Real-world data on transfusion needs of patients with myelodysplastic syndromes (MDS), and the impacts of disease-modifying therapies (DMTs) are sparse. In 2011, 5-azacitidine became the first funded DMT for MDS and chronic myelomonocytic leukaemia (CMML) patients in Australia and national patient blood management (PBM) guidelines were published. Describe red blood cell transfusion (RBC-T) and platelet transfusion (PLT-T) burden in MDS/CMML and any changes since 2011. Retrospective longitudinal cohort study of all MDS/CMML patients in the state of Victoria (population 6.5 million) from 2009 to 2022, linking hospital admissions dataset, cancer and death registries. 7043 patients (5715 MDS; 1328 CMML), with median follow-up 1.8 years (interquartile range [IQR]: 0.5-4.0 years), had 55 048 RBC-T and 10 749 PLT-T episodes. By 5 years post-diagnosis, patients had on average 14 RBC-T and 3 PLT-T admissions, with transfusion burden affected by MDS subtype. PLT-T were significantly higher post-2011 versus pre-2011 (0.8 more PLT-T admissions by 2 years, p < 0.001), with no difference in RBC-T admissions. Since 2011, RBC-T burden has remained stable, but PLT-Ts have increased. Given the potential costs and limited PLT inventories, this impacts blood supply planning and highlights the need for research to optimise PLT-T in MDS/CMML patients.

To ensure supply–demand balance in natural gas networks under uncertainty, a distributionally robust optimization (DRO) model is formulated that jointly optimizes supply allocation, pipeline expansion and storage scheduling. A satisfaction index, defined as the probability of meeting the demand, quantifies the network's reliability. Ambiguity sets can capture uncertainty in demand and storage costs; the model is equivalently reformulated as mixed-integer second-order cone programming (MISOCP). A tailored branch-and-cut algorithm is developed for efficient solutions. An anonymized case adapted from a real network is used to demonstrate the validity of the proposed DRO model: annual supply planning improves long-term returns, and tuning parameters enable a quantifiable risk–return trade-off to match operator's preferences. Relative to a baseline model, the proposed method can reduce the total cost while maintaining feasibility and provide practical and risk-aware decisions for a natural gas network. Supplemental data for this article can be accessed online at http://dx.doi.org/10.1080/0305215X.2026.2655339.

Abstract In recent years, our country’s electric ship industry has developed rapidly, and the application scale and technical level have jumped to the forefront of the world, providing important waterway transportation support for the implementation of the “double carbon” goal. Relying on the advantages of inland shipping resources, Huzhou actively promotes the development of electric ships and achieves initial results under the guidance of national and provincial policies. However, pure electric ships still face practical challenges such as insufficient endurance and imperfect charging and swapping infrastructure. Therefore, based on the actual situation in Huzhou, this paper innovatively proposes a comprehensive layout optimization method of “ship-shore-electricity” collaboration based on the systematic analysis of the applicable scenarios and layout logic of ship charging and swapping stations. By constructing a regional electricity demand model, measuring the scale of facilities, formulating power supply and expansion plans, and carrying out economic-social-environmental three-dimensional benefit assessments, a set of replicable and generalizable “Huzhou Plan” for the green transformation of inland waterway shipping has been formed, which has both theoretical value and practical guiding significance.

Ultra-short and short-term forecasting of photovoltaic solar energy using high temporal resolution data: Case study of Jaén (Spain)

Abstract Water supply systems are critical for sustainable development, public health, and economic growth. In recent decades, stakeholder involvement has emerged as a key factor influencing the success of planning and implementation of water supply projects. This study examines the role of stakeholder participation in enhancing planning quality, improving implementation efficiency, and ensuring long-term sustainability of water supply systems. The paper adopts a qualitative review of existing literature and case-based insights to analyze how stakeholder engagement contributes to decision-making, conflict resolution, and project acceptance. The findings indicate that inclusive stakeholder involvement leads to improved project outcomes, though challenges such as conflicting interests and coordination complexity persist. The study concludes with recommendations for strengthening participatory frameworks in water governance. Keywords: Stakeholder engagement, water supply systems, planning, implementation, participatory governance, project success

Global supply chains has been facing growing levels of instability caused by different sources of global disruptions including global pandemics such as the recent COVID 19 outbreak and its massive impact on the global economy and global supply chains, geopolitical conflicts such as the conflicts in the Gulf and its impact on global fuel prices and accordingly logistics prices, large scale wars such as the Russian-Ukrainian War and its impact on the upstream of global food supply chains, economical turbulences which is directing global supply chains to find more effective ways to adapt. The supply chains that were able to respond the fastest to every disruption gained an edge over its competitors as the responsive supply chains were able to secure their upstream by keeping it consistent and had more leverage in their downstream through efficient management of their inventory levels and distribution networks hence having an optimized overall sales and operations planning cycle (S&amp;OP). Sales and operations planning cycle is the process of synchronizing the demand requested by market with the company capabilities of supply including demand planning, supply planning, production planning, distribution network planning and inventory management which all together play a vital role in creating a unified goal for the supply chain which is responding to demand in the most efficient way, This study focuses on the effect of optimizing inventory levels and transportation management within the sales and operations planning cycle on overall supply chain resilience through response time in the food industry within the Egyptian market using insights from previous literature review and theoretical perspectives proposing a conceptual framework.

This article highlights the critical need for additional security measures in health care facilities during armed conflicts, emphasizing the importance of securing vital resources such as water, electricity, medical care, infrastructure, and essential medical equipment, medications, etc., to ensure proper care for the sick and vulnerable persons. We examine the impact of conflicts in Ukraine and Palestine. This article aims to draw lessons from historical experiences and propose strategies to enhance health care resilience, focusing on key topics such as essential infrastructure, protection of health care facilities, the physical preparedness of hospitals, and the availability of alternative sources for water and electricity. Enhancing the resilience of health services requires comprehensive disaster preparedness plans for hospitals that will ensure reliable power supplies in challenging situations and provide resilient physical protection for health facilities during times of conflict. Medical facilities must prepare for emergencies involving chemical, biological, radiological, or nuclear events, implementing water supply plans, and maintaining sufficient stocks of food and materials.

Accurate monthly electricity consumption (EC) forecasting is essential for power providers to allocate resources efficiently, develop reasonable sales plans, and support the creation of reliable smart grids and precise demand-side management policies. Given that factors such as climate, population, and economic conditions can significantly impact EC, it is crucial to consider a wide range of variables in medium-term EC forecasts. This paper addresses a gap in the existing literature by evaluating the performance of the M5 rule model—a relatively underutilized technique—in comparison with popular machine learning (ML) models like Random Forest (RF) and Support Vector Machine (SVM). The motivation for selecting the M5 rule regression technique stems from its effective feature selection process, which is simpler and more straightforward than the complex feature selection methods employed by other models. Using an aggregated dataset from the Czech Transmission System Operator, the study applies these three regression techniques independently to forecast monthly EC. The results demonstrate that the M5 rule regression model outperforms both SVM and RF models for monthly forecasts, achieving an impressive correlation coefficient (R²) value of 0.9063, compared to 0,8915 for SVM and 0,8598 for RF. Additionally, the M5 rule achieves the lowest Mean Absolute Error (MAE) of 16772.29, compared to 17477.57 for SVM and 21390.68 for RF, as well as the lowest Root Mean Squared Error (RMSE) of 22287.94, compared to 23114.17 for SVM and 26658.89 for RF. Furthermore, M5 rule shows superior performance in terms of relative errors, with a Relative Absolute Error (RAE) of 43.12% and a Relative Root Mean Squared Error (RRSE) of 45.74%, while RF and SVM show higher values. The M5 rule model also identifies air temperature, relative humidity, and clear sky surface irradiance as the most influential features in predicting EC. These findings offer valuable implications for power management companies, aiding in the strategic planning of power generation and supply. By accurately forecasting EC and understanding key influencing factors, companies can better avoid issues of overproduction or shortages, leading to more efficient and reliable power management.

Scientific planning and management of urban land supply are critical to the high-quality development of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). However, existing research lacks a systematic analysis of the evolution and risks of urban land supply-demand matching under different scenarios. This study integrates land suitability assessment with land-use simulation to develop an urban land supply-demand matching evaluation framework (S-LUS), based on the supply-demand ratio (SDR) and spatial matching degree (SMD). The empirical results show that under the economic development scenario, the supply-demand ratio continues to decline, with the risk of urban land supply-demand imbalance emerging as early as 2060. Under the farmland protection scenario, the decline in the supply-demand ratio slows, but spatial matching continues to deteriorate, and the Macao-Zhuhai-Zhongshan-Jiangmen (MZZJ) urban cluster also faces a risk of supply-demand imbalance. Under the ecological restoration scenario, both the supply-demand matching degree and the supply-demand ratio remain relatively stable, although urban land demand is constrained. These findings reveal the complex dynamics of urban land supply-demand matching in the GBA under different scenarios and highlight the challenges of coordinating multiple objectives, underscoring the necessity of integrating economic development, ecological protection, and land protection goals in land-use policy design.

Manufacturing companies aim to balance the flexibility and efficiency of manufacturing systems. The trend of improving the flexibility of product and material movements within manufacturing systems has recently gained more attention as an alternative to traditional flow-line manufacturing due to the growing opportunities for coordinating complex processes via digitalisation. One such concept is matrix-structured manufacturing systems (MMS), in which products and materials flow through stations arranged in a matrix-shaped grid. As multiple routes through the system may exist even for identical products, planning material supply to stations becomes drastically more complex. The scientific literature frequently addresses the design of MMS or related flexible types of manufacturing systems. However, material supply planning and its interdependencies with system design have not been previously addressed for MMS. This paper jointly assesses the interrelated problems of design planning and material supply planning for MMS. To this end, a mixed-integer programming model is proposed considering both interdepedent problems simultaneously. Numerical results illustrate that simultaneous planning is superior to sequential planning and leads to structurally different results. A genetic algorithm is developed and assessed for simultaneously solving design planning and material supply planning in large instances.

This policy commentary argues that forecasting and supply planning (FSP) for immunization systems must integrate gender equity and social inclusion (GESI) to bridge the gap between vaccine supply and actual utilization. While FSP traditionally focuses on supply-side efficiency, this narrow approach risks perpetuating inequities by leaving systematically excluded populations invisible in data and planning processes. Drawing on experience from the Immunization Collaborative Supply Planning Strengthening project, we demonstrate how contextual factors including gender dynamics, mobility patterns, seasonality and conflict shape vaccination demand among marginalized communities. The commentary outlines key practice implications, including strengthening supply-demand collaboration, embedding GESI expertise into FSP teams and improving data disaggregation. We conclude that this integration is essential to transforming forecasting into an inclusive mechanism that ensures equitable immunization access.

Purpose This study aims to explore how lean six sigma (LSS) may enhance demand management to improve customer value in South Africa’s automotive aftermarket. It addresses the need for structured, customer-centred strategies in volatile, resource-constrained supply chains. Design/methodology/approach A qualitative single-case study in a South African automotive aftermarket parts distributor. Data were collected via semi-structured interviews with staff across supply chain, planning and procurement. Analysis, guided by lean theory and the resource-based view, examined how LSS principles align with demand management practices and value delivery. Findings Customer value was defined by participants as availability, price and service reliability. Demand management was acknowledged as central but hindered by fragmented metrics, reactive practices and limited visibility. While LSS was not formally adopted, it was recognised as a relevant tool for improving consistency, responsiveness and problem-solving. Leadership, internal capabilities and cross-functional collaboration were identified as critical enablers. Research limitations/implications Findings are limited to one organisation and are based on perceived rather than observed LSS use. Practical implications Managers can use LSS to improve demand processes if supported by leadership and capability building. Originality/value This paper contributes practice-grounded evidence from a South African, service-oriented aftermarket context, clarifying the capability conditions, leadership commitment, analytical competence and cross-functional integration, under which LSS-aligned demand management supports value-oriented planning and fulfilment.

SummaryAccurate prediction of pediatric epidemic infectious diseases is critical for effective prevention and personalized treatment. Herein, we developed a deep learning framework for the epidemiological characteristics of the Chaoshan region, using electronic health records data from 278,506 pediatric outpatient and emergency visits at the Second Affiliated Hospital of Shantou University Medical College between 2017 and 2023. Our framework is designed to learn pediatric representations that capture local epidemic dynamics and to meet regional clinical prediction needs. Results demonstrate that the framework achieves strong predictive performance on the regional dataset. Our framework yields at least a 6.12% improvement over its counterparts in terms of average correlation coefficient; it achieves the lowest errors in both root-mean-square error (RMSE = 0.130) and mean absolute scaled error (MASE = 0.610). Our framework provided targeted decision support for local healthcare institutions in workforce allocation, medication, and supply planning, thereby contributing to improved prevention strategies.

Enhancing U.S. swine farm preparedness for infectious foreign animal diseases with rapid access to biosecurity information.

Reservoir water level forecasting is a critical component of effective water resources management, supporting flood mitigation, water supply planning, and sustainable reservoir operation, particularly under increasingly variable rainfall conditions. During periods of heavy rainfall, inaccurate or delayed water level prediction may increase flood risk, while during low rainfall seasons, poor forecasting can compromise water storage and operational efficiency. Artificial Neural Networks (ANNs) have been widely adopted for reservoir water level forecasting due to their capability to model nonlinear rainfall–reservoir relationships. However, existing studies largely focus on algorithm selection or architectural enhancement, with limited attention given to how rainfall data representation and dataset construction influence neural network performance. This study addresses this gap by analysing the impact of rainfall pattern dataset construction on ANN performance for reservoir water level forecasting. The primary aim is to evaluate how different rainfall representations affect predictive accuracy when the learning algorithm and training configuration are held constant. Two rainfall pattern datasets were constructed using the same raw rainfall and reservoir water level data from the Timah Tasoh Reservoir, Malaysia. The first dataset represents a compact abstraction of rainfall behaviour using rainfall change indicators derived from day-to-day observations. The second dataset enriches the feature space by incorporating both rainfall change and rainfall intensity categories for each upstream station. In both datasets, the reservoir water level category serves as the prediction target. Prior to model training, redundancy and conflicting data instances were removed to ensure data consistency. A consistent ANN architecture was employed for both datasets and evaluated using 10-fold cross-validation. Model performance was assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The experimental results demonstrate that the enriched rainfall pattern dataset achieved significantly lower RMSE and MAE values compared to the compact rainfall change dataset, indicating improved learning capability and generalisation performance. Although the enriched dataset required higher computational effort, the improvement in forecasting accuracy was substantial. The findings highlight that dataset construction plays a decisive role in neural-network-based reservoir water level forecasting.

Background: Inventory inefficiency remains a systemic barrier to responsive care delivery in low- and middle-income countries (LMICs), particularly in tertiary pediatric hospitals. Challenges include rigid procurement cycles, fragmented data architecture, and insufficient alignment between clinical needs and supply planning. These structural limitations are exacerbated by demand volatility and resource constraints, leading to recurrent stock-outs and waste. Objective: This study evaluates a hybrid inventory management approach combining ABC&amp;ndash;XYZ classification, discrete-event simulation (DES), and a context-adapted Just-in-Time (JIT) strategy. The objective is to assess whether these integrated tools can improve inventory responsiveness and cost-effectiveness within the administrative constraints of a public hospital system. Additionally, the study explores how risk-based stratification and simulation modeling can inform pragmatic policy adaptation in LMIC settings. Methods: A mixed-methods study was conducted at Children&amp;rsquo;s Hospital 1 (Vietnam) from 2019 to 2025. Inventory data were stratified using ABC&amp;ndash;XYZ analysis, and a DES model was applied to simulate the effects of JIT policies under real-world constraints. A pilot implementation was performed in the interventional cardiology unit, focusing on high-value, low-variability items.Triangulated data sources included retrospective performance indicators, audit reports, and stakeholder interviews to identify systemic bottlenecks and assess organizational readiness. Results: Simulation projected a 46% reduction in stock-outs, a 35% decrease in inventory days, and over 30% in cost savings. The JIT pilot confirmed feasibility, showing a 72.7% reduction in stock-outs and no clinical delays. However, systemic constraints&amp;mdash;procurement inflexibility, data fragmentation, and weak supplier accountability&amp;mdash;were identified as key bottlenecks to scalability.Feedback from clinical and logistics personnel further indicated improved predictability and coordination under the hybrid model. Conclusions: The findings highlight that technical innovations in inventory control must be embedded within broader administrative reform. Strategic procurement agility, digital integration, and governance restructuring are essential for sustainable inventory modernization in LMIC hospitals. JIT is most effective when selectively applied within a hybrid governance framework that balances operational efficiency with clinical risk mitigation.

H ow exactly are you going to meet our water use and water loss reduction goals over the next five years?"Elected officials and stakeholders didn't explicitly ask Austin Water about this during the utility's presentation of an updated water conservation plan and long-range supply plan, but it was implied.Coming out of a severe drought that lasted from 2008 to 2015, the City of Austin, Texas, had achieved significant water savings, which led to setting even higher goals for continued water efficiencies.When those water use and water loss reduction goals were not met by 2024, Austin Water needed to demonstrate all its activities underway to meet future targets.

This study examines the sectoral dynamics of natural gas consumption in Colombia by applying additive and multiplicative Holt–Winters exponential smoothing models. The analysis covers the main demand segments (Thermal Generation, Industrial, Residential, Refinery, Compressed Natural Gas for Vehicles (GNVC), Commercial, Petrochemical, and SNT Compressor Stations) using official monthly data from the Colombian Mercantile Exchange for the period April 2020 to July 2025. Model configurations were optimized by minimizing the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE) to identify the most appropriate structure for each sector. The results confirm that natural gas consumption in Colombia does not follow a uniform seasonal pattern. Instead, each segment exhibits distinct dynamics shaped by operational conditions, production schedules, mobility-related behavior, or logistical planning. The Thermal Generation sector was best represented by the multiplicative model, reflecting proportional variability associated with electricity dispatch and system-level operational changes. In contrast, the Industrial, Residential, GNVC, Commercial, and SNT Compressor Stations sectors showed superior performance under the additive model, consistent with relatively stable or constant-magnitude seasonal effects. The Petrochemical and Refinery sectors displayed short-term cyclical behavior, with model accuracy depending on the performance metric prioritized. These findings demonstrate that energy forecasting must incorporate the structural heterogeneity of demand systems rather than treating natural gas consumption as a homogeneous aggregate. Practically, the results provide insights for improving supply planning, contract allocation, and regulatory segmentation. The study also offers a replicable methodological basis for forecasting in emerging economies characterized by diverse consumption profiles.

Access to safe drinking water remains a critical challenge in coastal West African urban centers, particularly in resource-limited settings such as Fresco, Côte d&amp;apos;Ivoire. This study evaluates the potabilization potential of the Bolo and Niouniourou rivers to inform sustainable water supply strategies in hydrogeologically complex estuarine environments. Water samples were collected from 20 stations during the peak flood period (July 2025) and analyzed for 24 physico-chemical parameters. Results revealed contrasting hydrochemical patterns between the two rivers driven by differential hydrodynamic forcing. The Bolo River maintained a freshwater facies (mean conductivity: 1,141µS/cm; dissolved oxygen: 6.28mg/L) under fluvial dominance, where high flood discharge effectively repelled saltwater intrusion through hydraulic flushing mechanisms. Conversely, the Niouniourou River exhibited severe mineralization (conductivity: 3,308µS/cm; chlorides: 912mg/L), attributable to tidal inertia and saltwater trapping that persists despite elevated discharge during the monsoon season. Compliance assessment against WHO drinking water guidelines confirmed the Bolo River&amp;apos;s suitability for conventional treatment pathways, whereas the Niouniourou River&amp;apos;s chronic salinity burden renders it unsuitable for potabilization without prohibitively expensive desalination technologies. These findings underscore the fundamental importance of hydrodynamic forcing in governing coastal water resource quality and accessibility. The study demonstrates that site-specific hydrodynamic assessment is essential for evidence-based water supply planning in estuarine contexts.

Abstract Does information about the long-run financial costs of reduced labor supply increase mothers’ working hours? We document descriptively that long-term financial factors are not top of mind when mothers decide on their employment level. Moreover, a substantial share of women holds overly optimistic expectations about pension receipt and wage growth under part-time work. In a large-scale field experiment in Switzerland, we randomly assign mothers working part-time as teachers to receive objective information about the long-run costs of reduced labor supply. The treatment increases both demand for financial information and future labor supply plans, in particular among women who underestimate the costs of part-time work. Leveraging linked employer administrative data one year post-intervention, we find that this group of mothers increases working hours by 7 percent. These findings underscore that policies reducing information frictions in labor supply decisions may help address remaining gender gaps in the labor market.