Partial shading fragments the power–voltage characteristic of series-connected photovoltaic (PV) arrays into multiple local peaks, rendering conventional trackers ineffective and exposing the reliance of metaheuristic global maximum power point tracking (GMPPT) methods on random initialization and empirical tuning coefficients. This paper proposes TSB-GMPPT, a deterministic tanh-surrogate branch-and-bound GMPPT algorithm that eliminates both dependencies. A control-oriented hyperbolic-tangent surrogate of the PV I–V characteristic admits a non-iterative Lambert W asymptotic approximation for the maximum power point voltage. During discovery, a single saturation-region measurement per candidate is sufficient to construct a calibrated optimistic bound, safely pruning the search space before final peak localization. An offline polynomial calibration recovers the remaining parameters from short-circuit current. During discovery, an optimistic branch-and-bound rule maintains an upper power bound for each unconfirmed layer and permanently discards candidates that cannot exceed the incumbent confirmed power, contracting the admissible search space without exhaustive probing. A five-state finite-state machine governs initialization, shading-topology detection, discovery, winner refitting, convergence, and adaptive reinitialization. Experimental validation using a Chroma 62150H solar array simulator and a dSPACE DS1104 platform on five-module benchmark cases and additional eight-module complex multi-peak shading cases demonstrates average tracking efficiencies of 99.94% under partial shading and 99.98% under uniform irradiance, with corresponding average convergence times of 0.25 s and 0.21 s, respectively, for the five-module test set. Further validation under eight-module complex multi-peak shading cases and dynamic irradiance transitions confirms rapid re-tracking, accurate GMPP identification, and robust operation under realistic operating conditions.
This study presents a novel Machine Learning (ML) approach for predicting the central and minimum film thickness in isothermal elastohydrodynamically lubricated (EHL) smooth line contacts under non-Newtonian conditions. A modular, generalized ML approach based on the similarity analysis is proposed and compared with a direct one. The Moes dimensionless parameters and pressure-viscosity adaptation are employed to account for different operating conditions, solid body, and lubricant properties, incorporating non-Newtonian effects and correction for compressibility. Both ML approaches are trained using a database generated by solving the coupled generalized Reynolds, linear elasticity, load balance, and shear stress equations for EHL line contacts using a Finite Element Method (FEM)-based numerical model. The database comprises 1055 data points for Newtonian conditions and 1004 data points for non-Newtonian conditions. The widely used ML regression algorithms are implemented comprehensively for both approaches. Prediction accuracy and robustness are then analyzed in detail for the best-performing ML model configurations. The modular approach achieves a slightly lower predictive accuracy, but enhanced robustness, compared to the direct approach. Moreover, it offers greater flexibility in terms of decoupling physical effects and reducing dataset requirements and provides good generalization capability. This could potentially extend the proposed modular approach to thermal EHL, and incorporate other non-conventional configurations (e.g., surface coatings and mixed lubrication).
Despite growing interest in salespeople’s social media use, the role of customers in motivating and shaping such use has received limited scholarly attention. Moreover, little is known about whether—and how—social media use enhances salespeople’s effectiveness in managing service recovery situations. Using the conservation of resources theory and analyzing matched survey responses from B2B salespeople and their managers, we find that while customer interest in digital technology has a curvilinear (inverted U-shape) relationship with salesperson social media use, this relationship decreases at higher levels of job engagement, indicating that salespeople with high job engagement have greater resources to manage the stress caused by customer interest in digital technology. Moreover, social media use improves manager-rated salesperson service recovery performance. The study offers significant implications for both theory and practice.
Despite extensive research, brain lateralization remains theoretically fragmented, especially regarding the functional integration of hemispheric asymmetries, bilateral contributions to cognition, and the significance of lateralization degree for development and clinical practice. This fragmentation is reflected in often contradictory findings across various domains such as language, social cognition, creativity, and psychopathology. To address these challenges, this paper offers theoretical and methodological reinterpretations of lateralization grounded in Cultural-Historical Activity Theory (CHAT). Building on the law of extracortical organization of the brain, we argue that hemispheric specialization largely reflects the intracerebral manifestation of a dual organization of internal psychological activity, by emphasizing self-realization and alienation in the social structure. Specifically, we conceptualize internal activity as two interrelated yet non-coinciding spheres: a self (unconscious personality)-relevant sphere of meaning emergence, and an “I” (conscious personality)-relevant sphere of personal sense-making. By mapping CHAT principles onto lateralization findings—including bilateral language involvement, creativity, and links between lateralization-degree and psychopathology—we propose that these dual activities are associated with the cerebral hemispheres, with the right hemisphere preferentially supporting self-relevant and unconscious meaning-related processes, and the left hemisphere supporting language-mediated, hierarchical, and consciously regulated sense-making. More broadly, our proposed framework contributes to ongoing efforts to address reductionism and fragmentation in psychology and neuropsychology. We conclude by outlining implications for developmental, clinical, and neuropsychological research, and propose empirically testable directions for future studies.
PurposeThis study examines the sector-level herding and herding spillover across 11 US-listed Real Estate Investment Trust (REIT) sectors. Design/methodology/approachWe examine herding behaviour of REITs employing returns-based methods in the context of the standard linear model, along with extensions that capture any time-varying component of herding. FindingsA standard linear model shows no herding behaviour for all sectors, except for the lodging and resorts sector; whereas, a more robust quantile regression reveals significant herding in all 11 sectors and for the overall market at the lower tails of the distribution of cross-sectional return dispersion. The time-varying parameter ordinary least squares approach demonstrates spasmodic switches between herding and anti-herding behaviours during the sample period across all sectors and the overall market. A spillover analysis highlights significant and original herding spillover effects across REIT sectors. Practical implicationsOur results could be useful for investment management purposes since herding can drive asset price volatility to a higher level and undermine the effects of portfolio diversification. Thus, investors should pay attention to sectors that are involved in significant herding spillovers for the sake of portfolio and risk management inferences in the US REIT sectors. Regulators should monitor the developments and deploy effective policies to mitigate the effects of herding since it is widely known that herding could ultimately pose a threat to market stability. Originality/valueThis study contributes to the dynamic nature of behavioural biases of investors in US equity REITs and enhances our understanding of contagious effects of herding across sectors.
