Tourism demand forecasting is methodologically mature, but it typically treats accommodation supply as fixed or exogenous. In platform-mediated short-term rentals, supply is elastic, decision-driven, and co-evolves with demand through pricing, information design, and interventions. I reframe the core issue as endogenous stock-out censoring: realized booked nights satisfy B_{k,t} <= min(D_{k,t}, S_{k,t}), so booking models that ignore supply learn a regime-specific ceiling and become fragile under policy changes and supply shocks. This narrated review synthesizes work from tourism forecasting, revenue management, two-sided market economics, and Bayesian time-series methods; develops a three-part coupling framework (behavioral, informational, intervention); and illustrates the identification failure with a toy simulation. I conclude with a focused research agenda for jointly forecasting supply, demand, and their compositions.
Abstract The open channel and pressurized pipe combined water delivery systems usually have long pipelines, many overflow structures, and especially with two different flow regimes, making the transient simulations complicated and time-consuming. A graphics processing unit (GPU)-accelerated lattice Boltzmann model (LBM) is proposed to solve the above problem. The LBM model for water hammer in pipes is improved by introducing the one dimensional with two lattice velocities discrete model (D1Q2). Compared with the existing the one dimensional with three lattice velocities discrete model, the D1Q2 model has reduced the occupation of computational resources, and the boundary processing has become simple. By simulating the transient process of a pipe network system, the results are in good agreement with those of the method of characteristics (MOC), and the speedup ratio reaches 60.5. Then, the water hammer LBM model and shallow water LBM model are coupled to simulate the transient process of the open channel and pressurized pipe combined water delivery system, and its GPU parallel computing scheme is achieved. Practical application shows that the results agree well with those of MOC, and the maximum speedup ratio reaches 92.96, indicating the great application potential of the proposed model.
Reguia Boudraa, Atmane Djermoune, Djahida Touati-Talantikite
et al.
Abstract The growing environmental pollution caused by organic contaminants and the limitations of conventional treatment methods have made the development of sustainable degradation strategies increasingly urgent. This study proposes a green synthesis as an eco-friendly alternative to the traditional solvent-based preparation of photocatalysts. Ag/Ag3O4/CuO nanocomposites were successfully synthesized using Inula viscosa leaf extract, a natural reducing and stabilizing agent. Physicochemical analyses confirmed the formation of crystalline heterostructures (50.0–57.6 nm) with band gap energies ranging from 1.76 to 2.81 eV. The AAC-4 sample, containing 30% silver, achieved 99.3% degradation of Safranin O dye within 100 min under visible light and in the presence of 10 mM potassium persulfate (PDS). The synthesized catalyst maintained good efficiency over five reuse cycles. Machine learning (DT_LSBOOST) accurately predicted the degradation results (R = 0.9981, RMSE = 0.1116), and the dragonfly algorithm identified optimal conditions with only a 0.34% deviation from experimental data. These results highlight the synergistic effect of green nanomaterials and artificial intelligence for cost-effective and eco-friendly wastewater treatment.
Abstract This study investigates the impact of climate change on streamflow dynamics in the Berach-Banas catchment of Rajasthan through climate projections and hydrological modeling. This study employs the MIKE Hydro River and NAM (Nedbor-Afstromings Model) Rainfall-Runoff modules, integrating data from 14-meteorological stations and two streamflow stations (Chittorgarh and Bigod) for period 2000–2022. Climate projections are derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) under the SSP2-4.5 (Shared Socioeconomic Pathways) scenario for the period 1951–2100. Sixteen downscaled Global Climate Models (GCMs) from various institutes are utilized to simulate future conditions for 2030, 2050, and 2090. The hydrological model incorporates ten water storage structures and delineates the catchments into 13 sub-catchments. The calibration period (2011–2015) demonstrated strong model performance at Chittorgarh (R2 = 0.92 with a water balance error (WBL) of 1.41%) and Bigod (R2 = 0.95, WBL of 0.99%). Similarly, the validation period (2017–2022) exhibited good performance at Chittorgarh (R2 = 0.91, WBL = 1.64%) and Bigod (R2 = 0.94, WBL = 1.13%). Sensitivity analysis identified CQOF (overland flow runoff coefficient), CK1,2 (time constants for routing overland flow), and Lmax (maximum water content in root zone storage) as critical parameters, consistent with findings from previous studies on Indian river basins. The climate change impact analysis indicated a consistent increase in streamflow rates for 2030, 2050, and 2090 compared to 2022, likely driven by rising temperatures and changes in precipitation patterns. The projected increase in streamflow rates underscore potential future challenges for water management, highlighting the need for effective adaptation strategies. The novelty of the study lies in its comprehensive integration of future climate scenarios with hydrological modeling, offering valuable insights for sustainable water resource planning in the region. The results highlight the substantial hydrological changes anticipated in the coming decades, enhancing the overall understanding of climate change impacts on water systems.
Water supply for domestic and industrial purposes, Environmental sciences
Supply chain disruptions and volatile demand pose significant challenges to the UK automotive industry, which relies heavily on Just-In-Time (JIT) manufacturing. While qualitative studies highlight the potential of integrating Artificial Intelligence (AI) with traditional optimization, a formal, quantitative demonstration of this synergy is lacking. This paper introduces a novel stochastic learning-optimization framework that integrates Bayesian inference with inventory optimization for supply chain management (SCM). We model a two-echelon inventory system subject to stochastic demand and supply disruptions, comparing a traditional static optimization policy against an adaptive policy where Bayesian learning continuously updates parameter estimates to inform stochastic optimization. Our simulations over 365 periods across three operational scenarios demonstrate that the integrated approach achieves 7.4\% cost reduction in stable environments and 5.7\% improvement during supply disruptions, while revealing important limitations during sudden demand shocks due to the inherent conservatism of Bayesian updating. This work provides mathematical validation for practitioner observations and establishes a formal framework for understanding AI-driven supply chain resilience, while identifying critical boundary conditions for successful implementation.
Karan Bhuwalka, Hari Ramachandran, Swati Narasimhan
et al.
Surging demand for graphite in energy storage applications has led to concerns about supply chai security for manufacturers and nations globally. Currently, China produces over 92% of graphite for anodes, posing a risk for industries reliant on graphite supply. Here, we systematically assess the costs of producing natural and synthetic battery-grade graphite in the U.S. and China using process-based cost models. We find that production costs in the U.S. are higher than those in China by 100-200%, so scaling production in the short-term will require significant policy support. We use our models to explore opportunities to improve the competitiveness of graphite production outside China, finding that lower financing rates and improved shaping yields can together reduce costs by 25-30%. Further implementing other cost reduction strategies, such as improving process throughput or lowering equipment costs, can achieve 35-40% lower costs. Finally, we discuss how innovative graphite production processes like methane pyrolysis and catalytic graphitization may provide competitive pathways.
Modelling how shocks propagate in supply chains is an increasingly important challenge in economics. Its relevance has been highlighted in recent years by events such as Covid-19 and the Russian invasion of Ukraine. Agent-based models (ABMs) are a promising approach for this problem. However, calibrating them is hard. We show empirically that it is possible to achieve speed ups of over 3 orders of magnitude when calibrating ABMs of supply networks by running them on GPUs and using automatic differentiation, compared to non-differentiable baselines. This opens the door to scaling ABMs to model the whole global supply network.
The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles in R&D work-flow. In this paper we discussed the required qualities of industrial designer in the new era, according to an investigation among Chinese enterprises. Additionally, how to promote these qualities though educational program.
Water is essential not only for life but also as the foundation of a nation’s green economy. However, inadequate access to clean and safe drinking water severely jeopardizes public health, especially in developing countries such as Kenya. This study was conducted in Murang'a County, Kenya. The aim was to evaluate the effectiveness of the Karie Wastewater Treatment Plant for residential use by examining key physical parameters (temperature, PH, Dissolved solids, electrical conductivity and Total Dissolved Solids) of wastewater before and after treatment. A total of 45 samples (500 ml each) were collected as grab samples from three sites, that is, the plant inlet, outlet, and a point along River Karie. The study was carried out during the dry season (January–February 2023) and the wet season (May–June 2023). In situ measurements of temperature, PH, dissolved oxygen (DO), electrical conductivity (EC), and total dissolved solids (TDS) were performed using standard methods and appropriate instruments (mercury thermometer, PH meter, DO meter, and conductivity meter). Results were compared against guidelines from the Kenya Bureau of Standards (KEBS) and the World Health Organization (WHO) to assess suitability for household water use. During the dry season, temperature and PH increased significantly, with mean values of 24.52 ± 1.20°C and 7.5 ± 0.5, respectively (p < 0.05). In the wet season, TDS and DO levels were notably higher, with overall mean values of 23.42 ± 0.2°C (temperature), 7.4 ± 0.3 (PH), 234 ± 0.52 (TDS), 5.7 ± 0.3 (DO), and 593 ± 0.13 (EC). While temperature and PH did not significantly differ among the sampling stations (p > 0.05), several physical parameters varied significantly between the two seasons. Although most parameters were within allowable limits for domestic water use, the elevated electrical conductivity indicates that additional treatment processes such as advanced filtration, ion exchange, or reverse osmosis are necessary to prevent pollution of River Karie.
Rasmus E. Benestad, Cristian Lussana, Andreas Dobler
Abstract Both the total amount of precipitation falling on Earth’s surface and the fraction of the surface area on which it falls represent two key global climate indicators for Earth’s global hydrological cycle. We show that the fraction of Earth’s surface area receiving daily precipitation is closely connected to the global statistics of local wet-day frequency and mean precipitation intensity, based on the ERA5 reanalysis. Our analysis of the global statistical distribution of local temporal mean precipitation intensity $$\mu$$ μ revealed a close link between (1) its global spatial average $$\langle \mu \rangle$$ ⟨ μ ⟩ and (2) the total daily precipitation falling on Earth’s surface divided by the global surface area fraction on which it falls. This correlation highlights an important connection, since the wet-day frequency and the mean precipitation intensity represent two key parameters that may be used to approximately infer the probability of heavy rainfall on local scales. We also found a close match between the global mean surface temperature and both the total mass of 24-h precipitation falling on Earth’s surface as well as surface area receiving 24-h precipitation in the ERA5 data, highlighting the dependency between the greenhouse effect and the global hydrological cycle. Moreover, the total planetary precipitation and the daily precipitation area represent links between the global warming and extreme precipitation amounts that traditionally have not been included in sets of essential climate indicators. A simple back-of-the-envelope calculation suggests that half of $$\Delta \langle \mu \rangle /\Delta T = 0.47\, \text{mm}/\text{day}$$ Δ ⟨ μ ⟩ / Δ T = 0.47 mm / day can be explained by increased 24-h precipitation and half by a reduced fractional area of 24-h precipitation.
Water supply for domestic and industrial purposes, Environmental sciences
Abstract Microplastics are commonly found in aquatic ecosystems and can pose environmental threats to aquatic organisms. While the threats of microplastic ubiquity are recognized, few studies have concomitantly quantified microplastic abundance and heavy metals along a rural–urban river continuum. In the current study, we studied changes in microplastics and heavy metals (using lead as a proxy) by collecting sediment and water samples along a rural–urban river over two seasons (temperate spring and summer) and across five sites in a North American River. Our results revealed that microplastics decreased in a downstream direction in surface water but did not change predictably in sediment samples collected along the river continuum. Regarding the relationship between microplastic abundance and lead concentrations, we found a positive relationship between microplastics in sediment samples and lead concentrations. Contrariwise, we found no discernible correlation between microplastics in surface water and lead concentrations along the river continuum. Given the presence of microplastics at every site and moderate lead pollution documented in the Wolf River, our results provide baseline data that can aid in the concurrent assessment of microplastics and heavy metals in river systems. These findings can inform environmental managers in planning pollution management strategies for waterways flowing through rural–urban areas.
Water supply for domestic and industrial purposes, Environmental sciences
Abstract Flooding is the most frequent type of natural disaster, inducing devastating damage at large and small spatial scales. Flood exposure analysis is a critical part of flood risk assessment. While most studies analyze the exposure elements separately, it is crucial to perform a multi-parameter exposure analysis and consider different types of flood zones to gain a comprehensive understanding of the impact and make informed mitigation decisions. This research analyzes the population, properties, and road networks potentially exposed to the 100, 200, and 500-year flood events at the county level in the State of Iowa using geospatial analytics. We also propose a flood exposure index at the county level using fuzzy overlay analysis to help find the most impacted county. During flooding, results indicate that the county-level percentage of displaced population, impacted properties, and road length can reach up to 46%, 41%, and 40%, respectively. We found that the most exposed buildings and roads are laid in residential areas. Also, 25% of the counties are designated as very high-exposure areas. This study can help many stakeholders identify vulnerable areas and ensure equitable distribution of investments and resources toward flood mitigation projects.
Water supply for domestic and industrial purposes, Environmental sciences
Daniel Ovalle, Joshua L. Pulsipher, Yixin Ye
et al.
Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation , among other factors. In such context, we propose a multiperiod mixed-integer linear programming model that integrates production, scheduling, shipping, and order management to minimize the financial impact of such disruptions. The model accommodates arbitrary supply chain topologies and incorporates various disruption scenarios, offering adaptability to real-world complexities. A case study from the chemical industry demonstrates the scalability of the model under finer time discretization and explores the influence of disruption types and order management costs on optimal schedules. This approach provides a tractable, adaptable framework for developing responsive operational plans in supply chain and manufacturing networks under uncertainty.
Pranjol Sen Gupta, Md Rajib Hossen, Pengfei Li
et al.
Freshwater scarcity is a global problem that requires collective efforts across all industry sectors. Nevertheless, a lack of access to operational water footprint data bars many applications from exploring optimization opportunities hidden within the temporal and spatial variations. To break this barrier into research in water sustainability, we build a dataset for operation direct water usage in the cooling systems and indirect water embedded in electricity generation. Our dataset consists of the hourly water efficiency of major U.S. cities and states from 2019 to 2023. We also offer cooling system models that capture the impact of weather on water efficiency. We present a preliminary analysis of our dataset and discuss three potential applications that can benefit from it. Our dataset is publicly available at Open Science Framework (OSF)
In today's globalized economy, comprehensive supply chain visibility is crucial for effective risk management. Achieving visibility remains a significant challenge due to limited information sharing among supply chain partners. This paper presents a novel framework leveraging Knowledge Graphs (KGs) and Large Language Models (LLMs) to enhance supply chain visibility without relying on direct stakeholder information sharing. Our zero-shot, LLM-driven approach automates the extraction of supply chain information from diverse public sources and constructs KGs to capture complex interdependencies between supply chain entities. We employ zero-shot prompting for Named Entity Recognition (NER) and Relation Extraction (RE) tasks, eliminating the need for extensive domain-specific training. We validate the framework with a case study on electric vehicle supply chains, focusing on tracking critical minerals for battery manufacturing. Results show significant improvements in supply chain mapping, extending visibility beyond tier-2 suppliers. The framework reveals critical dependencies and alternative sourcing options, enhancing risk management and strategic planning. With high accuracy in NER and RE tasks, it provides an effective tool for understanding complex, multi-tiered supply networks. This research offers a scalable, flexible method for constructing domain-specific supply chain KGs, addressing longstanding challenges in visibility and paving the way for advancements in digital supply chain surveillance.
Green supply chain is an emerging approach in supply chain management to reduce environmental impact of the process concerning the flow of goods and materials. As a discrete-event system, supply chain can be modeled using Petri Nets. Colored Petri Nets (CPNs) extend the classical Petri net formalism with data, time and hierarchy. These extensions makes it possible to deal with the green aspects of the supply chain.This paper deals with the Colored Petri Net approach to model and simulate a green supply chain system. The forward supply chain network starts from the raw material suppliers, to the manufacturers, wholesalers, retailers and to the final customers. The reverse supply chain network is made of collecting points, recycling plants, disassembly plants,and secondary material market. A government environment agency plays the role of regulation of the recycling process. A colored Petri net model of the green supply chain is developed and simulated with CPN Tools, a dedicated software for CPN models. The simulation results as well as the state-space analysis results validate the correctness of the model.
Supply chain networks describe interactions between products, manufacture facilities, storages in the context of supply and demand of the products. Supply chain data are inherently under graph structure; thus, it can be fertile ground for applications of graph neural network (GNN). Very recently, supply chain dataset, SupplyGraph, has been released to the public. Though the SupplyGraph dataset is valuable given scarcity of publicly available data, there was less clarity on description of the dataset, data quality assurance process, and hyperparameters of the selected models. Further, for generalizability of findings, it would be more convincing to present the findings by performing statistical analyses on the distribution of errors rather than showing the average value of the errors. Therefore, this study assessed the supply chain dataset, SupplyGraph, with better clarity on analyses processes, data quality assurance, machine learning (ML) model specifications. After data quality assurance procedures, this study compared performance of Multilayer Perceptions (MLP), Graph Convolution Network (GCN), and Graph Attention Network (GAT) on a demanding forecasting task while matching hyperparameters as feasible as possible. The analyses revealed that GAT performed best, followed by GCN and MLP. Those performance improvements were statistically significant at $α= 0.05$ after correction for multiple comparisons. This study also discussed several considerations in applying GNN to supply chain networks. The current study reinforces the previous study in supply chain benchmark dataset with respect to description of the dataset and methodology, so that the future research in applications of GNN to supply chain becomes more reproducible.
The environmental protection industry has become an important support entity for the construction of ecological civilization and economic growth in China. However, there is little research on the competitiveness of environmental protection enterprises (EPEs). The construction of a set of scientific, comprehensive, practical, qualitative, and quantitative evaluation index systems is an important prerequisite for the sustainable and healthy development of the industry. Based on the literature analysis, semi-structured interviews with experts, and the Delphi method, the evaluation indicators for the competitiveness of EPEs were determined. Qualitatively, the evaluation index system of the competitiveness of EPEs contained 5 primary indicators, 12 secondary indicators, and 39 tertiary indicators. The analytic hierarchy process was used to determine the weights of indicators at each level. The primary indicators in order of weighting were organizational management capability, business environment, financial capability, innovation capability, and social responsibility, with corresponding weights of 26.13, 24.82, 21.76, 19.60, and 7.68%, respectively. Eight A-share listed EPEs in the water sector were selected for competitive evaluation. The evaluation index system of EPEs' competitiveness, being scientific-practical, combined, and quantitative, was constructed to provide a reference for the comprehensive evaluation of enterprises and the sustainable and healthy development of the industry.
HIGHLIGHTS
The evaluation index system was constructed with five dimensions.;
Five primary indicators, 12 secondary indicators, and 39 tertiary indicators were determined in the evaluation index system.;
Eight A-share-listed EPEs in the water sector were selected for an empirical study of their competitiveness.;
Jan Pennekamp, Roman Matzutt, Christopher Klinkmüller
et al.
Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010-2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points.