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Electrode-Driven Nitrogen Conversion in Bioelectrochemical Systems: Mechanisms, Advances, and Perspectives
No full textExtracellular electron transfer (EET) mediates the microbial conversion of diverse nitrogen species and makes the possible application of a bioelectrochemical system (BES) to remediate the environment and recover resources. Electrodes are the sole electron donor and acceptor of BES for driving nitrate reduction, ammonia oxidation, and nitrogen fixation. Previous findings in this topic advance our understanding of microbial metabolism, EET mechanisms, and factors influencing the performance of BES for nitrogen species conversion. The electrode-driven ammonia oxidation to nitrogen performs the conversion rates ranging from 1 mg/L/d to 151 mg/L/ d and the rates range from 3 mg/L/d to 81 mg/L/d when the nitrate/nitrite as the products. The rate for nitrate reduction driven by biocathode can be from 0.34 mg/L/d to 192 mg/L/d as the different electrode materials are used. This review highlights the mechanisms of electrodes driving the microbial conversion of diverse nitrogen species in BES and the performance is summarized. In addition, the challenges and outlooks are identified for future research on this topic. Future research should aim to uncover novel electroactive microorganisms and optimize system configurations to enhance nitrogen conversion efficiency. These advances will be critical for scaling up BES applications in environmental remediation and sustainable resource recovery.This work was supported by the National Natural Science Foundation of China (22106146, 52300068), the China Postdoctoral Science Foundation (2024M762997) and National Key Research and Development Program of China (2024YFC3713700) .National Natural Science Foundation of China [22106146, 52300068]; China Postdoctoral Science Foundation [2024M762997]; National Key Research and Development Program of China [2024YFC3713700
Optimal Modelling of Position Errors with Respect to Observation Duration for Online GNSS Processing Services
No full textIn this thesis, the positioning capabilities offered by GNSS (Global Navigation Satellite System) technology were evaluated through online data processing services, and the relationship between measurement time and positioning accuracy was examined. Commonly used internet-based services such as AUSPOS and CSRS-PPP were analysed in comparison with the scientific software GAMIT-GLOBK. While the AUSPOS service does not provide solutions for measurements shorter than 1 hour, the CSRS-PPP service provides results for all measurements longer than 5 minutes. Accuracy, repeatability, and differences between software were investigated in the horizontal and vertical components using a total of 250 static GNSS data sets collected at different intervals and repeatedly. The study concluded that position accuracy is directly related to observation duration, with longer observation periods yielding more meaningful results, particularly in the vertical component. While centimeter-level accuracy can be achieved with 1–3 hour observations in the horizontal component, 24-hour observations are required for milimeter-level accuracy in the vertical component. Additionally, coordinate differences between software were analysed, and systematic deviations were identified, particularly in the vertical component. The findings contribute to the development of error models that will assist users in making informed decisions regarding measurement duration and service selection when planning GNSS observations.Bu tez çalışmasında, GNSS (Global Navigation Satellite System) teknolojisinin sunduğu konum belirleme imkânları çevrimiçi veri işleme servisleri üzerinden değerlendirilmiş ve ölçüm süresi ile konum doğruluğu arasındaki ilişki incelenmiştir. AUSPOS ve CSRS-PPP gibi yaygın kullanılan internet tabanlı servisler ile GAMIT-GLOBK bilimsel yazılımı karşılaştırmalı olarak analiz edilmiştir. AUSPOS servisi 1 saaten kısa süreli ölçülerde çözüm vermezken, CSRS-PPP 5 dakikadan uzun süreli tüm ölçüler için sonuç vermiştir. Farklı sürelerde ve tekrarlı olarak toplanan toplam 250 adet statik GNSS verisi üzerinden yatay ve düşey bileşenlerde doğruluk, tekrarlanabilirlik ve yazılımlar arası farklar araştırılmıştır. Çalışma sonucunda, konum doğruluğunun gözlem süresi ile doğrudan ilişkili olduğu ve özellikle yükseklik bileşeninde uzun gözlem sürelerinin daha anlamlı sonuçlar verdiği belirlenmiştir. Yatay bileşenlerde 1–3 saatlik gözlemlerle santimetre düzeyinde doğruluğa ulaşılabilirken, düşey bileşende mm seviyesinde doğruluk için 24 saatlik gözlem gerektiği görülmüştür. Ayrıca yazılımlar arası koordinat farkları analiz edilmiş, özellikle yukarı bileşende sistematik sapmalar tespit edilmiştir. Elde edilen bulgular, kullanıcıların GNSS gözlem planlaması yaparken ölçüm süresi ve servis seçimi konularında bilinçli kararlar almasına yardımcı olacak hata modellerinin geliştirilmesine katkı sağlamaktadır
Gıda Ambalaj Uygulamaları için Antibakteriyel Özellikte Pullulan-Temelli Yenilebilir Film Üretimi
No full textThe environmental damage caused by the use of plastic packaging is increasing day by day. In order to minimise this impact, considerable attention has been given to the use of biodegradable biopolymers derived from natural sources in recent years. In view of consumer demands and attitudes towards environmental protection, the aim is to prolong food shelf life, minimise colour, odour and flavour changes, and improve food quality by increasing the use of biodegradable packaging materials. The growing awareness of food safety and environmental pollution has led to considerable attention being given to edible films produced using naturally derived protein, polysaccharide and lipid-based biopolymers, due to their potential benefits for food packaging. In this field, antibacterial films and coatings are used to protect all food products under active packaging methods. Thanks to the antimicrobial agents added to their structure, biopolymer-based edible films can prevent antimicrobial degradation in food. This study marks the first time that an environmentally friendly, antibacterial pullulan-based edible film has been produced for use in active packaging in line with green approach principles. Edible films were produced using the biopolymers pullulan and chitosan, which are polysaccharide-based. Glycerol from the plasticiser group was included in the film formulation to ensure sufficient elasticity properties. Antibacterial properties were imparted to the films by doping them with metallic nanoparticles (silver nanoparticles, AgNPs; bismuth nanoparticles, BiNPs; strontium nanoparticles, SrNPs), which were obtained using a green synthesis method involving a plant extract (Agrimonia eupatoria). Characterization studies of the synthesized nanoparticles were carried out, resulting in the identification of an antibacterial nanoparticle following a preliminary evaluation. As the expected antibacterial activity of BiNP and SrNP could not be achieved, edible film formulations containing varying concentrations of AgNPs in a pullulan/chitosan matrix were created. The structural and physical properties of the obtained edible films were evaluated using appropriate analysis methods. The structure of the films was examined using FTIR, XRD and TGA analysis. The thickness, moisture content, water vapour transmission rate, solubility, swelling index, opacity, wettability, biodegradability, mechanical strength, antibacterial activity and food preservation ability were evaluated using appropriate tests. The disk diffusion test showed that the AgNP-containing films were significantly active against Escherichia coli and Staphylococcus aureus bacteria. The results obtained from the food preservation application showed that pullulan/chitosan films and AgNP-containing pullulan/chitosan films had a greater preservative effect than polyethylene film. Overall, this study will provide a theoretical basis for the application of polysaccharide-based films in active food packaging, which is an area that has seen significant recent growth. The general concept of the thesis is an approach that is both safe and environmentally friendly, and that can be used to create low-cost active packaging for this sector.Plastik ambalaj kullanımının çevreye olan zararları gün geçtikçe artmaktadır. Bu zararı en aza indirgemek için doğal kaynaklı ve biyolojik olarak parçalanabilen biyopolimerlerin kullanımı son zamanlarda artış göstermektedir. Tüketici isteklerinin de doğayı korumaya karşı tutumları göz önüne alındığında biyobozunur ambalaj malzemelerinin artmasıyla gıdanın rafta bulunma süresinin uzaması, gıdadaki renk, koku, lezzet değişimlerinin en aza indirgenmesi ve gıdanın kalitesinin arttırılması amaçlanmaktadır. Gıda güvenliği ve çevre kirliliği konusunda artan farkındalık nedeniyle, doğal kaynaklı protein, polisakkarit ve lipit bazlı biyopolimerlerın kullanımı ile üretilen yenilebilir filmler gıda ambalajlamaları için potansiyel faydaları açısından dikkate değer ilgi görmektedir. Bu alanda antibakteriyel film ve kaplamaların kullanımı, aktif ambalajlama yöntemleri altında tüm gıda ürünlerinde uygulama alanı bulan bir koruma tekniğidir. Biyopolimer esaslı yenilebilir filmler yapılarına eklenen antimikrobiyal ajanlar sayesinde gıdalarda oluşabilecek tüm antimikrobiyal bozunmayı önleme yeteneğine sahip olmaktadırlar. Bu çalışmada, ilk kez tüm bileşenleriyle çevre dostu olan ve yeşil yaklaşım ilkeleriyle aktif ambalajlamada kullanılmak üzere antibakteriyel özellikli pullulan temelli yenilebilir film üretimi gerçekleştirilmiştir. Yenilebilir filmler, polisakkarit esaslı pullulan ve kitosan biyopolimerleri kullanılarak üretilmiştir. Yeterli elastikiyet özelliklerinin sağlanabilmesi için plastikleştirici grubundan gliserol, film formülasyonuna dahil edilmiştir. Filmlere antibakteriyel özellik kazandırmak için katkılanan metalik nanoparçacıklar (Gümüş nanoparçacık, AgNP; Bizmut nanoparçacık, BiNP; Stronsiyum nanoparçacık, SrNP) yeşil sentez yöntemiyle bitki ekstraktı (Koyun otu-Agrimonia Eupatoria) kullanılarak elde edilmiştir. Sentezlenen nanoparçacıkların karakterizasyon çalışmaları yapılmış, bir ön değerlendirme ile antibakteriyel aktiviteye sahip nanoparçacık tespit edilmiştir. BiNP ve SrNP'den beklenen antibakteriyel aktivite sağlanamadığı için yenilebilir film formülasyonları değişen konsantrasyonlarda AgNP'lerin pullulan/kitosan matrisinde kullanımı ile oluşturulmuştur. Elde edilen yenilebilir filmlerin yapısal ve fiziksel özelliklerini değerlendirmek için uygun analiz yöntemleri kullanılmıştır. Filmlerin yapısı FTIR, XRD, TGA, TEM analizleri ile incelenmiş, kalınlık, nem içeriği, su buharı iletim hızı, çözünürlük, şişme indeksi, opaklık, ıslanabilirlik, biyobozunurluk, mekanik dayanım, antibakteriyel aktivite ve gıda koruma yetenekleri uygun testler yardımı ile değerlendirilmiştir. AgNP içerikli filmler için uygulanan disk difüzyon testi sonucunda, Escherichia coli ve Staphylococcus aureus bakterilerine karşı kayda değer bir aktivite gösterdikleri görülmüştür. Gıda koruma uygulamasından elde edilen sonuçlara göre, pullulan/kitosan filmleri ve AgNP içerikli pullulan/kitosan filmlerinin polietilen filme kıyasla daha aktif bir koruma yeteneğine sahip olduğu tespit edilmiştir. Genel olarak, bu çalışma polisakkarit bazlı filmlerin aktif gıda ambalajında uygulanması için teorik bir temel sağlayacaktır. Tez çalışmasının genel konsepti, bu sektör için çevreye uyumlu, düşük maliyetli aktif ambalajlama fikrine katkı sağlayan güvenli bir yaklaşım olması yönüyle çalışmayı özgün kılmaktadır
Fog-Enabled Machine Learning Approaches for Weather Prediction in IoT Systems: a Case Study
No full textKaya, Sukru Mustafa/0000-0003-2710-0063Temperature forecasting is critical for public safety, environmental risk management, and energy conservation. However, reliable forecasting becomes challenging in regions where governmental institutions lack adequate measurement infrastructure. To address this limitation, the present study aims to improve temperature forecasting by collecting temperature, pressure, and humidity data through IoT sensor networks. The study further seeks to identify the most effective method for the real-time processing of large-scale datasets generated by sensor measurements and to ensure data reliability. The collected data were pre-processed using Discrete Wavelet Transform (DWT) to extract essential features and reduce noise. Subsequently, three wavelet-processed deep-learning models were employed: Wavelet-processed Artificial Neural Networks (W-ANN), Wavelet-processed Long Short-Term Memory Networks (W-LSTM), and Wavelet-processed Bidirectional Long Short-Term Memory Networks (W-BiLSTM). Among these, the W-BiLSTM model yielded the highest performance, achieving a test accuracy of 97% and a Mean Absolute Percentage Error (MAPE) of 2%. It significantly outperformed the W-LSTM and W-ANN models in predictive accuracy. Forecasts were validated using data obtained from the Turkish State Meteorological Service (TSMS), yielding a 94% concordance, thereby confirming the robustness of the proposed approach. The findings demonstrate that the W-BiLSTM-based model enables reliable temperature forecasting, even in regions with insufficient governmental measurement infrastructure. Accordingly, this approach holds considerable potential for supporting data-driven decision-making in environmental risk management and energy conservation
Yenilikçi Bir Teknik Olan Mekanik Çelik Dikişlerle Güçlendirilmiş Kesme Kapasitesi Yetersiz Betonarme Elemanların Depreme Benzeştirilen Çevrimsel Yükler Altındaki Davranışları
No full textBu çalışmada, yetersiz kesme kapasitesine sahip betonarme konsol kirişler, mekanik çelik dikişlerle (MÇD/MSS) güçlendirilmiştir. Çalışma, esas olarak MÇD ile güçlendirilmiş yetersiz kesme kapasitesine sahip betonarme konsol kirişlerin tersinir-tekrarlanır çevrimsel yüklerin etkisi altındaki davranışının incelenmesine odaklanmıştır. Her bir numuneye 45° X şeklinde MÇD'ler uygulanarak güçlendirmenin etkinliğini araştırılmıştır. Çalışma kapsamında, 8 gruba ayrılan toplam 26 adet betonarme konsol kiriş numunesi, depremi simüle eden tersinir ve tekrarlanır yüklere maruz bırakılmıştır. Deney gruplarında sırasıyla MÇD çapı, etriye aralığı, kiriş boyuna donatı çapı, hedef betonun basınç dayanımı, kesme açıklığı/ faydalı yükseklik oranı ve betonarme kiriş geometrisinin yükseklik parametreleri analiz edilmiştir. 1/2 geometrik ölçekli konsol kirişler yatay yükleme noktasından temel üst kotuna kadar olan mesafesi sırasıyla 540 mm, 701 mm ve 1049 mm'dir. Çalışma, özellikle birçok betonarme binanın yetersiz kesme kapasitesine sahip olduğu Türkiye gibi deprem açısından aktif bölgeler için yenilikçi, pratik ve uygun maliyetli çözümler geliştirme vizyonunun bir parçası olabilir. MÇD yöntemi ile mevcut yapıların hızlı ve daha az maliyetli bir çözüm sunması sağlanabilir. Deneysel sonuçlar, farklı numunelerin yapısal performansının karşılaştırmalı olarak değerlendirilmesini sağlamak için yük-deplasman eğrilerini, rijitlik değerlerini, kümülatif enerji tüketimi ve süneklik değerlerini içermektedir. Deneylerin ayrı ayrı sonuçları analiz edildikten sonra, her gruptaki farklı sonuçlar birbirleriyle karşılaştırılmış ve deneysel sonuçlar buna göre yorumlanmıştır. Sonuç olarak 45°açılı X şeklinde MÇD ile güçlendirme yöntemi, yetersiz kesme kapasitesine sahip betonarme konsol kirişlerin tersinir-tekrarlanır çevrimsel yüklerin etkisi altında etkili bir yöntemi olduğu anlaşılmıştır. Bu yöntem özelikle düşük a/d oranına sahip kirişlerin kesme kapasitesini ve enerji tüketim performansını artırmak amacıyla oldukça etkili bir çözüm sunmaktadır. Ancak, bu yöntemi uygulanması sırasında kiriş yüksekliği ve MÇD çapı gibi parametrelerin dikkatlice değerlendirilmesi önem taşımaktadır.In this study, reinforced concrete cantilever beams with insufficient shear capacity were reinforced with mechanical steel stitches (MÇD/MSS). The study mainly focused on the study of the behavior of reinforced concrete cantilever beams with insufficient shear capacity reinforced with MÇD under the influence of reversible-repetitive cyclic loads. The effectiveness of reinforcement was investigated by applying 45° X-shaped MÇDs to each sample. Within the scope of the study, a total of 26 reinforced concrete cantilever beam samples, divided into 8 groups, were exposed to reversible and repetitive loads simulating earthquakes. In the experimental groups, the diameter of the MÇD, stirrup spacing, beam longitudinal reinforcement diameter, compressive strength of the target concrete, shear span/useful height ratio and height parameters of the reinforced concrete beam geometry were analyzed, respectively. The distance from the horizontal loading point to the top elevation of the foundation in the 1/2 geometric scale cantilever beams is 540 mm, 701 mm, and 1049 mm, respectively. The study could be part of a vision to develop innovative, practical and cost-effective solutions, especially for earthquake-active regions such as Turkey, where many reinforced concrete buildings have insufficient shear capacity. With the MÇD method, it can be ensured that existing structures offer a fast and less costly solution. Experimental results include load-displacement curves, stiffness values, cumulative energy consumption, and ductility values to enable a comparative evaluation of the structural performance of different samples. After analyzing the results of the experiments separately, the different results in each group were compared with each other and the experimental results were interpreted accordingly. As a result, it has been understood that the reinforcement method with 45° angled X-shaped MCD is an effective method of reinforced concrete cantilever beams with insufficient shear capacity under the effect of reversible-repetitive cyclic loads. This method offers a very effective solution to increase the cutting capacity and energy consumption performance of beams with a low a/d ratio. However, it is important to carefully evaluate parameters such as beam height and MÇD diameter during the application of this method
Search for Jet Quenching with Dijets From High-Multiplicity pPb Collisions at √sNN=8.16 TeV
No full textHall, Geoffrey/0000-0002-6299-8385; Csanad, Mate/0000-0002-3154-6925; Grandi, Claudio/0000-0001-5998-3070; Yazgan, Efe/0000-0001-5732-7950; Chatterjee, Suman/0000-0003-2660-0349; Navarro-Tobar, Alvaro/0000-0003-3606-1780; Tapper, Alexander/0000-0003-4543-864X; Vannerom, David/0000-0002-2747-5095; Smith, Nicholas/0000-0002-0324-3054; Kyberd, Paul/0000-0002-7353-7090; Barroso Ferreira, Mapse/0000-0003-3904-0571; D'Anzi, Brunella/0000-0002-9361-3142; Pasztor, Gabriella/0000-0003-0707-9762; Wilson, Graham/0000-0003-0917-4763; Garcia, Francisco/0000-0002-4023-7964; Forthomme, Laurent/0000-0002-3302-336X; Pesaresi, Mark/0000-0002-9759-1083; Giacomelli, Paolo/0000-0002-6368-7220; Zhang, Yousen/0000-0002-6812-761XThe first measurement of the dijet transverse momentum balance x(j) in proton-lead (pPb) collisions at a nucleon-nucleon center-of-mass energy of root s(NN) = 8.16 TeV is presented. The x(j) observable, defined as the ratio of the subleading over leading jet transverse momentum in a dijet pair, is used to search for jet quenching effects. The data, corresponding to an integrated luminosity of 174.6 nb(-1), were collected with the CMS detector in 2016. The x(j) distributions and their average values are studied as functions of the charged-particle multiplicity of the events and for various dijet rapidity selections. The latter enables probing hard scattering of partons carrying distinct nucleon momentum fractions x in the proton- and lead-going directions. The former, aided by the high-multiplicity triggers, allows probing for potential jet quenching effects in high-multiplicity events (with up to 400 charged particles), for which collective phenomena consistent with quark-gluon plasma (QGP) droplet formation were previously observed. The ratios of x(j) distributions for high- to low-multiplicity events are used to quantify the possible medium effects. These ratios are consistent with simulations of the hard-scattering process that do not include QGP production. These measurements set an upper limit on medium-induced energy loss of the subleading jet of 1.26% of its transverse momentum at the 90% confidence level in high multiplicity pPb events.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); MICIU/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 (U.S.A.). 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 Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); 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), among others, 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, TKP2021-NKTA-64, and 2021-4.1.2-NEMZ_KI-2024-00036 (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 Artificial 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; MICIU/AEI/10.13039/501100011033, ERDF/EU, "European Union NextGenerationEU/PRTR", 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 B39G670016 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.).FWF; FNRS; FWO (Belgium); CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); BNSF (Bulgaria); MoST; NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG [MoER TK202]; Academy of Finland; MEC; CEA; CNRS/IN2P3 (France); SRNSF; BMBF; DFG; HGF (Germany); NKFIH (Hungary); DAE; DST; IPM; SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE; UM (Malaysia); BUAP; CONACYT; UASLP-FAI (Mexico); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA; TUBITAK; DOE; NSF; Marie-Curie program; European Research Council; Horizon 2020 Grant [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee [22rl-037]; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Beijing Municipal Science ; Technology Commission [Z191100007219010]; Fundamental Research Funds for the Central Universities (China); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Shota Rustaveli National Science Foundation [FR-22-985]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 400140256 -GRK2497]; Hellenic Foundation for Research and Innovation (HFRI) [2288]; Hungarian Academy of Sciences [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, 2021-4.1.2-NEMZ_KI-2024-00036]; Council of Science and Industrial Research, India - NextGenerationEU program (Italy); Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; Fundacao para a Ciencia e a Tecnologia [CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund [MICIU/AEI/10.13039/501100011033]; ERDF/EU; Programa Severo Ochoa del Principado de Asturias (Spain); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation [B39G670016]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.
Performance Evaluation and Misclassification Distribution Analysis of Pre-Trained Lightweight CNN Models for Hot-Rolled Steel Strip Surface Defect Classification Under Degraded Imaging Conditions
No full textSurface defects in hot-rolled steel strip alter the material's properties and degrade its overall quality. Especially in real production environments, due to time sensitivity, lightweight Convolutional Neural Network models are suitable for inspecting these defects. However, in real-time applications, the acquired images are subjected to various degradations, including noise, motion blur, and non-uniform illumination. The performance of lightweight CNN models on degraded images is crucial, as improved performance on such images reduces the reliance on preprocessing techniques for image enhancement. Thus, this study focuses on analyzing pre-trained lightweight CNN models for surface defect classification in hot-rolled steel strips under degradation conditions. Six state-of-the-art lightweight CNN architectures-MobileNet-V1, MobileNet-V2, MobileNet-V3, NasNetMobile, ShuffleNet V2 and EfficientNet-B0-are evaluated. Performance is assessed using standard classification metrics. The results indicate that MobileNet-V1 is the most effective model among those used in this study. Additionally, a new performance metric is proposed in this study. Using this metric, the misclassification distribution is evaluated for concentration versus homogeneity, thereby facilitating the identification of areas for model improvement. The proposed metric demonstrates that the MobileNet-V1 exhibits good performance under both low and high degradation conditions in terms of misclassification robustness
Constraints on the Higgs Boson Self-Coupling From the Combination of Svingle and Double Higgs Boson Production in Proton-Proton Collisions at √s=13 Tev
No full textShopova, Mariana/0000-0001-6664-2493; Giommi, Luca/0000-0003-3539-4313; Chatterjee, Suman/0000-0003-2660-0349; Tiwari, Praveen Chandra/0000-0002-3667-3843; /0000-0002-1153-816X; Dragicevic, Marko/0000-0003-1967-6783; Fernandez Perez Tomei, Thiago Rafael/0000-0002-1809-5226The Higgs boson (H) trilinear self-coupling, lambda(3), is constrained via its measured properties and limits on the HH pair production using the proton-proton collision data collected by the CMS experiment at root s = 13 TeV. The combination of event categories enriched in single-H and HH events is used to measure k(lambda), defined as the value of lambda(3) normalized to its standard model prediction, while simultaneously constraining the Higgs boson couplings to fermions and vector bosons. Values of k(lambda) outside the interval -1.2 k(lambda) 7.5 are excluded at 2 sigma confidence level, which is compatible with the expected range of -2.0 k(lambda) 7.7 under the assumption that all other Higgs boson couplings are equal to their standard model predicted values. Relaxing the assumption on the Higgs couplings to fermions and vector bosons the observed (expected) k(lambda) interval is constrained to be within -1.4 k(lambda) 7.8 (-2.3 k(lambda) 7.8) at 2 sigma confidence level.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). 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 (FRIABelgium); 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 FR22985 (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 SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).FWF; FNRS; FWO (Belgium) [30820817]; CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); BNSF (Bulgaria); MoST; NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG [MoER TK202]; Academy of Finland; MEC; CEA; CNRS/IN2P3 (France); SRNSF; BMBF; DFG; HGF (Germany); NKFIH (Hungary); DAE; DST; IPM; SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE; UM (Malaysia); BUAP; CONACYT; UASLP-FAI (Mexico); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA; TUBITAK; DOE; NSF (USA); Marie-Curie program; European Research Council; Horizon 2020 Grant [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee [22rl-037]; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIABelgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); Beijing Municipal Science ; Technology Commission [Z191100007219010]; Fundamental Research Funds for the Central Universities (China); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Shota Rustaveli National Science Foundation [FR22985]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 390833306, 400140256 - GRK2497]; Hellenic Foundation for Research and Innovation (HFRI) [2288]; Hungarian Academy of Sciences [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]; Council of Science and Industrial Research, India - NextGenerationEU program (Italy); Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; Fundacao para a Ciencia e a Tecnologia [CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund; ERDF "a way of making Europe" [MDM-2017-0765]; Programa Severo Ochoa del Principado de Asturias (Spain); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation [B37G660013]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA
Determination of the Optimal Process Parameters for Copper Solvent Extraction Using Robust Design Method
No full textCopper (Cu) solvent extraction (SX) from sulphate leach solution was investigated using Robust design (Taguchi design). The effect of some parameters, including pH (1.25-5), phase ratio (O/A: 1/3-2/1), concentration of the extractant (2.5-10% v/v) and extraction duration (5-20 min) were examined. In the investigation of SX, the ranges and experimental parameters chosen are as follows: pH, 3.75; concentration of the extractant, 7.5% v/v; Aqueous /Organic ratio, 3/1 and extraction duration, 10 min. Under these conditions, Cu extraction of 94.62% was achieved. Variance analysis technique (ANOVA) suggested that, the most effective parameter was pH and the less effective parameter was the aqueous /organic ratio. Using of 1.5 M H2SO4 allowed for a stripping efficiency of 98% Cu from the organic phase