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Obtaining a Multi-Factor Optimum Blend Using Scrap Within the Scope of Sustainable and Environmentally Friendly Steel Production: Application in a Steel-Casting Company
No full textThis study tackles the challenge of optimizing scrap blends in steel production to achieve sustainability and environmental consciousness. Focusing on a steel-casting company as a case study, we develop a mathematical model that minimizes cost, emissions, and energy consumption while maximizing scrap utilization. This model considers the specific elemental composition of various scrap piles and pure elements, alongside their associated costs and environmental impacts in the production of GS52 steel in a foundry company. Through the GAMS program and further verification with Microsoft Excel, we demonstrate that the optimal blend significantly reduces raw material costs by prioritizing scrap (99.7%) over pure elements. Moreover, this optimized blend minimizes energy consumption and associated carbon emissions, thus contributing to a more sustainable and environmentally friendly steel production process. This study offers valuable insights and a practical framework for the steel industry to adopt cost-effective and eco-conscious practices, aligning with global efforts towards sustainable manufacturing
Mixed Static and Reconfigurable Metasurface Deployment in Indoor Dense Spaces: How Much Reconfigurability Is Needed?
No full text25th IEEE Wireless Communications and Networking Conference, WCNC 2024 -- 21 April 2024 through 24 April 2024 -- Dubai -- 200793In this paper, we investigate how metasurfaces can be deployed to deliver high data rates in a millimeter-wave (mmWave) indoor dense space with many blocking objects. These surfaces can either be static metasurfaces (SMSs) that reflect with fixed phase-shifts or reconfigurable intelligent surfaces (RISs) that can reconfigure their phase-shifts to the currently served user. The latter comes with an increased power, cabling, and signaling cost. To see how reconfigurability affects the network performance, we propose an iterative algorithm based on the feasible point pursuit successive convex approximation method. We jointly optimize the types and phase-shifts of the surfaces and the time portion allocated to each user equipment to maximize the minimum data rate achieved by the network. Our numerical results demonstrate that the minimum data rate improves as more RISs are introduced but the gain diminishes after some point. Therefore, introducing more reconfigurability is not always necessary. Another result shows that to reach the same data rate achieved by using 22 SMSs, at least 18 RISs are needed. This suggests that when it is costly to deploy many RISs, as an inexpensive alternative solution, one can reach the same data rate just by densely deploying more SMSs. © 2024 IEEE.6G; Horizon 2020; ECSEL, (876124); VINNOVA, (2020- 1.2.3-EUREKA-2021-000006
Search for Dark Mesons Decaying To Top and Bottom Quarks in Proton-Proton Collisions at √s=13 Tev With the Atlas Detector
No full textLagouri, Theodota/0000-0001-7509-7765; Mohamed Farook, Mohamed Hijas/0000-0002-2082-8134; Mlinarevic, Marin/0000-0003-3587-646X; Herrmann, Linda/0000-0002-1857-6310; Corchia, Federico Andrea Guillaume/0000-0002-1788-3204; Teixeira-Dias, Pedro/0000-0001-9977-3836; Camplani, Alessandra/0000-0002-6386-9788; Riu, Imma/0000-0002-3742-4582; Mungo, Davide Pietro/0000-0002-2567-7857; Haley, Joseph/0000-0002-6938-7405; Gonnella, Francesco/0000-0003-0885-1654; Zaid, Estifa'A/0009-0008-3614-0562; Stanislaus, Beojan/0000-0001-9007-7658; Mitsou, Vasiliki A./0000-0002-1533-8886; Konstantinidis, Nikolaos/0000-0002-4140-6360; Fiorini, Luca/0000-0002-5070-2735; Petersen, Troels/0000-0003-0221-3037; Butterworth, Jonathan/0000-0002-5905-5394; Etzion, Erez/0000-0001-6871-7794; Staszewski, Rafal/0000-0001-7708-9259; Pintucci, Laura/0000-0001-9842-9830; Lyubushkin, Vladimir/0000-0003-0136-233X; Nellist, Clara/0000-0002-5171-8579; Soto, Orlando/0000-0002-8613-0310A search for dark mesons originating from strongly-coupled, SU(2) dark flavor symmetry conserving models and decaying gaugephobically to pure Standard Model final states containing top and bottom quarks is presented. The search targets fully hadronic final states and final states with exactly one electron or muon and multiple jets. The analyzed data sample corresponds to an integrated luminosity of 140 fb(-1) of proton-proton collisions collected at root s = 13 TeV with the ATLAS detector at the Large Hadron Collider. No significant excess over the Standard Model background expectation is observed and the results are used to set the first direct constraints on this type of model. The two-dimensional signal space of dark pion masses m(pi D) and dark rho-meson masses m(rho D) is scanned. For m(pi D)/m(rho D) = 0.45, dark pions with masses m(pi D) 940 GeV are excluded at the 95% CL, while for m(pi D)/m(rho D) = 0.25 masses m(pi D) 740 GeV are excluded.We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, The Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; TENMAK, Turkiye; STFC/UKRI, United Kingdom; DOE and NSF, United States of America. Individual groups and members have received support from BCKDF, CANARIE, CRC and DRAC, Canada; CERN-CZ, FORTE and PRIMUS, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members wish to acknowledge support from Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1230812, FONDECYT 1230987, FONDECYT 1240864); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700), National Natural Science Foundation of China (NSFC-12175119, NSFC 12275265, NSFC-12075060); Czech Republic: Czech Science Foundation (GACR -24-11373S), Ministry of Education Youth and Sports (FORTE CZ.02.01.01/00/22_008/0004632), PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC -101002463); European Union: European Research Council (ERC -948254, ERC 101089007), Horizon 2020 Framework Programme (MUCCA -CHIST-ERA-19-XAI-00), European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU PE00000013), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-0002), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG -469666862, DFG -CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU), Ministero dell'Universita e della Ricerca (PRIN -20223N7F8K -PNRR M4.C2.1.1); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227, JSPS KAKENHI JP23KK0245); The Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020 -VI.Veni.202.179); Norway: Research Council of Norway (RCN-314472); Poland: Ministry of Science and Higher Education (IDUB AGH, POB8, D4 no 9722), Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085); Slovenia: Slovenian Research Agency (ARIS grant J1-3010); Spain: Generalitat Valenciana (Artemisa, FEDER, ID-IFEDER/2018/048), Ministry of Science and Innovation (MCIN ; NextGenEU PCI2022135018-2, MICIN ; FEDER PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/027); Sweden: Swedish Research Council (Swedish Research Council 2023-04654, VR 2018-00482, VR 2022-03845, VR 2022-04683, VR 2023-03403, VR grant 2021-03651), Knut and Alice Wallenberg Foundation (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Switzerland: Swiss National Science Foundation (SNSF PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004), Royal Society (NIF-R1-231091); United States of America: U.S. Department of Energy (ECA DE-AC02-76SF00515), Neubauer Family Foundation.ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; BMBF, Germany; MPG, Germany; Hong Kong SAR, China; ISF, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; MICINN, Spain; Wallenberg Foundation, Sweden; SERI, Switzerland; MOST, Taiwan; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; Compute Canada, Canada; Czech Republic [PRIMUS 21/SCI/017, UNCE SCI/013]; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Skodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France; Investissements d'Avenir Idex , France; ANR, France; DFG , Germany; AvH Foundation, Germany; Herakleitos programme - EU-ESF, Greece; Thales programme - EU-ESF, Greece; Aristeia programme - EU-ESF, Greece; Greek NSRF, Greece; BSF-NSF, Israel; MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN, Poland; NAWA, Poland; La Caixa Banking Foundation, Spain; CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; GenT Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; STFC, United Kingdom; TENMAK, Turkiye; Canton of Geneva, Switzerland; Canton of Bern, Switzerland; SNSF, Switzerland; SRC, Sweden; DSI/NRF, South Africa; NWO, Netherlands; Benoziyo Center, Israel; RGC, China; GSRI, Greece; HGF, Germany; SRNSFG, Georgia; Minciencias, Colombia; MOST, China; CAS, China; ANID, Chile; CERN; NRC, Canada; CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo [FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230812, FONDECYT 1230987]; China: National Natural Science Foundation of China [NSFC - 12175119, NSFC 12275265, NSFC-12075060]; Czech Republic: PRIMUS Research Programme [PRIMUS/21/SCI/017]; European Union: European Research Council [ERC - 948254]; European Union: Horizon 2020 Framework Programme [MUCCA - CHIST-ERA-19-XAI-00]; European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU) [PE00000013]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); Marie Sklodowska-Curie Actions (EU H2020 MSC IF GRANT) [101033496]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022]; France: Investissements d'Avenir Idex [ANR-11-LABX-0012]; France: Investissements d'Avenir Labex [ANR-11-LABX-0012]; Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme); Germany: Deutsche Forschungsgemeinschaft [DFG - 469666862, DFG - CR 312/5-1]; Italy: Istituto Nazionale di Fisica Nucleare (FELLINI) [754496]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [22KK0227, JP21H05085, JP22H01227, JP22H04944]; Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020) [VI.Veni.202.179]; Norway: Research Council of Norway [RCN-314472]; Poland: Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Poland: Polish National Science Centre [NCN 2021/42/E/ST2/00350, 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187]; Slovenia: Slovenian Research Agency (ARIS grant) [J1-3010]; Spain: BBVA Foundation [LEO22-1-603]; Generalitat Valenciana (Artemisa, FEDER) [IDIFEDER/2018/048]; La Caixa Banking Foundation [LCF/BQ/PI20/11760025]; Ministry of Science and Innovation (MCIN NextGenEU) [PCI2022-135018-2]; Ministry of Science and Innovation (MICIN FEDER) [PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I]; PROMETEO Programme Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; GenT Programme Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; Sweden: Swedish Research Council [VR 2018-00482, VR 2022-03845, VR 2022-0468, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2017.0100, KAW 2018.0157, KAW 2018.0458, KAW 2019.0447]; Switzerland: Swiss National Science Foundation [SNSF - PCEFP2_194658]; United Kingdom: Leverhulme Trust [RPG-2020-004]; United States of America: Neubauer Family Foundatio
Solar-Related Performance-Oriented Digital Workflow
No full textIV. International Architectural Sciences and Applications Symposium (IArcSAS 2024) May 30-31, 2024, Girne-Turkish Republic of Northern CyprusIn the current era of global energy challenges, energy efficiency is a critical focus within architectural practices and building design. There are many current research in the field, however integration of energy efficiency as a design parameter from the initial stages of the design still appears as a challenge. Access to comprehensive climatic data during initial design stages can significantly expand design possibilities. Architects typically begin projects with limited data, relying on their expertise to explore diverse design scenarios within predefined parameters such as location, site boundaries, and client preferences. Enhancing access to environmental data empowers architects to develop design proposals aligned with energy efficiency goals. Solar -oriented design has emerged as a primary consideration in achieving energy efficiency, with the geometry of the sun playing a crucial role in design decisions regarding orientation, layout, facade, and material choices. However, implementing energyefficient design strategies presents challenges, such as the potential for late design decisions to compromise earlier energy-saving efforts. Therefore, establishing a systematic approach to the design process is essential to ensure the effectiveness of energy-efficient solutions. This study investigates necessity of the passive strategies to enhance energy efficiency in buildings and proposes their integration into the early design phase. By adopting a linear workflow, architects can effectively track and evaluate design decisions, ensuring that energyefficient design strategies are prioritized throughout the project. The findings of this study contribute to a better understanding of how energy-efficient design principles can be successfully implemented in architectural practice
Symbolic Self Completion As A Mediator Between Nicotine Dependence And Quit Intention: Evidence From A Nationally Representative Survey
No full textThe 7th Global Public Health Conference 22nd – 23rd February 2024 Bangkok, Thailand (GLOBEHEAL 2024)This study aims to investigate the relationship between nicotine dependence and quit intentions, and the degree to which symbolic completion of the self through smoking mediates this relationship. This study adopted a cross-sectional methodology using a population-based sample of cigarette smokers residing in Turkey. A survey was designed to measure nicotine dependence (using the heaviness of smoking index), intention to quit smoking, and the absence of symbolic self-completion. Mediation analysis was conducted using the Hayes model #4 to investigate the degree to which self-symbolizing smoking is implicated in the association between dependence and quit intention. Based on results, nicotine dependence is found to decrease the intention to quit (coefficient=-0,0198; p-value0,000), while an absence of symbolic self-completion is found to increase quit intentions (coefficient=0,6118; p-value0,000). Symbolic self-completion is found to be a significant (partial) mediator between dependence and quit intentions. Also, dependence is found to increase the symbolic attachment. The overall conclusion is that smoking is an activity that is publicly noticeable, thereby serving as a symbol of the self. The symbolic power of smoking should be considered along with physical dependence as one of the major barriers to quit smoking
Turkiye Elektrik Iletim Sebekesinde Batarya Enerji Depolama Sistemlerinin Boyutlandrlmas Ve Konumlandrlmas
No full text43. Yöneylem Araştırması Ve Endüstri Mühendisliği Ulusal Kongresi Afet Yönetiminde YA/EM 2-4 Ekim 2024 Karadeniz Teknik Üniversitesi Prof. Dr. Osman Turan Kültür ve Kongre Merkezi Trabzon[No Abstract Available
Yapay Zekânın Cezai Sorumluluğu ve Yapay Zekâya Uygulanacak Yaptırım Sorunu
No full textBu çalışma kapsamında, yapay zekânın ceza hukukunun konusu olup olamayacağı, literatür taraması araştırma yöntemi kullanılarak incelenmiştir. Bu bağlamda öncelikle yapay zekânın tarihsel süreç içerisindeki gelişimi ele alınmış, felsefi kökenleri irdelenmiş ve teknik açıdan öğrenme yöntemleri incelenmiştir. Yapay zekânın hukuk düzeni içerisinde kişiliğinin varlığı sorgulanmış ve literatürde yer alan hukuki statü önerileri açıklanmıştır. Sonrasında cezai sorumluluk kavramı açıklanmış ve yapay zekânın üreticisi, programlayıcısı, kullanıcısı ve kendisinin cezai sorumluluğu tartışılmıştır. Son olarak yapay zekâya uygulanacak yaptırım sorunu ele alınmış ve bu hususa yönelik önerilere yer verilmiştir. Çalışma kapsamında yapay zekânın dahil olduğu suçlara yönelik mevcut düzenlemelerin yeterliliği tartışılmış ve yeni düzenlemelerin gerekliliği sorgulanmıştır.This study examines whether artificial intelligence can be subject to criminal law, utilizing a literature review research method. In this context, the historical development of artificial intelligence has been addressed, its philosophical foundations analyzed, and its learning methods from a technical perspective examined. The existence of AI's personality within the legal system has been questioned, and legal status proposals in the literature have been explained. Subsequently, the concept of criminal liability has been clarified, and the criminal liability of AI's producer, programmer, user, and the AI itself has been discussed. Finally, the issue of sanctions to be applied to AI has been addressed, and recommendations on this matter have been included. The study discusses the adequacy of current regulations regarding crimes involving AI and questions the necessity of new regulations