One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population’s variety is increased by using the SPM chaotic mapping at initialization; secondly, the WHO and Golden Sine Algorithm (Golden-SA) are hybridized to improve the WHO’s accuracy and arrive at faster convergence; Thirdly, the IWHO can escape from a local optimum and broaden the search space by using opposition-based learning and the Cauchy variation strategy. The results indicate that the IWHO has the best capacity for optimization by contrasting the simulation tests with seven algorithms on 23 test functions. Finally, three sets of coverage optimization experiments in different simulated environments are designed to test the effectiveness of this algorithm. The validation results demonstrate that the IWHO can achieve better and more effective sensor connectivity and coverage ratio compared to that of several algorithms. After optimization, the HWSN’s coverage and connectivity ratio attained 98.51% and 20.04%, and after adding obstacles, 97.79% and 17.44%, respectively.

The black widow spider optimization algorithm (BWOA) had the problems of slow convergence speed and easily to falling into local optimum mode. To address these problems, this paper proposes a multi-strategy black widow spider optimization algorithm (IBWOA). First, Gauss chaotic mapping is introduced to initialize the population to ensure the diversity of the algorithm at the initial stage. Then, the sine cosine strategy is introduced to perturb the individuals during iteration to improve the global search ability of the algorithm. In addition, the elite opposition-based learning strategy is introduced to improve convergence speed of algorithm. Finally, the mutation method of the differential evolution algorithm is integrated to reorganize the individuals with poor fitness values. Through the analysis of the optimization results of 13 benchmark test functions and a part of CEC2017 test functions, the effectiveness and rationality of each improved strategy are verified. Moreover, it shows that the proposed algorithm has significant improvement in solution accuracy, performance and convergence speed compared with other algorithms. Furthermore, the IBWOA algorithm is used to solve six practical constrained engineering problems. The results show that the IBWOA has excellent optimization ability and scalability.

The relationship between height and diameter (H-D) is an important component in forest growth and yield models, and a better understanding of the relationship will improve forest monitoring, management, and biomass estimation. Sixteen nonlinear growth functions were fitted to H-D data for 1261 trees from a Cryptomeria fortunei plantation in the Pingba region of Guizhou Province, China. Of the 1261 trees, 80 % were randomly selected for model calibration, while the remaining trees were reserved for model validation. All models were evaluated and compared by means of multiple-model performance criteria. Although all 16 models showed a good fit to the dataset and each of them accounted for more than 75 % of the total variation in height, a large difference in asymptotic estimates was observed. The Chapman–Richards, Weibull, and Näslund models were recommended for C. fortunei plantations, due to their satisfactory height prediction and biological interpretability.

Complex optimization (CO) problems have been solved using swarm intelligence (SI) methods. One of the CO problems is the Wireless Sensor Network (WSN) coverage optimization problem, which plays an important role in Internet of Things (IoT). A novel hybrid algorithm is proposed, named hybrid particle swarm butterfly algorithm (HPSBA), by combining their strengths of particle swarm optimization (PSO) and butterfly optimization algorithm (BOA), for solving this problem. Significantly, the value of individual scent intensity should be non-negative without consideration of the basic BOA, which is calculated with absolute value of the proposed HPSBA. Moreover, the performance of the HPSBA is comprehensively compared with the fundamental BOA, numerous potential BOA variants, and tried-and-true algorithms, for solving the twenty-six commonly used benchmark functions. The results show that HPSBA has a competitive overall performance. Finally, when compared to PSO, BOA, and MBOA, HPSBA is used to solve the node coverage optimization problem in WSN. The experimental results demonstrate that the HPSBA optimized coverage has a higher coverage rate, which effectively reduces node redundancy and extends WSN survival time.

Introduction: Research on the effect of close-to-nature management (CTNM) on stand states and dynamics of forests is crucial for the evaluation of CTNM implemented and sustainable development of forestry. Outcomes: We analyzed and compared the stand states and dynamics of Masson pine ( Pinus massoniana ) plantations in the young, middle-aged, and near-mature stages under CTNM eight years after selective cutting and unmanaged control. Both paired-sample T-tests and RDA ordination results demonstrated that the overall stand states with CTNM were superior to the control treatment in all three stages. The interaction of the CTNM and stand age significantly affected The Shannon-Wiener index of diameter class and regeneration density. Discussion and Conclusion: CTNM and stand age jointly affected the stand state of forests and CTNM accelerated the forest development, which promoted an increase in the heterogeneity of stand parameters and shortened the formation time of a target stand. Greater tree size differentiation and adequate regeneration are particularly important aspects for improving the quality of Masson pineforests during the management processes.

Quasi-Industrial Districts (QIDs) in Japan allow the coexistence of industrial, residential, and commercial functions. However, under pressures such as deindustrialization, demographic decline, and urban restructuring, their functional balance has been increasingly disrupted. This study investigates the spatiotemporal evolution of QIDs in Kitakyushu and develops a GIS-based framework to quantify changes in land-use structure. Using historical zoning and building floor-area data from 1986 to 2024, ternary diagram analysis is applied to examine relationships among the three functional types and identify transformation trajectories. Results show that while the total QID area expanded by 38.8%, internal structures changed significantly. Industry-dominant districts declined, commerce-oriented districts increased, and residential–industrial mixed types largely disappeared, indicating a shift toward commercial and residential functions. These findings reveal a growing mismatch between zoning designations and actual land use. To address this, the study proposes combining industrial concentration with clearer residential zoning, supported by periodic evaluation based on functional deviation thresholds. The framework provides a quantitative tool for adaptive land-use governance in shrinking industrial cities.