Two-step Model Research Articles

A two-step optimization model for virtual power plant participating in spot market based on energy storage power distribution considering comprehensive forecasting error of renewable energy output

As a complement to the medium and long-term market, the spot market plays an important role in maintaining the security and stability of the power grid. However, as spot trading is more proximate to the actual operation of the power system, the virtual power plant (VPP) is exposed to greater volatility in renewable energy output as well as market prices. To enhance its market competitiveness, this paper constructs a two-step optimization model for VPP participation in the spot market. Based on long-term transaction, the energy storage power is distributed based on the comprehensive forecasting error of renewable energy output. This makes the VPP obtain the maximum profit in the day-ahead market. The trading deviation penalty is reduced by adjusting the energy storage operation plan in the real-time market. The conditional value at risk is used to measure the risk of a trading strategy. The profit of VPP participation in the spot market is analyzed under different risk levels, thus providing a basis for decision makers with different risk appetites. The results show that: (1) The sensitivity of the growth rates of EtVaRαand EtCVaRα to risk level shows an opposite trend. And there is an excess of arbitrage power at low risk level. (2) Although the total cost decreases as the risk level increases, the actual profit in the spot market tends to increase and then decrease. The risk level of the VPP should be set at about 0.4 to make full use of energy storage and obtain the maximum market benefit. (3) The energy storage power can be distributed more accurately according to the forecasting error of renewable energy. This not only enhances its arbitrage ability, but also ensures its regulation ability, thus improving the overall benefit of VPP at different risk levels. (4) The sensitivity of the trend in the unit profit of traded quantity in the spot market to the trading proportion in the medium and long-term market is not monotonic.

Machine learning-based acoustic emission technique for corrosion-induced damage monitoring in reinforced concrete structures

Early-stage detection of corrosion in reinforcement embedded inside concrete, generally considered as the major problem in structures under extreme marine environments, can help to schedule timely maintenance/repair and to avoid any catastrophic failure. However, quantitative evaluation of the degree of deterioration, especially at a very early stage, i.e., before its appearance on the surface, is extremely difficult due to the complex process in heterogeneous material. This study aims to characterize the critical damage stages in reinforced concrete specimens under accelerated conditions using the Acoustic emission (AE) damage quantification methods supported by machine learning (ML) techniques. The stages of corrosion-induced damage are predicted by a two-step predictive model that involve both regression and classification tasks. Three supervised regression models — support vector regression (SVR), relevance vector machine (RVM) and kernel ridge regression (KRR) —are first adopted to evaluate the amount of current. Then the output from regression model is given as an input to the classification problem to predict the discrete class label output. Characterized damage stages are validated based on mass loss and crack width measurements. The results show that the corrosion initiation (at day 3) and crack formation (at day 7) can be determined from the sudden change in acoustic parameters. Further, a double layer (supervised-unsupervised) model is proposed which is found be more effective in correctly detecting the progression of damage. The findings indicate that the proposed approach that combines corrosion detection by analysing AE signals with ML technique can accurately and robustly predict corrosion severity.

Hierarchical machine learning models can identify stimuli of climate change misinformation on social media

Misinformation about climate change poses a substantial threat to societal well-being, prompting the urgent need for effective mitigation strategies. However, the rapid proliferation of online misinformation on social media platforms outpaces the ability of fact-checkers to debunk false claims. Automated detection of climate change misinformation offers a promising solution. In this study, we address this gap by developing a two-step hierarchical model. The Augmented Computer Assisted Recognition of Denial and Skepticism (CARDS) model is specifically designed for categorising climate claims on Twitter. Furthermore, we apply the Augmented CARDS model to five million climate-themed tweets over a six-month period in 2022. We find that over half of contrarian climate claims on Twitter involve attacks on climate actors. Spikes in climate contrarianism coincide with one of four stimuli: political events, natural events, contrarian influencers, or convinced influencers. Implications for automated responses to climate misinformation are discussed.

Pakistan’s path to universal health coverage: national and regional insights

BackgroundUniversal Health Coverage (UHC) is a common health policy objective outlined in the Sustainable Development Goals. With provincial governments taking the initiative, Pakistan has implemented and extended UHC program amid a complex public health landscape. In this context, we assess Pakistan’s progress toward achieving UHC at the national and subnational level.MethodsWe use data from the Demographic and Health Surveys and the Household Integrated Economic Survey to construct a UHC index at the national and subnational level for 2007, 2013, and 2018. Furthermore, we use Concentration Index (CI) and CI decomposition methodologies to assess the primary drivers of inequality in accessing medical services. Logistic regression and Sartori’s two-step model are applied to examine the key determinants of catastrophic health expenditure (CHE).ResultsOur analysis underscores Pakistan’s steady progress toward UHC, while revealing significant provincial disparities in UHC progress. Provinces with lower poverty rate achieve higher UHC index, which highlights the synergy of poverty alleviation and UHC expansion. Among the examined indicators, child immunization remains a key weakness that one third of the children are not fully vaccinated and one sixth of these not-fully-vaccinated children have never received any vaccination. Socioeconomic status emerges as a main contributor to disparities in accessing medical services, albeit with a declining trend over time. Household socioeconomic status is negatively correlated with CHE incidence, indicating that wealthier households are less susceptible to CHE. For individuals experiencing CHE, medicine expenditure takes the highest share of their health spending, registering a staggering 70% in 2018.ConclusionPakistan’s progress toward UHC aligns closely with its economic development trajectory and policy efforts in expanding UHC program. However, economic underdevelopment and provincial disparities persist as significant hurdles on Pakistan’s journey toward UHC. We suggest continued efforts in UHC program expansion with a focus on policy consistency and fiscal support, combined with targeted interventions to alleviate poverty in the underdeveloped provinces.

Photo Fenton-like oxidation of real textile wastewater: Operating conditions, kinetic modelling and cost analysis

The reusability of the pretreated textile wastewater was improved by using bismuth ferrite/activated carbon hybrid photocatalysts in photo-Fenton-like oxidation process. BiFeO3/WAC (WAC: walnut shell based activated carbon) and BiFeO3/CAC (CAC: Commercial activated carbon) catalysts were synthesized and used for the photocatalytic oxidation of in-situ pretreated textile wastewater. The influences of the mixing speed, oxidant dosage, light source, catalyst loading, and temperature were studied. The highest TOC removal efficiencies were evaluated as 56.3% and 76.2% in the presence of BiFeO3/WAC and BiFeO3/CAC, respectively. A kinetic study was performed to derive the kinetic models expressing the TOC removal as a basis for the semi-pilot and industrial scale studies. The kinetic data fit to a two-step second order reaction rate model in the presence of both of the catalysts. The activation energies of the first and the second steps were calculated as 36.0 and 16.0 kJ/mol, respectively, on BiFeO3/WAC catalyst while 43.4 and 13.3 kJ/mol of activation energies were calculated on BiFeO3/CAC.

Heatmap-Based Active Shape Model for Landmark Detection in Lumbar X-ray Images.

Medical staff inspect lumbar X-ray images to diagnose lumbar spine diseases, and the analysis process is currently automated using deep-learning techniques. The detection of landmarks is necessary in the automatic process of localizing the position and identifying the morphological features of the vertebrae. However, detection errors may occur owing to the noise and ambiguity of images, as well as individual variations in the shape of the lumbar vertebrae. This study proposes a method to improve the robustness of landmark detection results. This method assumes that landmarks are detected by a convolutional neural network-based two-step model consisting of Pose-Net and M-Net. The model generates a heatmap response to indicate the probable landmark positions. The proposed method then corrects the landmark positions using the heatmap response and active shape model, which employs statistical information on the landmark distribution. Experiments were conducted using 3600 lumbar X-ray images, and the results showed that the landmark detection error was reduced by the proposed method. The average value of maximum errors decreased by 5.58% after applying the proposed method, which combines the outstanding image analysis capabilities of deep learning with statistical shape constraints on landmark distribution. The proposed method could also be easily integrated with other techniques to increase the robustness of landmark detection results such as CoordConv layers and non-directional part affinity field. This resulted in a further enhancement in the landmark detection performance. These advantages can improve the reliability of automatic systems used to inspect lumbar X-ray images. This will benefit both patients and medical staff by reducing medical expenses and increasing diagnostic efficiency.

Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection

BackgroundThe government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space–time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022.MethodsSpatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level.ResultsMalaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010–2011, and with P. vivax incidence in the north and central regions during 2012–2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south.ConclusionsMalaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

Translational T-box riboswitches bind tRNA by modulating conformational flexibility

T-box riboswitches are noncoding RNA elements involved in genetic regulation of most Gram-positive bacteria. They regulate amino acid metabolism by assessing the aminoacylation status of tRNA, subsequently affecting the transcription or translation of downstream amino acid metabolism-related genes. Here we present single-molecule FRET studies of the Mycobacterium tuberculosis IleS T-box riboswitch, a paradigmatic translational T-box. Results support a two-step binding model, where the tRNA anticodon is recognized first, followed by interactions with the NCCA sequence. Furthermore, after anticodon recognition, tRNA can transiently dock into the discriminator domain even in the absence of the tRNA NCCA-discriminator interactions. Establishment of the NCCA-discriminator interactions significantly stabilizes the fully bound state. Collectively, the data suggest high conformational flexibility in translational T-box riboswitches; and supports a conformational selection model for NCCA recognition. These findings provide a kinetic framework to understand how specific RNA elements underpin the binding affinity and specificity required for gene regulation.

Molecular basis of product recognition during PIP5K-mediated production of PI(4,5)P2 with positive feedback

The ability for cells to localize and activate peripheral membrane-binding proteins is critical for signal transduction. Ubiquitously important in these signaling processes are phosphatidylinositol phosphate (PIP) lipids, which are dynamically phosphorylated by PIP lipid kinases on intracellular membranes. Functioning primarily at the plasma membrane, phosphatidylinositol-4-phosphate 5-kinases (PIP5K) catalyzes the phosphorylation of PI(4)P to generate most of the PI(4,5)P2 lipids found in eukaryotic plasma membranes. Recently, we determined that PIP5K displays a positive feedback loop based on membrane-mediated dimerization and cooperative binding to its product, PI(4,5)P2. Here, we examine how two motifs contribute to PI(4,5)P2 recognition to control membrane association and catalysis of PIP5K. Using a combination of single molecule TIRF microscopy and kinetic analysis of PI(4)P lipid phosphorylation, we map the sequence of steps that allow PIP5K to cooperatively engage PI(4,5)P2. We find that the specificity loop regulates the rate of PIP5K membrane association and helps orient the kinase to more effectively bind PI(4,5)P2 lipids. After correctly orienting on the membrane, PIP5K transitions to binding PI(4,5)P2 lipids near the active site through a motif previously referred to as the substrate or PIP-binding motif (PIPBM). The PIPBM has broad specificity for anionic lipids and serves a role in regulating membrane association invitro and invivo. Overall, our data supports a two-step membrane-binding model where the specificity loop and PIPBM act in concert to help PIP5K orient and productively engage anionic lipids to drive the positive feedback during PI(4,5)P2 production.

Opening the “Black box” of the labor market in Macedonia: Youth unemployment

The youth unemployment in Macedonia has been high and persistent for the last three decades, despite the many active labour policies for young persons. This paper examines the determinants of youth unemployment in the Macedonian labour market using quantitative and qualitative analysis. In order to empirically estimate the relevant determinants of youth unemployment in Macedonia we employ the two-step conditional mixed processes model (CMP) using the school to work transition survey (SWTS) by the International Labour Organization. The quantitative analysis is accompanied by qualitative analysis to further explore and explain the social-economic issues. The qualitative analysis includes semi-structured interviews and focus groups conducted in different regions of Macedonia. The empirical results indicate that gender, age, the wealth of the household, the education level and regional characteristics are the main determinants of youth unemployment in Macedonia. According to the findings of the qualitative analysis, the most of the young people pursue education and live with their families who provide them with financial support to cover their expenses. Consequently, the young persons have a diminished interest to search for a job and this attitude on long run could have a dubious effect and influence on the personal choices. Overall, there is a long school to work transition period, which discourages young people to actively participate in the labor market.

China’s foreign direct investment: driving green growth or seeking pollution havens? Based on the perspective of green total factor productivity

China has significantly increased its foreign direct investment (FDI). Although these investments help to boost global development, their impacts on the environment are still controversial and deserve careful investigation. This paper uses the super-efficiency SBM model to measure the green total factor productivity (GTFP) of 123 host countries of China’s FDI. On this basis, we adopt the two-step system GMM and dynamic panel threshold effect model to analyze the impact of China’s FDI on the GTFP of host countries. The research results show that: 1) The impact of China’s FDI on the GTFP of host countries is mainly through green technology efficiency. Its impact is inhibitory, followed by a promotional one, with the latter being significantly larger than the former. 2) There is no evidence of the “pollution haven effect” associated with China’s FDI. In fact, for countries with lower environmental regulations, China’s FDI promotes the growth of GTFP more effectively. 3) The innovation level of host countries moderates the relationship between China’s FDI and GTFP growth. For host countries with stronger innovation level, their GTFP growth can be enhanced to a greater extent by China’s FDI.

Modulation of H atom photoelectron momentum distribution by chirped laser pulses of different wavelengths

The wavelength dependence of the holographic interference structure in the photoelectron momentum distribution (PMD) of hydrogen atoms under a few-cycle chirped laser field is investigated using a two-dimensional (2D) semiclassical two-step (SCTS) model. Under the constraint of a fixed chirp parameter, we observe an amplification in the impact of the chirp parameter on the laser field waveform as the wavelength increases. This enhancement facilitates effective modulation of the ionization time window. As the wavelength reaches a certain threshold, the double-slit interference pattern undergoes an observable expansion towards the left. Furthermore, based on the variations in X-axis momentum (Px) concerning ionization time at different wavelengths and the corresponding distribution of classical electron trajectories at those wavelengths, we present a more comprehensive explanation for the expansion of interference patterns. This expansion can be attributed to changes in wavelength, which result in modifications to the laser field’s waveform, consequently leading to a significant acceleration of ionized electrons when exposed to chirped laser pulses. It is worth noting that by tuning the negative chirp parameter, the leftward expansion of the double-slit interference pattern can also be achieved, particularly when shorter wavelengths of chirped laser pulses are employed.

A thermal fluid dynamic model for the melt region during the laser powder bed fusion of polyamide 11 (PA11)

Studying the physical characteristics of the melt region in polymer-based laser powder bed fusion (PBF-LB/P) offers good comprehension that is essential for quality control, process optimization, understanding of material behavior, and preserving process consistency. Among the few numerical modelling investigations of PBF-LB/P, this study focuses on 3D multiphysics simulation of melt pool morphologies, beginning with powder bed preparation incorporating the actual particle size distribution using the discrete element method (DEM). A thermal fluid dynamic numerical model is then developed based on the finite volume method (FVM) using Flow-3D Weld to simulate PBF-LB while using polyamide 11 (PA11) as the primary material. The cooling after sintering plays a crucial role for the formation of the melt region due to the high viscosity of the polymer material effectively resulting in creeping flow. Here, we propose a model for both sintering as well as the subsequent cooling step, a two-step model capable of simulating the finished structure accurately with relatively low computational resources. The model's accuracy is verified through a mesh dependency analysis and its predictive power via validation against internal single-track experiments providing dimension data on the melt region width and depth. The simulation results allow for a detailed exploration of the effects of laser settings on melt region morphologies and the occurrence of defects. The model's scope is further extended to multi-track simulation, revealing the connection between surface roughness and laser settings. In general, this study provides significant contributions to the comprehension of PBF-LB/P methodologies which in turn can be used for further process improvement.

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data

Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils absorbed by plants is a nonlinear process, traditional multiple linear regression (MLR) models failed to provide accurate predictions. Zinc (Zn) was chosen as the objective element in this case. Using soil geochemical data, artificial neural networks (ANN) were utilized to develop predictive models that accurately estimated Zn content within wheat grains. A total of 4036 topsoil samples and 73 paired rhizosphere soil-wheat samples were collected for the simulation study. Through Pearson correlation analysis, the total content of elements (TCEs) of Fe, Mn, Zn, and P, as well as the available content of elements (ACEs) of B, Mo, N, and Fe, were significantly correlated with the Zn bioaccumulation factor (BAF). Upon comparison, ANN models outperformed MLR models in terms of prediction accuracy. Notably, the predictive performance using ACEs as input factors was better than that using TCEs. To improve the accuracy, a two-step model was established through multiple testing. Firstly, ACEs in the soil were predicted using TCEs and properties of the rhizosphere soil as input factors. Secondly, the Zn BAF in grains was predicted using ACE as input factors. Consequently, the content of Zn in wheat grains corresponding to 4036 topsoil samples was predicted. Results showed that 85.69 % of the land was suitable for cultivating Zn-rich wheat. This finding offers a more accurate method to predict the uptake of trace elements from soils to grains, which helps to warn about abnormal levels in grains and prevent potential health risks.

Cardboard packaging minimization: Autonomous box selection by RGB-depth vision system and complementary deep learning classification

Amidst the dramatic expansion of online shopping, wasted cardboard packaging has become a significant challenge in modern-day society. In this paper, we propose an innovative approach to address the issue of box packaging waste by efficiently selecting an optimal cardboard box for a set of items. Employing depth vision and deep learning classification techniques, the proposed system analyzes depth images to compute an optimal cuboid for unpackaged items while accounting for fragility characteristics. The system, facilitated by two Intel RealSense RGB-D cameras, first captures the overhead and sideview images to determine the dimensions of an unknown object. Then, the developed vision system utilizes averaging and morphological opening filters to denoise the acquired images, calculates the difference between the background image and object image, and employs thresholding by Otsu’s algorithm to find the minimum cuboid of the irregular object. After, a two-step deep learning model is used to determine the general category and characteristics of an object, considering factors, such as fragility, that may necessitate wrapping or separate packaging. Finally, a modified largest area first fit algorithm uses the outputted dimensions for irregular-shaped objects and packaged objects to determine the optimal fitting box from the standard Amazon box catalog. The computer simulation shows that the proposed method can be implemented in a cardboard packaging warehouse for optimal box selection, effectively minimizing cardboard waste.

Nurturing Peer Review Proficiency in the Curriculum for Early Career Medical Researchers: Fostering Academic Connectivity

Abstract Peer review is being criticized for its sluggishness and review quality. Swiftly identifying available reviewers and cultivating a pool of specialized experts is crucial to avoid delays and ensure a seamless peer review process. A promising strategy to enhance the quality of peer reviewers is to encourage and facilitate early career researchers (ECRs) to engage in co-reviewing with their mentors as part of their curriculum training. In addition, it holds significant potential to bridge gaps in research quality and discourage unethical ghostwriting of peer review reports. Encouraging co-reviewing practices in medical, dental, nursing, and other professional institutions will foster the development of critical skills, research awareness, mentorship, and collaborative abilities among ECRs. This paper proposes learning peer review modules in a two-step model, i.e., foundational learning and practical learning. Emphasizing peer review within the curriculum can be a significant and transformative step.

Estimating Risk Factor Time Paths Among People with Type 2 Diabetes and QALY Gains from Risk Factor Management.

Most type 2 diabetes simulation models utilise equations mapping out lifetime trajectories of risk factors [e.g. glycated haemoglobin (HbA1c)]. Existing equations, using historic data or assuming constant risk factors, frequently underestimate or overestimate complication rates. Updated risk factor time path equations are needed for simulation models to more accurately predict complication rates. (1) Update United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2) risk factor time path equations; (2) compare quality-adjusted life-years (QALYs) using original and updated equations; and (3) compare QALY gains for reference case simulations using different risk factor equations. Using pooled contemporary data from two randomised trials EXSCEL and TECOS (n = 28,608), we estimated: dynamic panel models of seven continuous risk factors (high-density lipoprotein cholesterol, low density lipoprotein cholesterol, HbA1c, haemoglobin, heart rate, blood pressure and body mass index); two-step models of estimated glomerular filtration rate; and survival analyses of peripheral arterial disease, atrial fibrillation and albuminuria. UKPDS-OM2-derived lifetime QALYs were extrapolated over 70 years using historical and the new risk factor equations. All new risk factor equation predictions were within 95% confidence intervals of observed values, displaying good agreement between observed and estimated values. Historical risk factor time path equations predicted trial participants would accrue 9.84 QALYs, increasing to 10.98 QALYs using contemporary equations. Incorporating updated risk factor time path equations into diabetes simulation models could give more accurate predictions of long-term health, costs, QALYs and cost-effectiveness estimates, as well as a more precise understanding of the impact of diabetes on patients' health, expenditure and quality of life. ClinicalTrials.gov NCT01144338 and NCT00790205.

Development of tuberculosis treatment decision algorithms in children below 5 years hospitalised with severe acute malnutrition in Zambia and Uganda: a prospective diagnostic cohort study

In children with severe acute malnutrition (SAM) tuberculosis is common, challenging to diagnose, and often fatal. We developed tuberculosis treatment decision algorithms (TDAs) for children under the age of 5 years with SAM. In this prospective diagnostic study, we enrolled and followed up children aged <60 months hospitalised with SAM at three tertiary hospitals in Zambia and Uganda from 4 November 2019 to 20 June 2022. We included children aged 2-59 months with SAM as defined by WHO and hospitalised following the WHO clinical criteria. We excluded children with current or history of antituberculosis treatment within the preceding 3 months. They underwent tuberculosis symptom screening, clinical assessment, chest X-ray, abdominal ultrasound, Xpert MTB/RIF Ultra (Ultra) and culture on respiratory and stool samples with 6 months follow-up. Tuberculosis was retrospectively defined using the 2015 standard case definition for childhood tuberculosis. We used logistic regression to develop diagnostic prediction models for a one-step diagnosis and a two-step screening and diagnostic approaches. We derived scores from models using WHO-recommended thresholds for sensitivity and proposed TDAs. This study is registered with ClinicalTrials.gov, NCT04240990. Of 1906 children hospitalised with SAM during the study period, 1230 were screened, 1152 were eligible and 603 were enrolled. Of the 603 children enrolled-median age 15 (inter-quartile range (IQR): 11-20) months and 65 (11.0%) living with HIV-114 (18.9%) were diagnosed with tuberculosis, including 51 (8.5%) with microbiological confirmation and 104 (17.2%) initiated treatment at a median of 6(IQR: 2-10) days after inclusion. 108 children were retrospectively classified as having tuberculosis resulting in a prevalence of 17.9% (95% confidence intervals (CI): 15.1; 21.2). 75 (69.4%) children with tuberculosis reported cough of any duration, 32 (29.6%) cough ≥2 weeks and 11 (10.2%) tuberculosis contact history. 535 children had complete data and were included in the diagnostic prediction model. The one-step diagnostic model had 15 predictors, including Ultra, clinical, radiographic, and abdominal features, an area under the receiving operating curve (AUROC) of 0.910, and derived TDA sensitivity of 86.14% (95% CI: 78.07-91.56) and specificity of 80.88% (95% CI: 76.91-84.30). The two-step model had AUROCs of 0.750 and 0.912 for screening and diagnosis, respectively, and derived combined TDA sensitivity of 79.21% (95% CI: 70.30-85.98) and a specificity of 83.64% (95% CI: 79.87-86.82). Tuberculosis prevalence was high among hospitalised children with SAM, with atypical clinical features. TDAs achieved satisfactory diagnostic accuracy and could be used to improve diagnosis in this vulnerable group. Unitaid.

Real-time ozonation monitoring and control in wastewater tertiary treatment: An ultraviolet-visible monitoring approach

To address the challenge of refractory organics removal in wastewater tertiary treatment, this study investigates the application of ozonation, coupled with Ultraviolet-Visible (UV–Vis) spectroscopy, for enhanced tertiary treatment in a wastewater treatment plant (WWTP) in Qingdao, China. Our approach integrates ozonation with real-time UV–Vis spectral monitoring to precisely control ozone dosing, targeting the effective degradation of persistent organics characterized by high Chemical Oxygen Demand (COD) values. Through the deployment of mathematical models, including Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), we developed a robust two-step ozone dosing control model. The model's efficacy is validated by its ability to predict the need for ozonation with a high degree of accuracy (94.29 % for LDA), significantly optimizing the ozonation process. Key findings indicate that this method not only achieves substantial removal of recalcitrant organics (with COD removal rates varying between 16 % and 71 %) but also offers considerable energy savings and operational cost reductions by adjusting ozone dosing in real-time based on water quality data. This study presents a novel, sustainable approach to improving WWTP efficiency and compliance, showcasing the potential of integrating advanced oxidation processes with precision monitoring techniques for environmental protection and water reuse initiatives.

A scalable optimization approach to the intervention planning of complex interconnected infrastructures

The functioning of infrastructure networks is vital for modern communities. Maintenance should be planned to ensure infrastructure's functionality and safety at the lowest cost. Interconnected infrastructure networks can affect each other's functionality, and maintenance on one network can impact the serviceability of others. Planned intervention grouping across infrastructures reduces set-up costs and service interruption, improving infrastructure availability and serviceability at lower costs.Finding the best grouping strategy is a known NP-hard problem, with several optimization strategies have been proposed, mainly based on nonlinear models which are computationally expensive and do not guarantee scalability. Furthermore, infrastructure intervention planning models mostly focus on grouping of interventions which are considered as given. In this paper, we propose a new efficient optimization model to optimize intervention grouping for interconnected infrastructure networks. We develop a scalable two-step optimization model where we first plan each individual intervention type based on a preventive maintenance policy accounting for the degradation behavior of objects, then group interventions to minimize the net costs, considering dependencies within and accross infrastructure networks.We formulate the grouping problem as an Integer Linear Program, which can be solved exactly with standard solvers. The model accounts for interactions between infrastructure networks and considers the impact on all stakeholders. It also accommodates various intervention types like maintenance, removal, and upgrading.Using a demonstrative application, we show that our model significantly reduces net costs and outperforms alternative nonlinear formulations and related heuristics in terms of both solution quality and computation performance. Additionally, the optimal intervention plan shows repetitive patterns, which suggests that a rolling horizon strategy could be used where the optimization problem is solved for shorter time horizons, leading to significant computational benefits.