Tumble dryers are convenient but energy-intensive, and their performance depends on coupled heat and mass transfer within the drum and air circuit. This review evaluates mathematical modeling approaches for these processes, spanning 0-D lumped-parameter models, 1-D heat and moisture transfer models, and kinematic and image-processing methods, across vented, condenser, and heat-pump dryer types. Literature was drawn from peer-reviewed sources published roughly since the 1990s. The Chilton–Colburn analogy remains the dominant framework for evaporation-rate modeling, but its reliance on constant transfer coefficients, uniform textile temperature, and saturated surface assumptions limits its accuracy during the falling-rate drying period, when evaporation slows and a larger fraction of supplied energy may be diverted to heating the textiles and drum rather than moisture removal. This review’s contribution lies in systematically comparing classical models (Lambert, Deans) against newer 1-D, regression-based, kinematic, and image-processing strategies, clarifying the assumptions, applicability boundaries, and engineering trade-offs of each. The findings point toward hybrid, uncertainty-aware models that couple energy-balance formulations with variable transfer coefficients, textile-motion data, and data-driven tools as the most promising path forward for energy-efficient dryer design and control.

The Fiji–Tonga–Kermadec convergent margin represents one of the most seismically volatile regions globally, defined by complex subduction dynamics and a high density of deep-focus events. This study performs a high-resolution exploratory investigation of a 1000-event seismic catalog to resolve the intricate interplay between rupture magnitude, focal depth, and spatial distribution. Moving beyond baseline descriptive summaries, the study employs Exploratory Spatial Data Analysis (ESDA), including non-parametric 2D kernel density estimation and hexagonal binning, to delineate the latent tectonic architecture of the region. Our analysis identifies a pronounced bimodal depth signature, with focal concentrations in the shallow crust (<100 km) and the transition zone (>500 km). These findings empirically validate the thermal and phase-transformation mechanics of the Wadati–Benioffzone. Multivariate diagnostics further reveal a robust linear dependency 𝑟 = 0.71 between seismic magnitude and the global monitoring footprint, providing a quantitative metric for energy-dependent detectability across the station network. By bridging classical geostatistical workflows with modern spatial intensity modeling, this research provides a reproducible, data-driven foundation for enhancing seismic hazard zoning. These insights are particularly critical for improving infrastructure resilience and disaster risk reduction (DRR) strategies in Small Island Developing States (SIDS) facing cascading tectonic risks.

The textile coloration industry often discharges dye-rich effluents that pose both environmental and health risks. To address these risks, research on the use of bio-based dyes is ongoing. This study reports on the sustainable extraction and dyeing of cotton fabric using an extract from Camellia sinensis (Tea) leaves waste. The dyeing process utilized infrared (IR) dyeing, which reduces water consumption in the dye solution and uses heat for dye fixation instead of electrolytes, thereby contributing to sustainable dyeing. The optimal dyeing process was carried out at 60 °C for 60 min, both with and without mordants. Tannic acid, aluminium potassium sulphate (alum) and ferrous sulphate were selected as affordable and less toxic mordant. The unmordanted sample exhibited the highest colour strength (K/S = 29.85), while while ferrous sulphate mordanted sample confers the best color fastness overall. Mechanical analysis indicated an increase in bursting strength of 27% in the unmordanted dyed fabric compared with the raw fabric, along with improved drapability, while air permeability decreased by 6.8%. The scanning electron microscopy (SEM) image confirms the dye particles within the cotton fabric, and Fourier Transform Infrared Spectroscopy (FTIR) analysis confirms the presence of polyphenolic and aromatic groups responsible for dye-fiber interaction.

Tin-based halide perovskites have drawn considerable scientific attention as promising candidates for next-generation photovoltaic devices, primarily because they circumvent the toxicological and regulatory challenges associated with lead-containing counterparts. In the present work, a lead-free n-i-p type perovskite heterostructure solar cell was numerically designed and systematically optimized via the one-dimensional Solar Cell Capacitance Simulator (SCAPS-1D). The proposed device adopts the configuration FTO/CH₃NH₃SnCl₃/CH₃NH₃SnBr₃/CH₃NH₃SnI₃/Au, in which the tin chloride perovskite functions as the electron transport layer (ETL), the tin bromide perovskite serves as the intrinsic absorber, and the tin iodide perovskite acts as the hole transport layer (HTL). A systematic parametric investigation was carried out, covering layer thickness, electron affinity, shallow doping concentration, and trap-state density for each functional layer. Simulation outcomes demonstrate that an absorber thickness of 750 nm yields the best balance between photon harvesting and charge-carrier collection. Furthermore, elevated doping in the absorber was found to reduce device performance through enhanced Shockley–Read–Hall (SRH) recombination, while a reduced trap density markedly improved all photovoltaic figures of merit. Following comprehensive optimization, the device achieved a power conversion efficiency (PCE) of 31.11%, a short-circuit current density (J_SC) of 33.89 mA/cm², an open-circuit voltage (V_OC) of 1.0410 V, and a fill factor (FF) of 88.18%. The influence of ambient temperature was also studied, revealing that 300 K represents the optimal operating condition. The findings underline the viability of all-tin halide perovskite architectures for sustainable, high-efficiency photovoltaics.

Lamb waves (LWs) are guided waves (GWs) with unique properties that make them ideal for Non-Destructive Testing (NDT) and Structural Health Monitoring (SHM). Over the last two decades, LW-based techniques have gained significant traction in academic and industrial research. A primary advantage of LWs is their ability to propagate over long distances in plate or shell structures, navigating curves and reaching hidden or buried areas, capabilities essential for detecting interlaminar damage in composites. As LW wave structures are frequency and phase-velocity-dependent, they are now suitable for various engineering applications, offering a promising, cost-effective solution for automated, real-time structural integrity monitoring. This paper provides a comprehensive overview of LW behavior, modeling, applications, and limitations in detecting fatigue damage within composite structures. Furthermore, the study explores the integration of Artificial Intelligence (AI) algorithms, including Artificial Neural Networks (ANNs), Machine Learning (ML), and Deep Learning (DL), which have emerged as powerful tools for predicting fatigue damage. By leveraging historical data from LW sensing systems, these AI models can accurately forecast structural conditions without the need for complex analytical modeling, significantly reducing processing time.

Textile fiber waste in Pakistan is significantly produced; however, studies are needed to direct the reuse or recycling. This study demonstrated the preliminary market- based survey findings in the production of pre-consumer textile fiber waste, its collection, and the possible reuse in developing a new garment. Presently, this waste is mainly disposed of through burning or landfilling, creating an increased environmental footprint. This survey enhances the understanding of textile waste production for collecting, sorting and using in new garment making. The waste is collected from 35 commercial places in Karachi. Collected fabric pieces were used in the garment design by patchwork, and one case is demonstrated. The use of fiber dimensions, type, color, and variety found in the survey was demonstrated through a case study. It was found that the pre-consumer textile waste can be used in garment development through patch or applique designs. Several case studies were performed to apply waste fabric pieces in the design and development of fashion garments. Finally, one case study is demonstrated using patchwork to show the importance of waste fabric in garment making.