The selection of stock pairs in pairs trading strategy (PTS) is critical to the strategy’s success, as it determines the ability to exploit price movements and manage risk effectively. Existing methods like correlation-based, cointegration-based, and machine learning often overlook deeper relationships, and short-term fluctuations and struggle with data complexity, performing poorly in volatile periods like COVID-19. This paper focuses on a novel pair selection method that enhances performance by leveraging volatility and residuals for improved long-term stability and profitability. This approach improves pair selection precision and enhances trading performance by integrating volatility metrics with regression-derived residuals. Analyzing historical data, the proposed strategy reveals significantly superior returns, showcasing its peak performance in a volatile market environment.
Conventional scanning electron microscopy (SEM) readily shows cracks, particles, defects, and surface morphology but does not provide definitive composition signatures without additional methods or sectioning. This paper presents Extrudate Fracture Subsurface SEM (EFS-SEM), a rapid, non-sectioning workflow that generates impact-fractured particles from twin-screw-extruded PP/PVC nanocomposites to expose fresh subsurface faces for direct imaging. EFS augments mechanical fractography by revealing composition-linked particle signatures at 200× magnification and internal structural formation at 1000× magnification that standard test-mode surfaces often miss. The approach reduces preparation time, preserves native fracture textures, and enables building a reference library of particle-shape and interfacial signatures to support material identification, validation, and confirmation alongside complementary spectroscopic and compositional techniques. While Fourier-transform infrared spectroscopy (FTIR) confirms polymer functional groups and general filler presence without clearly differentiating carbon black and graphene, and energy‑dispersive X‑ray spectroscopy (EDS) detects elemental composition mainly confirming increased carbon content from fillers but not specific filler type, EFS-SEM provides direct morphological and interfacial contrast at the microscale, uniquely distinguishing filler shape and dispersion. Applied across PP/PVC ratios (60/40, 50/50, 40/60), EFS-SEM provides a high-throughput pathway to map subsurface morphology and composition to processing history and performance, addressing a key gap in conventional SEM-based analyses.
Rapid urbanization and climate change pose unprecedented challenges to urban water systems, necessitating a holistic and integrated approach to sustainable water resource management. Few studies have focused on enabling water-sensitive development by co-creating a Water Sensitive Cities (WSC) framework with fit-for-purpose guidelines, addressing site-specific issues, knowledge, practices, and proposing new interventions and policies. This review summarizes the evolution of urban water management practices, highlighting the WSC framework as a blueprint for achieving resilient, equitable, and sustainable urban water systems. An initial search in Google Scholar and Web of Science using relevant keywords yielded 831 articles. After filtering for topic relevance, 741 articles were selected for this review. The reviewed studies cover a wide geographical range, including Global North (United Kingdom, Germany, France, Singapore, USA, and Australia) and Global South regions (India, China, Sub-Saharan Africa, and parts of Latin America). Socio-ecological factors are considered to guide the selection of appropriate technology and infrastructure. The diverse experiences and challenges in constructing WSCs are analyzed through case studies from different geographies around the globe. Additionally, the review advocates for decentralized solutions, green infrastructure, and active community engagement to ensure successful WSC implementation. The review concludes with recommendations for future research and practical applications in urban water management.
Groundwater is a vital and sustainable resource. Precise groundwater level (GWL) modelling and prediction are vital for effective groundwater management and to maintain the supply–demand balance. This study evaluates five artificial intelligence (AI) models Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and M5 Tree against a persistence baseline model for seasonal (pre-, peak-, and post-monsoon) GWL prediction in Surat District, India, using data from 1995 to 2025. Input predictors include meteorological data relative humidity, maximum temperature, minimum temperature, precipitation, wind speed, profile soil moisture, Nino 3.4, and historical GWL. Before building the models, data quality assessment, Thomas Fiering model for missing observations, min–max normalisation, multicollinearity analysis and autocorrelation (ACF) and partial autocorrelation (PACF) analysis are carried out to obtain information about the temporal behaviour of GWLs. The models were developed based on a training–testing chronological split of 70:30 and then tested using the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE) and relative root mean square error (RRMSE). Statistical significance between the best models was evaluated using the Wilcoxon signed-rank test. SVM demonstrated the highest predictive accuracy during the pre- and peak-monsoon seasons, whereas ELM was superior during the post-monsoon season, with all AI models outperforming the persistence baseline model. Wilcoxon test results indicated that there was no statistically significant difference between the errors of prediction of SVM and ELM models. The proposed framework supports government agencies in sustainable groundwater management and water resource planning.
Aluminium alloys are widely used in aerospace and cryogenic industries, where the components must withstand a complex multiaxial loading. In this study, the deformation behaviour of AA2219 in ‘O’ condition is investigated by testing biaxial tensile at room temperature, elevated temperature (150 °C), and sub-zero temperature (− 40 °C). The biaxial tensile experiments are used to construct a temperature-dependent yield locus of AA2219, allowing us to understand the initial yielding and anisotropic plastic response. Furthermore, to correlate the mechanical response and microstructure evolution, microstructural characterisation has been carried out using electron backscattered diffraction (EBSD). The results highlight the temperature-dependent deformation mechanism, under multiaxial loading, governed primarily by changes in dislocation activity, grain morphology, and recovery mechanisms. These findings provide valuable insights for AA2219 components performance.
