Abstract The improvement of urban wastewater treatment systems remains a key priority across Europe in order to achieve the long-term objectives of the EU Urban Waste Water Treatment Directive 91/271/EEC and reduce nutrient loading to the Baltic Sea basin. This study evaluates the temporal development of wastewater treatment performance in Kaunas City (Lithuania) over a 25-year period (2000–2025), based on continuous laboratory monitoring and technological modernization of the treatment system. Five core parameters were analysed—biochemical oxygen demand (BOD7), chemical oxygen demand (COD), suspended solids, total nitrogen and total phosphorus—to quantify removal efficiencies and identify long-term performance trends. The results demonstrate substantial improvement in treatment effectiveness, with BOD7 removal reaching up to 98%, nitrogen up to 90%, and phosphorus 90–95%, while COD and suspended solids removal consistently exceeded 85%. Performance in most years met or exceeded EU regulatory thresholds. Since treated effluents are discharged into the Nemunas River—one of the largest contributors to the Baltic Sea catchment—enhanced nutrient removal contributes to broader international efforts aimed at reducing eutrophication pressure in this ecologically sensitive marine basin. This study highlights Kaunas as a relevant example of long-term transition toward advanced biological nutrient removal in Central and Eastern Europe. The findings provide evidence-based guidance for wastewater utilities and policymakers aiming to strengthen nitrogen removal stability and address future challenges such as emerging pollutants, pharmaceuticals and microplastics.
Water supply for domestic and industrial purposes, Environmental sciences
Eslam Ibrahim, Yury Lysogorskiy, Ralf Drautz
et al.
Water's phase diagram remains one of the most intricate and challenging benchmarks in molecular modeling. In this study, we compute the phase diagram of water using an Atomic Cluster Expansion (ACE) potential trained on density-functional theory (DFT) calculations based on the revPBE-D3 exchange and correlation functional. We compute solid-liquid chemical potential differences and melting points using biased coexistence simulations with the On-the-Fly Probability Enhanced Sampling (OPES) method. Starting from these points, we trace coexistence lines using Gibbs-Duhem integration. This combination of methods allows us to consistently map pressure-temperature phase boundaries and reconstruct the full phase diagram between approximately 100-500 K and 0-4 GPa. The stability regions of the main ice polymorphs (Ih, II, V, VI, and VII) are reproduced in close agreement with experiments. As in earlier studies based on DFT, ice III is metastable and there are systematic shifts of coexistence lines with respect to experimental results. Our results demonstrate the capability of our general-purpose ACE potential to capture the complex phase behavior of water across wide thermodynamic conditions.
Temirbolat Maratuly, Pakizar Shamoi, Timur Samigulin
Purifying sour water is essential for reducing emissions, minimizing corrosion risks, enabling the reuse of treated water in industrial or domestic applications, and ultimately lowering operational costs. Moreover, automating the purification process helps reduce the risk of worker harm by limiting human involvement. Crude oil contains acidic components such as hydrogen sulfide, carbon dioxide, and other chemical compounds. During processing, these substances are partially released into sour water. If not properly treated, sour water poses serious environmental threats and accelerates the corrosion of pipelines and equipment. This paper presents a fuzzy expert system, combined with a custom-generated digital twin, developed from a documented industrial process to maintain key parameters at desired levels by mimicking human reasoning. The control strategy is designed to be simple and intuitive, allowing junior or non-expert personnel to interact with the system effectively. The digital twin was developed using Honeywell UniSim Design R492 to simulate real industrial behavior accurately. Valve dynamics were modeled through system identification in MATLAB, and real-time data exchange between the simulator and controller was established using OPC DA. The fuzzy controller applies split-range control to two valves and was tested under 21 different initial pressure conditions using five distinct defuzzification strategies, resulting in a total of 105 unique test scenarios. System performance was evaluated using both error-based metrics (MSE, RMSE, MAE, IAE, ISE, ITAE) and dynamic response metrics, including overshoot, undershoot, rise time, fall time, settling time, and steady-state error. A web-based simulation interface was developed in Python using the Streamlit framework. Although demonstrated here for sour water treatment, the proposed fuzzy expert system is general-purpose.
Maeti Antoinette George, Qatsa Leshota, Stella Thoala
Abstract Inadequate management of municipal solid waste (MSW) is a global environmental concern for human health and ecosystems. In Lesotho, open dumping is the oldest method of solid waste disposal that threatens nearby water bodies, especially groundwater. There is under-presentation of monitoring data from the Ts’osane dumpsite, and this study intended to address the gap. The study investigated groundwater quality and level of non-carcinogenic health hazard for residents who consume the groundwater. Chemical oxygen demand (COD), chlorides (Cl), copper (Cu), electrical conductivity (EC), iron (Fe), lead (Pb) and pH) were analysed from 30 samples for five private boreholes within the 900 m radius from the dumpsite, following the American Public Health Association (APHA) standard methods. Data analysis entailed descriptive analysis, and correlation coefficient established relationships amongst the parameters. Hazard Quotient (HQ) and Hazard Index (HI) were used to assess communities’ risk. The results were as follows: Cl (191–288.7 mg/L), COD (54–82.25 mg/L), Fe (0.18–1.22 mg/L) and Pb (0.05–0.08 mg/L) and were in the order Pb > Fe > COD > Cl. The mean of 1173 µS/cm for EC was slightly below the permissible threshold of 1500 µS/cm. Cl, COD, Fe and Pb exceeded the World Health Organisation (WHO) limit for drinking water. The dumpsite may have influenced groundwater quality, however, health risk assessment revealed that HQ and HI were below 1, indicating a low likelihood of adverse health effects. The study recommends utilisation of anti-seepage systems to avert further seepage of pollutants into groundwater.
Water supply for domestic and industrial purposes, Environmental sciences
Semra Yılmazer Keskin, Nursena Demir, Can Serkan Keskin
et al.
Abstract The dye removal properties of Penicillium funiculosum mold were investigated in live and inactive forms. Acid Violet 90 and Direct Blue 86 textile dyes were used as target pollutant molecules. Bioaccumulation occurred when live mold was used. The dyes were gathered by living cells metabolically. The experiments were performed by adding mold spores and dyes to the nutrient medium in an orbital shaker with varied conditions. 100% removal efficiencies were achieved using 100 mg/L dye concentrations at the natural pH of the growth medium (pH 6.7), agitation speed of 160 rpm, 28 °C, and 4 days. The mold was killed in an autoclave for biosorption. The uptake occurred through the interaction of the dye molecules with the functional groups in the cell wall of dead mold. The biosorption experiments were done in batch mode at different pH, time, mixing speed, temperature, dead mold amount, and initial dye concentrations. The removal rates reached ~ 99% for AV90 and ~ 98% for DB86 using 0.1 g of mold at pH 4, 200 rpm shaking speed, 60 min reaction time, and 40 °C with 100 mg/L dye concentrations. The removal percentages were 100% when 0.25 g of mold was used in the same conditions. The kinetic, isotherm, and thermodynamic parameters of the biosorption were calculated. The biosorption is well fitted to the pseudo-second-order kinetic model and Langmuir isotherm. The used mold efficiently removed textile dyes in both live and inactive forms. The uptaken dyes can be seen in the microscope images of the treated molds.
C. S. Tshangana, S. T. Nhlengethwa, S. Glass
et al.
Abstract Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are highly resistant to degradation because of the strong C-F bond and their unique physico-chemical properties. Several techniques, both destructive and non-destructive, have been explored for removing PFAS from contaminated water. However, the most desirable techniques, ideally capable of effective separation and complete PFAS destruction and mineralization, have not progressed beyond bench-scale testing. This paper provides an overview of the existing treatment techniques demonstrated at laboratory, pilot, and industrial scales, and their associated treatment mechanisms. Insufficient data on pilot-scale and full-scale applications for PFAS remediation has limited the optimization and advancement of these systems at a large scale. Most research related to PFAS-remediation is based on laboratory-scale studies under ideal conditions that do not represent the complexity of PFAS-contaminated media. Factors such as inhibition by competing background compounds and secondary water or air pollution limit the application of some PFAS removal techniques at full-scale. Additionally, high energy intensity, cost, and inappropriate reactor design restrict the scalability of some proposed innovations. Here, we propose integrated systems and treatment trains as potential approaches to effectively remove and destroy PFAS from contaminated waters. This review also offers and contextualizes implementation barriers and scalable approaches for PFAS treatment.
Juulia-Gabrielle Moreau, Argo Jõeleht, Anna Losiak
et al.
Sedimentary rocks often form the upper layers or the entire target rocks in impact events. Thermodynamic properties of sedimentary rocks related to porosity and water saturation affect the process of impact crater formation. The heterogeneous distribution of sedimentary facies can complicate the development and distribution of shock effects, especially in numerical modeling. This work focuses on the shock thermodynamic properties of carbonate rocks with differing porosity textures (e.g., isolated pores, interstitial porosity, elongated pores) and water saturation levels. Using mesoscale numerical modeling, we found that water saturation reduces shock temperatures compared to those in dry, porous carbonate rocks. The orientation of elongated pores and porosity lineations influences the shock temperature distribution and rock deformation at angles of 50-90° to the shock front. Additionally, due to complex shock wave interactions, interstitial porosity is key in creating temperature zonations around larger grains.
The growing use of smart home devices poses considerable privacy and security challenges, especially for individuals like migrant domestic workers (MDWs) who may be surveilled by their employers. This paper explores the privacy and security challenges experienced by MDWs in multi-user smart homes through in-depth semi-structured interviews with 26 MDWs and 5 staff members of agencies that recruit and/or train domestic workers in China. Our findings reveal that the relationships between MDWs, their employers, and agencies are characterized by significant power imbalances, influenced by Chinese cultural and social factors (such as Confucianism and collectivism), as well as legal ones. Furthermore, the widespread and normalized use of surveillance technologies in China, particularly in public spaces, exacerbates these power imbalances, reinforcing a sense of constant monitoring and control. Drawing on our findings, we provide recommendations to domestic worker agencies and policymakers to address the privacy and security challenges facing MDWs in Chinese smart homes.
Giovanni Micheli, Laureano F. Escudero, Francesca Maggioni
et al.
In this paper we address the challenge of designing optimal domestic renewable energy systems under multiple sources of uncertainty appearing at different time scales. Long-term uncertainties, such as investment and maintenance costs of different technologies, are combined with short-term uncertainties, including solar radiation, electricity prices, and uncontrolled load variations. We formulate the problem as a multistage multi-horizon stochastic Mixed Integer Linear Programming (MILP) model, minimizing the total cost of a domestic building complex's energy system. The model integrates long-term investment decisions, such as the capacity of photovoltaic panels and battery energy storage systems, with short-term operational decisions, including energy dispatch, grid exchanges, and load supply. To ensure robust operation under extreme scenarios, first- and second-order stochastic dominance risk-averse measures are considered preserving the time consistency of the solution. Given the computational complexity of solving the stochastic MILP for large instances, a rolling horizon-based matheuristic algorithm is developed. Additionally, various lower-bound strategies are explored, including wait-and-see schemes, expected value approximations, multistage grouping and clustering schemes. An extensive computational experiment validates the effectiveness of the proposed approach on a case study based on a building complex in South Germany. We tackle models with over 43 million constraints and 12 million binary, 700 hundred integer and 10 million continuous variables; they are solved with up to 0.32% optimality gap in reasonable computing time, where the value of the stochastic decisions as well as the benefit of the integrated risk-averse measures are quantified.
Tristan Montoya, Andrés M. Rueda-Ramírez, Gregor J. Gassner
We introduce discontinuous spectral-element methods of arbitrary order that are well balanced, conservative of mass, and conservative or dissipative of total energy (i.e., a mathematical entropy function) for a covariant flux formulation of the rotating shallow water equations with variable bottom topography on curved manifolds such as the sphere. The proposed methods are based on a skew-symmetric splitting of the tensor divergence in covariant form, which we implement and analyze within a general flux-differencing framework using tensor-product summation-by-parts operators. Such schemes are proven to satisfy semi-discrete mass and energy conservation on general unstructured quadrilateral grids in addition to well balancing for arbitrary continuous bottom topographies, with energy dissipation resulting from a suitable choice of numerical interface flux. Furthermore, the proposed covariant formulation permits an analytical representation of the geometry and associated metric terms while satisfying the aforementioned entropy stability, conservation, and well-balancing properties without the need to approximate the metric terms so as to enforce discrete metric identities. Numerical experiments on cubed-sphere grids are presented in order to verify the schemes' structure-preservation properties as well as to assess their accuracy and robustness within the context of several standard test cases characteristic of idealized atmospheric flows. Our theoretical and numerical results support the further development of the proposed methodology towards a full dynamical core for numerical weather prediction and climate modelling, as well as broader applications to other hyperbolic and advection-dominated systems of partial differential equations on curved manifolds.
The integration of AI into CAD systems transforms how engineers plan and develop infrastructure projects involving water and power transportation across industrial and remote landscapes. This paper discusses how AI-driven CAD systems improve the efficient, effective, and sustainable design of infrastructure by embedding automation, predictive modeling, and real-time data analytics. This study examines how AI-supported toolsets can enhance design workflows, minimize human error, and optimize resource allocation for projects in underdeveloped environments. It also addresses technical and organizational challenges to AI adoption, including data silos, interoperability issues, and workforce adaptation. The findings demonstrate that AI-powered CAD enables faster project delivery, enhanced design precision, and increased resilience to environmental and logistical constraints. AI helps connect CAD, GIS, and IoT technologies to develop self-learning, adaptive design systems that are needed to meet the increasing global demand for sustainable infrastructure.
Engome R. Wotany, Samuel N. Ayonghe, Valery Ayuk
et al.
Water quality monitoring is a critical aspect of environmental protection. The Tiko-Douala coastline represents a key ecological and economic zone stretching along the Atlantic coast in the Southwest Region which is vulnerable to pollution, with little water quality data across. This study investigates the seasonal variations in physicochemical parameters of surface and groundwater. Fifty-two water samples were collected from 27 locations during the rainy and dry seasons and analyzed for pH, total dissolved solids (TDS), major ions and nutrients. The results showed acidic pH levels (mean: 5.43 dry season, 5.79 rainy season), and rising ammonium concentrations during the wet season (range 0- 1.44 mg/L) and dry season (0-2.16 mg/L). During the rainy season, the dominant water was of Ca-Cl (48%), Ca-HCO3 (22%), Na-HCO3 (11%), Na-Cl (11%), mixed Ca-Mg-Cl (11%) and Na-K-HCO3 (3.7%) types; during the dry season, the dominant water was only of Ca-Cl (44%), mixed Ca-Mg-Cl (15%), Ca-HCO3 (22%), and NaCl (19%) types. Water-rock interaction is the main geochemical process controlling the water chemistry in the dry and rainy seasons. Water quality index classification revealed a decline in water quality in the wet season, with the poor and very poor categories increasing from 35% (dry) to 45% (wet). Agricultural suitability showed a decline in the dry season due to increase in salinity while industrial water quality showed a seasonal drop in the percentage. These findings highlight the significant impact of seasonal and anthropogenic influences on water quality, calling for adaptive management strategies. Le suivi de la qualité de l’eau est un aspect essentiel de la protection de l’environnement. La ligne de côte Tiko–Douala représente une zone écologique et économique clé s’étendant le long de la côte atlantique dans la région du Sud-Ouest, qui est vulnérable à la pollution et où les données sur la qualité de l’eau sont limitées. Cette étude examine les variations saisonnières des paramètres physicochimiques des eaux de surface et souterraines. Cinquante-deux échantillons d’eau ont été prélevés sur 27 sites pendant les saisons pluvieuse et sèche, puis analysés pour le pH, les solides totaux dissous (TDS), les ions majeurs et les nutriments. Les résultats ont montré des niveaux de pH acides (moyenne : 5,43 en saison sèche, 5,79 en saison des pluies) et une augmentation des concentrations d’ammonium pendant la saison des pluies (etendue 0- 1.44 mg/L) saison secche (0-2.16 1,09mg/L).Pendant la saison des pluies, les types d’eau dominants étaient Ca-Cl (48 %), Ca-HCO3 (22 %), Na-HCO3 (11 %), Na-Cl (11 %), Ca-Mg-Cl mixte (11 %) et Na-K-HCO3 (3.7 %). En saison sèche, les types d’eau dominants étaient Ca-Cl (44 %), Ca-Mg-Cl mixte (15 %), Ca-HCO3 (22 %) et NaCl (19 %). L’interaction eau-roche est le principal processus géochimique contrôlant la chimie de l’eau pendant les saisons sèche et pluvieuse. La classification de l’indice de qualité de l’eau a révélé une baisse de la qualité de l’eau en saison des pluies, avec une augmentation des catégories Médiocre et Très Médiocre de 35 % (saison sèche) à 45 % (saison des pluies). L’aptitude agricole a montré une diminution en saison sèche en raison de l’augmentation de la salinité, tandis que la qualité de l’eau industrielle a également enregistré une baisse saisonnière. Ces résultats mettent en évidence l’impact significatif des influences saisonnières et anthropiques sur la qualité de l’eau, soulignant la nécessité de stratégies de gestion adaptatives.
In Hungary, maize is one of the most widely grown crops, with a stable area of 0.8–1 million hectares. The reason for this is the exceptional yield of the crop, which allows a significant amount of value to be produced per unit area. Domestic production is mainly used for animal feed, particularly in the poultry and pig sectors, and for feeding ruminants. Its use is not only as food or fodder crops, but is also increasingly important for the production of oil, bioethanol and energy. The intrinsic values of maize – protein, starch and oil – are crucial for its use in industry, feed and food. The nutrient supply of maize is essential to ensure plant development. Adequate nutrient supply is essential to ensure sustainable farming and high yields. The nutrient rates applied must be adapted to the needs of the crop so that the hybrids tolerate stress caused by seasonal effects well and yield security is maintained. Water deficit is one of the most serious abiotic stresses that negatively affect plant growth, development and yield. Extreme weather conditions reduce yields and threaten stable production. The content, quality and industrial use of maize are closely linked to genetic, ecological and agrotechnical factors. By selecting the appropriate hybrid and applying the appropriate cultivation technology, yield indicators can be adapted to different purposes. In the agrotechnical studies for 2024, the main yield determinants were analysed, and weather was evaluated for each agrotechnical element and phenophase. The research is mainly based on meteorological measurements at the Látókép Experimental Station of the University of Debrecen. In the winter period 2023/24, 283 mm of precipitation fell in 6 months, 69 mm above the long-term average. In June, the weather was free of extremes, with evenly distributed temperatures, but above the multi-year average. The above average rainfall (66 mm), combined with soil moisture in the deeper layers of the soil, ensured a good water supply. The average temperatures in both July and August were close to record highs (24.2 °C). The exceptional warmth in August (mid to late August) was mainly due to the shortening of the ripening phase. The 29 mm of precipitation in July was less than half the multi-year average and the following month of August was also dry (33 mm). The summer total was 128 mm. In early September, the unseasonably warm weather continued, with the first decade showing a positive anomaly of nearly 7 °C. The physiological maturity of the maize and its rapid drainage and drying allowed early harvesting. The year 2024 was marked by a marked dichotomy in terms of maize production. Our field maize experiments allowed us to record the phenophases of the plants throughout the growing season (Hanway scale). As a new result, our analyses showed that, especially in the generative phase, more accurate data were obtained when taking into account the useful heat sum (HU) calculations. From emergence to silking, 60 days passed using 545 HU of heat sum. From silking to waxy maturation (R4) 32 days and 422 HU were needed. It was found that from silking to physiological maturation, typical of the genotype, 815 HU were required. The yield of maize hybrid H470 under irrigation is excellent (20.76 t/ha). The dry matter incorporation dynamics of the hybrid is outstanding. Dry matter gain was measured weekly. At the physiological maturation phenophase (30 August 2024), using 1360 HU, the dry matter content was 77.1%. The dry matter measurements allowed the evaluation of the water loss dynamics of a maize hybrid with excellent yield potential. Measurements and analyses were performed every seven days. The water loss rate was 5.5% in the first week, 5.8% in the second week, 4.6% in the third week and 6.9% in the fourth week. At physiological maturation, grain moisture showed a favourable value (22.9%). After physiological maturation, the daily water loss was 0.23% during the 21-day period.
This research seeks to examine the nexus between industrialization and economic growth in Nigeria. The specific purpose of the study is to analyze the effects of manufacturing output, mining, electricity supply, construction, water/sewage/waste management, and labor force participation on Nigeria’s real gross domestic product growth rate. This study adopts an ex-post facto research design. The period covered spans from 1990 to 2024. Data were collected as annual time series secondary data from the Central Bank of Nigeria (CBN) statistical bulletin (various years), World Development Indicators, and World Energy Statistics from the International Energy Agency. The data were analyzed using the Error Correction Model. Additional tests conducted include unit root, cointegration, and autocorrelation tests. The research employs an econometric approach. The results reveal that manufacturing, mining, electricity supply, construction, and water/sewage/waste management had a negative effect on economic growth in Nigeria in the short run. However, only the effects of manufacturing, electricity, construction, and waste management on the Nigerian economy were statistically significant. In conclusion, industrialization has a negative effect on Nigeria’s economic growth. Nigeria’s industrialization efforts have not yielded the expected positive effects on the economy, leading to declining outputs in manufacturing, mining, electricity supply, construction, and water/sewage/waste management sectors. When electricity supply and distribution to the industrial sector are adequately enhanced, coupled with increased productive capacity, Nigeria’s economy will be on the path to long-term growth.
Chia-Yang Chen, Sheng-Wei Wang, Hyunook Kim
et al.
Due to the growing and diverse demands on water supply, exploitation of non-conventional sources of water has received much attention. Since water consumption for irrigation is the major contributor to total water withdrawal, the utilization of non-conventional sources of water for the purpose of irrigation is critical to assuring the sustainability of water resources. Although numerous studies have been conducted to evaluate and manage non-conventional water sources, little research has reviewed the suitability of available water technologies for improving water quality, so that water reclaimed from non-conventional supplies could be an alternative water resource for irrigation. This article provides a systematic overview of all aspects of regulation, technology and management to enable the innovative technology, thereby promoting and facilitating the reuse of non-conventional water. The study first reviews the requirements for water quantity and quality (i.e., physical, chemical, and biological parameters) for agricultural irrigation. Five candidate sources of non-conventional water were evaluated in terms of quantity and quality, namely rainfall/stormwater runoff, industrial cooling water, hydraulic fracturing wastewater, process wastewater, and domestic sewage. Water quality issues, such as suspended solids, biochemical/chemical oxygen demand, total dissolved solids, total nitrogen, bacteria, and emerging contaminates, were assessed. Available technologies for improving the quality of non-conventional water were comprehensively investigated. The potential risks to plants, human health, and the environment posed by non-conventional water reuse for irrigation are also discussed. Lastly, three priority research directions, including efficient collection of non-conventional water, design of fit-for-purpose treatment, and deployment of energy-efficient processes, were proposed to provide guidance on the potential for future research.
Technology based modern Irrigation systems are the recent requirement in every part of world today. The irrigation water is being used for agricultural, industrial and domestic purposes. Owing to mismanagement and inequitable distribution of water, it is necessary to have a fool-proof system where water is supplied to the end-users judiciously. Due to the variable atmospheric circumstances these conditions sometimes may vary from place to place, which makes very difficult to operate the canal gate manually and instantly. Therefore, we proposed. The “GSM based Canal gate and Flood monitoring and control system’’ canal gates are monitored and controlled after sensing the water level and flow speed. In our project the water level and flow will be calculated automatically and send towards the operator through SMS and the Canal Gate is operated according to the collected data, this system also Announce flood in case of high level of water and speed of water in River or canals by the help of Alarming as well as lightning tower with green, yellow and red lights indication of normal, intermediate and flood conditions respectively.
Abstract Forecasting and extending streamflow is a critical aspect of hydrology, especially where the time series are locally unavailable for a variety of reasons. The necessity of preprocessing, model fine-tuning, feature selection, or sampling to enhance prediction outcomes for streamflow forecasting using ML techniques is evaluated in this study. In this regard, the monthly streamflow at Pol-Chehr station is analyzed using various monthly rainfall and streamflow time series data from different stations. The results of streamflow prediction in the k-folds cross-validator approach are generally better than those of the time series approach, except when raw data with no preprocessing or feature selection is used. Applying the simple SVR model to raw data leads to the weakest result, but using the GA-SVR model on raw data significantly increases the Nash coefficient by about 215% and 72%, decreases the NRMSE by about 48% and 36% in the k-fold and time series approaches, even with no feature selection. On the other hand, standardization produces highly accurate model predictions in both the k-fold and time series approaches, with a minimum Nash coefficient of 0.83 and 0.73 during the test period in the simple SVR model, respectively. Finally, using optimization algorithms like GA to fine-tune ML models and feature selection does not always yield improved prediction accuracy, but it depends on whether raw or preprocessed data is chosen. In conclusion, combining k-fold cross-validator and preprocessing typically yields highly accurate predictive results, with an R value exceeding 93.7% (Nash = 0.83, SI = 0.55, NRMSE = 0.09), without requiring any additional fine-tuning or optimization. Using feature selection is only significant when utilizing the TS approach as well.
The exponential growth of open-source package ecosystems, particularly NPM and PyPI, has led to an alarming increase in software supply chain poisoning attacks. Existing static analysis methods struggle with high false positive rates and are easily thwarted by obfuscation and dynamic code execution techniques. While dynamic analysis approaches offer improvements, they often suffer from capturing non-package behaviors and employing simplistic testing strategies that fail to trigger sophisticated malicious behaviors. To address these challenges, we present OSCAR, a robust dynamic code poisoning detection pipeline for NPM and PyPI ecosystems. OSCAR fully executes packages in a sandbox environment, employs fuzz testing on exported functions and classes, and implements aspect-based behavior monitoring with tailored API hook points. We evaluate OSCAR against six existing tools using a comprehensive benchmark dataset of real-world malicious and benign packages. OSCAR achieves an F1 score of 0.95 in NPM and 0.91 in PyPI, confirming that OSCAR is as effective as the current state-of-the-art technologies. Furthermore, for benign packages exhibiting characteristics typical of malicious packages, OSCAR reduces the false positive rate by an average of 32.06% in NPM (from 34.63% to 2.57%) and 39.87% in PyPI (from 41.10% to 1.23%), compared to other tools, significantly reducing the workload of manual reviews in real-world deployments. In cooperation with Ant Group, a leading financial technology company, we have deployed OSCAR on its NPM and PyPI mirrors since January 2023, identifying 10,404 malicious NPM packages and 1,235 malicious PyPI packages over 18 months. This work not only bridges the gap between academic research and industrial application in code poisoning detection but also provides a robust and practical solution that has been thoroughly tested in a real-world industrial setting.
Hyung-il Ahn, Young Chol Song, Santiago Olivar
et al.
Successful supply chain optimization must mitigate imbalances between supply and demand over time. While accurate demand prediction is essential for supply planning, it alone does not suffice. The key to successful supply planning for optimal and viable execution lies in maximizing predictability for both demand and supply throughout an execution horizon. Therefore, enhancing the accuracy of supply predictions is imperative to create an attainable supply plan that matches demand without overstocking or understocking. However, in complex supply chain networks with numerous nodes and edges, accurate supply predictions are challenging due to dynamic node interactions, cascading supply delays, resource availability, production and logistic capabilities. Consequently, supply executions often deviate from their initial plans. To address this, we present the Graph-based Supply Prediction (GSP) probabilistic model. Our attention-based graph neural network (GNN) model predicts supplies, inventory, and imbalances using graph-structured historical data, demand forecasting, and original supply plan inputs. The experiments, conducted using historical data from a global consumer goods company's large-scale supply chain, demonstrate that GSP significantly improves supply and inventory prediction accuracy, potentially offering supply plan corrections to optimize executions.
Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation. RDVDL employs a sparse Bayesian dictionary learning to preserve the dynamic mechanism information of AWE process which allows the easy interpretation of fault detection results. To improve the robustness to measurement uncertainty, a low-rank vector autoregressive (VAR) method is derived to reliably extract the serial correlation from process variables. The effectiveness of the proposed approach is demonstrated with an industrial hydrogen production process, and RDVDL can efficiently detect and diagnose critical AWE faults.