Interest In Mining Research Articles

Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS.

The potential water quality impacts of hard-rock lithium mining: Insights from a legacy pegmatite mine in North Carolina, USA

The global green energy transition has spurred increased lithium exploration and extraction, yet the water quality impacts from lithium mining are understudied. This study investigates the potential water quality impacts from a legacy hard-rock lithium mine through comprehensive geochemical analyses of groundwater, surface waters, ore grade rocks, tailings, and waste rocks from a mine site in North Carolina, USA. The concentrations of regulated contaminants (e.g. As, Pb) in both groundwater and surface water emerging from the mine site were low, below drinking water and ecological standards. Yet Li (up to 46.8 mg/L), Rb (up to 169 μg/L), and Cs (up to 21 μg/L) were elevated relative to local background waters. Leaching experiments of the pegmatite ores, waste rocks, and tailing consistently demonstrate low mobilization of regulated contaminants and high leachability of Li, Rb, and Cs. Leaching experiments also reveal that water-rock interactions of the rocks and solid wastes from the mine site generate alkaline conditions, and that both phosphate and spodumene minerals are primary sources of Li and play a major role in formation of alkaline conditions during early stages of water-rock interactions. Over longer time scales, their direct impact on water quality is decreased. Given the global interest in hard-rock lithium mines, our findings highlight the potential occurrence of Li, Rb, and Cs in water resources adjacent to hard-rock lithium mines.

Negotiating the legitimacy of conservation at mining frontiers: Evidence from Madagascar and the DR Congo

Political ecologists are increasingly interested in the dynamics of power and authority where mining and conservation converge. We contribute to this emerging literature by applying an 'actor-oriented' approach to explore the creation of protected areas in African mining frontiers. Concretely, we look at how 'multiple-use' protected areas have been layered on top of mining frontiers in eastern DRC and northern Madagascar. Both of these mining frontiers include artisanal, semi-industrial, and industrial mining activities and permits. In the DRC case, various state and non-state armed actors are also implicated in mining. Our results show that the legitimacy of conservation at mining frontiers evolves as a process involving indiscrete phases of negotiation, coercion, resistance and cooperation. When authorities and alliances supporting mining interests preclude coercive conservation strategies, conservation authorities need to negotiate their way through different phases of resistance and cooperation. We find that in both cases, local actors living at the extraction and conservation nexus assert their agency and 'forum shop', by switching their allegiances between conservation and mining actors.

Interest HD: An Interest Frame Model for Recommendation Based on HD Image Generation.

This work is inspired by high-definition (HD) image generation techniques. When the user's interests are viewed as different frames of varying clarity, the unclear parts of one interest frame can be clarified by other interest frames. The user's overall HD interest portrait can be viewed as a fusion of multiple interest frames through detail compensation. Based on this inspiration, we propose a model for generating HD interest portrait called interest frame for recommendation (IF4Rec). First, we present a fine-grained pixel-level user interest mining method, Pixel embedding (PE) uses positional coding techniques to mine atomic-level interest pixel matrices in multiple dimensions, such as time, space, and frequency. Then, using an atomic-level interest pixel matrix, we propose Item2Frame to generate several interest frames for a user. The similarity score of each item is calculated to fill the multi-interest pixel clusters, through an improved self-attention mechanism. Finally, stimulated by HD image generation techniques, we initially present an interest frame noise compensation method. By utilizing the multihead attention mechanism, pixel-level optimization and noise complementation are performed between multi-interest frames, and an HD interest portrait is achieved. Experiments show that our model mines users' interests well. On five publicly available datasets, our model outperforms the baselines.

Metallic Nodules Create Oxygen in the Ocean’s Abyss

These nodules, a focus of seabed mining interests, could be natural “geobatteries” and play a larger-than-expected role in the deep-sea ecosystem.

Genomic Analysis of Aspergillus Section Terrei Reveals a High Potential in Secondary Metabolite Production and Plant Biomass Degradation.

Aspergillus terreus has attracted interest due to its application in industrial biotechnology, particularly for the production of itaconic acid and bioactive secondary metabolites. As related species also seem to possess a prosperous secondary metabolism, they are of high interest for genome mining and exploitation. Here, we present draft genome sequences for six species from Aspergillus section Terrei and one species from Aspergillus section Nidulantes. Whole-genome phylogeny confirmed that section Terrei is monophyletic. Genome analyses identified between 70 and 108 key secondary metabolism genes in each of the genomes of section Terrei, the highest rate found in the genus Aspergillus so far. The respective enzymes fall into 167 distinct families with most of them corresponding to potentially unique compounds or compound families. Moreover, 53% of the families were only found in a single species, which supports the suitability of species from section Terrei for further genome mining. Intriguingly, this analysis, combined with heterologous gene expression and metabolite identification, suggested that species from section Terrei use a strategy for UV protection different to other species from the genus Aspergillus. Section Terrei contains a complete plant polysaccharide degrading potential and an even higher cellulolytic potential than other Aspergilli, possibly facilitating additional applications for these species in biotechnology.

Inhibition of Monilinia fructicola sporulation and pathogenicity through eucalyptol-mediated targeting of MfCat2 by Streptomyces lincolnensis strain JCP1-7.

Peach brown rot, attributed to Monilinia fructicola, presents a significant threat to postharvest peach cultivation, causing losses of up to 80%. With an increasing number of countries, spearheaded by the European Union, imposing bans on chemical agents in fruit production, there is a growing interest in mining highly active antibacterial compounds from biological control strains for postharvest disease management. In this study, we highlight the unique ability of Streptomyces lincolnensis strain JCP1-7 to inhibit M. fructicola sporulation, despite its limited antimicrobial efficacy. Through GC-MS analysis, eucalyptol was identified as the key compound. Fumigation of diseased fruits with eucalyptol at a concentration of 0.0335 μg cm-3 demonstrated an invivo inhibition rate against M. fructicola of 93.13%, completely suppressing spore formation. Transcriptome analysis revealed the impact of eucalyptol on multiple pathogenesis-related pathways, particularly through the inhibition of catalase 2 (Cat2) expression. Experiments with a MfCat2 knockout strain (ΔMfCat2) showed reduced pathogenicity and sensitivity to JCP1-7 and eucalyptol, suggesting MfCat2 as a potential target of JCP1-7 and eucalyptol against M. fructicola. Our findings elucidate that eucalyptol produced by S. lincolnensis JCP1-7 inhibits M. fructicola sporulation by regulating MfCat2, thereby effectively reducing postharvest peach brown rot occurrence. The use of fumigation of eucalyptol offers insights into peach brown rot management on a large scale, thus making a significant contribution to agricultural research.

Utilizing environmental DNA and imaging to study the deep-sea fish community of Takuyo-Daigo Seamount

The increase in interest of mining at seamounts means there is a critical need to establish baseline inventories through environmental survey, with the aim of promoting the conservation and stewardship of these remote habitats. To efficiently evaluate fish biodiversity around a seamount, we compared environmental DNA (eDNA) methods using seawater and sponge samples against methods using imagery obtained with a remotely operated vehicle (ROV) and a free-fall deep-sea camera lander called the Edokko Mark I on the Takuyo-Daigo Seamount (153.0°E, 23.5°N) in the northwestern Pacific Ocean. We detected a total of 18 fish families by these methods. The fish fauna detected on the seamount included many families commonly found in deep-sea areas and were similar to the fish fauna of other seamounts located at similar latitudes in the northwestern Pacific. Significant differences in the patterns of detection of fish families between the eDNA and imaging methods is attributed to the differing powers of detection of some fish groups between methods (related to primer compatibility and fish size). For deep-sea fish, the difference in fish composition at the family level between seawater and sponge eDNA methods was not significant, but the difference between Edokko Mark I and ROV methods was significant; the latter difference is likely due to whether or not bait is used to attract fish. Although the eDNA workflow implemented here requires improvements, the use of eDNA and imaging methods in combination provided better insight into the biodiversity of deep-sea fishes in the deep-sea around a seamount, where our knowledge of the fish fauna has been extremely limited. Our recovery of eDNA from seawater and sponge samples around the seamount demonstrates the potential of these methods for facilitating environmental baseline surveys and impact assessments of mining activities to obtain results not previously possible with the use of visual methods only.

Rare metal concentration features at a caldera type deposit in the Miocene-Quaternary boron-lithium province of North America. Search for analogues

The fact that lithium has a wide range of applications in many fields including the production of lithium-ion batteries determines an increased interest in lithium mining. The most common types of lithium raw material sources are underground brines, saline lakes (“salars”), and ore minerals. In 2021, the first and unique deposit of lithium clays was discovered in the McDermitt caldera (Nevada, USA). Its resources are estimated at 13.7 million tons of lithium carbonate with the lithium concentration of 2231 mg/l. The uniqueness of this deposit raises the interest in the formation of model ideas about lithiumclay genesis to search for analogous deposits and explore them. The purpose of the article is to provide an overview of the geological structure and describe the main development periods of the McDermitt caldera. The authors also characterize the potential sources of lithium (felsic igneous rocks and hydrothermal fluids), migration paths of lithium-bearing brines as well as the formation mechanism of clays with a high lithium content (hectorite, illite and smectite). A generalized formation model of this type of deposits is proposed. Particular attention is paid to the role of hydrothermal fluids as a potential additional source of lithium “supply” to the caldera basin. Key criteria characteristic of industrial accumulations of lithium of this type have been formed in order to explore and identify analogous deposits. In conclusion, the authors put forward a hypothesis about the presence of deposits that are analogous to the Thacker Pass in the McDermitt caldera in the lithium province on the Altiplano-Puna plateau in one of the calderas of the Altiplano-Puna volcanic complex, and in Eastern Kamchatka.

Enhancing cross-market recommendations by addressing negative transfer and leveraging item co-occurrences

Real-world multinational e-commerce companies, such as Amazon and eBay, serve in multiple countries and regions. Some markets are data-scarce, while others are data-rich. In recent years, cross-market recommendation (CMR) has been proposed to bolster data-scarce markets by leveraging auxiliary information from data-rich markets. Previous CMR algorithms have employed techniques such as sharing market-agnostic parameters or incorporating inter-market similarity to optimize the performance of CMR. However, the existing approaches have several limitations: (1) They do not fully utilize the valuable information on item co-occurrences obtained from data-rich markets (such as the consistent purchase of mice and keyboards). (2) They ignore the issue of negative transfer stemming from disparities across diverse markets. To address these limitations, we introduce a novel attention-based model that exploits users’ historical behaviors to mine general patterns from item co-occurrences and designs market-specific embeddings to mitigate negative transfer. Specifically, we propose an attention-based user interest mining module to harness the potential of common items as bridges for mining general knowledge from item co-occurrence patterns through rich data derived from global markets. In order to mitigate the adverse effects of negative transfer, we decouple the item representations into market-specific embeddings and market-agnostic embeddings. The market-specific embeddings effectively model the inherent biases associated with different markets, while the market-agnostic embeddings learn generic representations of the items. Extensive experiments conducted on seven real-world datasets illustrate our model’s effectiveness.11Our codes and checkpoints are available at https://github.com/laowangzi/ACMR. Our model outperforms the suboptimal model by an average of 4.82%, 6.82%, 3.87%, and 5.34% across four variants of two metrics. Extensive experiments and analysis demonstrate the effectiveness of our proposed model in mining general item co-occurrence patterns and avoiding negative transfer for data-sparse markets.

Enjoy the silence: Analysis of stochastic Petri nets with silent transitions

Capturing stochastic behaviour in business and work processes is essential to quantitatively understand how nondeterminism is resolved when taking decisions within the process. This is of special interest in process mining, where event data tracking the actual execution of the process are related to process models, and can then provide insights on frequencies and probabilities. Variants of stochastic Petri nets provide a natural formal basis to represent stochastic behaviour and support different data-driven and model-driven analysis tasks in this spectrum. However, when capturing business processes, such nets inherently need a labelling that maps between transitions and activities. In many state of the art process mining techniques, this labelling is not 1-on-1, leading to unlabelled transitions and activities represented by multiple transitions. At the same time, they have to be analysed in a finite-trace semantics, matching the fact that each process execution consists of finitely many steps. These two aspects impede the direct application of existing techniques for stochastic Petri nets, calling for a novel characterisation that incorporates labels and silent transitions in a finite-trace semantics. In this article, we provide such a characterisation starting from generalised stochastic Petri nets and obtaining the framework of labelled stochastic processes (LSPs). On top of this framework, we introduce different key analysis tasks on the traces of LSPs and their probabilities. We show that all such analysis tasks can be solved analytically, in particular reducing them to a single method that combines automata-based techniques to single out the behaviour of interest within an LSP, with techniques based on absorbing Markov chains to reason on their probabilities. Finally, we demonstrate the significance of how our approach in the context of stochastic conformance checking, illustrating practical feasibility through a proof-of-concept implementation and its application to different datasets.

The Influence of Geological Potential and Investment Climate on Botswana’s Competitiveness

Objective: This article's objective is to evaluate Botswana's attractiveness to investors in mineral exploration. Botswana's competitiveness and ability to attract exploration and mining investment have declined in recent years. Method: Questionnaires were used as part of a mixed methodology to collect data that was subjected to empirical analysis. From a possible 231 respondents including exploration and mining companies, mining consultants, a research firm, and a mining association, a representative sample of 100 were chosen and given the survey through a stratified random sampling process. The data was analyzed using SPSS and both descriptive and inferential analyses were utilized. Result: This study finds that Botswana is only moderately successful in luring exploration investment. Botswana's ability to entice exploration investment is influenced by factors including the country's political stability, mining tax, mineral policy, license approval, quality of geological data, availability of skilled labor, adequate infrastructure, a streamlined licensing process, effective relevant institutions, and clear rules and regulations. Conclusion: The study recommends that Botswana regulators should consider improving mining tax incentives and continue removing bureaucratic requirements that delay the licensing process, as this may result in a more favorable mining environment that encourages both domestic and international mining interests.

Deep learning techniques in concrete powder mix designing

Abstract The water-cement ratio is multiple phases procedure in which we aim to determine the most optimal combination for producing high-performing concrete. In modern literature and business practice, there are various methods for designing concrete mixes, although the Three Equation Method-inspired procedures are by far the most widely used. Concrete compressive strength is one of the fundamental properties that determines its class. Foreseeable compressive strength concrete is necessary to promote the use of concrete structures. The primary feature of its durability and safety is. Deep learning has recently received a lot of attention, and the prospects for this technology are even brighter. Machine learning algorithms have advanced to the point that they can recognize patterns, which are difficult for humans to recognize. This has sparked interest in data mining on enormous datasets. In this research, we aim to utilize cutting-edge developments in machine learning techniques for the production of concrete mixes. To provide the ideal structure of a synthetic neural network that has been chosen, we compiled a comprehensive dataset of concrete mixtures, complete with laboratory destructive test results. A mathematical formula that may be used in practical applications has been developed from the creation of an artificial neural network.

A gene cluster encoding a nonribosomal peptide synthetase-like enzyme catalyzes γ-aromatic butenolides

Sea cucumber-derived fungi have attracted much attention due to their capacity to produce an incredible variety of secondary metabolites. Genome-wide information on Aspergillus micronesiensis H39 obtained using third-generation sequencing technology (PacBio-SMRT) showed that the strain contains nonribosomal peptide synthetase (NRPS)-like gene clusters, which aroused our interest in mining its secondary metabolites. 11 known compounds (1–11), including two γ-aromatic butenolides (γ-AB) and five cytochalasans, were isolated from A. micronesiensis H39. The structures of the compounds were determined by NMR and ESIMS, and comparison with those reported in the literature. From the perspective of biogenetic origins, the γ-butyrolactone core of compounds 1 and 2 was assembled by NRPS-like enzyme. All of the obtained compounds showed no inhibitory activity against drug-resistant bacteria and fungi, as well as compounds 1 and 2 had no anti-angiogenic activity against zebrafish.

Short and decadal impacts of seafloor physical perturbation on the abundances of Lebensspuren ‘traces of life’ in the Peru Basin manganese nodule province

Interest in deep-sea mining for polymetallic nodules as an alternative source to onshore mines for various high-technology metals has risen in recent years, as demands and costs have increased. The need for studies to assess its short- and long-term consequences on polymetallic nodule ecosystems is therefore also increasingly prescient. Recent image-based expedition studies have described the temporal impacts on epi-/megafauna seafloor communities across these ecosystems at particular points in time. However, these studies have failed to capture information on large infauna within the sediments or give information on potential transient and temporally limited users of these areas, such as mobile surface deposit feeders or fauna responding to bloom events or food fall depositions. This study uses data from the Peru Basin polymetallic nodule province, where the seafloor was previously disturbed with a plough harrow in 1989 and with an epibenthic sled (EBS) in 2015, to simulate two contrasting possible impact forms of mining disturbance. To try and address the shortfall on information on transient epifauna and infauna use of these various disturbed and undisturbed areas of nodule-rich seafloor, images collected 6 months after the 2015 disturbance event were inspected and all Lebensspuren, ‘traces of life’, were characterized by type (epi- or infauna tracemakers, as well as forming fauna species where possible), along with whether they occurred on undisturbed seafloor or regions disturbed in 1989 or 2015. The results show that epi- and endobenthic Lebensspuren were at least 50% less abundant across both the ploughed and EBS disturbed seafloors. This indicates that even 26 years after disturbance, sediment use by fauna may remain depressed across these areas.

Two new melanodrymiid snails from the East Pacific Rise indicate the potential role of inactive vents as evolutionary stepping-stones

Since their discovery in 1977, animals specialized to life in active deep-sea hydrothermal vents have been the focus of many studies. Inactive spires in the vent periphery, however, have received little attention. Recent shifts of deep-sea mining interests from active vents to inactive sulphide deposits have led to an urgent need to characterize the inactive vent fauna. Here, we report two new species of the vent-endemic genus Melanodrymia that are apparently specific to surfaces of inactive sulphides on the East Pacific Rise. Melanodrymia laurelin sp. nov. and M. telperion sp. nov. are easily distinguished from other congeners by shell shape and sculpture; their standings as distinct species are supported by molecular analyses using the mitochondrial cytochrome c oxidase subunit I (COI) gene. They were collected together with numerous specimens of M. galeronae, another species originally described some distance away from active vents. Phylogenetic reconstruction indicates Melanodrymia species living in active vents likely evolved from ancestors specializing in inactive spires, and in turn from sunken wood. Our findings add to the growing evidence that inactive sulphides host unique species, bolstering the need to better understand these systems before any anthropogenic exploitation takes place. https://zoobank.org/urn:lsid:zoobank.org:pub:D8E8EB3F-B246-4962-B70C-1CD94AEB134D

An Empirical Study on Personalized Product Recommendation Based on Cross-Border E-Commerce Customer Data Analysis

Thanks to the rapid growth of cross-border e-commerce platforms, numerous cross-border items are now available to customers. Several serious issues with cross-border e-commerce platforms related to item promotion and consumer product screening have arisen. Particular importance should be placed on studying and implementing personalized recommendation systems based on international e-commerce. In light of the quick expansion of commodities, when making individualized suggestions, traditional recommendation algorithms have had to deal with issues such as scant data, a chilly start to the market, and trouble identifying user preferences. To automatically mine the implicit and latent relationships between users and objects in recommendation systems, this study employs deep learning with nonlinear learning capabilities, which resolves the challenges of user interest mining. The weaknesses of the existing global recommendation research are emphasized, the study of conventional recommendation algorithms mixed with deep learning technology is deep factorization machine (DeepFM) and neural matrix factorization (NeuMF) models. Both models excel in recommending implicit feedback data. The DeepFM model yields the lowest loss function values, while the NeuMF model outperforms the competing models in terms of HR@20 (a commonly used indicator to measure the recall rate) and loss functions. In summary, this research addresses critical issues in cross-border e-commerce by developing personalized recommendation systems and integrating deep learning with traditional recommendation algorithms to enhance global recommendations.

Correcting Measurement Error in Regression Models with Variables Constructed from Aggregated Output of Data Mining Models

The burgeoning interest in data mining has catalyzed a proliferation of innovative techniques in extracting useful information from unstructured data sources, such as text and images in social sciences. One typical research design involves a two-stage process. In the first stage, researchers apply the classification algorithm to predict an individual-level categorical variable. In the second stage, the researchers aggregate the predicted values to construct a group-level variable for further regression analysis. For example, text classification has been applied to classify whether a review is positive or negative. The predicted review sentiment is aggregated at the product level as a focal independent variable in a regression model to examine the impact of the average review sentiment on product sales. Since the first-stage classification inevitably has errors, the aggregated variable may suffer from the measurement error in the regression analysis. Our study attempts to systematically investigate the theoretical properties of the estimation bias and introduce solutions rooted in theory to mitigate the issue of measurement error. We propose one exact solution and two approximated solutions based on the Central Limit Theorem (CLT) and the Law of Large Numbers (LLN), respectively. Our theoretical analysis and experimentation confirm that the consistency of regression estimators can be recovered across all examined scenarios and the approximated solutions offer a significantly reduced computational complexity compared to the exact solution. We also provide heuristic guidelines to choose one of three solutions.

The “CELL” initiative as an instrument for integrated care in Limburg (Flanders): a provincial living lab approach

Introduction: The primary care system in Flanders (Belgium) was substantially reoriented and restructured. At local level Primary Care Zones (PCZs) were installed to support better coordination and connect care and welfare organizations with local authorities. The province of Limburg counts 8 active PCZs and is one of the five provinces of Flanders. The province boasts an interesting mining history, forests and historic towns. Collaboration and exchange between primary care actors and knowledge institutions (universities of applied sciences and universities) offers opportunities to translate and implement evidence-based concepts and innovations in the sector. Vice versa, the sector's practical knowledge feeds scientific research and education in the knowledge institutions.
 Description: Limburg has a well-established tradition of collaboration between the healthcare and welfare sector and the knowledge institutions (Hasselt University and University Colleges PXL and UCLL). The PCZs and knowledge institutions aim to further strengthen this existing collaboration and embed it in the Health Campus Limburg (as a tangible meeting ground for primary care in Limburg). This creates a unique partnership between practice (healthcare & welfare), knowledge (research and education) and campus development (innovation ecosystem). The initiative - called 'CELL' was launched on October 5th of 2022 in Hasselt. A charter was signed by 17 Limburg organizations. In addition to 6 PCZs, the 3 knowledge institutions and Health Campus Limburg, the 2 Limburg hospital networks (South-West and North-East Limburg), the 3 mental healthcare networks (Reling, Noolim, Ligant), the palliative care network (Pallion) and the Logo Limburg (prevention network) also joined. The 3 knowledge institutes will collaborate intensively within the CELL initiative in the coming years to strengthen primary care in Limburg. CELL aims to facilitate integrated care at a regional level through innovation processes and projects. Limburg can act as a testing ground (Living Lab) for Flanders to test, monitor and implement innovations in the fields of population management, digitization, community-oriented care, etc.
 Discussion: The CELL initiative is embedded in the Hasselt University Fund and together with the partner organizations the first 2 years of funding is insured. At this point the governance structure is realized and the PCZs performed a needs assessment to establish their annual action plan. CELL will identify the main common denominators in order to accomplish joint initiatives. CELL will not operate parallel to the PZCs, but act as a supportive and academic umbrella. CELL is open to work with all organizations in- and outside the region, that share the same goals. It strives towards a good cooperation and exchange with the Flemish government and its organizations (including the Flemish Institute for Primary Care, VIVEL).
 Conclusion: The interaction between the PCZs themselves and the collaboration with knowledge institutions thus contribute to the further development of high-quality integrated care and welfare and offers opportunities to translate and implement new concepts and (digital) innovations in the sector. This directly benefits citizens and patients in the province of Limburg.

Municipal Solid Waste Fly Ash-Derived Zeolites as Adsorbents for the Recovery of Nutrients and Heavy Metals—A Review

Though fly ash from the incineration of municipal solid waste (MSW-FA) is considered hazardous waste, its huge and increasing volumes and potential value due to high concentrations of salts and heavy metals such as copper and zinc have attracted commercial interest in mining it for resources. The associated treatments used for extracting these resources may make it economically feasible to extract other constituents from the residuals, adding another potential pathway towards a zero-waste society. This review assesses the feasibility of using zeolites, synthesized from precursors extracted from MSW-FA, as sorbents for the recovery of nutrients (ammonium, nitrate, and phosphate) and heavy metals. It is possible to tailor the properties of the zeolites; however, the large variability in reported adsorption capacities and specificities, as well as the inherent heterogenic nature and variable composition and concentrations of most nutrient- or heavy metal-rich waste streams, make such tailoring challenging. A remaining important issue is the transfer of unwanted micropollutants from the MSW-FA or waste stream to the final products and the loss of surfactants from surfactant-modified zeolites during adsorption and/or desorption of nitrate and/or phosphate. Nonetheless, the recovery process is benefited by high concentrations of the target compounds and low concentrations of competing ions.