Identifying the impact scope and scale is critical for software supply chain vulnerability assessment. However, existing studies face substantial limitations. First, prior studies either work at coarse package-level granularity, producing many false positives, or fail to accomplish whole-ecosystem vulnerability propagation analysis. Second, although vulnerability assessment indicators like CVSS characterize individual vulnerabilities, no metric exists to specifically quantify the dynamic impact of vulnerability propagation across software supply chains. To address these limitations and enable accurate and comprehensive vulnerability impact assessment, we propose a novel approach: (i) a hierarchical worklist-based algorithm for whole-ecosystem and call-graph-level vulnerability propagation analysis and (ii) the Vulnerability Propagation Scoring System (VPSS), a dynamic metric to quantify the scope and evolution of vulnerability impacts in software supply chains. We implement a prototype of our approach in the Java Maven ecosystem and evaluate it on 100 real-world vulnerabilities. Experimental results demonstrate that our approach enables effective ecosystem-wide vulnerability propagation analysis, and provides a practical, quantitative measure of vulnerability impact through VPSS.
Sathish Krishna Anumula, SVSV Prasad Sanaboina, Ravi Kumar Nagula
et al.
The growing need to automate processes in industrial settings has led to tremendous growth in the robotic systems and especially the robotic arms. The paper assumes the design, modeling and control of a robotic arm to suit industrial purpose like assembly, welding and material handling. A six-degree-of-freedom (DOF) robotic manipulator was designed based on servo motors and a microcontroller interface with Mechanical links were also fabricated. Kinematic and dynamic analyses have been done in order to provide precise positioning and effective loads. Inverse Kinematics algorithm and Proportional-Integral-Derivative (PID) controller were also applied to improve the precision of control. The ability of the system to carry out tasks with high accuracy and repeatability is confirmed by simulation and experimental testing. The suggested robotic arm is an affordable, expandable, and dependable method of automation of numerous mundane procedures in the manufacturing industry.
Water is one of the central molecules for the formation and habitability of planets. In particular, the region where water freezes-out, the water snowline, could be a favorable location to form planets in protoplanetary disks. We use high resolution ALMA observations to spatially resolve H$_2$O, H$^{13}$CO$^+$ and SO emission in the HL Tau disk. A rotational diagram analysis is used to characterize the water reservoir seen with ALMA and compare this to the reservoir visible at mid- and far-IR wavelengths. We find that the H$_2$O 183 GHz line has a compact central component and a diffuse component that is seen out to ~75 au. A radially resolved rotational diagram shows that the excitation temperature of the water is ~350 K independent of radius. The steep drop in the water brightness temperature outside the central beam of the observations where the emission is optically thick is consistent with the water snowline being located inside the central beam ($\lesssim 6$ au) at the height probed by the observations. Comparing the ALMA lines to those seen at shorter wavelengths shows that only 0.02%-2% of the water reservoir is visible at mid- and far-IR wavelengths, respectively, due to optically thick dust hiding the emission whereas 35-70% is visible with ALMA. An anti-correlation between the H$_2$O and H$^{13}$CO$^+$ emission is found but this is likely caused by optically thick dust hiding the H$^{13}$CO$^+$ emission in the disk center. Finally, we see SO emission tracing the disk and for the first time in SO a molecular outflow and the infalling streamer out to ~2". The velocity structure hints at a possible connection between the SO and the H$_2$O emission. Spatially resolved observations of H$_2$O lines at (sub-)mm wavelengths provide valuable constraints on the location of the water snowline, while probing the bulk of the gas-phase reservoirs.
As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness ``6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.
Decarbonising the industrial sector is vital to reach net zero targets. The deployment of industrial decarbonisation technologies is expected to increase industrial electricity demand in many countries and this may require upgrades to the existing electricity network or new network investment. While the infrastructure requirements to support the introduction of new fuels and technologies in industry, such as hydrogen and carbon capture, utilisation and storage are often discussed, the need for investment to increase the capacity of the electricity network to meet increasing industrial electricity demands is often overlooked in the literature. This paper addresses this gap by quantifying the requirements for additional electricity network capacity to support the decarbonisation of industrial sectors across Great Britain (GB). The Net Zero Industrial Pathways model is used to predict the future electricity demand from industrial sites to 2050 which is then compared spatially to the available headroom across the distribution network in GB. The results show that network headroom is sufficient to meet extra capacity demands from industrial sites over the period to 2030 in nearly all GB regions and network scenarios. However, as electricity demand rises due to increased electrification across all sectors and industrial decarbonisation accelerates towards 2050, the network will need significant new capacity (71 GW + by 2050) particularly in the central, south, and north-west regions of England, and Wales. Without solving these network constraints, around 65% of industrial sites that are large point sources of emissions would be constrained in terms of electric capacity by 2040. These sites are responsible for 69% of industrial point source emissions.
Aimira Baitieva, David Hurych, Victor Besnier
et al.
Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily consist of images without anomalies, limiting the practical application of AD methods in production settings. To address this challenge, we present (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial dataset comprising 5000 images, including 2000 instances of challenging real defects across more than 20 subclasses. Acknowledging that traditional AD methods struggle with this dataset, we introduce (2) Segmentation-based Anomaly Detector (SegAD). First, SegAD leverages anomaly maps as well as segmentation maps to compute local statistics. Next, SegAD uses these statistics and an optional supervised classifier score as input features for a Boosted Random Forest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset (+0.4% AUROC). The code and the models are publicly available.
Pierre-Alexis Roy, Björn Benneke, Caroline Piaulet
et al.
Recent work on the characterization of small exoplanets has allowed us to accumulate growing evidence that the sub-Neptunes with radii greater than $\sim2.5\,R_\oplus$ often host H$_2$/He-dominated atmospheres both from measurements of their low bulk densities and direct detections of their low mean-molecular-mass atmospheres. However, the smaller sub-Neptunes in the 1.5-2.2 R$_\oplus$ size regime are much less understood, and often have bulk densities that can be explained either by the H$_2$/He-rich scenario, or by a volatile-dominated composition known as the "water world" scenario. Here, we report the detection of water vapor in the transmission spectrum of the $1.96\pm0.08$ R$_\oplus$ sub-Neptune GJ9827d obtained with the Hubble Space Telescope. We observed 11 HST/WFC3 transits of GJ9827d and find an absorption feature at 1.4$μ$m in its transit spectrum, which is best explained (at 3.39$σ$) by the presence of water in GJ9827d's atmosphere. We further show that this feature cannot be caused by unnoculted star spots during the transits by combining an analysis of the K2 photometry and transit light-source effect retrievals. We reveal that the water absorption feature can be similarly well explained by a small amount of water vapor in a cloudy H$_2$/He atmosphere, or by a water vapor envelope on GJ9827d. Given that recent studies have inferred an important mass-loss rate ($>0.5\,$M$_\oplus$/Gyr) for GJ9827d making it unlikely to retain a H-dominated envelope, our findings highlight GJ9827d as a promising water world candidate that could host a volatile-dominated atmosphere. This water detection also makes GJ9827d the smallest exoplanet with an atmospheric molecular detection to date.
The use of free and open source software (FOSS) components in all software systems is estimated to be above 90%. With such high usage and because of the heterogeneity of FOSS tools, repositories, developers and ecosystem, the level of complexity of managing software development has also increased. This has amplified both the attack surface for malicious actors and the difficulty of making sure that the software products are free from threats. The rise of security incidents involving high profile attacks is evidence that there is still much to be done to safeguard software products and the FOSS supply chain. Software Composition Analysis (SCA) tools and the study of attack trees help with improving security. However, they still lack the ability to comprehensively address how interactions within the software supply chain may impact security. This work presents a novel approach of assessing threat levels in FOSS supply chains with the log model. This model provides information capture and threat propagation analysis that not only account for security risks that may be caused by attacks and the usage of vulnerable software, but also how they interact with the other elements to affect the threat level for any element in the model.
Lily M. Turkstra, Tanya Bhatia, Alexa Van Os
et al.
People who are blind employ unique strategies when performing instrumental activities of daily living (iADLs), often relying on multiple sensory modalities and assistive technologies. While prior research has extensively explored adaptive strategies for outdoor activities like wayfinding and navigation, less emphasis has been placed on the information needs and problem-solving strategies for managing domestic activities. To address this gap, our study presents insights from 16 semi-structured interviews with individuals who are either legally or completely blind, highlighting both the current use and potential future applications of technologies for home-based iADLs. Our findings reveal several underexplored challenges, including the difficulty of locating misplaced objects, a structured problem-solving approach where digital tools are a last resort, and limited awareness of assistive training programs. Participants also faced persistent usability barriers as software updates disrupted accessibility features. Participants utilize a variety of low-tech and high-tech solutions, with tactile labeling systems and digital assistance apps being particularly prevalent. However, existing assistive technologies often fail to integrate seamlessly with users' preferred strategies, leading to frustration and underutilization. Addressing these barriers is crucial for enhancing the adoption of assistive technologies and ultimately improving the quality of life for people who are blind.
We experience air traffic delays every day, but are there any recurrent patterns in these delays? In this study, we investigate the recurrence of delay propagation patterns in Japan's domestic air transport network in 2019 by integrating delay causality networks and temporal network analysis. Additionally, we examine characteristics unique to delay propagation by comparing delay causality networks with corresponding randomized networks generated by a directed configuration model. As a result, we found that the structure of the delay propagation patterns can be classified into several groups. The identified groups exhibit statistically significant differences in total delay time and average out-degree, with different airports playing central roles in spreading delays. The results also suggest that some delay propagation patterns are particularly prominent during specific times of the year, which could be influenced by Japan's seasonal and geographical factors. Moreover, we discovered that specific network motifs appear significantly more (or less) frequently in delay causality networks than their corresponding randomized counterparts. This characteristic is particularly pronounced in groups with more significant delays. These results suggest that delays propagate following specific directional patterns, which could significantly contribute to predicting air traffic delays. We expect the present study to trigger further research on recurrent and non-recurrent natures of air traffic delay propagation.
A novel mathematical nonlinear theory of surface gravity waves in deep water is presented, in which analytical analysis of the classical nonlinear equations of fluid dynamics is performed under less restrictive assumptions than those applied by existing theories. In particular, the new theory ensures uniqueness of the solution without the need to employ the so-called radiation condition, and its solutions are such that the liquid always remains at rest at infinity, and the energy supplied to the water by a source of disturbances is finite at all times - all that in contrast with conventional approaches that operate in terms of spatially-infinite harmonic waves. The new theory accounts for the non-linearity of the problem, and yields solutions valid at all times, from zero (the time of setting initial conditions) to infinity. The author describes previously unknown patterns in wave evolution, and confirms those patterns with the experimental results by other researchers.
Industrial big data is an important part of big data family, which has important application value for industrial production scheduling, risk perception, state identification, safety monitoring and quality control, etc. Due to the particularity of the industrial field, some concepts in the existing big data research field are unable to reflect accurately the characteristics of industrial big data, such as what is industrial big data, how to measure industrial big data, how to apply industrial big data, and so on. In order to overcome the limitation that the existing definition of big data is not suitable for industrial big data, this paper intuitively proposes the concept of big data cloud and the 3M (Multi-source, Multi-dimension, Multi-span in time) definition of cloud-based big data. Based on big data cloud and 3M definition, three typical paradigms of industrial big data applications are built, including the fusion calculation paradigm, the model correction paradigm and the information compensation paradigm. These results are helpful for grasping systematically the methods and approaches of industrial big data applications.
Nazneen N Sultana, Hardik Meisheri, Vinita Baniwal
et al.
This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains. The problem description and solution are both adapted from a real-world business solution. The novelty of this problem with respect to supply chain literature is (i) we consider concurrent inventory management of a large number (50 to 1000) of products with shared capacity, (ii) we consider a multi-node supply chain consisting of a warehouse which supplies three stores, (iii) the warehouse, stores, and transportation from warehouse to stores have finite capacities, (iv) warehouse and store replenishment happen at different time scales and with realistic time lags, and (v) demand for products at the stores is stochastic. We describe a novel formulation in a multi-agent (hierarchical) reinforcement learning framework that can be used for parallelised decision-making, and use the advantage actor critic (A2C) algorithm with quantised action spaces to solve the problem. Experiments show that the proposed approach is able to handle a multi-objective reward comprised of maximising product sales and minimising wastage of perishable products.
Peter Klimek, Katharina Ledebur, Michael Gyimesi
et al.
Many countries faced challenges in their health workforce supply like impending retirement waves, negative population growth, or a suboptimal distribution of resources across medical sectors even before the pandemic struck. Current quantitative models are often of limited usability as they either require extensive individual-level data to be properly calibrated or (in the absence of such data) become too simplistic to capture key demographic changes or disruptive epidemiological shocks like the SARS-CoV-2 pandemic. We propose a novel population-dynamical and stock-flow-consistent approach to health workforce supply forecasting that is complex enough to address dynamically changing behaviors while requiring only publicly available timeseries data for complete calibration. We demonstrate the usefulness of this model by applying it to 21 European countries to forecast the supply of generalist and specialist physicians until 2040, as well as how Covid-related mortality and increased healthcare utilization might impact this supply. Compared to staffing levels required to keep the physician density constant at 2019 levels, we find that in many countries there is indeed a significant trend toward decreasing density for generalist physicians at the expense of increasing densities for specialists. The trends for specialists are exacerbated in many Southern and Eastern European countries by expectations of negative population growth. Compared to the expected demographic changes in the population and the health workforce, we expect a limited impact of Covid on these trends even under conservative modelling assumptions. It is of the utmost importance to devise tools for decision makers to influence the allocation and supply of physicians across fields and sectors to combat these imbalances.
Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately normed (SCAN) density functional, provides such a description of water. SCAN accurately describes the balance among covalent bonds, hydrogen bonds, and van der Waals interactions that dictates the structure and dynamics of liquid water. Notably, SCAN captures the density difference between water and ice I{\it h} at ambient conditions, as well as many important structural, electronic, and dynamic properties of liquid water. These successful predictions of the versatile SCAN functional open the gates to study complex processes in aqueous phase chemistry and the interactions of water with other materials in an efficient, accurate, and predictive, ab initio manner.
If the nodes of a graph are considered to be identical barrels - featuring different water levels - and the edges to be (locked) water-filled pipes in between the barrels, one might consider the optimization problem of how much the water level in a fixed barrel can be raised with no pumps available, i.e. by opening and closing the locks in an elaborate succession. This problem originated from the analysis of an opinion formation process and proved to be not only sufficiently intricate in order to be of independent interest, but also algorithmically complex. We deal with both finite and infinite graphs as well as deterministic and random initial water levels and find that the infinite line graph, due to its leanness, behaves much more like a finite graph in this respect.
The present study was aimed to create new methods for extraction and analysis of land elevation contour lines, automatic extraction of water bodies (river basins and lakes), from the digital elevation models (DEM) of a test area. And extraction of villages which are fell under critical water scarcity regions for agriculture and drinking water with respect to their elevation data and available natural water resources.