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Reweighting simulated events using machine-learning techniques in the CMS experiment
No full textData analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detectors are used to produce large samples of simulated events for analysis by the LHC experiments. These simulations come at a high computational cost, where the detector simulation and reconstruction algorithms have the largest CPU demands. This article describes how machine-learning (ML) techniques are used to reweight simulated samples obtained with a given set of parameters to samples with different parameters or samples obtained from entirely different simulation programs. The ML reweighting method avoids the need for simulating the detector response multiple times by incorporating the relevant information in a single sample through event weights. Results are presented for reweighting to model variations and higher-order calculations in simulated top quark pair production at the LHC. This ML-based reweighting is an important element of the future computing model of the CMS experiment and will facilitate precision measurements at the High-Luminosity LHC.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 (USA); Marie-Curie programme; 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 (FRIA-Belgium); FWO (Belgium) under the Excellence of Science - EOS [30820817]; 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, 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, K147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA64]; Council of Science and Industrial Research, India - NextGenerationEU program (Italy); Latvian Council of 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; Research and Innovation [B39G670016]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C1845]; Weston Havens Foundation (USA)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 programme 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 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), 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, K147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA64 (Hungary); the Council of Science and Industrial Research, India; ICSC - National Research Centre 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; theMinistry 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 Cientifica 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& InstitutionalDevelopment, Research and Innovation, grant B39G670016 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C1845; and the Weston Havens Foundation (USA)
The transboundary effects of climate change and global adaptation: the case of the Euphrates–Tigris water basin in Turkey and Iraq
No full textDrought has erupted across the Middle East due to climate change and global warming, leading to a considerable reduction in rainfall and snowfall and a substantial drop in water resources. The Euphrates–Tigris water basin is a significant source of water supply for Turkey and Iraq, where the latter is a downstream riparian country, and the former is an upstream country. We aim to explore the impact of climate change shocks on the economic welfare of households in Iraq. The empirical analysis relies on data from the Iraqi Household Socio-Economic Survey conducted in 2012 and the 2017 Rapid Welfare Monitoring Survey. We apply simultaneous unrelated regression equations with probit models. We further extend the analysis by incorporating an instrumental variables approach. The findings show a significant impact of climate change-related shocks on income, assets, food production and stock, and the overall economic situation of households in Iraq. © 2025 Elsevier B.V., All rights reserved.Economic Research Forum, ER
The Effect of Chemotherapy-Induced Alopecia on Distress and Quality of Life in Male Patients With Cancer
No full textPURPOSE: To investigate the effects of chemotherapy-induced alopecia (CIA) on the distress and quality of life of male patients with cancerand to identify characteristics that place these patients at risk for higher distress and lower quality of life. PARTICIPANTS & SETTING: 146 patients with alopecia seen in an outpatient chemotherapy unit and receiving at least one cycle of chemotherapy participated in the study from March to December2023. METHODOLOGICAPPROACH: Data were collected via face-to-face interviews using an individual information form, the National Comprehensive Cancer Network Distress Thermometer, and the CIA Quality-of-Life Scale. FINDINGS: Education level, marital status, pretreatment hair loss status, alopecia severity, hair accessory use because of alopecia, self-reported mood change from alopecia, and distress score significantly affected CIA and explained 72% of the variance in CIA Quality-of-Life Scale scores. IMPLICATIONS FOR NURSING: Oncology nurses are pivotal in the prevention and early management of CIA. Patients who are expected to lose their hair because of chemotherapy should be informed before treatment and given suggestions to mitigate the impact of changes in appearance. Patients should be educated about pharmacologic and nonpharmacologic approaches that can be used to cope with CIA
Evaluating Physician and Manager Perspectives on EHR Usage in Turkey: A Factor Analysis Approach
No full textAims and objectives: Understanding the perspectives of health professionals about Electronic Health Records (EHRs) is pivotal for better management of health information systems (HIS). The purpose of this study is to examine explanatory factors of usage of EHRs according to the physician and hospital manager's evaluations. Methods: A survey was administered in three hospitals in the & Idot;zmir metropolitan area, and 202 physicians and hospital administrators participated in this study. The internal consistency of the questionnaire was assessed using Cronbach's Alpha (0.74), and the suitability of the factor analytical model was assessed using KMO (0.79) and Bartlett's test (X-2 = 1720.97, p < 0.001). Exploratory factor analysis was performed with Varimax rotation to determine the factors underlying the model. Then, confirmatory factor analysis (CFA) was performed to reveal the latent structure of the model. Results: The performance of CFA model is statistically significant (p < 0.0001), acceptable at moderate level (X-2/df = 3,81) the goodness-of-fit indices are good (CFI = 0,87; GFI = 0,76; NFI = 0,83; AGFI = 0,70). Three factors explain the latent structure of this model and evaluations of physician and hospital managers towards the usage of EHRs named as: benefits of usage of EHRs; concerns about the usage of EHRs and the effect of EHRs on the quality of work, efficiency, access to the information and safety. Conclusion: Study results highlight the necessity of comprehending the EHR from the perspectives of health professionals and managers by focusing on the advantages of EHRs, concerns towards the deployment of EHR systems, and the improvement effects of EHR in work quality, efficiency, and HIS
Electronic and Optical Insights into NaBi(WO4)2: A Promising Candidate for Optoelectronic Applications
No full textThis study provides an in-depth exploration of the electronic and optical properties of NaBi(WO4)2 crystal, with a focus on its potential applications in optoelectronic technologies. Absorbance measurements revealed a bandgap energy of 3.45 eV, highlighting the crystal's suitability for ultraviolet-visible light interactions. Thermally stimulated current (TSC) analysis uncovered a hole defect center, offering insights into charge trapping and transport mechanisms within the crystal. A prominent TSC peak was observed around 194 K, corresponding to a trap activation energy of 0.32 eV. Photoluminescence (PL) spectra displayed multiple emission peaks in the ultraviolet, blue, and green regions, emphasizing the crystal's ability to emit across a broad spectrum. These findings reveals the band structure and defect dynamics of NaBi(WO4)2, establishing its promise for advanced optoelectronic applications, particularly in blue and green light-emitting devices
Teachers predict ADHD more accurately than parents: findings from a large epidemiological survey
No full textObjectiveObservational reports of parents and teachers might conflict in the diagnostic process of pediatric Attention-Deficit/Hyperactivity Disorder (ADHD). This study investigates the diagnostic accuracy of parents and teachers in identifying ADHD in children, focusing on the influences of parental education level, child gender, and age. MethodsData were derived from the Turkish Epidemiological Survey in Childhood Psychopathologies, encompassing 5,830 children aged 6-13 years. ADHD diagnoses were determined using a semi-structured interview and impairment ratings from both parents and teachers. Both groups completed the ADHD Rating Scale-IV to identify ADHD-related symptoms. Diagnostic accuracy was evaluated by comparing sensitivity, specificity, positive predictive value, and negative predictive value across informants. Parental education was categorized into lower (LEL) and higher education levels (HEL). ResultsTeachers exhibited significantly higher diagnostic accuracy (93.7%) compared to parents (89.9%, p < 0.001), a trend consistent across gender and age groups. Teachers predicted ADHD in girls (95.2%) with greater accuracy than boys (92.1%), and similar patterns were observed for parents (girls: 92.0%, boys: 88.0%, p < 0.001). Parents with HEL demonstrated better diagnostic performance (91.3%) than those with LEL (89.4%, p < 0.05), though both were outperformed by teachers. Accuracy slightly declined in older children (10-13 years), but the differences were statistically insignificant. ConclusionThe findings highlight teachers' superior ability to predict ADHD, likely due to their comparative observational advantages in structured settings. Parental education and child gender also influenced diagnostic performance. These results underscore the importance of incorporating teacher reports into diagnostic protocols while addressing socio-educational disparities to improve parent-reported accuracy
Evaluation of Predictive Factors for Success of Single-Dose Methotrexate Treatment in Tubal Ectopic Pregnancy With Low Beta- Human Chorionic Gonadotropin Levels
No full textBackground Ectopic pregnancy (EP) is the most important cause of first-trimester maternal deaths. Objectives The aim of this study was to evaluate the clinical findings, ultrasound results, and systemic hematologic markers of inflammation associated with the success of single-dose methotrexate (MTX) treatment at low ?-hCG levels in the medical management of tubal EP. Methods This retrospective study included 145 patients diagnosed with tubal EP at a tertiary referral hospital. Following approval from the hospital ethics committee, patients with ?-hCG levels 0.05). However, significant differences were found in the initial ?-hCG levels, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), which were higher in the unsuccessful group (p < 0.05). In the successful group, a 27.2% decrease in median ?-hCG levels was observed between days 0 and 4, whereas the unsuccessful group experienced a 19.6% increase during the same period. Conclusion A high initial ?-hCG level, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) may predict treatment failure with single-dose MTX at low ?-hCG levels. A decrease in ?-hCG levels between days 0 and 4 may be an early indicator of successful treatment. © 2025 Elsevier B.V., All rights reserved
Technology-Enhanced Multimodal Learning Analytics in Higher Education: A Systematic Literature Review
No full textMultimodal learning analytics (MMLA) is an emerging field of learning analytics and promises a more comprehensive analysis of the learning process thanks to advances in technological devices and data science. The purpose of this study was to explore technology-enhanced multimodal learning analytics in higher education systematically. A systematic literature review was performed using the PRISMA guidelines, and 45 studies published between January 2012 and June 2024 were determined. The findings demonstrated that China, the USA, Australia, and Chile were the leading contributors to MMLA research, with a notable surge in publications in 2021. Audio recorders, cameras, webcams, eye trackers, and wristbands were the most used devices. Most studies were conducted in experiment rooms or laboratories, though studies in authentic classroom settings have been growing. Data were primarily collected during activities such as programming, simulation exercises, presentations, discussions, writing, watching videos, reading, or exams, as well as throughout the entire instructional process, predominantly in computer science, health, and engineering courses. The studies were mainly predictive or descriptive whereas quite a few studies were prescriptive. Frequently tracked data types included audio, gaze, log, facial expression, physiological, and behavioral data. Traditional machine learning and basic statistics were the commonly used analytical methods whilst advanced statistics and deep learning were relatively less utilized. Test performance, engagement, emotional state, debugging performance, and learning experience were the popular target variables. The studies also pointed out several implications and future directions, with a significant portion highlighting the development of interventions, frameworks, or adaptive systems using MMLA
The adaptor protein SKT interacts with PSD-95 and SHANK3 and affects synaptic functions
No full textPostsynaptic density (PSD) is atightly interconnected protein network ensuring synaptic function through the interaction of neurotransmitter receptors, structural adaptor proteins, and signaling molecules. Disruption of PSD may cause neurological diseases, including autism spectrum disorders and cognitive impairment. We demonstrate that the SKT adaptor distinctly localizes within dendritic spines as an integral component of the synaptic network, binding PSD-95 and SHANK3. SKT-knockout (KO) mice show significant abnormalities in dendritic spine density and morphology, consistent with RhoA and Rac1 GTPase dysregulated activity. KO-derived neuronal cultures display delayed neuronal synchronization and maturation associated with glutamatergic pre-and postsynaptic impairment. Behavioral tests on KO mice reveal increased self-grooming activity and impaired motor coordination, with altered cognitive and executive functions compared to wild-type mice. Overall, SKT emerges as a key contributor to the structural and functional PSD organization, regulating synaptic function through its interactions with PSD components.Italian Ministry of University and Research (MUR) [2022WYAEWE, 20228HRTJ2]; AIRC [27353]; Fondazione CRT [2020.1798]; RILO University of Torino [IG 11904, IG 15538]; Italian Ministry of Health [RF-2021-12371961]; Finanziato dall'Unione Europea-NextGenerationEU [1.4-529 CN00000041]; RILO University of Torino; Italian Ministry of University and Research (MUR) national project Dipartimenti di Eccellenza 2023/27Dr. Carola Eva prematurely passed away in November 2023. The colleagues who had the privilege to collaborate with her dedicate this manuscript to her memory. The research leading to these results has received funding from Italian Ministry of University and Research (MUR), PRIN 2022 project number 2022WYAEWE, PI P.D., and PRIN 2022 project number 20228HRTJ2, PI E.C., and from AIRC under IG 2022-ID 27353 project, PI P.D. This work was also supported by Fondazione CRT 2020.1798, RILO University of Torino (IG 11904, IG 15538), and Italian Ministry of Health (MSAL) (RF-2021-12371961) to P.D.; PNRR M4C2-Investimento 1.4-529 CN00000041 Finanziato dall'Unione Europea-NextGenerationEU to P.D.; and RILO University of Torino to I.B. This work was also supported by the Italian Ministry of University and Research (MUR) national project Dipartimenti di Eccellenza 2023/27 awarded to the Department of Neuroscience Rita Levi Montalcini (University of Torino)
The role of acemetacin in pain management
No full textAnalgesics can be divided into three main groups as non-opioid, opioid, and adjuvant treatment options. In this review, the place of acemetacin, which is among the non-steroidal anti-inflammatory drugs (NSAIDs) in the non-opioid group, in pain treatment was evaluated. Acemetacin is a prodrug and acts by converting to indomethacin in the body. Clinical studies have shown that acemetacin is an effective and safe treatment option for acute and chronic pain. The main gastrointestinal side effects of acemetacin are complaints such as loss of appetite and nausea, and the severity of the side effects is usually mild to moderate. In conclusion, acemetacin is a good option that is effective orally, has a sufficient analgesic effect, has a low gastrointestinal side effect profile, and does not cause tolerance and addiction in pain treatment