Search Results (278)

To remain competitive in the decarbonization process of the economy worldwide, energy companies must preserve their market value to attract new investors and remain resilient throughout the transition to net zero. This article examines the market value of US energy companies during the period 2012–2024 in relation to their power generation mix. Panel regression analyses reveal that Tobin’s q and price-to-book ratios increase significantly for solar and wind power, while they experience moderate increases for natural gas power. In contrast, Tobin’s q and price-to-book ratios decline for nuclear and coal power. Furthermore, accounting-based profitability, measured by the return on assets (ROA), does not show significant variation with any type of power generation. The findings suggest that market investors prefer solar, wind, and natural gas power generation, thereby attributing greater value (that is, demanding lower risk compensation) to green companies compared to traditional ones. These insights provide guidance to executives, investors, and policy makers on how the power generation mix can influence strategic decisions in the energy sector. Full article

This paper proposes a novel hierarchical control architecture that combines Model Predictive Control (MPC) with Explicit Model-Following Control (EMFC) to enable accurate and efficient trajectory tracking for quadrotor electric Vertical Takeoff and Landing (eVTOL) aircraft operating in urban environments. The approach addresses the challenges of strong nonlinear dynamics, multi-axis coupling, and stringent safety constraints by separating the planning task from the fast-response control task. The MPC layer generates constrained velocity and yaw rate commands based on a simplified inertial prediction model, effectively reducing computational complexity while accounting for physical and operational limits. The EMFC layer then compensates for dynamic couplings and ensures the rapid execution of commands. A high-fidelity simulation model, incorporating rotor flapping dynamics, differential collective pitch control, and enhanced aerodynamic interference effects, is developed to validate the controller. Four representative ADS-33E-PRF tasks—Hover, Hovering Turn, Pirouette, and Vertical Maneuver—are simulated. Results demonstrate that the proposed controller achieves accurate trajectory tracking, stable flight performance, and full compliance with ADS-33E-PRF criteria, highlighting its potential for autonomous urban air mobility applications. Full article

Introduction: Hip abductor strength is essential for pelvic stability, lower limb alignment, and injury prevention. Weaknesses of the gluteus medius and minimus contribute to various musculoskeletal conditions. Lateral band walks and monster walks are elastic resistance exercises commonly used to target the hip abductors and external rotators in functional, weight-bearing tasks. Therefore, the aim was to summarize the current evidence on the biomechanics, muscle activation, and clinical applications of lateral and monster band walks. Methods: This narrative review was conducted following the SANRA guideline. A comprehensive literature search was performed across PubMed, Scopus, Web of Science, and SPORTDiscus up to April 2025. Studies on the biomechanics, electromyography, and clinical applications of lateral band walks and monster walks were included, alongside relevant evidence on hip abductor strengthening. Results: A total of 13 studies were included in the review, of which 4 specifically investigated lateral band walk and/or monster walk exercises. Lateral and monster walks elicit moderate to high activation of the gluteus medius and maximus, especially when performed with the band at the ankles or forefeet and in a semi-squat posture. This technique minimizes compensation from the tensor fasciae latae and promotes selective gluteal recruitment. Proper execution requires control of the trunk and pelvis, optimal squat depth, and consistent band tension. Anatomical factors (e.g., femoral torsion), sex differences, and postural variations may influence movement quality and necessitate tailored instruction. Full article

Frequency instability poses a significant challenge to the overall stability of islanded microgrid systems. Deep reinforcement learning (DRL)-based intelligent control strategies are drawing considerable attention for their ability to operate without the need for previous system dynamics information and the capacity for autonomous learning. This paper proposes an intelligent frequency secondary compensation solution that divides the traditional secondary frequency control into two layers. The first layer is based on a PID controller and the second layer is an intelligent controller based on DRL. To address the typically extensive training durations associated with DRL controllers, this paper integrates transfer learning, which significantly expedites the training process. This scheme improves control accuracy and reduces computational redundancy. Simulation tests are executed on an islanded microgrid with four distributed generators and an IEEE 13-bus system is utilized for further validation. Finally, the proposed method is validated on the OPAL-RT real-time test platform. The results demonstrate the superior performance of the proposed method. Full article

Hand gestures are essential for interaction in augmented and virtual reality (AR/VR), allowing users to intuitively manipulate virtual objects and engage with human–machine interfaces (HMIs). Accurate gesture recognition is critical for effective task execution. However, users often encounter difficulties due to the lack of immediate and clear feedback from head-mounted displays (HMDs). Current tracking technologies cannot always guarantee reliable recognition, leaving users uncertain about whether their gestures have been successfully detected. To address this limitation, haptic feedback can play a key role by confirming gesture recognition and compensating for discrepancies between the visual perception of fingertip contact with virtual objects and the actual system recognition. The goal of this paper is to compare a simple vibrotactile ring with a full glove device and identify their possible improvements for a fundamental gesture like pinching and fine moving of objects using Microsoft HoloLens 2. Where the pinch action is considered an essential fine motor skill, augmented reality integrated with haptic feedback can be useful to notify the user of the recognition of the gestures and compensate for misaligned visual perception between the tracked fingertip with respect to virtual objects to determine better performance in terms of spatial precision. In our experiments, the participants’ median distance error using bare hands over all axes was 10.3 mm (interquartile range [IQR] = 13.1 mm) in a median time of 10.0 s (IQR = 4.0 s). While both haptic devices demonstrated improvement in participants precision with respect to the bare-hands case, participants achieved with the full glove median errors of 2.4 mm (IQR = 5.2) in a median time of 8.0 s (IQR = 6.0 s), and with the haptic rings they achieved even better performance with median errors of 2.0 mm (IQR = 2.0 mm) in an even better median time of only 6.0 s (IQR= 5.0 s). Our outcomes suggest that simple devices like the described haptic rings can be better than glove-like devices, offering better performance in terms of accuracy, execution time, and wearability. The haptic glove probably compromises hand and finger tracking with the Microsoft HoloLens 2. Full article

Infrastructure facility development and modernization highly contribute to national economic growth, but at the same time, such development also causes local negative impacts on the use of specific land plots, creating losses for their right holders. In Russia, some prerequisites have already been laid down on the issue of compensation for the losses associated with restrictions on the rights and prohibitions of economic activity within zones with special territory use conditions (ZSTUCs). However, the impacts of such facilities lead to environmental pollution and land use disadvantages, such as irregular parcels. The aim of this work is to substantiate a set of approaches to compensating for the cumulative negative impact of infrastructure facilities. The factors causing the negative impacts of infrastructure facilities are grouped into three areas: rights restrictions, territorial deficiencies and environmental pollution. This work uses the SWOT analysis method with the possibility of element-by-element analysis, as a result of which the approaches to the compensation for negative impacts under different external and internal conditions are determined. As a result of this study, a justification for a set of approaches to compensating for the negative impacts of infrastructure facilities on land use was executed, and a new algorithm to compensate the right holders of the land, industry sector or state for such negative impacts was developed. The following approaches to compensating for negative impacts were identified: loss assessment; the establishment of environmental payments; cadastral value adjustment; compensation for industry sector losses; and the use of state regulation tools. The first two approaches were identified as the main ones. The proposed algorithm can be realized only with the help of the abovementioned methodological approaches, which form a basis for further research. Full article

This paper proposes a dual-mode CMOS analog front-end (AFE) circuit for short-wave infrared (SWIR) image sensors, which integrates a hybrid readout circuit (ROIC) and a 12-bit two-step single-slope analog-to-digital converter (TS-SS ADC). The ROIC dynamically switches between buffered-direct-injection (BDI) and direct-injection (DI) modes, thus balancing injection efficiency against power consumption. While the DI structure offers simplicity and low power, it suffers from unstable biasing and reduced injection efficiency under high background currents. Conversely, the BDI structure enhances injection efficiency and bias stability via an input buffer but incurs higher power consumption. To address this trade-off, a dual-mode injection architecture with mode-switching transistors is implemented. Mode selection is executed in-pixel via a low-leakage transmission gate and coordinated by the column timing controller, enabling low-current pixels to operate in low-noise BDI mode, whereas high-current pixels revert to the low-power DI mode. The TS-SS ADC employs a four-terminal comparator and dynamic reference voltage compensation to mitigate charge leakage and offset, which improves signal-to-noise ratio (SNR) and linearity. The prototype occupies 2.1 mm × 2.88 mm in a 0.18 µm CMOS process and serves a 64 × 64 array. The AFE achieves a dynamic range of 75.58 dB, noise of 249.42 μV, and 81.04 mW power consumption. Full article

Applying the resource-based view and dynamic capability theory, this study employs panel data analysis to examine how financial flexibility influences corporate innovation efficiency from an integrated resource-capability perspective. Analyzing data from Chinese A-share listed companies during 2013–2022, we discovered three key results. First, as an organizational liquidity buffer, financial flexibility reduces transaction costs, enhances incentives for technical talent retention, and better aligns executive compensation with innovation objectives. Second, as a manifestation of financial dynamic capabilities, financial flexibility significantly boosts a firm’s overall dynamic capabilities, thereby increasing innovation efficiency. Third, institutional investor engagement positively moderates this relationship through enhanced governance oversight. These investors strengthen governance oversight and reduce information asymmetry. Our findings advance the financial flexibility literature and offer actionable strategies to optimize innovation resource allocation and sustain R&D competitiveness. Companies should strategically build financial reserves to enhance innovation efficiency and achieve sustainable development. Full article

This article presents a mathematically grounded approach to increasing the operational reliability of current collectors in electric transport systems by ensuring a constant contact force between the collector shoe and the power rail. The core objective is achieved through the development and analysis of a mechanical system incorporating spring and cam elements, which is specifically designed to provide a nearly invariant contact pressure under varying operating conditions. A set of equilibrium equations was derived to determine the stiffness ratios of the springs and the geometric conditions under which the contact force remains constant despite wear or displacement. Additionally, the paper introduces a method for synthesizing the cam profile that compensates for nonlinear spring deformation, ensuring force constancy over a wide range of movement. The analytical results were validated through parametric simulations, which assessed the influence of wear depth, rail inclination, and external vibrations on the system’s force output. These simulations, executed within a numerical framework using scientific computing tools, demonstrated that the deviation of the contact force does not exceed a few percent under typical disturbances. Experimental verification further confirmed the theoretical predictions. The study exemplifies the effective use of mathematical modeling, nonlinear mechanics, and numerical methods in the design of energy transmission components for transport applications, contributing to the development of robust and maintainable systems. Full article

This study investigates the interactive link between bank performance and financial innovation in Tunisian banking using a mixed-methods research framework that combines econometric approaches and institutional factors. The empirical analysis uses a panel data of 11 commercial banks from the period of 2000–2024 and employs an Autoregressive distributed lag (ARDL) model to estimate short- and long-run impacts of innovation on return on equity (ROE). A composite indicator of Fintech investment, digital service adoption, and innovation productivity characterizes financial innovation. Governance factors like the presence of risk management departments and executive compensation are taken into account. The results reveal a robust positive impact of financial innovation on bank performance in the long run, especially in more concentrated market settings. Risk management supports performance, while inefficient executive compensation is negatively associated with profitability. These findings are confirmed by robustness tests with HAC standard errors. This research contributes to the literature by situating financial innovation in the context of an emerging North African market and produces practitioner-relevant information for policymakers and bank executives interested in ensuring that performance results are consistent with innovation strategy. Full article

This research investigates the effect of board characteristics on environmental, social, and governance (ESG) disclosure among firms listed on the Saudi Stock Exchange (Tadawul) from 2021 to 2023. Motivated by the global shift toward sustainable development and the Saudi Vision 2030 agenda, this study examines how board size, gender diversity, independence, expertise, and compensation impact ESG disclosure practices. Drawing on stakeholder and agency theories, the regression model uses a sample of 78 Saudi-listed companies. ESG disclosure is measured using a content analysis-based checklist that conforms to international and Saudi ESG reporting frameworks. The findings indicate that background and skills, female representation, and compensation positively correlate with ESG disclosure. Conversely, board size and independence do not show significant relationships. The results highlight the pivotal role of board composition in emphasizing business practices for sustainability in emerging markets, particularly within the unique institutional setting of Saudi Arabia. The study contributes to the growing body of ESG literature by offering factual proof from an under-researched context and practical ramifications for investors, legislators, and business executives, as well as seeking to enhance transparency and accountability through effective board governance. Full article

Malware remains a central tool in cyberattacks, and systematic research into adversarial attack techniques targeting malware is crucial in advancing detection and defense systems that can evolve over time. Although numerous review articles already exist in this area, there is still a lack of comprehensive exploration into emerging artificial intelligence technologies such as reinforcement learning from the attacker’s perspective. To address this gap, we propose a foundational reinforcement learning (RL)-based framework for adversarial malware generation and develop a systematic evaluation methodology to dissect the internal mechanisms of generative models across multiple key dimensions, including action space design, state space representation, and reward function construction. Drawing from a comprehensive review and synthesis of the existing literature, we identify several core findings. (1) The scale of the action space directly affects the model training efficiency. Meanwhile, factors such as the action diversity, operation determinism, execution order, and modification ratio indirectly influence the quality of the generated adversarial samples. (2) Comprehensive and sensitive state feature representations can compensate for the information loss caused by binary feedback from real-world detection engines, thereby enhancing both the effectiveness and stability of attacks. (3) A multi-dimensional reward signal effectively mitigates the policy fragility associated with single-metric rewards, improving the agent’s adaptability in complex environments. (4) While the current RL frameworks applied to malware generation exhibit diverse architectures, they share a common core: the modeling of discrete action spaces and continuous state spaces. In addition, this work explores future research directions in the area of adversarial malware generation and outlines the open challenges and critical issues faced by defenders in responding to such threats. Our goal is to provide both a theoretical foundation and practical guidance for building more robust and adaptive security detection mechanisms. Full article

This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, which runs artificial intelligence (AI) workloads. The real-time sensor data are subject to errors, such as time-varying bias and thermal stress. To compensate for these drifts, the traditional calibration method based on a linear model is applicable, and unfortunately, it does not work with nonlinear errors. The algorithm devised by this study to reduce such errors adopts Radial Basis Function Neural Networks (RBF-NNs). This method does not rely on the classical adoption of the backpropagation algorithm. Due to its low complexity, it is deployable using kibyte memory and in software runs on the DSP, thus performing interleaved in-sensor learning and inference by itself. This avoids using any off-package computing processor. The learning process is performed periodically to achieve consistent sensor recalibration over time. The devised solution was implemented in both 32-bit floating-point data representation and 16-bit quantized integer version. Both of these were deployed into the Intelligent Sensor Processing Unit (ISPU), integrated into the LSM6DSO16IS Inertial Measurement Unit (IMU), which is a programmable 5–10 MHz DSP on which the programmer can compile and execute AI models. It integrates 32 KiB of program RAM and 8 KiB of data RAM. No permanent memory is integrated into the package. The two (fp32 and int16) RBF-NN models occupied less than 21 KiB out of the 40 available, working in real-time and independently in the sensor package. The models, respectively, compensated between 46% and 95% of the accelerometer measurement error and between 32% and 88% of the gyroscope measurement error. Finally, it has also been used for attitude estimation of a micro aerial vehicle (MAV), achieving an error of only 2.84°. Full article

The relative performance evaluation (RPE) hypothesis posits that executive compensation should not be influenced by uncontrollable exogenous shocks. However, prior studies often find limited empirical support for this hypothesis, partly because identifying peers exposed to the same exogenous shocks is challenging. We propose a new method for identifying peers and testing the RPE hypothesis within the context of exchange rate risk. Specifically, we select peers based on the sensitivity of their stock returns to exchange rate fluctuations. We find evidence that firms respond to significant exchange rate movements by ex post adjusting their peer selection to include peers with similar exchange rate risk exposure. Furthermore, after accounting for ex post peer group adjustments, we find much stronger support for the RPE hypothesis than prior studies. Full article

This research empirically investigates how the adoption of restricted stock units (RSUs) affects earnings per share (EPS) and operating profit, focusing on variations across industries. RSUs have emerged as a strategic compensation tool for promoting alignment between employee interests and long-term organizational objectives, while overcoming the short-sighted focus typically associated with conventional stock options. However, previous studies have mainly focused on analyzing the average effect of RSU or verifying only whether there is a short-term improvement in performance after its introduction, and there has been no sufficient review of the long-term effects. In addition, there is a lack of analyses on how the effects of RSU differ by industry. To fill this research gap, this study applied hierarchical regression analysis to S&P 500 company data from 1997 to 2023 to compare and analyze the differential effects of RSU by industry. The analysis showed that the EPS and operating income were only significantly affected by some industries in the early stages of RSU introduction, but the EPS and operating income were significantly improved in all industries in the long term. In addition, it was confirmed that the effects of RSU differ depending on the characteristics of the industry. This study empirically verifies the long-term effects of RSU and the differences by industry, offering practical insights for executives and shareholders when utilizing RSUs as a forward-looking compensation mechanism that fosters sustainable performance and enduring corporate value. Full article