This paper examines the relationship between industrial specialization and employment growth across Mexican municipalities over the period 1986–2019. The analysis is framed within the debate on Marshall–Arrow–Romer (MAR), Porter, and Jacobs externalities. Using the Krugman Specialization Index (KSI) and a spatial autoregressive (SAR) panel data model with municipality and time fixed effects, we estimate the impact of specialization on total and sectoral employment. The results indicate that higher specialization is generally associated with lower employment growth at the municipal level. However, this relationship varies across sectors: the negative effect is strongest in commerce and services, not statistically significant in manufacturing, and positive in mining. In addition, we find strong evidence of positive spatial spillovers, suggesting that interactions across neighboring municipalities shape employment dynamics. These findings suggest that specialization may enhance productivity without necessarily generating employment gains. More broadly, the results highlight the role of productive structure, sectoral heterogeneity, and spatial interdependence in shaping regional labor outcomes. Policy should therefore emphasize balanced and context-specific development strategies rather than relying on specialization alone.
Nowadays, the rapid growth of antibiotic resistance, mainly due to the non-rational use of antibiotics, is one of the threats to global public health. In this context, mathematical modelling of the dynamics of antibiotic resistance, considering the randomness intrinsic to the biological process, would allow studying the long-term evolution of the resistance. In this work, we present an antibiotic resistance model and we propose a novel method to calibrate and estimate an empirical joint distribution of the model parameters which is representative of a real-world scenario. Unlike other approaches, our method does not assume any a priori family of parameter distributions or independence between them. The model calibration has been carried out using the Particle Swarm Optimization (PSO) evolutionary algorithm. Based on the record of their multiple evaluations, our method consists of taking a sample from the parameter space that represents equidistributedly the different zones of the error function explored by the PSO. The sample selection is carried out first with a k-means clustering, and then with two antagonistic objective functions that choose the set of solutions most representative of the data, i.e. that best fit and best capture their uncertainty within a certain confidence interval. From this final sample, an estimation of the empirical joint distribution is obtained. The method has been successfully applied to the case study: the colistin-resistant Acinetobacter baumannii dynamics in the Valencian Community, Spain. The method has been tested under different calibration conditions by varying the number of PSO iterations. The results show that the parameters distributions are representative of the data and the method has been shown to be robust under all calibration scenarios. In all cases, the model simulations of the final sample adequately fit both the calibration and validation resistance data series and capture a high proportion of randomness (around 70
The growing scale of modern farms has led to an increased demand for efficient and adaptive strategies for pest, weed, and disease management. Traditional methods, such as manual inspections and blanket pesticide spraying, often result in excessive chemical use, resource wastage, and environmental degradation. Uncrewed aerial vehicles (UAVs) have emerged as a promising solution for precision agriculture, offering the potential for targeted spraying that enhances operational efficiency. However, existing UAV-based approaches face limitations, including restricted battery life, small payload capacity, and inefficient large-area coverage, particularly when relying on single-UAV systems. Multi-UAV coordination strategies have been proposed to address these challenges, yet many of these frameworks still rely on uniform spraying, neglecting important factors such as infestation severity, UAV dynamics, and energy efficiency. This article introduces a density-driven optimal control ((DOC)-O-2) framework, leveraging optimal transport (OT) theory to optimize UAV-based pesticide spraying in large-scale agricultural environments. The proposed method facilitates adaptive resource allocation based on infestation severity, reducing unnecessary chemical application. UAVs are modeled as a linear time-varying (LTV) system, accounting for variations in mass and moment of inertia due to payload changes. The (DOC)-O-2-based control strategy, derived through Lagrangian mechanics, ensures efficient fleet coordination, balanced workload distribution, and optimized mission duration. Simulation results demonstrate that the proposed approach outperforms traditional methods, such as uniform coverage and spectral multiscale coverage (SMC), in terms of coverage efficiency, chemical reduction, and sustainability, providing a scalable and resource-efficient solution for smart farming.
This paper presents a decentralized control framework for distribution matching in multi-agent systems (MAS), where agents collectively achieve a prescribed terminal spatial distribution. The problem is formulated using optimal transport (Wasserstein distance), which provides a principled measure of distributional discrepancy and serves as the basis for the control design. To avoid solving the global optimal transport problem directly, the distribution-matching objective is reformulated into a tractable per-agent decision process, enabling each agent to identify its desired terminal locations using only locally available information. A sequential weight-update rule is introduced to construct feasible local transport plans, and a memory-based correction mechanism is incorporated to maintain reliable operation under intermittent and range-limited communication. Convergence guarantees are established, showing cycle-wise improvement of a surrogate transport cost under both linear and nonlinear agent dynamics. Simulation results demonstrate that the proposed framework achieves effective and scalable distribution matching while operating fully in a decentralized manner.
The discovery of 2024 YR4 presented the planetary defense community with the most significant impact threat in almost two decades, reaching level 3 on the Torino scale. The community, now mature and well-organized, responded with a global observational effort. Astrometric measurements, forming the basis for orbital refinement and impact prediction, were a central component of this response. In this paper, we present the astrometric data collected by the international community, from the time of discovery until the object became too faint for all existing observational assets, including JWST. We also discuss the coordination role played by the International Asteroid Warning Network, and the importance of publicly available image archives to enable precovery searches.
