Hasil untuk "Water supply for domestic and industrial purposes"

Sara AlMahri, Liming Xu, Alexandra Brintrup

Modern supply chains are increasingly exposed to disruptions from geopolitical events, demand shocks, trade restrictions, to natural disasters. While many of these disruptions originate deep in the supply network, most companies still lack visibility beyond Tier-1 suppliers, leaving upstream vulnerabilities undetected until the impact cascades downstream. To overcome this blind-spot and move from reactive recovery to proactive resilience, we introduce a minimally supervised agentic AI framework that autonomously monitors, analyses, and responds to disruptions across extended supply networks. The architecture comprises seven specialised agents powered by large language models and deterministic tools that jointly detect disruption signals from unstructured news, map them to multi-tier supplier networks, evaluate exposure based on network structure, and recommend mitigations such as alternative sourcing options. \rev{We evaluate the framework across 30 synthesised scenarios covering three automotive manufacturers and five disruption classes. The system achieves high accuracy across core tasks, with F1 scores between 0.962 and 0.991, and performs full end-to-end analyses in a mean of 3.83 minutes at a cost of \$0.0836 per disruption. Relative to industry benchmarks of multi-day, analyst-driven assessments, this represents a reduction of more than three orders of magnitude in response time. A real-world case study of the 2022 Russia-Ukraine conflict further demonstrates operational applicability. This work establishes a foundational step toward building resilient, proactive, and autonomous supply chains capable of managing disruptions across deep-tier networks.

Bruno H. S. Mendonça, Elizane E. de Moraes, João P. K. Abal et al.

In this study, using nonequilibrium molecular dynamics simulation, the water flow in carbon nanocones is studied using the TIP4P/2005 rigid water model. The results demonstrate a nonuniform dependence of the flow on the cone apex angle and the diameter of the opening where the flow is established, leading to a significant increase in the flow in some cases. The effects of cone diameter and pressure gradient are investigated to explain flow behavior with different system structures. We observed that some cones can optimize the water flow precisely. Nanocones with a larger opening facilitate the sliding of water, significantly increasing the flow, thus being promising membranes for technological use in water impurity separation processes. Nanocones with narrower opening angles limited water mobility due to excessive confinement. This phenomenon is linked to the ability of water to form a larger hydrogen-bond network in typical systems with diameters of this size, obtaining a single-layer water structure. Nanocones act as selective nanofilters capable of allowing water molecules to pass through while blocking salts and impurities. The conical shape of their structures creates a directed flow that improves separation efficiency. Membranes based on carbon nanocones are becoming promising for clean, smart, and efficient technologies. The combination of transport speed, selectivity, and structural control put them ahead of other nanostructures for various purposes.

Ammar S. Easa, Mohamed T. Tolan, Ahmed R. S. Essa et al.

Abstract Water is essential for life on Earth. Desalination may reduce water shortages by turning salty water into drinkable water. We study a solar desalination process that combines a humidifier and a dehumidifier. The present work measures solar HDH desalination distinctively using an air-pressurized humidifier. This humidification technique employs an air-pressurized humidifier to increase water droplet dispersion and decrease droplet size. The experiment examines compressed air flow, air pressure, nozzle diameter, and humidification pattern breadth. Water output increases due to the broader spraying pattern and smaller air-pressurized nozzle. The suggested system produces 27.8 kg of distilled water every day for 0.0066 $ per liter.

Stephen G. Fildes, Ian F. Clark, David Bruce et al.

Abstract In remote arid regions of South Australia, local industries, agriculture, mining, and households rely on limited groundwater resources. Data scarcity often leads to drilling unproductive wells when siting new bores. This study introduces an innovative geospatial method for groundwater exploration using an ensemble mapping approach. It combines knowledge- and data-driven machine learning methods: fuzzy analytic hierarchy process (FAHP), multi-influencing factor (MIF), frequency ratio (FR), random forest (RF) and maximum entropy (MaxEnt) to map groundwater potential. The approach leverages the strengths of each method without relying on the bias of a single approach. Morris sensitivity analysis filters areas of higher uncertainty, enhancing knowledge-driven methods before ensemble integration. Spatial representation shortcomings are addressed for key parameters, including drainage density weighted by stream order, terrain curvature integrated into slope models, yield-distance analysis for lineament density, and combining underlying lithology with surface geology to represent water- and non-water-bearing formations at depth. Each groundwater potential model’s performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC), with the MIF model producing the lowest AUC of 85.41%. Although the study focuses on the arid township of Leigh Creek in the northern Flinders Ranges, the methodology is applicable to other regions with minimal well datasets worldwide. This research also contributes to addressing the scarcity of geospatial groundwater potential studies in Australia.

Daisuke Ishikura, Hiroe Hara-Yamamura, Rion Igarashi et al.

Abstract Water quality monitoring is essential for ensuring the safety and security of our drinking water supplies. However, conventional methods, which assume the use of pristine water sources, rely on a limited set of regulated indicators and may overlook unregulated contaminants, especially when such clean sources are not available. In this study, we developed a complementary water quality monitoring tool for water sources potentially influenced by anthropogenic activities, using anomaly detection model Deep Support Vector Data Description (Deep SVDD), based on excitation-emission matrix (EEM) data of drinking water samples collected across Japan. The model effectively identified deviations from baseline water quality (i.e., tap water and spring water for potable use) in a variety of non-drinking water samples, including river water, treated wastewater, chemically spiked water, and diluted industrial discharge, demonstrating high sensitivity to low-level contamination. Although a similar model was also developed using high-resolution mass spectrometry (HRMS) data, it showed limited ability to characterize the sample origins. The explainable AI was further applied to the EEM-based model, revealing that fluorescence features associated with the transphilic neutral organic matter contributed significantly to anomalies. These results demonstrate that fluorescent fingerprints-based anomaly detection, enhanced by explainable AI, offers a rapid approach for identifying subtle anthropogenic sign in water sources, with potential applications in early warning in water quality monitoring systems.

Chizoba A. Ozochi, Christopher C. Okonkwo, Emmanuel C. Adukwu et al.

AbstractCholera infection results from the ingestion of water or food contaminated with toxigenic Vibrio cholerae. This study evaluated the occurrence of toxigenic V. cholerae in Asata River, Enugu Metropolis, Nigeria, and estimated V. cholerae infection risks from use of the River water for drinking, domestic and recreational purposes. Vibrio was detected and quantified using membrane filtration technique and thiosulfate–citrate–bile salts–sucrose agar. Isolates were screened by PCR, using specific primers targeting the internal transcribed spacer (its) region between 16 and 23S rRNA and the cholera toxin (ctx) gene. Sequenced 16SrRNA gene amplicons of two selected isolates were used for phylogenetic analysis. Quantitative microbial risk assessment (QMRA) was conducted using the β-Poisson dose–response model. About 81% (58/72) of Asata River samples recorded Vibrio counts above 1.0 $$\times$$ ×  103 cfu/100 mL. Of the fifty Vibrio isolates screened, its was detected in 54% (27/50), out of which 74% (20/27) had the ctx gene of toxigenic V. cholerae. Evolutionary relatedness of the sequenced isolates to V. cholerae was revealed. The estimated risks of cholera infection in persons exposed to untreated Asata River water were above 0.5 for all the exposure scenarios, for both the rainy and dry seasons. The risks were highest (~ 0.9) for exposure via drinking water and annual risk of infection was deduced to have a probability of 1.0. Therefore, dependence on the untreated Asata River water for drinking, recreational, domestic and irrigation purposes may present a potential public health risk of cholera outbreak. We recommend increased monitoring and surveillance of River water for Vibrio abundance and that Asata River be protected from further degradation.

Lennon Ó Náraigh

We introduce two models of industrial drying - a simplified one-equation model, and a detailed three-equation model. The purpose of the simplified model is rigorous validation of numerical methods for PDE-constrained optimal control. The purpose of the detailed model is to be able to predict and control the behaviour of an industrial disk drier. For both models, we introduce a fully validated numerical method to compute the optimal source term to maintain the outlet temperature as close as possible to the set-point temperature. By performing simulations using realistic parameters for industrial driers, we illustrate potential applications of the method.

Quentin Kral, Paul Huet, Camille Bergez-Casalou et al.

To date, the most widespread scenario is that the Earth originated without water and was brought to the planet mainly due to impacts by wet asteroids coming from further out in space. However, many uncertainties remain regarding the exact processes that supply water to inner terrestrial planets. This article explores a new mechanism that would allow water to be efficiently transported to planets without impacts. We propose that primordial asteroids were icy and that when the ice sublimated, it formed a gaseous disk that could then reach planets and deliver water. We have developed a new model that follows the sublimation of asteroids and evolves the subsequent gas disk using a viscous diffusion code. We can then quantify the amount of water that can be accreted onto each planet in a self-consistent manner. We find that this new disk-delivery mechanism can explain the water content on Earth as well as on other planets. Our model shows most of the water being delivered between 20 and 30 Myr after the birth of the Sun. Our scenario implies the presence of a gaseous water disk with substantial mass for 100s Myr, which could be one of the key tracers of this mechanism. We show that such a watery disk could be detected in young exo-asteroid belts with ALMA. We propose that viscous water transport is inevitable and more generic than the impact scenario. We also suggest it is a universal process that may also occur in extrasolar systems. The conditions required for this scenario to unfold are indeed expected to be present in most planetary systems: an opaque proto-planetary disk that is initially cold enough for ice to form in the exo-asteroid belt region, followed by a natural outward-moving snow line that allows this initial ice to sublimate after the dissipation of the primordial disk, creating a viscous secondary gas disk and leading to the accretion of water onto the exoplanets.

Agathe Balayn, Mireia Yurrita, Fanny Rancourt et al.

With the widespread proliferation of AI systems, trust in AI is an important and timely topic to navigate. Researchers so far have largely employed a myopic view of this relationship. In particular, a limited number of relevant trustors (e.g., end-users) and trustees (i.e., AI systems) have been considered, and empirical explorations have remained in laboratory settings, potentially overlooking factors that impact human-AI relationships in the real world. In this paper, we argue for broadening the scope of studies addressing `trust in AI' by accounting for the complex and dynamic supply chains that AI systems result from. AI supply chains entail various technical artifacts that diverse individuals, organizations, and stakeholders interact with, in a variety of ways. We present insights from an in-situ, empirical study of LLM supply chains. Our work reveals additional types of trustors and trustees and new factors impacting their trust relationships. These relationships were found to be central to the development and adoption of LLMs, but they can also be the terrain for uncalibrated trust and reliance on untrustworthy LLMs. Based on these findings, we discuss the implications for research on `trust in AI'. We highlight new research opportunities and challenges concerning the appropriate study of inter-actor relationships across the supply chain and the development of calibrated trust and meaningful reliance behaviors. We also question the meaning of building trust in the LLM supply chain.

Yi Yang, Haowen Lin, Yuxi Long et al.

Abstract Catalytic films work well in degradation of organic matters. However, catalytic activity and stability of films are challenging factors. A nanoscale zero-valent iron (NZVI) incorporated porous PAN fiber (Fe-PAN) film was thus developed through a one-step cryogenic auxiliary electrospinning method. The Fe-PAN film overcame the problem in the traditional multistep preparation process. The excellent intrinsic properties of the polymer in the film were maintained. It exhibited high catalytic activity (> 95% conversion in just 4 min) and excellent stability and reusability, due to the synergistic interaction between PAN and NZVI. The degradation process was optimized by the Box-Behnken design, leading to the optimal condition: pH = 2.8, temperature = 56 °C, and oxidant concentration = 4.2 mmol/L. The degradation followed the 2nd order kinetic equation and was due to the reactions by ·OH and O2 -· radicals. This study demonstrates the great potentials of the Fe-PAN film for industrial applications.

Jianhui Meng, Xiaolong Wu, Tairan Ye et al.

A three-phase isolated AC-DC-DC power supply is widely used in the industrial field due to its attractive features such as high-power density, modularity for easy expansion and electrical isolation. In high-power application scenarios, it can be realized by multiple AC-DC-DC modules with Input-Parallel Output-Parallel (IPOP) mode. However, it has the problems of slow output voltage response and large ripple in some special applications, such as electrophoresis and electroplating. This paper investigates an improved Adaptive Linear Active Disturbance Rejection Control (A-LADRC) with flexible adjustment capability of the bandwidth parameter value for the high-power DC supply to improve the output voltage response speed. To reduce the DC supply ripple, a control strategy is designed for a single module to adaptively adjust the duty cycle compensation according to the output feedback value. When multiple modules are connected in parallel, a Hierarchical Delay Current Sharing Control (HDCSC) strategy for centralized controllers is proposed to make the peaks and valleys of different modules offset each other. Finally, the proposed method is verified by designing a 42V/12000A high-power DC supply, and the results demonstrate that the proposed method is effective in improving the system output voltage response speed and reducing the voltage ripple, which has significant practical engineering application value.

Federico Rollo, Gennaro Raiola, Nikolaos Tsagarakis et al.

Robots with a high level of autonomy are increasingly requested by smart industries. A way to reduce the workers' stress and effort is to optimize the working environment by taking advantage of autonomous collaborative robots. A typical task for Human-Robot Collaboration (HRC) which improves the working setup in an industrial environment is the \textit{"bring me an object please"} where the user asks the collaborator to search for an object while he/she is focused on something else. As often happens, science fiction is ahead of the times, indeed, in the \textit{Iron Man} movie, the robot \textit{Dum-E} helps its creator, \textit{Tony Stark}, to create its famous armours. The ability of the robot to comprehend the semantics of the environment and engage with it is valuable for the human execution of more intricate tasks. In this work, we reproduce this operation to enable a mobile robot with manipulation and grasping capabilities to leverage its geometric and semantic understanding of the environment for the execution of the \textit{Bring Me} action, thereby assisting a worker autonomously. Results are provided to validate the proposed workflow in a simulated environment populated with objects and people. This framework aims to take a step forward in assistive robotics autonomy for industries and domestic environments.

Liming Xu, Stephen Mak, Maria Minaricova et al.

Trade restrictions, the COVID-19 pandemic, and geopolitical conflicts have significantly exposed vulnerabilities within traditional global supply chains. These events underscore the need for organisations to establish more resilient and flexible supply chains. To address these challenges, the concept of the autonomous supply chain (ASC), characterised by predictive and self-decision-making capabilities, has recently emerged as a promising solution. However, research on ASCs is relatively limited, with no existing studies specifically focusing on their implementations. This paper aims to address this gap by presenting an implementation of ASC using a multi-agent approach. It presents a methodology for the analysis and design of such an agent-based ASC system (A2SC). This paper provides a concrete case study, the autonomous meat supply chain, which showcases the practical implementation of the A2SC system using the proposed methodology. Additionally, a system architecture and a toolkit for developing such A2SC systems are presented. Despite limitations, this work demonstrates a promising approach for implementing an effective ASC system.

Gholamreza Rafiee, Fateh Moezzi, Hadi Poorbagher et al.

The present study was conducted to assess the water quality of the Balighli-Chai and Ghare-Sou Rivers, the main rivers of Ardabil Province, Iran. The levels of dissolved oxygen (DO), biochemical oxygen demand (BOD), nitrate (NO3) and phosphate (PO4) were measured and simulated using the QUAL2Kw model during low- and high-water periods. Also, self-purification capacity and total maximum daily load (TMDL) levels of water quality parameters were calculated. The obtained results indicated considerable differences between the levels simulated by the model from the measured data for both rivers. Most of the river sections had low self-purification capacities. Maximum self-purification capacity (%) were: high water period: DO = -226.61; BOD5 = 90/30; NO3 = 99.88; PO4 = 96.49; low water period: DO = -281.71; BOD5 = 89.13; NO3 = 94.74; PO4 = 90.21. TMDL scores for DO, BOD5 and NO3 were higher during the high-water period, but entire sections of both rivers showed high excess loads of PO4. The results showed that most ranges of both rivers didn't have appropriate water quality conditions. Therefore, it is necessary to make proper decisions to control pollution levels and improve water quality in this basin

Fereshteh Nourmohammadi Dehbalaei, Arash Azari, Ali Akbar Akhtari

Abstract Accurate forecasting of runoff as an important hydrological variable is a key task for water resources planning and management. Given the importance of this variable, in the current study, a multivariate linear stochastic model (MLSM) is combined with a multilayer nonlinear machine learning model (MNMLM) to generate a hybrid model for the spatial and temporal simulation of runoff in the Quebec basin, Canada. Monthly hydrological data from 2001 to 2013, including precipitation and runoff data from nine stations and Normalized Difference Vegetation Index (NDVI) extraction of MODIS data, are applied as input to the proposed hybrid model. At the first step of the hybrid modeling, data normality and stationary were examined by performing various tests. In the second step, MLSM was developed by defining four different scenarios and as a result 15 sub-scenarios. The first and second scenarios were developed based on one exogenous variable (precipitation or NDVI). In contrast, the second and third scenarios were developed based on two additional variables. In the first and third scenarios, the data are modeled without preprocessing. In the second and fourth scenarios, a preprocessing step is performed on the data. Then, in the third step, various combinations based on different time delays from runoff data were applied for developing nonlinear model. The comparisons are made between observed and simulated time series at various stations and based on the root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R) and Akaike information criterion (AIC). The efficiency of the proposed hybrid model is compared with a novel machine learning model that was introduced in 2021 by Sultani et al., and it was also compared with the results obtained from the linear and nonlinear models. In most stations, delays (t-1) and (t-24) are identified as the most effective delays in hybrid and nonlinear modeling of runoff. Also, in most stations, the use of climatic parameters and physiographic factors as exogenous variables along with runoff data improves the results compared to the use of one variable. Results showed that at all stations, proposed hybrid model generally leads to more accurate estimates of runoff compared with various linear and nonlinear models. More accurate estimates of peak runoff values at all stations were another excellence of proposed hybrid model than other models.

Ali Hussain, Ali Hasan, Shahid Sherzada et al.

In the current scenario of the need for cost-effective remediation, our study aimed to assess the remedial potential of bacteria obtained from metal-rich wastewater. To simulate the conditions, we prepared wastewater containing five toxic metals (Cu, Cr, Ni, Fe, and Pb). Two types of metal-resistant bacteria were isolated from a prominent wastewater drain in Lahore, Pakistan. These isolated bacteria were thoroughly characterized, both phenotypically and genotypically. Subsequently, the isolated bacteria were exposed to the wastewater solution containing each of the aforementioned metals at a concentration of 250 ppm. The exposed isolates were then incubated for a duration of 15 days. After 5 days, we measured the uptake of metals by the bacterial isolates. Following the 15-day incubation period, we observed that the bacterial isolates demonstrated the maximum efficiency in removing metals, with approximately 47.5% of Fe, 77% of Ni, 75.75% of Cu, 64% of Cr, and 82.5% of Pb being removed. These findings have significant implications for the development of environmentally friendly and cost-effective strategies for metal ion remediation. HIGHLIGHTS Assess the remedial potential of bacteria obtained from metal-rich wastewater.; Isolated bacteria were thoroughly characterized, both phenotypically and genotypically.; Bacterial isolates demonstrated maximum efficiency in removing metals, with approximately 47.5% of Fe, 77% of Ni, 75.75% of Cu, 64% of Cr, and 82.5% of Pb being removed.;

Zhaocai Wang, Xian Wu, Huifang Wang et al.

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.

Klara Nagode, Tjaša Kanduč, Branka Bračič Železnik et al.

Urban water supply systems (WSS) are complex and challenging to manage since the properties of water in the WSS change from source to the end user over time. However, understanding these changes requires a more profound knowledge of the WSS. This study describes the urban water cycle within the WSS of Ljubljana, Slovenia, where different water parameters such as temperature, electrical conductivity, total alkalinity, δ2H, δ18O, and δ13CDIC were monitored from September to November 2018. Altogether 108 samples were collected, including from the source (3) and at different levels of the WSS: wells (41), joint exits from water pumping stations (7), reservoirs (22), water treatment locations (2), drinking fountains (13), taps (19) and wastewater system (1). The data show that although the ranges of δ2H and δ18O values were small, each well is represented by a unique fingerprint when considering additional parameters. A statistically significant difference was observed between sampling months, and temperature and most parameters showed higher variability within the wells than across the WSS, suggesting a more unified WSS. Finally, based on δ13CDIC values, a distinction could be made between river/groundwater interactions within the WSS and between shallower and deeper wells and their distance from the river bank.

Jin-Zhu Yü, Chencheng Cai, Jianxi Gao

The progressive amplification of fluctuations in demand as the demand travels upstream the supply chains is known as the bullwhip effect. We first analytically characterize the bullwhip effect in general supply chain networks in two cases: (i) all suppliers have a unique layer position, where our method is founded on the control-theoretic approach, and (ii) not all suppliers have a unique layer position due to the presence of intra-layer links or inter-layer links between suppliers that are not positioned in consecutive layers, where we use both the absorbing Markov chain and the control-theoretic approach. We then investigate how network structures impact the BWE of supply chain networks. In particular, we analytically show that (i) if the market demand is generated from the same stationary process, the structure of supply networks does not affect the layer-wise bullwhip effect of supply networks, and (ii) if the market demand is generated from different stationary or non-stationary market processes, wider supply networks lead to a lower level of layer-wise bullwhip effect. Finally, numerical simulations are used to validate our propositions.

Jose I. Caiza, Ian Walter, Jitesh H. Panchal et al.

In this paper, we formulate an optimal ordering policy as a stochastic control problem where each firm decides the amount of input goods to order from their upstream suppliers based on the current inventory level of its output good. For this purpose, we provide a closed-form solution for the optimal request of the raw materials for given a fixed production policy. We implement the proposed policy on a 15-firm acyclic network based on a real product supply chain. We first simulate ideal demand situations, and then we implement demand-side shocks (i.e., demand levels outside of those considered in the policy formulation) and supply-side shocks (i.e., halts in production for some suppliers) to evaluate the robustness of the proposed policies.