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AIGCodeSet: Yapay Zeka Üretimli Kod Tespiti İçin Yeni Bir Veri Kümesi
No full textIsik UniversityWhile large language models provide significant convenience for software development, they can lead to ethical issues in job interviews and student assignments. Therefore, determining whether a piece of code is written by a human or generated by an artificial intelligence (AI) model is a critical issue. In this study, we present AIGCodeSet, which consists of 2.828 AI-generated and 4.755 human-written Python codes, created using CodeLlama, Codestral, and Gemini. In addition, we share the results of our experiments conducted with baseline detection methods. Our experiments show that a Bayesian classifier outperforms the other models. © 2025 Elsevier B.V., All rights reserved
Precision Measurement of the B0 Meson Lifetime Using B0→J/Ψk∗0 Decays with the ATLAS Detector
No full textA measurement of the B0 meson lifetime using B0→J/ψK∗0 decays in data from 13 TeV proton–proton collisions with an integrated luminosity of 140fb-1 recorded by the ATLAS detector at the LHC is presented. The measured effective lifetime is (Formula presented.) The average decay width extracted from the effective lifetime, using parameters from external sources, is (Formula presented.) where the uncertainties are statistical, systematic and from external sources. The earlier ATLAS measurement of Γs in the Bs0→J/ψϕ decay was used to derive a value for the ratio of the average decay widths Γd and Γs for B0 and Bs0 mesons respectively, of (Formula presented.) The measured lifetime, average decay width and decay width ratio are in agreement with theoretical predictions and with measurements by other experiments. This measurement provides the most precise result of the effective lifetime of the B0 meson to date. © 2025 Elsevier B.V., All rights reserved
Charged-Hadron and Identified-Hadron (Ks0, Λ, Ξ-) Yield Measurements in Photonuclear Pb Plus Pb and P Plus Pb Collisions at √snn=5.02 Tev with ATLAS
No full textTishelman-Charny, Abraham/0000-0002-7332-5098; Angerami, Aaron/0000-0001-7834-8750; Kirk, Julie/0000-0001-8096-7577; Mete, Alaettin Serhan/0000-0002-5508-530X; Valero, Alberto/0000-0002-9776-5880; Schultz-Coulon, Hans-Christian/0000-0002-0860-7240; Morii, Masahiro/0000-0001-9324-057X; Merlassino, Claudia/0000-0002-5445-5938; Beretta, Matteo Mario/0000-0002-7026-8171; Petersen, Troels/0000-0003-0221-3037; Castro, Nuno/0000-0001-8491-4376; D'Uffizi, Matteo/0000-0003-2499-1649; Warburton, Andreas/0000-0002-2298-7315; White, Martin/0000-0001-5474-4580; Simsek, Sinem/0000-0002-9650-3846; Gaudio, Gabriella/0000-0002-6833-0933; Chan, Jay/0000-0001-7069-0295; Terzo, Stefano/0000-0003-3388-3906; Escobar Ibanez, Carlos/0000-0003-4442-4537; Dinu, Ioan-Mihail/0000-0002-2683-7349; Meloni, Federico/0000-0001-7075-2214; Volkotrub, Yuriy/0000-0002-3114-3798; Rompotis, Nikolaos/0000-0003-2577-1875; Grinstein, Sebastian/0000-0002-6460-8694; Koffas, Thomas/0000-0001-9612-4988; Bruschi, Marco/0000-0002-4319-4023; Pleier, Marc-Andre/0000-0002-9461-3494; Mclean, Christine/0000-0002-7450-4805; Iuppa, Roberto/0000-0001-5038-2762; Kretzschmar, Jan/0000-0002-8515-1355; Chwastowski, Janusz/0000-0002-6190-8376; Bouhova-Thacker, Evelina/0000-0002-5103-1558; De La Torre Perez, Hector/0000-0002-4516-5269; Moser, Brian/0000-0001-6750-5060; Onyisi, Peter/0000-0003-4201-7997; Novak, Tadej/0000-0002-3053-0913; Doglioni, Caterina/0000-0002-1509-0390; Konstantinidis, Nikolaos/0000-0002-4140-6360; Camarda, Stefano/0000-0003-0479-7689; Das, Sruthy Jyothi/0000-0003-2693-3389; Price, Darren/0000-0003-2750-9977; Schmitt, Stefan/0000-0001-8387-1853; Vincter, Manuella/0000-0002-5338-8972; Panizzo, Giancarlo/0000-0002-0352-4833; Mondal, Santu/0000-0002-6965-7380; Haley, Joseph/0000-0002-6938-7405; Munoz Sanchez, Francisca/0000-0002-6374-458X; Azuelos, Georges/0000-0003-4241-022X; Chu, Ming-Chung/0000-0002-1971-0403; Mlinarevic, Marin/0000-0003-3587-646X; Martoiu, Sorin/0000-0002-4963-9441; Butterworth, Jonathan/0000-0002-5905-5394; Vigl, Matthias/0000-0003-2281-3822; Montella, Alessandro/0000-0002-5578-6333; Di Luca, Andrea/0000-0002-9074-2133; Sciandra, Andrea/0000-0001-7163-501X; Quinn, Ryan/0000-0002-0879-6045; Kowalewski, Robert/0000-0002-7314-0990; Barakat, Marawan/0000-0001-5740-1866; Stark, Giordon/0000-0001-6616-3433; Aad, Georges/0000-0002-6665-4934; Martin Dit Latour, Bertrand/0000-0003-3420-2105; Keeler, Richard/0000-0002-0510-4189; Jia, Jiangyong/0000-0002-5725-3397; Cunha Sargedas Sousa, Mario Jose/0000-0001-7991-593X; Ghosh, Aishik/0000-0003-0819-1553; Beck, Hans Peter/0000-0001-7212-1096; Alimonti, Gianluca/0000-0002-7128-9046; Winter, Benedict Tobias/0000-0001-9606-7688; Lacasta, Carlos/0000-0002-2623-6252; Santra, Arka/0000-0003-4644-2579; Rousseau, David/0000-0001-7613-8063; Cheu, Elliott/0000-0002-2562-9724; Carmignani, Joseph (Joe)/0000-0002-1705-1061; Bella, Gideon/0000-0002-4009-0990; Worm, Steven/0000-0002-3865-4996; Sahinsoy, Merve/0000-0002-7400-7286; Dong, Qichen/0000-0002-0117-7831; Ernani Martins Neto, Daniel/0000-0003-2793-5335; Hoppesch, Matthew/0000-0002-7773-3654; Umaka, Ejiro/0000-0001-7725-8227; Klein, Lucas/0000-0002-0145-4747; Onofre, Antonio/0000-0003-3471-2703; Varvell, Kevin/0000-0003-1017-1295; Vecchio, Valentina/0000-0002-1351-6757; Berta, Peter/0000-0003-0780-0345; Mcpherson, Robert/0000-0001-9211-7019; Bhatta, Somadutta/0000-0002-9045-3278; Stanislaus, Beojan/0000-0001-9007-7658; Kumar, Mukesh/0000-0003-3681-1588; Martinez-Agullo, Pablo/0000-0001-8925-9518; Smirnova, Oxana/0000-0003-2517-531X; Citron, Zvi/0000-0003-1831-6452; Yabsley, Bruce/0000-0002-2680-0474; Su, Dong/0000-0001-6980-0215; Affolder, Anthony/0000-0002-9058-7217; Elsing, Markus/0000-0002-1213-0545; Kaji, Toshiaki/0000-0002-6532-7501; Cheong, Sanha/0000-0002-2797-6383; Beau, Tristan/0000-0002-2022-2140; Jackson, Paul/0000-0002-0847-402X; Cranmer, Kyle/0000-0002-5769-7094; Hance, Michael/0000-0001-8392-0934; Lloyd, Stephen/0000-0002-5073-2264; Bahmani, Marzieh/0000-0003-4173-0926; Islam, Wasikul/0000-0002-5624-5934; Fox, Harald/0000-0003-3089-6090; Held, Alexander/0000-0002-8924-5885; Mitsou, Vasiliki A./0000-0002-1533-8886; Bona, Marcella/0000-0002-9660-580X; Gwilliam, Carl/0000-0002-9401-5304; Mckee, Shawn/0000-0002-4551-4502; Ahmadov, Faig/0000-0003-3644-540X; Cristoforetti, Marco/0000-0002-0127-1342; Kontaxakis, Pantelis/0000-0002-4860-5979; Sampsonidou, Despoina/0000-0003-0384-7672; Sadrozinski, Hartmut/0000-0003-0019-5410; Koch, Simon Florian/0000-0002-2676-2842;This paper presents the measurement of charged-hadron and identified-hadron (K-S(0), Lambda, Xi(-)) yields in photonuclear collisions using 1.7 nb(-1) of root s(NN) = 5.02 TeV Pb + Pb data collected in 2018 with the ATLAS detector at the Large Hadron Collider. Candidate photonuclear events are selected using a combination of tracking and calorimeter information, including the zero-degree calorimeter. The yields as a function of transverse momentum and rapidity are measured in these photonuclear collisions as a function of charged-particle multiplicity. These photonuclear results are compared with 0.1nb(-1) of root s(NN) = 5.02 TeV p + Pb data collected in 2016 by ATLAS using similar charged-particle multiplicity selections. These photonuclear measurements shed light on potential quark-gluon plasma formation in photonuclear collisions via observables sensitive to radial flow, enhanced baryon-to-meson ratios, and strangeness enhancement. The results are also compared with the Monte Carlo DPMJET-III generator and hydrodynamic calculations to test whether such photonuclear collisions may produce small droplets of quark-gluon plasma that flow collectively.CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands), PIC (Spain); BNL (USA); ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW; FWF, Austria; ANAS; CNPq; FAPESP, Brazil; NSERC; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF; DNSRC, Denmark; IN2P3-CNRS; CEA-DRF/IRFU, France; BMBF; MPG, Germany; RGC and Hong Kong SAR, China; ICHEP; Academy of Sciences and Humanities, Israel; INFN, Italy; MEXT; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSSR, Slovakia; Wallenberg Foundation, Sweden; SNSF and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; STFC/UKRI, United Kingdom; DOE; NSF; BCKDF; CANARIE; CRC; DRAC, Canada; FORTE; PRIMUS, Czech Republic; ERC; ERDF; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex; ANR, France; DFG; AvH Foundation, Germany - EU-ESF; Greek NSRF, Greece; BSF-NSF; NCN [UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, UMO-2023/51/B/ST2/00920, UMO-2024/53/N/ST2/00869]; La Caixa Banking Foundation; CERCA Programme Generalitat de Catalunya; PROMETEO; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society [NIF-R1-231091]; Leverhulme Trust, United Kingdom; Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research; Chile: Agencia Nacional de Investigacion y Desarrollo; FONDECYT [1240864]; China: Chinese Ministry of Science and Technology [MOST-2023YFA1605700, MOST-2023YFA1609300]; National Natural Science Foundation of China; NSFC [12275265]; Czech Republic: Czech Science Foundation; GACR [24-11373S]; Ministry of Education Youth and Sports [ERC-CZ-LL2327, FORTE CZ.02.01.01/00/22_008/0004632]; PRIMUS Research Programme [PRIMUS/21/SCI/017]; EU: H2020 European Research Council; European Union: European Research Council; BARD [101116429]; European Regional Development Fund [SMASH COFUND 101081355]; (SLO ERDF); Horizon 2020 Framework Programme [MUCCA-CHIST-ERA-19XAI-00]; European Union [FAIR-NextGenerationEU PE00000013, EuroHPC-EHPC-DEV-2024D11-051]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC); France: Agence Nationale de la Recherche [ANR-21-CE31-0013, ANR-21-CE31-0022]; Germany: Baden-Wurttemberg Stiftung [BW Stiftung-Postdoc Eliteprogramme]; Deutsche Forschungsgemeinschaft [DFG-469666862, DFG-CR 312/5-2]; China: Research Grants Council (GRF); Istituto Nazionale di Fisica Nucleare (ICSC); Ministero dell'Universita e della Ricerca [PRIN20223N7F8K M4C2.1.1]; Japan Society for the Promotion of Science; JSPS KAKENHI [JP23KK0245]; Norway: Research Council of Norway [RCN-314472]; Ministry of Science and Higher Education [9722]; Polish National Science Centre; NCN OPUS [2022/47/B/ST2/03059]; Portugal: Foundation for Science and Technology (FCT); Spain: Generalitat Valenciana (Artemisa, FEDER) [IDIFEDER/2018/048]; Ministry of Science and Innovation (MCIN ; NextGenEU Grant) [PCI2022-135018-2]; MICIN FEDER [PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I]; Carl Trygger Foundation (Carl Trygger Foundation CTS) [22:2312]; Swedish Research Council (Swedish Research Council) [2023-04654, VR 2021-03651, VR 2022-03845]; VR [2022-04683, VR 2023-03403, VR 2024-05451]; Knut and Alice Wallenberg Foundation [KAW 2018.0458, KAW 2022.0358]; Swiss National Science Foundation; SNSF [PCEFP2_194658]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020-004]; United States of America; U.S. Department of Energy; ECA [DE-AC02-76SF00515]; Neubauer Family FoundationWe thank CERN for the very successful operation of the LHC and its injectors, as well as the support staff at CERN and at our institutions worldwide without whom ATLAS could not be operated efficiently. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide, and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [55]. We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ICHEP and Academy of Sciences and Humanities, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSTDI, Serbia; MSSR, Slovakia; ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; TENMAK, Turkiye; STFC/UKRI, United Kingdom; DOE and NSF, USA. Individual groups and members have received support from BCKDF, CANARIE, CRC and DRAC, Canada; CERN-CZ, FORTE and PRIMUS, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members acknowledge support from Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research (CERN DOCT); Chile: Agencia Nacional de Investigacion y Desarrollo (Grants No. FONDECYT 1230812, No. FONDECYT 1230987, and No. FONDECYT 1240864); China: Chinese Ministry of Science and Technology (Grants No. MOST-2023YFA1605700 and No. MOST-2023YFA1609300), National Natural Science Foundation of China (Grants No. NSFC 12175119 and No. NSFC 12275265); Czech Republic: Czech Science Foundation (Grant No. GACR 24-11373S), Ministry of Education Youth and Sports (ERC-CZ-LL2327, FORTE CZ.02.01.01/00/22_008/0004632), PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (Grant No. ERC 101002463); European Union: European Research Council (Grants No. ERC 948254, No. ERC 101089007, and No. ERC, BARD, 101116429), European Regional Development Fund (SMASH COFUND 101081355, SLO ERDF), Horizon 2020 Framework Programme (MUCCA-CHIST-ERA-19XAI-00), European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU PE00000013), Horizon 2020 (EuroHPC-EHPC-DEV-2024D11-051), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (Grants No. ANR-21-CE31-0013, No. ANR-21-CE31-0022, and No. ANR-22-EDIR-0002); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (Grants No. DFG-469666862 and No. DFG-CR 312/5-2); China: Research Grants Council (GRF); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU), Ministero dell'Universita e della Ricerca (NextGenEU PRIN20223N7F8K M4C2.1.1); Japan: Japan Society for the Promotion of Science (Grants No. JSPS KAKENHI JP22H01227, No. JSPS KAKENHI JP22H04944, No. JSPS KAKENHI JP22KK0227, and No. JSPS KAKENHI JP23KK0245); Norway: Research Council of Norway (Grant No. RCN-314472); Poland: Ministry of Science and Higher Education (IDUB AGH, POB8, D4 No. 9722), Polish National Science Centre (Grants No. NCN 2021/42/E/ST2/00350, No. NCN OPUS 2023/51/B/ST2/02507, No. NCN OPUS nr 2022/47/B/ST2/03059, No. NCN UMO-2019/34/E/ST2/00393, No. UMO-2020/37/B/ST2/01043, No. UMO-2022/47/O/ST2/00148, No. UMO-2023/49/B/ST2/04085, No. UMO-2023/51/B/ST2/00920, and No. UMO-2024/53/N/ST2/00869); Portugal: Foundation for Science and Technology (FCT); Spain: Generalitat Valenciana (Artemisa, FEDER, Grant No. IDIFEDER/2018/048), Ministry of Science and Innovation (MCIN ; NextGenEU Grant No. PCI2022-135018-2, MICIN ; FEDER Grants No. PID2021-125273NB, No. RYC2019-028510-I, No. RYC2020-030254-I, No. RYC2021-031273-I, and No. RYC2022-038164-I); Sweden: Carl Trygger Foundation (Carl Trygger Foundation CTS Grant No. 22:2312), Swedish Research Council (Swedish Research Council Grants No. 2023-04654, No. VR 2021-03651, No. VR 2022-03845, No. VR 2022-04683, No. VR 2023-03403, No. VR 2024-05451), Knut and Alice Wallenberg Foundation (Grants No. KAW 2018.0458, No. KAW 2022.0358, and No. KAW 2023.0366); Switzerland: Swiss National Science Foundation (Grant No. SNSF PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust Grant No. RPG-2020-004), Royal Society (Grant No. NIF-R1-231091); United States of America: U.S. Department of Energy (Grant No. ECA DE-AC02-76SF00515), Neubauer Family Foundation
Bromide Ion Additive for High-Performance Photo-Assisted Rechargeable Zinc Ion Hybrid Supercapacitor
No full textIncreasing the capacitance of photo-assisted rechargeable zinc ion hybrid supercapacitor can be accomplished through the utilization of bromide ions in conjunction with a supportive electrolyte, as demonstrated by this work. In this study, it is also proved that the combination of bromide and sulfate salts of zinc has a positive impact on the photosensitivity of metal oxide supercapacitors. It has been demonstrated that cyclic voltammetry and galvanic charge-discharge tests both exhibit an increase in capacitance when the conditions are either dark or illuminated. Both 1.95M ZnSO4 and 0.05M ZnBr2 electrolytes were found to be effective for CuO, while 1.9M ZnSO4 and 0.1M ZnBr2 electrolytes were found to be effective for MnO2. The maximum solar gain in MnO2-based electrodes was %129 for 0.05M ZnBr2 added electrolyte tested via cyclic voltammetry at a scan rate of 0.05Vs-1. Furthermore, the electrode stability test was conducted for both MnO2 and CuO-based electrodes. Over the course of one week, each electrode was subjected to 10,000 GCD cycles.TUBITAK [122F390]The authors would like to thank TUBITAK 122F390 for their financial support
Microstrip Stub Filter Design with Enhanced Performance Inspired by Siw Structures Operating at 1.93 Ghz GSM Band
No full textThis paper reports a microstrip stub filter design operating at 1.93 GHz GSM band with enhanced performance inspired by SIW structures. In the designed filter additional vias are placed around the microstrip lines to enhance the encasing of the electromagnetic fields while propagating through the filter to develop the filter performance. The filter was examined with electromagnetic simulations for various numbers of vias and different via to microstrip line distances. Results show that the maximum transmission coefficient (S21 parameter) magnitude value reached in the pass band of the filter increases with the number of the vias and as the vias get closer to the lines. On the other hand, when the via number increases and the space between them and the lines narrows, the frequency at which the maximum S21 value is attained shifts to lower frequencies. The designed filters were manufactured, too. Results obtained in the measurements agree well with the simulation results. Additionally, a receiver system operating at 1.93 GHz band was constructed. System experiments were carried out with the constructed prototype for the manufactured filters. Results show that a greater signal level in the filter pass band is achieved and unwanted signals outside the filter pass band are suppressed more in the system where the filter with vias is used instead of the filter without any additional via. The findings indicate that the designed filters inspired by SIW structures are promising for applications requiring high signal quality
Kalmannet-Aided Target Tracking with 1-Bit Decisions in Wireless Sensor Networks
No full textIn wireless sensor networks (WSNs), 1-bit quantization provides an energy-efficient and bandwidth-conserving solution, albeit at the expense of considerable information loss in state estimation tasks. This paper introduces a modified KalmanNet architecture tailored for scenarios where the process noise statistics are known, but the measurement noise characteristics are unknown due to binary quantization. In contrast to the original KalmanNet, which processes temporally differenced, continuous-valued observations, the proposed model operates directly on 1-bit sensor decisions, interpreting them as independent Bernoulli samples at each time step. To ensure scalability in dense sensor deployments, we incorporate architectural compression inspired by SqueezeNet, significantly reducing the number of trainable parameters without sacrificing model expressiveness. The proposed approach is benchmarked against the Extended Kalman Filter (EKF) using raw sensor measurements, and the Particle Filter (PF) with 1-bit decisions under both adaptive and non-adaptive thresholding schemes driven by approximate mutual information and Fisher information criteria, namely Mutual Information Upper Bound - Adaptive (MIUB-A), Mutual Information Upper Bound - Shared (MIUB-S) and the Fisher Information Matrix Adaptive (FIM-A) designs. Simulation results show that the non-adaptive KalmanNet not only outperforms PF without adaptive thresholding, but also closely approaches the estimation accuracy of a representative adaptive scheme (MIUB-S). These findings underscore the potential of data-driven filtering in quantized WSNs, paving the way for robust, scalable, and feedback-free tracking systems. © 2025 Elsevier B.V., All rights reserved
Apoptosis Modulation by the Antidepressant Escitalopram in Alemtuzumab-Treated Multiple Sclerosis Patients: An Ex Vivo Approach
No full textTUBITAK [2209-A-1919B012211950]This study was supported by TUBITAK 2209-A-1919B012211950
Building Anatomy: Rethinking Internal and External Dynamics in Architecture
No full textConventional frameworks often reduce architectural production to a linear sequence of deterministic technical and managerial stages. This study challenges that paradigm, arguing that such a view overlooks the adaptive, multi-layered, and context-responsive nature of contemporary built environment creation. Grounded in systems theory, biomimicry, and human physiology, a novel "Building Anatomy" model is proposed that treats architectural practice as a living organism. This conceptual framework is first established, and its validity is then tested through a mixed-methods empirical study conducted with 126 Turkish architects, analyzing the continuous feedback loops between internal (e.g., designer identity, team dynamics) and external (e.g., regulations, socio-cultural currents) factors. It was confirmed that the dynamic interaction between these internal and external factors is central to architectural processes. "Systemic dysfunctions" were identified and diagnosed that arise from breakdowns in these metabolic feedback loops, providing empirical evidence for the model's explanatory power. By offering a systemic lens, this study shifts the focus from a product-centric to a process-oriented view of design. The Building Anatomy model demonstrates its potential for diagnosing "metabolic failures" and redefining the architect's agency, ultimately advocating for more adaptive, responsive, and resilient architectural outcomes
Measurements of the Production Cross-Sections of a Higgs Boson in Association With a Vector Boson and Decaying Into WW* With the ATLAS Detector at S = 13 TeV
No full textMeasurements of the total and differential Higgs boson production cross-sections, via WH and ZH associated production using H → WW* → ℓνℓν and H → WW* → ℓνjj decays, are presented. The analysis uses proton-proton events delivered by the Large Hadron Collider at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector between 2015 and 2018. The data correspond to an integrated luminosity of 140 fb−1. The sum of the WH and ZH cross-sections times the H → WW* branching fraction is measured to be 0.44−0.09+0.10stat.−0.05+0.06syst. pb, in agreement with the Standard Model prediction. Higgs boson production is further characterised through measurements of the differential cross-section as a function of the transverse momentum of the vector boson and in the framework of Simplified Template Cross-Sections. © 2025 Elsevier B.V., All rights reserved
Investigation of the Relationship Between Nfe2l2 Rs6721961 T/G Genetic Polymorphism and Relapsing-Remitting Multiple Sclerosis
No full textTUBITAK [323S265]This study is supported by TUBITAK (323S265)