The growing need for efficient and sustainable urban water management has accelerated the adoption of smart monitoring infrastructures based on wireless sensor networks (WSNs). This study proposes a connectivity-aware methodology for the optimal deployment of wireless sensor networks (WSNs) in smart water metering systems. The approach models the wireless sensors as nodes embedded in household water meters and determines the minimal yet sufficient set of Data Aggregation Points required to ensure complete network coverage and transmission reliability. A scalable and hierarchical topology is generated by integrating an enhanced minimum spanning tree algorithm with set covering techniques and geographic constraints, leading to a robust intermediate layer of aggregation nodes. These nodes are wirelessly linked to a single cellular base station, minimizing infrastructure costs while preserving communication quality. Simulation results on realistic urban layouts demonstrate that the proposed strategy reduces network fragmentation, improves energy efficiency, and simplifies routing paths compared to traditional ad hoc designs. The results offer a practical framework for deploying resilient and cost-effective smart water metering solutions in densely populated urban environments.

In the field of acoustic gas sensing, the qualitative and quantitative detection of gas mixtures remains a significant challenge. Traditional methods based on sound speed dispersion require measurements at both ultra-low and ultra-high frequencies, which significantly increase system complexity and raise experimental costs. To address it, we propose a Mixture-of-Experts network for gas detection that takes narrowband sound speed dispersion sequences as input. This model integrates a convolutional neural network as the classification router and bidirectional temporal convolutional attention networks as regression sub-experts. Validation on a ternary gas mixture dataset (CO₂, CH₄, N₂), generated by the analytical sound-speed dispersion expression, demonstrates that our model achieves 99.16% classification accuracy, with regression root mean square errors of 0.168%, 0.276%, and 0.268% for the three components. Experimental results on the gas mixture (CO₂, N₂) further confirm its robustness, with an average absolute error of 0.18%. Notably, with only 2.16M parameters and 5.49M floating-point operations, the model exhibits its potential for embedded deployment.

Molecular vibrational relaxation is responsible for the sound relaxational absorption in most excitable gases. However, it is desirable to calculate the contribution of each vibrational mode to sound multi-relaxation absorption. In this paper, first, a sound multi-relaxation absorption curve is decomposed into the sum of single-relaxation curves; second, based on this decomposed characteristic, a model to quantitatively analyze the vibrational mode contributions to sound absorption is proposed. The simulation results quantitatively demonstrates that the primary relaxation process connected with the lowest mode is the decisive factor for sound relaxational absorption, and the mode with lower vibrational frequency supplies higher contribution to the primary relaxation process in pure polyatomic gases.

Magnetorheological elastomers (MREs) are field-responsive composites whose stiffness can be tuned by a magnetic field. In chain-like MREs, strong local magnetic interactions and Maxwell tractions do not necessarily translate into a measurable macroscopic response, because interfacial tractions may be internally balanced after elastic propagation. This study develops a Green-function-weighted traction-transfer framework describing how generalized misalignment modulates magnetoelastic transduction. Local interfacial magnetic traction is projected onto a prescribed macroscopic observable through an output-specific elastic influence field, and a transfer-efficiency index quantifies the contribution that survives weighted cancellation. A minimal two-source model and a dimensionless illustration show that misalignment can produce an output through magnetic-traction imbalance, elastic-transfer imbalance, or their combination, whereas compensating imbalances suppress it. A three-dimensional strong-field finite-element study (flux density up to 0.58 T, constant relative permeability µ _r = 200) resolves two effects in aligned and longitudinally offset chains under tension, compression, and shear: a strongly mode-dependent magneto-induced effect, reaching +36.0% in shear while tension is weak (+6.4%) and the compressive nominal stress is reduced, and an output-specific geometric effect in which, at matched strain, field, and particle volume fraction, the offset chain is stiffer in shear and compression while the two configurations are essentially equal in tension. The solver is verified against an analytic single-sphere solution (0.13% error) and a zero-field hyperelastic degeneracy test (0.0003% error). The transfer-efficiency index is evaluated directly from the finite-element fields and cross-validated against the macroscopic reaction force, giving near-unity transfer in the field-aligned channel and near-complete cancellation in the cross-mode channel. The framework yields a design guideline rather than a universal rule: enhancement occurs only when misalignment increases the product of available weighted traction and transfer efficiency for the selected output.