Birhane Ataklti, Fethangest Woldemariyam Tesema, Ermias Hagos
et al.
ABSTRACT The study area, located in central Tigray, Ethiopia, covers 234.5 km2 and primarily relies on groundwater as its main water source. As water quality concerns grow, detailed studies on groundwater hydrogeochemistry and its suitability for consumption remain insufficient. This study investigates groundwater hydrogeochemistry and evaluates its quality for drinking purposes. In May 2020, 19 water samples were collected from various locations and analyzed for physicochemical parameters, to gain insights into groundwater quality and its influencing factors. Hydrogeochemical classification utilized ionic ratios, Piper and Schoeller diagrams, statistical methods, and water quality assessment via the water quality index (WQI). The findings revealed Ca2+ > Mg2+ > Na+ + K+ and HCO3− > SO42− > Cl− as predominant ions, with Ca–Mg–SO4–HCO3 dominating. Gibbs diagrams and scatter plots revealed water–rock interaction and silicate dissolution as key hydrogeochemical factors, supplemented by ion exchange processes and human activities. Total dissolved solid and electrical conductivity exhibited a strong correlation and were moderately correlated with the major ions. The WQI ranged from 25.6 to 215.94, averaging 69.09, classifying groundwater as excellent (15.8%), good (78.9%), and very poor (5.3%) for drinking use. These insights provide valuable input to maintain the optimal use of groundwater resources in the area.
Esther Madrid, Ma. Laura Sampedro, Ma. del Carmen Maganda
et al.
La cuenca del río Atoyac atraviesa los municipios de Atoyac de Álvarez y Benito Juárez en el estado de Guerrero, México. Desde el año 2000 existen evidencias de que sus aguas están contaminadas en las zonas más pobladas. Este trabajo tuvo por objetivo investigar la calidad del agua superficial en la zona alta, media y baja de la cuenca del río Atoyac en Guerrero. La metodología incluyó nueve sitios de muestreo, para analizar 11 parámetros fisicoquímicos y ocho metales pesados, en seis momentos diferentes, que abarcaron épocas de lluvia y estiaje. Los datos obtenidos se compararon con los criterios ecológicos de la calidad del agua y con los indicadores del semáforo de la calidad del agua. Para conocer las diferencias en la calidad del agua en las tres zonas de la cuenca, se realizó un análisis estadístico cuantitativo con diseño cuasi experimental, transversal y comparativo sobre la DBO5. Los resultados respecto a DBO5 y a los compuestos nitrogenados (N-NH3, N-NO2- y N-NO3-) demuestran que el agua del río Atoyac, Guerrero, en la cuenca alta, media y baja no se considera apta como fuente de abastecimiento de agua potable, riego agrícola o protección de la vida acuática. Además, que la cuenca baja del río Atoyac, Guerrero, se encuentra contaminada por la presencia de Cd y Hg, lo cual puede constituir un problema para la salud pública debido a las características tóxicas de los metales pesados.
Hydraulic engineering, Water supply for domestic and industrial purposes
<p style="text-align: left;"><strong>Introduction:</strong> Water is considered one of the main foundations of sustainable development of societies, while clean water resources are a major prerequisite for environmental protection and economic, political, social and cultural development. The increasing demand for water, increasing living standards and the spread of water resource pollution due to the development of agricultural, urban and industrial activities have led to a chaotic environmental situation and intensified water resource pollution, which will make it difficult to control.</p>
<p style="text-align: left;"><strong>Methods:</strong> Multivariate statistical methods and data mining have been used to investigate water quality in many studies. Cluster analysis (CA) and discriminant analysis (DA) were used to identify pollution sources in river basins. In order to systematically compare the assumptions of the analytical methods used, the theoretical foundations of each method were examined. Nonparametric methods such as percentage elimination (PR) and sign test (ST) were applicable without the need to assume a specific data distribution, while classical multivariate methods including PCA and FA were used with the assumption of multivariate normality and linear relationships between variables (as confirmed by KMO and Bartlett tests). Machine learning models including Random Forest and XGBoost with the ability to analyze nonlinear relationships and resist collinearity, SVM with sensitivity to feature scaling and the need for separable space, and regression methods such as PLS and Stepwise with the assumption of linear relationships and the need for cross-validation to prevent overfitting were used.</p>
<p style="text-align: left;"><strong>Findings:</strong> According to the results obtained from the statistical methods of percentage elimination and sign test, it was observed that the wetland plays a fundamental and key role in the entire drainage system; therefore, using the statistical methods of LDA, PCA/FA and HACA, all water quality factors in the wetland are examined. Also, principal component analysis (PCA) plays a positive role in prioritizing the importance of each factor in pollution, so that it places the more important factors in the first component and the less important factors in the subsequent components. The results obtained from the principal component analysis show that the components with more than one eigenvalue are considered the most important components that justify the variance.</p>
Toni Seibold, Fabian Neumann, Falko Ueckerdt
et al.
Greenhouse gas emissions from the steel, fertiliser and plastic industries can be mitigated by producing their precursors with green hydrogen. In Germany, green production may be economically unviable due to high energy costs. This study quantifies the 'renewables pull' of cheaper production abroad and high-lights trade-offs between cost savings and import dependence. Using a detailed European energy system model coupled to global supply curves for hydrogen and industry precursors (hot briquetted iron, ammonia and methanol), we assess five scenarios with increasing degrees of freedom with respect to imports. We find that precursor import is preferred over hydrogen import because there are significant savings in hydrogen infrastructure. Cost savings in the German industry sector from shifting precursor production to European partners compared to domestic production are at 4.1 bnEUR/a or 11.2 %. This strategy captures 47.7 % of the cost savings achievable by precursor import from non-European countries, which lowers industry costs by 8.6 bnEUR/a (23.3 %). Moving energy-intensive precursor production abroad allows Germany to save costs while still retaining a substantial share of subsequent value-creating industry. However, cost savings must be weighed against the risks of import dependence, which can be mitigated by sourcing exclusively from regional partners.
Jonathan Benchimol, Inon Gamrasni, Michael Kahn
et al.
The global financial crisis (GFC) triggered the use of macroprudential policies imposed on the banking sector. Using bank-level panel data for Israel for the period 2004-2019, we find that domestic macroprudential measures changed the composition of bank credit growth but did not affect the total credit growth rate. Specifically, we show that macroprudential measures targeted at the housing sector moderated housing credit growth but tended to increase business credit growth. We also find that accommodative monetary policy surprises tended to increase bank credit growth before the GFC. We show that accommodative monetary policy surprises increased consumer credit when interacting with macroprudential policies targeting the housing market. Accommodative monetary policy interacted with nonhousing macroprudential measures to increase total credit.
Mohammadhossein Golchin, Siyu Chen, Shubham Sharma
et al.
Water flushing to remove particle sediment is essential for safe and continuous transport of many industrial slurries through pipelines. An efficient flushing strategy may reduce water consumption and the cost associated with water usage, and help water conservation for sustainability. In this study, a computational fluid dynamics (CFD) model coupled with the kinetic theory of granular flow for the flushing process is presented. The CFD models were validated against field data collected from a coal slurry pipeline of 128 $km$ in length, 0.575~$m$ in diameter, achieving an average error of less than 15\% for outlet solid concentration over time. A parametric study evaluated the effects of water velocity (1.88-5.88~$m/s$), bubble size (50~$μm$, 150~$μm$, and 1000~$μm$) and bubble volume fraction (0.05-0.2) on flushing performance including pipeline cleanness, cleanness efficiency, and water consumption. The obtained outcomes indicate that higher water velocity is preferred and an increase in water velocity from $1.88~m/s$ to $5.88~m/s$ reduces the water consumption by $28\%$. Large bubbles may hinder the flushing process and increase the water consumption by $23\%$. Remarkably, small bubbles facilitates the flushing process and lead to $35\%$ reduction in water consumption. These effects are attributed to the turbulent characteristics in the pipelines in presence of microbubbles.
Abstract Eliminating antibiotics from wastewater remains a critical challenge. This work introduces durable, high-performance adsorptive membranes formed by polymerizing tannic acid (TA) and Fe3+ ions on commercial substrates, known as TA-Fe, to enhance antibiotic removal. Using a filtration process, the TA-Fe modified membrane (MCM) demonstrated excellent ciprofloxacin hydrochloride removal efficiency, permeance, and reusability. The MCM achieved high removal capability for antibiotics, with a permeance of 3815 L m−2 h−1 bar−1 and 96.7% removal efficiency within 1.3 min—115 times faster than traditional methods. Furthermore, the MCM sustained an impressive adsorptive capacity of 99.4% even after 10 consecutive cycles. This work profoundly progresses the domain of adsorptive membrane technology, offering a promising avenue for the sustainable adsorption and separation of antibiotics from contaminated wastewater.
Faramarz Zargar Yaghoubi, Mahdi Sarai Tabrizi, Ali Mohammadi Torkashvand
et al.
Abstract This study aimed to investigate the simultaneous effects of drought and salinity on irrigation management coefficients in maize farms. A three-year field research was conducted in the form of a 3 × 3 factorial experiment with a randomized complete block design and three replications from 2020 to 2022 in a maize farm, in Aliabad Fashafoye, Qom province, Iran. The applied treatments included three levels of salinity (S0 = 1.8, S1 = 5.2, and S2 = 8.6 dS/m) and three levels of irrigation (W0 = 100%, W1 = 75%, and W2 = 50% of field capacity). Evapotranspiration stress coefficient (KS) due to W0S1 and W0S2 treatments was (0.975 and 0.934), (0.974 and 0.932), and (0.962 and 0.935) in 2020, 2021, and 2022, respectively. According to the results, KS decreased by increasing the salinity level of irrigation water, so a 1-unit increase in salinity level above the tolerance threshold of the crop to salinity decreased KS by 0.78 and 1.76% for S1 and S2, respectively. Moreover, each percent of volumetric moisture decrease from field capacity decreased KS by 5.9 and 13.3% in W1 and W2, respectively. Also, with the increase in the intensity of the stresses, the readily available water (RAW) of treatments decreased. The sole application of salinity stress decreased the decreasing slope of RAW by 3.2%, while the application of both stresses resulted in the decreasing slopes of 4.9, 5.7, and 7.8% at the salinity levels of S0, S1, and S2, respectively, compared to the control. The findings of this study show that the accurate estimation of crop evapotranspiration and RAW can help to improve the irrigation schedule, and the amount of irrigation water used is less than in non-stress conditions due to the reduction of total evapotranspiration and less water uptake in environmental stresses in maize farms.
El análisis de frecuencias de crecientes trivariado, del gasto máximo (Q), el volumen escurrido (V) y la duración total (D) permite estimar con mayor exactitud el hidrograma de la creciente de diseño. Para procesar registros anuales conjuntos de Q y V disponibles se propuso estimar D como la duración del hidrograma Gamma hasta el 0.1 % del Q. Después, a cada registro de Q, V y D se le busca su distribución de probabilidades idónea para obtener las funciones marginales. En seguida, se adopta la función Cópula (FC) que mejor representa a las variables conjuntas Q-V, Q-D y V-D. Para estas búsquedas y las trivariadas subsecuentes, se trabajó con las FC de Clayton, Frank, Gumbel-Hougaard y Joe. En ambos casos, la selección de la mejor FC se basa en los errores de ajuste entre las probabilidades empíricas y teóricas. A las ternas de datos Q, V y D se les buscó las FC de mejor ajuste simétricas y asimétricas de las cuatro familias citadas. A continuación se calculan los periodos de retorno conjuntos de tipo OR, AND y de Kendall. Estos últimos permiten la estimación de los eventos de diseño de Q, V y D. Se describe el análisis de frecuencias trivariado para las 55 crecientes anuales de la estación hidrométrica La Cuña de la Región Hidrológica No. 12-3 (Río Santiago), México. Por último, se formulan las conclusiones, que destacan la sencillez de los análisis de frecuencias trivariados cuando se realizan con FC.
Hydraulic engineering, Water supply for domestic and industrial purposes
The rapid advancement of large language models (LLMs) has revolutionized artificial intelligence, introducing unprecedented capabilities in natural language processing and multimodal content generation. However, the increasing complexity and scale of these models have given rise to a multifaceted supply chain that presents unique challenges across infrastructure, foundation models, and downstream applications. This paper provides the first comprehensive research agenda of the LLM supply chain, offering a structured approach to identify critical challenges and opportunities through the dual lenses of software engineering (SE) and security & privacy (S\&P). We begin by establishing a clear definition of the LLM supply chain, encompassing its components and dependencies. We then analyze each layer of the supply chain, presenting a vision for robust and secure LLM development, reviewing the current state of practices and technologies, and identifying key challenges and research opportunities. This work aims to bridge the existing research gap in systematically understanding the multifaceted issues within the LLM supply chain, offering valuable insights to guide future efforts in this rapidly evolving domain.
Ensuring safe water supplies requires effective water quality monitoring, especially in developing countries like Nepal, where contamination risks are high. This paper introduces various hybrid deep learning models to predict on the CCME dataset with multiple water quality parameters from Canada, China, the UK, the USA, and Ireland, with 2.82 million data records feature-engineered and evaluated using them. Models such as CatBoost, XGBoost, and Extra Trees, along with neural networks combining CNN and LSTM layers, are used to capture temporal and spatial patterns in the data. The model demonstrated notable accuracy improvements, aiding proactive water quality control. CatBoost, XGBoost, and Extra Trees Regressor predicted Water Quality Index (WQI) values with an average RMSE of 1.2 and an R squared score of 0.99. Additionally, classifiers achieved 99% accuracy, cross-validated across models. SHAP analysis showed the importance of indicators like F.R.C. and orthophosphate levels in hybrid architectures' classification decisions. The practical application is demonstrated along with a chatbot application for water quality insights.
The ongoing modernization of the power system, involving new equipment installations and upgrades, exposes the power system to the introduction of malware into its operation through supply chain attacks. Supply chain attacks present a significant threat to power systems, allowing cybercriminals to bypass network defenses and execute deliberate attacks at the physical layer. Given the exponential advancements in machine intelligence, cybercriminals will leverage this technology to create sophisticated and adaptable attacks that can be incorporated into supply chain attacks. We demonstrate the use of reinforcement learning for developing intelligent attacks incorporated into supply chain attacks against generation control devices. We simulate potential disturbances impacting frequency and voltage regulation. The presented method can provide valuable guidance for defending against supply chain attacks.
We study deregulated power markets with strategic power suppliers. In deregulated markets, each supplier submits its supply function (i.e., the amount of electricity it is willing to produce at various prices) to the independent system operator (ISO), who based on the submitted supply functions, dispatches the suppliers to clear the market with minimal total generation cost. If all suppliers reported their true marginal cost functions as supply functions, the market outcome would be efficient (i.e., the total generation cost is minimized). However, when suppliers are strategic and aim to maximize their own profits, the reported supply functions are not necessarily the true marginal cost functions, and the resulting market outcome may be inefficient. The efficiency loss depends crucially on the topology of the underlying transmission network. This paper provides an analytical upper bound of the efficiency loss due to strategic suppliers, and proves that the bound is tight under a large class of transmission networks (i.e., weakly cyclic networks). Our upper bound sheds light on how the efficiency loss depends on the transmission network topology (e.g., the degrees of nodes, the admittances and flow limits of transmission lines).
Water is routinely exposed to external electric fields (EFs). Whether, e.g., at physiological conditions, in contact with biological systems, or at the interface of polar surfaces in countless technological and industrial settings, water responds to EFs on the order of a few V/Å in a manner that is still under intense investigation. Dating back to the $19^{th}$ century, the possibility of solidifying water upon applying an EF instead of adjusting temperature and pressure -- a process known as electrofreezing -- is an alluring promise that has canalized major efforts since, with uncertain outcomes. In this work, we perform long \emph{ab initio} molecular dynamics simulations \textcolor{black}{of water at ambient conditions exposed at EFs of different intensities. While the response of single water molecules is almost instantaneous, the cooperativity of the hydrogen bonds induces slower reorganizations that can be captured by dividing the trajectories in disjoint time windows and by performing analysis on each of them separately. Upon adopting this approach, we find} that EFs of $0.10\leq$EFs$\leq0.15$~V/Å induce electrofreezing \textcolor{black}{occurring after $\sim150$~ps. We observe a continuous transition to a disordered state characterized by frozen dynamical properties, damped oscillations, lower energy, and enhanced local structural properties. Therefore, we ascribe this state to} a new ferroelectric amorphous phase, which we term f-GW (ferroelectric glassy water). Our work represents the first evidence of electrofreezing of liquid water at ambient conditions and therefore impacts several fields, from \textcolor{black}{fundamental chemical physics to} biology \textcolor{black}{and} catalysis.
Hossein Mirzaee, Hamed Samarghandi, Keith Willoughby
Disasters and disruptions such as the COVID-19 pandemic can significantly interrupt supply chains and industries. To control these disruptions, decision-makers must focus on supply chain resiliency. This paper proposes a multi-stage, multi-period green supply chain design model and six resilience strategies, with downstream and upstream disruptions taken into account to analyze both the ripple and bullwhip effect, respectively. To control the mentioned disruptions and handle the uncertainties of parameter estimations, a two-stage stochastic optimization approach is devised. The objectives are to minimize the total cost of disruption, and $CO_{2}$ emission under the cap-and-trade mechanism as a government-issued emission regulation. The proposed decision-making framework and solution approach are validated using a numerical experiment followed by sensitivity analysis. The results show the optimum structure of the supply chain and the best resilient strategies to mitigate the ripple effect. Moreover, the effect of a decline in capacity of facilities on the optimal solution and the applied resilient strategies is investigated. This study provides managerial insights to help governments set the proper amount of cap, and supply chain managers to predict the demand behaviour of essential and non-essential products in the event of disruptions.
Md. Mahabub Hasan, Md. Bengir Ahmed Shuvho, Mohammad Asaduzzaman Chowdhury
et al.
Abstract Pasur river is one of the largest rivers in the World Heritage Sundarbans mangrove forest region of the southwestern part of Bangladesh. Due to lack of alternative sources, more than 1 million inhabitants living in the Pasur river basin area rely heavily on the river water for domestic, irrigation, and industrial purposes without proper and reliable information on the water qualities and contamination types. The study aimed at evaluating the suitability and sustainability for irrigation and consumption practices, and suitable hydrogeochemical techniques and quality of Pasur river water of Sundarbon region of Bangladesh were investigated. Water samples were collected from six locations during pre-monsoon and post-monsoon seasons and assessed for suitability for drinking and irrigation application. The water quality index (WQI) was calculated to evaluate the suitability for drinking. WQI indicates that the river water samples during both the seasons are safe for drinking in the good category. Sodium percentage (Na%), sodium adsorption ratio (SAR), magnesium hazard (MH), residual sodium carbonate (RSC) were investigated to assess the feasibility for agricultural applications. Most of the indices, such as SAR, Na%, and RSC results recommend that the river water is safe for irrigation. A suggestion is made that MH in river water should be controlled for the use of water in irrigation. United States Salinity Laboratory (USSL) diagram and Wilcox diagram analysis also identified that river water as a usable category for irrigation purposes is feasible during both seasons.
Ebelia Del Angel, Mayra Agustina Pantoja, Rosendo López
et al.
Los residuos de la industria de la caña de azúcar representan uno de los principales agro-residuos. A menudo se consideran como basura y se pueden utilizar como materia prima barata para obtener carbón activado (CA). Por otra parte, el agua residual doméstica contiene residuos de grasas y aceites causantes del taponamiento de las tuberías; se puede lograr la adsorción de estas sustancias con carbón activado. En este trabajo se obtuvo CA a partir de residuos de bagazo de caña de azúcar mediante un método simplificado de dos pasos: activación química con H3PO4 y pirólisis a 973 K. La caracterización textural y estructural fue por fisisorción N2, microscopía electrónica de barrido (SEM) y difracción de rayos X (RDX). El carbono obtenido (SBET de 914 m2g-1) mostró una estructura análoga al grafito y una superficie microporosa característica de los carbonos activados. El carbono sintetizado se evaluó en la adsorción de grasas y aceites de aguas residuales domésticas. Los resultados mostraron el valor agregado que se obtiene del bagazo de caña para eliminar eficientemente grasas y aceites de aguas residuales, ya que después de ocho horas se encontró que su CA adsorbió el 94 % de las grasas y aceites contenidas en las aguas residuales, lo cual es similar a la adsorción de un CA comercial (96 %). También se evaluó la cinética de adsorción con las ecuaciones de pseudo primer orden, pseudo segundo orden e intrapartícula; los datos experimentales mostraron un mejor ajuste con el modelo de pseudo segundo orden.
Hydraulic engineering, Water supply for domestic and industrial purposes
Soroush Goodarzi, Vahid Kayvanfar, Alireza Haji
et al.
Vaccinating the global population against Covid-19 is one of the biggest supply chain management challenges humanity has ever faced. Rapid supply of Covid-19 vaccines is essential for successful global immunization, but its effectiveness depends on a transparent supply chain that can be monitored. In this research, we have proposed an approach based on blockchain technology, which is used to ensure seamless distribution of the Covid-19 vaccine with transparency, data integrity, and full traceability of the supply chain to reduce risk, ensure safety, and immutability. A vaccine supply chain needs to update the status of the vaccine at every stage, and any problem in the supply and distribution path can lead to irreparable damage. Currently, the research conducted on the use of blockchain in supply chains is still in the early stages. In this paper, the use of blockchain technology to monitor the vaccine supply and distribution system will be investigated. A model close to reality of today's vaccine supply chains in developing countries is considered and then a new intelligent system for vaccine monitoring in the vaccine supply chain is designed based on the considered model. Also, smart contracts based on a blockchain network is designed to check consumer vaccination records as well as vaccine circulation from beginning to end. The implementation and design of the vaccine supply chain is done using smart contracts on the Ethereum blockchain network. Additionally, the system has been tested on both local networks, the HardHat suite and Rinkbey's test network. The system has also been developed to work seamlessly when it is using an integrated IoT chip that can automatically update a batch's location, temperature, and other physical conditions periodically.