En Machala durante los últimos 30 años, el sector del comercio informal en las calles y avenidas ha registrado un crecimiento desbordante. La presente investigación tiene como objetivo analizar el comercio informal, su incidencia y sus perjuicios que arrastran al comercio formal, desde una perspectiva socioeconómica y política en la ciudad de Machala. La metodología utilizada es de tipo empírica descriptiva, analítico en campo y método lógico deductivo en gabinete. En la actualidad la economía informal que produce el comercio es uno de los problemas medulares del desarrollo económico y social de la ciudad, al generar precariedad, desorden, insalubridad, competencia desigual con los que sí pagan tributos en sus locales propios. La informalidad, lejos de ser vista con pasividad, al considerarla como una válvula de escape ante los desequilibrios del mercado de trabajo, representa un problema sustantivo que requiere de atención urgente de la política económica y social por parte de las autoridades locales. Dada esta situación de caos social causada por la pandemia Convid-19 el GAD Municipal de Machala precedida por el Ing. Darío Macas realizó el estudio de factibilidad para construir cuatros mercados en los diferentes puntos de la ciudad, pero las socializaciones con los comerciantes han mencionado que solo quieren un solo mercado, que desde el punto de vista técnico no es factible ante una ciudad que crece de forma acelera en su población.

Machine learning has enhanced rice grain classification by enabling accurate, automated, and objective morphological analysis, supporting quality control and varietal selection. This study compared the performance of several algorithms in identifying three Ecuadorian rice varieties (INIAP-11, INIAP-12, and INIAP-20) using a balanced dataset of morphological features. Five models were trained with cross-validation and evaluated using multi-class metrics. Significant differences among varieties particularly in area, length, and eccentricity confirmed their discriminative potential. Initially, models were trained using all morphological variables. However, to optimize training time and computational cost, the study also evaluated model performance after applying dimensionality reduction through Principal Component Analysis (PCA). This approach enabled assessing whether reduced feature spaces could maintain competitive predictive performance while improving efficiency. Overall, all algorithms performed well, but only the Artificial Neural Network (ANN) and Support Vector Classifier (SVC) demonstrated strong generalization without overfitting. In contrast, Random Forest achieved perfect accuracy in training but decreased performance in testing. In conclusion, ANN and SVC emerged as the most robust alternatives for rice grain morphological classification, while the PCA results highlight the value of dimensionality reduction as a strategy to enhance computational scalability without substantially compromising accuracy. The objective of the present study is to train, evaluate, and compare different machine learning algorithms for the classification of three types of rice grains, in order to determine the best model for this task based on seven morphological characteristics of the grains applying machine learning algorithms with and without dimensional reduction.

This article models the Ecuadorian shrimp market as a nonlinear system with recurring latent regimes that affect margins and planning decisions. A multivariate Hidden Markov Model (HMM) with Gaussian emissions in log space is estimated via the Baum–Welch algorithm to segment the joint dynamics of pounds produced, dollars invoiced, and average price. The analysis uses monthly data from January 2017 to May 2025 (T = 101). The selected four-state specification shows strong fit and outperforms linear alternatives (log likelihood = 480.9; AIC = 859.8; BIC = 729.5). The dominant regime (State 2) concentrates high prices (~USD 2.97/lb) with intermediate production and acts as an attractor (stationary probability ≈ 1), while States 0 and 1 capture orderly expansion and oversupply conditions, and State 3 reflects episodic demand rallies. Adverse regimes (States 0–1) exhibit expected durations of 6–8 months, suggesting natural reversion toward the profitable regime. These estimates enable probabilistic regime forecasting and Monte Carlo scenario simulation to support hedging, inventory management, and financial stress testing. Overall, the proposed HMM framework provides an operational decision tool for producers, traders, and policymakers seeking to anticipate regime shifts, mitigate oversupply cycles, and stabilize margins.

La extracción de la concha prieta (Anadara tuberculosa) es una actividad productiva ancestral, que se realiza de manera espontánea e intuitiva y cuya importancia no ha sido suficientemente ponderada, destacada y reconocida en los diversos ámbitos gubernamentales, empresariales y académicos del Ecuador y de otros países que también la producen. Esta investigación tiene por objetivo indagar sobre los indicadores que determinan la importancia económica, social y ambiental a nivel global y nacional de la producción del molusco bivalvo concha prieta. A tal fin, se aplicó una metodología de investigación bibliográfica de fuentes primarias, secundarias y terciarias. Se obtuvo como resultado que su aporte al PIB ecuatoriano es de 15 millones de USD al año, si se incluye el valor que se agrega a la concha prieta a nivel de restaurante, picanterías y cevicherías, que genera unos 8000 puestos de trabajo de tiempo completo y parcial, la mayoría por cuenta propia. Contribuye a preservar más de 156 mil hectáreas de superficie de manglar existentes todavía en las costas ecuatorianas. Se concluye que la producción de la concha prieta registra una relevancia significativa en términos económicos, sociales y ambientales, con un gran potencial para insertarse de manera formal en el circuito exportador ecuatoriano, de mejorar los niveles de ingresos y calidad de vida de los concheros y de contribuir al manejo sostenible del ecosistema manglar. Se recomienda profundizar en el estudio de las variables económicas, sociales y ambientales que tienen mayor incidencia sobre sus niveles de sostenibilidad y competitividad.

Shrimp aquaculture increasingly depends on precise water quality management, yet most farms still rely on fragmented measurements and qualitative assessments. This study aimed to evaluate whether routine physicochemical data from commercial ponds can reliably discriminate between operational categories of acceptable and residual water and thus support early warning systems. We compiled water quality records from shrimp ponds in several coastal provinces, focusing on a reduced set of variables related to salinity, alkalinity, hardness and inorganic nitrogen. Supervised and unsupervised machine learning models were trained and compared using standard classification metrics. Tree-based ensembles and margin-based models achieved high accuracy and F1 scores when predicting water status from routine variables, while clustering methods only reproduced similar patterns after an ex post mapping of clusters to classes. These results indicate that latent nitrogen loads and subtle shifts in water chemistry are systematically captured by basic monitoring data and can be translated into operational signals of risk. The study demonstrates the feasibility of integrating data-driven classification into shrimp farm monitoring and outlines a pathway toward low-cost, scalable decision support tools for aquaculture 4.0 in data-limited settings.