This study investigates the economic complexity of Indian states by constructing a state-industry bipartite network using firm-level data on registered companies and their paid-up capital. We compute the Economic Complexity Index and apply the fitness-complexity algorithm to quantify the diversity and sophistication of productive capabilities across the Indian states and two union territories. The results reveal substantial heterogeneity in regional capability structures, with states such as Maharashtra, Karnataka, and Delhi exhibiting consistently high complexity, while others remain concentrated in ubiquitous, low-value industries. The analysis also shows a strong positive relationship between complexity metrics and per-capita Gross State Domestic Product, underscoring the role of capability accumulation in shaping economic performance. Additionally, the number of active firms in India demonstrates a persistent exponential growth at an annual rate of 11.2%, reflecting ongoing formalization and industrial expansion. The ordered binary matrix displays the characteristic triangular structure observed in complexity studies, validating the applicability of complexity frameworks at the sub-national level. This work highlights the usefulness of firm-based data for assessing regional productive structures and emphasizes the importance of capability-oriented strategies for fostering balanced and sustainable development across Indian states. By demonstrating the usefulness of firm registry data in data constrained environments, this study advances the empirical application of economic complexity methods and provides a quantitative foundation for capability-oriented industrial and regional policy in India.
Azmine Toushik Wasi, Mahfuz Ahmed Anik, Abdur Rahman
et al.
Supply chain management is growing increasingly complex due to globalization, evolving market demands, and sustainability pressures, yet traditional systems struggle with fragmented data and limited analytical capabilities. Graph-based modeling offers a powerful way to capture the intricate relationships within supply chains, while Digital Twins (DTs) enable real-time monitoring and dynamic simulations. However, current implementations often face challenges related to scalability, data integration, and the lack of sustainability-focused metrics. To address these gaps, we propose a Graph-Based Digital Twin Framework for Supply Chain Optimization, which combines graph modeling with DT architecture to create a dynamic, real-time representation of supply networks. Our framework integrates a Data Integration Layer to harmonize disparate sources, a Graph Construction Module to model complex dependencies, and a Simulation and Analysis Engine for scalable optimization. Importantly, we embed sustainability metrics - such as carbon footprints and resource utilization - into operational dashboards to drive eco-efficiency. By leveraging the synergy between graph-based modeling and DTs, our approach enhances scalability, improves decision-making, and enables organizations to proactively manage disruptions, cut costs, and transition toward greener, more resilient supply chains.
Supply networks are essential for modern production, yet their critical properties remain understudied. We present a stochastic model with random production capacities to analyze material flow to a root node, focusing on topology and buffer stocks. The critical demand, where unsatisfied demand diverges, is examined mostly through numerical simulations. Without stocks, minimal production dictates behavior, making topology irrelevant. With stocks, memory effects arise, making topology crucial. Increased local connectivity is beneficial: firms should favor broad, short supply chains over long, narrow ones.
Lucas Landwehrkamp, Minja Bogunović Koljaja, Munima Sultana
et al.
Abstract Increasingly stringent water quality standards are forcing more water treatment facilities to implement adsorption steps. Activated carbon is efficient but has a high environmental impact due to CO₂ emissions and energy demand. Adsorbents derived from water treatment residuals offer a potential solution. In this study, a novel laboratory rotary furnace was designed to produce clay-carbon composite adsorbents from drinking water treatment residues. The process was optimized using a statistical design of experiments, representing the first comprehensive statistical analysis of the thermal activation of such residuals. Thermal activation increased the specific surface area almost tenfold (112–201 m²/g). The adsorbents were tested for removal of ibuprofen, caffeine, diclofenac (1 µg/L), and brilliant blue FCF (5 mg/L). Response surface models showed that heating rate (p < 0.003) and ramp duration (p < 0.00002) significantly influenced adsorption capacity. Mass balance calculations suggest on-site production could fully substitute activated carbon and generate surplus material.
Eva M. García-del-Toro, M. Isabel Más-López, Luis F. Mateo
et al.
Abstract This research proposes the use of machine learning tools to assess groundwater quality in the semiarid Mediterranean region of Murcia, Spain, with a focus on the risk of aquifer salinization. Two groundwater quality indices were defined: one for drinking water (DWQI) and another for irrigation purposes (IWQI), calculated using ten and fifteen parameters, respectively. The weights of the parameters such as pH, electrical conductivity (EC), major ion concentrations, as well as the Kelly ratio, KR; magnesium hardness, MH; potential salinity, PS; sodium absorption rate, SAR; and the percentage of soluble sodium, %Na in the calculation of these indices were determined through principal component analysis (PCA). The developed artificial neural network (ANN) models included a resilient backpropagation multilayer perceptron (RProp-MLP) and a probabilistic neural network with dynamic decay adjustment (PNN DDA), both implemented within a KNIME framework. Input variables were selected based on Spearman correlation analysis, PCA, and scientific criteria related to the risk of saline intrusion and irrigation water infiltration. The dataset consisted of 1962 groundwater samples collected from 159 sampling points between 2000 and 2023, covering 38 groundwater bodies with diverse hydrochemical characteristics. Both models demonstrated strong predictive performance, with the RProp-MLP model outperforming the PNN DDA across all evaluated metrics. The best results were obtained using RProp-MLP with seven-input variables (Ca2+, Cl‒, Mg2+, Na+, NO3 ‒, SO4 2‒ and EC), although satisfactory accuracy was also achieved using only five-input variables. This study highlights the effectiveness of ANN-based models for groundwater quality assessment and management, contributing to the sustainable use of water resources in semiarid regions.
Internet of Everything (IoE) is a newly emerging trend, especially in homes. Marketing forces toward smart homes are also accelerating the spread of IoE devices in households. An obvious risk of the rapid adoption of these smart devices is that many lack controls for protecting the privacy and security of end users from attacks designed to disrupt lives and incur financial losses. Today the smart home is a system for managing the basic life support processes of both small systems, e.g., commercial, office premises, apartments, cottages, and largely automated complexes, e.g., commercial and industrial complexes. One of the critical tasks to be solved by the concept of a modern smart home is the problem of preventing the usage of IoE resources. Recently, there has been a rapid increase in attacks on consumer IoE devices. Memory corruption vulnerabilities constitute a significant class of vulnerabilities in software security through which attackers can gain control of an entire system. Numerous memory corruption vulnerabilities have been found in IoE firmware already deployed in the consumer market. This paper aims to analyze and explain the resource usage attack and create a low-cost simulation environment to aid in the dynamic analysis of the attack. Further, we perform controlled resource usage attacks while measuring resource consumption on resource-constrained victims' IoE devices, such as CPU and memory utilization. We also build a lightweight algorithm to detect memory usage attacks in the IoE environment. The result shows high efficiency in detecting and mitigating memory usage attacks by detecting when the intruder starts and stops the attack.
Several major rivers within the La Plata Basin (LPB), the third largest basin in the world, have experienced record-low water levels between 2019 and 2022, with significant impacts for the economy of the region. This hydrological drought originated from a precipitation deficit over the headwaters of the Paraná, Paraguay, and Uruguay rivers, in response to an unusual multi-year La Niña episode. The objective of this study is to characterize the hydrological drought and quantify its unusualness by analyzing a set of indices based on daily, monthly, and annual streamflow and water levels of the main rivers of LPB, using centennial records. The results indicate that the recent hydrological drought was unprecedented in the context of the past 50 years in terms of severity and duration, featuring extreme drought conditions and duration over 25 months. The atmospheric drivers of the drought are analyzed, and future perspectives for water management are discussed.
The increasing frequency of extreme weather events caused by climate change has had a significant impact on cities worldwide. Resilient cities have become a prominent topic of discussion in both domestic and international urban planning. This paper examines the challenges and barriers faced in building resilient cities, as well as the current state of resilience construction in China amidst global flooding issues. By analyzing the impact factors of urban flooding, a comprehensive urban flood resilience evaluation model is proposed, comprising spatial resilience, engineering resilience, and management resilience. Previous studies have not closely integrated the influencing factors with the flood disaster process. The CRITIC assignment method takes into account the correlation between the indicators while objectively considering the magnitude of the differences between them. Therefore, this paper adopts the CRITIC assignment method to categorize and integrate the evaluating factors, aiming to enhance the overall resilience to urban floods. The flood resilience evaluation factors are reassessed in this study to ensure that the various systems and segments of the city can prevent, effectively respond to, and efficiently recover from flood disasters while achieving sustainable development.
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes
Md Abrar Jahin, Md Sakib Hossain Shovon, Md. Saiful Islam
et al.
Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. However, traditional machine-learning models struggle with large-scale datasets and complex relationships, hindering real-world data collection. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets. Experimental evaluations on a benchmark dataset demonstrate QAmplifyNet's superiority over classical models, quantum ensembles, quantum neural networks, and deep reinforcement learning. Its proficiency in handling short, imbalanced datasets makes it an ideal solution for supply chain management. To enhance model interpretability, we use Explainable Artificial Intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, enabling proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, providing superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.
We consider the problem of supply chain data visibility in a blockchain-enabled supply chain network. Existing methods typically record transactions happening in a supply chain on a single blockchain and are limited in their ability to deal with different levels of data visibility. To address this limitation, we present FoodFresh -- a multi-chain consortium where organizations store immutable data on their blockchains. A decentralized hub coordinates the cross-chain exchange of digital assets among the heterogeneous blockchains. Mechanisms for enabling blockchain interoperability help to preserve the benefits of independent sovereign blockchains while allowing for data sharing across blockchain boundaries.
There seems to exist significant similarities between a reactor system and a supply chain from collection to delivery. In the reactor case, neutrons are continuously produced and absorbed in nuclear fuel. In a supply system case, items are continuously collected and continuously delivered to destinations. Stable reactor operation is ensured by keeping the ratio of neutrons produced to neutrons absorbed in the reactor equal to one. Profitable and qualitative supply operation is ensured by keeping the ratio of items delivered to items collected as close to unity as possible. The analogy between the two systems is obvious. This text, which is provided as is and has not undergone any peer review process, proposes transferring parts of the nuclear reactor's neutron transport and diffusion theory to deterministically model supply processes. To this end a set of assumptions and definitions are provided as needed along with the introduction of reactions or interactions like collections, deliveries, and losses occurring into the supply chain. The interaction rates are calculated with the method used in reactors employing analogy factors and interactors with which the items in the chain interact. The main aim is to describe losses escape in steady state and in parallel estimate the analogy factors and optimize for the correct selection of the interactors pool. The model as proposed seems to be a tool for a different insight method into supply problems. The model, if proven and applied, is discussed to be a strong optimization tool, which could deterministically pinpoint flaws in existing supply systems or stochastically efficiently organize proposed supplied chains.
Abstract
Introduction: Many factors are involved in agricultural development such as climatic conditions, soil moisture, evapotranspiration and etc. For their effectiveness, it is necessary to examine the key parameters. Timely and accurate monitoring of these committees with the help of satellite imagery is a necessity in this regard. Herat plain is one of the plains where soil salinity and lack of moisture has led to a critical situation of gardens and agricultural lands.
Methods: In this research, we have tried to study soil salinity, soil moisture and actual evapotranspiration using the MODIS sensor data for the four months of February, May, August and November 2017.
Findings: The first stage of vegetation survey shows 0.4 in May (growing season). While the maximum land surface temperature was recorded in August (54 ° C) and May (45.15 ° C). Then, in the next step, using the results of two indicators of vegetation and land surface temperature, the humidity of the area is investigated by TVDI. The humidity of the region was divided into five classes from zero to 0.5, which indicates the low soil moisture and dryness in the Herat plain. Finally, due to the dryness of the area and to verify the TVDI method, field soil samples were taken from different parts of Herat and especially its agricultural lands to estimate the soil salinity (EC, PH and soil moisture). The results showed that the soil moisture content of the samples at a depth of 5 cm above the ground varies between 0 and 0.3. Also, out of 12 soil samples, 6 samples have saline soils and one sample has saline-acid soils. Of course, it is also important to note that some of the agricultural lands whose soils are in the saline group are dry and left to their own devices.
Finally, the study of actual evapotranspiration with the SEBAL algorithm showed that in this region, despite the lack of moisture, actual evapotranspiration is very high, especially in the hot month of August.
Rapidly growing social networks and other graph data have created a high demand for graph technologies in the market. A plethora of graph databases, systems, and solutions have emerged, as a result. On the other hand, graph has long been a well studied area in the database research community. Despite the numerous surveys on various graph research topics, there is a lack of survey on graph technologies from an industry perspective. The purpose of this paper is to provide the research community with an industrial perspective on the graph database landscape, so that graph researcher can better understand the industry trend and the challenges that the industry is facing, and work on solutions to help address these problems.
Relatively little is known about mobile phone use in a Supply Chain Management (SCM) context, especially in the Bangladeshi Ready-Made Garment (RMG) industry. RMG is a very important industry for the Bangladeshi economy but is criticized for long product supply times due to poor SCM. RMG requires obtaining real-time information and enhanced dynamic control, through utilizing information sharing and connecting stakeholders in garment manufacturing. However, a lack of IT support in the Bangladeshi RMG sector, the high price of computers and the low level of adoption of the computer-based internet are obstacles to providing sophisticated computer-aided SCM. Alternatively, the explosive adoption of mobile phones and continuous improvement of this technology is an opportunity to provide mobile-based SCM for the RMG sector. This research presents a mobile phone-based SCM framework for the Bangladeshi RMG sector. The proposed framework shows that mobile phone-based SCM can positively impact communication, information exchange, information retrieval and flow, coordination and management, which represent the main processes of effective SCM. However, to capitalize on these benefits, it is also important to discover the critical success factors and barriers to mobile SCM systems.
Yigermal Bassie, Mohammed Mahmud, and Mulugeta Bekele
A large number of water molecules are each placed on a lattice far apart so that they are very weakly interacting with each other and in contact with a heat bath at temperature $T$. A strong static electric field, $E_{0}$, is applied to these molecules along a $z$-axis causing three level split energy values. A weak AC electric field that acts for a finite time $τ$ applied in the $xy-$plane induces transitions between the three levels. This weak AC field acts as a protocol $ζ(t)$, that is switched on at $t=0$ and switched off at $t=τ$. Through this protocol, the system is taken from an initial thermodynamic equilibrium state $F(T,0)$ to the non-equilibrium state $F_{non-equil}(T, τ)$ recorded right when the AC field is switched off at time $t=τ$. Once again the AC field is switched on and let it act for the same finite amount of time $τ$ and its non-equilibrium state $F_{non-equil}(T, τ)$ recorded right when the AC field is switched off. The same cyclic process is repeated for a large number of times. The data available for this finite-time non-equilibrium process allowed us to extract equilibrium thermodynamic quantities like free energy, which is what we call Jarznski equality and its relation to the second law of thermodynamics. The work distributions of the three-level system in the optimum condition is obtained. Besides, the average work of the system as a function of $ω$ and time around the optimum frequency are evaluated, where $ω$ is the frequency of the AC electric field.
Klemens Schumann, Luis Böttcher, Philipp Hälsig
et al.
In order to achieve the climate targets, electrification of individual mobility is essential. However, grid integration of electrical vehicles poses challenges for the electrical distribution network due to high charging power and simultaneity. To investigate these challenges in research studies, the network-referenced supply task needs to be modeled. Previous research work utilizes data that is not always complete or sufficiently granular in space. This is why this paper presents a methodology which allows a holistic determination of residential supply tasks based on orthophotos. To do this, buildings are first identified from orthophotos, then residential building types are classified, and finally the electricity demand of each building is determined. In an exemplary case study, we validate the presented methodology and compare the results with another supply task methodology. The results show that the electricity demand deviates from the results of a reference method by an average 9%. Deviations result mainly from the parameterization of the selected residential building types. Thus, the presented methodology is able to model supply tasks similarly as other methods but more granular.
We report a computational study of the structural and energetic properties of water clustersand singly-charged water cluster anions containing from 20 to 573 water molecules. We have used both a classical and a quantum description of the molecular degrees of freedom. Water intra and inter-molecular interactions have been modelled through the SPC/F model, while the water-excess electron interaction has been described via the well-known Turi-Borgis potential. We find that in general the quantum effects of the water degrees of freedom are small, but they do influence the cluster-size at which the excess electron stabilises inside the cluster, which occurs at smaller cluster sizes when quantum effects are taken into consideration.
Mohammad Nazeri Tahroudi, Mirali Mohammadi, Keivan Khalili
Abstract Statistical analysis and simulation of annual maximum discharge values, while considering the corresponding maximum daily rainfall, provide a comprehensive view of flood management. This research presents the application of copula functions for simulating and modeling two variables of annual maximum discharge and corresponding precipitation. In this research, the performance of copula-based models and ARCH-based models including VAR-GARCH, copula, and copula-GARCH models was then evaluated to simulate the annual maximum discharge values. The simulation results of all three models were evaluated using NSE and NRMSE statistics. According to the 95% confidence intervals, the accuracy of all three models was confirmed. The correlation results of the studied pair variables confirmed the possibility of using copula-based models. The results of simulations revealed that a higher accuracy of the copula-GARCH approach compared with two models copula and VAR-GARCH. Considering 76% efficiency (NSE = 0.76) of the copula-GARCH approach, the results indicated 20 and 2.7% improvements in the performance of the proposed approach compared to both VAR-GARCH and copula models. The results also illustrated that by combining nonlinear ARCH models with copula-based simulations, the reliability of simulation results increased. The results obtained in this study suggest that the proposed method is very effective for increasing the certainty of frequency analysis of two variables. Because the copula-GARCH approach simulates the average values, the first and third quarters, as well as the amplitude of changes of 5 and 95% of the data better than the other two models. Graphical abstract Violin plot of AMD series in copula scale
A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world -- especially in problematic regions unable to cope with these conditions. However, the movement to a low-carbon energy supply can not happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required, however, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. A well choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods.