Saline intrusion in Vietnam’s coastal areas has been significantly increased by climate change and anthropogenic activities, particularly water resource exploitation. This paper presents a comprehensive assessment of salinity levels within river systems supplying domestic water, with a focus on the Mekong Delta, Central Coast, and Northern regions. The findings indicate that saline intrusion fronts have penetrated inland distances ranging from tens to hundreds of kilometers. This penetration results in pronounced seasonal and diurnal (hourly) variations in river water salinity. Measured concentrations range from several hundred to tens of thousands of mg/L (quantified as Total Dissolved Solids, TDS), or up to several tens of parts per thousand (‰). River systems exhibiting high average salinity (> 1 ‰) include the Re, Cam, and Ninh Co rivers (North); the Yen, Cai, and Ma rivers (Central); and the Cua Tieu, Cua Dai, and Co Chien rivers (Mekong Delta). A critical associated issue is the elevated concentration of bromide ions (Br-) in saline-affected waters. This presence significantly increases the formation potential of carcinogenic disinfection byproducts (DBPs) during the chlorination process. Consequently, this study proposes technical solutions designed to mitigate and adapt to these adverse impacts on domestic water supplies
Jesse Ponnock, Grace Kenneally, Michael Robert Briggs
et al.
New technologies in generative AI can enable deeper analysis into our nation's supply chains but truly informative insights require the continual updating and aggregation of massive data in a timely manner. Large Language Models (LLMs) offer unprecedented analytical opportunities however, their knowledge base is constrained to the models' last training date, rendering these capabilities unusable for organizations whose mission impacts rely on emerging and timely information. This research proposes an innovative approach to supply chain analysis by integrating emerging Retrieval-Augmented Generation (RAG) preprocessing and retrieval techniques with advanced web-scraping technologies. Our method aims to reduce latency in incorporating new information into an augmented-LLM, enabling timely analysis of supply chain disruptors. Through experimentation, this study evaluates the combinatorial effects of these techniques towards timeliness and quality trade-offs. Our results suggest that in applying RAG systems to supply chain analysis, fine-tuning the embedding retrieval model consistently provides the most significant performance gains, underscoring the critical importance of retrieval quality. Adaptive iterative retrieval, which dynamically adjusts retrieval depth based on context, further enhances performance, especially on complex supply chain queries. Conversely, fine-tuning the LLM yields limited improvements and higher resource costs, while techniques such as downward query abstraction significantly outperforms upward abstraction in practice.
Henry J. Tanudjaja, Najat A. Amin, Adnan Qamar
et al.
Abstract Detecting and quantifying biofouling is a challenging process inside a seawater reverse osmosis (SWRO) module due to its design complexity and operating obstacles. Herein, deep Convolutional Neural Network (CNN) models were developed to accurately calculate the cross-sectional biofilm thickness (vertical plane) through membrane surface images (horizontal plane). Models took membrane surface image as input; the classification model (CNN-Class) predicted fouling classification, while the regression model (CNN-Reg) predicted the average biofilm thickness on the membrane surface. CNN-Class model showed 90% accuracy, and CNN-Reg reached a moderate mean difference of ±24% in predicting the classification and biofilm thickness, respectively. Both models performed well and validated with 80% accuracy in classification and a mean difference of ±18% in biofilm thickness prediction from a new set of unseen live OCT images. The developed CNN models are a novel technology that has the potential to be implemented in desalination plants for early decision-making and biofouling mitigation.
Abstract This research exploration emerged from the critical need to revolutionize heat transfer techniques, particularly in pivotal domains like nuclear technologies, electronics and energy-efficient systems. The motivation for this study endeavour stemmed from the complex interrelation among nanofluids, magnetic fields and their potential for enhancing heat exchange. A pragmatic numerical approach is utilized to examine the Cu–H2O nanofluid flow situation within an enclosure featuring cooled vertical walls and a heat-generating source, while ensuring insulation for the remaining edges. The evaluation analyses the contribution of entropy, including total, viscous and thermal entropies, establishing a connection to real-world heat transfer challenges. The Galerkin finite element algorithm is utilized to solve the partial differential system of the modelled problem. The phenomena of entropy generation, fluid flow and heat transfer are studied under the influence of parameters such as the Hartmann number, Rayleigh number, magnetic field inclination angle and nanoparticle volume fraction. The study reveals that irreversibility increases with the magnetic field inclination angle, while entropy generation decreases with an increase in the Hartmann number. The primary innovation of this study is uncovering new dimensions with widespread practical implications by deciphering the complex dynamics of nanofluid convection with entropy generation and inclined magnetic influence. This research holds significant potential for advancing heat transfer applications in water treatment and resource management, aligning with the journal’s focus on sustainable and innovative water solutions.
O. A. Abdel Moamen, E. M. Abu Elgoud, B. A. Masry
et al.
Abstract The growing adoption of computer-aided design (CAD) and computer-aided manufacturing (CAM) technologies in the dental industry has led to increase production of zirconia waste powder (ZWP), generated during the milling process of dental prostheses. Efficient recycling and processing of ZWP are essential to recover valuable metals and mitigate environmental impacts. This study examines the separation of zirconium Zr(IV) and yttrium Y(III) from ZWP via the use of Amberlite A27 chloride form, a strong base anion exchange sorbent, in acidic media (0.25 M H2SO4). This paper focuses on understanding the effects of initial Zr(IV) and Y(III) concentrations, sorbent amount, residence time, and system temperature on the sorption process. The optimum sorption conditions were found to be initial concentrations of [Zr] = [Y] = 100 mg/L, sorbent amount of 0.1 g, and a contact time of 30 min. A time transient study was conducted using pseudo-first-order (PFO), pseudo-second-order (PSO) and fractal time transient models. The results showed that higher initial concentrations of the metal ions decreased the sorption percentage of the sorbent. An optimal maximum sorbent amount was determined to maximize metal ion uptake and found to be 0.25 g. Time transient studies revealed that the sorption of the studied ions onto Amberlite A27 followed the pseudo-second-order model, indicating chemisorption as the predominant mechanism. Additionally, the fractal time transient model provided insights into the complexity and heterogeneity of the sorption sites on the sorbent surface. The best-fitted (R2 > 0.95) fractal-like pseudo-second-order (PSO) model demonstrated energetic site heterogeneity. The overall findings showed that Amberlite A27 chloride form selectively recovered zirconium more than yttrium from an acidic sulfuric acid medium. Desorption studies showed that the loaded yttrium scrapped with deionized water and 1 M HCl was completely stripped zirconium with an efficiency of 95%.
Bishwambhar Mishra, Parashuram Kallem, Rajasri Yadavalli
et al.
Abstract The treatment of industrial wastewater, containing various contaminants like chemicals, dyes, and heavy metals, has emerged as a significant environmental issue. Traditional treatment procedures, although successful, frequently include synthetic chemicals that are non-biodegradable and present hazards to both ecosystems and human health. Bio-flocculants, especially those originating from extracellular polymeric substances (EPS) generated by microorganisms, provide an environmentally benign and sustainable option. These bioflocculants utilize microbial enzymes and polymeric substances to efficiently agglomerate and eliminate contaminants, adhering to the principles of green chemistry. Recent studies have concentrated on enhancing bioflocculant manufacturing technologies and their utilization in industrial wastewater treatment. Research is directed on improving biodegradability, cost-efficiency, and pollutant removal efficacy. This paper analyzes the function of microbial-based bioflocculants in industrial wastewater treatment, emphasizing their ecological advantages, biodegradability, and economic efficiency. It also examines progress in bioflocculant synthesis, concentrating on the production and application of EPS. Contemporary research trends focus on enhancing bioflocculant manufacturing technologies and broadening their industrial uses. Future initiatives focus on improving the efficiency and scalability of bioflocculants to facilitate new and sustainable wastewater management solutions.
Abstract Anaerobic digestion (AD) is a crucial bioenergy source widely applied in wastewater treatment. However, its efficiency improvement is hindered by complex microbial communities and sensitivity to feedstock properties. We, thus, propose a hybrid quantum-classical machine learning (Q-CML) regression algorithm using a quantum circuit learning (QCL) strategy. Combining a variational quantum circuit with a classical optimiser, this approach predicts biogas production from operational data of 18 full-scale mesophilic AD sites in the UK. Tailored for noisy intermediate-scale quantum (NISQ) devices, the low-depth QCL model outperforms conventional regression methods (R²: 0.53) and matches the performance of a classical multi-layer perceptron (MLP) regressor (R²: 0.959) with significantly fewer parameters and better scalability. Comparative analysis highlights the advantages of quantum superposition and entanglement in capturing intricate correlations in AD data. This study positions Q-CML as a cutting-edge solution for optimising AD processes, boosting energy recovery, and driving the circular economy.
Bruno Chaves Figueiredo, Maria Alexandra Oliveira, João Nuno Silva
The Alqueva Multi-Purpose Project (EFMA) is a massive abduction and storage infrastructure system in the Alentejo, which has a water quality monitoring network with almost thousands of water quality stations distributed across three subsystems: Alqueva, Pedrogão, and Ardila. Identification of pollution sources in complex infrastructure systems, such as the EFMA, requires recognition of water flow direction and delimitation of areas being drained to specific sampling points. The transfer channels in the EFMA infrastructure artificially connect several water bodies that do not share drainage basins, which further complicates the interpretation of water quality data because the water does not flow exclusively downstream and is not restricted to specific basins. The existing user-friendly GIS tools do not facilitate the exploration and visualisation of water quality data in spatial-temporal dimensions, such as defining temporal relationships between monitoring campaigns, nor do they allow the establishment of topological and hydrological relationships between different sampling points. This thesis work proposes a framework capable of aggregating many types of information in a GIS environment, visualising large water quality-related datasets and, a graph data model to integrate and relate water quality between monitoring stations and land use. The graph model allows to exploit the relationship between water quality in a watercourse and reservoirs associated with infrastructures. The graph data model and the developed framework demonstrated encouraging results and has proven to be preferred when compared to relational databases.
To meet carbon emission targets, governments around the world seek electricity consumers to invest in self-sufficiency technologies such as solar photovoltaic and battery storage. Such behaviour is sought in markets typically characterised by an oligopoly amongst generating firms. In this work, we study the interactions between investment decisions on the demand side and the supply side, and we investigate how price-making behaviour on the supply side affects these interactions compared to a situation with perfect competition. To do so, we introduce a novel stochastic mixed complementarity problem to model several players in an oligopolistic electricity market. On the supply side, we consider generating firms who make operational and investment decisions. On the demand side, we consider both industrial and residential consumers, each of whom may invest in self-sufficiency technologies. The uncertainties of wind and solar generation are the sources of the model's stochasticity. We apply the model to a case study of a stylised Irish electricity system in 2030. Our results demonstrate that price-making on the supply side increases investment in self-sufficiency on the demand side, leading to a reduction in prices and carbon emissions. We also find that both market power and self-sufficiency alter the investment and decommissioning decisions made by generation firms. Counter-intuitively, we also observe that the absence of a feed-in premium increases investment in solar generation on the demand side. Our findings highlight the importance of including both demand and supply side investment in models of electricity markets characterised by an oligopoly.
Globally, the water crisis has become a significant problem that affects developing and industrialized nations. Water shortage can harm public health by increasing the chance of contracting water-borne diseases, dehydration, and malnutrition. This study aims to examine the causes of the water problem and its likely effects on human health. The study scrutinizes the reasons behind the water crisis, including population increase, climate change, and inefficient water management techniques. The results of a lack of water on human health, such as the spread of infectious diseases, a higher risk of starvation and dehydration, and psychological stress, are also concealed in the study. The research further suggests several ways to deal with the water situation and lessen its potential outcomes on human health. These remedies include enhanced sanitation and hygiene procedures, water management, and conservation techniques like rainwater gathering and wastewater recycling.
Akanksha Saini, Arash Shaghaghi, Zhibo Huang
et al.
The challenges of healthcare supply chain management systems during the COVID-19 pandemic highlighted the need for an innovative and robust medical supply chain. The healthcare supply chain involves various stakeholders who must share information securely and actively. Regulatory and compliance reporting is also another crucial requirement for perishable products (e.g., pharmaceuticals) within a medical supply chain management system. Here, we propose Multi-MedChain as a three-layer multi-party, multi-blockchain (MPMB) framework utilizing smart contracts as a practical solution to address challenges in existing medical supply chain management systems. Multi-MedChain is a scalable supply chain management system for the healthcare domain that addresses end-to-end traceability, transparency, and collaborative access control to restrict access to private data. We have implemented our proposed system and report on our evaluation to highlight the practicality of the solution. The proposed solution is made publicly available.
Abstract In this study, we investigate the capabilities of magnetohydrodynamic bioconvective micropolar nanofluids, considering the impact of Soret and Dufour effects using a non-similarity analysis. Our objective is to forecast the complex heat and mass transfer phenomena observed in both biological and industrial systems. In recent years, notable progress in energy applications has spurred our inquiry and exploration. To augment thermal conductivity and explore potential biocompatibility, we utilize blood as the base fluid, incorporating silver $$\left({\text{Ag}}\right)$$ Ag and copper oxide $$({\text{CuO}})$$ ( CuO ) . This distinctive configuration offers improved control over thermal properties and supports the exploration of advanced applications across various domains. In our analysis, we also consider factors such as viscous dissipation, the influences of Soret and Dufour effects, the existence of a magnetic field, and the occurrence of heat generation. The governing PDEs and their corresponding boundary conditions are transformed into dimensionless form through the use of suitable non-similar transformations. The outcomes generated by the modified model are obtained through the application of a local non-similarity approach, extended up to the second degree of truncation, and integrated with a finite difference code (bvp4c). Furthermore, the effects of different factors on fluid flow, micro-rotation, heat transfer, volume fraction, and microorganism properties in the analyzed flow scenarios are demonstrated and examined through visual representations, following the attainment of satisfactory agreement between the obtained results and those reported in prior studies. The tables are designed to present numerical variations for the drag coefficient and Nusselt number. A comparative analysis is conducted on previously published work, despite certain limitations, in order to evaluate the accuracy of the numerical scheme. It can be shown that the material parameter $$K$$ K has two effects on micropolar fluid dynamics: it increases the micro-rotation profile, which leads to higher fluid stiffness, and it reduces the velocity profile in response to an angled magnetic field. Furthermore, in bio-convective micropolar fluid, greater $$K$$ K values are correlated with an elevated temperature profile, showing enhanced heat transfer efficiency via increased fluid speed and kinetic energy production. The velocity profiles in bioconvective micropolar fluids rise with higher magnetic field values $$(M)$$ ( M ) , highlighting the significance of magnetic field orientations for a thorough comprehension of the behavior of fluids in these systems. Increasing the Dufour effect $$({\text{Du}})$$ ( Du ) raises the temperature profile, whereas increasing the Soret effect $$({\text{Sr}})$$ ( Sr ) lowers the concentration profile. Furthermore, increasing the bio-convective Lewis numbers $$({\text{Le}})$$ ( Le ) results in larger concentrations of moving microorganisms, but increasing the Peclet number $$({\text{Pe}})$$ ( Pe ) results in a drop in microbe concentrations. The main focus of our study is to devise unique transformations customized to address the intricacies of the specific problem under investigation. These transformations aim to produce precise and efficient outcomes, offering valuable insights for future research in the realm of nanofluid flows, particularly concerning pressure ulcer problems.
Abstract The time difference of arrival is a common method to find the leakage point of water pipeline. The leakage point localization is achieved by calculating the time delay between the signals reaching different sensors. Mainstream time delay estimation algorithms based on signal correlation analysis are susceptible to the introduction of noise signals, low sampling rates, and signal clipping, resulting in inaccurate localization results. The article analyzed the impact of different interference factors and proposed a new time delay estimation algorithm based on signal cross-zero information modulation (CZIM) to address these problems. By normalizing the amplitude of the two signals at the detection points on both sides of the pipeline leakage position, two sets of sparse signal sequences with only two eigenvalues of 0 and 1 are obtained. The error coefficient function is calculated by a similar traversal method to finally index the time delay. In this paper, the principle and characteristics of the algorithm are analyzed and compared with the most commonly used GCC method. In both numerical simulations and actual pipe leakage localization experiments, the CZIM algorithm has shown its wide applicability, low impact by low sampling rate, and adaptability to low signal-to-noise ratios, etc. At the same time, the algorithm is simple in design and has a small amount of calculation and can meet the demand for real-time data processing, providing a new idea for the development of acoustic localization technology.
Abstract With the rapid development of urbanization and the continuous improvement of living standards, China's domestic water consumption shows a growing trend. However, in some arid and water deficient areas, the shortage of water resources is a crucial factor affecting regional economic development and population growth. Therefore, it is essential to reliably predict the future water consumption data of a region. Aiming at the problems of poor prediction accuracy and overfitting of non-growth series in traditional grey prediction, this paper uses residual grey model combined with Markov chain correction to predict domestic water consumption. Based on the traditional grey theory prediction, the residual grey prediction model is established. Combined with the Markov state transition matrix, the grey prediction value is modified, and the model is applied to the prediction of domestic water consumption in Shaanxi Province from 2003 to 2019. The fitting results show that the accuracy grade of the improved residual grey prediction model is “good”. This shows that the dynamic unbiased grey Markov model can eliminate the inherent error of the traditional grey GM (1,1) model, improve the prediction accuracy, have better reliability, and can provide a new method for water consumption prediction.
The evaporation of water is ubiquitous in nature and industrial technologies. The known mechanism for evaporation is "thermal evaporation" which highlights the energy input for evaporation is via heat. Due to the weak absorption of water to visible light, the first step to using solar energy to evaporate water is usually by converting it into thermal energy through photothermal processes via additional absorbing materials. Contrary to this conventional wisdom, we report here strong absorption of photons in the visible spectrum at the water-vapor interface by direct cleavage of water clusters via a process we call photomolecular effect. We show that this process happens at the water-vapor interface by measuring the dependence of the photomolecular evaporation rate on the wavelength, the angle of incidence, and the polarization of the incident light. The spectra signatures in the vapor phase further support the photomolecular effect. Despite the long propagation lengths of visible light in bulk water, we demonstrate that they can heat a thin layer of fog easily, suggesting that this process is ubiquitous. The photomolecular effect will have significant implications for the earth's water cycle, global warming, plant transpiration, as well as different technologies involving the evaporation of liquids from drying to power generation
We investigate the tumor boundary instability induced by nutrient consumption and supply based on a Hele-Shaw model derived from taking the incompressible limit of a cell density model. We analyze the boundary stability/instability in two scenarios: 1) the front of the traveling wave; 2) the radially symmetric boundary. In each scenario, we investigate the boundary behaviors under two different nutrient supply regimes, in vitro, and in vivo. Our main conclusion is that for either scenario, the in vitro regime always stabilizes the tumor's boundary regardless of the nutrient consumption rate. However, boundary instability may occur when the tumor cells aggressively consume nutrients, and the nutrient supply is governed by the in vivo regime.
Mohammad Emamjome Kashan, Alan S. Fung, John Swift
In Canada, more than 80% of energy in the residential sector is used for space heating and domestic hot water (DHW) production. This study aimed to model and compare the performance of four different systems, using solar energy as a renewable energy source for DHW production. A novel microchannel (MC) solar thermal collector and a microchannel-based hybrid photovoltaic/thermal collector (PVT) were fabricated (utilizing a microchannel heat exchanger in both cases), mathematical models were created, and performance was simulated in TRNSYS software. A water-to-water heat pump (HP) was integrated with these two collector-based solar systems, namely MCPVT-HP and MCST-HP, to improve the total solar fraction. System performance was then compared with that of a conventional solar-thermal-collector-based system and that of a PV-resistance (PV-R) system, using a monocrystalline PV collector. The heat pump was added to the systems to improve the systems’ efficiency and provide the required DHW temperatures when solar irradiance was insufficient. Comparisons were performed based on the temperature of the preheated water storage tank, the PV panel efficiency, overall system efficiency, and the achieved solar fraction. The microchannel PVT-heat pump (MCPVT-HP) system has the highest annual solar fraction among all the compared systems, at 76.7%. It was observed that this system had 10% to 35% higher solar fraction than the conventional single-tank solar-thermal-collector-based system during the wintertime in a cold climate. The performance of the two proposed MC-based systems is less sensitive than the two conventional systems to collector tilt angle in the range of 45 degrees to 90 degrees. If roof space is limited, the MCPVT-HP system is the best choice, as the MCPVT collector can perform effectively when mounted vertically on the facades of high-rise residential and commercial buildings. A comparison among five Canadian cities was also performed, and we found that direct beam radiation has a great effect on overall system solar faction.
The Gompertz growth curve is used to describe the urban water population, the linear function is used to represent the per capita disposable income, and the domestic water demand is described combined with the factors of population, income, and the water-saving consciousness. The VES production function is used to describe the production function of the domestic water supply. Combined with system dynamics, the supply and demand management model of urban domestic water in Jiangsu province, China, is developed. The process of water supply investment and labor input in the urban domestic water system is studied with two depreciation methods: the straight-line depreciation method and the sum of years digits method. In the case that the water consumption population is expected to decline, four water demand scenarios composed of different per capita disposable income and the growth rate of water-saving consciousness are investigated. Investment and labor input are taken as control variables to conduct water supply and demand simulations for the four scenarios. The results show that the control schemes in all four scenarios reach a balance between water supply and demand. Moreover, the investment of the sum of years digits method is larger than that of the straight-line depreciation method in 2005–2019 but less than that of the straight-line depreciation method in 2020–2034. The sum of years digits method has the characteristics of more depreciation in the early stage and less depreciation in the later stage, which is conducive to timely compensation for the large loss of fixed assets in the early stage.
Melika Abolhassani, Hossein Esfandiari, Yasamin Nazari
et al.
In this work, we study a scenario where a publisher seeks to maximize its total revenue across two sales channels: guaranteed contracts that promise to deliver a certain number of impressions to the advertisers, and spot demands through an Ad Exchange. On the one hand, if a guaranteed contract is not fully delivered, it incurs a penalty for the publisher. On the other hand, the publisher might be able to sell an impression at a high price in the Ad Exchange. How does a publisher maximize its total revenue as a sum of the revenue from the Ad Exchange and the loss from the under-delivery penalty? We study this problem parameterized by \emph{supply factor $f$}: a notion we introduce that, intuitively, captures the number of times a publisher can satisfy all its guaranteed contracts given its inventory supply. In this work we present a fast simple deterministic algorithm with the optimal competitive ratio. The algorithm and the optimal competitive ratio are a function of the supply factor, penalty, and the distribution of the bids in the Ad Exchange. Beyond the yield optimization problem, classic online allocation problems such as online bipartite matching of [Karp-Vazirani-Vazirani '90] and its vertex-weighted variant of [Aggarwal et al. '11] can be studied in the presence of the additional supply guaranteed by the supply factor. We show that a supply factor of $f$ improves the approximation factors from $1-1/e$ to $f-fe^{-1/f}$. Our approximation factor is tight and approaches $1$ as $f \to \infty$.
From cutting costs to improving customer experience, forecasting is the crux of retail supply chain management (SCM) and the key to better supply chain performance. Several retailers are using AI/ML models to gather datasets and provide forecast guidance in applications such as Cognitive Demand Forecasting, Product End-of-Life, Forecasting, and Demand Integrated Product Flow. Early work in these areas looked at classical algorithms to improve on a gamut of challenges such as network flow and graphs. But the recent disruptions have made it critical for supply chains to have the resiliency to handle unexpected events. The biggest challenge lies in matching supply with demand. Reinforcement Learning (RL) with its ability to train systems to respond to unforeseen environments, is being increasingly adopted in SCM to improve forecast accuracy, solve supply chain optimization challenges, and train systems to respond to unforeseen circumstances. Companies like UPS and Amazon have developed RL algorithms to define winning AI strategies and keep up with rising consumer delivery expectations. While there are many ways to build RL algorithms for supply chain use cases, the OpenAI Gym toolkit is becoming the preferred choice because of the robust framework for event-driven simulations. This white paper explores the application of RL in supply chain forecasting and describes how to build suitable RL models and algorithms by using the OpenAI Gym toolkit.