Okan University GCRIS Standard Database
Not a member yet
7992 research outputs found
Sort by
Modulational Instability, Bifurcation Study, Sensitivity Analysis, and Jacobi Elliptic Waves of a Resonant Nonlinear Schrodinger Equation
No full textHosseini, Kamyar/0000-0001-7137-1456The current paper formally investigates the propagation of specific waves modeled by a resonant nonlinear Schrodinger equation (RNLSE). In particular, in-depth research is conducted on the RNLSE, which involves various effects such as Bohm potential, detuning effect, etc. The study begins with the modulational instability (MI) of the governing model and goes on with its bifurcation analysis (BA) using the dynamical system theory. Additionally, a sensitivity analysis (SA) is performed to ensure that minor changes in seed values do not adversely affect the solution's stability. The paper ends with retrieving several Jacobi elliptic and soliton waves and analyzing the impact of nonlinear parameters on the dynamics of such waves. The outcomes effectively show how to control the width and amplitude of Jacobi elliptic and soliton waves.National Science, Research and Innovation Fund (NSRF); King Mongkut's University of Tech-nology North Bangkok [KMUTNB-FF-68-B-18]This research budget was allocated by National Science, Research and Innovation Fund (NSRF) , and King Mongkut's University of Tech-nology North Bangkok (Project no. KMUTNB-FF-68-B-18) .Science Citation Index Expande
Energy-Scaling Behavior of Intrinsic Transverse-Momentum Parameters in Drell-Yan Simulation
No full textBenaglia, Andrea Davide/0000-0003-1124-8450; Pesaresi, Mark/0000-0002-9759-1083; Mcginnis, Michael/0000-0002-9833-6316; Ruiz, Jose/0000-0002-3306-0363; Zhang, Licheng/0000-0001-7947-9007; Diaz, Daniel/0000-0001-6834-1176; Bhowmik, Sandeep/0000-0003-1260-973X; Smith, Nicholas/0000-0002-0324-3054; Chou, Pin-Chun/0000-0002-5842-8566; Santpur, Sai Neha/0000-0001-6467-9970; Vami, Tamas Almos/0000-0002-0959-9211; Longo, Luigi/0000-0002-2357-7043; Tytgat, Michael/0000-0002-3990-2074; Kreczko, Luke/0000-0003-2341-8330; Ventura, Sandro/0000-0002-8938-2193; Wittich, Peter/0000-0002-7401-2181; Brooke, James/0000-0003-2529-0684; Kim, Youngwan/0000-0002-4856-5989; Cussans, David/0000-0001-8192-0826; Ivanov, Andrew/0000-0002-9270-5643; Leonidou, Christos/0009-0008-6993-2005; Hall, Geoffrey/0000-0002-6299-8385; Evdokimov, Olga/0000-0002-1250-8931; Mora Herrera, Maria Clemencia/0000-0003-3915-3170; Stylianou, Nicolas/0000-0002-0113-6829; Ngadiuba, Jennifer/0000-0002-0055-2935; Tornago, Marta/0000-0001-6768-1056; Cardini, Andrea/0000-0003-1803-0999; Heath, Helen/0000-0001-6576-9740; Ligabue, Franco/0000-0002-1549-7107; Whalen, Kathleen/0000-0002-9383-8763; Duarte, Javier Mauricio/0000-0002-5076-7096; Benato, Lisa/0000-0001-5135-7489; Chokheli, Davit/0000-0001-7535-4186; Trocino, Daniele/0000-0002-2830-5872; Pradhan, Raghunath/0000-0001-7000-6510; Safdari, Murtaza/0000-0001-8323-7318; Giammanco, Andrea/0000-0001-9640-8294; Portales, Louis/0000-0002-9860-9185; Murillo Quijada, Javier Alberto/0000-0003-4933-2092; Tapper, Alexander/0000-0003-4543-864X; Csanad, Mate/0000-0002-3154-6925; Garcia, Francisco/0000-0002-4023-7964; Dolek, Furkan/0000-0001-7092-5517; Lu, Meng/0000-0002-6999-3931; Tiras, Emrah/0000-0002-5628-7464; Grunewald, Martin/0000-0002-5754-0388An analysis is presented based on models of the intrinsic transverse momentum (intrinsic k(T)) of partons in nucleons by studying the dilepton transverse momentum in Drell-Yan events. Using parameter tuning in event generators and existing data from fixed-target experiments and from hadron colliders, our investigation spans 3 orders of magnitude in center-of-mass energy and 2 orders of magnitude in dilepton invariant mass. The results show an energy-scaling behavior of the intrinsic k(T) parameters, independent of the dilepton invariant mass at a given center-of-mass energy.SC (Armenia); BMBWF (Austria); FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); MES (Bulgaria); BNSF (Bulgaria); CERN; CAS (China); MoST (China); NSFC (China); MINCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, (Estonia); RVTT3 (Estonia); MoER TK202 (Estonia); Academy of Finland (Finland); MEC(Finland); HIP (Finland); CEA (France); CNRS/IN2P3 (France); SRNSF (Georgia); BMBF(Germany); DFG (Germany); HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP (Republic of Korea); NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES (Poland); NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI (Spain); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI (Thailand); NSTDA (Thailand); TUBITAK (Turkey); TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE (USA); NSF (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).Science Citation Index Expande
Multilayer Neural Networks Enhanced with Hybrid Methods for Solving Fractional Partial Differential Equations
No full textThis paper introduces a novel multilayer neural network technique to solve partial differential equations with non-integer derivatives (FPDEs). The proposed model is a deep feed-forward multiple layer neural network (DFMLNN) that is trained using advanced optimization approaches, namely adaptive moment estimation (Adam) and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), which integrate neural networks. First, the Adam method is employed for training, and then the model is further improved using L-BFGS. The Laplace transform is used, concentrating on the Caputo fractional derivative, to approximate the FPDE. The efficacy of this strategy is confirmed through rigorous testing, which involves making predictions and comparing the outcomes with exact solutions. The results illustrate that this combined approach greatly improves both precision and effectiveness. This proposed multilayer neural network offers a robust and reliable framework for solving FPDEs. © 2025 John Wiley & Sons Ltd.Kementerian Pendidikan Malaysia, MOE, (FRGS/1/2022/STG06/UPM/02/2); Kementerian Pendidikan Malaysia, MOEScience Citation Index Expande
Effect of Using Wire Coils and Aluminum Oxide Nanofluid on Heat Transfer in a Double-Pipe Heat Exchanger and Predicting Data With Artificial Neural Networks
No full textThe present study aims to experimentally investigate the Nusselt number and friction factor in a double-pipe heat exchanger equipped with wire coils and aluminum oxide nanofluid, with a particle size of approximately 55 nm, in Reynolds numbers from 4000 to 14000, volume fractions of 0.02, 0.04, and 0.06 %, and pitch ratios of 0, 1, 1.6, and 2.4. Then, a proposed correlation for the Nusselt number is presented, and finally, the experimental data are evaluated using an artificial neural network. The optimum increase of 135.6 % in the Nusselt number with aluminum oxide nanofluid occurs at a volume fraction of 0.06 %, a Reynolds number of 14000, and a pitch ratio of 1. The increase in the friction factor with nanofluid and wire coils, compared to the base fluid (water) without the wire coils, is approximately 7.06 %. The correlation coefficient, mean squared error, root mean squared error, and mean absolute error are calculated for the proposed correlation and artificial neural network. Furthermore, the maximum and minimum deviation margins obtained are +3.4211 and -3.2120, respectively. The results indicated that perceptron neural network of a 3-22-1 topology with Levenberg-Marquardt algorithm has successfully predicted the experimental data.Science Citation Index Expande
Solution of an Uncertain EPQ Model Using the Neutrosophic Differential Equation Approach
No full textTime is a very crucial factor in controlling demand patterns for certain products. In manufacturing processes, the production rate must be regulated according to the demand pattern and available stock as a part of effective lot size management policies. We incorporate this fundamental idea for constructing the production rate as a function of demand and stock, which is the primary contribution of this paper. Predicting demand patterns and adjusting the production rate inherently involve vagueness. We use neutrosophic logic, an advanced mathematical tool for addressing imprecision in decision planning. Neutrosophic calculus-based analysis of uncertainty involved with the proposed model is the secondary contribution in this paper. Numerical results indicate that the proposed approach yields superior results compared to the crisp environment and traditional neutrosophic approaches for cost minimization. Furthermore, it is worth noting that Case 1 of the proposed neutrosophic differential approach guarantees better results than Case 2.Emerging Sources Citation Inde
Aso Visual Abstract: Breast Cancer Recurrence in Initially Clinically Node-Positive Patients Undergoing Sentinel Lymph Node Biopsy After Neoadjuvant Chemotherapy in the Neosentiturk-Trials Mf18-02/18-03
No full textScience Citation Index Expande
Natural Convection of Water/Titanium Oxide Nanofluid Inside a Closed Enclosure at Different Angles of Attack
No full textIn most industrial applications, several situations are associated with closed enclosures, such as avionics, automotive, cooling/heating systems in buildings, electronic equipment, food, and phase change materials. In this paper, the natural convection (NC) of a Newtonian fluid inside a Non-Square Closed Enclosure (NSCE) is numerically simulated. The working fluid is a water/Titanium oxide nanofluid (NF) with volume fractions in the range of φ = 0 to 4 % and experiences a laminar flow with Rayleigh numbers (Ra) from 103 to 105. To benefit from better flow mixing, NSCE undergoes five different angles of attack -90°, -45°, 0°, 45°, and 90° degrees (cases 1 to 5, respectively). This research was solved using a computer code in two-dimensional space in steady state using the finite volume method. The solid-fluid suspension is considered homogeneous, single-phased, and Newtonian. The Boussinesq approximation is used for the density term. A SIMPLE algorithm is used for decoupling pressure and velocity fields. The results suggest that increasing the Ra number strengthens the fluid velocity components in the Closed Enclosure (CE). In all cases, the maximum Nusselt number (Nu) occurs at the interface between the fluid and the hot surface. In cases (1) and (5), due to the elongation of the fluid path, the circulation effects become more important, creating an anomaly in the friction factor for the Ra = 105. A symmetric pattern in the Nu number diagrams in cases (2) and (4) is evident which is due to the invariance of this parameter in these two cases. Entropy generation is influenced by fluid circulation and rotation. In all cases and conditions, the use of solid nanoparticles reduces the temperature gradient, which significantly affects the removal of hot spots with high entropy and consequently reduces the average entropy generation. Increasing the angle of attack of the closed enclosure compared to the smooth case (case 3) at Rayleigh numbers 103 and 104 can increase the friction coefficient by a factor of 1.62. Also, at Rayleigh number 104, changes in the angle of attack of the closed enclosure will experience a decrease in the Nusselt number and average heat flux by <8 % compared to the smooth case. At Rayleigh number 103, the 10 % increase in the average Nusselt number and heat flux is only due to the increase in the volume fraction of the solid nanoparticle and is somewhat independent of the angle of attack of the closed enclosure. © 2025 The Author(s
Recognizing the Unexpected: a Rare Cause of Rice Bodies in Brucellosis
No full textBrucella is a gram-negative zoonotic agent transmitted by consumption of raw milk and infected meat. Among musculoskeletal manifestations, axial involvement such as spondylodiscitis and sacroiliitis is well documented, while peripheral manifestations like tenosynovitis and rice bodies remain underreported. In this case, flexor tenosynovitis and associated rice body involvement due to Brucella, which developed progressive swelling, pain, and restricted movement in the wrist and third finger of the hand after a minor abrasion, were investigated ultrasonographically. As far as we know, this is the first reported case of primary brucellosis with flexor tenosynovitis and associated rice bodies demonstrated ultrasonographically in the literature.Science Citation Index Expande
Effects of Initial Temperature Changes on Swelling Percentage, Mechanical and Thermal Attributes of Polyacrylamide-Based Hydrogels Using the Molecular Dynamics Simulation
No full textPolyacrylamide hydrogels are widely used in various applications due to their unique swelling properties and mechanical performance. However, the effect of temperature on their behavior is not well understood. This study's goal is to use the LAMMPS software to do molecular dynamics simulations to examine how temperature affects the thermal characteristics, mechanical strength, and expansion of polyacrylamide hydrogels. As the temperature raised from 300 K to 350 K, the findings show that the elongation of hydrogels rose significantly, from 193.4 % to 224.4 %, due to enhanced water absorption and polymer chain mobility. As the temperature rose, the mechanical strength decreases from 0.0333 MPa to 0.0302 MPa, which is caused by the structure relaxing as the polymer chains got more flexible. Additionally, when the temperature rose, the thermal conductivity and heat flux rose as well, reaching 0.61 W/m & sdot;K and 1711 W/m2, respectively, as shown by the improved heat transfer. These results have a major influence on the design and development of polyacrylamide hydrogels for use in wound healing, tissue engineering, and drug delivery systems.Science Citation Index Expande
Measurement of the (Formula Presented.) and tH Production Rates in the (Formula Presented.) Decay Channel Using Proton-Proton Collision Data at (Formula Presented.) TeV
No full textAn analysis of the production of a Higgs boson (H) in asSociation with a top quark-antiquark pair (tt¯H) or a single top quark (tH) is presented. The Higgs boson decay into a bottom quark-antiquark pair (H → bb¯) is targeted, and three different final states of the top quark decays are considered, defined by the number of leptons (electrons or muons) in the event. The analysis utilises proton-proton collision data collected at the CERN LHC with the CMS experiment at s = 13 TeV in 2016–2018, which correspond to an integrated luminosity of 138 fb−1. The observed tt¯H production rate relative to the standard model expectation is 0.33 ± 0.26 = 0.33 ± 0.17(stat) ± 0.21(syst). Additionally, the tt¯H production rate is determined in intervals of Higgs boson transverse momentum. An upper limit at 95% confidence level is set on the tH production rate of 14.6 times the standard model prediction, with an expectation of 19.3−6.0+9.2. Finally, constraints are derived on the strength and structure of the coupling between the Higgs boson and the top quark from simultaneous extraction of the tt¯H and tH production rates, and the results are combined with those obtained in other Higgs boson decay channels. © The Author(s) 2025.Ministry of Education and Science, MES; Benemérita Universidad Autónoma de Puebla, BUAP; Center for African Studies, CAS; Fundação para a Ciência e a Tecnologia, FCT; Department of Atomic Energy, Government of India, DAE; PCTI; National Academy of Sciences of Ukraine, NASU; National Science and Technology Development Agency, NSTDA; Fundamental Research Funds for the Central Universities; MSES; Ministry of Education of the People's Republic of China, MOE; National Science Foundation, NSF; Missouri University of Science and Technology, MST; Institut National de Physique Nucléaire et de Physique des Particules, IN2P3; Science and Technology Facilities Council, STFC; F.R.S.-FNRS; Council of Science and Industrial Research; Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, CINVESTAV; Ministério da Educação e Ciência, MEC; MEYS; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, FAPERJ; Ministerio de Ciencia e Innovación, MICINN; Universiti Malaya, UM; Bundesministerium für Bildung, Wissenschaft und Forschung, BMBWF; FAIR; National Science Council, NSC; Ministry of Science,Technology and Research, MoSTR; Hispanics in Philanthropy, HIP; Hungarian Academy of Sciences; Secretaría de Educación Pública, SEP; Austrian Science Fund, FWF; Department of Science and Technology, Ministry of Science and Technology, India, DSTIndia; Chulalongkorn Academic; Consejo Nacional de Humanidades, Ciencias y Tecnologías; ERDF; Centre National de la Recherche Scientifique, CNRS; Bundesministerium für Bildung und Forschung, BMBF; Fonds Wetenschappelijk Onderzoek, FWO; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITAK; LMTLT; Research Council of Finland, AKA; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Pakistan Atomic Energy Commission, PAEC; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; California Earthquake Authority, CEA; Türkiye Enerji, Nükleer ve Maden Araştırma Kurumu, TENMAK; Nvidia Corporation; Hellenic Foundation for Research and Innovation; Belgian Federal Science Policy Office; Deutsche Forschungsgemeinschaft, DFG; LNS; Alfred P. Sloan Foundation; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture; Science Foundation Ireland, SFI; U.S. Department of Energy, ENERGYGOV; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Cosmetic Surgery Foundation, CSF; Agencia Estatal de Investigación, AEI; ICSC; FRIA-Belgium; Ministry of Education, Youth and Sports; Ministry of Science and Technology of the People's Republic of China, MOST; Programa Severo Ochoa del Principado de Asturias; General Secretariat for Research and Innovation, GSRI; Bulgarian National Science Fund, BNSF; Latvian Council of Science; Hugh Green Foundation, HGF; Ministerio de Ciencia, Tecnología e Innovación; Maryland Ornithological Society, MOS; Ministry of Higher Education, Science, Research and Innovation, Thailand, MHESI; Fonds De La Recherche Scientifique - FNRS, FNRS; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, FAPERGS; Shota Rustaveli National Science Foundation, SRNSF; Institute for Research in Fundamental Sciences, IPM; Leventis Foundation; Swiss Funding Agencies; European Research Council; CERN, CERN; National Retail Federation, NRF; Ministry of Science ICT and Future Planning, MSIP; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, SENESCYT; Ministry for Business Innovation and Employment, MBIE; Istituto Nazionale di Fisica Nucleare, INFN; Weston Havens Foundation; Universidad Autónoma de San Luis Potosí, UASLP; National Natural Science Foundation of China, NSFC; Kavli Foundation; Fundação para a Ciência e a Tecnologia, (CEECIND/01334/2018); Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, (MDM-2017-0765); Deutsche Forschungsgemeinschaft, (400140256 — GRK2497, 390833306); National Science Center, (2021/43/B/ST2/01552, 2021/41/B/ST2/01369); National Science, Research and Innovation Fund, (B39G670016); Shota Rustaveli National Science Foundation, (FR-22-985); Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFIH, (K 133046, K 131991, K 146914, 2020-2.2.1-ED-2021-00181, K 138136, K 143477, K 143460, TKP2021-NKTA-64, K 147048, K 146913); Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFIH; Qatar National Research Fund, (MCIN/AEI/10.13039/501100011033); Excellence of Science, (30820817); Engineering Research Centers, ERC, (MoER TK202); Engineering Research Centers, ERC; COST, (CA16108); Horizon 2020, (101002207, 724704, 101115353, 752730, 758316, 765710, 824093, 675440); Welch Foundation, (C-1845); Alexander von Humboldt Foundation, (22rl-037); Ministry of Education and Science, (2022/WK/14); Beijing Municipal Science & Technology Commission, (Z191100007219010)Science Citation Index Expande