Ayman Ibrahim, Nahed El Mahallawy, Islam Elsebaee
et al.
Abstract In the globe, there is a rise in water demand for agricultural, industrial, and domestic purposes. Single-basin solar stills (SBSS) have been a subject of research in various countries, particularly in regions with water scarcity or limited access to clean drinking water. In this work, SBSS for desalinating high-salinity water were developed, tested, and evaluated based on a developed numerical model using MATLAB R2021a program to predict the best productivity through the best selection of raw materials used to develop the SBSS. A four-inclined SBSS was fabricated and examined experimentally according to numerical model findings for best design parameters at Marsa Matrouh, 31° 21′ 10.44″N, 27°14′14.10″ E, Agricultural Station—Agricultural Research Center (ARC), Egypt. The hourly experimental results are compared with the numerical results. A good correlation between the numerical and the experimental results with variations in water, and glass temperatures of 9, and 18% respectively, and a variation in cumulative productivity by 11%. The results clearly showed that instantaneous productivity increases by decreasing water depth to 10 mm and using the SBSS unit partially insulated from the bottom of the basin. Adding insulation in front of the sides and back of tempered glass increases the shading area and decreases water temperature hence the cumulative productivity by 15%. The cumulative productivity reached 3 L for the SBSS unit partially insulated from the bottom of the basin with an area of 0.6 m2 for only 12 h working system at a water depth of 10 mm.
Abstract The fast expansion of aquaculture to fulfil rising worldwide seafood demand poses considerable environmental concerns, particularly in the appropriate management of nutrient-rich wastewater. Traditional wastewater treatment methodologies often prove insufficient for addressing the high concentrations of organic matter and dissolved nutrients like nitrogen and phosphorus, characteristic of aquaculture discharge. This inadequacy underscores the urgent need for advanced and innovative approaches to mitigate the ecological impacts of aquaculture operations, including eutrophication and degradation of aquatic ecosystems. Recently, “algal-based hollow fibre membrane bioreactors” (A-HFMBRs) have surfaced as a viable solution for green and effective wastewater treatment. These bioreactors effectively remove organic and inorganic matter, reduce the risk of eutrophication, and generate valuable by-products. They also offer advantages such as low energy consumption, high algal biomass yield, and efficient water reclamation. This review critically evaluates advanced methodologies for aquaculture wastewater treatment, with a particular focus on membrane bioreactor (MBR) systems and A-HFMBR. It discusses the novel approaches for fouling prevention in MBR systems. The review delves into the critical factors impacting the efficiency of A-HFMBR systems, including hydraulic retention time, nutrient removal, etc. It also evaluates the environmental and cost-effective feasibility of these technologies to assess their suitability for large-scale deployment and highlights their significant challenges. By identifying key challenges and proposing future research directions, this study aims to advance the development of A-HFMBRs as a sustainable solution for aquaculture wastewater treatment.
Mehdi Khaleghi, Sobhan Sheykhivand, Nastaran Khaleghi
et al.
The sustainability of supply chain plays a key role in achieving optimal performance in controlling the supply chain. The management of risks that occur in a supply chain is a fundamental problem for the purpose of developing the sustainability of the network and elevating the performance efficiency of the supply chain. The correct classification of products is another essential element in a sustainable supply chain. Acknowledging recent breakthroughs in the context of deep networks, several architectural options have been deployed to analyze supply chain datasets. A novel geometric deep network is used to propose an ensemble deep network. The proposed Chebyshev ensemble geometric network (Ch-EGN) is a hybrid convolutional and geometric deep learning. This network is proposed to leverage the information dependencies in supply chain to derive invisible states of samples in the database. The functionality of the proposed deep network is assessed on the two different databases. The SupplyGraph Dataset and DataCo are considered in this research. The prediction of delivery status of DataCo supply chain is done for risk administration. The product classification and edge classification are performed using the SupplyGraph database to enhance the sustainability of the supply network. An average accuracy of 98.95% is obtained for the ensemble network for risk management. The average accuracy of 100% and 98.07% are obtained for sustainable supply chain in terms of 5 product group classification and 4 product relation classification, respectively. The average accuracy of 92.37% is attained for 25 company relation classification. The results confirm an average improvement and efficiency of the proposed method compared to the state-of-the-art approaches.
The widespread adoption of AI in recent years has led to the emergence of AI supply chains: complex networks of AI actors contributing models, datasets, and more to the development of AI products and services. AI supply chains have many implications yet are poorly understood. In this work, we take a first step toward a formal study of AI supply chains and their implications, providing two illustrative case studies indicating that both AI development and regulation are complicated in the presence of supply chains. We begin by presenting a brief historical perspective on AI supply chains, discussing how their rise reflects a longstanding shift towards specialization and outsourcing that signals the healthy growth of the AI industry. We then model AI supply chains as directed graphs and demonstrate the power of this abstraction by connecting examples of AI issues to graph properties. Finally, we examine two case studies in detail, providing theoretical and empirical results in both. In the first, we show that information passing (specifically, of explanations) along the AI supply chains is imperfect, which can result in misunderstandings that have real-world implications. In the second, we show that upstream design choices (e.g., by base model providers) have downstream consequences (e.g., on AI products fine-tuned on the base model). Together, our findings motivate further study of AI supply chains and their increasingly salient social, economic, regulatory, and technical implications.
The combination of embodied intelligence and robots has great prospects and is becoming increasingly common. In order to work more efficiently, accurately, reliably, and safely in industrial scenarios, robots should have at least general knowledge, working-environment knowledge, and operating-object knowledge. These pose significant challenges to existing embodied intelligent robotics (EIR) techniques. Thus, this paper first briefly reviews the history of industrial robotics and analyzes the limitations of mainstream EIR frameworks. Then, a new knowledge-driven technical framework of embodied intelligent industrial robotics (EIIR) is proposed for various industrial environments. It has five modules: a world model, a high-level task planner, a low-level skill controller, a simulator, and a physical system. The development of techniques related to each module are also thoroughly reviewed, and recent progress regarding their adaption to industrial applications are discussed. A case study of real-world assembly system is given to demonstrate the newly proposed EIIR framework's applicability and potentiality. Finally, the key challenges that EIIR encounters in industrial scenarios are summarized and future research directions are suggested. The authors believe that EIIR technology is shaping the next generation of industrial robotics and EIIR-based industrial systems supply a new technological paradigm for intelligent manufacturing. It is expected that this review could serve as a valuable reference for scholars and engineers that are interested in industrial embodied intelligence. Together, scholars can use this research to drive their rapid advancement and application of EIIR techniques. The authors would continue to track and contribute new studies in the project page https://github.com/jackyzengl/EIIR
This research paper explores the impact of Artificial intelligence (AI) on the global economy, with particular emphasis on its influence on gross domestic product (GDP). The paper begins with an overview of AI, followed by a discussion of its potential benefits and Drawbacks of economic growth. Next, the The paper examines empirical evidence and case studies to Analyze the relationship between AI adoption and GDP growth across different countries and regions. Finally, The paper concludes by providing policy Recommendations for governments seeking to harness The potential of AI to foster economic growth.
Saad S. M. Hassan, Mohamed E. Mahmoud, Rana M. Tharwat
et al.
Abstract Two-dimensional bismuthene material is characterized with promising and superior optical, electrical and other characteristics. The application of 2D-bismuthene or its composites in water remediation of As(V) was not previously investigated. Consequently, embedded bismuthene into zinc aluminum bismuth-layered double hydroxide (ZnAlBi LDHs-embedded-Biene) was simply fabricated as a novel nanosorbent. Bismuthene (Biene) was prepared by bottom up hydrothermal reaction, while ZnAlBi LDHs was synthesized by a coprecipitation method followed by hydrothermal treatment process. Characterization of ZnAlBi LDHs-embedded-Biene referred to a crystalline mesoporous structure of globular particles with 5–8 nm. It was confirmed that the nanosorbent exterior surface is functionalized with metal oxides and metal oxyhydroxide, while exchangeable carbonate anion existed in the nanosorbent inner layer. Therefore, arsenate uptake was favored by both exterior electrostatic attraction and anion exchange processes. The highest uptake capacity of As(V) by ZnAlBi LDHs-embedded-Biene was detected at pH 3 and contact time 30 min providing 94.67% removal by using 5 mg L−1 As(V) concentration. The ionic strength factor proved a good selectivity of the nanosorbent toward As(V) ions. Thermodynamic behavior of interaction between As(V) and nanosorbent was emphasized as exothermic and spontaneous process, while the kinetic evaluation indicated that the pseudo-second order was the best-fitting expression. The application of ZnAlBi LDHs-embedded-Biene in the adsorptive uptake process of As(V) ions from various water samples referred to elevated uptake percentages as 93.29% and 90.52% by 5 mg L−1 and 10 mg L−1 As(V), respectively. The adsorbed As(V) onto ZnAlBi LDHs-embedded-Biene exhibited excellent recyclability and re-usage up to five cycles to affirm that the designed ZnAlBi LDHs-embedded-Biene has a great prospect for utilization in water purification from As(V).
Imane Smatti-Hamza, Dounia Keddari, Smail Mehennaoui
et al.
Abstract The present study assesses the level of heavy metals concerning sediment from the Koudiet Medouar dam. This dam is intended for the production of drinking water and irrigation. In order to assess the level of contamination of the dam by toxic metals, 216 sediment samples were taken at nine stations upstream and downstream of the dam from 2012 to 2014. At the same time, the physical characteristics of the water and the physicochemical parameters of the sediments were determined. The results, expressed by the mean ± standard deviation, are for water: temperature, 15.5 ± 7 °C; potential of hydrogen, 8.05 ± 0.36; conductivity, 1125 ± 228 μS/cm. For sediments the values are potential of hydrogen, 8.55 ± 0.22; conductivity, 730 ± 347 μS/cm; carbonates, 49.18 ± 18.1%; fraction less than 63 μm 27.06 ± 6.95%; organic matter 3.02 ± 1.2%. Trace metal concentrations followed the order: Mn > Zn > Cr > Cu > Co > Ni > Pb > Cd. The strong correlation among trace metal indicates that these elements have common sources suggesting their association with silted sands. The geo-accumulation index, contamination factors, degree of contamination, and sediment pollution index reveal a polymetallic contamination dominated by two or more elements in which Cd, Cr, and Cu are of greatest concern. The levels of trace metals in the sediments record high concentrations upstream of the dam, especially in the second station of the village, near the dam. Our results reflect the footprint of anthropogenic inputs of cadmium, chromium, and copper resulting from agricultural activities by runoff water and soil erosion as well as domestic water discharges.
Abhilasha Maheshwari, Shamik Misra, Ravindra Gudi
et al.
Tanker water systems play critical role in providing adequate service to meet potable water demands in the face of acute water crisis in many cities globally. Managing tanker movements among the supply and demand sides requires an efficient scheduling framework that could promote economic feasibility, ensure timely delivery, and avoid water wastage. However, to realize such a sustainable water supply operation, inherent uncertainties related to consumer demand and tanker travel time need to accounted in the operational scheduling. Herein, a two-stage stochastic optimization model with a recourse approach is developed for scheduling and optimization of tanker based water supply and treatment facility operations under uncertainty. The uncertain water demands and tanker travel times are combinedly modelled in a computationally efficient manner using a hybrid Monte Carlo simulation and scenario tree approach. The maximum demand fulfillment, limited extraction of groundwater, and timely delivery of quality water are enforced through a set of constraints to achieve sustainable operation. A representative urban case study is demonstrated, results are discussed for two uncertainty cases (i) only demand, and (ii) integrated demand-travel time. Value of stochastic solution over expected value and perfect information model solutions are analyzed and features of the framework for informed decision-making are discussed.
Lukas Hueller, Tim Kuffner, Matthias Schneider
et al.
Enabling supply chain transparency (SCT) is essential for regulatory compliance and meeting sustainability standards. Multi-tier SCT plays a pivotal role in identifying and mitigating an organization's operational, environmental, and social (ESG) risks. While research observes increasing efforts towards SCT, a minority of companies are currently publishing supply chain information. Using the Design Science Research approach, we develop a collaborative platform for supply chain transparency. We derive design requirements, formulate design principles, and evaluate the artefact with industry experts. Our artefact is initialized with publicly available supply chain data through an automated pipeline designed to onboard future participants to our platform. This work contributes to SCT research by providing insights into the challenges and opportunities of implementing multi-tier SCT and offers a practical solution that encourages organizations to participate in a transparent ecosystem.
The academic job market for fresh Ph.D. students to pursue postdoctoral and junior faculty positions plays a crucial role in shaping the future orientations, developments, and status of the global academic system. In this work, we focus on the domestic Ph.D. hiring network among universities in China by exploring the doctoral education and academic employment of nearly 28,000 scientists across all Ph.D.-granting Chinese universities over three decades. We employ the minimum violation rankings algorithm to decode the rankings for universities based on the Ph.D. hiring network, which offers a deep understanding of the structure and dynamics within the network. Our results uncover a consistent, highly structured hierarchy within this hiring network, indicating the imbalances wherein a limited number of universities serve as the main sources of fresh Ph.D. across diverse disciplines. Furthermore, over time, it has become increasingly challenging for Chinese Ph.D. graduates to secure positions at institutions more prestigious than their alma maters. This study quantitatively captures the evolving structure of talent circulation in the domestic environment, providing valuable insights to enhance the organization, diversity, and talent distribution in China's academic enterprise.
Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima
et al.
Domestic violence is commonly viewed as a gendered issue that primarily affects women, which tends to leave male victims largely overlooked. This study presents a novel, data-driven analysis of male domestic violence (MDV) in Bangladesh, highlighting the factors that influence it and addressing the challenges posed by a significant categorical imbalance of 5:1 and limited data availability. We collected data from nine major cities in Bangladesh and conducted exploratory data analysis (EDA) to understand the underlying dynamics. EDA revealed patterns such as the high prevalence of verbal abuse, the influence of financial dependency, and the role of familial and socio-economic factors in MDV. To predict and analyze MDV, we implemented 10 traditional machine learning (ML) models, three deep learning models, and two ensemble models, including stacking and hybrid approaches. We propose a stacking ensemble model with ANN and CatBoost as base classifiers and Logistic Regression as the meta-model, which demonstrated the best performance, achieving $95\%$ accuracy, a $99.29\%$ AUC, and balanced metrics across evaluation criteria. Model-specific feature importance analysis of the base classifiers identified key features influencing their decision-making. Model-agnostic explainable AI techniques, such as SHAP and LIME, provided both local and global insights into the decision-making processes of the proposed model, thereby increasing transparency and interpretability. Statistical validation using paired $t$-tests with 10-fold cross-validation and Bonferroni correction ($α= 0.0036$) confirmed the superior performance of our proposed model over alternatives. Our findings challenge the prevailing notion that domestic abuse primarily affects women, emphasizing the need for tailored interventions and support systems for male victims.
Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-scale system, the challenge for ads supply lies in modeling the counterfactual effects of a conservative supply treatment (e.g., a small density change) over an extended duration. In this paper, we present a streamlined framework for personalized ad supply. This framework optimally utilizes information from data collection policies through the doubly robust learning. Consequently, it significantly improves the accuracy of long-term treatment effect estimates. Additionally, its low-complexity design not only results in computational cost savings compared to existing methods, but also makes it scalable for billion-scale applications. Through both offline experiments and online production tests, the framework consistently demonstrated significant improvements in top-line business metrics over months. The framework has been fully deployed to live traffic in one of the world's largest social media platforms.
Patrick Herbke, Sid Lamichhane, Kaustabh Barman
et al.
Supply chain data management faces challenges in traceability, transparency, and trust. These issues stem from data silos and communication barriers. This research introduces DIDChain, a framework leveraging blockchain technology, Decentralized Identifiers, and the InterPlanetary File System. DIDChain improves supply chain data management. To address privacy concerns, DIDChain employs a hybrid blockchain architecture that combines public blockchain transparency with the control of private systems. Our hybrid approach preserves the authenticity and reliability of supply chain events. It also respects the data privacy requirements of the participants in the supply chain. Central to DIDChain is the cheqd infrastructure. The cheqd infrastructure enables digital tracing of asset events, such as an asset moving from the milk-producing dairy farm to the cheese manufacturer. In this research, assets are raw materials and products. The cheqd infrastructure ensures the traceability and reliability of assets in the management of supply chain data. Our contribution to blockchain-enabled supply chain systems demonstrates the robustness of DIDChain. Integrating blockchain technology through DIDChain offers a solution to data silos and communication barriers. With DIDChain, we propose a framework to transform the supply chain infrastructure across industries.
Recent studies of multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images have highlighted the importance of exploiting the redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multimodal IAD in practical production lines remains a work in progress. It is essential to consider the trade-offs between the costs and benefits associated with the introduction of new modalities while ensuring compatibility with current processes. Existing quality control processes combine rapid in-line inspections, such as optical and infrared imaging with high-resolution but time-consuming near-line characterization techniques, including industrial CT and electron microscopy to manually or semi-automatically locate and analyze defects in the production of Li-ion batteries and composite materials. Given the cost and time limitations, only a subset of the samples can be inspected by all in-line and near-line methods, and the remaining samples are only evaluated through one or two forms of in-line inspection. To fully exploit data for deep learning-driven automatic defect detection, the models must have the ability to leverage multimodal training and handle incomplete modalities during inference. In this paper, we propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of a Multi-modal Training, Few-modal Inference (MTFI) pipeline. Our findings show that the MTFI pipeline can more effectively utilize incomplete multimodal information compared to applying only a single modality for training and inference. Moreover, we investigate the reasons behind the asymmetric performance improvement using point clouds or RGB images as the main modality of inference. This provides a foundation for our future multimodal dataset construction with additional modalities from manufacturing scenarios.
Motivated by the recently experienced systemic shocks (the COVID-19 pandemic and the full-fledged Russia's war of aggression against Ukraine) - that have created new forms of uncertainties to our supplies - this paper explores the supply chain robustness under risk aversion and ambiguity aversion. We aim to understand the potential consequences of deeply uncertain systemic events on the supply chain resilience and how does the information precision affect individual agents' choices and the chain-level preparedness to aggregate shocks. Augmenting a parsimonious supply chain model with uncertainty, we analyse the relationship between the upstream sourcing decisions and the supply chain survival probability. Both risk-averse and ambiguity-averse individually-optimising agents' upstream sourcing paths are efficient but can become vulnerable to aggregate shocks. In contrast, a chain-level coordination of downstream firm sourcing decisions can qualitatively improve the robustness of the entire supply chain compared to the individual decision-making baseline. Such a robust decision making ensures that in the presence of an aggregate shock - independently of its realisation - part of upstream suppliers will survive and the final goods' supply will be ensured even under the most demanding circumstances. Our results also indicate that an input source diversification extracts a cost in foregone efficiency.
This research was carried out to investigate the physical and chemical properties of the soils obtained from dredging of the drainage drains in Abbas Irrigation channels in Ilam province. The area encompasses 16 thousand hectares. In this study sampling was carried out from two major branches. The drainage No. 1 with 21 km length, is flooded to the Rafahyeh river and drainage No. 2 with 5 km length is flooded to the Chikhab River. Sampling was done from drainage soil, dredged soil and the arable land around drainage. A comparison was performed between the three regions of drainage floor area, drainage agricultural land and dredged soil. Soil physico-chemical properties such as particle and bulk density, potassium, magnesium, sodium, calcium and phosphorus, carbonate and bicarbonate, sodium absorption ratio, exchangeable sodium percentage, organic matter, organic carbon, cation exchange capacity, sulfate, base saturation percentage and nitrogen. The results showed that the amount of potassium in dredged soil is more than recommended for soil. Phosphorus concentration was also higher than recommended for soil. Sodium, magnesium and calcium was lower than soil recommended levels. Due to the presence of calcareousmaterial,the amount of limein soils washigh and the amount of gypsum was high as well, due to the warm and dry climate and low rainfall. Soil pH of the region was mostly higher than 7 so they are categorized as alkaline and semi-alkaline soil. The studied soil is classified as saline soil due to high electrical conductivity.Soil texture was sandy clay loam.
Trade disruptions, the pandemic, and the Ukraine war over the past years have adversely affected global supply chains, revealing their vulnerability. Autonomous supply chains are an emerging topic that has gained attention in industry and academia as a means of increasing their monitoring and robustness. While many theoretical frameworks exist, there is only sparse work to facilitate generalisable technical implementation. We address this gap by investigating multi-agent system approaches for implementing autonomous supply chains, presenting an autonomous economic agent-based technical framework. We illustrate this framework with a prototype, studied in a perishable food supply chain scenario, and discuss possible extensions.
The rapid spread of antimicrobial resistance (AMR) poses a significant threat to public safety. The use of water containing resistant bacteria could increase the risk of spreading AMR. This study assessed the quality of 143 dug wells used for domestic purposes in some communities in Nigeria and determined the resistance profile of isolated <em>Escherichia coli (E. coli)</em>. The Microbact<sup>TM</sup> identification kit was used to identify the isolates, and the susceptibility profile was evaluated using the Kirby-Bauer disc diffusion method. The combination disc technique was used to test all isolates for extended spectrum beta lactamase (ESBL) production. Polymerase chain reaction was used to identify ESBL genes, Integrons, and plasmid-mediated quinolone resistance genes. A total of 110 (76.9%) wells were contaminated by coliform bacteria. Of these, 94 (84.45%) wells yielded 202 <em>E. coli</em> strains. The isolates were commonly resistant to ampicillin (60.9%) but were all susceptible to meropenem. Seventy-seven (38.1%) isolates were multi-drug resistant. Two isolates harbored blaCTX-M and blaTEM separately while four (19%) ciprofloxacin-resistant isolates carried the oqxAB/aac-lb-cr gene. All isolates with resistance genes harbored class 1 and/2 Integrons. Most wells had coliform counts far above the World Health Organization’s recommended limit, indicating that they are unsafe to drink. The presence of multidrug-resistant isolates in well water poses a serious risk to consumers since it might lead to outbreaks of untreatable water-borne diseases.