kuşatma mevsiminin geçmesi sebebiyle bir sonuç elde edememişlerdir
What is the Level of Language Development of Children at Risk of Developmental Language Disorder 2 Years Later?
No full textBackground: This study investigates the language development of children at risk for developmental language disorder (DLD) 2 years after initial assessment and evaluates the impact of parental training on language outcomes. Methods: Sixteen children (9 boys and 7 girls) who were at risk for DLD 2 years ago and whose parents were trained after the initial assessment were re-evaluated. During the study period, children's language development was evaluated using the Denver II Developmental Screening Test and the Turkish Adaptation of the Test of Early Language Development (TEDIL). Parents' perceptions of their children's language skills were also assessed. Results: At the second evaluation, 31.25% of the children were diagnosed with DLD, while 68.75% reached a normal language development level. TEDIL test results showed a significant increase in expressive and overall spoken language scores in children without DLD. Parents reported that their children's overall levels of communication, expression, and intelligibility increased significantly over 2 years. Conclusion: In this study, it was determined that some of the children at risk of DLD were diagnosed with DLD after 2 years. Long-term follow-ups of children at risk of DLD were also observed. Early parent training is beneficial in supporting language development in children at risk for DLD, emphasizing the importance of early intervention
KARŞILAŞTIRMALI ANAYASA HUKUKUNDA ÇİFT MECLİS SİSTEMİNE İLİŞKİN BİR DEĞERLENDİRME
No full textParlamento, modern demokrasilerin temsili niteliğini somutlaştıran en önemli kurumlarından birini oluşturmakla birlikte yapısal tasarımı her devlette farklılık göstermektedir. Parlamentoların yapısında iki meclisin bulunmasını ifade eden ve toplumsal, sınıfsal bölünmelerin siyasete yansıması sonucu ortaya çıkan çift meclisli sistem, tek meclisli sisteme göre daha köklü bir tarihsel geçmişe sahiptir. Ancak günümüzde tek meclisli sistemin daha yaygın bir şekilde tercih edildiği gözlemlenmektedir. Temel olarak federal yapıları koruma, siyasi güçlerin dengesini, toplumsal uzlaşıyı sağlama ve çoğunluğun tek taraflı baskın iradesini sınırlama amaçlarıyla kabul edilen çift meclis sisteminin avantaj ve dezavantajlarına ilişkin olarak karşılaştırmalı anayasa hukukunda farklı görüşler öne sürülmüştür. Bu bakımdan devletlerin parlamentolarında tercih ettikleri çift meclis sistemin özelliklerinin ortaya konulması, hangi gerekçelerle tercih edildiğinin açıklanması ve anayasal açıdan tartışılması gerekmektedir. Çalışmada çift meclis sisteminin temel özellikleri ortaya konularak, bir devletin tek meclis yerine çift meclisli bir yasama organına sahip olmasının ne gibi avantajlar ve dezavantajlar yaratacağı irdelenmiştir. Bu kapsamda meclis sistemini değiştiren devletlerin anayasal çerçevede değişiklik gerekçeleri ele alınmıştır. Karşılaştırmalı anayasa hukukundaki güncel gelişmelerden hareketle üniter devletlerde çift meclis sitemine ilişkin değerlendirmeler ve Türkiye’de çift meclis sistemi önerilerine ilişkin tartışmalar ortaya konulmuştur