Mohammad Behroozi, Mohammad Hadi Fattahi, Abolghasem Sayadi
<p style="text-align: left;">Drought is a complex phenomenon that affects hydrological and geohydrological processes. Given the importance of the river as a major water source, studying the impact of drought on flow patterns is very important. In this study, the Atrak River watershed in Iran was analyzed by considering daily data from 1978 to 2018 and using the tools of Streamflow Drought Index (SDI), Lyapunov Exponent (LE), Approximate Entropy (ApEn), and finally Pearson correlation. The findings showed that the river flow had a level of chaos. The river flow was also predictable to an acceptable level. The chaos and the amount of deterministic and random elements in the river flow were dominated by multi-scale (multi-fractal) behavior. Finally, the results revealed that the degree of fractality of hydrological drought had a positive and direct effect on the multi-scale behavior of chaos and the amount of deterministic and random elements of the river flow. The Pearson correlation values between SDI and ApEn and SDI and LE were 0.93 and 0.99, respectively. This study provides a new perspective on the effects of hydrological drought on river flow dynamic patterns. The findings will have great application in the fields of flow forecasting, drought monitoring, and water resources management.</p>
Melika Mohammadbeigi, Mohammad Saber Tehrani, Mohammad Hadi Givianrad
Abstract S. In this study, the nZVI@CS nano-absorbent was synthesized with a chemical reduction method and then characterized by FTIR, XRD, FESEM, EDS mapping, and BET analyses. The nZVI@CS had a uniform morphology and suitable functional groups for dye removal. After characterization, the nZVI@CS nano-absorbent was used to remove the dye from real and synthetic wastewater. For this goal, the operation condition was optimized by array L16 of the Taguchi design experiment. The results indicated that the dosage of nano-absorbent with 52% impact had the most effect on the dye removal efficiency. At the optimum condition (pH = 6, T = 25 °C, nanocomposite dosage = 0.01 g, and t = 60 min), the nZVI@CS nano-absorbent could be removed 99%, 96% and 88% of reactive red 81, real wastewater and reactive blue 41, respectively. Additionally, isotherms, the kinetic and thermodynamic of the adsorption reaction were assessed. The kinetic reaction has been similar to the pseudo-first-order model. Also, the isotherm followed the Langmuir isotherm. Moreover, thermodynamic investigation outcomes indicated absorption reactions were exothermic. The reuse ability experiment indicated that the synthesized nZVI@CS nano-absorbent could be used several times without remarkable loss of the sorption ability. Finally, it confirmed that the nZVI@CS nanocomposite has sufficient sorption capacity for dye wastewater on a large scale.
Van Thi Thanh Tran, Osamu Nakagoe, Hideaki Sano
et al.
Abstract This study highlights the importance of utilizing banana peel, an abundant and low-cost agricultural waste, as a sustainable precursor material for producing activated carbon for removing heavy metals in wastewater treatment. Adsorption experiments were conducted with single-metal (Pb2+) and mixed-metal (Pb2+, Cd2+, and Cu2+) solutions to evaluate the effects of synthetic sequences, weight ratios, and competitive adsorption. The results demonstrated that the activation sequence played a critical role in adsorption performance, with banana peel-derived activated carbon (BPAC) activated with ZnCl2 after pyrolysis (BPAC(I)) achieving around 5% higher Pb2+ adsorption compared to activation before pyrolysis (BPAC(II)). BPAC(I) was modified with Al2O3 and chitosan to significantly enhance its adsorption capacity for heavy metal ions. Composite adsorbents with varying weight ratios of BPAC, Al2O3 and chitosan (4:2:1 (denoted as 421), 3:2:1 (321), 3:1:2 (312) and 2:1:1 (211)) were synthesized and evaluated. Among all samples, 321 showed the highest adsorption performance, with a maximum adsorption capacity of 39.0 mg/g, reaching almost 100% Pb2+ removal after 24 h, suggesting that the increased weight ratio of hydrophilic Al2O3 with abundant surface –OH groups enhances the adsorption amount of Pb2+. A similar trend was also observed for other heavy metals in mixed solutions, with the adsorption percentage Pb2+, Cu2+ and Cd2+ were 85.1%, 88.9% and 26.5%, respectively. The effects of different experimental parameters (including adsorbent mass, pH level, and initial concentration of the solution) on the adsorption of Pb2+ ions were studied. The adsorption isotherms revealed that BPAC(I) and 312 fitted both the Langmuir and Freundlich isotherm models, with the latter providing a slightly better fit, suggesting heterogeneous surface adsorption. Regeneration tests found that the adsorption capacity of the adsorbent could be reduced to approximately one-third with four repeated adsorption–desorption cycles owing to irreversible adsorption and detachment of surface modifiers.
Stéphane Chevalier, Meguya Ryu, Jean-Christophe Batsale
et al.
This study investigates water transport in a polymer electrolyte membrane (PEM) electrolyzer using operando infrared spectroscopic imaging. By testing different H2SO4 anolyte concentrations, it examines electrochemical performance, water diffusion, and membrane hydration. Higher anolyte concentrations increased standard deviations in current densities and led to water diffusion gradients revealed by infrared imaging and confirming localized water transport variations. The study highlights the need for improved water management and optimized electrolyzer design for stable and efficient PEM electrolysis in industrial applications.
Software supply chain attacks have increased exponentially since 2020. The primary attack vectors for supply chain attacks are through: (1) software components; (2) the build infrastructure; and (3) humans (a.k.a software practitioners). Software supply chain risk management frameworks provide a list of tasks that an organization can adopt to reduce software supply chain risk. Exhaustively adopting all the tasks of these frameworks is infeasible, necessitating the prioritized adoption of tasks. Software organizations can benefit from being guided in this prioritization by learning what tasks other teams have adopted. The goal of this study is to aid software development organizations in understanding the adoption of security tasks that reduce software supply chain risk through an interview study of software practitioners engaged in software supply chain risk management efforts. An interview study was conducted with 61 practitioners at nine software development organizations that have focused efforts on reducing software supply chain risk. The results of the interviews indicate that organizations had implemented the most adopted software tasks before the focus on software supply chain security. Therefore, their implementation in organizations is more mature. The tasks that mitigate the novel attack vectors through software components and the build infrastructure are in the early stages of adoption. Adoption of these tasks should be prioritized.
Marco Cardia, Stefano Chessa, Alessio Micheli
et al.
The quality of water is key for the quality of agrifood sector. Water is used in agriculture for fertigation, for animal husbandry, and in the agrifood processing industry. In the context of the progressive digitalization of this sector, the automatic assessment of the quality of water is thus becoming an important asset. In this work, we present the integration of Ultraviolet-Visible (UV-Vis) spectroscopy with Machine Learning in the context of water quality assessment aiming at ensuring water safety and the compliance of water regulation. Furthermore, we emphasize the importance of model interpretability by employing SHapley Additive exPlanations (SHAP) to understand the contribution of absorbance at different wavelengths to the predictions. Our approach demonstrates the potential for rapid, accurate, and interpretable assessment of key water quality parameters.
The availability and sustainability of good quantities and qualities of water supplies for human needs and support development should be warranted; therefore, existing water resources should be managed sustainably. A multidisciplinary rapid appraisal method called multidimensional scaling (MDS) is an approach for a comprehensive analysis of the sustainability statuses of domestic water supplies. This study aims to analyze the index and sustainability status of raw water management from three dimensions of sustainability. The results that were obtained from a specific multidimensional scaling analysis method called Rapid Appraisal for Air Baku (Rapaku) are expressed in the form of indices and sustainability statuses. Based on different dimensions of the sustainability status review, the analysis results showed that Bandung’s domestic raw water was “less sustainable” (42.34%). Of the 35 attributes that were analyzed, there were 13 sensitive attributes that affected the index and sustainability status with a very small error at a 95% confidence level.
Hickmat Hossen, Ahmed S. Nour-Eldeen, Ismail Abd-Elaty
et al.
Abstract Groundwater levels vary from region to another and sometimes in different zones in the same country due to different boundary conditions and extraction rates. Therefore, understanding intricate aquifer systems and predicting how they will react to hydrological changes require the use of groundwater models. In Egypt, the groundwater levels in the Nile Delta aquifer decrease causing problems to the delta ecosystem while it is rising in Aswan area due to the presence of Nasser Lake causing several damages to the city’s buildings and infrastructures. In order to maximize its benefits and lessen the harm brought on by inadequate groundwater management in the city of Aswan, the height of the groundwater level in that city was examined, appraised, and groundwater management scenarios were established in this study. To achieve the objectives of the study, a simulation of Aswan aquifer’s groundwater system is built based on a quasi-three-dimensional transient groundwater flow model using MODFLOW. The model was calibrated and verified. Four management scenarios are tested. The fifth scenario, in this scenario, the four scenarios combined together at the same time and with the same conditions and ratios were proposed to be implemented. The results of the proposal to implement the four scenarios together showed that the rates of decline in groundwater levels in the last stage will be 12.44%. The study results reveal that a better understanding of the simulated long-term average spatial distribution of water balance components is useful for managing and planning the available water resources in the Aswan aquifer.
Abstract: Given the threat of fossil fuel depletion, it is essential to proactively strive for carbon neutrality and promote clean, low-carbon and efficient energy use. This study used ERA5 reanalysis data to assess wind energy resources in the Pacific Northwest region. By analyzing key indicators such as wind power density, effective wind speed occurrence, and energy level occurrence, climate statistics and empirical orthogonal function analysis (EOF) were used to examine the spatial distribution and long-term trend of offshore wind energy resources in the Northwest Pacific. The results suggest that there are abundant wind energy resources in this region, which are beneficial for the development of offshore wind energy. The rich areas are the East China Sea, Taiwan islands and reefs in the South China Sea and the eastern shore of Japan, with prevailing wind power density (500 ∼ 2500 W/m2) and effective wind speed occurrence (80–90%) and energy level occurrence (60% ∼ 90%). Offshore wind energy resources in the Pacific Northwest are more abundant in fall and winter than in summer. The time coefficient of the first mode shows that the offshore wind energy in the Northwest Pacific has no obvious change trend, and the wind energy resources are relatively stable.
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes
Eman Abu Ishgair, Marcela S. Melara, Santiago Torres-Arias
The software supply chain comprises a highly complex set of operations, processes, tools, institutions and human factors involved in creating a piece of software. A number of high-profile attacks that exploit a weakness in this complex ecosystem have spurred research in identifying classes of supply chain attacks. Yet, practitioners often lack the necessary information to understand their security posture and implement suitable defenses against these attacks. We argue that the next stage of software supply chain security research and development will benefit greatly from a defense-oriented approach that focuses on holistic bottom-up solutions. To this end, this paper introduces the AStRA model, a framework for representing fundamental software supply chain elements and their causal relationships. Using this model, we identify software supply chain security objectives that are needed to mitigate common attacks and systematize knowledge on recent and well-established security techniques for their ability to meet these objectives. We validate our model against prior attacks and taxonomies. Finally, we identify emergent research gaps and propose opportunities to develop novel software development tools and systems that are secure-by-design.
Richar Cayo-Dominguez, Claudia Montalvo-Achic-Huamán, Noe Benjamin Pampa-Quispe
El objetivo de la investigación fue estudiar el proceso de adsorción de iones de As (III) con carbón activado de estructura nanoporosa obtenido de lodos orgánicos de aguas residuales. La obtención del carbón activado se realizó mediante una activación química usando ZnCl2 y una activación térmica a 650 °C. Los ensayos de adsorción se realizaron colocando en contacto 16 g/l de carbón activado con soluciones de 0.247, 0.406, 0.564, 0.683 y 0.801 mg/l de As (III) en vasos precipitados de 1 l a un tiempo de 24 horas. Todos los ensayos fueron sometidos a una velocidad de agitación de 720 RPM, a temperatura de 28 °C ± 0.5 y al pH natural de las muestras en laboratorio, el cual fue 3. Los resultados de la caracterización de carbón indicaron que este adsorbente presentó una estructura nanoporosa con presencia de grupos funcionales (hidroxilo y carboxilo). En cuanto a los ensayos de adsorción de As (III), se determinó que el carbón activado logró reducir la concentración del metal hasta 0.004 mg/l, valor que está por debajo de los establecidos por la Organización Mundial de la Salud (OMS) para el consumo de agua. Finalmente se concluye que el carbón activado presentó una eficiencia de 98.4 % de adsorción de iones de As (III) y los datos experimentales mostraron un mayor ajuste al modelo de pseudo-segundo orden y a la isoterma de Freundlich, lo cual indica que el proceso de adsorción de As (III) se realiza en centros enérgicamente heterogéneos mediante una interacción físico-química entre el metal y el adsorbente.
Hydraulic engineering, Water supply for domestic and industrial purposes
Abstract One of the priorities in the management of agricultural inputs, such as water and fertilizers, involves investigating the variations in the crop yields under actual field conditions, subjected to the effects of various simultaneous stresses. The current study is mainly aimed at investigating the simultaneous effects of triple water, salinity, and nitrogen stresses on basil, Mazandaran mass cultivar, and determining its production functions in such situations. The study was conducted at Doshan Tappeh Agricultural Experiment Station with an area of one ha in Tehran, Iran. A factorial experiment was conducted in the form of a randomized complete block design with different irrigation levels as the main treatment. In addition, two sub-treatments, i.e., salinity and fertilization, were conducted in three replications by a water and soil laboratory in 2016 and 2017. The irrigation treatments included full irrigation (FI) in 100% (W1) and deficit irrigation (DI) in 80% (W2), 60% (W3), and 40% (W4) of crop water requirements. The salinity treatment involved 1.175 ds m−1 (S1) (control treatment), 3 ds/m (S2), and 5 ds/m (S3), while the fertilization treatment involved 100% (F1) (control treatment), 75% (F2), and 50% (F3) of the recommended fertilizer requirement. Overall, results indicated that under a constant fertilizer treatment, the rise in the salinity and water stress reduced the basil yield, while under the water-fertilizer double stress, the basil yield rate first decreased and then had a notable increase. By applying water and salinity stresses, the crop yield experienced a steeper reduction under the water stress than the salinity stress. Contrary to expectations, fertilization reduced basil yield under these conditions.
Shivarajappa, L. Surinaidu, Pankaj Kumar Gupta
et al.
Use of wastewater for irrigation remains a disagreement among policymakers and researchers. However, it is fact that wastewater may contains many contaminants including heavy metals that can negatively impact ecosystem health. The present study is an attempt to assess the impact of wastewater use on soil, crop, and water and associated health risks based on exposure risk model where wastewater irrigation is more prominent in Hyderabad city, South India by analyzing physical (pH, EC, turbidity, oil and grease, TSS), chemical (Zn, Cr, Pb, Mn, Cu and Ni) and biological (BOD, COD, DO) parameters. The results indicated that biological contamination and the presence of heavy metals with the rising groundwater salinity. Low to moderate cancer risks are also inferred if humans are exposed to these waters for a long time. However, the study suggests continuous monitoring of the water and soil quality in wastewater irrigated areas to take remedial actions for sustainable agriculture development and protect ecosystems.
In this note, we illustrate the computation of the approximation of the supply curves using a one-step basis. We derive the expression for the L2 approximation and propose a procedure for the selection of nodes of the approximation. We illustrate the use of this approach with three large sets of bid curves from European electricity markets.
While the growth of TNCs took a substantial part of ridership and asset value away from the traditional taxi industry, existing taxi market policy regulations and planning models remain to be reexamined, which requires reliable estimates of the sensitivity of labor supply and income levels in the taxi industry. This study aims to investigate the impact of TNCs on the labor supply of the taxi industry, estimate wage elasticity, and understand the changes in taxi drivers' work preferences. We introduce the wage decomposition method to quantify the effects of TNC trips on taxi drivers' work hours over time, based on taxi and TNC trip record data from 2013 to 2018 in New York City. The data are analyzed to evaluate the changes in overall market performances and taxi drivers' work behavior through statistical analyses, and our results show that the increase in TNC trips not only decreases the income level of taxi drivers but also discourages their willingness to work. We find that 1% increase in TNC trips leads to 0.28% reduction in the monthly revenue of the yellow taxi industry and 0.68% decrease in the monthly revenue of the green taxi industry in recent years. More importantly, we report that the work behavior of taxi drivers shifts from the widely accepted neoclassical standard behavior to the reference-dependent preference (RDP) behavior, which signifies a persistent trend of loss in labor supply for the taxi market and hints at the collapse of taxi industry if the growth of TNCs continues. In addition, we observe that yellow and green taxi drivers present different work preferences over time. Consistently increasing RDP behavior is found among yellow taxi drivers. Green taxi drivers were initially revenue maximizers but later turned into income targeting strategy
With the advent of new technologies and endeavors for automation in almost all day-to-day activities, the recent discussions on the metaverse life have a greater expectation. Furthermore, we are in the era of the fifth industrial revolution, where machines and humans collaborate to maximize productivity with the effective utilization of human intelligence and other resources. Hence, Industry 5.0 in the metaverse may have tremendous technological integration for a more immersive experience and enhanced communication.These technological amalgamations are suitable for the present environment and entirely different from the previous perception of virtual technologies. This work presents a comprehensive review of the applications of the metaverse in Industry 5.0 (so-called industrial metaverse). In particular, we first provide a preliminary to the metaverse and industry 5.0 and discuss key enabling technologies of the industrial metaverse, including virtual and augmented reality, 3D modeling, artificial intelligence, edge computing, digital twin, blockchain, and 6G communication networks. This work then explores diverse metaverse applications in Industry 5.0 vertical domains like Society 5.0, agriculture, supply chain management, healthcare, education, and transportation. A number of research projects are presented to showcase the conceptualization and implementation of the industrial metaverse. Furthermore, various challenges in realizing the industrial metaverse, feasible solutions, and future directions for further research have been presented.
Ramakrishna Kanungo, Swamynathan Siva, Nathaniel Bleier
et al.
Mitigating losses from supply and demand volatility in the semiconductor supply chain and market has traditionally been cast as a logistics and forecasting problem. We investigate how the architecture of a family of chips influences how it is affected by supply and demand uncertainties. We observe that semiconductor supply chains become fragile, in part, due to single demand paths, where one chip can satisfy only one demand. Chip architects can enable multiple paths to satisfy a chip demand, which improves supply chain resilience. Based on this observation, we study composition and adaptation as architectural strategies to improve resilience to volatility and also introduce a third strategy of dispersion. These strategies allow multiple paths to satisfy a given chip demand. We develop a model to analyze the impact of these architectural techniques on supply chain costs under different regimes of uncertainties and evaluate what happens when they are combined. We present several interesting and even counterintuitive observations about the product configurations and market conditions where these interventions are impactful and where they are not. In all, we show that product redesign supported by architectural changes can mitigate nearly half of the losses caused by supply and demand volatility. As far as we know, this is the first such investigation concerning chip architecture.
By using low-cost microcontrollers and TinyML, we investigate the feasibility of detecting potential early warning signs of domestic violence and other anti-social behaviors within the home. We created a machine learning model to determine if a door was closed aggressively by analyzing audio data and feeding this into a convolutional neural network to classify the sample. Under test conditions, with no background noise, accuracy of 88.89\% was achieved, declining to 87.50\% when assorted background noises were mixed in at a relative volume of 0.5 times that of the sample. The model is then deployed on an Arduino Nano BLE 33 Sense attached to the door, and only begins sampling once an acceleration greater than a predefined threshold acceleration is detected. The predictions made by the model can then be sent via BLE to another device, such as a smartphone of Raspberry Pi.