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Being a Child in Vulnerable Cities and Child-Focused Disaster Management
No full textThroughout history, societies have created living spaces for their own needs such as nutrition, shelter and protection. When faced with any situation that threatened these habitats, they either adapted to the new life by improving their existing areas or continued their lives by relocating. When we look at the reasons for relocation throughout history, it is seen that this is mostly due to wars and natural disasters. The earthquake disaster, which cannot be prevented but the effects of which can be minimized, is also an important factor in the relocation or re-establishment of societies. Turkey, an earthquake country that has experienced many devastating earthquakes in the historical process, experienced the most recent and severe consequences of the earthquake with the earthquake that occurred on February 6, 2023, which is called the 'disaster of the century'. Hayat province suffered the most devastating damage in the 7.7 and 7.6 magnitude earthquakes centered in Koramangala and Elista, which affected many cities and settlements. As a result of these earthquakes, many people lost their lives, homes, relatives, schools, neighborhoods and almost all the cities. A significant portion of the people who lost their lives are 'children', who are one of the most vulnerable groups of society with their physical characteristics and their undeveloped mental states. These vulnerable characteristics of children also increase the rate of being affected by disasters as disaster victims. The absence of a child-oriented disaster management plan in our country further aggravates this situation. Within the framework of the approach to the subject, the aim of this thesis is to understand the needs of children who experienced the February 6 earthquake in Hatay province, what they experienced in the process, how they were affected, and from this point of view, to create a 'guide' for the preparation of a 'child-centered disaster management plan' that includes what needs to be done for children before and after the disaster. Based on this purpose, the study aims to determine what the physical/spatial and psychological needs of children who have experienced the earthquake disaster are, what kind of decisions should be taken and what kind of steps should be taken in the settlements to be planned in the future, what are the duties of the competent institutions and organizations and the society, and how to ensure spatial and social recovery by understanding the earthquake reality from the perspective of children. In addition to source research, face-to-face questionnaire interviews and observations in the field have been guided in achieving the set objectives. The methodology of the study consisted of questionnaire interviews and on-site observations conducted with a total of 390 children in the age group of 6-18 years old, who are studying in primary, middle and high schools in Hatay Province, selected by random sampling techniques. The questionnaire interview data obtained from the field was entered into the system through the SPSS application and interpreted through frequency and correlation analysis. In addition, the observations made in the field were supported by photographs and included in the study. Within the scope of the study, the thoughts, wishes and expectations of children divided into different age groups regarding their accommodation, education, and psychological needs were questioned. According to the findings obtained as a result of the study, recommendations have been prepared to meet the needs.Tarih boyunca toplumlar kendileri için bir gereksinim olan beslenme, barınma ve korunma gibi amaçlarla yaşam alanları oluşturmuşlardır. Bu yaşam alanlarını tehdit eden herhangi bir durumla karşılaştıklarında ise ya mevcut alanlarını iyileştirerek yeni yaşama adapte olmuşlar ya da yer değiştirerek yaşamlarını sürdürmüşlerdir. Tarih boyunca yer değiştirme nedenlerine bakıldığında ise çoğunlukla bu durumun savaşlar ve doğal afetler yoluyla olduğu da görülmektedir. Önlenmesinin mümkün olmadığı fakat yarattığı etkilerin en aza indirgenebileceği deprem afeti de toplumların yer değiştirmesinde veya yeniden yaşam alanı kurmasında önemli bir etkendir. Tarihsel süreçte çok sayıda yıkıcı depremler yaşamış ve bir deprem ülkesi olan Türkiye, depremin en son ve ağır sonuçlarını 'yüzyılın felaketi' olarak adlandırılan 6 Şubat 2023 tarihinde meydana gelen deprem ile yaşamıştır. Birçok kenti ve yerleşmeyi etkileyen, Kahramanmaraş ve Elbistan merkezli 7.7 ve 7.6 büyüklüğünde meydana gelen depremlerde en çok ve yıkıcı hasarı ise Hatay İli almıştır. Bu depremler sonucunda birçok kişi hayatını, evini, yakınını, okulunu, mahallesini ve neredeyse kentini kaybetmiştir. Hayatını kaybeden kişilerin önemli bir kısmını da gerek fiziksel özellikleri gerekse de henüz gelişimi tamamlanmamış ruhsal durumlarıyla toplumun en kırılgan gruplarından biri olan 'çocuklar' oluşturmaktadır. Onların bu kırılgan özellikleri afetzede olarak afetlerden etkilenme oranını da arttırmaktadır. Ülkemizde çocuk odaklı bir afet yönetim planının bulunmayışı bu durumu daha da ağırlaştırmaktadır. Konuya yaklaşım çerçevesinde bu tez çalışmasının amacı; 6 Şubat depremini Hatay ilinde yaşamış çocukların ihtiyaçlarını, süreçte ne yaşadıklarını, nasıl etkilendiklerini anlamak ve buradan hareketle çocuklar için afet öncesi ve sonrasında yapılması gerekenleri içeren 'çocuk merkezli afet yönetim planı' hazırlanması için bir 'rehber' oluşturmaktır. Bu amaçtan hareketle çalışma kapsamında, deprem afetini yaşamış çocukların fiziksel/mekânsal ve psikolojik gereksinimlerinin neler olduğu, ilerleyen süreçte planlanacak yerleşimlerde çocukları ön plana alan ne tür kararların alınması ne tür adımların atılmasının gerektiği, yetkili kurum ve kuruluşlara, topluma düşen görevlerin neler olduğunun tespit edilmesi ve çocuk bakış açısıyla deprem gerçeğini anlayarak mekânsal ve sosyal iyileşmenin nasıl sağlanacağına cevap aranması hedeflenmektedir. Belirlenen hedeflere ulaşmada kaynak araştırması yanı sıra alanda yüz yüze yapılan anket görüşmeleri ve gözlemler yol gösterici olmuştur. Hatay İli genelindeki ilkokul, ortaokul ve liselerde öğrenim gören, 6-18 yaş grubundaki, rastgele örnekleme tekniği ile seçilen toplamda 390 çocukla gerçekleştirilen anket görüşmeleri ve yerinde yapılan gözlemler çalışmanın yöntemini oluşturmaktadır. Alandan elde edilen anket görüşmesi verileri SPSS uygulaması aracılığı ile sisteme girilmiş, frekans ve ilişkililik analizleri aracılığı ile yorumlanmıştır. Ayrıca alanda yapılan gözlemler de fotoğraflarla desteklenerek çalışma kapsamına dahil edilmiştir. Çalışma kapsamında farklı yaş gruplarına ayrılan çocukların barınma, eğitim, psikolojik ihtiyaçlarına yönelik düşünce, istek ve beklentileri sorgulanmıştır. Çalışma sonucunda elde edilen bulgular doğrultusunda, çocukların fiziksel, mekânsal ve psikolojik gereksinimlerinin karşılanmasına yönelik çeşitli öneriler geliştirilmiştir
Effect of Seasonal-Trend Decomposition on Machine Learning-Based Suspended Sediment Load Prediction Performance
No full textSedimentin tahmin edilmesi, su kaynakları yönetimi için hayati önem taşımaktadır. Bu çalışmada, Kızılırmak Nehri'nin Bulakbaşı istasyonundaki askıda sediment yükünün (SSL) makine öğrenmesi tabanlı tahmin performansı araştırılmıştır. Ayrıca mevsimsel ayrıştırmanın tahmin performansı üzerindeki etkisi incelenmiştir. Bu doğrultuda, Destek Vektör Makinesi (SVM), Adaptif Boosting (AdaBoost) ve Genelleştirilmiş Regresyon Sinir Ağı (GRNN) algoritmaları SSL tahmini için kullanılmıştır. Hiperparametre optimizasyonu için Grid Search (GS) algoritması tercih edilmiştir. Mevsimsel bileşen, Mevsimsel-Trend ayrıştırması LOESS (STL) yöntemi kullanılarak elde edilmiştir. Akış (Qt), akış gecikmesi (Qt-1) ve SSL'nin mevsimsel bileşeni (S-SSLt) kullanılarak altı girdi kombinasyonu oluşturulmuştur. Bulgulara göre AdaBoost (M6-NSEEğitim=0,914, M4-NSETest=0,765), SVM (M6-NSEEğitim=0,912, M6-NSETest=0,863) ve GRNN (M6-NSEEğitim=0,912, M4-NSETest=0,834) modelleri oldukça tutarlı sonuçlar üretmiştir. Test aşamasında, SVM-M6 (R2=0,893, NSE=0,863) çeşitli değerlendirme ölçütlerine göre en başarılı modeldir. SSL'nin mevsimsel bileşeninin eklendiği son üç girdi kombinasyonunun genel olarak performansı artırdığı da gözlemlenmiştir. En başarılı model olan test aşamasındaki SVM için mevsimsel bileşenin olmadığı kombinasyonda (M3) R2=0,873, NSE=0,820 ve mevsimsel bileşenin olduğu kombinasyonda (M6) R2=0,893, NSE=0,863 değerleri elde edilmiştir
Reusing Cast Iron Slag Waste as a Material Development by Flash Sintering
No full textCetinkaya, Zeynep/0000-0002-4591-2332The materials sintered with FS are determined by considering temperature, time, energy, cost, environmental pollution, and human health. In this study, cast iron slag wastes (CISW) were utilized in powder form and sintered using flash sintering (FS). The outcomes of both FS and conventional sintering (CS) processes were assessed regarding their physical, chemical, and mechanical properties. The CS process was performed at 1000 degrees C for 4 h. FS experiments were conducted under 20, 25, and 30 V/mm electric fields. CISW was sintered using the FS method resulting in lower temperatures and shorter processing times, thus yielding energy savings. Through this method, it was observed that the interatomic spaces narrowed due to the electric field and temperature applied to the sample. Physical, chemical, and mechanical tests (3-point bending and hardness) were carried out on all sintered materials. Experimental results indicated that the sample sintered under the 20 V/mm electric field at 517 degrees C for 15 s exhibited better mechanical properties compared to CS. On the other hand, the sample flash sintered under 30 V/mm electric field had lower temperatures (478 degrees C) compared to all FS processes that were carried out with perfect intergranular interactions. However, the mechanical properties were lower than the others because the structures may have passed into the liquid phase. Consequently, it has been proven that this product obtained from CISWs can be used in floor and wall tiles according to ISO10545-4 and BS-EN14411:2016 standards. It has better mechanical strengths than all other sintering processes with FS under 20 V/mm electric field.Scientific and Technological Research Council of Turkiye (TUBITAK); Scientific Research Projects Coordination Unit of Konya Technical University [BAP- 221019015]Open access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK). The authors gratefully acknowledge the financial support provided by the Scientific Research Projects Coordination Unit of Konya Technical University (Contract # BAP- 221019015)
Search for Charged-Lepton Flavor Violation in the Production and Decay of Top Quarks Using Trilepton Final States in Proton-Proton Collisions at √s=13 Tev
No full textKreczko, Luke/0000-0003-2341-8330; Bhowmik, Sandeep/0000-0003-1260-973X; Ivanov, Andrew/0000-0002-9270-5643; Csanad, Mate/0000-0002-3154-6925; Heath, Helen/0000-0001-6576-9740; Cussans, David/0000-0001-8192-0826; Garcia, Francisco/0000-0002-4023-7964; Brooke, James/0000-0003-2529-0684; Stylianou, Nicolas/0000-0002-0113-6829; Smith, Nicholas/0000-0002-0324-3054A search is performed for charged-lepton flavor violating processes in top quark (t) production and decay. The data were collected by the CMS experiment from proton-proton collisions at a center-of-mass energy of 13 TeV and correspond to an integrated luminosity of 138 fb(-1). The selected events are required to contain one opposite-sign electron-muon pair, a third charged lepton (electron or muon), and at least one jet of which no more than one is associated with a bottom quark. Boosted decision trees are used to distinguish signal from background, exploiting differences in the kinematics of the final states particles. The data are consistent with the standard model expectation. Upper limits at 95% confidence level are placed in the context of effective field theory on the Wilson coefficients, which range between 0.024-0.424 TeV-2 depending on the flavor of the associated light quark and the Lorentz structure of the interaction. These limits are converted to upper limits on branching fractions involving up (charm) quarks, t -> e mu u (t -> e mu c), of 0.032(0.498) x 10(-6), 0.022(0.369) x 10(-6), and 0.012(0.216) x 10(-6) for tensorlike, vectorlike, and scalarlike interactions, respectively.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); MoER, ERC PUT and ERDF (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); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, Contract No. 675440, 724704, 752730, 758316, 765710, 824093, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, Project No. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWTBelgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science-EOS"-be.h Project No. 30820817; the Beijing Municipal Science ; Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, Grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy-EXC 2121 "Quantum Universe"-390833306, and under project number 400140256-GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program-UNKP, the NKFIH research Grants No. K 124845, No. K 124850, No. K 128713, No. K 128786, No. K 129058, No. K 131991, No. K 133046, No. K 138136, No. K 143460, No. K 143477, No. 2020-2.2.1-ED-2021-00181, and No. TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC-National Research Center for High Performance Computing, Big Data and Quantum Computing, funded by the EU NexGeneration program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, Contracts No. Opus 2021/41/B/ST2/01369 and No. 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant No. CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe," and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, Grant No. MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation, Grant No. B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, Contract C-1845; and the Weston Havens Foundation (USA).SC (Armenia); FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); BNSF (Bulgaria); MoST (China); NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); NKFIH (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CONACYT (Mexico); UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA (Thailand); TUBITAK (Turkey); NASU (Ukraine); NSF (USA); Marie-Curie program (European Union); European Research Council (European Union); Horizon 2020 Grant (European Union) [675440, 724704, 752730, 758316, 765710, 824093, 884104]; COST Action (European Union) [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); FWO (Belgium) under the "Excellence of Science - EOS - be.h project [30820817]; Beijing Municipal Science ; Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Hellenic Foundation for Research and Innovation (HFRI) (Greece) [2288]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 390833306, 400140256 - GRK2497]; Hungarian Academy of Sciences (Hungary); Council of Science and Industrial Research, India; Latvian Council of Science; National Science Center (Poland) [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; National Priorities Research Program by Qatar National Research Fund; MCIN/AEI, ERDF "a way of making Europe"; Programa Severo Ochoa del Principado de Asturias (Spain); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation (Thailand) [B05F650021]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA); BMBWF (Austria); MES (Bulgaria); CERN; CAS (China); MINCIENCIAS (Colombia); MSES (Croatia); ERC PUT (Estonia); HIP (Finland); GSRI (Greece); MSIP (Republic of Korea); LAS (Lithuania); CINVESTAV (Mexico); LNS (Mexico); SEP (Mexico); MOS (Montenegro); MES (Poland); NSC (Poland); MCIN/AEI (Spain); MST (Taipei); MHESI (Thailand); TENMAK (Turkey); STFC (United Kingdom); DOE (USA); F.R.S.-FNRS (Belgium); New National Excellence Program - UNKP (Hungary); NKFIH (Hungary) [K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Ministry of Education and Science [2022/WK/14]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu (Spain) [MDM-2017-0765
A Novel Alginate/Fruit Waste/Fe3O4 Hydrogel Biobeads for Highly Efficient Removal of Methylene Blue From Aqueous Solution
No full textToday, for effective wastewater management, water resources need to be treated in an environmentally friendly, cost-effective and less complex manner. It is very important to develop alternative low-cost, high adsorption capacity adsorbents based on sustainable materials. For this purpose, glutaraldehyde cross-linked alginate-coated magnetic orange peel composite beads (ALG-Op@Fe3O4) and nectarine peel composite beads (ALG-Np@Fe3O4) were synthesized using fruit waste as an alternative adsorbent and alginate as a biopolymer. The dropping and pH precipitation method was used to alginate containing hydrogel beads n-Fe3O4, and fruit waste with a well-defined structure. FTIR, SEM, EDX-mapping and XRD analyses of synthesized new biobeads were performed. The adsorption effects of experimental parameters such as pH (3–9), adsorbent dosage (1–8 g/L), time (5–360 min), temperature (25–55 °C) and dye concentration (10–300 ppm) were investigated. Langmuir, Freundlich, Scarthard, D–R and Temkin adsorption isotherm models were applied for the adsorption of methylene blue onto newly synthesized adsorbents through equilibrium studies. Kinetic constants were determined by pseudo-first-order, pseudo-second-order, Elovich and intraparticle diffusion models. The adsorption process was found to fit better with Langmuir isotherm model, and adsorption capacity of 188.7 mgg−1 and 100.0 mgg−1 was obtained for ALG-Op@Fe3O4 and ALG-Np@Fe3O4, respectively. The adsorption process followed the pseudo-second-order kinetic model, and thermodynamic studies revealed that it was an exothermic and spontaneous process. Waste fruit peels have been shown to be an effective and alternative material when compared to different adsorbents in the removal of methylene blue molecules from aqueous media due to reasons such as high removal capacity, easy availability, low cost, usability, recyclability and the fact that alginate as a biopolymer does not harm the environment. © The Author(s) 2025.Scientific and Technological Research Council of Türkiye; TÜBİTAK; Konya Teknik Üniversitesi, KTUN, (231016053); Konya Teknik Üniversitesi, KTU
Exploring Deep Learning Approaches for Walnut Phenotype Variety Classification
No full textThe efficient classification of agricultural commodities like walnuts is crucial for assessing quality and managing the supply chain. This scholarly article analyses various deep learning and data science methods for walnut fruit classification. For this purpose, first, a dataset comprising images of walnuts from Chandler, Fernor, Howard, and Oguzlar varieties was collected. Two different experiments were conducted. In the first experiment, only deep learning methods were used as classifiers. In this experiment, InceptionV3 demonstrated the highest classification accuracy, followed by VGG-19 and VGG-16. In the second experiment, deep learning algorithms were used for feature extraction, followed by support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (k-NN) algorithms for classification. These models resulted in an improvement in overall success rates. The most effective classification was achieved with the InceptionV3 and LR combination, achieving the highest success rate. These results highlight the efficacy of deep learning methodologies in swiftly and accurately classifying agricultural products based on visual information, indicating the potential to strengthen classification systems within the agricultural sector
Girth and Groomed Radius of Jets Recoiling Against Isolated Photons in Lead-Lead and Proton-Proton Collisions at √snn=5.02 Tev
No full textHussain, Priya Sajid/0000-0002-4825-5278; Chatterjee, Suman/0000-0003-2660-0349; Dragicevic, Marko/0000-0003-1967-6783; /0000-0002-1153-816X; Navarrete Ramos, Efren/0000-0002-5180-4020This Letter presents the first measurements of the groomed jet radius R-g and the jet girth g in events with an isolated photon recoiling against a jet in lead-lead (PbPb) and proton-proton (pp) collisions at the LHC at a nucleon-nucleon center-of-mass energy of 5.02 TeV. The observables R-g and g provide a quantitative measure of how narrow or broad a jet is. The analysis uses PbPb and pp data samples with integrated luminosities of 1.7 nb(-1) and 301 pb(-1), respectively, collected with the CMS experiment in 2018 and 2017. Events are required to have a photon with transverse momentum p(T)(gamma) > 100 GeV and at least one jet back-to-back in azimuth with respect to the photon and with transverse momentum p(T)(jet) such that p(T)(jet)/p(T)(gamma) > 0.4. The measured R-g and g distributions are unfolded to the particle level, which facilitates the comparison between the PbPb and pp results and with theoretical predictions. It is found that jets with p(T)(jet)/p(T)(gamma) > 0.8, i.e., those that closely balance the photon p(T)(gamma), are narrower in PbPb than in pp collisions. Relaxing the selection to include jets with p(T)(jet)/p(T)(gamma) > 0.4 reduces the narrowing of the angular structure of jets in PbPb relative to the pp reference. This shows that selection bias effects associated with jet energy loss play an important role in the interpretation of jet substructure measurements.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 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).r Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no.22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science --EOS'' --be.h project n. 30820817; the Beijing Municipal Science ; Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy --EXC 2121 `` Quantum Universe'' -390833306, and under project number 400140256 -GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program -UNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC -National Research Center for High Performance Computing, Big Data and Quantum Computing and FAIR --Future Artficial Intelligence Research, funded by the NextGenerationEU program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe'', and the Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation, grant B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the Super-Micro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA). TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).r Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science --EOS'' --be.h project n. 30820817; the Beijing Municipal Science ; Technology Commission, No.Z191100007219010 and Fundamental Research Funds for the Central Universities (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy --EXC 2121 `` Quantum Universe'' -390833306, and under project number 400140256 -GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program -UNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC -National Research Center for High Performance Computing, Big Data and Quantum Computing and FAIR --Future Artficial Intelligence Research, funded by the NextGenerationEU program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe'', and the Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation, grant B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the Super-Micro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).FWF (Austria); SC (Armenia); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); BNSF (Bulgaria); MoST (China); NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER TK202 (Estonia); RVTT3 (Estonia); Academy of Finland (Finland); MEC (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); NKFIH (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CONACYT (Mexico); UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA (Thailand); TUBITAK (Turkey); NASU (Ukraine); NSF (USA); Marie-Curie program (European Union); European Research Council (European Union); Horizon 2020 Grant (European Union) [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action (European Union) [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); FWO (Belgium) under the "Excellence of Science - EOS - be.h project [30820817]; Beijing Municipal Science AMP; Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Hellenic Foundation for Research and Innovation (HFRI) (Greece) [2288]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 390833306, 400140256 - GRK2497]; Hungarian Academy of Sciences (Hungary); Council of Science and Industrial Research, India; Latvian Council of Science; National Science Center (Poland) [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; National Priorities Research Program by Qatar National Research Fund; MCIN/AEI, ERDF "a way of making Europe"; Programa Severo Ochoa del Principado de Asturias (Spain); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources AMP; Institutional Development, Research and Innovation (Thailand) [B05F650021]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA); BMBWF (Austria); MES (Bulgaria); CERN; CAS (China); MINCIENCIAS (Colombia); MSES (Croatia); ERC PRG (Estonia); HIP (Finland); GSRI (Greece); MSIP (Republic of Korea); LAS (Lithuania); CINVESTAV (Mexico); LNS (Mexico); SEP (Mexico); MOS (Montenegro); MES (Poland); NSC (Poland); MCIN/AEI (Spain); MST (Taipei); MHESI (Thailand); TENMAK (Turkey); STFC (United Kingdom); DOE (USA); F.R.S.-FNRS (Belgium); New National Excellence Program - UNKP (Hungary); NKFIH (Hungary) [K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Ministry of Education and Science [2022/WK/14]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu (Spain) [MDM-2017-0765]; Science Committee (Armenia) [22rl-037]; Shota Rustaveli National Science Foundation (Georgia) [FR-22-985]; ICSC -National Research Center for High Performance Computing, Big Data and Quantum Computing (Italy); FAIR-Future Artficial Intelligence Research - NextGenerationEU program (Italy
Rinex Scan: Open-Source Rinex Epoch and Frequency Scanning Software
No full textSince the introduction of the Receiver Independent Exchange Format (RINEX) version 2 in 1990, the community of Global Navigation Satellite System (GNSS) rapidly adapted to its use and continued to develop the RINEX format. Because of its widespread adoption and user-friendly format across all GNSS receivers, GNSS companies typically offer conversion software that enables users to convert raw receiver-dependent data into the RINEX observation format. As new GNSS signals and constellations continue to emerge, revising the RINEX format has become a necessary requirement over time. RINEX SCAN is a new open-source software for multi-GNSS epoch and frequency (GNSS observation codes) scanning, providing availability information for epochs and frequencies in RINEX files. This software is free and open source, released under the MIT License., and written in object-oriented programming language C#. The software supports GPS, GLONASS, Galileo, BeiDou-2, BeiDou-3, and QZSS constellations data in RINEX observation 2.xx and 3.xx formats. Thanks to parallel computing architecture, users can easily obtain large-scale data quantity results for epochs and multi-GNSS frequencies when working with big data sets.The author thanks the IGS-MGEX service for providing the data. All plots are created using MATLAB 2022b, license number:41008612.Konya Technical Universit
Removal of Hexavalent Chromium (cr6+) From Aqueous Solutions by Chitosan-Halloysite Nanotubes Composite Hydrogel Beads
No full textBackground: In recent years, low-cost biomaterials have been used for sustainable applications to reduce the impact of wastewater treatment. The preparation of bio-based materials with a strong affinity for chromate plays a crucial role in the adsorption process. Methods: In this study, chitosan-halloysite nanotubes composite hydrogel beads (Ch-HNTs) were prepared for the removal of hexavalent chromium (Cr6+) from aqueous solutions. Ch-HNTs hydrogel beads were generated by incorporating HNTs into chitosan using a glutaraldehyde solution to achieve efficient crosslinking. The structure of the Ch-HNTs was characterized by SEM and FTIR analysis. These novel adsorbents were then tested for the adsorption of Cr6+ in serial batch experiments. For this purpose, the effect of pH, contact time, temperature, concentration of adsorbate, and adsorbent concentration on the extent of adsorption were investigated. Results: The adsorption rate for Cr6+ was maximum at an initial pH of 2 in 60 minutes of contact time. The experimental data were fitted to Langmuir adsorption isotherm. The adsorption data were fitted to the Langmuir adsorption isotherm. The maximum adsorption capacity of 72.22 mg Cr6+/g for Ch-HNTs was obtained according to the Langmuir adsorption isotherm. Conclusion: It is proposed that Ch-HNTs can be potential adsorbents for Cr6+ removal from dilute solutions. Nonetheless, further studies on adsorbing and removing various heavy metals using these novel beads in column systems can be planned. © 2025 Bentham Science Publishers
Design and Practical Implementation of a Novel Hyperchaotic System Generator Based on Apery's Constant
No full textModern chaotic systems necessitate high levels of randomness and complexity, which can be achieved through adaptable seed functions. This paper proposes a new 2D Apeacute accent>ry chaotic system generator (2D-ACG) based on Apeacute accent>ry numbers to fulfill this need. The 2D-ACG generates various chaotic systems using classical seed functions. The effectiveness and the capabilities of 2D-ACG are demonstrated on three well-known example chaotic maps using pairs of seed functions such as Cos-Cos, Sin-Sin and Cos-Sin. The reliability of chaos metrics, such as the Lyapunov exponent (LE), sample entropy (SE), correlation dimension (CD), Kolmogorov entropy (KE), C0 test, and sensitivity, confirms the chaotic performance of these maps. This is further supported by a comparison with reported 2D chaotic systems. Furthermore, one of the maps derived from 2D-ACG has been implemented into an image encryption algorithm and has successfully passed the cryptanalysis tests. Additionally, the hardware implementation of 2D-ACG has been tested on a field programmable gate array (FPGA), thereby confirming its efficacy. The superior results obtained indicate that the proposed 2D-ACG, with its enhanced diversity and complex structure derived from the Apeacute accent>ry's constant, exhibits higher-performance chaotic characteristics