A key challenge in developing fiber–wireless (FiWi) access networks is determining the optimal positions of optical network units (ONUs) to maximize network performance and minimize development costs. This paper introduces backtracking search-based weighted fuzzy C-means (BWFCM), an optimized unsupervised method for ONU placement in FiWi. The proposed method integrates the local refinement strength of weighted fuzzy C-means (WFCM) with the global exploration capability of the power mutation-based backtracking search algorithm (PBSA). It is enhanced by an adaptive weighting mechanism that dynamically balances load imbalance and average communication distance without manual parameter configuration. The objective is to minimize the average Euclidean distance between ONUs and their primary wireless users while ensuring fair load distribution. Extensive simulations on standard test cases demonstrate BWFCM’s superiority over state-of-the-art ONU placement algorithms, including chaotic local search-based Levy flight distribution (CLSLFD), Harris-Hawks optimization (HHO), marine predators algorithm (MPA), and arithmetic optimization combined with fuzzy c-means (AOFCM). BWFCM achieves competitive load balance across all test cases and reduces the total combined cost compared to its counterparts, with the minimum improvement (0.24%) over MPA and the maximum (12.17%) over HHO. Scalability and complexity analyses confirm BWFCM as a tractable, competitive, and parameter-free solution for ONU placement in next-generation broadband access networks.
Surfactants with capability to bind to organics might modify reaction network in hydrothermal carbonization (HTC), probably affecting the properties of resulting hydrochar and/or derived activated carbons (AC). This was studied herein by conducting HTC of tangerine peel (TP) at 200 degrees C in the presence of cetyltrimethylammonium bromide (CTAB) or sodium dodecyl sulfate (SDS) and further activation of hydrochar with ZnCl2 at 550 degrees C. The results indicated that CTAB may have interacted with intermediates containing oxygen-containing functionalities (i.e. -OH, C=O and C-O-C), potentially suppressing polymerization reactions and reducing hydrochar yield by 24.6% with hydrochar-blank as a base for comparison. In-situ IR measurement confirmed highly aliphatic nature of the hydrochar-CTAB, which facilitated development of porous structures in activation, producing AC of much higher SBET and pore volume (1726.9 m2g-1 and 1.24 cm3g-1) than that from AC-char-Blank (1245.8 m2g-1 and 0.83 cm3g-1). In comparison, SDS presence generated electronegative micelle structures serving as seeds for formation of hydrochar, which increased significantly yields of hydrochar (70.4% versus 40.3%) by wrapping SDS-derivatives inside the hydrochars. The SDS-derivatives could not be activated by ZnCl2, producing AC of lower SBET (1067.7 m2g-1). However, cracking of the SDS-derivatives increased abundance of mesopores by nearly 3 folds.
Global warming represents a significant environmental challenge, prompting extensive research into Carbon Capture, Utilization, and Storage (CCUS) technologies. Calcium oxide (CaO)-based sorbents, particularly within the Calcium Looping (CaL) process, have emerged as a primary focus for high-temperature post-combustion CO2 capture and thermochemical energy storage (TCES). To address sintering and sustain sorbent performance over multiple CaL cycles, various enhancement strategies for nanostructured and granule-shaped CaO have been developed. Nevertheless, comprehensive reviews of recent advancements remain limited, underscoring the necessity to critically assess these developments for practical implementation. To this end, a timely review is required to identify the more sophisticated and efficient method for developing the best structurally operational CaO sorbents, considering all effective operational and structural conditions. This study, motivated by the significant impact of CaO texture on its CO2 capture activity and TCES performance, first examines conventional CaO-based preparation methodologies, followed by a systematic analysis of contemporary advancements in the development of modified sorbents. Particular emphasis is placed on comprehensively evaluating optimized modification strategies for enhanced CO2 capture capacity. This review thoroughly examines the CO2 capture capacity and TCES potential of modified CaO powders and granules to assess the effectiveness of different textural and functional improvements. It systematically compares the performance of various enhancement methods for nanostructured and granule-shaped CaO sorbents under laboratory and semi-practical conditions. The paper also discusses latest advances in improving the fluidity of CaO-based sorbents in fluidized-bed systems are also discussed, along with recent developments in using CaL for TCES. Persistent issues like sorbent sintering are addressed through strategies such as doping, particle design, and advanced synthesis, offering a comprehensive outlook on the progress and future prospects of CaL technology.
The uniform linear array (ULA) remains the reference geometry for direction-of-arrival (DoA) estimation in wireless communications, radar, sonar, and electronic surveillance. Subspace estimators such as MUSIC and ESPRIT resolve closely spaced sources when the array is well calibrated and the assumed signal model holds, but their accuracy falls away at low signal-to-noise ratio (SNR), under coherent multipath, and when few snapshots are available. Learning-based estimators do not invert an assumed model. They instead fit a mapping from measured array data to angle, and this review organizes the resulting literature along the two axes that govern what such a mapping can represent. The first axis is the representation used to encode the received data, namely the sample covariance matrix, the spatial pseudo-spectrum, or raw in-phase and quadrature samples. The second is the strategy used to produce angle estimates, whether by classification, regression, multi-label prediction, or sequential prediction. Shallow, deep, hybrid, and physics-embedded architectures are compared within this framework, with particular attention to how much prior signal-processing structure each one retains. Dataset construction is treated as a design problem in its own right, covering simulation parameter selection, measurement campaigns, and the transfer of models trained on synthetic data to physical hardware. Accuracy, complexity, and generalization are examined jointly, since the surveyed studies rarely address all three at once. Two obstacles recur throughout. The absence of shared benchmarks makes reported figures difficult to compare, and most models provide little insight into how an angle estimate is formed. Physics-informed constraints, compact architectures for embedded platforms, and attention-based temporal models are discussed as responses to these obstacles in 5G/6G and automotive sensing.
High entropy alloys (HEAs) have become the focus of research and industrial attention as a new class of advanced engineering materials due to their exceptional properties and performance. Generally, HEAs exhibit superior performance compared to traditional alloys, especially when they form FCC or BCC single-phase solid solutions. However, achieving such systems is beyond simple mixing of elements and is not always straightforward. It strongly depends on element selection, deliberate design of compositions, processing strategies, and thermodynamic control. Given the vast compositional space of HEAs, the use of predictive tools is Inevitable. Phase formation rules, as an empirical approach, have become effective and valuable tools for accelerating the screening of single-phase FCC or BCC alloys and minimizing trial-and-error efforts due to their low cost, time-saving nature, and simplicity. In this regard, the present review elaborated quantitatively and conceptually the thermodynamic parameters such as Gibbs free energy of mixing (ΔGmix), configurational entropy (ΔSmix), enthalpy of mixing (ΔHmix), and the prediction parameter for solid solution formation (Ω), along with Hume-Rothery criteria, including atomic size difference (δ), valence electron concentration (VEC), and electronegativity difference (Δχ). In addition, the processing and manufacturing routes, economic aspects, and applications of HEAs are discussed. This review provides a comprehensive overview of HEA with a practical framework from element selection, design, and fabrication techniques to the relationship between phase, properties, and performance. The present work bridges the gap between HEAs design, manufacturing processes, practical implementation, and application. Additionally, an overview of cost and manufacturing considerations has highlighted future research directions in targeted design, scalable production, and the industrial application of HEAs.
