Search Results (32)

Monitoring water quality is crucial for achieving clean water and sanitation goals, particularly in remote areas. The project “Morbidity vs. Water Quality for Human Consumption in Tonosí: A Pilot Study” aimed to enhance water quality assessments in Panama using advanced analytical techniques to assess volatile organic compounds, heavy metals, and microbiological pathogens. To support this, the Technical Unit for Water Quality (UTECH) was established, featuring a novel mobile laboratory with cutting-edge technology for accurate testing, minimal chemical reagent use, reduced waste generation, and equipped with a solar-powered battery system. The aim of this paper is to explore the design, deployment, and impact of the UTECH. Furthermore, this study presents results from three sampling points in Tonosí, where several parameters exceeded regulatory limits, demonstrating the capabilities of the UTECH and highlighting the need for ongoing monitoring and intervention. The study also assesses the environmental, social, and economic impacts of the UTECH in alignment with the Sustainable Development Goals and national initiatives. Finally, a SWOT analysis illustrates the UTECH’s potential to improve water quality assessments in Panama while identifying areas for sustainable growth. The study showcases the successful integration of advanced mobile laboratory technologies into water quality monitoring, contributing to sustainable development in Panama and offering a replicable model for similar initiatives in other regions. Full article

Marine cyanobacteria have gained momentum in recent years as a source of novel bioactive small molecules. This paper describes the structure elucidation and pharmacological evaluation of two new (veraguamide O (1) and veraguamide P (2)) and one known (veraguamide C (3)) analogs isolated from a cyanobacterial collection made in the Las Perlas Archipelago of Panama. We hypothesized that these compounds would be cytotoxic in cancer cell lines. The compounds were screened against HEK-293, estrogen receptor positive (MCF-7), and triple-negative breast cancer (MDA-MB-231) cells as well as against a broad panel of membrane-bound receptors. The planar structures were determined based on NMR and MS data along with a comparison to previously isolated veraguamide analogs. Phylogenetic analysis of the collection suggests it to be an Okeania sp., a similar species to the cyanobacterium reported to produce other veraguamides. Veraguamide O shows no cytotoxicity (greater than 100 μM) against ER-positive cells (MCF-7) with 13 μM IC₅₀ against MDA-MB-231 TNBC cells. Interestingly, these compounds show affinity for the sigma2/TMEM-97 receptor, making them potential leads for the development of non-toxic sigma 2 targeting ligands. Full article

Species distribution modeling is fundamental to biodiversity, evolution, conservation science, and the study of invasive species. Given environmental data and species distribution data, model selection techniques are frequently used to help identify relevant features. Existing studies aim to find the relevant features by selecting the best models using different criteria, and they deem the predictors in the best models as the relevant features. However, they mostly consider only a given model family, making them vulnerable to model family misspecification. To address this issue, this paper introduces the Bayesian information-theoretic minimum message length (MML) principle to species distribution model selection. In particular, we provide a framework that allows the message length of models from multiple model families to be calculated and compared, and by doing so, the model selection is both accurate and robust against model family misspecification and data aggregation. To find the relevant features efficiently, we further develop a novel search algorithm that does not require calculating the message length for all possible subsets of features. Experimental results demonstrate that our proposed method outperforms competing methods by selecting the best models on both artificial and real-world datasets. More specifically, there was one test on artificial data that all methods got wrong. On the other 10 tests on artificial data, the MML method got everything correct, but the alternative methods all failed on a variety of tests. Our real-world data pertained to two plant species from Barro Colorado Island, Panama. Compared to the alternative methods, for both the plant species, the MML method selects the simplest model while also having the overall best predictions. Full article

A large amount of cargo is transported between European and Southeast Asian countries. Ships sometimes take different routes when sailing between ports due to the best commercial speed; navigational, economical, and hydrometeorological conditions; and political and military situations. Several routes are available for sailing between Europe and Southeast Asia: sailing the Suez Canal, sailing around the African continent, sailing the Panama Canal, as well as sailing the Northern Sea route. This article analyzes the possible sailing routes between Southeast Asia and Europe and presents a developed methodology for the evaluation of sailing routes. This sea route evaluation methodology is based on a comparative mathematical model that evaluates the main factors of cargo transportation by sea: transportation cost and time, possible maximum ship parameters, transportation energy (fuel) demand, and other possible factors, such as the probability of various restrictions. This paper presents a case study of cargo transportation between Rotterdam (The Netherlands) and Shanghai (China) using different possible sea routes. Assessments of various possible routes are presented; the main topics of discussion and conclusions are formulated. Full article

In recent years, despite a decline in international trade and disruptions in the supply chain caused by COVID-19, the main container terminals in Latin America and the Caribbean (LAC) have increased their container volumes. This growth has necessitated significant adaptations by seaports and their authorities to meet new demands. Consequently, there has been a focused analysis on the performance, efficiency, and competitiveness, particularly their most relevant logistical aspects. In this paper, a multi-objective hybrid approach was employed. The Principal Component Analysis (PCA) technique was combined with the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to rank LAC container terminals and identify operational criteria affecting efficiency. The analysis considered all input variables (berth/quay length, quay draught, yard area, number of quay cranes (portainer), number of yard cranes (trastainer), reachstacker, multicranes, daily montainer movement capacity, number of station reefer container type, number of terminals, and distance to the Panama Canal) and output variable (port performance expressed in TEUs from 2014 to 2023). The results revealed noteworthy findings for several terminals, particularly Colón, Santos, or Cartagena, which stands out as the main container port in LAC not only in annual TEUs throughput, but also in resource utilization. Full article

The objective of this study was to conduct a detailed analysis of the available flow series in the Chiriquí Viejo River basin in Panama. This paper examines the patterns of variation within these series and calculates various hydrological indexes indicative of the region’s hydrology. Utilizing advanced hydrological indexes within the Chiriquí Viejo River basin in Panama, which spans an area of 1376 km² and supports an estimated population of 100,000 inhabitants, analytical methods were employed to compute indexes such as the Daily Flow Variation Index (QVAR), the Slope of the Flow Duration Curve (R2FDC), the Hydrological Regulation Index (IRH), and the average duration of low (DLQ75) and high (DHQ25) flow pulses. The results indicate moderate flow variability (QVAR of 0.72) and a Hydrological Regulation Index (IRH) of 2.32, signifying a moderate capacity for flow regulation. Notably, low flow events (DLQ75) lasted approximately 3.73 days, while high flow events (DHQ25) lasted around 4.08 days. The study highlights a significant capacity to respond to extreme events, with maximum annual flows reaching 80.25 m³/s and minimum flows dropping to 3.01 m³/s. Despite the significant contribution of the basin to hydroelectric power generation and other economic activities, there is an observed need for sustainable management that accommodates hydrological fluctuations and promotes resource conservation. The conclusions indicate that these findings are critical for future planning and conservation strategies in the region, emphasizing the importance of integrating multidisciplinary approaches for Hydro-Social Sustainability. This novel and holistic approach underscores the interdependence between hydrological dynamics, socio-economic activities, and environmental sustainability, aiming to ensure the long-term resilience of the Chiriquí Viejo basin and its communities. Full article

Tropical coastal karst areas represent dynamic, fragile, and biodiverse environments. Central America’s karst regions have been scarcely studied, with most of the research focused on the northern part of the region and on several larger cave systems. The coastal carbonate zones of the Central American region represent a unique karstic landscape, which, so far, has been insufficiently studied. Therefore, in this paper, we aim to describe the (i) landscape geomorphology and (ii) chemical conditions that define Ciénega de El Mangle in Panama as a distinctive karstic site. Carried geomorphological mapping and the characterization of karstic features have resulted in the identification of the different karstic forms and processes that are present within this unique karstic area. Considering that the chosen karstic study area is located in a marine–coastal fringe on the periphery of a lagoon, it is affected by a combination of several factors and processes, including seawater intrusion (through sinkholes), the formation of conchiferous limestone (CaCO₃), and NaCl precipitation related to efflorescence. Due to the seasonally humid tropical climate, the chemical weathering processes are intense, thus forming alkaline soils that are hindering the development of mangrove vegetation. The geomorphology of the area results from intense evaporation combined with an influx of brackish groundwater, due to which a landscape has evolved in the marine–coastal strips, of seasonal tropical climates, that exhibit saline beaches, known as a littoral shott. In total, 24 karstic microdolines have evolved within the shott, of which six represent domical geoforms formed by gradual evaporitic precipitation, while seven other geoforms represent active karstic sinkholes filled with brackish water. These results are key for understanding the past and present climate interactions and conditions that have led to the formation of tropical karst environments. Full article

The paper presents a proposed queuing model based on Kendall’s notation for the intersection of two streets in Panama City (53 East and 56 East). The proposed model is based on a set of traffic lights that controls the flow of vehicles at the intersection according to a predetermined schedule. The model analyzes the stability of the system and simulations are performed to evaluate its performance. The main objective of the paper is to optimize the vehicle flow by minimizing the waiting time for passage. In the study, it was observed that the current traffic light system on Calle 50 (50th Street) is unstable and oversaturated during weekdays, which generates large vehicle queues with no estimated exit times. It was proposed to increase the system capacity to 1300 vehicles per hour to achieve reasonable stability and provide a solution to improve traffic signal timing on 50th Street. The need to increase the system capacity has been demonstrated and an optimal value has been suggested. The evaluation of other models and the use of AI can further strengthen the system and improve the prediction accuracy in different traffic scenarios. Full article

Sporotrichosis is known as a subacute or chronic infection, which is caused by thermodimorphic fungi of the genus Sporothrix. It is a cosmopolitan infection, which is more prevalent in tropical and subtropical regions and can affect both humans and other mammals. The main etiological agents causing this disease are Sporothrix schenckii, Sporothrix brasiliensis, and Sporothrix globosa, which have been recognized as members of the Sporothrix pathogenic clade. Within this clade, S. brasiliensis is considered the most virulent species and represents an important pathogen due to its distribution and prevalence in different regions of South America, such as Brazil, Argentina, Chile, and Paraguay, and Central American countries, such as Panama. In Brazil, S. brasiliensis has been of great concern due to the number of zoonotic cases that have been reported over the years. In this paper, a detailed review of the current literature on this pathogen and its different aspects will be carried out, including its genome, pathogen-host interaction, resistance mechanisms to antifungal drugs, and the caused zoonosis. Furthermore, we provide the prediction of some putative virulence factors encoded by the genome of this fungal species. Full article

This paper blends conservation science with legal and policy analysis to assess the primary threats to global shark populations and explores innovative approaches to conservation building upon the philosophy of Earth law, including the Rights of Nature legal framework. Using a case study of Panamá’s national Rights of Nature law, this paper highlights approaches to improve the protection and restoration of shark populations and their habitats. By examining the ecological, social, and economic aspects of conservation holistically, this study offers an interdisciplinary perspective on the urgency for shark protection and presents Rights of Nature as a valuable approach to shark conservation, with potential applications to other species globally. Full article

This paper aims to identify typologies of Latin American cities based on socioeconomic urban environment patterns. We used census data from 371 urban agglomerations in 11 countries included in the SALURBAL project to identify socioeconomic typologies of cities in Latin America. Exploratory factor analysis was used to select a set of variables, and finite mixture modelling (FMM) was applied to identify clusters to define the typology of cities. Despite the heterogeneities among the Latin American cities, we also found similarities. By exploring intersections and contrasts among these clusters, it was possible to define five socioeconomic regional typology patterns. The main features of each one are low-education cities in Northeast Brazil; low-unemployment cities in Peru and Panama; high-education cities in Argentina, Chile, Colombia, Costa Rica, Nicaragua and Mexico; high female labor participation, with high primary education in Argentina and low primary education in Brazil; and low female labor participation and low education in Brazil, Colombia, El Salvador, Guatemala, and Mexico. Identifying clusters of cities with similar features underscores understanding of the urban social and economic development dynamics and assists in studying how urban features affect health, the environment, and sustainability. Full article

Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score. Full article

Reservoir sedimentation is often seen as a site-specific process and is usually assessed at an individual reservoir level. At the same time, it takes place everywhere in the world. However, estimates of storage losses globally are largely lacking. In this study, earlier proposed estimates of sedimentation rates are applied, for the first time, to 47,403 large dams in 150 countries to estimate cumulative reservoir storage losses at country, regional, and global scales. These losses are estimated for the time horizons of 2022, 2030, and 2050. It is shown that 6316 billion m³ of initial global storage in these dams will decline to 4665 billion m³ causing a 26% storage loss by 2050. By now, major regions of the world have already lost 13–19% of their initially available water storage. Asia-Pacific and African regions will likely experience relatively smaller storage losses in the next 25+ years compared to the Americas or Europe. On a country level, Seychelles, Japan, Ireland, Panama, and the United Kingdom will experience the highest water storage losses by 2050, ranging between 35% and 50%. In contrast, Bhutan, Cambodia, Ethiopia, Guinea, and Niger will be the five least affected countries losing less than 15% of storage by 2050. The decrease in the available storage by 2050 in all countries and regions will challenge many aspects of national economies, including irrigation, power generation, and water supply. The newly built dams will not be able to offset storage losses to sedimentation. The paper is an alert to this creeping global water challenge with potentially significant development implications. Full article

A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in Panama. Deep learning is a more advanced learning paradigm in the machine learning field that continues to have significant breakthroughs in domain areas such as electricity forecasting, object detection, speech recognition, etc. We identified that the main predictors of electricity demand in the short term: the previous week’s load, the previous day’s load, and temperature. We found that the deep learning regression model achieved the best performance, which yielded an R squared (R²) of 0.93 and a mean absolute percentage error (MAPE) of 2.9%, while the AdaBoost model obtained the worst performance with an R² of 0.75 and MAPE of 5.70%. Full article

Global warming trends and the rapid reduction of summer Arctic sea ice extent have increased the feasibility of transarctic transport. How the process of glacier melting affects the existing containerized sea–rail shipping network and container flow assignment has become a challenging economic and policy issue. This paper first examines the meteorological influences on glacier melting and the assignment of container flow over the existing sea–rail network. Then, a three-layer simulation framework is constructed, with the upper layer simulating glacier melting based on the raster grid, the middle layer combining a grid and topology analysis to simulate the evolution of the global sea–rail network and the lower layer establishing a concave cost network flow model to simulate the container flow assignment. Finally, we use MicroCity to achieve the dynamic optimization and simulation of global container flow assignment, solving the large-scale sea–rail shipping network traffic assignment problem. The simulation results show that the proposed model and solution algorithm are feasible and effective, revealing the variation of container flow assignment in the global sea–rail shipping network under different Arctic ice melting scenarios. For instance, in the summer of 2050, the Arctic routes will share the global container flows, resulting in a significant reduction of container flows in the Malacca Strait, Suez Canal and Panama Canal. Full article