Objectives: This study investigates the relationship between occupational noise, solvent exposure, and audiometric abnormalities in male automotive industry workers, focusing on factors associated with audiometric abnormalities, including age, body mass index, and biochemical markers. Methods: This cross-sectional study was conducted among 500 male workers who were admitted to the outpatient clinic of Ankara Occupational and Environmental Diseases Hospital for periodic health examinations between September 1, 2020, and July 1, 2021. Audiometric results and biochemical markers, including hippuric acid, trichloroacetic acid, and manganese, were assessed alongside demographic and occupational variables. Multivariate logistic regression was used to identify predictive factors for abnormal audiometry results. Results: Among the participants, 61.2 % exhibited abnormal audiometry results, and 11.4 % had noise-induced hearing loss. Age strongly predicted abnormal audiometry (OR = 1.11, 95% CI: 1.07–1.15, p < 0.001), with an 11% increase in odds per year. Elevated trichloroacetic acid (TCA) levels were also associated with a 9% increase in odds of abnormal audiometry (OR = 1.09, 95% CI: 1.01–1.18, p = 0.027). No significant associations were found for body mass index, duration of work, or hippuric acid levels. Conclusions: Age and TCA levels were significantly associated with audiometric abnormalities among workers exposed to occupational noise and solvents. These findings highlight the need for targeted interventions, including regular audiometric evaluations and minimizing solvent exposure, to reduce the risk of hearing loss in industrial settings. Further research is needed to explore the underlying mechanisms linking solvent exposure and auditory dysfunction.

The automotive industry has always demanded innovation in renewable materials that are environmentally friendly. One of the natural materials that has the potential as a constituent material to make motor vehicle parts is blood clam shell waste (Anadara Granosa) which can be used as a raw material for making brake pads. This study has the purpose to analyze the effect of the addition of blood shell powder on the wear, toughness, and hardness value of epoxy resin matrix composite as a motorcycle brake pad. This study is a type of experimental research by comparing the experimental group with the control group (Honda Genuine Parts brand brake pads). The test results showed that the addition of 30% of blood shells powder got the most optimal results with a wear value of 1.27×10-6 mm2/kg, impact value 2.41×10-3 J/mm2, hardness of 19.98 kgf/mm2. The 30% variation has the closest results to the test value on the brake pads of the Honda Genuine Parts brand motorcycle. With this, it can be concluded that the more volume of blood shells powder, the more the strength of the composite increases. With these results, blood shell powder composite can be recommended as an alternative to brake pad friction material that is more environmentally friendly.

In the automotive industry, ultrasonic welding (USW) stands out as a highly effective solid-state welding technique. It is specifically designed for joining components within lithium-ion battery modules and energy storage systems. To achieve an optimal weight-to-body ratio, dissimilar materials like aluminum, copper, and nickel are joined to reduce the weight of electrical and electronic devices. This study presents a systematic analysis of aluminum (Al) and cupro-nickel (CuNi) dissimilar joints using USW, with variations in welding parameters. The lap shear strength results show that sonotrode pressure and welding energy have a greater impact on joint strength than the amplitude of vibration at all tested levels. Joint strength reached peak values of 942N and 236N, respectively, before declining sharply as weld energy increased due to the high interfacial temperature. Resistance measurements revealed that resistance decreased with increasing welding energy. Additionally, Al-CuNi specimens exhibited higher hardness values at the faying surface compared to their base metals. A metallographic analysis of the weld cross-section was conducted to evaluate diffusion across the joint interface, supported by X-ray energy-dispersive spectroscopy, electron backscatter diffraction, and X-ray diffraction to assess weld quality.

The present study emphasises the critical phenomena and characterisation of DC magnetisation and AC susceptibility measurements in La0.7Bi0.3MnO3 perovskite manganite. The compound has been synthesised using a conventional solid-state reaction method. Rietveld analysis of powder X-ray diffraction (PXRD) patterns shows that the compound exhibits rhombohedral crystal structure indexed to the R 3 ¯ c space group. The DC magnetisation versus temperature exhibits a high-temperature paramagnetic (PM) to low-temperature frustrated ferromagnetic (FM) transition. Using Bloch’s law, the existence of critical inhomogeneous FM agglomerates around blocking temperature (T K) has been confirmed. Further, the critical fluctuations at low temperatures have been understood using the Kouvel–Fisher (KF) method, which indicates the existence of the critical phenomena. By using the critical scaling analysis and KF method, the obtained values of β, γ and δ are comparable with the 3D Heisenberg-type spin interactions. The Langevin fit to the DC magnetisation versus magnetic field confirms the short-range interactions with inhomogeneities in the randomly dispersed magnetic spins. Hence, indicating temperature-dependent distortions in the magnetisation ordering. This type of investigation on critical phenomena and the results of L a 0.7 B i 0.3 Mn O 3 discussed in this paper finds importance in devising advanced sensors useful for automotive and chemical catalytic degradation industries.

The reinforcement of nylon-12 with lemon grass-derived carbon (LGC) as a filler can be a promising sustainable strategy to reduce both biowastes and the negative environmental impact of plastics. In this work, LGC is incorporated as a sustainable filler to nylon-12 polymer matrix in varying filler concentrations, i.e., 0.25, 1, and 1.5 wt %. Highly semicrystalline carbon obtained from the pyrolysis of lemon grass at 850 °C is investigated by several characterizations, such as XRD, TEM, Raman, SEM, surface area, and pore-size distribution analysis to understand its basic properties. The incorporation of LGC improved the thermal, mechanical, and viscoelastic properties of the composites. The tensile strength at an optimized concentration of 0.25 wt % is improved by 13% along with the increase in crystallinity, tensile modulus, complex viscosity, and storage modulus. The tensile modulus increased by ∼36% at the highest filler concentration of 1.5 wt %, with the tensile strength improving by ∼5%. The improved tensile strength and modulus in LGC-incorporated nylon-12 composites by injection molding have the potential to be used in the automotive industry.

Industrial and domestic wastewaters, nonpoint pollution sources, and climate change affect stream ecosystems, water quantity, and quality. Within the scope of this study, the water quality of Nilüfer Stream was evaluated using the Water Quality Index (WQI), One-Way ANOVA, the Kruskal-Wallis Test, and Principal Component Analysis (PCA). In the study, 4686 water quality data from seven sampling stations between 2008 and 2024 were used. WQI results showed a distinct decrease in water quality from the upstream to the downstream of the Stream. Average WQI values for the stations were found to be between 140.83 and 487.83. The lowest WQI value was found at Station 1 and the highest WQI value was found at Station 7. According to WQI, the ranking of the stations by magnitude was St7 > St4 > St5 > St6 > St2 > St3 > St1. A statistically significant difference was observed between the stations in terms of WQI, ANOVA, and Kruskal-Wallis Test (p < 0.05), and water quality was found to be seasonally diverse. Generally, at stations (except for two stations), the seasonal WQI values ranked by magnitude were autumn > summer > winter > spring. The PCA showed that relationships among parameters originating from industrial wastewater associated with the textile, automotive, and metal industries were stronger (component loadings > 0.75), whereas the groups identified in the upstream basin indicated domestic pollution and agricultural pollution from fertilizers and pesticides. PCA conducted between meteorological parameters and the WQI values of the stations showed that climate change could be effective at only two stations. It was determined that the region located before the wastewater treatment plant (St4) was associated with precipitation, humidity, and evaporation, while the downstream region (St7) was related to wind speed. It was observed that water quality was more influenced by industrial, urban, and agricultural pollution sources than by climate change.

A PVB solid polymer electrolyte film with an ionic conductivity as high as 4.65 × 10-5 S cm-1 and a tensile strength of 25 MPa was successfully fabricated using electrostatic spinning and immersion techniques. Based on this film, electrochromic laminated safety glass was constructed by a hot-pressing process. It can be quickly turned from transparent to dark blue opaque within 4 s by applying voltage, and the colour can be maintained even after the voltage is removed. This electrochromic laminated glass can be widely used in the automotive and construction industries owing to its safety, stability, and energy efficiency.

ABSTRACT This article examines the dynamics of industrial restructuring in the contemporary automotive GVC based on the Marxian critique of political economy. To do so, it firstly sketches out the main tenets of the theoretical approach, which, in a nutshell, establish how the system-wide ‘laws of production and appropriation of value’ become mediated through a stratified qualitative differentiation of individual capitals (i.e. firms) and a concomitant quantitative hierarchy of profitability. Subsequently, it examines through those lenses the recent trajectory of the automotive GVC, which is shown to revolve around the changing relationship between automakers and so-called ‘mega suppliers’ resulting from the twofold process of, on the one hand, vertical disintegration by means of outsourcing of the manufacturing of parts and components and, on the other, the growing concentration and centralisation of capital among first-tier suppliers. On these grounds, the article finally moves to its second focal point and key contribution, namely: the discussion of a methodologically-informed framework for the construction of quantitative metrics of profitability, which are then used to provide an analysis of profitability differentials between automakers and suppliers based on data obtained from the balance sheets of firms.

This study aims to analyze the effect of work ethic on the performance of Daihatsu workshop mechanics in Japan. The research background is based on the observation that existing studies in the Japanese automotive industry have focused predominantly on assembly-line workers in manufacturing, while the after-sales service sector (workshops) remains understudied. Using a quantitative survey approach, questionnaires were distributed to 31 respondents selected by purposive sampling from nine Daihatsu branches across various prefectures in Japan, measured with a Likert scale. Data were analyzed using classical assumption tests, simple linear regression, t-test, and coefficient of determination (R²). Results show that work ethic has a positive and statistically significant association with mechanic performance (regression coefficient = 0.797; sig. = 0.000 &lt; 0.05). The R² value of 0.612 indicates that work ethic explains 61.2% of the variance in mechanic performance. These findings suggest that strengthening work discipline, responsibility, work commitment, and cooperation can help companies improve work quality and mechanic productivity.

Polyurethane (PU) adhesives are widely used in aerospace and automotive industries for bonding dissimilar materials. However, accurately predicting the lap-shear strength of such joints is challenging due to complex, non-linear interactions among parameters like bondline thickness, curing time, substrate types, and aging behaviour. This study presents a data-driven machine learning (ML) framework to predict both the mechanical performance (lap-shear strength and extension) and the dominant failure mode of PU adhesive joints. An experimental dataset comprising 330 data points was generated using aluminium (Al), galvanized steel, and glass fiber-reinforced polymer (GFRP) substrates bonded with PU adhesive. Seven input features (three numerical, four categorical) were used to train and validate multiple ML models, including random forest (RF), gradient boosting (GB), extreme GB (XGB), and multilayer perceptron (MLP). For regression tasks, GB and XGB models demonstrated superior accuracy and robustness. On the final test set, GB and XGB achieved the highest R 2 scores for predicting lap-shear strength (0.82) and extension (0.93), both with the lowest mean absolute error. For classification of dominant failure modes, which involved an imbalanced dataset, logistic regression (LR) model emerged as the most suitable model. Although GB attained the highest test accuracy (0.89), LR model was uniquely capable of correctly identifying all critical substrate/substrate (S/S) failures. These results demonstrate the potential of ML-based approaches to efficiently and accurately predict the complex behaviour of adhesive joints.

This study examines Japan’s carbon-neutral policy in the automotive sector. To tackle carbon neutrality, the Japanese government and industry have introduced two manufacturing-related strategies. The first is the Multi-Pathway Strategy, which promotes not only battery electric vehicles but also a variety of powertrain vehicles by using a technology-neutral approach. The second is the establishment of green supply chain networks. These strategies align with the life-cycle assessment concept that calculates the total carbon dioxide (CO2) emissions of the whole automotive supply chain network but are based on the not commonly used Tank-to-Wheel method that calculates CO2 emissions during the driving process only. This study asserts that a technology-neutral approach based on life-cycle assessment can be a more practical policy for greenhouse gas reduction under the uncertainty of the automotive technology and energy policy. Lastly, the article reveals key issues in sustainable global automotive development.

This study investigates the role of symmetric probabilistic models in predicting financial distress in the automotive industry, with a focus on companies operating in the Slovak Republic. Financial distress prediction represents a binary classification problem characterized by an inherent symmetry between healthy and distressed firms. To capture this structure, two widely used symmetric models—logit and probit—are applied and systematically compared. The modeling framework incorporates LASSO regression for variable selection, enabling dimensionality reduction while preserving the most informative financial indicators. The empirical analysis is conducted on a dataset of 351 manufacturing enterprises. The results indicate that both models achieve comparable predictive performance, with the logit model reaching an accuracy of 78.9% and the probit model 77.8%. The area under the ROC curve further confirms the strong discriminatory power of both approaches. The findings highlight that the symmetric nature of the applied link functions contributes to model stability, interpretability, and balanced classification behavior. This study extends existing research by explicitly linking symmetry concepts with financial distress prediction in a sector-specific context. The proposed approach provides a transparent and practically applicable framework for early risk identification in industrial enterprises.

Differentiated pathways for enhancing green innovation quality in the automotive industry guided by ESG frameworks and energy policies

Life Cycle Assessment of material substitution in manufacturing equipment by wood-based structures in the automotive industry

This review examines the vehicle communication systems, its evaluation measures, security concern and impact of contemporary technology. By making electronic searches through different databases, 20 articles were identified to include in the study. Findings have demonstrated that more sophisticated protocols are being implemented, e.g., FlexRay and Dedicated Short-Range Communication (DSRC), though older protocols, e.g., Controller Area Network (CAN) and Local Interconnect Network (LIN), remain widespread. Additionally, the use of Ethernet-based systems in automotive communications is increasing. However, many of these protocols have substantial vulnerabilities, which pose significant security challenges. The findings suggest adopting enhanced communication and security measures supported by Artificial Intelligence (AI) and Machine Learning (ML) for future vehicles. Overall, this work systematically evaluates in-vehicle communication protocols and proposes methods for addressing contemporary security challenges in the automotive industry.

From policy to practice: Challenges and enablers of electric mobility in South Africa’s automotive industry

• Several new combinations of metal matrix composites have been produced in recent decades. • Aluminum composites are considered among the most advanced structural materials, classified as metal matrix composites (MMCs), due to their superior properties for automotive and aerospace applications. • Al MMCs were synthesized using different fabrication methods. The formations of stable reinforcement particles in the composites resulted in superior mechanical and wear properties for Al MMCs. • Recent advances in processing, microstructure, wear, and mechanical properties of Al matrix composites increased with innumerable types of particles are covered in this review paper. • At the end of the study, future directions for these composites will also be discussed. • These articles outline the production process and highlight the mechanical and wear properties of AMCs with various reinforcements. • A brief discussion of the advantages and limitations of different methods is provided. • The choice of a fabrication process is mainly dependent on the required quality, cost, and efficiency of the process. • The technique for improving UTS, impact resistance, and hardness has been thoroughly explored. • Orowan strengthening, along with the load-bearing effect, strongly affects the strength of the composites. • The effect of various parameters on the wearing characteristics of AMCs was also explained in detail. • The mechanisms are briefly discussed, along with relevant research papers on the wear surfaces. • This review aids in identifying the research background and challenges associated with the processing of AMCs, despite their good performance in current applications. Aluminum matrix composites (AMCs) have received attention over the past decades in the automotive and aerospace industries due to their high strength, low weight, and wear resistance. This review presents a detailed analysis of the microstructure, mechanical, and tribological properties of AMCs. It begins with the primary fabrication processes, including stir casting, liquid-state processing, squeeze casting, and solid-state processing. The review then explores the correlation between microstructural properties and processing conditions, including reinforcement distribution, phase development, porosity formation, grain refinement, and interfacial bonding. Particular attention was given to the effect of nanoparticles and hybrid reinforcement on the strengthening mechanism, microstructure, and mechanical properties of AMC. The strengthening mechanisms, Orwan strengthening, load transfer, grain boundary strengthening, and thermal mismatch were examined to explain variation in wear properties, tensile strength, and hardness. Recent advances in processing, microstructure, wear, and mechanical properties of Al matrix composites have increased, with innumerable types of particles covered in this review paper. At the end of the study, future directions for these composites will also be discussed.

Ultra-thin and ultra-lightweight meta-panel for low-frequency broadband sound insulation based on a computationally efficient design framework

The shift toward battery electric vehicles (EVs) represents the most profound disruption in the automotive industry in over a century, with far-reaching consequences for European automotive regions. For decades, incremental improvements in internal combustion engine (ICE) technologies have served as the dominant decarbonization pathway, and many regions have built strong innovation systems around these legacy technologies. To assess how European NUTS2 regions are positioned for the transition to electric mobility, we analyze regional technological capabilities using technology-specific relatedness and knowledge complexity to investigate how EV- and ICE-related capabilities affect regional EV innovation. Using patent data from the OECD REGPAT database, we map regional innovation potentials and estimate econometric models to assess how these capability structures affect EV patenting. We find that higher relatedness density to EV technologies is positively associated with EV patenting, while relatedness to ICE improvements also shows a significant but weaker positive association, indicating that ICE capabilities do not necessarily impede EV innovation. However, when ICE-related capabilities substantially outweigh EV-related ones, we observe a negative effect on EV patenting, pointing to emerging lock-in risks. This effect is markedly stronger in low-complexity regions. Interregional co-patenting analysis shows that regions with low complexity and low EV-relatedness are doubly disadvantaged, as they are weakly positioned internally and less integrated into innovation networks. Focusing on four regions of this type, we illustrate how targeted partnerships could unlock untapped potentials. The results inform a policy discussion highlighting the need for place-sensitive innovation strategies addressing both emerging and legacy capability structures.

Latent attention operator network with augmented representation for complex PDE systems in intricate geometries