5916 research outputs found
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Mistreated but still resilient! Unraveling the role of servant leadership in mitigating the adverse consequences of care recipients' incivility
No full textIn many countries, social care workers suffer from mistreatment from social care recipients. Such mistreatment poses a significant challenge from the human resource management (HRM) perspective as finding and retaining competent social care workers is a global challenge. However, only a few studies focus on the relationship between such mistreatment and social care workers' job and psychological resources. Drawing on the conservation of resources (COR) theory, our study sheds light on the relationship between social care recipients' incivility and the resilience of social care workers. Specifically, our study examines the mediating role of work meaningfulness on the care recipient incivility-care worker resilience link, and the moderating role of servant leadership on this mediated relationship. To test the proposed moderated mediation model, two studies were conducted in social care organizations in England (n = 248) and Romania (n = 296). Our results revealed that perceived care recipient incivility is indirectly and negatively related to care workers' resilience by undermining their perceptions of work meaningfulness. Moreover, when social care workers work under a servant leader, this indirect relationship becomes weaker. The discussion elaborates on the findings of our model as well as the theoretical and practical implications for the management of human resources in social care organizations.Publisher versio
Optimal prototype filter design in GFDM systems for self-interference elimination: A novel signal processing approach
No full textWe present an innovative conceptual framework and a comprehensive mathematical model to advance the understanding and mitigation of self-interference phenomena within generalized frequency division multiplexing (GFDM). By introducing a novel analytical perspective, we decompose the self-interference effects inherent to GFDM into two orthogonal constituents through a vectorized representation. Our elucidation of the self-interference components in terms of prototype filter parameters in the frequency domain is of particular significance. This theoretical characterization allows us to derive explicit analytical expressions, thereby paving the way for the proposition of an optimal filter design strategy that effectively mitigates self-interference distortions within GFDM systems. Our investigation reveals a noteworthy linkage between the required bandwidth allocation for individual subcarriers and the sub-symbol configuration within the proposed optimal prototype filter. This relationship underscores the filter's adeptness in optimizing spectrum utilization across the system. Through an analytical examination of the bit error rate (BER) performance within the GFDM framework, we establish the superior efficacy of our proposed optimal filter design relative to contemporary approaches documented in extant literature. Validation of our analytical findings is conducted via meticulous computer simulations, where a strong concurrence between the analytical predictions and the observed simulation outcomes is manifest
Evaluation of polycarboxylate ether-based grinding aids on clinker grinding performance: The influence of Ph
No full textThis study introduces an innovative approach by synthesizing PCE-based grinding aids (GAs) at three different pH levels (4, 7, and 9) to investigate how pH-induced structural variations impact grinding performance and cement quality. The GAs were applied at dosages of 0.025%, 0.05%, and 0.1% (by total weight of clinker and gypsum). Milling performance and final product properties were assessed, while thermogravimetric analysis (TGA) evaluated the thermal stability of the synthesized GAs. Additionally, molecular dynamics (MD) simulations were conducted to quantify the adsorption energies of the GAs on C-A and C-S clinker phases. Results showed that the low-pH PCE-based GA significantly enhanced grinding efficiency, achieving up to 17% improvement over the control, and delivered superior cement performance. This research provides new insights into the pH-dependent behavior of PCE-based GAs and offers a novel strategy for optimizing molecular design to achieve high-efficiency, performance-driven cement production.Bursa Uludag University Science and Technology Center (BAP) ; TÜBİTA
Unconventional pathways: An autoethnographic exploration of nontraditional academic journeys
No full textPurpose - In this study, I employ an autoethnographic approach to critically examine the concept of privilege within academia. I investigate how systemic privilege in traditional academic settings contributes to disparities in support, resources and recognition for individuals like myself who follow nontraditional academic paths or work in less established fields within traditional academic structures. Design/methodology/approach - My nontraditional academic background, combined with my involvement in a nontraditional field, became a lens through which I observed and experienced the often-unspoken privileges within academia. I use autoethnography to reveal how systemic privilege within academia creates barriers for nontraditional scholars and those working in fields that deviate from established academic norms. With this approach, I invite readers to connect emotionally and intellectually with my narrative. Findings - I illuminate the unique challenges faced by individuals who enter academia through nontraditional routes. Drawing on my personal journey and years of observation, I highlight how systemic privilege creates barriers that foster exclusion for those who deviate from the traditional academic mold. I reveal the significant hurdles nontraditional academics encounter in adapting to academic norms and expectations. These challenges contribute to feelings of marginalization and alienation, complicating the journey toward achieving success and recognition. Ultimately, my study underscores the urgent need for a more inclusive and equitable academic environment that values diverse forms of expertise and experience. Practical implications - The insights gained from this research can inform policy changes at both institutional and regulatory levels, promoting more inclusive environments for academics with diverse backgrounds and career trajectories. Originality/value - This research highlights the privilege dynamics and systemic barriers encountered by nontraditional academics and those in nontraditional disciplines, an underexplored area in the existing literature
Measurement of multidifferential cross sections for dijet production in proton-proton collisions at √s=13TeV
No full textA measurement of the dijet production cross section is reported based on proton–proton collision data collected in 2016 at s=13TeV by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of up to 36.3fb-1. Jets are reconstructed with the anti-kT algorithm for distance parameters of R=0.4 and 0.8. Cross sections are measured double-differentially (2D) as a function of the largest absolute rapidity |y|max of the two jets with the highest transverse momenta pT and their invariant mass m1,2, and triple-differentially (3D) as a function of the rapidity separation y∗, the total boost yb, and either m1,2 or the average pT of the two jets. The cross sections are unfolded to correct for detector effects and are compared with fixed-order calculations derived at next-to-next-to-leading order in perturbative quantum chromodynamics. The impact of the measurements on the parton distribution functions and the strong coupling constant at the mass of the Z boson is investigated, yielding a value of αS(mZ)=0.1179±0.0019. © The Author(s) 2024.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); MoER, ERC PUT and ERDF (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); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 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 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 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, funded by the EU NexGeneration 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 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 & Institutional Development, Research and Innovation, grant B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).Publisher versio
A cross-national study on prosocial behaviors in emerging adulthood during the COVID-19 Pandemic
No full textEmerging adulthood is marked by changes and exploration of life directions and is significantly impacted by crises like the COVID-19 pandemic. This cross-national study examined the psychological, relational, and contextual factors associated with prosocial behaviors- adherence to COVID-19 measures and helping strangers-among emerging adults (ages 18-25) from 14 countries during the pandemic. We assessed empathy, social identification with those affected by COVID-19, family and friend support, and perceived pandemic-related burden while exploring the moderating effects of country-level restrictions and cultural values. Results showed that empathy and social identification were consistently linked to adherence and helping behaviors, with stronger associations observed in countries with moderate to high COVID-19 restrictions. The findings highlighted the complex role of empathy and social identity in fostering prosocial behavior under varying cultural contexts and suggested pathways for enhancing community resilience during global crises.Faculty of Social Sciences of Utrecht University to fund data collectionPublisher versio
Vortex-enhanced jet impingement and the role of impulse generation rate in heat removal using additively manufactured synthetic jet devices
No full textThis article presents an approach to the design and fabrication of synthetic jet devices (SJDs) using rapid prototyping via additive manufacturing, marking the first study to employ this method for such devices. This manufacturing technique empowers researchers with complete design freedom, enabling the production of ultrathin SJDs-as thin as 4 mm-without mechanical fasteners and facilitating the rapid fabrication of multiple devices with varying geometries. To showcase the potential of this method, SJDs with conical and cylindrical cavities and orifices ranging from 1.6 mm to 7 mm were designed, fabricated, and tested. These devices achieved air jet exit velocities exceeding 106 m/s using a single piezoelectric diaphragm-among the highest reported in the literature-validating the effectiveness of this manufacturing approach. This high jet velocity is significant for practical applications requiring efficient thermal management, such as cooling high-power-density electronics, where compact and energy-efficient solutions are essential. Beyond achieving high velocities, it was revealed that maximizing jet velocity alone is not always optimal for heat removal. The hydrodynamic impulse generation rate was introduced as a more significant factor influencing heat transfer performance. By fabricating and testing multiple SJDs with different geometries, it was demonstrated that the impulse generation rate, which accounts for both jet velocity and flow rate, better correlates with enhanced heat transfer capabilities than jet velocity alone. This insight addresses an often-overlooked parameter in SJD design and has substantial implications for optimizing heat removal performance. Moreover, lumped element modeling, tuned solely on diaphragm deflection behavior, accurately predicted device performance and was validated using a hotwire anemometer. This model effectively characterizes center-axis orifice devices and confirms its applicability to thin-cavity designs, providing a valuable tool for future SJD development. Despite moderate volume flow rates (0.2 to 0.8 m3/h), the fabricated SJDs delivered significant improvements in heat transfer. Compared to natural convection, these devices achieved over 13 times greater heat removal rates, with an average heat transfer coefficient exceeding 120 W/m2 center dot K over a 30 mm x 30 mm heated surface. These findings demonstrate the practicality and effectiveness of vortex-enhanced synthetic jet impingement for targeted and efficient cooling of localized hot spots. This approach offers multiple advantages over traditional rotary cooling systems like fans, including increased reliability, lower profile, while consuming less than 100 mW. The ability to rapidly prototype and optimize SJDs using additive manufacturing accelerates research and development in this field, paving the way for advanced thermal management solutions in real-world applications.Auburn University Samuel Ginn College of Engineerin
Extracting airline emission KPIs from sustainability reports using large language models (LLMs)
No full textThe extraction of environmental Key Performance Indicators (KPIs) from airline sustainability reports is essential for assessing environmental sustainability metrics and regulatory compliance within the European aviation sector. Manual extraction from extensive, unstructured documents is laborious and often inconsistent. This study systematically investigates the potential of advanced Large Language Models (LLMs) -specifically -GPT-4.0, o3mini, and Deepseek R1- to automate the extraction of emissions-related KPIs from the 2023 sustainability reports of 16 publicly traded European airline groups. Utilizing the Perplexity platform, the research contrasts manual expert extraction with automated approaches, exploring various models, prompt strategies, and data formats. Results indicate that the accuracy of LLM extraction depends significantly on prompt specificity. Attempts to extract data from unstructured documents without guidance yielded low accuracy. However, incorporating explicit KPI terms into prompts increased accuracy from below 30% to above 70%. The format of the data source was also influential, with HTML formats producing superior extraction results compared to PDFs. Despite ongoing challenges in standardizing data and extracting precise KPI metrics, the findings demonstrate that LLMs can substantially streamline environmental, social and governance (ESG) data collection when prompt engineering and source standardization are prioritized. This study represents a novel, interdisciplinary approach by combining advances in large language models (LLMs) with expertise in environmental, social, and governance (ESG) analysis within the aviation sector, offering empirical benchmarking of LLM performance in real-world regulatory contexts. Recommendations for LLM integration into ESG analysis workflows are provided, and future research directions for advancing automation in sustainability reporting are discussed.European Union (EU) ; European Research Executive Agency (REA)Publisher versio
The Turkish Constitutional Court’s struggle with the European human rights law: An evaluation of the court’s case-law on the crime of ‘Defamation against the President’ in light of the jurisprudence of the European Court of Human Rights
No full textThis paper evaluates the Turkish Constitutional Court's norm review and individual application decisions concerning Article 299 of the Turkish Penal Code containing the crime of 'Defamation against the President'. The evaluation is conducted within the context of international human rights law, specifically on the European Court of Human Rights' jurisprudence on crimes that protect heads of state. While the Turkish Constitutional Court did not see an existential problem with Article 299, this paper argues that the Court overlooks significant issues regarding the provision's legality, purpose and proportionality. The view presented is that the Constitutional Court's open disregard of the European Court of Human Rights' judgments, even when these judgments specifically focus on the Turkish crime of 'Defamation against the President' is an example of judicial restraint in which the Court refrains from contesting the President's standing in the current political setting. According to this paper, such a stance cannot be taken, especially in light of the problems of the provision, and the Court should acknowledge that the only way to protect the freedom of expression effectively is to get rid of Article 299
Challenges and opportunities: Integrating AI into accounting systems
No full textThis study investigates the integration of Artificial Intelligence (AI) into accounting systems, identifying key challenges, and potential opportunities. The objective is to provide a critical assessment of the impact that AI has on modern accounting practices and systems. Methodologically, the research involves a review and analysis of secondary data, including official company portals, comprehensive industry analyses, product documentation, and insightful whitepapers of 10 major companies pioneering AI adoption. The study reveals challenges such as the compatibility of AI with existing accounting infrastructures and the need for workforce reskilling to adapt to AI-driven processes. Conversely, the study highlights opportunities such as enhanced operational efficiency, improved accuracy, and the facilitation of strategic decision-making through predictive analytics. The research underscores the necessity for strategic planning and the development of adaptable systems to leverage AI technologies effectively in accounting