Search Results (320)

Using a firm-level panel of China’s heavily polluting industries from 2014 to 2023, this paper employs two-way fixed-effects regressions and a battery of robustness checks to examine how ESG performance affects corporate financing constraints and the channels through which effects operate. We uncover a paradox: overall ESG performance is associated with reduced financing constraints, whereas the environmental subcomponent alone significantly aggravates firms’ financing difficulties. Moderating analyses show that stricter regional environmental regulations and higher persistence in firms’ innovation outputs weaken the easing effect of aggregate ESG performance and may even fully offset it under certain conditions. Mechanism tests reveal that ESG mitigates constraints mainly by enhancing corporate reputation and curbing green agency costs. Heterogeneity analyses further indicate that the environmental-induced tightening effect is more pronounced in state-owned enterprises, firms in eastern provinces, and those located in regions with lower levels of new-quality productivity. These findings point to a trade-off between the short-term compliance costs of environmental investment and the longer-run signaling and informational benefits of ESG disclosure. Policy implications include the need for targeted green-finance support, improved ESG transparency and verification, and measures to accelerate innovation pathways that shorten the payback period for environmental investments. Full article

Strategic mergers and acquisitions (M&A) can support sustainable business models by enabling firms to adapt their capabilities and competitive positions as conditions change. This study examines how target listing status (public vs. private) and acquirer size shape short-term shareholder wealth in Korean SMEs (Small- and medium-sized enterprise), and links announcement reactions to subsequent operating outcomes. Using an event study and multivariate regressions on 155 M&A announcements by KOSDAQ-listed SMEs (Korean Securities Dealers Automated Quotations) (2016–2020), we find that smaller acquirers earn significantly higher announcement-period cumulative abnormal returns (CAR)—i.e., smaller firm size is positively associated with superior market-adjusted performance around M&A events. Although acquisitions of privately held targets and diversifying deals show higher unadjusted means, their effects become statistically insignificant once firm fundamentals and size are controlled for. To connect M&A strategy with business-model sustainability, we operationalize sustainability as the alignment between short-term market expectations (CAR) and realized operating performance over 1–2 years, measured by return on operating cash flow (ROCF); medium-term checks indicate that the short-run “size effect” attenuates, underscoring the role of execution and scale in longer-run outcomes. Overall, the evidence highlights the primacy of firm-specific fundamentals, strategic fit, and integration capacity in guiding M&A decisions that advance both near-term performance and longer-term resilience. The Korean SME setting—marked by concentrated ownership, resource constraints, and a chaebol-influenced market and policy environment—provides a stringent context for these tests. Full article

This article examines the impact of an undetected Instrument Landing System (ILS) failure on crew situational awareness. A literature review of similar aviation accidents is presented, highlighting the recurring challenge of misleading instrument indications and their influence on approach safety. The research environment consisted of flight simulator replicating both ideal and accident-weather conditions at two airports, with the final scenario involving a simulated ILS receiver malfunction providing erroneous yet seemingly valid indications. Six pilots with varying flight hours participated, conducting four simulated approaches under different conditions. Flight path stability, deviation from glide slope and course, approach speed, and decision-making were recorded and analyzed. The results indicate that experienced pilots detected inconsistencies more quickly, maintained more stable control inputs, and initiated go-arounds earlier, while less experienced pilots required more time but were still able to correctly assess the risks. The primary goal of this research was to identify cognitive mechanisms and operational decision-making processes under simulated conditions, not to establish universally generalizable outcomes. The findings underline the importance of simulator-based training incorporating unexpected navigation system failures to reinforce cross-checking habits, enhance situational awareness, and improve decision-making during critical phases of flight. Full article

This work presents the results of a research study focused on the development and evaluation of an algorithmic optimal control framework for energy-efficient operation of screw compressors in smart power systems. The proposed approach is based on the Pontryagin maximum principle (PMP), which enables the synthesis of a mathematically grounded regulator that minimizes the total energy consumption of a nonlinear electromechanical system composed of a screw compressor and a variable-frequency induction motor. Unlike conventional PID controllers, the developed algorithm explicitly incorporates system constraints, nonlinear dynamics, and performance trade-offs into the control law, allowing for improved adaptability and energy-aware operation. Simulation results obtained using MATLAB/Simulink confirm that the PMP-based regulator outperforms classical PID solutions in both transient and steady-state regimes. Experimental tests conducted in accordance with standard energy consumption evaluation methods showed that the proposed PMP-based controller provides a reduction in specific energy consumption of up to 18% under dynamic load conditions compared to a well-tuned basic PID controller, while maintaining high control accuracy, faster settling, and complete suppression of overshoot under external disturbances. The control system demonstrates robustness to parametric uncertainty and load variability, maintaining a statistical pressure error below 0.2%. The regulator’s structure is compatible with real-time execution on industrial programmable logic controllers (PLCs), supporting integration into intelligent automation systems and smart grid infrastructures. The discrete-time PLC implementation of the regulator requires only 10³ arithmetic operations per cycle and less than 10² kB of RAM for state, buffers, and logging, making it suitable for mid-range industrial controllers under 2–10 ms task cycles. Fault-tolerance is ensured via range and rate-of-change checks, residual-based plausibility tests, and safe fallbacks (baseline PID or torque-limited speed hold) in case of sensor faults. Furthermore, the proposed approach lays the groundwork for hybrid extensions combining model-based control with AI-driven optimization and learning mechanisms, including reinforcement learning, surrogate modeling, and digital twins. These enhancements open pathways toward predictive, self-adaptive compressor control with embedded energy optimization. The research outcomes contribute to the broader field of algorithmic control in power electronics, offering a scalable and analytically justified alternative to heuristic and empirical tuning approaches commonly used in industry. The results highlight the potential of advanced control algorithms to enhance the efficiency, stability, and intelligence of energy-intensive components within the context of Industry 4.0 and sustainable energy systems. Full article

Laser Powder Bed Fusion (L-PBF) parts combine geometric freedom with process-induced rough surfaces that challenge residual-stress metrology. We evaluated the accuracy of the incremental hole-drilling (IHD) method with electronic speckle pattern interferometry (ESPI) by applying defined stresses via four-point bending to stress-relieved AlSi10Mg coupons, rather than measuring unknown process stresses. Flat specimens (2 mm, thin per ASTM E837) were analyzed on up-skin, side-skin, and CNC-milled surfaces; thin-specimen calibration coefficients were used. After a preliminary inter-specimen check (three specimens per surface; spread < 8 MPa), one representative specimen per surface was tested with three drill sites to assess intra-specimen uniformity. Measured IHD–ESPI stresses agreed best at 70 MPa: deviations were ~4.1% (up-skin), 6.0% (side-skin), and 6.24% (CNC-milled). At 10 MPa the relative errors increased (23.6%, 18.4%, and 1.40%), consistent with reduced ESPI signal-to-noise and fixture compliance in the low-stress regime. At 140 MPa, deviations rose again (21.1%, 14.3%, and 13.1%), reflecting operation near the ~60% Rp0.2 elastic limit of hole-drilling and potential local plasticity. Surface-dependent artifacts also mattered as follows: the side-skin required no coating and performed comparably to CNC-milled, whereas the up-skin’s roughness plus matting spray introduced fringe distortions and chip/coating debris near the hole. This controlled study indicates that IHD–ESPI can provide reliable results on L-PBF AlSi10Mg in the mid-stress range when surface preparation, coating, and rig compliance are carefully managed. Limitations include excluding down-skin surfaces and testing only one specimen per condition; thus, results should be generalized cautiously. Full article

The air transportation industry is challenged to address airport curbside delay problems that affect landside service quality and can potentially impact check-in operations. Methodological advances guided by industry requirements are needed to support curbside improvement studies. Existing methods require verification of assumptions prior to application or need expensive surveys to acquire data for use in microsimulations. A probability-based macrosimulation method is advanced for the evaluation of the level of service and capacity of the curbside processor. A key component of the method is the simulation of the stochastic balance of demand and available curb space for unloading/loading tasks using the Monte Carlo simulation model. The method meets the planning and operation requirements with the ability to analyze conditions commonly experienced at the curb area. Example applications illustrate the flexibility of the method in evaluating existing as well as planned facilities of diverse designs and sizes. The developed method can contribute to curbside processor delay reduction and due to the macroscopic nature of the method, the data requirements can be met by an airport authority without costly surveys. Full article

The use of commercial off-the-shelf smart devices in digital signage for sentient spaces is emerging as a promising solution within smart city environments. In such scenarios, these devices are often required to execute resource-intensive applications despite limited local computational capacity. Although cloud and fog infrastructures have been proposed to offload demanding workloads, they are not always suitable due to privacy and security concerns. As a result, executing sentient space applications directly on smart devices may exceed their processing capabilities. To address this limitation, state-of-the-art solutions have introduced load balancing techniques for smart devices. However, these approaches typically rely on centralized coordination or require extensive system profiling, making them unsuitable for sentient spaces, where device availability is intermittent and cooperative behavior must remain lightweight, adaptive, and decentralized. This paper proposes a distributed load balancing strategy tailored for sentient spaces that operate without reliance on cloud or fog infrastructures. The approach is based on reactive cooperation among neighboring devices and employs a local feasibility-check mechanism to determine when to offload computation and which neighboring devices are available to process it. The proposed solution is evaluated in a laboratory setting that emulates a real-world sentient space scenario within a commercial mall. Experimental results show the effectiveness of the proposed approach in maintaining real-time performance and mitigating local computational overload without relying on centralized infrastructure. Even under dynamic operating conditions, the system achieves a load balancing execution time of 5 ms on an ARM Cortex-A53 processor integrated in an AMD Zynq UltraScale+ platform. Full article

When a transformer operates under three-phase unbalanced conditions, the location of the winding hot-spot temperature (HST) is no longer fixed on a certain phase. Taking an S13-M-100 kVA/10 kV transformer as the research object, this paper proposes a streamline inversion method for inverting the winding HST based on the analysis of oil flow morphology. The study employs the finite volume method for coupled calculations of a transformer’s thermal fluid field and combines a support vector regression (SVR) model for the HST inversion. An orthogonal experimental method is used to construct the training and testing sample sets, and the grid search method is utilized to optimize the parameters of the SVR model. In response to variations in hot-spot locations under three-phase unbalanced conditions, representative streamlines are reasonably selected, and a genetic algorithm-based dimensionality reduction optimization is performed on the feature quantities. The research results indicate that the established inversion model exhibits high inversion accuracy under three-phase unbalanced conditions, with a maximum temperature difference of 3.71 K, and the robustness check verifies the stability of the model. Full article

As the automotive industry continues to evolve rapidly, there is a growing demand for high-throughput reliable communication systems within vehicles. This paper presents the implementation and verification of a fault-tolerant Ethernet-based communication protocol tailored for automotive applications operating at 1 Gbps and above. The proposed system introduces a multi-port Network Interface Controller (NIC) architecture that supports real-time communication and robust fault handling. To ensure adaptability across various in-vehicle network (IVN) scenarios, the system allows for configurable packet sizes and transmission rates and supports diverse data formats. The architecture integrates cyclic redundancy check (CRC)-based error detection, real-time recovery mechanisms, and file-driven data injection techniques. Functional validation is performed using Verilog HDL simulations, demonstrating deterministic timing behavior, modular scalability, and resilience under fault injection. This paper presents a fault-tolerant Network Interface Controller (NIC), architecture incorporating CRC-based error detection, real-time recovery logic, and file-driven data injection. The system is verified through Verilog HDL simulation, demonstrating correct timing behavior, modular scalability, and robustness against injected transmission faults. Compared to conventional dual-port NICs, the proposed quad-port architecture demonstrates superior scalability and error tolerance under injected fault conditions. Experimental results confirm that the proposed NIC architecture achieves stable multi-port communication under embedded automotive environments. This study further introduces a novel quad-port NIC with an integrated fault injection algorithm and evaluates its performance in terms of error tolerance. Full article

Rydberg atoms play a crucial role in testing atomic structure theory, quantum computing and simulation. Measurements of transition frequencies from the $2^{1,3}S$ states to Rydberg ${}^{1,3}P$ states have reached a precision of several kHz, which poses significant challenges for theoretical calculations, since the accuracy of variational energy calculations decreases rapidly with increasing principal quantum number n. Recently the complex “triple” Hylleraas basis was employed to attain the ionization energy of helium $24{}^{1}P$ state with high accuracy. Different from it, we extended the correlated B-spline basis functions (C-BSBFs) to calculate the Rydberg states of helium. The nonrelativistic energies of $1snp$${}^{1,3}P$ states up to $n=27$ achieve at least 14 significant digits using a unified basis set, thereby greatly reducing the complexity of the optimization process. Results of geometric structure parameters and cusp conditions were presented as well. Both the global operator and direct calculation methods are employed and cross-checked for contact potentials. This C-BSBF method not only obtains high-accuracy energies across all studied levels but also confirms the effectiveness of the C-BSBFs in depicting long-range and short-range correlation effects, laying a solid foundation for future high-accuracy Rydberg-state calculations with relativistic and QED corrections included in helium atom and low-Z helium-like ions. Full article

Image segmentation is a foundation for autonomous driving frameworks that empower vehicles to explore and navigate their surrounding environment. It gives a fundamental setting to the dynamic cycles by dividing the image into significant parts like streets, vehicles, walkers, and traffic signs. Precise segmentation ensures safe navigation and the avoidance of collisions, while following the rules of traffic is very critical for seamless operation in self-driving cars. The most recent deep learning-based image segmentation models have demonstrated impressive performance in structured environments, yet they often fall short when applied to the complex and unpredictable conditions encountered in autonomous driving. This study proposes an Adaptive Ensemble Attention (AEA) mechanism within a Generative Adversarial Network architecture to deal with dynamic and complex driving conditions. The AEA integrates the features of self, spatial, and channel attention adaptively and powerfully changes the amount of each contribution as per input and context-oriented relevance. It does this by allowing the discriminator network in GAN to evaluate the segmentation mask created by the generator. This explains the difference between real and fake masks by considering a concatenated pair of an original image and its mask. The adversarial training will prompt the generator, via the discriminator, to mask out the image in such a way that the output aligns with the expected ground truth and is also very realistic. The exchange of information between the generator and discriminator improves the quality of the segmentation. In order to check the accuracy of the proposed method, the three widely used datasets BDD100K, Cityscapes, and KITTI were selected to calculate average IoU, where the value obtained was 89.46%, 89.02%, and 88.13% respectively. These outcomes emphasize the model’s effectiveness and consistency. Overall, it achieved a remarkable accuracy of 98.94% and AUC of 98.4%, indicating strong enhancements compared to the State-of-the-art (SOTA) models. Full article

Carbon fiber-reinforced polymer (CFRP) laminates have been widely coated on aged and damaged structures for recovering or enhancing their structural performance. The health conditions of the coated composite structures have been given high attention, as they are critically important for assessing operational safety and residual service life. However, the current problem is the lack of an efficient, long-term, and stable monitoring technique to characterize the structural behavior of coated composite structures in the whole life cycle. For this reason, bare and packaged fiber Bragg grating (FBG) sensors have been specially developed and designed in sensing networks to monitor the structural performance of CFRP-coated composite beams under different loads. Some optical fibers have also been inserted in the CFRP laminates to configure the smart CFRP component. Detailed data interpretation has been conducted to declare the strengthening process and effect. Finite element simulation and simplified theoretical analysis have been conducted to validate the experimental testing results and the deformation profiles of steel beams before and after the CFRP coating has been carefully checked. Results indicate that the proposed FBG sensors and sensing layout can accurately reflect the structural performance of the composite beam structure, and the CFRP coating can share partial loads, which finally leads to the downward shift in the centroidal axis, with a value of about 10 mm. The externally bonded sensors generally show good stability and high sensitivity to the applied load and temperature-induced inner stress variation. The study provides a straightforward instruction for the establishment of a structural health monitoring system for CFRP-coated composite structures in the whole life cycle. Full article

For power electronic interfaces, Direct Power Control (DPC) has emerged as a leading control technique, especially in applications such as synchronous motors, induction motors, and other electric drives; renewable energy sources (such as photovoltaic inverters and wind turbines); and converters that are grid-connected, such as Virtual Synchronous Generator (VSG) and Static Compensator (STATCOM) configurations. DPC accomplishes several significant goals by avoiding the inner current control loops and doing away with coordinating transformations. The application of STATCOM based on three- and five-level diode-clamped inverters is covered in this work. The study checks the abilities of DPC during power control adjustments during diverse grid operation scenarios while detailing how multilevel inverters affect system stability and power reliability. Proportional Integral (PI) controllers are used to control active and reactive power levels as part of the control approach. This study shows that combining DPC with Sinusoidal Pulse Width Modulation (SPWM) increases the system’s overall electromagnetic performance and control accuracy. The performance of STATCOM systems in power distribution and transient response under realistic operating conditions is assessed using simulation tools applied to three-level and five-level inverter topologies. In addition to providing improved voltage quality and accurate reactive power control, the five-level inverter structure surpasses other topologies by maintaining a total harmonic distortion (THD) below 5%, according to the main findings. The three-level inverter operates efficiently under typical grid conditions because of its straightforward design, which uses less processing power and computational complexity. Full article

Both ZnO and BeO semiconductors crystallize in the hexagonal wurtzite (wz), cubic rock salt (rs), and zinc-blende (zb) phases, depending upon their growth conditions. Low-dimensional heterostructures ZnO/Be_xZn_1-xO and Be_xZn_1-xO ternary alloy-based devices have recently gained substantial interest to design/improve the operations of highly efficient and flexible nano- and micro-electronics. Attempts are being made to engineer different electronic devices to cover light emission over a wide range of wavelengths to meet the growing industrial needs in photonics, energy harvesting, and biomedical applications. For zb materials, both experimental and theoretical studies of lattice dynamics ${\mathsf{\omega }}_{\mathrm{j}}\left(\overrightarrow{\mathrm{q}}\right)$ have played crucial roles for understanding their optical and electronic properties. Except for zb ZnO, inelastic neutron scattering measurement of ${\mathsf{\omega }}_{\mathrm{j}}\left(\overrightarrow{\mathrm{q}}\right)$ for BeO is still lacking. For the Be_xZn_1-xO ternary alloys, no experimental and/or theoretical studies exist for comprehending their structural, vibrational, and thermodynamical traits (e.g., Debye temperature ${\mathsf{\Theta }}_{\mathrm{D}}\left(\mathrm{T}\right);$ specific heat ${\mathrm{C}}_{\mathrm{v}}\left(\mathrm{T}\right))$. By adopting a realistic rigid-ion model, we have meticulously simulated the results of lattice dynamics, and thermodynamic properties for both the binary zb ZnO, BeO and ternary Be_xZn_1-xO alloys. The theoretical results are compared/contrasted against the limited experimental data and/or ab initio calculations. We strongly feel that the phonon/thermodynamic features reported here will encourage spectroscopists to perform similar measurements and check our theoretical conjectures. Full article

Friction behaviour at the wheel–rail interface is of critical importance for railway operations and maintenance and is generally characterised by creep curves. The V-Track test rig was used in this study to measure both the lateral and longitudinal creep curves with uncontaminated dry interface conditions, utilising contact pressures representative of operational railway wheel–rail systems. The novelties of this study are threefold. 1. With proper representations of train/track components, the V-Track tests revealed the effects of structural dynamics on measuring wheel–rail creep curves in real life. 2. Pure lateral and longitudinal creepage conditions were produced with two distinct experimental principles—displacement- and force-controlled—on the V-Track, i.e., by carefully controlling the angle of attack and the traction/braking torque, respectively, and thus the coefficient of friction from lateral and longitudinal creep curves measured on the same platform could be cross-checked. 3. The uncertainties in the measured creep curves were analysed, which was rarely addressed in previous studies on creep curve measurements. In addition, the measured creep curves were compared against the theoretical creep curves obtained from Kalker’s CONTACT. The influence of wheel rolling speed and torque direction on the creep curve characteristics was then investigated. The measurement results and findings demonstrate the reliability of the V-Track to measure wheel–rail creep curves and study the wheel–rail frictional rolling contact. Full article