Hasil untuk "Water supply for domestic and industrial purposes"

Amali A Amali, Timothy Foster, Angela Harris

As competition for water resources intensifies, especially in water-scarce regions, there is a growing need to manage water usage effectively, particularly in irrigated agriculture. However, data on agricultural water use and abstractions are often unavailable or only at coarse spatial resolutions. Water use by crops can be physically characterised by the landscape’s rate of evapotranspiration (ET). Satellite-based monitoring of actual ET provides one potential solution to this data gap, but significant knowledge gaps remain about the uncertainty in satellite-based estimates of irrigation water use (IWU) and their associated implications for policy and management. In this model-intercomparison study, we attempt to address the relevance of model choice on satellite-based estimates of IWU by assessing the variability resulting from different model estimates of satellite-based IWU. We utilised six satellite-based ET datasets from OpenET and five precipitation datasets to estimate field-level IWU over 6 years in the high plains aquifer, United States. Results reveal substantial variability in IWU estimates, particularly at field and seasonal scales, which reduces when aggregated spatially or temporally. ET, rather than precipitation, was the primary driver of variability in IWU estimates. These findings highlight the challenges of using satellite data to estimate IWU at fine spatial and temporal scales or in areas where irrigation supplements rainfall. Aggregating IWU estimates reduces variability but emphasises the importance of model choices when monitoring irrigation water usage both at the farm and at regional levels.

Momo Gweama Stevens, Paul Okemo Owuor, John Maingi Muthini et al.

Abstract Water is fundamental to every life component on earth, including humans, animals, and plants. It is a component of food, an essential source of mineral nutrients, and plays a key role in various metabolic processes, hence underscoring the need for safe drinking water. However, research on the composition of water in cities like Nairobi, with its rapidly growing population, remains very limited. Therefore, there is a need to assess the water quality determining components in different years and seasons. The study assesses seasonal variations in water quality parameters, including pH, turbidity, conductivity, iron, manganese, total dissolved solids (TDS), and determines their safety for consumption. Nairobi River was sampled purposively since it is the main river, and the borehole, tap, and bottled water were sampled randomly in the selected study area. A total of 192 samples were collected from multiple locations representing each water source. The study employed standard laboratory methods for water quality analysis. Data were analyzed using SPSS, with a one-way ANOVA and post hoc Tukey tests to identify statistically significant differences between sources and seasons (α = 0.05). The study revealed significant differences in water quality parameters in the different water sources: tap, borehole, river, and bottled water (p < 0.05). River water showed the highest level in color turbidity, iron, and nitrate. During the wet season, river water exhibited high turbidity (14.37 ± 1.79 NTU), iron (0.46 ± 0.04 mg/L), and manganese (0.28 ± 0.04 mg/L). The turbidity and pollutant levels in river water significantly exceeded those in bottled and tap water, with bottled water showing the lowest turbidity (0.05 ± 0.03 NTU). Key findings revealed significant seasonal variations in river and borehole water quality. Borehole water demonstrated the highest conductivity (556.20 ± 43.79 µS/cm) and TDS (297.50 ± 21.94 mg/L), particularly in the dry season, due to the concentration of dissolved minerals as groundwater levels decreased. Sodium levels in borehole water were also notably high, reaching 149.2 ± 15.06 mg/L. Tap water, sourced from municipal systems, showed consistent quality across seasons, with minor increases in turbidity (2.39 ± 0.56 NTU) and color in the wet season. However, its overall conductivity (69.04 ± 2.33 µS/cm) and TDS (41.77 ± 1.33 mg/L) were lower compared to river and borehole water, indicating effective treatment. Bottled water was the most stable across all parameters and seasons, with conductivity at 94.23 ± 8.89 µS/cm and TDS at 56.56 ± 5.70 mg/L. In conclusion, while bottled and tap water remain the safest options for year-round consumption, river and borehole water present health risks, especially during the wet season when turbidity and pollutant levels increase. This shows the need for enhanced treatment systems and water management strategies, particularly for sources prone to contamination, such as rivers and boreholes. The study’s results provide crucial insights for public health policy and water safety, underscoring the necessity for interventions that ensure access to safe drinking water year-round.

Che Zhao, Yaoxin Xiao, Ying Gu et al.

Abstract Interfacial solar desalination has been widely considered as a promising technology for fresh water production because of its green, pollution-free and sustainable characteristics. Compared with the 2D solar evaporator, the 3D solar evaporator has a larger evaporation area. But traditional 3D evaporators could not utilize solar energy effectively due to its inefficient internal and peripheral spaces. Consequently, based on silk cocoon shell and its natural cavity structure, a 3D solar evaporator with internal and external double evaporation interface was designed and manufactured in this paper. The prepared evaporator had double evaporation surfaces which increased the actual evaporation area and the conversion efficiency of solar energy. Besides, the evaporator of Up-FSCE exhibited excellent thermal management performance, its evaporation rate reached 2.32 kg m−2 h−1 under 1 sun illumination. This study offers new insights into the structural design of 3D solar evaporators and further promotes the practical application of interfacial solar desalination.

Oumaima Sarhir

Global supply lines have been severely disrupted by the COVID-19 epidemic and the conflict between Russia and Ukraine, which has sharply increased the price of commodities and generated inflation. These incidents highlight how critical it is to improve supply chain resilience (SCRES) in order to fend off unforeseen setbacks. Controlling both internal and external interruptions, such as transportation problems brought on by natural catastrophes and wars, is the responsibility of SCRES. Enhancing resilience in supply chains requires accurate and timely information transfer. Promising answers to these problems can be found in the Metaverse and ChatGPT, two new digital technologies. The Metaverse may imitate real-world situations and offer dynamic, real-time 3D representations of supply chain data by integrating blockchain, IoT, network connection, and computer power.Large-scale natural language processing model ChatGPT improves communication and data translation accuracy and speed. To manage risk and facilitate decision making in Supply Chain management, firms should increase information transmission, Speed and quality. This study aim to show the importance of ChatGPT and Metaverse technologies to improve SCRES, with an emphasis on the most important criteria for SCRES, and maturity factor that can influence directly the SC development.

Muhammad Azmi Umer, Chuadhry Mujeeb Ahmed, Aditya Mathur et al.

This work focuses on validation of attack pattern mining in the context of Industrial Control System (ICS) security. A comprehensive security assessment of an ICS requires generating a large and variety of attack patterns. For this purpose we have proposed a data driven technique to generate attack patterns for an ICS. The proposed technique has been used to generate over 100,000 attack patterns from data gathered from an operational water treatment plant. In this work we present a detailed case study to validate the attack patterns.

Dalga Merve Özkan, Sergio Lucia, Sebastian Engell

This paper presents a general mathematical programming framework for the design and optimization of supply chain infrastructures for the upcycling of plastic waste. For this purpose, a multi-product, multi-echelon, multi-period mixed-integer linear programming (MILP) model has been formulated. The objective is to minimize the cost of the entire circular supply chain starting from the collection of post-consumer plastic waste to the production of virgin-equivalent high value polymers, satisfying a large number of constraints from collection quota to the quality of the feedstock. The framework aims to support the strategic planning of future circular supply chains by determining the optimal number, locations and sizes of various types of facilities as well as the amounts of materials to be transported between the nodes of the supply chain network over a specified period. The functionality of the framework has been tested with a case study for the upcycling of rigid polyurethane foam waste coming from construction sites in Germany. The economic potential and infrastructure requirements are evaluated, and it has been found that from a solely economic perspective, the current status of the value chain is not competitive with fossil-based feedstock or incineration. However, with the right economic incentives, there is a considerable potential to establish such value chains, once the upcycling technology is ready and the economic framework conditions have stabilized.

Dimitra I. Pomoni, Maria K. Koukou, Michail Gr. Vrachopoulos et al.

The consumption of natural resources and waste production raises questions and concerns for the global scientific community and government decision-makers. This review article provides several literature references related to the concept of the circular economy and how the transition from a linear to a more circular system would prove to be a particularly sustainable practice in resource and waste management, ensuring the sustainable use and minimized consumption of resources, but also the reduced production, the reuse and the controlled disposal of waste as nutrients of a subsequent system. Also, bibliographic references give information about the actions and the action plan of the European Union in a sustainable policy, as well as the expected goals from this activity. This article review provides adequate literature references regarding the principles of the circular economy in the agri-food sector as well as the necessity of its implementation to address the existing challenges that the specific sector must face and the benefits that can arise from such a transition.

Ahmed Elbeltagi, Okan Mert Katipoğlu, Veysi Kartal et al.

Abstract Various critical applications, spanning from watershed management to agricultural planning and ecological sustainability, hinge upon the accurate prediction of reference evapotranspiration (ETo). In this context, our study aimed to enhance the accuracy of ETo prediction models by combining a variety of signal decomposition techniques with an Artificial Bee Colony (ABC)–artificial neural network (ANN) (codename: ABC–ANN). To this end, historical (1979–2014) daily climate variables, including maximum temperature, minimum temperature, mean temperature, wind speed, relative humidity, solar radiation, and precipitation from four arid and semi-arid regions in Egypt: Al-Qalyubiyah, Cairo, Damietta, and Port Said, were used. Six techniques, namely, Empirical Mode Decomposition, Variational Mode Decomposition, Ensemble Empirical Mode Decomposition, Local Mean Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and Empirical Wavelet Transform were used to evaluate signal decomposition efficiency in ETo prediction. Our results showed that the highest ETo prediction accuracy was obtained with ABC-ANN (Train R 2: 0.990 and Test R 2: 0.989), (Train R 2: 0.986 and Test R 2: 0.986), (Train R 2: 0.991 and Test R 2: 0.989) and (Train R 2: 0.988 and Test R 2: 0.987) for Al-Qalyubiyah, Cairo, Damietta, and Port Said, respectively. The impressive results of our hybrid model attest to its importance as a powerful tool for tackling the problems associated with ETo prediction.

Jun Xiao, Shuang Hao, Yiwen Qin et al.

Abstract Many fouling-resistant materials have been grafted or coated on the RO membrane surface for fouling-resistance. However, these modified RO membranes still exhibit a fast flux drop towards small charged organic foulants. Herein, we creatively use the quantum chemistry method to screen the thiol group having a close to zero interaction energy with small charged organic foulants. Thus, we selected a small molecule of 2-aminoethanethiol (AET) having a fouling-resistant thiol group and a reactive amine group for RO membrane surface modification. The water permeance of the AET-grafted RO membrane increases from 2.6 ± 0.1 L m−2 h−1 bar−1 to 3.2 ± 0.05 L m−2 h−1 bar−1, 23% higher than that of the pristine membrane. Moreover, the AET-grafted RO membrane exhibits excellent fouling resistance against charged surfactants. Our study offers insights on the design of fouling-resistant molecules for antifouling surface modification of RO membranes towards small charged organic foulants.

Tareq Alsaleh, Bilal Farooq

While the transition to electric vehicles (EVs) is essential for decarbonizing the transportation system, the production and distribution of EVs entail substantial carbon costs. To ensure these emissions are accurately accounted for and effectively mitigated, this research introduces a digital twin of the EV's supply chain, addressing a critical gap in current EV life cycle analyses and providing the first comprehensive quantification of its environmental sustainability and resilience. This simulation model replicates global market dynamics and captures the complexity and uncertainty of the EV supply chain, enabling a thorough evaluation of its carbon footprint, sustainability, resilience, and what-if counterfactual scenarios for alternative market structures. The results reveal that average supply chain emissions range from 6.42 to 6.94 Kg e-CO2/KWh across different battery technologies. Additionally, the mass flow analysis shows unbalanced dependencies at all supply phases, with one geographical region significantly dominating the supply chain structure, highlighting the current supply chain architecture's low resilience and high vulnerability. In light of these findings, the study introduces an optimization model for hub and resource allocation configuration, effectively reducing vulnerability levels and supply chain emissions by up to 80%.

Wenshuo Chao, Zhaopeng Qiu, Likang Wu et al.

The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain a competitive edge in the labor market. Existing efforts in this area either rely on domain-expert knowledge or regarding skill evolution as a simplified time series forecasting problem. However, both approaches overlook the sophisticated relationships among different skills and the inner-connection between skill demand and supply variations. In this paper, we propose a Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework for joint skill demand-supply prediction. Specifically, CHGH is an encoder-decoder network consisting of i) a cross-view graph encoder to capture the interconnection between skill demand and supply, ii) a hierarchical graph encoder to model the co-evolution of skills from a cluster-wise perspective, and iii) a conditional hyper-decoder to jointly predict demand and supply variations by incorporating historical demand-supply gaps. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed framework compared to seven baselines and the effectiveness of the three modules.

Amirfarhad Farhadi, Homayoun Safarpour Motealegh Mahalegi, Abolfazl Pourrezaeian Firouzabad et al.

The supply chain is a critical segment of the product manufacturing cycle, continuously influenced by risky, uncertain, and undesirable events. Optimizing flexibility in the supply chain presents a complex, multi-objective, and nonlinear programming challenge. In the poultry supply chain, the development of mass customization capabilities has led manufacturing companies to increasingly focus on offering tailored and customized services for individual products. To safeguard against data tampering and ensure the integrity of setup costs and overall profitability, a multi-signature decentralized finance (DeFi) protocol, integrated with the IoT on a blockchain platform, is proposed. Managing the poultry supply chain involves uncertainties that may not account for parameters such as delivery time to retailers, reorder time, and the number of requested products. To address these challenges, this study employs an adaptive neuro-fuzzy inference system (ANFIS), combining neural networks with fuzzy logic to compensate for the lack of data training in parameter identification. Through MATLAB simulations, the study investigates the average shop delivery duration, the reorder time, and the number of products per order. By implementing the proposed technique, the average delivery time decreases from 40 to 37 minutes, the reorder time decreases from five to four days, and the quantity of items requested per order grows from six to eleven. Additionally, the ANFIS model enhances overall supply chain performance by reducing transaction times by 15\% compared to conventional systems, thereby improving real-time responsiveness and boosting transparency in supply chain operations, effectively resolving operational issues.

Miranda Bihler, Hala Nelson, Erin Okey et al.

We propose two neural network based and data-driven supply and demand models to analyze the efficiency, identify service gaps, and determine the significant predictors of demand, in the bus system for the Department of Public Transportation (HDPT) in Harrisonburg City, Virginia, which is the home to James Madison University (JMU). The supply and demand models, one temporal and one spatial, take many variables into account, including the demographic data surrounding the bus stops, the metrics that the HDPT reports to the federal government, and the drastic change in population between when JMU is on or off session. These direct and data-driven models to quantify supply and demand and identify service gaps can generalize to other cities' bus systems.

Jonathan L. Fasig, Barry K. Gilbert, Erik S. Daniel

As ASIC supply voltages approach one volt, the source-impedance goals for power distribution networks are driven ever lower as well. One approach to achieving these goals is to add decoupling capacitors of various values until the desired impedance profile is obtained. An unintended consequence of this approach can be reduced power supply stability and even oscillation. In this paper, we present a case study of a system design which encountered these problems and we describe how these problems were resolved. Time-domain and frequency-domain analysis techniques are discussed and measured data is presented.

Daniel Francisco Campos-Aranda

Las obras hidráulicas como embalses, diques, puentes y el drenaje urbano se planean, diseñan, construyen y revisan (las existentes) bajo una seguridad hidrológica, con base en las Crecientes de Diseño. Tales estimaciones del gasto máximo anual asociadas a bajas probabilidades de excedencia, se llaman Predicciones y se obtienen a través del llamado análisis de frecuencia de crecientes. Técnica que consiste en seleccionar una función de distribución de probabilidades (FDP), que represente a la muestra de gastos disponible y con base en tal modelo, realizar las predicciones buscadas. Cuando el registro de crecientes es reducido, poco confiable o no existe en el sitio de interés, se recurre al análisis regional; enfoque que amplía la longitud de los registros disponibles para reducir los errores en las estimaciones y además, permite estimar predicciones en localidades sin datos. En este estudio se realizó un contraste, entre las predicciones locales y las regionales; obtenidas estas últimas, según dos métodos diferentes en la Región Hidrológica No. 10 (Sinaloa), México. En el primero, se procesaron sus registros de gasto máximo anual y sus respectivas fechas de ocurrencia, para definir las regiones de influencia de cada uno y en la segunda, la cual se expone con detalle, se utiliza la misma información hidrométrica y se aplica el método del índice de crecientes, utilizando como FDP regional a la distribución TCEV (two-component extreme value), sugerida para registros de gastos que fueron generados por dos mecanismos físicamente diferentes y que no es posible separarlos. El método regional expuesto es mucho más simple y conduce a predicciones bastante aproximadas por déficit y más exactas por exceso, según los errores relativos evaluados. Por lo anterior, se recomienda su aplicación sistemática, en otras regiones hidrológicas del país.

Hemant Kumar Joshi, Mahesh Chandra Vishwakarma, Rajesh Kumar et al.

Abstract Heavy metals cause outrageous ecological risks when released into the environment from many point and non-point sources. Biosorbents prepared from the leaves of Eupatorium adenophorum (AEA) and Acer oblongum (AAO) were used as practical solutions to remove the toxic heavy metal cadmium (Cd2+) from wastewater. Biosorption of Cd2+ was investigated using AEA and AAO biomass under batch conditions. The effect of operating variables like temperature, contact time, the pH impact, and initial metal concentration and biosorbent portion on Cd2+ removal has been studied. The optimal pH and the sorbent dosage were found to be 7.0 and 2.0 g L−1, respectively, and removal efficiency attained was 93.3% with an equilibrium removal time of 90 min. The equilibrium uptake of Cd2+ was evaluated by Freundlich, Langmuir, and Temkin isotherm models. The Langmuir isotherm model was proved fit confirming single layer of sorption. The biosorption of Cd2+ onto activated AEA and AAO biomass achieved were 45.45 mg g−1 and 44.64 mg g−1 respectively. The adsorption affinity of AEA toward Cd2+ was discovered a lot more prominent than AAO biomass. The kinetic data of Cd2+ biosorption onto activated AEA and AAO, fitted with a pseudo-second-order well with higher values of R2 (> 0.99). Thermodynamics disclosed that the adsorption process was spontaneous (∆G0 < 0), endothermic (∆H0 > 0), and feasible (ΔS0 > 0). The adsorption of Cd2+ onto AEA was more exothermic and spontaneous than the AAO biosorbent. Additionally, FT-IR and SEM analysis uncovered that Cd2+ were adsorbed onto selected biomassdue to –NH–, –COOH, –OH, and –NH2 groups. Ionic, coordination bond formation, and electrostatic interaction with Cd2+ demonstrated that they were promising biosorbent for wastewater treatment.

Roberto Figliè, Riccardo Amadio, Marios Tyrovolas et al.

Today, one of the biggest challenges for digital transformation in the Industry 4.0 paradigm is the lack of mutual understanding between the academic and the industrial world. On the one hand, the industry fails to apply new technologies and innovations from scientific research. At the same time, academics struggle to find and focus on real-world applications for their developing technological solutions. Moreover, the increasing complexity of industrial challenges and technologies is widening this hiatus. To reduce this knowledge and communication gap, this article proposes a mixed approach of humanistic and engineering techniques applied to the technological and enterprise fields. The study's results are represented by a taxonomy in which industrial challenges and I4.0-focused technologies are categorized and connected through academic and grey literature analysis. This taxonomy also formed the basis for creating a public web platform where industrial practitioners can identify candidate solutions for an industrial challenge. At the same time, from the educational perspective, the learning procedure can be supported since, through this tool, academics can identify real-world scenarios to integrate digital technologies' teaching process.

Alessandro Ferrari

I study the role of industries' position in supply chains in shaping the transmission of final demand shocks. First, I use a novel shift-share design leveraging destination-specific final demand shocks and a new measure of destination exposure accounting for direct and indirect linkages. I find that demand shocks amplify significantly as they propagate upstream, with upstream industries experiencing output elasticities up to three times larger than final good producers, consistent with the bullwhip effect. To rationalize these empirical results, I develop a tractable production network model with inventories and study how the properties of the network and the cyclicality of inventories interact to determine whether final demand shocks amplify or dissipate upstream. I test the mechanism by directly estimating the model-implied relationship between output growth and demand shocks, mediated by network position and inventories. I find that the presence of inventories increases output elasticities by 18% on average, highlighting the macroeconomic significance of this channel. Finally, I use the model to quantitatively study the effects of long-run trends of lengthening supply chains and rising inventories on the volatility of the economy.

Junjie Dong, Rebecca A. Fischer, Lars P. Stixrude et al.

Water has been stored in the Martian mantle since its formation, primarily in nominally anhydrous minerals. The short-lived early hydrosphere and intermittently flowing water on the Martian surface may have been supplied and replenished by magmatic degassing of water from the mantle. Estimating the water storage capacity of the solid Martian mantle places important constraints on its water inventory and helps elucidate the sources, sinks, and temporal variations of water on Mars. In this study, we applied a bootstrap aggregation method to investigate the effects of iron on water storage capacities in olivine, wadsleyite, and ringwoodite, based on high-pressure experimental data compiled from the literature, and we provide a quantitative estimate of the upper bound of the bulk water storage capacity in the FeO-rich solid Martian mantle. Along a series of areotherms at different mantle potential temperatures ($T_{p}$), we estimated a water storage capacity equal to $9.0_{-2.2} ^{+2.8}$ km Global Equivalent Layer (GEL) for the present-day Martian mantle at $T_{p}$ = 1600 K and $4.9_{-1.5}^{+1.7}$ km GEL for the initial Martian mantle at $T_{p}$ = 1900 K. The water storage capacity of the Martian mantle increases with secular cooling through time, but due to the lack of an efficient water recycling mechanism on Mars, its actual mantle water content may be significantly lower than its water storage capacity today.

Ludovic Courtès

The software supply chain is becoming a widespread analogy to designate the series of steps taken to go from source code published by developers to executables running on the users? computers. A security vulnerability in any of these steps puts users at risk, and evidence shows that attacks on the supply chain are becoming more common. The consequences of an attack on the software supply chain can be tragic in a society that relies on many interconnected software systems, and this has led research interest as well as governmental incentives for supply chain security to rise. GNU Guix is a software deployment tool and software distribution that supports provenance tracking, reproducible builds, and reproducible software environments. Unlike many software distributions, it consists exclusively of source code: it provides a set of package definitions that describe how to build code from source. Together, these properties set it apart from many deployment tools that center on the distribution of binaries. This paper focuses on one research question: how can Guix and similar systems allow users to securely update their software? Guix source code is distributed using the Git version control system; updating Guix-installed software packages means, first, updating the local copy of the Guix source code. Prior work on secure software updates focuses on systems very different from Guix -- systems such as Debian, Fedora, or PyPI where updating consists in fetching metadata about the latest binary artifacts available -- and is largely inapplicable in the context of Guix. By contrast, the main threats for Guix are attacks on its source code repository, which could lead users to run inauthentic code or to downgrade their system. Deployment tools that more closely resemble Guix, from Nix to Portage, either lack secure update mechanisms or suffer from shortcomings. Our main contribution is a model and tool to authenticate new Git revisions. We further show how, building on Git semantics, we build protections against downgrade attacks and related threats. We explain implementation choices. This work has been deployed in production two years ago, giving us insight on its actual use at scale every day. The Git checkout authentication at its core is applicable beyond the specific use case of Guix, and we think it could benefit to developer teams that use Git. As attacks on the software supply chain appear, security research is now looking at every link of the supply chain. Secure updates are one important aspect of the supply chain, but this paper also looks at the broader context: how Guix models and implements the supply chain, from upstream source code to binaries running on computers. While much recent work focuses on attestation -- certifying each link of the supply chain -- Guix takes a more radical approach: enabling independent verification of each step, building on reproducible builds, "bootstrappable" builds, and provenance tracking. The big picture shows how Guix can be used as the foundation of secure software supply chains.