Abstract Accurate prediction of Chemical Oxygen Demand (COD) is vital for effective water quality management and pollution control. This study compares six ensemble boosting models, AdaBoost, CatBoost, XGBoost, LightGBM, HistGBRT, and NGBoost, for estimating COD from multiple water quality parameters, including pH, dissolved oxygen, suspended solids, and specific conductance. Data from two monitoring stations in South Korea (Toilchun and Hwangji) were used to train and validate the models. Model performance was evaluated using RMSE, MAE, R, NSE, and PBIAS, while interpretability was assessed through SHapley Additive exPlanations (SHAP). Results showed that NGBoost achieved the highest predictive accuracy at Toilchun (R = 0.979, NSE = 0.958, RMSE = 0.397 mg/L), while CatBoost performed best at Hwangji (R = 0.861, NSE = 0.733, RMSE = 0.477 mg/L). As NGBoost provides predictive probability distributions rather than single estimates, its results also reflect model uncertainty, supporting a more robust quantification of COD variability. SHAP analysis identified total organic carbon (TOC), biochemical oxygen demand (BOD₅), and suspended solids (SS) as the most influential variables controlling COD dynamics.
This paper presents the outputs of the exploratory phase of a global audit of Large Language Models (LLMs) project. In this exploratory phase, I used the Global AI Dataset (GAID) Project as a framework to stress-test the Llama-3 8B model and evaluate geographic and socioeconomic biases in technical AI governance awareness. By stress-testing the model with 1,704 queries across 213 countries and eight technical metrics, I identified a significant digital barrier and gap separating the Global North and South. The results indicate that the model was only able to provide number/fact responses in 11.4% of its query answers, where the empirical validity of such responses was yet to be verified. The findings reveal that AI's technical knowledge is heavily concentrated in higher-income regions, while lower-income countries from the Global South are subject to disproportionate systemic information gaps. This disparity between the Global North and South poses concerning risks for global AI safety and inclusive governance, as policymakers in underserved regions may lack reliable data-driven insights or be misled by hallucinated facts. This paper concludes that current AI alignment and training processes reinforce existing geoeconomic and geopolitical asymmetries, and urges the need for more inclusive data representation to ensure AI serves as a truly global resource.
WU Hongchun1, ZHENG Youqi1, CAO Liangzhi1, DU Xianan1, WANG Xuesong2, ZU Tiejun1, LIU Zhouyu1, HE Qingming1, CHEN Ronghua1, GE Li1, YANG Rui2, GAO Xinzhao2, WANG Shixi2, A Reai2
Liquid-metal fast reactor is a key link between the preceding and the next in the “three steps” strategy of nuclear energy development in China. High-precision numerical analysis software of liquid-metal fast reactor is the basis for improving the research and development level of fast reactor in China. At present, the industry departments still use the numerical analysis methods and computing software formed through the digestion of imported software since the 1980s and 1990s, and face the technical problems such as large model approximation and narrow application range, and it is urgent for theoretical breakthrough and the research and development of a new generation of high-performance numerical analysis software. This paper focused on the physical characteristics and numerical analysis needs of liquid-metal fast reactor, focusing on six aspects: nuclear data processing, nuclear reactor physics, thermal hydraulics, system safety analysis, fuel performance analysis, and radiation shielding analysis. It proposed a set of numerical analysis methods for liquid-metal fast reactor with advanced theoretical models, high computational accuracy, and strong adaptability to reactor types. With the support of national projects, a fully independent code system, named LoongSystem, was developed. To validate the LoongSystem, the physical experiments and operational measurement data based on China Experimental Fast Reactor (CEFR) were utilized to verify the proposed theoretical model and the developed code system. The results indicate that the maximum error in critical calculations for the CEFR startup physical experiments is 321 pcm, and the maximum relative error in control rod value is 11.60%. The trends in the thermal hydraulics and system safety analysis calculations of the reactor core are consistent with the experimental measurement results, with relative deviations of key parameters such as outlet temperature being less than 2%. The aforementioned results indicate that the newly proposed model and the developed computational software exhibit excellent computational accuracy. The findings suggest that employing advanced numerical simulation algorithms can circumvent the inherent shortcomings of existing methods and software in terms of theoretical models, addressing the issue of computational accuracy that fails to meet engineering requirements due to model defects. Consequently, these algorithms demonstrate superior versatility and scalability, providing instrumental support for the research and development of China’s new generation of liquid-metal fast reactor.
ObjectiveSediment supply is an important control on sediment transport and riverbed morphology. Influenced by earthquake, bank collapse, landslides or debris flows, the sediment supply process in mountain rivers has changed significantly, resulting in abrupt adjustment of riverbed and frequent sediment-related disasters. Up to the present, the movement characteristics of sediment body have mainly focused on the episodic sediment supply induced by landsides or debris flows, the bedload transport law and riverbed evolution in mountain rivers under variable sediment supply conditions are still unclear. During flash flood in mountain rivers, a large amount of sediment enters rivers in a short period of time, exceeding the transport capacity of the flow and leading riverbed aggradation and water level rising so as to increase the flood risk. The previous studies have rarely addressed the evolution of sediment body under such excessive sediment supply conditions, resulting in inadequate estimation of hazard impacts, human casualties and property losses in vulnerable areas, which cause significant challenges for the prevention and control of sediment-related disasters. Therefore, this paper conducted a series of experiments to study the movement characteristics and bed evolution under varying sediment supply conditions, and revealed the influence of sediment supply process, magnitude (i.e., mass), and texture (i.e., grain size distributions) of variable sediment supply on translation and dispersion of sediment body. The results of this study can provide theoretical and technical support for sediment-related disasters control and river government.MethodsAll experiments were conducted in a 10 m long, 0.6 m wide, and 0.8 m deep rectangular glass-walled water recirculating flume at the State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, China. The uniform sediment with the median grain size <italic>D</italic><sub>50</sub> = 3.6 mm and geometric standard deviation <italic>σ</italic><sub>g</sub> = 1.18 and the non-uniform sediment with <italic>D</italic><sub>50</sub> = 4.8 mm and <italic>σ</italic><sub>g</sub> = 2.54 were used in experiments. 12 test runs were conducted with a constant flow discharge and four sediment supply modes, including a constant sediment supply regime and three stepped variable sediment supply regimes. During experiments, the flow discharge was measured using a rectangular weir installed upstream of the flume inlet. The water level and bed profile of a 5.5 m long test reach downstream of the sediment feeder were recorded and measured every 0.5 m using webcams with transparent grid sheets on the glass wall of the flume with an accuracy of ±2 mm. After the bed was dry, the high-resolution digital elevation models (DEMs) were obtained using structure-from-motion (SfM) photogrammetry to analyze the evolution of bed elevation and bed morphology.Results and Discussions (1) For the uniform sediment supply and bed material, the channel had net deposition under the same sediment supply mass and different sediment supply process, the total mass of sediment deposition for each test was consistent and the averaged bed elevation also remained the same as that before sediment supply. However, there were some differences in bedforms (i.e., the aggradation and degradation areas) under different sediment supply conditions, which may be related to the randomness of particle movement and the inhomogeneity of sediment supply along the flume width. For the non-uniform sediment supply and bed material, the bed deposition in the upstream was stronger than that in the downstream, and the more the sediment supply, the greater the degree of bed aggradation. (2) For the constant sediment supply regime and stepped variable sediment supply regime, the sediment body presented a wedge shape, the longitudinal profile of sediment body shown a process of increasing and lengthening first, then decreasing and shortening, and finally stabilizing. According to the movement process of the mass center of sediment body, it can be seen that the trajectory of the mass center was clockwise during riverbed aggradation and degradation process, and the speed of aggradation was greater than that of degradation. (3) The sediment propagation form can be assessed based on the downstream cumulative difference of bed elevation (CED). The CED curve rotates clockwise and fades with time for dispersion, migrates to downstream entirely without any deformations for translation, and shows both a progressive translation to downstream and a rotational decline in the slope of the curve for the mixed dispersion and translation. The results of the current study shown that the sediment body propagates primarily though dispersion to downstream for uniform sediment supply and bed material. The relative dispersion and translation of the sediment body induced by excessive sediment supply can be further quantified by plotting the CED interquartile range (IQR, the length enclosing the central 50% volume) against the CED center location (i.e., the median of the CED curves). The slope of IQR against the center location ranges from 0.98 to 1.38 for uniform sediment tests, according to the previous studies, the sediment body primarily though dispersion to downstream. (4) According to the downstream cumulative distribution of elevation difference (CED) and the relationship of CED interquartile range and CED longitudinal center location, the movement forms of sediment body are divided into three intervals. For the large-fine and large-coarse sediment supply, the sediment body propagates primarily though dispersion to downstream. For the small-fine and small-coarse sediment supply, the deposited sediment propagates primarily through dispersion-translation to downstream.ConclusionsThe present study conducted a series of experiments to investigate the movement characteristics of sediment body in mountain rivers under excessive sediment supply conditions. The sediment supply process, sediment supply mass and sediment supply grain size distribution on the movement law of sediment body and the characteristics of riverbed evolution were revealed. The downstream cumulative difference of bed elevation (CED) and the CED interquartile range (IQR, the length enclosing the central 50% volume) against the CED center location can be used to assess the sediment propagation form of dispersion and translation. The results of this study can provide theoretical and technical support for sediment-related disasters control and river government.
Fluid-structure interaction (FSI) problems are characterized by strong nonlinearities arising from complex interactions between fluids and structures. These pose significant challenges for traditional control strategies in optimizing structural motion, often leading to suboptimal performance. In contrast, deep reinforcement learning (DRL), through agent interactions within numerical simulation environments and the approximation of control policies using deep neural networks (DNNs), has shown considerable promise in addressing high-dimensional FSI problems. Furthermore, the training of DRL models necessitates a stable numerical environment, particularly for FSI problems. Smoothed particle hydrodynamics (SPH) offers a flexible and efficient computational approach for modeling large deformations, fractures, and complex interface movements inherent in FSI, outperforming traditional grid-based methods. This work presents DRLinSPH, an open-source Python platform that integrates the SPH-based numerical environment provided by the open-source software SPHinXsys with the mature DRL platform Tianshou to enable parallel training for FSI problems. DRLinSPH has been successfully applied to four FSI scenarios: sloshing suppression using rigid and elastic baffles by controlling displacement or introducing deformation, achieving a maximum wave height reduction of 68.81% and 42.92%, respectively; wave energy harvesting optimization with an 8.25% improvement through an oscillating wave surge converter (OWSC) by regulating the damping characteristics of the Power Take-Off (PTO) system; and muscle-driven fish swimming control in a straight line within vortices. The results demonstrate the platform's accuracy, stability, and scalability, highlighting its potential to advance industrial solutions for complex FSI challenges.
Automatic speech recognition (ASR) has witnessed remarkable progress in recent years, largely driven by the emergence of LLM-based ASR paradigm. Despite their strong performance on a variety of open-source benchmarks, existing LLM-based ASR systems still suffer from two critical limitations. First, they are prone to hallucination errors, often generating excessively long and repetitive outputs that are not well grounded in the acoustic input. Second, they provide limited support for flexible and fine-grained contextual customization. To address these challenges, we propose Index-ASR, a large-scale LLM-based ASR system designed to simultaneously enhance robustness and support customizable hotword recognition. The core idea of Index-ASR lies in the integration of LLM and large-scale training data enriched with background noise and contextual information. Experimental results show that our Index-ASR achieves strong performance on both open-source benchmarks and in-house test sets, highlighting its robustness and practicality for real-world ASR applications.
Kazi Reyazul Hasan, Mubasshira Musarrat, A. B. M. Alim Al Islam
et al.
Large language models work well for technical problem solving in English but perform poorly when the same questions are asked in Bangla. A simple solution would be to translate Bangla questions into English first and then use these models. However, existing Bangla-English translation systems struggle with technical terms. They often mistranslate specialized vocabulary, which changes the meaning of the problem and leads to wrong answers. We present BanglaSTEM, a dataset of 5,000 carefully selected Bangla-English sentence pairs from STEM fields including computer science, mathematics, physics, chemistry, and biology. We generated over 12,000 translations using language models and then used human evaluators to select the highest quality pairs that preserve technical terminology correctly. We train a T5-based translation model on BanglaSTEM and test it on two tasks: generating code and solving math problems. Our results show significant improvements in translation accuracy for technical content, making it easier for Bangla speakers to use English-focused language models effectively. Both the BanglaSTEM dataset and the trained translation model are publicly released at https://huggingface.co/reyazul/BanglaSTEM-T5.
BACKGROUND AND OBJECTIVES: Agriculture plays a significant role in the overall consumption of freshwater resources, accounting for approximately 70 percent of the total use. Energy is also essential at various stages of agriculture and the food chain. On a global scale, the agricultural and food industries account for approximately 30 percent of the overall energy consumption, with a significant portion derived from fossil fuel sources. This underscores the intricate interconnection between food, energy, and water resources. This study utilizes a linear programming model to determine the optimal cropping pattern while minimizing water usage in agriculture. This study's primary contribution lies in its dual approach: it identifies the optimal cropping patterns for a specified objective function through linear programming while simultaneously conducting a comprehensive analysis of these patterns by integrating considerations of food, energy, and water nexus.METHODS: The constraints applied in linear programming involve maintaining nearly the same agricultural revenue, conserving the total cultivation area, and limiting the change intervals in cultivation area by 5 percent. Using linear programming, crop patterns were determined for the periods of 2017, 2025-2050, 2050-2075, and 2075-2100. In each scenario, calculations were conducted to assess water consumption, energy demands, agricultural income, and carbon dioxide emissions, all framed within the context of the food-energy-water nexus. The various scenarios were subsequently analyzed to assess their effects. An evaluation was conducted regarding the sustainability of water usage in agricultural production.FINDINGS: All the scenarios examined resulted in lower water usage, reduced energy requirements, and decreased carbon dioxide emissions. Throughout various time periods, scenario 6, which permits a 5 percent variation in cultivation areas without imposing a total cultivation area limit, consistently proved to be the most favorable choice. It achieved an average reduction of 3.94 percent in water usage, 2.95 percent in energy requirements, and 1.62 percent in carbon dioxide emissions compared to the base scenario. Scenario 11, allowing for a 5 percent variation in cultivation areas while maintaining a total cultivation area limit, was evaluated as the second most effective scenario in terms of water conservation. It achieved an average reduction in water use of 3.45 percent, an average reduction in energy requirements of 1.85 percent, and a minimal reduction in carbon dioxide emissions of 0.11 percent across all time periods.CONCLUSION: The outcomes of this study reveal that the proposed model can play a crucial role in advancing sustainable agricultural management strategies. The obtained results indicate that significant reductions in water usage, energy requirements, and carbon dioxide emissions can be achieved simply by modifying the crop pattern, while agricultural income remains largely at the same level. The imposition of a constraint requiring the total cultivation area to be equal to or greater than that of the baseline scenario in the linear programming model has been found to restrict the extent of these reductions.
Anshul Yadav, Marwan A. Hassan, Conor McDowell
et al.
Abstract In this study, we use field observations augmented with model simulations to examine gravel dispersion over nine years (2007–2015) in Halfmoon Creek. The observations of flow, entrainment, and dispersion were used to develop a forward model utilizing the Einstein‐Hubbell‐Sayre (EHS) compound Poisson process. The observed mean virtual velocity of the tracer population slows down with cumulative excess energy after the 2010 large event. The forward model deviates from the observations in representation of tails, overpredicts mean displacements, and shows a narrower spatial distribution. The heavy‐tailed resting times indicate prolonged immobilization of some grains, suggesting the preferential movement of other most mobile grains. As such, 34% of most mobile grains constitute 50% of the total entrainments. The consideration of preferential movement explains the longitudinal spread but still overpredicts the displacement after the 2010 event. The model was then explored to consider additional transport‐related mechanisms causing deviations, such as reduction in virtual velocity, entrainment probability, and morphological trapping of meander bends, which helps to adequately recreate the observed dispersive behavior. The available historical flow records used for simulating dispersive behavior over multiple decades reveal an abrupt increase in displacements for exceptionally large events, suggesting the exhumation of deeply buried grains back in transport. The simulation results highlight the need for tracer studies with large sample sizes and improved recovery rates for longer time frames experiencing floods of widely varying magnitudes. Such models, inspired by Einstein's stochastic theory can be valuable for various river research applications.
This paper presents a curriculum, "AI Literacy for All," to promote an interdisciplinary understanding of AI, its socio-technical implications, and its practical applications for all levels of education. With the rapid evolution of artificial intelligence (AI), there is a need for AI literacy that goes beyond the traditional AI education curriculum. AI literacy has been conceptualized in various ways, including public literacy, competency building for designers, conceptual understanding of AI concepts, and domain-specific upskilling. Most of these conceptualizations were established before the public release of Generative AI (Gen-AI) tools like ChatGPT. AI education has focused on the principles and applications of AI through a technical lens that emphasizes the mastery of AI principles, the mathematical foundations underlying these technologies, and the programming and mathematical skills necessary to implement AI solutions. In AI Literacy for All, we emphasize a balanced curriculum that includes technical and non-technical learning outcomes to enable a conceptual understanding and critical evaluation of AI technologies in an interdisciplinary socio-technical context. The paper presents four pillars of AI literacy: understanding the scope and technical dimensions of AI, learning how to interact with Gen-AI in an informed and responsible way, the socio-technical issues of ethical and responsible AI, and the social and future implications of AI. While it is important to include all learning outcomes for AI education in a Computer Science major, the learning outcomes can be adjusted for other learning contexts, including, non-CS majors, high school summer camps, the adult workforce, and the public. This paper advocates for a shift in AI literacy education to offer a more interdisciplinary socio-technical approach as a pathway to broaden participation in AI.
Context: Technical debt (TD) refers to the additional costs incurred due to compromises in software quality, providing short-term advantages during development but potentially compromising long-term quality. Accurate TD forecasting and prediction are vital for informed software maintenance and proactive management. However, this research area lacks comprehensive documentation on the available forecasting techniques. Objective: This study aims to explore existing knowledge in software engineering to gain insights into approaches proposed in research and industry for forecasting TD evolution. Methods: To achieve this objective, we conducted a Systematic Literature Review encompassing 646 distinct papers published until 2023. Following established methodology in software engineering, we identified and included 14 primary studies for analysis. Result: Our analysis unveiled various approaches for TD evolution forecasting. Notably, random forest and temporal convolutional networks demonstrated superior performance compared to other methods based on the result from the primary studies. However, these approaches only address two of the fifteen identified TD types, specifically Code debt and Architecture debt, while disregarding the remaining types. Conclusion: Our findings indicate that research on TD evolution forecasting is still in its early stages, leaving numerous challenges unaddressed. Therefore, we propose several research directions that require further investigation to bridge the existing gaps. Keywords: Systematic literature review, Technical debt, Technical debt prediction, Technical debt forecasting, Technical debt metrics
This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed to address the unique challenges posed by video data, such as interpreting spatiotemporal information, to offer nuanced video content comprehension across various lengths. This technical report overviews Pegasus-1's architecture, training strategies, and its performance in benchmarks on video conversation, zero-shot video question answering, and video summarization. We also explore qualitative characteristics of Pegasus-1 , demonstrating its capabilities as well as its limitations, in order to provide readers a balanced view of its current state and its future direction.
Bhuwan Paudel, Javier Gonzalez-Huerta, Ehsan Zabardast
et al.
Background: Software companies must balance fast value delivery with quality, a trade-off that can introduce technical debt and potentially waste developers' time. As software systems evolve, technical debt tends to increase. However, estimating its impact on lead time still requires more empirical and experimental evidence. Objective: We conduct an empirical study investigating whether technical debt impacts lead time in resolving Jira issues. Furthermore, our aim is to measure the extent to which variance in lead time is explainable by the technical debt. Method: We conducted an industrial case study to examine the relationship in six components, each of which was analyzed individually. Technical debt was measured using SonarQube and normalized with the component's size, while lead time to resolve Jira issues was collected directly from Jira. Results: We found a set of mixed results. Technical debt had a moderate positive impact on lead time in two components, while we did not see a meaningful impact on two others. A moderate negative impact was found in the remaining two components. Conclusion: The findings show that technical debt alone can not explain all the variance in lead time, which ranges from 5% up to 41% across components. So, there should be some other variables (e.g., size of the changes made, complexity, number of teams involved, component ownership) impacting lead time, or it might have a residual effect that might manifest later on. Further investigation into those confounding variables is essential.
Abstract The thermal hydraulics simulation of the reactor coolant system for a typical three-loop pressurized water reactor was conducted based on numerical solution of Reynolds-averaged Navier-Stokes equations by commercial CFD software. This study aims to obtain the three-dimensional, global and localized flow features of the reactor coolant system. The completed model of the reactor coolant system is built including reactor pressure vessel and internals, core, steam generator, primary pump and linking pipe. The flow and the temperature have been investigated under normal steady operating condition with the full core thermal power and unbalanced operating condition with the failure of any primary pump during cold shutdown state. The local thermal hydraulic features of the reactor pressure vessel head dome, the thermal stratification in the reactor pressure vessel upper plenum and the hot legs, and the swirling flow of the primary pump are characterized to give reference to the reactor safety operation. This analysis practice provides an effective evaluation for the three-dimensional thermal hydraulics phenomena, and these encouraging results allow performing the comprehensive analysis for the reactor coolant system.
This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
En Guatemala, el método de desinfección más económico para el tratamiento de aguas residuales es la cloración, generando trihalometanos (THM) como principal contaminante. Este estudio evaluó los niveles de trihalomentanos totales (TTHM) en plantas de tratamiento de agua residual (PTAR) aerobias y anaerobias que utilizan cloración. Se empleó el método HACH 10132 para análisis, diseñado para agua potable adaptado a aguas residuales domésticas. Se tomaron muestras de seis PTAR en cinco zonas de la ciudad de Guatemala. De las 30 muestras analizadas, el 83% superó los límites de TTHM establecidos por la USEPA (80 µg/l). Los resultados de cloro residual indicaron que el tricloro fue la fuente predominante de TTHM. Se identificaron correlaciones significativas (coeficiente > 0.6) entre el tricloro y parámetros como DBO5, sólidos suspendidos y turbiedad en el 50% de las variables estudiadas. Estas asociaciones sugieren la influencia de la materia orgánica en la formación de TTHM. Este estudio destaca la necesidad de reconsiderar la eficacia y sostenibilidad de la cloración en el tratamiento de aguas residuales, así como la importancia de monitorear y controlar los niveles de TTHM para garantizar la calidad ambiental y la salud pública.
Masod Sadipour, Mohammad Masoud Momeni, Majid Soltani
In this study, a computational simulation is employed to place two essential parameters, the permeability of vessels and hydraulic conductivity, under assessment. These parameters impact the movement of drug particles through vessels, and normal and tumoral tissue to examine the concentration of nanoparticles, interstitial pressure, and velocity. To provide a geometric model detailing the capillary network under normal and tumoral tissue conditions, the geometry is extracted via real image processing. Subsequently, the real conditions were considered to solve the equations pertaining to drug transport and intravascular and interstitial flows in the tissue. The results showed that an increase in permeability and hydraulic conductivity leads to an increase in drug concentration in the tumor. Finally, Methotrexate drug has the most effect in the treatment of tumors. Overall, the computational model for anti-cancer delivery provides a powerful tool for understanding and optimizing drug delivery strategies for the treatment of cancer.
La cuenca del rio Coyolate es de importancia económica y ecológica para Guatemala, en esta se producen los cultivos con mayor aporte al PIB nacional siendo estos la caña de azúcar, maíz, banano, café y hule entre otros. El estudio generó tres escenarios de erosión hídrica utilizando cuatro niveles de erosión nula a ligera, moderada, fuerte y muy fuerte, que corresponden a tasas de erosión menor a 10, de 10 a 50, de 50 a 200 y mayor a 200 toneladas por hectárea anuales. En este se determinó la intensidad de uso de los suelos de la cuenca, comparando su capacidad de uso con su uso actual utilizando el modelo USLE para el cálculo de la erosión hídrica. Los escenarios presentaron las siguientes coberturas: a) 96% agricultura, 1.5% bosque y 2.5% otros usos; b) 65% agricultura, 28% bosque y 7% de otros usos; y c) 69% agricultura, 29% bosque y el 2% otros usos. El estudio concluyó que una de las principales causas de la erosión hídrica en la cuenca del río Coyolate es el uso inadecuado de sus suelos sin tomar en cuenta su capacidad de uso y características geomorfológicas. Se encontró que, aunque se incrementara el área para agricultura, una adecuada distribución de uso generaría menor erosión hídrica. Para mitigar la erosión hídrica en la cuenca, se debe mejorar su gestión, adecuando el uso de sus suelos e implementando prácticas para su conservación.
Este artículo presenta un compendio sobre los microorganismos filamentosos en el tratamiento de aguas residuales por lodos activados, con el propósito de disminuir o reducir los efectos adversos que este tipo de bacterias causan cuando se ausentan o se da un crecimiento excesivo de las mismas, debido a problemas operacionales o de diseño en las plantas de tratamiento de agua residual (PTAR). Estos microorganismos son la columna vertebral de la estructura del flóculo, pero cuando crecen en grandes densidades, ocasionan dificultad en la correcta sedimentación y compactación del lodo activo. Con respecto a su control, en el punto de dosificación de Hipoclorito sódico no deben alcanzarse los 35 g Cl/m3 de caudal de recirculación, para no dañar de forma irreversible la fauna. Y se recomienda que las dosificaciones del agente químico antes mencionado no superen los 15 kg Cl/t SSLM*día.
El tratamiento de las aguas residuales generadas en una industria de producción de grasas vegetales se realiza mediante un proceso de lodos activados. Esta planta de tratamiento de aguas residuales produce, como subproducto, de una a dos toneladas diarias de lodos. Su disposición representa un riesgo para el medio ambiente, así como también genera un alto costo para la empresa implicada. Por esta razón, este estudio consiste en caracterizar estos lodos para impulsar alternativas de reúsos de forma sanitaría y atribuirle un valor agregado. Se opta por un análisis cuantitativo de parámetros físicos, químicos y biológicos bajo una investigación cuantitativa de método exploratorio y se generan los datos a través de la recolección de 16 muestras. Se obtiene como resultado un déficit de nitrógeno, con una relación carbono - nitrógeno promedio de 49:1 y una humedad promedio de 77%. Encontrándose estos parámetros por encima del rango ideal para darle al lodo un reúso como abono orgánico, por lo cual se requiere una adecuación previa para este tipo de reúso. Por medio de esta investigación se muestra la oportunidad de aprovechar un subproducto considerado generalmente como un desecho.