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Allocation Without Transfers: a Welfare-Maximizing Mechanism Under Incomplete Information
No full textThis paper studies the allocation of multiple copies of indivisible objects to agents with multi-object demands in the absence of monetary transfers. We look for a welfare-maximizing ordinal mechanism in an incomplete information setting where agents' preferences are privately known. Our main finding establishes the significant welfare gains of the so-called Ranking mechanism. When each agent's type (values for objects) is independently drawn from an exchangeable distribution, the Ranking mechanism yields higher interim utility for all agents compared to any symmetric equilibrium of any other symmetric ordinal mechanism, regardless of the agents' cardinal values
Effect of Safflower Oil Supplementation in Quail (coturnix Coturnix Japonica) Diets on Growth Performance, Blood Antioxidant Status, Caecal Short-Chain Fatty Acid Content, and Biomechanical Properties of Bones
No full textThe aim of this study was to investigate the effect of safflower oil supplementation in quail diets on growth performance, blood antioxidant status, caecal short-chain fatty acid (SCFA) concentrations, and tibia–femur biomechanical properties. A total of 180 one-day-old quail chicks were randomly divided into three groups, each containing 60 chicks. Each group was randomly divided into six subgroups, each containing 10 chicks. All chicks were fed a diet based on corn and soybean meal. The control group was fed the basal ration and experimental groups were fed the basal ration plus 0.5% and 2% safflower oil. The use of safflower oil in quails did not affect the growth performance parameters. Malondialdehyde, glutathione, superoxide dismutase, glutathione peroxidase, and catalase exhibited a linear response to the addition of safflower. Ceruloplasmin, albumin, total protein, and globulin were not affected by the addition of safflower oil. Acetic acid and SCFA were linearly associated with safflower oil content. There were no statistical differences in propionic, butyric, isobutyric, valeric, isovaleric, isocaproic, and caproic acids and BCFA in quails fed different percentages of safflower oil. Feeding a diet containing safflower oil did not affect the biomechanical properties of the tibia and femur in quails. It was concluded that diets containing safflower oil can be used to improve antioxidant status and caecal short-chain fatty acid content in quails. © 2024, South African Journal for Animal Science. All rights reserved
Effects of situational and structural factors on co-creation in retail stores
No full textThis paper focuses on the dialogical components of a service relationship and their effects on co-creation. We attempt to investigate the effects of one situational variable (partner’s perceptions of the other partner’s resources) and one non-situational variable (store brand perceptions) on co-creative behavior. To test the proposed model, dyadic survey data of 364 pairs was collected in retail stores where one customer and one salesperson interacted in a sales exchange. Data were analyzed using the structural equation modeling technique. Results support that store brand perceptions, directly and indirectly, increase co-creation through the perceptions of the partners’ resources.Bu makale, bir hizmet ilişkisinin diyalojik bileşenlerine odaklanmakta ve bunların ortak yaratım üzerindeki etkilerini incelemektedir. Bir durumsal değişkenin (bir partnerin, diğerinin kaynaklarına yönelik algıları) ve bir durumsal olmayan değişkenin (mağazanın marka algısı) ortak yaratım davranış üzerindeki etkilerini araştırılmaktadır. Önerilen modeli test etmek için, perakende mağazalarda bir müşteri ile bir satış elemanının bir satış ortamında etkileşimde bulunduğu 364 çiftlik diyadik anket verisi toplandı. Veriler, yapısal denklem eşitleme tekniği kullanılarak analiz edildi. Sonuçlar, mağazanın marka algılarının, partnerlerin kaynaklarına yönelik algılar aracılığıyla doğrudan ve dolaylı olarak ortak yaratımı artırdığı bulgusunu desteklemektedir
Electronic Properties of Dna Origami Nanostructures Revealed by in Silico Calculations
No full textDNA origami is a pioneering approach for producing complex 2- or 3-D shapes for use in molecular electronics due to its inherent self-assembly and programmability properties. The electronic properties of DNA origami structures are not yet fully understood, limiting the potential applications. Here, we conduct a theoretical study with a combination of molecular dynamics, first-principles, and charge transmission calculations. We use four separate single strand DNAs, each having 8 bases (4 x G(4)C(4) and 4 x A(4)T(4)), to form two different DNA nanostructures, each having two helices bundled together with one crossover. We also generated double-stranded DNAs to compare electronic properties to decipher the effects of crossovers and bundle formations. We demonstrate that density of states and band gap of DNA origami depend on its sequence and structure. The crossover regions could reduce the conductance due to a lack of available states near the HOMO level. Furthermore, we reveal that, despite having the same sequence, the two helices in the DNA origami structure could exhibit different electronic properties, and electrode position can affect the resulting conductance values. Our study provides better understanding of the electronic properties of DNA origamis and enables us to tune these properties for electronic applications such as nanowires, switches, and logic gates.Division of Electrical, Communications and Cyber Systems; TUBITAK 2214-A International Doctoral Research FellowshipWe acknowledge using the Hyak supercomputer system at the University of Washington. Busra Demir further acknowledges a TUBITAK 2214-A International Doctoral Research Fellowship. We also acknowledge TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources)
Measurement of the Centrality Dependence of the Dijet Yield in P+pb Collisions at Sqrt[s_{nn}]=8.16 Tev With the Atlas Detector
No full textATLAS measured the centrality dependence of the dijet yield using 165 nb^{-1} of p+Pb data collected at sqrt[s_{NN}]=8.16 TeV in 2016. The event centrality, which reflects the p+Pb impact parameter, is characterized by the total transverse energy registered in the Pb-going side of the forward calorimeter. The central-to-peripheral ratio of the scaled dijet yields, R_{CP}, is evaluated, and the results are presented as a function of variables that reflect the kinematics of the initial hard parton scattering process. The R_{CP} shows a scaling with the Bjorken x of the parton originating from the proton, x_{p}, while no such trend is observed as a function of x_{Pb}. This analysis provides unique input to understanding the role of small proton spatial configurations in p+Pb collisions by covering parton momentum fractions from the valence region down to x_{p}∼10^{-3} and x_{Pb}∼4×10^{-4}
Search for the Exclusive W Boson Hadronic Decays i>W/i>SUP>±/SUP> → πSUP>±/SUP>γ, i>W/i>SUP>±/SUP> → i>K/i>SUP>±/SUP>γ and i>W/i>SUP>±/SUP> → ρSUP>±/SUP>γ with the ATLAS Detector
No full textKretzschmar, Jan/0000-0002-8515-1355; Fernandez-Martinez, Pablo/0000-0002-7818-6971; Petersen, Troels/0000-0003-0221-3037; Mazzeo, Elena/0000-0002-8406-0195; Pintucci, Laura/0000-0001-9842-9830; Gwilliam, Carl/0000-0002-9401-5304; Soto, Orlando/0000-0002-8613-0310; Camplani, Alessandra/0000-0002-6386-9788; Mitsou, Vasiliki A./0000-0002-1533-8886; /0000-0001-5765-1750; Rompotis, Nikolaos/0000-0003-2577-1875; Zivkovic, Lidija/0000-0003-4236-8930; Oh, Alexander/0000-0001-9025-0422; Etzion, Erez/0000-0001-6871-7794; Stabile, Alberto/0000-0002-6868-8329; Teixeira-Dias, Pedro/0000-0001-9977-3836; Butterworth, Jonathan/0000-0002-5905-5394; Aad, Georges/0000-0002-6665-4934; McKee, Shawn/0000-0002-4551-4502; Fiorini, Luca/0000-0002-5070-2735; Calafiura, Paolo/0000-0002-1692-1678; Ragusa, Francesco/0000-0002-4064-0489; KHWAIRA, Yahya/0000-0001-8538-1647; Carbone, Antonio/0000-0002-4117-3800; Konstantinidis, Nikolaos/0000-0002-4140-6360; Ventura, Andrea/0000-0002-3368-3413; Staszewski, Rafal/0000-0001-7708-9259; Haley, Joseph/0000-0002-6938-7405; Gonnella, Francesco/0000-0003-0885-1654; Mlinarevic, Marin/0000-0003-3587-646X; Stanislaus, Beojan/0000-0001-9007-7658; D'Auria, Saverio/0000-0003-3393-6318A search for the exclusive hadronic decays W-+/- -> pi(+/-)gamma, W-+/- -> K-+/-gamma and W-+/- -> rho(+/-)gamma is performed using up to 140 fb(-1) of proton-proton collisions recorded with the ATLAS detector at a center-of-mass energy of root s = 13 TeV. If observed, these rare processes would provide a unique test bench for the quantum chromodynamics factorization formalism used to calculate cross sections at colliders. Additionally, at future colliders, these decays could offer a new way to measure the W boson mass through fully reconstructed decay products. The search results in the most stringent upper limits to date on the branching fractions B(W-+/- -> pi(+/-)gamma) 1.9 x 10(-6), B(W-+/- -> K-+/-gamma) 1.7 x 10(-6), B(W-+/- -> rho(+/-)gamma) 5.2 x 10(-6) at 95% confidence level.We thank CERN for the very successful operation of the LHC and its injectors, as well as the support staff at CERN and at our institutions worldwide without whom ATLAS could not be operated efficiently. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [61]. 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, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taipei; TENMAK, Turkiye; STFC, United Kingdom; DOE and NSF, USA. Individual groups and members have received support from BCKDF, CANARIE, CRC and DRAC, Canada; CERN-CZ, PRIMUS 21/SCI/017 and UNCE SCI/013, 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; Norwegian Financial Mechanism 2014-2021, Norway; 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 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); EU: H2020 European Research Council (ERC-101002463); European Union: European Research Council (ERC-948254), 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 No. 101033496); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022), Investissements d'Avenir Idex (ANR-11-LABX-0012), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG-CR 312/5-1); Italy: Istituto Nazionale di Fisica Nucleare (FELLINI G. A. No. 754496, ICSC, NextGenerationEU); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP21H05085, JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944); Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020VI.Veni.202.179); Norway: Research Council of Norway (RCN-314472); Poland: 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); Slovenia: Slovenian Research Agency (ARIS Grant No. 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, MICIN ; FEDER PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/023, CIDEGENT/2019/027); Sweden: Swedish Research Council (VR 2018-00482, VR 2022-03845, VR 2022-04683, VR Grant 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 (Leverhulme Trust RPG-2020-004); USA: U.S. Department of Energy (ECA DE-AC02-76SF00515), Neubauer Family Foundation.CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands), PIC (Spain); BNL (USA); ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW; FWF, Austria; ANAS; CNPq; FAPESP, Brazil; NSERC; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF; DNSRC, Denmark; IN2P3-CNRS; CEA-DRF/IRFU, France; BMBF; MPG, Germany; RGC and Hong Kong SAR, China; ISF; Benoziyo Center, Israel; INFN, Italy; MEXT; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS; MIZS, Slovenia; MICINN, Spain; SRC; Wallenberg Foundation, Sweden; SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taipei; DOE; NSF; BCKDF; CANARIE; CRC; DRAC, Canada [PRIMUS 21/SCI/017, UNCE SCI/013]; Czech Republic; ERC; ERDF; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex; ANR, France; DFG; AvH Foundation, Germany - EU-ESF; Greek NSRF, Greece; BSF-NSF; NCN [UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187]; La Caixa Banking Foundation [LCF/BQ/PI20/11760025]; CERCA Programme Generalitat de Catalunya; PROMETEO; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society; Leverhulme Trust, United Kingdom; Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT) [1190886]; FONDECYT [1230987]; China: National Natural Science Foundation of China [NSFC-12175119, NSFC 12275265, NSFC-12075060]; Czech Republic: PRIMUS Research Programme [PRIMUS/21/SCI/017]; EU [ERC-101002463]; European Union: European Research Council [ERC-948254, MUCCA-CHIST-ERA-19-XAI-00]; European Union [FAIR-NextGenerationEU PE00000013]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC); Marie Sklodowska-Curie Actions (EU) [101033496]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022]; Investissements d'Avenir Idex [ANR-11-LABX-0012]; Investissements d'Avenir Labex; Germany: Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG-CR 312/5-1, 754496]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [JP21H05085, JP22H01227, JP22H04944, NWO Veni 2020VI]; Norway: Research Council of Norway [RCN-314472]; 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 [2022/47/B/ST2/03059]; Slovenian Research Agency [J1-3010]; BBVA Foundation [LEO22-1-603]; Generalitat Valenciana (Artemisa, FEDER) [IDIFEDER/2018/048]; Ministry of Science and Innovation [NextGenEU PCI2022-135018-2]; MICIN FEDER [PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I]; GenT Programmes Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; Swedish Research Council [VR 2018-00482, VR 2022-03845, VR 2022-04683, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2017.0100, KAW 2018.0157, KAW 2018.0458]; Swiss National Science Foundation [SNSF-PCEFP2_194658]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020-004]; USA: U.S. Department of Energy [ECA DE-AC02-76SF00515]; Neubauer Family Foundatio
Compression Analysis of Automotive Radar Raw Data
No full textAutomotive radar system is one of the most important components of the advanced vehicle support systems. Basically, it stands out from other sensors with its capability of being less affected by weather conditions and precise range and speed measurement. Multiple radar sensors are used for precise detection and full coverage for near and far range. By using multiple radar sensors in the vehicle, precise detection and full coverage are provided at near and far range. Processing data received from multiple sensors in a single center enables both the commonality of data and the creation of higher capacity signal processing capabilities. For this purpose, a central processing analysis of the compression of radar raw data with the JPEG 2000 method was carried out. JPEG 2000 method with low loss for high compression ratios can be provided via hardware accelerators. In these analyses, image evaluation tools such as peak SNR and mean squared error, as well as the distortions in radar parameters such as range and speed estimation have been observed. It was determined that for the compression rates of up to 70%, there was no consequential decline in radar detection performance
Phase Coded Waveforms for Integrated Sensing and Communication Systems
No full textNowadays, especially with the developments in the automotive industry, it has become a highly popular topic for radar and communication systems to operate on the same platform and perform joint tasks to increase situational awareness. Sharing the hardware of the radar and communication systems and using a joint waveform for both functions eliminate interference problems between the systems, increasing the effectiveness of the joint task. In studies where radar signals are utilised for communication functions, continuous wave frequency modulation or intra-pulse frequency modulation is generally employed to transmit communication signals. There are a few studies in the literature on joint radar and communication waveforms using phase modulation. In these studies, phase modulation is not employed directly for both functions. As a contribution to the literature, this study evaluates the usage of joint radar and communications waveforms, such as Barker sequences and Zadoff-Chu sequences as opposed to linear frequency modulation. We compare these waveforms terms of range-speed detection and symbol error rate
Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence
No full textDynamic malware analysis plays a pivotal role in modern cybersecurity, offering insights into malware behavior through dynamic execution and network traffic analysis. In this study, we present a comprehensive approach to dynamic malware analysis using a sandbox environment and network traffic logs. Our methodology involves the extraction of relevant features from network traffic captured in pcap files. We conducted experiments using a virtualized Oracle VirtualBox environment, where benign and malicious software samples were executed within a Windows virtual machine controlled by Python scripts. For network emulation, we utilized tools from the REMnux distribution, including InetSim and FakeDNS, to simulate realistic network interactions during malware execution. The collected pcap data underwent preprocessing and feature extraction to capture essential behavioral patterns and network indicators. Machine learning and artificial intelligence models were developed to classify malware based on these extracted features. Our findings underscore the efficacy of dynamic analysis coupled with machine learning in detecting and classifying malware variants based on their network behavior. This research contributes to advancing techniques for real-time threat detection and response in cybersecurity, emphasizing the importance of dynamic malware analysis in mitigating evolving cyber threats