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In this paper, we investigate the generalized high-order coupled nonlinear Schrödinger system with modulating coefficients which is used to describe the optical pulses in inhomogeneous media. This work makes two fundamental advances: first, we rigorously derive the Hirota bilinear representation of this system, previously unreported in the literature; second, we use a novel parameter truncation method that enables to obtain the non-degenerate soliton solutions. Specifically, non-degenerate one-, two-soliton solutions are successfully obtained for the first time. Based on inference, we obtain the forms of non-degenerate N-soliton solutions for this system. In addition, by controlling high-order dispersion and self-steepening shift effect, u-shape, v-shape, w-shape, and other shapes of double-hump figures can also be provided. Finally, after selecting appropriate parameters, we analyse the dynamic behaviors of non-degenerate solitons during collisions.

Using a composite ecological remediation system for the long-term field application has been remaining a challenge. To cope with this problem, we fabricated a mussels-bacteria stereo integrated (MBSI) system deployed on a river bank to improve the river water quality. The MBSI system was operated for more than 4 months, and it was found that the turbidity, COD and TN of river water decreased significantly (P < 0.05) after the MBSI system treatment. The mussels layer was dominated by fermentation bacteria like Cetobacterium (7.6%), while ammoxidation bacteria of norank_NS9_marine_group (9.7%), denitrication bacteria of Thauera (9.4%) and anammox bacteria of Pirellula (3.7%) predominated in the immobilization microorganisms, demonstrating that the major responsibility was COD removal for mussels and nitrogen removal for immobilization bacteria. In summary, mussels and bacteria (immobilized) reinforced the synergistic effect, the combination of which provided a simple and feasible system for the improvement of river water quality and a promising tool for the engineering practice.

The generalized uncertainty principle (GUP), as a key model in quantum gravity, is crucial for exploring cosmological properties and related challenges. In this paper, we investigate the effects of a higher-order GUP on primordial Big Bang nucleosynthesis (BBN). First, using a new higher-order GUP, we derive the modified Friedmann equations and the corresponding thermodynamic properties of the universe within the quantum gravity framework. Next, we analyze BBN under these modifications. Finally, by incorporating observational data of BBN and the primordial light element abundances, respectively, we establish constraints on the GUP parameter. The results reveal a significant impact of the GUP on the BBN processes in the early universe. Notably, the GUP parameter is found to take both positive and negative values, which is different from the classical case.

Microplastic (MP) pollution in groundwater is a growing concern due to its toxic properties and harmful effects. Meanwhile, landfills and dumpsites act as storage areas for plastic materials, which gradually disintegrate into microplastics over time, leading to pollution of the surrounding environment. Knowledge of the presence of MPs in the groundwater is scarce, and it is the need of the hour. This article focuses on the MPs migration from the dumpsite to the surrounding groundwater by analyzing the MPs in leachate generated from the dumpsite and MPs found in the groundwater near the solid waste dumpsite region in Ariyamangalam, Tiruchirappalli, Tamil Nadu, India. In this study, the Nile Red staining method has been used to quantify the microplastics with sizes as small as 3.42 μm. The results indicated that the MPs abundance in groundwater is about 11 to 77 particles/L with an average size of 45.16 μm, and in leachate on average, 102 to 140 particles/L were identified with the average size of 152 μm. Based on appearance, most of the MPs are of a fragment’s nature; some films and fibers were also found in the groundwater. Meanwhile, in leachate, fragments (45%) and fibers (44%) were found to be in equal proportion, along with a smaller number of films (11%). From micro-Raman characterization, polyethylene was the dominating polymer, followed by polypropylene, polyethylene terephthalate, polystyrene, polyvinyl chloride, poly methyl methacrylate, polyamide, and polyvinyl alcohol in the groundwater. The risk assessment reveals that the groundwater near the dumpsite zone comes under risk category IV based on the polymer risk index, which means that there is a high risk due to the certain kind of highly toxic polymer present in the groundwater.

This study focused on the Daluxi River, a small watershed and a primary tributary of the Yangtze River. Based on the nonlinear characteristics of water quality parameters and environmental factors such as meteorological and hydrological influences, a comparative analysis was conducted using Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA). KPCA extracted four potential sources for both the upstream and downstream sections, accounting for 79% of the total variance in each case—an increase of 7% and 6% compared to PCA, respectively. To address the limitation of KPCA in directly revealing the relationship between principal components and the original water quality data, six machine learning algorithms—Extreme Learning Machine (ELM), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were employed to perform regression analysis between the kernel principal components and the original water quality parameters, thereby elucidating source characteristics. The results indicated that GBDT exhibited the best fitting performance (R² = 0.988, MAE = 0.05, RMSE = 7.13%). Based on the extracted KPC, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model was used to calculate the contribution rates of various pollution sources in the Wandang and Siming areas. The results indicate that combining KPCA with GBDT and APCS-MLR can effectively uncover the complex relationships among water quality, meteorological, and hydrological factors, thereby enhancing the accuracy and reliability of pollution source analysis. This study advances research by using KPCA to capture nonlinear relationships and integrating machine learning for enhanced pollution source analysis.

A large-area centimeter-scale (2 cm × 1 cm) high-quality continuous MoS₂ film was grown on a SiO₂/Si substrate via the Chemical Vapor Deposition (CVD) technique to ensure the scalability and uniformity of the MoS₂ film across a large area, rendering it suitable for wafer-scale applications. We further establish the transfer of MoS₂ film from the grown substrate (SiO₂/Si) to Interdigitated Electrodes (IDE) structures fabricated on GaAs substrate via a wet etching process utilizing Hydrogen Fluoride (HF) solution, effectively removing the MoS₂ layer from SiO₂/Si substrate within 2–3 min, while preserving the structural integrity and quality of the MoS₂ film. Characterization studies involving Raman analysis, Photoluminescence (PL) mapping, SEM imaging, and optoelectronics measurements confirm the high quality and integrity of the transferred MoS₂ film onto IDE structures fabricated on GaAs substrate for photodetection application. Optoelectronic measurements revealed a significant responsivity enhancement from 2.13 to 26.4 mA/W at a 20 V bias under 780 nm laser illuminations (5 mW), due to the incorporation of gold nanoparticles between the IDE fingers by employing RF sputtering. Thus, integrating nanoparticles in the active region of optoelectronic devices can markedly enhance the optical efficiency of 2D material-based optoelectronic systems. Overall, this CVD technique presents a viable approach for the scalable production of large-area MoS₂ films and their transfer onto fabricated structures, opening avenues for the integration of MoS₂ films into advanced technological devices and systems, particularly in micro and Nano-electromechanical systems.

This article provides a comprehensive review of the evolution of the nuclear shape concept, a cornerstone in nuclear physics. Tracing its historical development from the early 20th century, we highlight key milestones and paradigm shifts that have shaped our understanding. The review explores the transition from the initial spherical model to the introduction of nuclear deformation, emphasizing the contributions of the liquid drop model and the unified model. The pivotal role of nuclear shapes in elucidating various nuclear phenomena and their profound impact on both theoretical and experimental nuclear physics are discussed in depth. The article underscores the relevance of nuclear shape in contemporary physics, particularly in light of groundbreaking findings from ultra-relativistic heavy ion collisions. These recent results illustrate the enduring significance of nuclear shape in advancing our comprehension of nuclear structure and reactions.