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Investigation of Uncertainties in Tensile Test Results Due Tothe Material Extrusion-Based Production of Latticestructures
No full textTeknolojinin sürekli gelişimiyle birlikte, malzeme ekstrüzyonuna dayalı bir eklemeli imalat yöntemi olan ergiyik filament fabrikasyonu (EFF), mühendislikten tıbba kadar çeşitli alanlarda geniş bir kullanım alanı bulmuştur. 3 boyutlu (3B) baskı teknolojisi olarak da bilinen bu yöntem, tasarım esnekliği, maliyet etkinliği ve malzeme israfını azaltma gibi önemli avantajlar sunmaktadır. Bu teknoloji, geleneksel üretim yöntemlerine kıyasla daha karmaşık ve özgün tasarımların oluşturulmasına olanak tanıyarak, özellikle otomotiv, havacılık ve biyomedikal mühendislik gibi sektörlerde yenilikçi çözümler sunmaktadır. Ayrıca, hızlı prototipleme yetenekleri sayesinde tasarım süreçlerini hızlandırarak üretim döngülerini kısaltmaktadır. Son yıllarda 3B yazıcı teknolojilerindeki hızlı ilerlemeler, endüstriyel üretim süreçlerinde devrim yaratarak, daha hafif ve dayanıklı yapıların geliştirilmesine olanak sağlamıştır. Bu bağlamda, biyobozunur bir termoplastik polimer olan polilaktik asit (PLA) kullanılarak EFF yöntemiyle üretilen kafes yapılar, yenilikçi tasarımları ve çok yönlü uygulama potansiyelleriyle dikkat çekmektedir. Kafes yapılar, malzeme kullanımını optimize ederek yüksek performans ve düşük ağırlık 8 gereksinimlerini karşılamak üzere tasarlanmıştır. Özellikle araç gövdeleri, çarpışma enerjisi emilimi için tasarlanmış yapılar ve biyomedikal implantlar gibi kritik alanlarda başarılı sonuçlar elde edilmiştir. Bununla birlikte, EFF ile üretilen kafes yapıların mekanik özelliklerinde görülen belirsizlikler, bu yapıların tasarım ve üretim süreçlerini etkileyen önemli bir faktör olarak karşımıza çıkmaktadır. Bu çalışma, PLA kullanılarak EFF yöntemiyle üretilen tek kafes hücrelerinin çekme testi altındaki mekanik özelliklerindeki belirsizlikleri incelemeyi amaçlamaktadır. Araştırmada, üretim maliyetlerini minimize etmek için tek kafes hücreli numuneler kullanılmış ve iki farklı numune tasarımı geliştirilmiştir. İlk tasarımda, yüksek mukavemetli yapıştırıcıyla sabitlenen hücreler, ikinci tasarımda ise PLA'dan üretilen bütünleşmiş fikstürler kullanılmıştır. Çekme testleri sırasında deformasyon davranışları kamera görüntüleriyle detaylı olarak analiz edilmiş ve mekanik özellikler üzerindeki olasılık dağılım fonksiyonları incelenmiştir. Elde edilen sonuçlar, farklı kafes türleri ve çubuk çaplarının mekanik özellikler üzerindeki etkilerini ortaya koymuş ve bu yapıların tasarım ve üretim süreçlerinde daha yüksek güvenilirlik ve performans elde edilmesine yönelik önemli bulgular sunmuştur. Özellikle otomotiv, havacılık ve biyomedikal alanlarda EFF ile üretilen kafes yapıların kullanımı, bu sektörlerin ihtiyaçlarına yönelik etkili çözümler sunarak dikkat çekmektedir. Araştırma sonuçları, EFF yönteminin gelecekte daha karmaşık ve dayanıklı yapıların geliştirilmesine olanak tanıyacak potansiyele sahip olduğunu göstermektedir. Böylelikle hem mühendislik hem de biyomedikal uygulamalarda, bu teknolojinin kullanımının artması beklenmektedir. EFF ile üretilen kafes yapıların mekanik özelliklerindeki belirsizliklerin anlaşılmasını sağlayarak, bu belirsizliklerin kontrol edilmesi ve optimize edilmesine yönelik yol gösterici bir kaynak oluşturmayı hedeflemektedir. Araştırma, endüstriyel uygulamalarda daha verimli, hafif ve dayanıklı çözümler geliştirilmesine katkıda bulunmayı amaçlamaktadır.With the continuous development of technology, material extrusion-based additive manufacturing, also known as fused filament fabrication (FFF), has found widespread use in various fields ranging from engineering to medicine. This method, also referred to as 3 dimensional (3D) printing technology, offers significant advantages such as design flexibility, cost efficiency, and the reduction of material waste. Compared to traditional manufacturing methods, this technology enables the creation of more complex and unique designs, providing innovative solutions especially in sectors such as automotive, aerospace, and biomedical engineering. Furthermore, its rapid prototyping capabilities accelerate design processes and shorten production cycles. In recent years, the rapid advancements in 3D printing technologies have revolutionized industrial production processes, enabling the development of lighter and more durable structures. In this context, lattice structures produced using the FFF method with polylactic acid (PLA), a biodegradable thermoplastic polymer, stand out with their innovative designs and versatile application potential. These structures are 11 designed to optimize material use while meeting high-performance and low-weight requirements. Particularly in critical applications such as vehicle bodies, energy absorption structures, and biomedical implants, significant successes have been achieved. However, uncertainties observed in the mechanical properties of lattice structures produced via FFF, particularly in parameters such as tensile strength and stiffness, pose a major challenge to their design and production processes. This study aims to examine the uncertainties in the mechanical properties of single lattice cells manufactured using PLA through FFF under tensile testing. To minimize production costs, single-cell samples were used, and two different designs were developed. The first design employed cells fixed with high-strength adhesives, while the second incorporated integrated fixtures made of PLA. Deformation behaviors were thoroughly analyzed using video recordings captured during tensile tests, and probability distribution functions were applied to the mechanical property data. The findings revealed the effects of different lattice types and strut diameters on mechanical properties, providing significant insights for achieving higher reliability and performance in the design and production processes of these structures. The use of lattice structures produced via FFF, particularly in the automotive, aerospace, and biomedical sectors, has proven to be an effective solution addressing the specific needs of these industries. The results of the study suggest that the FFF method has the potential to facilitate the development of more complex and durable structures in the future, increasing its adoption in both engineering and biomedical applications. This study aims to provide a comprehensive understanding of the uncertainties in the mechanical properties of lattice structures produced via FFF, guiding the control and optimization of these uncertainties. The research contributes to the development of more efficient, lightweight, and durable solutions in industrial applications
Conditional Handover for Uav-Ues
No full textIn this study, basic handover (HO) and conditional handover (CHO) procedures for terrestrial and UAV users (UE) were investigated. Firstly, the operations of basic HO and CHO procedures were explained, and the differences between them were emphasized. Then, in the urban scenario, the HO performances of UEs receiving service from a terrestrial wireless network and navigating at different speeds and altitudes were evaluated according to the criteria of the number of HOs, the time spent in poor signal conditions, and the number of radio link failures. The results showed that the CHO procedure enabled the UEs to operate in better signal conditions while slightly increasing the number of HOs. Moreover, it was also demonstrated that as altitude and speed increase, the CHO provides increased HO performance gains compared to basic HO
Eeg Sinyalleri ve Derin Öğrenme Modelleri Kullanılarak Dikkat Eksikliği ve Hiperaktivite Bozukluğu Tespiti
No full textAttention deficit hyperactivity disorder (ADHD) is a common, comorbid neurodevelopmental disorder that begins in childhood. Diagnosis is made by professionals using behavioral rating scales, without biomarkers. Delays in accurate diagnosis and treatment result in worsening of other psychiatric conditions that co-occur with the disease and, as a result, increased utilization of healthcare services. Therefore, there is a need for potential biomarkers that can be used in the diagnosis of ADHD. In this thesis, it is aimed to propose a decision support algorithm which will reduce the dependence on subjective tests and provide rapid and successful diagnosis by examining different convolutional neural networks (CNN), one of the deep learning methods, that have high accuracy for ADHD diagnosis by using frequency-time features in EEG signals of children diagnosed with ADHD and healthy controls, and to discover how hidden information in the time-frequency domain have an impact on the diagnosis of ADHD by using explainable artificial intelligence (AI) methods. For this purpose, the data was pre-processed and noise was removed, and spectrogram images providing time-frequency information were created. Within the scope of the thesis, transfer learning was studied on CNN architectures AlexNet, GoogleNet, ResNet-50, and SqueezeNet. Firstly, CNN architectures were used with their own classifiers. The most successful performance was achieved with the AlexNet architecture with 97.78%. The signals divided into channels and frequency bands and the distinctiveness of brain regions and frequencies in diagnosis were examined. Alpha subband and parietal region channels were found to be most successful in predicting ADHD by using alone. An explainable model was obtained by visualizing it with gradient-weighted class activation mapping (Grad-CAM). Then, only features were extracted with the same CNN architectures and performance analyzes were carried out with different machine learning algorithms. Manual feature extraction was performed and compared with the previous stage. Decision Trees, Support Vector Machines (SVM), k-Nearest Neighborhood (k-NN) and Ensemble methods were used as classification algorithms. The most successful performance was obtained by classifying the features extracted from the AlexNet architecture with the SVM algorithm (98.33%). The high accuracy findings observed as a result of the study show that the proposed method can be used as a good decision support system in the diagnosis of ADHD.Dikkat eksikliği hiperaktivite bozukluğu (DEHB), çocukluk çağında başlayan, yaygın, komorbid bir nörogelişimsel bozukluktur. Teşhis, karar destek sistemi olmadan, profesyoneller tarafından davranış derecelendirme ölçekleri kullanılarak konulmaktadır. Doğru teşhis ve tedavideki gecikmeler, bu hastalık ile birlikte ortaya çıkan diğer psikiyatrik durumların kötüleşmesine ve sonuç olarak sağlık hizmetlerindeki kullanımın artmasına bağlı maliyete neden olur. Bu nedenle, DEHB tanısında kullanılabilecek potansiyel bir karar destek sistemine ihtiyaç vardır. Bu tez çalışmasında DEHB tanısı bulunan ve sağlıklı çocuklara ait EEG sinyallerinden frekans-zaman yapıları ile derin öğrenme yöntemlerinden biri olan evrişimsel sinir ağları (ESA) kullanılarak DEHB teşhisi için yüksek doğrulukla çalışan farklı tahmin modellerinin incelenmesi ile sübjektif testlere bağımlılığı azaltacak, tanı için hızlı ve başarılı bir karar destek algoritması önermek ve açıklanabilir yapay zeka (YZ) yöntemleri ile zaman-frekans alanındaki gizli bilgilerin DEHB tanısında nasıl bir etkiye sahip olduğunu keşfetmek amaçlanmaktadır. Bu amaçla, veriler bir ön işlemeden geçirilerek gürültülerinden arındırılmış ve zaman-frekans bilgisi sağlayan spektrogram görüntüleri oluşturulmuştur. Tez kapsamında ESA mimarileri AlexNet, GoogleNet, ResNet-50, ve SqueezeNet üzerinde transfer öğrenme çalışılmıştır. İlk aşamada ESA mimarileri kendi sınıflandırıcılarıyla birlikte kullanılmıştır. En başarılı performans %97.78 ile AlexNet mimarisi ile elde edilmiştir. Kanal ve frekans bantlarına ayrılan sinyaller ile beyin bölgelerinin ve frekansların tanıda ayırt ediciliği incelenmiştir. Alfa frekansı ve parietal bölge kanalları tek başına DEHB tahmininde en başarılı bulunmuştur. Gradyan ağırlıklı sınıf aktivasyon haritalama (Grad-CAM) ile görselleştirilerek açıklanabilir bir model elde edilmiştir. Daha sonra aynı ESA mimarileri ile sadece öznitelikler elde edilmiş ve farklı makine öğrenimi algoritmalarıyla performans analizleri gerçekleştirilmiştir. Manuel öznitelikler elde edilip bir önceki aşama ile karşılaştırılmıştır. Sınıflandırma algoritması olarak Karar Ağaçları, Destek Vektör Makineleri (DVM), k-En Yakın Komşuluk (k-NN) ve Ensemble yöntemi kullanılmıştır. En başarılı performans AlexNet mimarisinden elde edilen özniteliklerin DVM algoritması ile sınıflandırılması sonucu elde edilmiştir (%98.33). Çalışmanın sonucunda gözlemlenen yüksek doğruluktaki bulgular, önerilen yöntemin DEHB tanısında iyi bir karar destek sistemi olarak kullanılabileceğini göstermektedir
Evaluation of Single-Layer Versus Double-Layer Suturing of Low Transverse Uterine Incisions in Cesarean Section and Follow-Up of Scars by Ultrasound: a Prospective Randomized Controlled Study
No full textKutlucan, Hazal/0000-0003-3696-5499Background/aim: Cesarean section (CS) is a widely performed operation worldwide but data about uterine closure are lacking. We aimed to evaluate scar niches and compare single-layer and double-layer uterine closure at 6 months following CS. Materials and methods: This prospective randomized trial assessed 56 women undergoing single- or double-layer uterine closure. None of the patients had previous uterine surgery and all CS cases were elective. Transvaginal ultrasound was performed 6 months after CS to assess the uterine scars by measuring the width, depth, and length of the scar niche and residual myometrial thickness. An experienced sonographer was blinded to the uterine closure technique and the ultrasounds were conducted by practitioners unaware of the technique in the postoperative follow-up appointments. Results: Twenty-eight women were assigned to the single-layer closure group (Group 1) and 28 were assigned to the double-layer closure group (Group 2). The demographic and clinical characteristics of patients and the width, depth and diameter of the niche were similar between the groups, as was residual myometrial thickness. There was no difference in uterine scar volume under the incision between the two groups. The duration of surgery was approximately 5 min longer (p = 0.048) and hemoglobin decrease was about 0.5 g/ dL less (p = 0.039) in the double-layer group compared to the single-layer group. Postmenstrual spotting rates were similar between the groups. Group 1 had two and Group 2 had one spontaneous pregnancy within 6 months after CS. Conclusion: The single- and double-layer closure techniques do not produce different impacts on CS niche features at 6 months after delivery. Ultrasound might be an important noninvasive diagnostic tool for understanding CS scar remodeling
Algorithmic Stock Trading Based on Ensemble Deep Neural Networks Trained With Time Graph
No full textOzbayoglu, Murat/0000-0001-7998-5735Financial forecasting is generally implemented by analyzing the time series data related to the stock. This is accomplished widely with deep neural networks (DNNs) since DNNs can directly extract the related information that is otherwise hard to obtain. Time series is the core data representation of financial forecasting problem since it comes naturally. However recent studies show that even if time series representation is necessary, it still lacks certain aspects related to the problem. One of them is the relationship between the stocks of the market which can be captured through graph representation. Therefore, DNNs might solve the financial forecasting problem better when graph and time series representations are combined. In this study, we present different graph representations that can be used for this purpose. We also present an ensemble network that gives an investment strategy related to the stock market from stock predictions. Our proposed model returns an average of 20.09% annual profit on DOW30 dataset through daily buy-sell decisions based on close prices. Therefore, it can serve as a daily financial investment strategy, offering higher annual returns than conventional heuristic approaches
Implications of intracrystalline OC17 on the protection of lattice incorporated proteins
No full textBiogenic CaCO3 formation is regulated by crystallization proteins during crystal growth. Interactions of proteins with nascent mineral surfaces trigger proteins to be incorporated into the crystal lattice. As a result of incorporation, these intracrystalline proteins are protected in the lattice, an example of which is ancient eggshell proteins that have persisted in CaCO3 for thousands of years even under harsh environmental conditions. OC17 is an eggshell protein known to interact with CaCO3 during eggshell formation during which OC17 becomes incorporated into the lattice. Understanding protein incorporation into CaCO3 could offer insights into protein stability inside crystals. Here, we study the protection of OC17 in the CaCO3 lattice. Using thermogravimetric analysis we show that the effect of temperature on intracrystalline proteins of eggshells is negligible below 250 1C. Next, we show that lattice incorporation protects the OC17 structure despite a heat-treatment step that is shown to denature the protein. Because incorporated proteins need to be released from crystals, we verify metal chelation as a safe crystal dissolution method to avoid protein denaturation during reconstitution. Finally, we optimize the recombinant expression of OC17 which could allow engineering OC17 for engineered intracrystalline entrapment studies.H. B. C. would like to thank the Cambridge Commonwealth, European ; International Trust, theDepartmentofEngineering, the Nanoscience Centre, and Prof Jim Huntington from the Cambridge Institute for Medical Research, Department of Haematology, Cambridge, UK
The Relationship Between Cognitive Emotion Regulation and Negative Emotions Moderated by Exposure To Traumatic News
No full textBu çalışma, bilişsel duygu düzenleme stratejileri ile travmatik haberlere maruz kalmayla ilişkili duyguları araştırmayı hedeflemiştir. Bu kapsamda iki çalışma gerçekleştirilmiştir. Çalışma 1'de (n = 72) travmatik haberler belirlenmiş olup, Çalışma 2'de (n = 120) ise bu haberler ve Demografik Bilgi Formu, Bilişsel Duygu Düzenleme Ölçeği, Pozitif ve Negatif Duygu Ölçeği kullanılarak katılımcıların bilişsel duygu düzenleme stratejileri ve duyguları değerlendirilmiştir. Çalışma 2'de veri analizi, tanımlayıcı istatistikler, t-test analizi ve düzenleyici değişken analizi ile gerçekleştirilmiştir. Bulgulara göre, travmatik haberlere maruz kalanlar, nötr haberlere maruz kalanlara göre daha yüksek şiddette olumsuz duygu yaşamıştır. Düzenleyici değişken analizine göre travmatik haberlere maruz kalanlar tekrar eden düşüncelere odaklanmayı strateji olarak kullandıklarında daha fazla olumsuz duygulanım yaşamıştır. Ayrıca, erkekler ve kadınlar diğerlerini suçlama, felaketleştirme, tekrar eden düşüncelere odaklanma, plan yapmaya yeniden odaklanma, olumlu yeniden değerlendirme stratejilerinde farklılık göstermiştir. Sonuç olarak, travmatik haberlere maruz kalmanın duygular üzerinde olumsuz etkisi olduğu görülmektedir. Kullanılan bilişsel duygu düzenleme stratejisine göre bu etki azaltılabilir ya da ortadan kaldırılabilir. Psikoterapilerde ve önleyici hizmetlerde tekrar eden düşüncelere odaklanmak önemlidir. Bu çalışma ile haberlerin insanlar üzerindeki etkisine ve insanların bilişsel ve duygusal süreçlerine ışık tutulmuştur.This study aimed to investigate the relationship between cognitive emotion regulation strategies and emotions associated with exposure to traumatic news. Two studies were conducted in this context. In Study 1 (n = 72), traumatic news items were identified, while in Study 2 (n = 120), participants' cognitive emotion regulation strategies and emotions were assessed using the traumatic news selected, Demographic Information Form, Cognitive Emotion Regulation Questionnaire, and Positive and Negative Affect Scale. In Study 2, data analysis was conducted using descriptive statistics, t-test analysis, and moderation analysis. According to the findings, those exposed to traumatic news experienced more intense negative emotions compared to those exposed to neutral news. The moderator variable analysis showed that individuals exposed to traumatic news experienced greater negative emotion when they used rumination as a coping strategy. The results also showed that men and women show differences in cognitive emotion regulation strategies. In conclusion, it was observed that exposure to traumatic news has a negative impact on emotions. This effect can be mitigated or eliminated depending on the cognitive emotion regulation strategy used. Focusing on rumination is considered important, especially in psychotherapies and preventive approaches. This study highlights the impact of news on people and their cognitive and emotional processes
Combination of Searches for Higgs Boson Pair Production in Formula Presented Collisions at Formula Presented With the Atlas Detector
No full textThis Letter presents results from a combination of searches for Higgs boson pair production using Formula Presented of proton-proton collision data at Formula Presented recorded with the ATLAS detector. At 95% confidence level (CL), the upper limit on the production rate is 2.9 times the standard model (SM) prediction, with an expected limit of 2.4 assuming no Higgs boson pair production. Constraints on the Higgs boson self-coupling modifier Formula Presented, and the quartic Formula Presented coupling modifier Formula Presented, are derived individually, fixing the other parameter to its SM value. The observed 95% CL intervals are Formula Presented and Formula Presented, respectively, while the expected intervals are Formula Presented and Formula Presented in the SM case. Constraints obtained for several interaction parameters within Higgs effective field theory are the strongest to date, offering insights into potential deviations from SM predictions. © 2024 CERN, for the ATLAS Collaboration.BSF-NSF; Australian Research Council, ARC; DRAC; La Caixa Banking Foundation; BMWFW; Centre National pour la Recherche Scientifique et Technique, CNRST; Fundação para a Ciência e a Tecnologia, FCT; European Union, Future Artificial Intelligence Research; Cooperative Research Centres, Australian Government Department of Industry, CRCs; Center for Advancing Research Impact in Society, ARIS; National Science Foundation, NSF; CEA-DRF; Science and Technology Facilities Council, STFC; Horizon 2020, ICSC-NextGenerationEU; H2020 Marie Skłodowska-Curie Actions, MSCA; INFN-CNAF; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, FAPERJ; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministry of Science and Technology, Taiwan, MOST; Israel Science Foundation, ISF; Wallenberg Foundation; Leverhulme Trust; Baden-Württemberg Stiftung, BWS; MVZI; PROMETEO; Neubauer Family Foundation, NFF; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; Generalitat de Catalunya; Instituto Nazionale di Fisica Nucleare, INFN; Austrian Science Fund, FWF; Yerevan Physics Institute; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Helmholtz-Gemeinschaft, HGF; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Forskningsrådet om Hälsa, Arbetsliv och Välfärd, FORTE; Karlsruhe Institute of Technology, KIT; Canarie; GridKA; Göran Gustafssons Stiftelser; European Commission, EC; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; European Cooperation in Science and Technology, COST; EU-ESF; International Council of Shopping Centers, ICSC; RGC; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Institutul de Fizică Atomică, IFA; Natural Sciences and Engineering Research Council of Canada, NSERC; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; GenT Programmes Generalitat Valenciana, Spain; National Science and Technology Council, NSTC; Irish Rugby Football Union, IRFU; Cantons of Bern and Geneva; Chinese Academy of Sciences, CAS; Defence Science Institute, DSI; MNE; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Royal Society; Minerva Foundation; CERN-CZ; National Research Foundation, NRF; Ministerstwo Edukacji i Nauki, MNiSW; Generalitat Valenciana, GVA; CERN; National Research Council Canada, NRC; Alexander von Humboldt-Stiftung, AvH; Multiple Sclerosis Scientific Research Foundation, MSSRF; Caring Futures Institute, Flinders University, CFI; British Columbia Knowledge Development Fund, BCKDF; Ministry of Education, Culture, Sports, Science and Technology, MEXT; MICIN, (RYC2022-038164-I, RYC2021-031273-I, RYC2020-030254-I, RYC2019-028510-I, PID2021-125273NB); Narodowe Centrum Nauki, NCN, (2021/42/E/ST2/00350, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, UMO-2021/40/C/ST2/00187, 2022/47/B/ST2/03059, UMO-2020/37/B/ST2/01043, UMO-2019/34/E/ST2/00393); Narodowe Centrum Nauki, NCN; GenT Programmes Generalitat Valenciana, (CIDEGENT/2019/027); The Slovenian Research and Innovation Agency, ARRS, (J1-3010); The Slovenian Research and Innovation Agency, ARRS; Norges Forskningsråd, (RCN-314472); Norges Forskningsråd; Horizon 2020 Framework Programme, H2020, (MUCCA-CHIST-ERA-19-XAI-00); Horizon 2020 Framework Programme, H2020; Japan Society for the Promotion of Science, JSPS, (JP22KK0227, JP22H04944, JP23KK0245, JP22H01227); Japan Society for the Promotion of Science, JSPS; Vetenskapsrådet, VR, (VR 2022-04683, 2021-03651, VR 2018-00482, VR 2022-03845, 2023-04654, VR 2023-03403); Vetenskapsrådet, VR; Agence Nationale de la Recherche, ANR, (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-22-EDIR-0002, ANR-21-CE31-0022); Agence Nationale de la Recherche, ANR; Ministerio de Ciencia e Innovación, MCIN, (PCI2022-135018-2); Ministerio de Ciencia e Innovación, MCIN; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR, (PRIN—20223N7F8K—PNRR M4.C2.1.1); Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; Deutsche Forschungsgemeinschaft, DFG, (DFG-CR 312/5-2, DFG-469666862); Deutsche Forschungsgemeinschaft, DFG; Knut och Alice Wallenbergs Stiftelse, (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Knut och Alice Wallenbergs Stiftelse; European Regional Development Fund, ERDF, (IDIFEDER/2018/048); European Regional Development Fund, ERDF; U.S. Department of Energy, USDOE, (ECA DE-AC02-76SF00515); U.S. Department of Energy, USDOE; DNSRC, (IN2P3-CNRS); FAIR-NextGenerationEU, (PE00000013); National Natural Science Foundation of China, NSFC, (12275265, NSFC-12175119, NSFC-12075060); National Natural Science Foundation of China, NSFC; European Research Council, ERC, (ERC 101089007, ERC-948254); European Research Council, ERC; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT, (CZ.02.01.01/00/22_008/0004632, PRIMUS/21/SCI/017); Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; Grantová Agentura České Republiky, GAČR, (GACR-24-11373S); Grantová Agentura České Republiky, GAČR; H2020 European Research Council, ERC, (ERC-101002463); H2020 European Research Council, ERC; North Dakota Game and Fish Department, (CC-IN2P3); North Dakota Game and Fish Department; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF, (RPG-2020-004, NIF-R1-231091, SNSF—PCEFP2_194658); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Ministry of Science and Technology of the People's Republic of China, MOST, (MOST-2023YFA1605700); Ministry of Science and Technology of the People's Republic of China, MOST; Investissements d’Avenir Labex, (ANR-11-LABX-0012); Narodowa Agencja Wymiany Akademickiej, NAWA, (PPN/PPO/2020/1/00002/U/00001); Narodowa Agencja Wymiany Akademickiej, NAWA; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT, (1240864, 1230987, 1230812); Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECY