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Psikolojik Sermaye ve İş-Yaşam Anlamlılık Algılarının Psikolojik İyi Oluş Üzerindeki Etkisi
No full textRELIABLE AND EFFICIENT DESIGN OF RAILWAY ELECTRIFICATION SYSTEMS: STANDARDS AND CRITERIA
No full textMACHINE LEARNING-DRIVEN FAILURE MODE CLASSIFICATION FOR COLDFORMED STEEL BUILT-UP BEAMS SUBJECTED TO STATIC LOADING
No full textComparative Analysis of Life Cycle Assessment (LCA)-Process-Oriented Approach with Campus Sustainability Assessment Systems
No full textVARK öğrenme modelinin ortaokul öğrencilerinin akademik başarılarına ve çevreye karşı ilgilerine etkisi
No full textExamining the Levels of Social Integration and Acculturation of Turkishand Syrian Middle School Students
No full textEnhancing Nano-Optoelectronic Orthogonal Frequency Division Multiplexing Passive Optical Networks (OFDM-PONs) via SLM-DFOA: A Low-Complexity PAPR Reduction Approach
No full textHigh peak-to-average power ratio (PAPR) remains a critical challenge in orthogonal frequency division multiplexing passive optical networks, particularly in nano-optoelectronic applications where laser nonlinearities and power efficiency are paramount. Conventional PAPR reduction techniques like selective mapping (SLM) and partial transmit sequence (PTS) suffer from high computational complexity, limiting their practicality in high-speed optical communication systems. This paper introduces a novel SLM-based discrete forest optimization algorithm (SLM-DFOA) that significantly reduces PAPR while maintaining low computational overhead. We present a comprehensive simulation of a 40 Gb/s OFDM-PON transmitted over 50 km of single-mode fiber, modeled using OptiSystem and MATLAB. The proposed SLM-DFOA method achieves a remarkable 5.4 dB reduction in PAPR, lowering it from 10.5 dB to 5.1 dB at a complementary cumulative distribution function (CCDF) of 10-3, alonwith a 6.1 dB improvement in bit error rate (BER) compared to the original OFDM signal. The algorithm demonstrates superior computational efficiency, offering an 86.59% complexity reduction over PTS with discrete invasive weed optimization (PTS-DIWO) and 53.13% over genetic algorithm-based SLM (SLM-GA). Through detailed parameter analysis, we identify optimal DFOA configurations, including an area limit of 5 and local seeding changes (LSC) of 2, which ensure robust performance under nano-optoelectronic hardware constraints. With a power-saving efficiency of 51.4%, the SLM-DFOA technique emerges as a promising solution for energy-efficient, high-speed optical communication systems, including 5G fronthaul, visible light communication (VLC), and future nano-photonic networks
Unlocking the nexus of digitalization, servitization and financial performance
No full textPurposeThis research focuses on the association between digitalization, servitization, and financial performance (FP) and examines the roles of resource orchestration capability (ROC) and organizational improvisation (OI) by integrating resource orchestration theory (ROT) and dynamic capability view (DCV).Design/methodology/approachTo test the research model, covariance-based structural equation modeling (CB-SEM) is performed using data from a cross-sectional survey with 265 manufacturing firms.FindingsResults show that digitalization supports servitization and ROC mediates this relationship. Findings also reveal that ROC and servitization serially mediate the association between digitalization and FP. Moreover, findings demonstrate that servitization fosters FP, and OI strengthens this effect.Originality/valueDespite intense interest in digitalization and servitization, there is still a lack of understanding of how they are related and how they affect FP. Additionally, there are contradictions in the existing literature regarding these relationships, which may be explained by the digitalization and servitization paradoxes. To the best of our knowledge, this research is the first attempt to reveal the roles of ROC and OI in these associations. Therefore, this study contributes to literature by unearthing the mechanisms that are effective in relationships between digitalization, servitization and FP, enriches current knowledge and fulfills the gaps. Practically, this study offers a strategic roadmap and hints at success with digitalization and servitization for manufacturing firms
STREL - Naturalistic Dataset and Methods for Studying Mental Stress and Relaxation Patterns in Critical Leading Roles
No full textWe investigate mental stress and relaxation patterns in professionals occupying leadership roles in emergency care and special police units. A key finding is that on days that involve critical missions, these individuals experience negative mental stress (i.e., distress) that escalates as the day unfolds. In contrast, on non-leadership workdays, mental distress remains relatively stable, while on non-workdays, participants exhibit positive mental stress (i.e., eustress) that subsides over time, facilitating relaxation. These findings stem from a four-day naturalistic study of n=24 professionals, during which we collected physiological, mobility, and psychometric data. Using participant debriefings and sensor-based validation triggers (e.g., GPS, cadence), we labeled activities in 5-minute intervals and focused our analysis on sedentary periods. We defined stress during these periods as a normalized heart rate that exceeds two standard deviations above a personalized baseline, thus isolating mental stress uncontaminated by physical exertion. A logistic regression model based on this stress labeling method yielded results largely consistent with those obtained from Kubios' SNS Index, reinforcing its validity. In cases of disagreement, our method aligned better with participant reports and established literature, highlighting advantages in interpretability and specificity. Overall, our work makes three contributions: (a) to affective science, by quantifying the mentally stressful nature of leadership in high-stakes environments; (b) to affective computing, by proposing a wearable compatible method for estimating mental stress during sedentary activity in the wild; (c) to data science, by introducing a well-annotated, multimodal dataset suitable for machine learning benchmarking in stress detection
