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Operation and Performance of the ATLAS Tile Calorimeter in LHC Run 2
No full textThe ATLAS tile calorimeter (TileCal) is the hadronic sampling calorimeter covering the central region of the ATLAS detector at the Large Hadron Collider (LHC). This paper gives an overview of the calorimeter’s operation and performance during the years 2015–2018 (Run 2). In this period, ATLAS collected proton–proton collision data at a centre-of-mass energy of 13 TeV and the TileCal was 99.65% efficient for data-taking. The signal reconstruction, the calibration procedures, and the detector operational status are presented. The performance of two ATLAS trigger systems making use of TileCal information, the minimum-bias trigger scintillators and the tile muon trigger, is discussed. Studies of radiation effects allow the degradation of the output signals at the end of the LHC and HL-LHC operations to be estimated. Finally, the TileCal response to isolated muons, hadrons and jets from proton–proton collisions is presented. The energy and time calibration methods performed excellently, resulting in good stability and uniformity of the calorimeter response during Run 2. The setting of the energy scale was performed with an uncertainty of 2%. The results demonstrate that the performance is in accordance with specifications defined in the Technical Design Report. © 2025 Elsevier B.V., All rights reserved
Fair and Effective Vaccine Allocation During a Pandemic
No full textThis paper presents a novel model for the Vaccine Allocation Problem (VAP), which aims to allocate the available vaccines to population locations over multiple periods during a pandemic. We model the disease progression and the impact of vaccination on the spread of the disease and mortality to minimise total expected mortality and location inequity in terms of mortality ratios under total vaccine supply and hospital and vaccination centre capacity limitations at the locations. The spread of the disease is modelled through an extension of the well-established Susceptible–Infected–Recovered (SIR) epidemiological model that accounts for multiple vaccine doses. The VAP is modelled as a nonlinear mixed-integer programming model and solved to optimality using the Gurobi solver. A set of scenarios with parameters regarding the COVID-19 pandemic in the UK over 12 weeks are constructed using a hypercube experimental design on varying disease spread, vaccine availability, hospital capacity, and vaccination capacity factors. The results indicate the statistical significance of vaccine availability and the parameters regarding the spread of the disease. © 2024 Elsevier LtdTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: TR 220N017, 220N017; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; Newton Fund, NF: 623795194; Newton Fund, N
Moment-Based Approximation for Variance of Semi-Markovian Random Walk With Gamma Distributed Interference of Chance
No full textThis study proposed moment-based approximations for the expected value and variance of the ergodic distribution of the semi-Markovian random walk process (X (t)) with gamma distributed interference of chance. Many studies have investigated analogous moment problems by using an asymptotic approach. The key distinguishing aspect of this study from others in the literature is obtaining Kambo's approximations for the moments of X (t) instead of asymptotic expansions. Firstly, the approximation formulas for the moments of boundary functional S N(z) of X (t) were obtained. Then using these results, approximation formulas for the first two moments of the ergodic distributions of X (t) were derived. Finally, the expected value and variance of X (t) were calculated by using the Monte Carlo simulation method for two concrete distributions (Gaussian and Uniform)
The Cerium Oxide Nanoparticles Toxicity Induced Physiological, Histological and Biochemical Alterations in Freshwater Mussels, Unio Crassus
No full textIntroduction: Releasing of cerium oxide nanoparticles (nano-CeO2) to the nature has increased due to the widespread use in many fields ranging from cosmetics to the food industry. Therefore, nano-CeO2 has been included in the Organization for Economic Co-operation and Development's (OECD) priority list for engineering nanomaterials. In this study, the effects of nano-CeO2 on the freshwater mussels were investigated to reveal the impact on the freshwater systems on model organism. Methods: First, the chemical and structural properties of nano-CeO2 were characterized in details. Second, the freshwater mussels were exposed to environmentally relevant concentrations of nano-CeO2 as 10 mg, 25 mg and 50 mg/L during 48-h and 7-d. Third, after the exposure periods, hemolymph and tissue samples were taken to analyse the Total Hemocyte Counts (THCs) histology and oxidative stress parameters (total antioxidant status, glutathione, glutathione-S-transferase, and advanced oxidative protein products). Results: Significant decrease of the THCs was observed in the nano-CeO2 exposed mussels compared to the control group (P ;lt; 0.05). The histological results showed a positive association between nano-CeO2 exposure concentration in the water and level of tissue damage and histopathological alterations were detected in the gill and the digestive gland tissues. Oxidative stress parameters were slightly affected after exposure to nano-CeO2 (P ;gt; 0.05). In conclusion, this study showed that acute exposure of freshwater mussels to nano-CeO2 did not pose significant biological risk. However, it has been proven that mussels are able to accumulate nano-CeO2 significantly in their bodies. Conclusion: This suggests that nano-CeO2 may be a potential risk to other organisms in the ecosystem through trophic transfer in the food-web based on their habitat and niche in the ecosystem. © 2024 Elsevier Gmb
Statistical Combination of Atlas Run 2 Searches for Charginos and Neutralinos at the Lhc
No full textStatistical combinations of searches for charginos and neutralinos using various decay channels are performed using 139 fb^{-1} of pp collision data at sqrt[s]=13 TeV with the ATLAS detector at the Large Hadron Collider. Searches targeting pure-wino chargino pair production, pure-wino chargino-neutralino production, or Higgsino production decaying via standard model W, Z, or h bosons are combined to extend the mass reach to the produced supersymmetric particles by 30-100 GeV. The depth of the sensitivity of the original searches is also improved by the combinations, lowering the 95% C.L. cross-section upper limits by 15%-40%
Artificial Intelligence for Machining Process Monitoring
No full textSince the early 1980s, implementation of artificial intelligence (AI)-based intelligent machining process monitoring (MPM) has been advancing, parallel to the new AI models and machining technologies. These systems are critical for balancing the tradeoffs among productivity, quality, cost, and sustainability measures of machine tool shops and the broader manufacturing industry. Furthermore, increased demand for high-level process automation, pressure to use less workforce, and data explosion by widely used low-cost sensory equipment have increased the expectation from MPM to become an enabler of the dark factory. This chapter covers a broad range of AI models and techniques used in MPM by providing both algorithmic fundamentals and architectural examples from recent studies. © 2024 Elsevier Inc. All rights reserved
Xgboost-Based Qoe Prediction for Mobile Networks
No full textThe increasing demand for advanced video-based services necessitates operators to ensure the most suitable network performance while also considering user satisfaction with the service. QoS provides significant insights on the network side to deliver satisfactory user experiences. On the other hand, QoE informs about how a given service is perceived from the user's perspective. The more advanced video-based services to be offered with the more complex structure of 6G increase the importance of mapping QoS to QoE. This paper presents an XGBoost-based method for predicting QoE based on UE-based, network-based, and application-based QoS measurements obtained from a real and live mobile network. The results indicate that XGBoost is an effective method for user experience estimation
Damage Detection On Beam Structures Using Ho-Kalman Identification Method
No full text20th Internatıonal Conference On Machıne Desıgn And Productıon August 14-17, 2024, Ankara, Türkiye / 20. Uluslararası Makine Tasarım ve İmalat Kongresi (UMTIK 2024)Due to its non-destructive nature and real time monitoring capabilities, Vibration-Based Structural Health Monitoring Systems have a promising potential for damage assessment. Among possible methods, Sub-Space methods provide state-space model, and modal parameters of the system. Ho-Kalman method forms the backbone of Sub-Space Methods. Using impulse response of the structure, the modal parameters such as natural frequencies, damping ratios, and mode shapes can be captured. By investigating vibration characteristics of intact and damaged specimens, the effect of the damage on the modal parameters of Steel and Carbon Fiber Reinforced Polymer (CFRP) beams has been investigated.Titreşim Esaslı Yapısal Sağlık İzleme Sistemleri, tahribatsız ve gerçek zamanlı inceleme yenetekleri sayesinde hasar tespit sistemleri için gelecek vaad eden bir potansiyele sahiptir. Olası yöntemler arasından Alt-Uzay yöntemleri, sistemin durum-uzayı modelini ve modal parametrelerini belirlemek için kullanılabilmektedir. Ho-Kalman Metodu, Alt-Uzay Metodlarının temelini oluşturmaktadır, yapının darbe etkisine cevabı incelenerek, doğal frekansları, sönümleme oranları ve mod şekilleri gibi modal parametreleri belirlenebilmektedir. Hasarsız ve hasarlı numunelerin titreşim özellikleri incelenerek, hasarın Çelik ve Karbon Fiber Takviyeleri Polimer, CFRP, kirişlerin modal parametreleri üzerindeki etkisi araştırılmıştır.This study is funded by TUBITAK (The Scientific and Technological Research Council of Turkey) through project grant no 123M891
Yapısal Optimizasyon Problemlerinin Çözümü için Yeni Bir Hibrid Optimizasyon Yönteminin Geliştirilmesi
No full textOptimizasyon, herhangi bir problem için verilen koşullar altındaki bütün çözümleri arasından en uygun çözümü bulma işlemi olarak ifade edilebilir. Optimizasyon probleminin daha kolay çözülebilecek bir yapıda olması için problemin yapısına göre belirlenen matematiksel modeller oluşturulur. Doğadan ilham alan meta-sezgisel optimizasyon algoritmalarının davranışlarındaki kararsızlık ve düzensiz yapıları, birçok optimizasyon uygulamaları ve problemler için çözüm olabilmektedir. Meta-sezgisel optimizasyon algoritmalarının temel bileşenleri olan keşif, global arama yaparak çeşitli çözümler üretme sürecini, istifade ise yerel aramaya odaklanma ve en uygun aday çözümleri seçmeyi açıklamaktadır. Çalışmada, yapısal optimizasyon problemlerinin çözümüne yönelik yeni bir hibrid optimizasyon algoritması geliştirilmiştir. Literatürde mevcut ve güncel meta-sezgisel optimizasyon algoritması olan Yapay Tavşan Optimizasyonu (ARO) algoritmasının, kaotik haritalar ile entegre edilerek yakınsama hızının geliştirilmesi ve bu yeni hibrid algoritmanın çeşitli yapısal optimizasyon problemlerinin çözümünde kullanılması hedeflenmiştir. Bu çalışmada, Yapay Tavşan Optimizasyonu (ARO) algoritmasının matematiksel denklemlerindeki rastgele parametreler, literatürde iyi bilinen 10 kaotik harita ve önerilen 4 farklı modifikasyon yöntemi ile hibridlenmiştir ve tez kapsamında geliştirilen bu hibrid algoritmaya, Kaotik Yapay Tavşan Optimizasyonu (CARO) algoritması adı verilmiştir. CARO algoritmasının 23 klasik test fonksiyonları ile performansları test edilmiştir. CARO algoritması daha sonra 5 temel mühendislik tasarım problemi olan basınçlı kap tasarımı, rulman tasarımı, çeki/bası yay tasarımı, ankastre kiriş tasarımı ve dişli takımı tasarımına uygulanmıştır. CARO algoritması, literatürdeki güncel ve popüler 6 meta-sezgisel algoritma olan Yapay Tavşan Optimizasyonu (ARO) algoritması, Parçacık Sürü Optimizasyonu (PSO), Gri Kurt Optimizasyonu (GWO), Güve-Alev Optimizasyonu (MFO), Harris Şahinleri Optimizasyonu (HHO) ve Genetik Algoritma (GA) ile karşılaştırılmıştır. Tez kapsamında, mevcut bir taşıt fren pedalı parçasına göre daha az ağırlıkta ve çalışma koşullarındaki gereksinimlerini karşılayacak gerilme değerine sahip olan yeni bir fren pedalı parçasının CARO algoritması kullanılarak yapısal optimizasyonu yapılmıştır. Fren pedalı parçasının yapısal optimizasyon probleminin matematiksel modelini meydana getirmek için latin hiperküp örnekleme yöntemi ve radyal temelli fonksiyonlar ile meta-modelleme yöntemi kullanılmıştır. Topoloji ve şekil optimizasyonuna göre fren pedalı parçasında 6 adet delik açılarak ağırlık hafifletilmiş ve tasarım değişkenleri belirlenmiştir. Latin hiperküp örnekleme yöntemi ile 100 farklı fren pedalı modeli oluşturulmuştur. Radyal temelli fonksiyonlar ile meta-modelleme yöntemiyle amaç fonksiyonu olarak ağırlık ve kısıt fonksiyonu olarak gerilme değerleri için matemetiksel denklemler elde edilmiştir. Bu matematiksel denklemler kullanılarak, fren pedalı parçasının kütle minimizasyonu açısından, CARO algoritması literatürde bilinen 6 meta-sezgisel algoritmalarla mukayese edilerek çözülmüştür. Sonuç olarak, fren pedalı parçasının optimum tasarım ağırlığı 983.30 gr olarak bulunmuştur ve başlangıç tasarımı ağırlığı olan 1202 gr değerine göre %18.2 daha hafif çıkmıştır. Yeni geliştirilen Kaotik Yapay Tavşan Optimizasyonu (CARO) algoritması, literatürde ilk defa taşıt parçasının yapısal optimizasyon probleminde kullanılmıştır. Anahtar Kelimeler: Meta-sezgisel optimizasyon, Yapay tavşan optimizasyonu algoritması, Kaotik haritalar, Kaotik yapay tavşan optimizasyonu algoritması, Topoloji optimizasyonu, Şekil optimizasyonu, Mekanik tasarım, Fren pedalıOptimization can be defined as the process of finding the best solution among all possible solutions for a given problem under certain conditions. To make an optimization problem more manageable, mathematical models are created based on the problem's structure. The uncertainty and irregularity in the behavior of nature-inspired metaheuristic optimization algorithms can provide solutions for various optimization applications and problems. The fundamental components of metaheuristic optimization algorithms are exploration, which involves generating various solutions through global search, and exploitation, which focuses on local search to select the best candidate solutions. This study proposes a new hybrid optimization algorithm to solve structural optimization problems. The goal was to integrate chaotic maps into the Artificial Rabbits Optimization (ARO) algorithm, a current and well-known metaheuristic optimization algorithm, to improve its convergence speed. This new hybrid algorithm was then applied to solve various structural optimization problems. In this study, the random parameters in the mathematical equations of the Artificial Rabbit Optimization (ARO) algorithm were hybridized with 10 well-known chaotic maps and 4 proposed modification methods and this hybrid algorithm developed in this thesis is named as the Chaotic Artificial Rabbits Optimization (CARO) algorithm. The performance of the CARO algorithm was tested by using 23 classical test functions. Subsequently, the CARO algorithm was applied to 5 fundamental engineering design problems; pressure vessel, bearing, tension/compression spring, cantilever beam, and gear train. The CARO algorithm was compared with 6 current and popular metaheuristic algorithms in the literature; Artificial Rabbits Optimization (ARO) algorithm, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Moth-Flame Optimization (MFO), Harris Hawks Optimization (HHO) and Genetic Algorithm (GA). In the scope of the thesis, the structural optimization of a new brake pedal was performed using the CARO algorithm, aiming to create a light weight brake pedal that meets the stress requirements under working conditions compared to an existing brake pedal. The mathematical model of the brake pedal's structural optimization problem was created by using latin hypercube sampling method and radial basis functions meta-modeling method. 6 holes were drilled in the brake pedal according to topology and shape optimization, reducing the weight and determining the design variables. Using the latin hypercube sampling method, 100 different brake pedal models were created. Mathematical equations for the objective function (weight) and the constraint function (stress) were obtained using the radial basis functions meta-modeling method. Using these mathematical equations, CARO algorithm was solved in terms of the mass minimization of the brake pedal, by comparing with 6 well-known metaheuristic algorithms in the literature. As a result, the optimum design weight of the brake pedal was obtained as 983.30 grams, which is 18.2% lighter than the initial design weight of 1202 grams. Thus, the newly developed Chaotic Artificial Rabbits Optimization (CARO) algorithm was used for the first time in the literature to solve a structural optimization problem for a vehicle part. Keywords: Metaheuristic optimization, Artificial rabbits optimization algorithm, Chaotic maps, Chaotic artificial rabbits optimization algorithm, Topology optimization, Shape optimization, Mechanical design, Brake peda