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Article Citation - WoS: 2Citation - Scopus: 3Financial Constraints and the ESG-Firm Performance Nexus in the Automotive Industry: Evidence From a Global Panel Study(MDPI, 2025) Dincergok, Burcu; Pirgaip, BurakThis study examines the complex relationship between environmental, social, and governance (ESG) and financial performance in the automotive industry, with a particular focus on how financial constraints shape this relationship. Using a global data set for the period 2008 to 2023 and employing a range of panel data techniques, including those addressing endogeneity concerns, we find that higher ESG scores positively affect financial performance. Specifically, a one-point rise in ESG score corresponds to an estimated 1-1.7% increase in the market-to-book ratio, with the effect reaching approximately 1.6% for firms facing financial constraints. These findings highlight the economic significance of ESG engagement, particularly for resource-constrained companies. The novelty of this study is that it focuses on the automotive sector, an industry with limited ESG-specific research, and that it makes a theoretical contribution by linking ESG performance outcomes to financial constraints, an angle largely overlooked in prior research. The findings offer critical policy insights, emphasizing the strategic importance of ESG initiatives for value creation under varying financial conditions.Article Transcatheter Aortic Valve Implantation in Nonagenarians: A Comparative Analysis of Baseline Characteristics and 1-Year Outcomes(MDPI, 2025) Guney, Murat Can; Bozkurt, EnginBackground: Transcatheter aortic valve implantation (TAVI) is increasingly used in elderly patients with severe aortic stenosis, yet data on nonagenarians remain limited. This study aimed to compare clinical characteristics and outcomes of patients aged >= 90 years with those aged <90 years undergoing TAVI. Methods: We retrospectively analyzed 620 patients who underwent transfemoral TAVI. Patients were divided into two groups: <90 years (n = 545) and >= 90 years (n = 75). Baseline clinical, procedural, and outcome data were compared. Results: Nonagenarians had lower body mass index (BMI) and a lower prevalence of comorbidities such as diabetes, hyperlipidemia, and prior coronary artery bypass grafting CABG (all p < 0.05). All-cause mortality was higher in nonagenarians at 1 month (8.0% vs. 5.5%, p = 0.425), 6 months (9.3% vs. 7.9%, p = 0.838), and 1 year (21.3% vs. 16.7%, p = 0.405), though these differences were not statistically significant. In-hospital stroke occurred more frequently in patients >= 90 years (6.7% vs. 2.2%, p = 0.044). Conclusions: Despite a higher rate of in-hospital stroke, nonagenarians undergoing TAVI had comparable mortality outcomes to younger patients. These findings support the feasibility of TAVI in selected very elderly patients, while highlighting the need for tailored stroke prevention strategies. Trial Registration: The trial is retrospectively registered, and a clinical trial number is not applicable.Article Citation - Scopus: 1Benefits of Best Practice Guidelines in Spine Fusion: Comparable Correction in Ais With Higher Density and Fewer Complications(MDPI, 2023) Fernandes,P.; Flores,I.; Soares do Brito,J.Background: There is significant variability in surgeons’ instrumentation patterns for adolescent idiopathic scoliosis surgery. Implant density and costs are difficult to correlate with deformity correction, safety, and quality of life measures. Materials and Methods: Two groups of postoperative adolescents were compared based on exposure to a best practice guidelines program (BPGP) introduced to decrease complications. Hybrid and stainless steel constructs were dropped, and posterior-based osteotomies, screws, and implant density were increased to 66.8 ± 12.03 vs. 57.5 ± 16.7% (p < 0.001). The evaluated outcomes were: initial and final correction, rate of correction loss, complications, OR returns, and SRS-22 scores (minimum two-year follow-up). Results: 34 patients were operated on before BPGP and 48 after. The samples were comparable, with the exceptions of a higher density and longer operative times after BPGP. Initial and final corrections before BPGP were 67.9° ± 22.9 and 64.6° ± 23.7; after BPGP, the corrections were 70.6° ± 17.4 and 66.5° ± 14.9 (sd). A regression analysis did not show a relation between the number of implants and postoperative correction (beta = −0.116, p = 0.307), final correction (beta = −0.065, p = 0.578), or loss of correction (beta= −0.137, p = 0.246). Considering screw constructs only (n = 63), a regression model controlled for flexibility continued to show a slight negative effect of density on initial correction (b = −0.274; p = 0.019). Only with major curve concavity was density relevant in initial correction (b = 0.293; p = 0.038), with significance at 95% not being achieved for final correction despite a similar beta (b = 0.263; p = 0.069). Complications and OR returns dropped from 25.6% to 4.2%. Despite this, no difference was found in SRS-22 (4.30 ± 0.432 vs. 4.42 ± 0.39; sd) or subdomain scores pre- and post-program. Findings: Although it appears counterintuitive that higher density, osteotomies, and operative time may lead to fewer complications, the study shows the value of best practice guidelines in spinal fusions. It also shows that a 66% implant density leads to better safety and efficacy, avoiding higher costs. © 2023 by the authors.Article Citation - WoS: 1Citation - Scopus: 1The Effectiveness of Redistribution in Carbon Inequality: What About the Top 1%(MDPI, 2025) Boz, Arinc; Unalan, Gokhan; Caskurlu, ErenThis study investigates the impact of income redistribution on carbon emissions across 154 countries from 1995 to 2023, with a particular focus on carbon inequality. Using a dynamic panel approach with two-step System GMM estimations, the analysis considers three dependent variables: average per capita emissions, top 1% per capita emissions, and the ratio of top 1% per capita emissions to national average per capita emissions. Results show that income redistribution (measured in both absolute and relative terms) significantly reduces average per capita emissions in the short term. However, redistribution has no mitigating effect on the carbon emissions of the top 1%; in some models, it is even associated with increases in elite emissions and a widening of carbon inequality. These findings suggest that while redistribution may contribute to national emission reductions, it is insufficient to curb the carbon-intensive lifestyles of the wealthiest. The analysis confirms the Environmental Kuznets Curve (EKC) hypothesis and underscores the need for complementary policy tools to more effectively address the emissions of high-emitting individuals. Overall, this study contributes to the literature by linking income redistribution with emission disparities across income groups and highlights the importance of considering distributional dynamics in climate policy design.Article Citation - WoS: 7Citation - Scopus: 8Parameter Identification and Speed Control of a Small-Scale BLDC Motor: Experimental Validation and Real-Time PI Control with Low-Pass Filtering(MDPI, 2025) Abouseda, Ayman Ibrahim; Doruk, Resat Ozgur; Amini, AliThis paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional-integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors.Article Citation - WoS: 2Citation - Scopus: 2Physics-Informed Neural Network for Nonlinear Bending Analysis of Nano-Beams: A Systematic Hyperparameter Optimization(MDPI, 2025) Esfahani, Saba Sadat Mirsadeghi; Fallah, Ali; Aghdam, Mohammad MohammadiThis paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton's principle, incorporating nonlocal strain gradient theory, and based on Euler-Bernoulli beam theory. In the PINN method, the solution is approximated by a deep neural network, with network parameters determined by minimizing a loss function that consists of the governing equation and boundary conditions. Despite numerous reports demonstrating the applicability of the PINN method for solving various engineering problems, tuning the network hyperparameters remains challenging. In this study, a systematic approach is employed to fine-tune the hyperparameters using hyperparameter optimization (HPO) via Gaussian process-based Bayesian optimization. Comparison of the PINN results with available reference solutions shows that the PINN, with the optimized parameters, produces results with high accuracy. Finally, the impacts of boundary conditions, different loads, and the influence of nonlocal strain gradient parameters on the bending behavior of nano-beams are investigated.Article From Street Canyons To Corridors: Adapting Urban Propagation Models for an Indoor IQRF Network(MDPI, 2025) Doyan, Talip Eren; Yalcinkaya, Bengisu; Dogan, Deren; Dalveren, Yaser; Derawi, MohammadAmong wireless communication technologies underlying Internet of Things (IoT)-based smart buildings, IQRF (Intelligent Connectivity Using Radio Frequency) technology is a promising candidate due to its low power consumption, cost-effectiveness, and wide coverage. However, effectively modeling the propagation characteristics of IQRF in complex indoor environments for simple and accurate network deployment remains challenging, as architectural elements like walls and corners cause substantial signal attenuation and unpredictable propagation behavior. This study investigates the applicability of a site-specific modeling approach, originally developed for urban street canyons, to characterize peer-to-peer (P2P) IQRF links operating at 868 MHz in typical indoor scenarios, including line-of-sight (LoS), one-turn, and two-turn non-line-of-sight (NLoS) configurations. The received signal powers are compared with well-known empirical models, including international telecommunication union radio communication sector (ITU-R) P.1238-9 and WINNER II, and ray-tracing simulations. The results show that while ITU-R P.1238-9 achieves lower prediction error under LoS conditions with a root mean square error (RMSE) of 5.694 dB, the site-specific approach achieves substantially higher accuracy in NLoS scenarios, maintaining RMSE values below 3.9 dB for one- and two-turn links. Furthermore, ray-tracing simulations exhibited notably larger deviations, with RMSE values ranging from 7.522 dB to 16.267 dB and lower correlation with measurements. These results demonstrate the potential of site-specific modeling to provide practical, computationally efficient, and accurate insights for IQRF network deployment planning in smart building environments.Article Experimental Investigation of Energy Efficiency, SOC Estimation, and Real-Time Speed Control of a 2.2 kW BLDC Motor with Planetary Gearbox under Variable Load Conditions(MDPI, 2025) Abouseda, Ayman Ibrahim; Doruk, Resat; Emin, Ali; Lopez-Guede, Jose ManuelThis study presents a comprehensive experimental investigation of a 2.2 kW brushless DC (BLDC) motor integrated with a three-shaft planetary gearbox, focusing on overall energy efficiency, battery state of charge (SOC) estimation, and real-time speed control under variable load conditions. In the first stage, the gearbox transmission ratio was experimentally verified to establish the kinematic relationship between the BLDC motor and the eddy current dynamometer shafts. In the second stage, the motor was operated in open loop mode at fixed reference speeds while variable load torques ranging from 1 to 7 N.m were applied using an AVL dynamometer. Electrical voltage, current, and rotational speed were measured in real time through precision transducers and a data acquisition interface, enabling computation of overall efficiency and SOC via the Coulomb counting method. The open loop results demonstrated that maximum efficiency occurred in the intermediate-to-high-speed region (2000 to 2800 rpm) and at higher load torques (5 to 7 N.m) while locking the third gearbox shaft produced negligible parasitic losses. In the third stage, a proportional-integral-derivative (PID) controller was implemented in closed loop configuration to regulate motor speed under the same variable load scenarios. The closed loop operation improved the overall efficiency by approximately 8-20 percentage points within the effective operating range of 1600-2500 rpm, reduced speed droop, and ensured precise tracking with minimal overshoot and steady-state error. The proposed methodology provides an integrated experimental framework for evaluating the dynamic performance, energy efficiency, and battery utilization of BLDC motor planetary gearbox systems, offering valuable insights for electric vehicle and hybrid electric vehicle (HEV) drive applications.Article Citation - WoS: 1Citation - Scopus: 1An Experimental Study on Ultrasonic-Assisted Drilling of CFRP Composites with Minimum Quantity Lubrication(MDPI, 2025) Namlu, Ramazan Hakki; Sagener, Mustafa Burak; Kilic, Zekai Murat; Colak, Oguz; Kilic, Sadik EnginThe increasing use of carbon fiber reinforced polymer (CFRP) composites in industries such as aerospace, due to its high strength-to-weight ratio, durability, and resistance to corrosion has led to a growing demand for more efficient machining processes. However, the multilayered structure of CFRP composites, composed of densely packed fibers, presents significant challenges during machining. Additionally, when cutting fluids are used to improve effective cooling and lubrication, the material tends to absorb the fluid, causing damage and leading to problem of weaking of composite structure. To address these issues, this study compares ultrasonic-assisted drilling (UAD) and minimum quantity lubrication (MQL) techniques with conventional drilling (CD) and dry cutting to improve the performance of CFRP composite drilling. The results show that using UAD and MQL together reduced thrust force by up to 27%, improved surface roughness inside the holes by up to 31%, reduced improved hole diameter, cylindricity, roundness, and delamination.Article Citation - WoS: 1Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review(MDPI, 2025) Ayhan, Cagri; Mekhaeil, Marina; Channawi, Rita; Ozcan, Alp Eren; Akargul, Elif; Deger, Atakan; Soliman, OsamaAcute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today's data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a powerful tool to process this complexity and enable precision risk prediction. To date, AI has been applied across multiple aspects of aortic disease management, with mortality prediction being the most widely investigated. Machine learning (ML) and deep learning (DL) models-particularly ensemble algorithms and biomarker-integrated approaches-have frequently outperformed traditional clinical tools such as EuroSCORE II and GERAADA. These models provide superior discrimination and interpretability, identifying key drivers of adverse outcomes. However, many studies remain limited by small sample sizes, single-center design, and lack of external validation, all of which constrain their generalizability. Despite these challenges, the consistently strong results highlight AI's growing potential to complement and enhance existing prognostic frameworks. Beyond mortality, AI has expanded the scope of analysis to the structural and biomechanical behavior of the aorta itself. Through integration of imaging, radiomic, and computational modeling data, AI now allows virtual representation of aortic mechanics-enabling prediction of aneurysm growth rate, remodeling after repair, and even rupture risk and location. Such models bridge data-driven learning with mechanistic understanding, creating an opportunity to simulate disease progression in a virtual environment. In addition to mortality and growth-related outcomes, morbidity prediction has become another area of rapid development. AI models have been used to assess a wide range of postoperative complications, including stroke, gastrointestinal bleeding, prolonged hospitalization, reintubation, and paraplegia-showing that predictive applications are limited only by clinical imagination. Among these, acute kidney injury (AKI) has received particular attention, with several robust studies demonstrating high accuracy in early identification of patients at risk for severe renal complications. To translate these promising results into real-world clinical use, future work must focus on large multicenter collaborations, external validation, and adherence to transparent reporting standards such as TRIPOD-AI. Integration of explainable AI frameworks and dynamic, patient-specific modeling-potentially through the development of digital twins-will be essential for achieving real-time clinical applicability. Ultimately, AI holds the potential not only to refine risk prediction but to fundamentally transform how we understand, monitor, and manage patients with AAS and TAA.
