Modern software systems heavily rely on third-party dependencies, making software supply chain security a critical concern. We introduce the concept of software supply chain smells as structural indicators that signal potential security risks. We design and evaluate Dirty-Waters, a novel tool for detecting such smells in the supply chains of software packages. Through interviews with practitioners, we show that our proposed smells align with real-world concerns and capture signals considered valuable. A quantitative study of popular packages in the Maven and NPM ecosystems reveals that while smells are prevalent in both, they differ significantly across ecosystems, with traceability and signing issues dominating in Maven and most smells being rare in NPM, due to strong registry-level guarantees. Software supply chain smells support developers and organizations in making informed decisions and improving their software supply chain security posture.
Chile ha logrado entregar agua potable de manera amplia y segura en contextos rurales a través de un esquema de coproducción. Esta forma de operación ha funcionado por más de 60 años y entrega agua a más del 10 % de la población. Sin embargo, la literatura reporta heterogeneidad en los resultados de cada territorio. En tal contexto, se está implementando una nueva legislación que institucionaliza dicha forma de provisión generando nuevas demandas en el Estado. Ante esto, el artículo analiza si la ley avanza en tres aspectos de la gestión integrada del agua potable rural. A partir de un análisis de contenido de la nueva ley y su reglamentación se concluye que existen avances en participación, articulación intersectorial y descentralización, pero se proyectan posibles conflictos al interior de la administración del Estado y en la vinculación de éste con las organizaciones. Se concluye que es importante para la política pública de agua potable rural mayor coordinación con otros sectores y también mayor atención a las tendencias actuales en administración pública respecto de la gestión estatal que abogan por una mayor articulación, descentralización y participación.
Hydraulic engineering, Water supply for domestic and industrial purposes
Ermanno Bartoli, Dennis Rotondi, Kai O. Arras
et al.
Long-term planning for robots operating in domestic environments poses unique challenges due to the interactions between humans, objects, and spaces. Recent advancements in trajectory planning have leveraged vision-language models (VLMs) to extract contextual information for robots operating in real-world environments. While these methods achieve satisfying performance, they do not explicitly model human activities. Such activities influence surrounding objects and reshape spatial constraints. This paper presents a novel approach to trajectory planning that integrates human preferences, activities, and spatial context through an enriched 3D scene graph (3DSG) representation. By incorporating activity-based relationships, our method captures the spatial impact of human actions, leading to more context-sensitive trajectory adaptation. Preliminary results demonstrate that our approach effectively assigns costs to spaces influenced by human activities, ensuring that the robot trajectory remains contextually appropriate and sensitive to the ongoing environment. This balance between task efficiency and social appropriateness enhances context-aware human-robot interactions in domestic settings. Future work includes implementing a full planning pipeline and conducting user studies to evaluate trajectory acceptability.
In the rapidly evolving landscape of global supply chains, where digital disruptions and sustainability imperatives converge, traditional operational frameworks often struggle to adapt. This paper introduces the Design-Based Supply Chain Operations Research Model, a novel extension of the Design SCOR framework, which embeds operational research techniques to enhance decision-making, resilience, and environmental stewardship. Building on the foundational processes of DSCOR such as Design, Orchestrate, Plan, Order, Source, Transform, Fulfil, and Return DSCORM incorporates predictive analytics, simulation modelling, and optimization algorithms to address contemporary challenges like supply chain volatility and ESG (environmental, social, governance) compliance. Through a comprehensive literature synthesis and methodological approach involving case-based simulations, we explore DSCORM's hierarchical structure, performance metrics, implementation strategies, and digital modernization pathways. Results from simulated scenarios indicate potential efficiency gains of 15to25 percent, reduced carbon footprints by up to 20 percent, and improved agility in dynamic markets. Discussions delve into practical implications for industries like manufacturing and logistics, highlighting barriers such as data integration hurdles and the need for skilled workforces. By humanizing supply chain management emphasizing collaborative, adaptive strategies over rigid automation DSCORM positions itself as a blueprint for sustainable growth. Conclusions underscore its role in advancing digital transformation, with recommendations for future empirical validations in real-world settings
Supply chain networks are complex systems that are challenging to analyze; this problem is exacerbated when there are illicit activities involved in the supply chain, such as counterfeit parts, forced labor, or human trafficking. While machine learning (ML) can find patterns in complex systems like supply chains, traditional ML techniques require large training data sets. However, illicit supply chains are characterized by very sparse data, and the data that is available is often (purposely) corrupted or unreliable in order to hide the nature of the activities. We need to be able to automatically detect new patterns that correlate with such illegal activity over complex, even temporal data, without requiring large training data sets. We explore neurosymbolic methods for identifying instances of illicit activity in supply chains and compare the effectiveness of manual and automated feature extraction from news articles accurately describing illicit activities uncovered by authorities. We propose a question tree approach for querying a large language model (LLM) to identify and quantify the relevance of articles. This enables a systematic evaluation of the differences between human and machine classification of news articles related to forced labor in supply chains.
Stefano Genetti, Alberto Longobardi, Giovanni Iacca
In the context of Industry 4.0, Supply Chain Management (SCM) faces challenges in adopting advanced optimization techniques due to the "black-box" nature of most AI-based solutions, which causes reluctance among company stakeholders. To overcome this issue, in this work, we employ an Interpretable Artificial Intelligence (IAI) approach that combines evolutionary computation with Reinforcement Learning (RL) to generate interpretable decision-making policies in the form of decision trees. This IAI solution is embedded within a simulation-based optimization framework specifically designed to handle the inherent uncertainties and stochastic behaviors of modern supply chains. To our knowledge, this marks the first attempt to combine IAI with simulation-based optimization for decision-making in SCM. The methodology is tested on two supply chain optimization problems, one fictional and one from the real world, and its performance is compared against widely used optimization and RL algorithms. The results reveal that the interpretable approach delivers competitive, and sometimes better, performance, challenging the prevailing notion that there must be a trade-off between interpretability and optimization efficiency. Additionally, the developed framework demonstrates strong potential for industrial applications, offering seamless integration with various Python-based algorithms.
Relevance of the study. In the context of digital transformation and global changes in the agricultural sector, artificial intelligence (AI) is becoming a key tool for increasing the competitiveness of agricultural products. The introduction of AI in the Russian agro-industrial complex helps to optimize resource management, reduce costs, increase yields and improve product quality. However, despite significant potential, the digitalization process faces a number of barriers, including the high cost of technology, a shortage of specialists and insufficient infrastructure. This study is aimed at analyzing the impact of AI on Russian agriculture and developing a forecast for its development until 2035. Purpose of the study. To analyze the current state of AI implementation in Russian agricultural production, assess its impact on the competitiveness of agricultural products and develop a forecast for the development of AI technologies in the agro-industrial sector until 2035. Data and methods. The study used data from Rosstat, the Ministry of Agriculture of the Russian Federation, international organizations (FAO, World Bank) and analytical centers. The methods of statistical and comparative analysis, scenario forecasting and econometric modeling are applied. To assess the prospects for the introduction of AI in agriculture, the extrapolation method based on the second-order polynomial regression is used, taking into account technological trends and state policy in the field of digitalization. Results. The forecast until 2035 shows that the share of agricultural enterprises using AI will reach 70%, which will lead to an increase in yields by 35%, a decrease in costs by 25% and a reduction in the consumption of water and fertilizers by 20%. The introduction of AI in supply chain management will minimize logistics costs, increase export potential and compliance with international quality standards. However, the digitalization process requires significant investment, infrastructure development and personnel training. Conclusions. The integration of AI in agriculture is a strategically important direction for the development of the agro-industrial complex of Russia. The introduction of AI will not only increase productivity and reduce costs, but also strengthen the position of domestic agricultural products in the world market. Further growth of digitalization is possible subject to active government support, investments in technological solutions and training of specialists in the field of agro-digital technologies.
Ante la creciente incorporación de energías renovables en Uruguay en un contexto de gran variabilidad climática, desde el 2019 se encuentra operativo un modelo hidrológico para simular los caudales de aporte a las centrales hidroeléctricas del río Negro, acoplado con la simulación del sistema eléctrico del país. En este trabajo se propone una técnica de bajo costo computacional basada en la combinación de “Time-Lagged Ensembles” (TLE) para mejorar el desempeño del pronóstico por ensambles de precipitación del modelo GEFS (NCEP-NOAA) en la cuenca del río Negro. En particular, se busca mejorar la representación de la incertidumbre que enfrenta el sistema en el horizonte inmediato, ya que durante los primeros días del pronóstico la dispersión del ensamble es excesivamente baja en comparación con su error medio. Para ello, se construyeron super-ensambles equiprobables a partir de múltiples pronósticos de precipitación inicializados en diferentes momentos. Esta metodología demostró una mejora en la distribución del ensamble sin deteriorar su error, mejorando el cociente SPREAD/RMSE (en comparación con el desempeño del ensamble de la última inicialización disponible). Esto sugiere que el enfoque TLE aporta información valiosa que va más allá de aumentar el tamaño del ensamble.
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes
En el área del estratovolcán La Malinche la fuente de agua es el acuífero. El objetivo de este trabajo fue analizar la relación entre población y niveles estáticos ( ) en los acuíferos Alto Atoyac y Huamantla. Se utilizaron las siguientes variables: número de habitantes ( ), tasas de cambio anual de , porcentajes de cambio anual de ( ), tendencias de y . Los pozos se agruparon con análisis de componentes principales ( ). Los se compararon con un diseño factorial. La tasa de cambio anual promedio de fue igual a -0.159 m·año-1 y 6.7% de . Las tasas de cambio de fueron estadísticamente diferentes entre los acuíferos. La relación entre y que resalta fue igual a -16.5 cm·hab-1. Las tendencias de fueron mayores que las tendencias de . El diseño factorial arrojó que entre las temporadas de lluvia y estiaje, los no tuvieron diferencias significativas, pero entre los tipos de concesión sí los hubo. El ACP correlacionó 51 pozos con una componente. En resumen, este estudio reveló que los son mayores en las zonas agrícolas y urbanas. El en los pozos de uso industrial se abate cinco veces más que en los de uso público. La relación entre población y fue más clara en la montaña que en los valles. El ACP mostró que los pozos de los alrededores de La Malinche se diferencian de los demás del área de estudio.
Hydraulic engineering, Water supply for domestic and industrial purposes
Saliha Mebarki, Mohammed Amin Kendouci, Ali Bendida
Abstract Climate change has clearly affected the desert city of Bechar, located in southern Algeria, and this miserable situation for the supply of drinking water prompted the authorities to provide capabilities and funds to bring groundwater located 250 km away and transfer it to the city of Bechar. The characterization of these underground waters presents a bicarbonate-magnesian facies according to the diagram of Schöeller and Berkaloff; the representation of the data on the triangular Piper diagram shows that Boussir ground water has the magnesium bicarbonate facies. The calculation of the quality index (GWQI) shows that all samples taken from the boreholes belong to the good quality category. The long distance of diversion of this underground water and the quality of the materials used in the project under a dry desert climate made us carry out the process of monitoring and tracking the quality of the water from the well until it reaches the consumer. The results revealed that all the levels of the physic-chemical parameters do not exceed the WHO portability standards, except that a variation of certain values was observed at the level of the storage tank, this variation due to the mode of filling and the mixing of water in tubular form, without eliminating the effect of water stagnation. If we technically know how to produce high-quality drinking water, we cannot always ensure a safe and sustainable water supply of the same quality in distribution networks and reservoirs; it is from this principle that our article is based in order to reinforce the monitoring role.
In the current global economy, supply chain transparency plays a pivotal role in ensuring this security by enabling companies to monitor supplier performance and fostering accountability and responsibility. Despite the advancements in supply chain relationship datasets like Bloomberg and FactSet, supply chain transparency remains a significant challenge in emerging economies due to issues such as information asymmetry and institutional gaps in regulation. This study proposes a novel approach to enhance supply chain transparency in emerging economies by leveraging online content and large language models (LLMs). We develop a Supply Chain Knowledge Graph Mining System that integrates advanced LLMs with web crawler technology to automatically collect and analyze supply chain information. The system's effectiveness is validated through a case study focusing on the semiconductor supply chain, a domain that has recently gained significant attention due to supply chain risks. Our results demonstrate that the proposed system provides greater applicability for emerging economies, such as mainland China, complementing the data gaps in existing datasets. However, challenges including the accurate estimation of monetary and material flows, the handling of time series data, synonyms disambiguation, and mitigating biases from online contents still remains. Future research should focus on addressing these issues to further enhance the system's capabilities and broaden its application to other emerging economies and industries.
We conducted a study to assess the variations in groundwater quality and metal pollution and identify the sources in the U S Nagar district of Uttarakhand state of India using multivariate statistical techniques. The two essential indicators of any developed society are Safe drinking water and decontamination. This research aims to undertake drinking water quality analyses of the groundwater and the sources of contamination in Udham Singh Nagar district, Uttarakhand. We produced results of 250 samples collected from hand pumps (Govt. and Private) and artesian wells. We measured 19 parameters which nine physicochemical parameters (pH, electrical conductivity, total dissolved solids, dissolved oxygen, oxidation and reduction potential, salinity, fluoride, chloride, nitrate), 7 Heavy metals (Lead, nickel, chromium, copper, iron, manganese, zinc) along with three metals (potassium, magnesium, sodium). Water quality index, Heavy metal pollution index, PCA (Principal component analysis)/FA (factor analysis), and CA (Cluster analysis) methods were applied. WQI index shows five samples (2 %) comes under the excellent, 211 samples (84.4 %) fall under good quality, and 34 samples (13.6 %) have poor water quality wqi status as per Yadav index. Further, referring to the Ramakrishnaiah index, 216 samples (86.4 %) fall under excellent quality and only 13 samples (13.6 %) come under good water quality. For HPI, as per Indian Standard, nearly 40.4% of samples show a low degree of pollution, 33.2% of samples show a medium degree of pollution, and 26.4% show a High degree of pollution. According to the International HPI standard, 46% of samples show a low degree of pollution, 38% have a medium degree, and 16% show a high-grade degree of pollution. The results of PCA show that groundwater has mainly geogenic (geochemical alteration and weathering of source rock like carbonate, dolomite, quartzite, etc.) followed by anthropogenic sources (agrogenic, domestic sewage and industrial wastes etc.). The results obtained through the PCA are also moderately supported by Cluster analysis. The cations which were over the limit in groundwater samples are presented in chronological order Fe > Pb > Ni > Mg > Mn > Zn > Cu, and significant anions were over the limit F¯ > Cl¯, and the rest was under the limit. The highly heavy metal-contaminated drinking groundwater sample, being used for drinking purpose, is deteriorating and need a proper treatment strategy before domestic water supply.
Zinc is one of the heavy metals present in textile wastewater with high concentrations. However, the chronic toxic effects of zinc on aquatic vertebrates are still ambiguous. Zinc accumulation in zebrafish after chronic zinc exposure and toxic effects on the intestines, muscles, and gills were investigated in this study. The results showed that a significant accumulation of zinc in the intestine, muscle, and gill was observed after 25 d of zinc exposure. The toxic effects of zinc were mainly in the form of zinc-induced oxidative stress in zebrafish, potential neurotoxicity, and changes in intestinal microbes. Significant changes in the levels of superoxide dismutase, catalase, metallothionein, glutathione, and malondialdehyde indicated that zinc damaged the antioxidant system of adult zebrafish. Zinc exposure resulted in a significant decrease in acetylcholinesterase activity and abnormal neural signaling. Furthermore, zinc exposure resulted in increased intestinal microbial richness and decreased the Simpson index in adult zebrafish. At the phylum and genus levels, the predominant microbes in the intestine are altered by zinc. In summary, this study provides an analysis of the toxic effects of chronic zinc exposure on adult zebrafish and the potential mechanisms, which are important for assessing the dual effects of zinc on aquatic organisms.
HIGHLIGHTS
Zinc accumulation in adult zebrafish organs is significantly associated with oxidative stress.;
Differences in oxidative stress of different organs to chronic zinc exposure were found.;
Zinc adversely affects the nervous system of adult zebrafish.;
The effect of zinc on the intestinal microbiome of adult zebrafish is twofold.;
Organisations often struggle to identify the causes of change in metrics such as product quality and delivery duration. This task becomes increasingly challenging when the cause lies outside of company borders in multi-echelon supply chains that are only partially observable. Although traditional supply chain management has advocated for data sharing to gain better insights, this does not take place in practice due to data privacy concerns. We propose the use of explainable artificial intelligence for decentralised computing of estimated contributions to a metric of interest in a multi-stage production process. This approach mitigates the need to convince supply chain actors to share data, as all computations occur in a decentralised manner. Our method is empirically validated using data collected from a real multi-stage manufacturing process. The results demonstrate the effectiveness of our approach in detecting the source of quality variations compared to a centralised approach using Shapley additive explanations.
With increasing interest in adaptive clinical trial designs, challenges are present to drug supply chain management which may offset the benefit of adaptive designs. Thus, it is necessary to develop an optimization tool to facilitate the decision making and analysis of drug supply chain planning. The challenges include the uncertainty of maximum drug supply needed, the shifting of supply requirement, and rapid availability of new supply at decision points. In this paper, statistical simulations are designed to optimize the pre-study medication supply strategy and monitor ongoing drug supply using real-time data collected with the progress of study. Particle swarm algorithm is applied when performing optimization, where feature extraction is implemented to reduce dimensionality and save computational cost.
Mohammadali Athari, Hamid reza Azizi, Seyed shahab Hashemi
et al.
One of the most important issues that has been considered by many researchers in recent years is the study of the phenomenon of land subsidence. The purpose of studying subsidence is to examine the risks and consequences that can result from it over many years. In their research, most researchers considered the occurrence of earthquakes and excessive abstraction of groundwater aquifers due to the drilling of a large number of illegal wells as the most important causes of subsidence. The aim of this study was to obtain a statistical relationship between groundwater level changes and the rate of vertical movement of the earth's surface using linear regression and grade 3 models and using InSAR radar interferometry technique in Varamin plain between 2014 and 2019. Most of the surface of Varamin plain is covered by agricultural lands and therefore it can be said that the uncontrolled abstraction of groundwater is considered as the main cause of vertical movements of the earth. In order to analyze the subsidence that occurred in this plain, Snap software was used and by applying the desired filters to eliminate the noise in the initial images, surface displacement maps were obtained in Varamin plain. The images used for the surface of the earth from 2014 to 2019 were obtained by Sentinel-1 satellite SAR sensor as ascending, and finally, by comparing the obtained statistical models from the fluctuation of the aquifer and the ground, it was found that the linear regression model has a better predictive power than the 3rd degree regression model.
Mhamed Abali, Abdeljalil Ait Ichou, Ahmed Zaghloul
et al.
Abstract The study is carried out at the wastewater treatment plant (WWTP) of an agricultural cooperative that operates according to the activated sludge process. Dairy industry is enlisted as one of the top-most industries in the food industry. Dairy wastewater treatment is a big issue as dairy wastewater releases a high amount of chemical oxygen demand, inorganic and organic particles, biological oxygen demand, and nutrients. But, these processes partly degrade wastewater containing fats and nutrients as dairy wastewater. The aim of this study was to evaluate the purification performance of this treatment process. The qualitative analysis of decanted raw wastewater (DRWW) and purified wastewater (PWW) shows that the concentration of orthophosphate, nitrate and sulfate ions is slightly higher. Such contaminated water if not handled appropriately, it pollutes water bodies and largely affects our ecosystem and biodiversity. Hence, our proposal is to improve the WWTP performances by using the adsorption process onto dried Carpobrotus edulis as an inert biomaterial. This adsorption process is recognized as one of the best water treatment techniques, more and more works are oriented towards the search for new materials, cheaper and having a good adsorbent potential. This study opens the path for the use of natural and abundant local material to remove orthophosphate, nitrate and sulfate ions using the C. edulis plant particles shred. The surface micromorphology of the biomaterial was investigated using a scanning electron microscope; while the qualitative element composition was analyzed using energy dispersive X-ray and infrared spectroscopies. The found results of DRWW was about 57% for orthophosphates, 67% for sulfates and 73% for nitrates ions. For PWW, the percentage removal was found to be 62%, 73% and 84% for orthophosphates, sulfates and nitrates respectively. These results indicate that dried C. edulis plant, as an environmentally friendly adsorbent could be recommended for the removal of mineral pollutants. In conclusion, the C. edulis adsorbent can be integrated into the activated sludge process for wastewater treatment after identifying the optimal hydraulic loads, associated sizes, and shapes in continuous operations.
Sabyasachi Swain, Surendra Kumar Mishra, Ashish Pandey
et al.
Abstract Drought is amongst the most precarious natural hazards associated with severe repercussions. The characterization of droughts is usually carried out by the sector-specific (meteorological/agricultural/hydrological) indices that are mostly based on hydroclimatic variables. Groundwater is the major source of water supply during drought periods, and the socio-economic factors control the aftermaths of droughts; however, they are often ignored by the sector-specific indices, thereby failing to capture the overall impacts of droughts. This study aims to circumvent this issue by incorporating hydroclimatic, socio-economic and physiographic information to assess the overall drought vulnerability over Narmada River Basin, India, which is an agriculture-dominated basin highly dependent on groundwater resources. A Comprehensive Drought Vulnerability Indicator (CDVI) is proposed that assimilates the information on meteorological fluctuations, depth to groundwater level, slope, distance from river reach, population density, land use/land cover, soil type, and elevation through a geospatial approach. The CDVI showed a remarkable geospatial variation over the basin, with a majority (66.4%) of the area under highly to extremely vulnerable conditions. Out of 35 constituent districts of the basin, 9, 22, and 4 districts exhibited moderate, high, and extreme vulnerability to droughts, respectively. These results urge an immediate attention towards reducing drought vulnerability and enhancing resilience towards drought occurrences. The proposed multi-dimensional approach for drought vulnerability mapping would certainly help policy-makers to proactively plan and manage water resources over the basin, especially to ameliorate the pernicious impacts of droughts.
Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by \texttt{IBM Qiskit} and the \texttt{qBraid} system.