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Environmental Impacts of Micromobility
No full textGlobally, the e-scooter/e-bike network has already spread to more than 500 cities, 60 countries, and five continents, with the largest scooter rental companies counting tens of millions of registered trips. However, it is important to evaluate the environmental impact of these micromobility vehicles. Are these transportation options really as eco-friendly as the media and social networks claim? The fact that e-scooters/e-bikes do not emit gases from exhaust pipes does not necessarily mean that they do not create emissions and are completely eco-friendly. The environmental impact of these micromobility modes depends on their lifecycle, including production, distribution, and disposal. More than half of the emissions are generated from the extraction of necessary raw materials and the manufacturing process. Moreover, e-scooters/e-bikes require batteries that contain rare metals and whose production processes are energy-intensive. Shared e-scooter/e-bike services also involve energy-consuming charging and maintenance processes. Furthermore, e-scooters have a short lifespan, and improper disposal can pose environmental risks. E-scooters/e-bikes are frequently thrown into bodies of water, dropped from buildings, set on fire or otherwise damaged. The positive impact on the climate from manufacturing and using e-scooters/e-bikes could become a reality if significant attention is given to recyclable materials and sustainable development. Choosing renewable energy sources and optimizing every step of the service supply chain is also crucial for maintaining an efficient servicing system.Book Citation Index – Social Sciences & Humanities - Book Citation Index – Scienc
Smart Mxene-Based Microrobots for Targeted Drug Delivery and Synergistic Therapies
No full textMXenes and their composites exhibit remarkable electrical conductivity, mechanical flexibility, and biocompatibility, making them ideal candidates for microrobot fabrication. Their tunable surface chemistry allows for easy functionalization, which enhances their interaction with biological environments, thereby facilitating targeted therapies. Such smart microrobots can be engineered to navigate through complex biological systems with precision via the integration of responsive elements, such as stimuli-sensitive polymers or magnetic components. MXene-based microrobots are able to actively seek out specific tissues or cells. This capability is crucial for applications in cancer treatment, where localized drug delivery minimizes side effects and enhances therapeutic efficacy. The primary advantage of MXene-based microrobots lies in their ability to deliver therapeutic agents directly to diseased cells. Utilizing ligand-receptor interactions, these microrobots can bind to target cells and release their payload in a controlled manner. This targeted delivery system not only improves the effectiveness of the drug but also reduces the required dosage, thus mitigating potential side effects. Moreover, smart MXene-based microrobots can facilitate synergistic therapies by co-delivering multiple therapeutic agents. For instance, combining chemotherapy drugs with immunotherapeutic agents could enhance treatment outcomes in cancer therapy. The ability to simultaneously deliver different types of drugs allows for more comprehensive treatment strategies that can tackle tumor heterogeneity. Significant advancements are anticipated in synergistic therapies, particularly in chemo-photothermal, chemodynamic, and photothermal/photodynamic therapies. These strategies leverage multiple therapeutic modalities to enhance cancer treatment outcomes. Despite their outstanding potential, several challenges remain in the development of MXene-based microrobots namely matters pertaining to scalability, stability in biological environments, and associated regulatory hurdles which ought to be addressed. Future research should focus on optimizing the design and functionality of these microrobots, including enhancing their navigation capabilities and ensuring their safety and effectiveness in vivo. By presenting the innovative capabilities of MXene-based microrobots, this perspective aims to inspire additional explorations in the field of advanced targeted drug delivery systems and synergistic therapies, ultimately contributing to the future of personalized medicine and oncology. © 2025 The Royal Society of Chemistry.Science Citation Index Expande
Morphometric and Anatomic Characteristics of Pronator Quadratus Muscle
No full textSanliturk, Yusra Nur/0000-0003-1867-417X; Zeybek, Nursen/0000-0002-4401-1292Purpose: In surgical procedures commonly employed for the management of scaphoid and distal radial fractures, the incision and dissection of the pronator quadratus muscle play a pivotal role. Nevertheless, comprehensive investigations into the anatomical intricacies of the pronator quadratus muscle have been relatively scarce within the clinical community. In light of this, our study endeavors to make a substantive contribution to the medical literature by conducting a meticulous examination of the morphology and morphometry of this muscle. Methods: This study is a cross-sectional observational study conducted on 22 cadaveric upper extremities (44 sides) preserved between January 2005 and December 2018 at Istanbul University. The study included specimens with intact dissection areas and no prior surgical intervention. Observations focused on the morphometry of the pronator quadratus muscle and related anatomical structures. Statistical analysis was performed using SPSS v23.0, employing Student's t-test and paired t-test, with significance set at p < 0.05. Results: Significant differences were found in the morphometric measurements of the pronator quadratus muscle between the right and left upper extremities, particularly in the vertical distance between the proximal and distal attachment points of the pronator quadratus to the radius (p = 0.008). Additionally, significant differences were observed between male and female samples for radius length (p < 0.001), ulna length (p < 0.001), pronator quadratus width (p < 0.001), and the vertical distance between pronator quadratus attachment points on both the radius (p = 0.001) and ulna (p = 0.001). Furthermore, significant correlations were identified between radius length and parameters such as the vertical distance between pronator quadratus attachment points on both the radius (p = 0.002) and pronator quadratus width (p = 0.030), and between ulna length and parameters including the vertical distances on the radius (p = 0.001) and ulna (p = 0.024). Conclusion: In light of our comprehensive analysis, which encompasses not only the anatomical features of the pronator quadratus muscle but also its vascular supply and the organization of its neurovascular structures, we posit that our study holds significant implications for the field of orthopedic surgery. We anticipate that this research will furnish valuable insights that can inform and enhance orthopedic procedures. (c) 2024 Chinese Medical AsSociation. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Emerging Sources Citation Inde
Experimental Investigation and Correlation of Viscosity for MgO–MWCNT–CeO2/Water Hybrid Nanofluid
No full textThis study is focused on the viscosity of mono-nanofluids (MNFs) composed of CeO2/water, MWCNT/water, and MgO/water, as well as hybrid nanofluids (HNFs) namely MgO-MWCNT/water, MgO[sbnd]CeO2/water, and CeO2−MWCNT/water. Additionally, a ternary hybrid nanofluid (THNF) of MgO[sbnd]CeO2−MWCNT/water was examined. The study encompassed temperatures ranging from 20 to 60 ℃ and solid volume fractions (SVFs) of 0.1 % and 0.3 % for MNFs and HNFs, while THNF was studied at SVFs of 0.1 %, 0.2 %, 0.3 %, 0.4 %, and 0.5 %. Post-nanofluid preparation, zeta potential and dynamic light scattering (DLS) tests were performed to confirm stability, with tests conducted at the highest SVF (SVF = 0.5 %), indicating favorable nanofluid quality. Brookfield viscometer measurements were employed to assess nanofluid viscosity. The experimental findings revealed that viscosity decreases with rising temperature at a constant SVF, while it increases with rising SVF at a constant temperature. Notably, at SVF = 0.1 % and 0.3 %, the most significant viscosity increase occurred in the water/MWCNT MNF, reaching 2.7 times that of the base fluid (BF) at SVF = 0.3 % and T = 40 ℃. For THNF, the highest viscosity increase was observed at SVF = 0.5 % and T = 50 ℃, growing approximately 1.3 times that of BF. Subsequently, a mathematical model was proposed to predict THNF viscosity, demonstrating high consistency with laboratory results. © 2025 The Author(s)Emerging Sources Citation Inde
Meta-Heuristic Tuned with Gudermannian Neural Network for the Singular Neumann, Dirichlet and Neumann-Robin Boundary Conditions
No full textThe purpose of the current study is to design a feed forward Gudermannian neural networks for a singular Lane–Emden model based Neumann, Neumann-Robin and Dirichlet boundary conditions arising in numerous physical systems. The procedure based on the GNNs is exploited through the optimization of global and local search methods, i.e., genetic algorithm and active-set technique. A fitness function is constructed using the model and its boundary conditions, while the efficiency of the scheme is observed through the optimization with the hybridization of global and local search schemes. Four different nonlinear variants of the singular Lane–Emden model based Neumann, Neumann-Robin and Dirichlet boundary conditions have been numerically presented to validate the efficiency and accuracy of the model. The comparison of the obtained and exact results is used to verify the validity of the designed procedure. Moreover, different statistical measures have been implemented to certify the reliability of the proposed technique. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.Emerging Sources Citation Inde
A Computational Analysis of Hybrid Nanofluids on Heat Transfer Amelioration Through a Conical Helical Shell-And Heat Exchanger Under Turbulent Flow Conditions
No full textThis research work examines the pressure drop and heat transfer trends within a conical helical tube heat exchanger utilizing pure water and hybrid nanofluids, namely Water/ Ag-HEG and Water/ MOS2-Fe3O4. The numerical simulation is performed using a Computational Fluids Dynamics (CFD) code in three-dimensional space according to the Finite Volume Method (FVM) for all examined cases. Also, turbulent flow regimes are conducted in this analysis employing the standard k-epsilon turbulence model, within the Dean Number (De) range of 2200 < De < 4250. In this regard, the proposed thermo fluids are evaluated under the same geometric and thermal conditions to assess their thermal performance parameters. Subsequently, the fluid exhibiting superior thermal performance is further analyzed at specific volume fractions. Results revealed that Water/ MOS2-Fe(3)O(4 )outperforms the other two fluids in terms of thermal performance, and the Nusselt Number for the hybrid nanofluid of Water/ MOS2-Fe3O4 is superior to that of water at diverse volume fractions of the nanofluids. Moreover, according to the outcomes, it was found that the pressure drop caused by the presence of the MOS2-Fe3O4 nanoparticles at volume fractions of 0.1 %, 0.3 %, and 0.5 % are 11 %, 19 %, and 20 % more than that of water, respectively. Thereby, the hybrid nanofluid of Water/ MOS2-Fe(3)O(4 )can be an alternative heat transfer fluid to conventional fluids in conical helical tube heat exchangers, improving heat transfer while maintaining an adequate pressure drop. Furthermore, as the Dean Number augments, the heat transfer coefficient (HTC) demonstrates an upward trend for all considered volume fractions of the hybrid nanofluids. Additionally, this phenomenon is ascribed to the intensified fluid velocity and interaction with the twisted wire, creating rotational and turbulent motion within the fluid, which leads to more frequent interaction between the coil wall and the fluid, therefore ameliorating the HTC between the two fluids which this value will be higher for nanofluids with a more significant volume fraction. At a constant Dean Number of 4250, the HTC was improved by approximately 200 % using hybrid nanofluid Water/ MOS2-Fe(3)O(4 )with a phi= 0.7 in comparison with pure water. Besides, the optimum thermal performance across all Dean Numbers is observed in models with volume fractions phi(1) = phi(2) = 0.7, peaking at about 2.72 for the hybrid nanofluid of Water/ MOS2-Fe(3)O(4 )at De = 3560.Science Citation Index Expande
Girth and Groomed Radius of Jets Recoiling Against Isolated Photons in Lead-Lead and Proton-Proton Collisions at √snn=5.02 Tev
No full textBlekman, Freya/0000-0002-7366-7098; Vilela Pereira, Antonio/0000-0003-3177-4626; Kim, Tae Jeong/0000-0001-8336-2434; Jabeen, Shabnam/0000-0002-0155-7383; Kasemann, Matthias/0000-0002-0429-2448; Costa, Salvatore/0000-0001-9919-0569; Ligabue, Franco/0000-0002-1549-7107; Androsov, Konstantin/0000-0003-2694-6542; Janssen, Tahys/0000-0002-3998-4081; Bethani, Agni/0000-0002-8150-7043; Sanchez-Hernandez, Alberto/0000-0001-9548-0358; Rizzi, Andrea/0000-0002-4543-2718; Chatterjee, Suman/0000-0003-2660-0349; Gomez-Ceballos, Guillelmo/0000-0003-1683-9460; Konecki, Marcin/0000-0001-9482-4841; Benaglia, Andrea Davide/0000-0003-1124-8450; Raidal, Martti/0000-0001-7040-9491; Benitez, J.F./0000-0002-2633-6712; Reis, Thomas/0000-0003-3703-6624; Rappoccio, Salvatore/0000-0002-5449-2560; Collard, Caroline/0000-0002-5230-8387; Leonardo, Nuno/0000-0002-9746-4594; Spagnolo, Paolo/0000-0001-7962-5203; Golf, Frank/0000-0003-3567-9351; Cepeda, Maria/0000-0002-6076-4083; Chahal, Gurpreet Singh/0000-0003-0320-4407; Mitselmakher, Guenakh/0000-0001-5745-3658; Noll, Dennis Daniel Nick/0000-0002-0176-2360; Fernandez Perez Tomei, Thiago Rafael/0000-0002-1809-5226; Goldstein, Joel/0000-0003-1591-6014; Gonzalez Lopez, Oscar/0000-0002-4532-6464; Levchuk, Leonid/0000-0001-5889-7410; Hamel De Monchenault, Gautier/0000-0002-3872-3592; Ecklund, Karl/0000-0002-6976-4637; Dragicevic, Marko/0000-0003-1967-6783; Delgado Peris, Antonio/0000-0002-8511-7958; Hahn, Kristian/0000-0001-7892-1676; Malik, Sudhir/0000-0002-6356-2655; Alverson, George/0000-0001-6651-1178; Hinzmann, Andreas/0000-0002-2633-4696; Wardle, Nicholas/0000-0003-1344-3356; Walsh, Roberval/0000-0002-3872-4114; Belyaev, Andrey/0000-0003-1692-1173; Goy Lopez, Silvia/0000-0001-6508-5090; Titov, Maxim/0000-0002-1119-6614; Dasu, Sridhara/0000-0001-5993-9045; Yagil, Avi/0000-0002-6108-4004; Hajahmad (Wael Haj Ahmad), Vael/0000-0003-1491-0446; Kreczko, Luke/0000-0003-2341-8330; De Guio, Federico/0000-0001-5927-8865; Vischia, Pietro/0000-0002-7088-8557; Meola, Sabino/0000-0002-8233-7277; Azzurri, Paolo/0000-0002-1717-5654; Gershtein, Yuri/0000-0002-4871-5449; Kogler, Roman/0000-0002-5336-4399; Delaere, Christophe/0000-0001-8707-6021; Robert, Schoefbeck/0000-0002-2332-8784; Verdini, Piero Giorgio/0000-0002-0042-9507; Forthomme, Laurent/0000-0002-3302-336X; Rolandi, Luigi (Gigi)/0000-0002-0635-274X; Rebello Teles, Patricia/0000-0001-9029-8506; Evdokimov, Olga/0000-0002-1250-8931; Herndon, Matthew/0000-0003-3043-1090; Mundim, Luiz/0000-0001-9964-7805; Rabbertz, Klaus/0000-0001-7040-9846; Sznajder, Andre/0000-0001-6998-1108; /0000-0002-1153-816X; Bortignon, Pierluigi/0000-0002-5360-1454; Tinoco Mendes, Andre David/0000-0001-5854-7699; Sharma, Varun/0000-0003-1287-1471; Wallny, Rainer/0000-0001-8038-1613; Josa Mutuberria, Isabel/0000-0002-4985-6964; Canelli, Florencia/0000-0001-6361-2117; Pastrone, Nadia/0000-0001-7291-1979; Escalante Del Valle, Alberto/0000-0002-9702-6359; Everaerts, Pieter/0000-0003-3848-324X; Lethuillier, Morgan/0000-0001-6185-2045; Lipton, Ronald/0000-0002-6665-7289; Puerta Pelayo, Jesus/0000-0001-7390-1457; Botta, Cristina/0000-0002-8072-795X; Bloom, Kenneth/0000-0002-4272-8900; Wang, Dayong/0000-0002-9013-1199; Melo Da Costa, Eliza/0000-0002-5016-6434; Geurts, Frank/0000-0003-2856-9090; Hernandez Calama, Jose Maria/0000-0001-6436-7547; Missiroli, Marino/0000-0002-1780-1344; Feld, Lutz/0000-0001-9813-8646; Waltenberger, Wolfgang/0000-0002-6215-7228; Naimuddin, Md/0000-0003-4542-386X; Novaes, Sergio/0000-0003-0471-8549; Steggemann, Jan/0000-0003-4420-5510; Navarrete Ramos, Efren/0000-0002-5180-4020; Gonzalez Caballero, Isidro/0000-0002-8087-3199; Clement, Emyr/0000-0003-3412-4004; Taylor, Lucas/0000-0002-6584-2538; Tao, Junquan/0000-0003-2006-3490; Schroder, Matthias/0000-0001-8058-9828; Redondo, Ignacio/0000-0003-3737-4121; Ventura, Sandro/0000-0002-8938-2193; Alves, Gilvan/0000-0002-8369-1446; Bernardes, Cesar Augusto/0000-0001-5790-9563; Smith, Wesley/0000-0003-3195-0909; Tytgat, Michael/0000-0002-3990-2074; Padula, Sandra S./0000-0003-3071-0559; Hussain, Priya Sajid/0000-0002-4825-5278; Heredia De La Cruz, Ivan/0000-0002-8133-6467; Kole, Gouranga/0000-0002-3285-1497; Singh, Jasbir/0000-0001-9029-2462; Yazgan, Efe/0000-0001-5732-7950; Alcaraz Maestre, Juan/0000-0003-0914-7474; Tonelli, Guido Emilio/0000-0003-2606-9156; Verdier, Patrice/0000-0003-3090-2948; Galli Mercadante, Pedro/0000-0001-8333-4302; Dutta, Valentina/0000-0001-5958-829X; Rinkevicius, Aurelijus/0000-0002-7510-255X; Fouz Iglesias, Maria Cruz/0000-0003-2950-976X; Jafari, Abideh/0000-0001-7327-1870; Belyaev, Alexander/0000-0002-1733-4408; Petrucciani, Giovanni/0000-0003-0889-4726; Brooke, James/0000-0003-2529-0684; Marzocchi, Badder/0000-0001-6687-6214; Beaudette, Florian/0000-0002-1194-8556; Brigljevic, Vuko/0000-0001-5847-0062; Gutsche, Oliver/0000-0002-8015-9622; Martinez Ruiz Del Arbol, Pablo/0000-0002-7737-5121; Malgeri, Luca/0000-0002-0113-7389; Abbiendi, Giovanni/0000-0003-4499-7562; Moon, Chang-Seong/0000-0001-8229-7829; Pieri, Marco/0000-0003-3303-6301; Litov, Leandar/0000-0002-8511-6883; Li, Qiang/0000-0002-8290-0517; Gerosa, Raffaele/0000-0001-8359-3734; Heath, Helen/0000-0001-6576-9740; Kyberd, Paul/0000-0002-7353-7090; Kolberg, Ted/0000-0002-0211-6109; Haller, Johannes/0000-0001-9347-7657; Dewanjee, Ram Krishna/0000-0001-6645-6244; Lucchini, Marco Toliman/0000-0002-7497-7450; Grandi, Claudio/0000-0001-5998-3070; Fernandez Bedoya, Cristina/0000-0001-8057-9152; Fernandez Menendez, Javier/0000-0002-5213-3708; Manzoni, Riccardo Andrea/0000-0002-7584-5038; Duarte, Javier Mauricio/0000-0002-5076-7096; Saka, Halil/0000-0001-7616-2573; Landsberg, Greg/0000-0002-4184-9380This 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.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)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).Science Citation Index Expande
Multi-Objective Optimization of Vertical and Horizontal Solar Shading in Residential Buildings To Increase Power Output While Reducing Yearly Electricity Usage
No full textBaghoolizadeh, Mohammadreza/0000-0002-3703-0866Background: One of the effective strategies to reduce residential energy use is shading devices. Shading devices can be installed adjacent to windows in vertical or horizontal orientations to regulate the amount of sunlight entering a building. This study focuses on Photovoltaic Shading Devices (PVSDs), which combine traditional shading functions with photovoltaic (PV) technology. PVSDs are designed to block excessive sunlight and convert incident solar radiation into electricity, thereby serving dual purposes of energy conservation and renewable energy production. Methodology: This work presents a multi-objective optimization of PVSD configurations to maximize power output and reduce annual electricity consumption in residential buildings. Nine design variables were optimized using EnergyPlus software for energy simulation and JEPLUS + EA software for optimization. The study analyzed four cities representing different climate types. Results: The results showed that the use of solar shading for the city of Bandar Abbas reduces electricity consumption by 1000 kWh per year and also produces 1920 kWh of electricity. In the south, west, north, and east directions, electricity has improved between 15 and 37%, 10-29 %, 7-22 %, and 7-30 % annually. The findings highlight the effectiveness of PVSDs in balancing energy efficiency and renewable energy production, with significant variations depending on climate and building orientation.Project Name: Research on Key Technologies for Intelligent Information Processing of Fiber Optic Array Laser Ultrasonic Sensors [2024 KJHX076]; Project Name: Design of an Intelligent Disinfection Robot Model with Multi-Sensor Fusion [2024-KJHX103]; Project Name: Design of a High-Voltage Transmission Cable Ice Removal Robot Model [2024-KJHX087]; Project Name: Design of an Eco-Friendly Tree System for Carbon Reduction and Oxygen Increase Based on Wind and Solar Energy [2024-KJHX089]; Prince Sattam bin Abdulaziz University [PSAU/2025/R/1446]This paper was supported by 1.Project Name: Research on Key Technologies for Intelligent Information Processing of Fiber Optic Array Laser Ultrasonic Sensors, Grant No.: 2024 KJHX076. 2. Project Name: Design of an Intelligent Disinfection Robot Model with Multi-Sensor Fusion, Grant No.:2024-KJHX103. 3. Project Name: Design of a High-Voltage Transmission Cable Ice Removal Robot Model, Grant No.:2024-KJHX087. 4. Project Name: Design of an Eco-Friendly Tree System for Carbon Reduction and Oxygen Increase Based on Wind and Solar Energy, Grant No.:2024-KJHX089. This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2025/R/1446) .Science Citation Index Expande
Prediction of the Viscosity of Iron-Cuo/Water-Ethylene Glycol Non-Newtonian Hybrid Nanofluids Using Different Machine Learning Algorithms
No full textViscosity is a crucial parameter for heat transfer systems, governing pumping power, Rayleigh number, and Reynolds number; thus, viscosity prediction for hybrid nanofluids is important. Although some studies have employed ML algorithms for predicting viscosity, limited ML algorithms or specific nanofluid types were examined in previous studies, disregarding the complexities involved in the rheological behavior of a complex nanofluid system such as non-Newtonian hybrid nanofluids. To overcome this limitation, this study offers a practical contribution by utilizing 20 different machine-learning models to predict the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids. The influences of the input variables: solid volume fraction (SVF), temperature, and shear rate on viscosity prediction are systematically assessed. We evaluate the prediction accuracy and reliability of algorithms using ten performance metrics including RMSE, MAE, R2 and NSE. Multivariate Polynomial Regression (MPR) outperforms the other algorithms, which is evident in the highest correlation coefficient (R2 = 0.992) and lowest error metrics. At the other end, is the Extreme Learning Machine (ELM), which turns out to be the worst performer. A unique contribution of this paper is that we extract a mathematical equation from the MPR model that allows for straightforward calculation of viscosity, avoiding non-trivial ML computations. This simplicity aids in practical applications and increases usefulness for engineers and researchers alike. Using advanced data visualization techniques (heatmaps, box plots, KDE plots and Taylor diagrams), the relationships between input variables and viscosity as well as the model performance are explored. These results give a better understanding of the non-Newtonian hybrid nanofluid behavior and a solid predictor of design-efficient heat transfer systems. © 2025 The Author
Anti-Lipid Accumulation and Antioxidant Effects of the Root Extract From Rubus Discolor and Its Phytochemical Analysis
No full textSen, Ali/0000-0002-2144-5741Rubus species exhibit potent antioxidant properties due to their high phenolic compound content. In our study, phytochemical composition of the extract obtained from roots of Rubus discolor (RDE) was determined using LC-MS/MS. Subsequently, effect of this extract on lipid accumulation was investigated in HepG2 liver cells. For this purpose, HepG2 cells were treated with oleic acid to induce lipid accumulation. The effect of RDE on lipid accumulation was assessed using Oil-Red staining and measuring intracellular triglyceride, cholesterol levels. Melatonin was used as a positive control. Enzymes involved in lipid synthesis, including acetyl-CoA carboxylase (ACC1), fatty acid synthase (FAS), and transcription factor sterol regulatory element-binding protein 1 (SREBP-1c), as well as the antioxidant status, were evaluated by RT-PCR. As dose-dependent, RDE significantly reduced lipid accumulation and each concentrations (25, 50, 100 mu g/mL) of RDE showed 78%, 48%, 38% ratio, respectively for Oil Red O staining. TG was found 1.92 +/- 0.03, 1.76 +/- 0.40, 1.22 +/- 0.13, 1.41 +/- 0.08 mg/mg protein for oleic acid and each concentrations (25, 50, 100 mu g/mL) of RDE, respectively It significantly suppressed ACC1, FAS, and SREBP-1c (p g/mL. The extract also exhibited in vitro strong antioxidant and good anti-inflammatory properties. Pedunculagin isomer, ellagic acid and ellagic acid pentoside compounds were found to be the main compounds of RDE with high total phenolic content. The results indicate that RDE has the potential to prevent lipid accumulation. These findings could provide a basis for further studies on the potential therapeutic effect of Rubus discolor in the treatment of NAFLD.Scientific and Technological Research Council of Turkiye (TUBITAK); Marmara University Scientific Research Projects Commission [SAG-C-YLP- 110718-0432]Open access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK).This research was supported by Marmara University Scientific Research Projects Commission (Project No: SAG-C-YLP- 110718-0432). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Science Citation Index Expande