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This article proposes a novel methodology for estimating metallurgical copper recovery, a critical feature in mining project evaluations. The complexity of modeling this nonadditive variable using geostatistical methods due to low sampling density, strong heterotopic relationships with other measurements, and nonlinearity is highlighted. As an alternative, a copula-based conditional quantile regression method is proposed, which does not rely on linearity or additivity assumptions and can fit any statistical distribution. The proposed methodology was evaluated using geochemical log data and metallurgical testing from a simulated block model of a porphyry copper deposit. A highly heterotopic sample was prepared for copper recovery, sampled at 10% with respect to other variables. A copula-based nonparametric dependence model was constructed from the sample data using a kernel smoothing method, followed by the application of a conditional quantile regression for the estimation of copper recovery with chalcocite content as secondary variable, which turned out to be the most related. The accuracy of the method was evaluated using the remaining 90% of the data not included in the model. The new methodology was compared to cokriging placed under the same conditions, using performance metrics RMSE, MAE, MAPE, and R2. The results show that the proposed methodology reproduces the spatial variability of the secondary variable without the need for a variogram model and improves all evaluation metrics compared to the geostatistical method. [ABSTRACT FROM AUTHOR]

Lip seals are components subjected to high mechanical stress and they are responsible for many out-of-service in pneumatic cylinders. The aim of this work is the development of an experimental methodology to evaluate lifetime duration and analyse failures and damages of rod lip seals for pneumatic cylinders. A dedicated test bench was designed and manufactured which reproduces actual working conditions of the seals i.e., compressed air action (seal pressurisation) and relative linear reciprocating motion. Three types of seals made of two elastomers (NBR and polyurethane) were tested; dry condition was considered to speed up the tests. The influence of geometric parameters like seal seat dimension and seal axis misalignment with respect to the rod axis, was analysed by multiple experimental tests. Results in terms of seal life duration and failure modes are presented which allow comparison of seals performance and provide a helpful tool to end-users in a proper selection of seals geometry, material and key working parameters. [ABSTRACT FROM AUTHOR]

Among the amazing properties of graphene, superlubricity is one of the most promising properties. This property can be used in industrial field components to reduce friction without using liquid lubricants, and therefore, improve machines' efficiency and reliability with low environmental impact thanks to the elimination of oil or grease lubricants. In this paper, copper alloy samples for electrical purposes were coated with graphene by four different deposition processes. The investigated synthesis processes are direct grown graphene on bulk Cu, transferred graphene, and self-assembled graphene from graphene flakes. Ball-on-disk tests were performed to evaluate the tribological performance of samples. The aim was to compare the effect on the tribological performance given by different types of coatings, taking also into consideration industrial scalability. Interestingly, not all graphene nano-coatings being compared proved effective in reducing friction and wear in gross sliding conditions. The results show that the cost-effective self-assembled graphene is the longer-lasting nano-coating among those investigated in this work, and can reduce both friction and wear. Tests revealed that graphene coatings can be applied as a solid lubricant, reducing friction up to 78%, and reducing the average wear volume up to 40%. [ABSTRACT FROM AUTHOR]

Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research. [ABSTRACT FROM AUTHOR]

The definition of geostatistical domains is a stage in the estimation of mineral resources, in which a sample resulting from a mining exploration process is divided into zones that show homogeneity or minimal variation in the main element of interest or mineral grade, having geological and spatial meaning. Its importance lies in the fact that the quality of the estimation techniques, and therefore, the correct quantification of the mineral resource, will improve in geostatistically stationary areas. The present study seeks to define geostatistical domains of estimation for a mineral grade, using a non-traditional approach based on the k-prototype clustering algorithm. This algorithm is based on the k-means paradigm of unsupervised machine learning, but it is exempt from the one-time restriction on numeric data. The latter is especially convenient, as it allows the incorporation of categorical variables such as geological attributes in the grouping. The case study corresponds to a hydrothermal gold deposit of high sulfidation, located in the southern zone of Peru, where estimation domains are defined from a historical record of data recovered from 131 diamond drill holes and 37 trenches. The characteristics directly involved were the gold grade (Au), silver grade (Ag), type of hydrothermal alteration, and type of mineralization. The results obtained showed that clustering with k-prototypes is an efficient approach and can be used as an alternative or complement to the traditional methodology. [ABSTRACT FROM AUTHOR]

A general method for the synthesis of pyrrolizidine derivatives using an intramolecular hydroaminomethylation protocol (HAM) under microwave (MW) dielectric heating is reported. Starting from a 3,4-bis(benzyloxy)-2-[(benzyloxy)methyl]-5-vinylpyrrolidine, MW-assisted intramolecular HAM in the presence of gaseous H2 and CO gave the natural alkaloid hyacinthacine A2 protected as benzyl ether. The same approach gave a lentiginosine analogue starting from the corresponding vinyl N-hydroxypyrrolidine. The nature of the reaction products and the yields were strongly influenced by the relative stereochemistry of the starting pyrrolidines, as well as by the catalyst/ligand employed. The use of ethanol as a solvent provides environmentally friendly conditions, while the ligand/catalyst system can be recovered by separating the alkaloid product with an SCX column and recycling the ethanolic solution. HAM worked up to three times with the recycled catalyst solution without any significant impact on yield. [ABSTRACT FROM AUTHOR]

GM1 gangliosidosis is a rare lysosomal disease caused by the deficiency of the enzyme β-galactosidase (β-Gal; GLB1; E.C. 3.2.1.23), responsible for the hydrolysis of terminal β-galactosyl residues from GM1 ganglioside, glycoproteins, and glycosaminoglycans, such as keratan-sulfate. With the aim of identifying new pharmacological chaperones for GM1 gangliosidosis, the synthesis of five new trihydroxypiperidine iminosugars is reported in this work. The target compounds feature a pentyl alkyl chain in different positions of the piperidine ring and different absolute configurations of the alkyl chain at C-2 and the hydroxy group at C-3. The organometallic addition of a Grignard reagent onto a carbohydrate-derived nitrone in the presence or absence of a suitable Lewis Acid was exploited, providing structural diversity at C-2, followed by the ring-closure reductive amination step. An oxidation-reduction process allowed access to a different configuration at C-3. The N-pentyl trihydroxypiperidine iminosugar was also synthesized for the purpose of comparison. The biological evaluation of the newly synthesized compounds was performed on leucocyte extracts from healthy donors and identified two suitable β-Gal inhibitors, namely compounds 10 and 12. Among these, compound 12 showed chaperoning properties since it enhanced β-Gal activity by 40% when tested on GM1 patients bearing the p.Ile51Asn/p.Arg201His mutations. [ABSTRACT FROM AUTHOR]

The exponential growth of digitalisation and the continuous increase in sustainability needs are currently reshaping the European manufacturing industry through its entire value chain. Industrial sectors have undergone significant changes globally in recent years, and they will continue to face this deep transformation. The manufacturing sectors, more specifically, companies, need to develop a relevant strategy that can support their organisation to handle the upcoming future technological developments and sustainability requirements properly. In order to implement the strategy effectively and achieve an adequate digital and green transformation, their main focus should be the development of a multi-skilled workforce. This competent workforce can only be built by foreseeing the changes in the needed skills for the manufacturing industry and then updating the skills of the current workforce accordingly. As an answer to this need, we developed an automated skill database for the manufacturing industry, particularly transversal occupations of this sector related to the industrial symbiosis (IS) and energy efficiency (EE). Differently from the conventional ones, the generated database incorporated not only the current but also the future skill needs for each profile. During the development of the future skills for each occupation in the database, we identified the foreseen skill needs for the manufacturing industry through detailed desk research. Therefore, this paper presents a valuable perspective on the subject. Our work aimed to fill the gap for a database specifically developed for the manufacturing industry, which provides the end-users with data about the new skills requirements resulting from industrial changes and sustainability needs. We believe that companies, education and training institutions and policymakers can make use of the generated database as a complementary tool for developing their training programmes or strategy roadmaps to cover the emerging changes in each individual industrial sector. [ABSTRACT FROM AUTHOR]

The construction sector has always occupied a strategic place in the European economy. The European construction industry suffered during the 2007–2008 global financial crisis, and today the sector is undergoing a recovery process. Among all the construction subsectors, civil engineering has the highest growth rate. Currently, the sector has to face profound industrial changes emerging with digital transformations (Industry 4.0), sustainability, climate change and energy efficiency. To promote the growth of the civil engineering sector and accelerate the recovery, we need to create a highly qualified and competent workforce that can handle the challenges coming up with the technological progress and global competitiveness. The main condition to achieve this capable workforce is to define the expected evolution of skills requirements. For that purpose, our work focuses on identifying current and near-future key skills required by the civil engineering occupations. To achieve this, we developed an automated sectoral database for the current and near-future skills requirements of the selected professional profiles. It is our belief that this sectoral database is a fundamental framework that will guide the sector through the future changes. We also believe that our research can be used as a key tool for construction companies, policy-makers, academics and training centers to develop well-designed and efficient training programs for upskilling and reskilling the workforce. [ABSTRACT FROM AUTHOR]

Thanks to the digitalization of industry, maintenance is a trending topic. The amount of data available for analyses and optimizations in this field has increased considerably. In addition, there are more and more complex systems to maintain, and to keep all these devices in proper conditions, which requires maintenance management to gain efficiency and effectiveness. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, but often these programs are complex to manage and understand. The problem becomes more complex when equipment is analyzed in the context of a plant, where equipment can be more or less saturated, critical regarding quality, etc. Thus, this paper focuses on CBM optimization of a full industrial chain, with the objective of determining its optimal values of preventive intervention limits for equipment under economic criteria. It develops a mathematical plus discrete-event-simulation based model that takes the evolution in quality and production speed into consideration as well as condition based, corrective and preventive maintenance. The optimization process is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case, where the data gathered by the IoT (Internet of Things) devices at edge level can detect when some premises of the CBM model are no longer valid and request a new simulation. The simulation performed in a centralized way can thus obtain new optimal values who fit better to the actual system than the existing ones. Finally, these new optimal values can be transferred to the model whenever it is necessary. The approach developed has raised the interest of a partner of the Deusto Digital Industry Chair. [ABSTRACT FROM AUTHOR]

Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Nowadays, its importance is increasing thanks to the "Industry 4.0" or "fourth industrial revolution". There are more and more complex systems to maintain, and maintenance management must gain efficiency and effectiveness in order to keep all these devices in proper conditions. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. For this reason, several research papers propose approaches that are as simple as possible and can be understood by users and modified by experts. In this context, this paper focuses on CBM optimization in an industrial environment, with the objective of determining the optimal values of preventive intervention limits for equipment under corrective and preventive maintenance cost criteria. In this work, a cost-benefit mathematical model is developed. It considers the evolution in quality and production speed, along with condition based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case. [ABSTRACT FROM AUTHOR]

According to EU guidance SANCO/7525/VI/95 Rev. 10.3, residue data extrapolation from a surrogate major crop to a minor crop can be used for setting maximum residue levels (MRLs) with a reduced number of residue trials and representative selected pesticides. In this work, a QuEChERS method (citrate-buffered version and PSA with MgSO4 clean-up) and LC-ESI-MS/MS for the determination of boscalid, pyraclostrobin, fludioxonil, fluopyram and tebuconazole in persimmon was developed and validated according to EU Commission guidelines and afterwards used for the determination of residues in four field trials. Residue levels at harvest for each pesticide ranged between 0.347 and 0.028 mg/kg. After comparing EFSA residue data on apples, as the surrogate major crop, and conducting a consumer risk assessment, a proposal of residue data extrapolation to set MRLs in persimmons was performed. The results showed that pesticide residues in persimmons at harvest were consistently lower than residues in apples when substances were applied according to the same critical GAP. MRLs were set at 0.5 mg/kg for fludioxonil, 0.6 mg/kg for boscalid, 0.3 mg/kg for tebuconazole, 0.4 mg/kg for fluopyran and 0.3 mg/kg for pyraclostrobin. The ratio of the MRLs for apple/persimmon varied between 2.5 for boscalid and 1.25 for fluopyram, suggesting that residue extrapolation can be feasible, promoting the process of pesticide registration for minor crops and the settlement of MRL. [ABSTRACT FROM AUTHOR]

Among carbohydrate-processing enzymes, Jack bean α-mannosidase (JBα-man) is the glycosidase with the best responsiveness to the multivalent presentation of iminosugar inhitopes. We report, in this work, the preparation of water dispersible gold nanoparticles simultaneously coated with the iminosugar deoxynojirimycin (DNJ) inhitope and simple monosaccharides (β-d-gluco- or α-d-mannosides). The display of DNJ at the gold surface has been modulated (i) by using an amphiphilic linker longer than the aliphatic chain used for the monosaccharides and (ii) by presenting the inhitope, not only in monomeric form, but also in a trimeric fashion through combination of a dendron approach with glyconanotechnology. The latter strategy resulted in a strong enhancement of the inhibitory activity towards JBα-man, with a Ki in the nanomolar range (Ki = 84 nM), i.e., more than three orders of magnitude higher than the monovalent reference compound. [ABSTRACT FROM AUTHOR]

This paper presents the design of a multi-objective tool for sizing shell and tube heat exchangers (STHX), developed under a University/Industry collaboration. This work aims to show the feasibility of implementing artificial intelligence tools during the design of Heat Exchangers in industry. The design of STHX optimisation tools using artificial intelligence algorithms is a visited topic in the literature, nevertheless, the degree of implementation of this concept is uncommon in industrial companies. Thus, the challenge of this research consists of the development of a tool for the design of STHX using artificial intelligence algorithms that can be used by industrial companies. The approach is implemented using a simulated dataset contrasted with ARA TT, the company taking part in the project. The given dataset to develop a theoretical STHX calculator was modeled using MATLAB. This dataset was used to train seven neural networks (NNs). Three of them were mono-objective, one per objective to predict, and four were multi-objective. The last multi-objective NN was used to develop an inverse neural network (INN), which is used to find the optimal configuration of the STHXs. In this specific case, three design parameters, the pressure drop on the shell side, the pressure drop on the tube side and heat transfer rate, were jointly and successfully optimised. As a conclusion, this work proves that the developed tool is valid in both terms of effectiveness and user-friendliness for companies like ARA TT to improve their business activity. [ABSTRACT FROM AUTHOR]

The growth of the renewable energy industry is happening at a swift pace pushed, by the emergence of Industry 4.0. Smart technologies like artificial intelligence (AI), Big Data, the Internet of Things (IoT), Digital Twin (DT), etc. enable companies within the sector of renewable energies to drastically improve their operations. In this sectoral context, where upgraded sustainability standards also play a vital role, it is necessary to fulfil the human capital requirements of the imminent technological advances. This article aims to determine the current skills of the renewable energy industry workforce and to predict the upcoming skill requirements linked to a digital transition by creating a unified database that contains both types of skills. This will serve as a tool for renewable energy businesses, education centers, and policymakers to plan the training itinerary necessary to close the skills gap, as part of the sectoral strategy to achieve a competent future workforce. [ABSTRACT FROM AUTHOR]

The aim of this paper is to characterize the mechanical behavior of corrugated cardboard boxes using simple models that allow an approach to the load capacity and the deformation of the boxes. This is very interesting during a box design stage, in which the box does not exist yet. On the one hand, a mathematical model of strength and deformation of boxes with different geometry is obtained from experiments according to the Box Compression Test and Edge Crush Test standards. On the second hand, a finite element simulation is proposed in which only the material elastic modulus in the compression direction is needed. For that, corrugated cardboard sheets are glued to build billets for testing, and an equivalent elastic modulus is obtained. This idea arises from the fact that the collapse of the box is given by the local bucking of the corrugated cardboard panels, due to the slenderness itself, and the properties in the compression direction are predominant. As a result, the numerical models show satisfactory agreement with experiments, concluding that it is an adequate methodology to simulate in a simple and efficient way this type of boxes built with corrugated cardboard. [ABSTRACT FROM AUTHOR]

The machine tool industry, which is the starting point of all the metal producing activities, is presently undergoing rapid and continuous changes as a result of the fourth industrial revolution Industry 4.0. Manufacturing models are profoundly transforming with emerging digitalization. Smart technologies like artificial intelligence (AI), big data, the Internet of Things (IoT), digital twin, allow the machine tool companies to optimize processes, increase efficiency and reduce waste through a new phase of automation. These technologies, as well, enable the machine tool producers to reach the aim of creating products with improved performance, extended life, high reliability that are eco-efficient. Therefore, Industry 4.0 could be perceived as an invaluable opportunity for the machine tool sector, only if the sector has a competent workforce capable of handling the implementation of new business models and technological developments. The main condition to create this highly qualified workforce is reskilling and upskilling of the current workforce. Once we define the expected evolution of skills requirements, we can clarify the skills mismatch between the workers and job profiles. Only then, we can reduce them by delivering well-developed trainings. For this purpose, this article identifies the current and foreseen skills requirements demanded by the machine tool industry workforce. To this end, we generated an integrated database for the sector with the present and prospective skills needs of the metal processing sector professionals. The presented sectoral database is a fundamental structure that will make the sector acquire targeted industrial reforms. It can also be an essential instrument for machine tool companies, policymakers, academics and education or training centers to build well-designed and effective training programs to enhance the skills of the labor force. [ABSTRACT FROM AUTHOR]

Pharmacological chaperones (PCs) are small compounds able to rescue the activity of mutated lysosomal enzymes when used at subinhibitory concentrations. Nitrogen-containing glycomimetics such as aza- or iminosugars are known to behave as PCs for lysosomal storage disorders (LSDs). As part of our research into lysosomal sphingolipidoses inhibitors and looking in particular for new β-galactosidase inhibitors, we report the synthesis of a series of alkylated azasugars with a relative "all-cis" configuration at the hydroxy/amine-substituted stereocenters. The novel compounds were synthesized from a common carbohydrate-derived piperidinone intermediate 8, through reductive amination or alkylation of the derived alcohol. In addition, the reaction of ketone 8 with several lithium acetylides allowed the stereoselective synthesis of new azasugars alkylated at C-3. The activity of the new compounds towards lysosomal β-galactosidase was negligible, showing that the presence of an alkyl chain in this position is detrimental to inhibitory activity. Interestingly, 9, 10, and 12 behave as good inhibitors of lysosomal β-glucosidase (GCase) (IC50 = 12, 6.4, and 60 µM, respectively). When tested on cell lines bearing the Gaucher mutation, they did not impart any enzyme rescue. However, altogether, the data included in this work give interesting hints for the design of novel inhibitors. [ABSTRACT FROM AUTHOR]

This work presents the results obtained from a spatial modeling and analysis process on pollutants measured in the air through forty-three monitoring stations located in the three provinces of the Basque Autonomous Community (Spain). The pollutants measured correspond to the set of nitrogen oxides (nitric oxide, NO; nitrogen dioxide, NO 2 ; and nitrogen oxides, NO x ) and atmospheric particulate matter with a diameter less than or equal to 10 micrometers (PM 10 ). The objective of this work was to generate a map of the pollutants that exhaustively covers the entire area of the Basque Autonomous Community using geostatistical techniques, in such a way that it serves as a basis for short and midterm environmental studies. [ABSTRACT FROM AUTHOR]

Calculating adequate vehicle routes for collecting municipal waste is still an unsolved issue, even though many solutions for this process can be found in the literature. A gap still exists between academics and practitioners in the field. One of the apparent reasons why this rift exists is that academic tools often are not easy to handle and maintain by actual users. In this work, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. In order to guarantee that the solution is efficient, the performance of the genetic algorithm has been compared with another well-performing algorithm, the Variable Neighborhood Search (VNS). Three problems of different sizes have been solved and, in all cases, a significant improvement has been obtained. A total reduction of 40% of itineraries is attained with the subsequent reduction of emissions and costs. [ABSTRACT FROM AUTHOR]

The food industry has recently faced rapid and constant changes due to the current industrial revolution, Industry 4.0, which has also profoundly altered the dynamics of the industry overall. Due to the emerging digitalisation, manufacturing models are changing through the use of smart technologies, such as robotics, Artificial Intelligence (AI), Internet of Things (IoT), machine learning, etc. They are experiencing a new phase of automation that enables innovative and more efficient processes, products and services. The introduction of these novel business models demands new professional skills requirements in the workforce of the food industry. In this work, we introduce an industry-driven proactive strategy to achieve a successful digital transformation in the food sector. For that purpose, we focus on defining the current and near-future key skills and competencies demanded by each of the professional profiles related to the food industry. To achieve this, we generated an automated database of current and future professions and competencies and skills. This database can be used as a fundamental roadmap guiding the sector through future changes caused by Industry 4.0. The interest shown by the local sectorial cluster and related entities reinforce the idea. This research will be a key tool for both academics and policy-makers to provide well-developed and better-oriented continuous training programs in order to reduce the skill mismatch between the workforce and the jobs. [ABSTRACT FROM AUTHOR]

In the present study, the influence of the sampling density on the coestimation error of a regionalized, locally stationary and geo-mining nature variable is analyzed. The case study is two-dimensional (2D) and synthetic-type, and it has been generated using a non-conditional Sequential Gaussian Simulation (SGS), with subsequent transformation to Gaussian distribution, seeking to emulate the structural behavior of the aforementioned variable. A primary and an auxiliary variable with different spatial and statistical properties are constructed using the same methodology. The collocated ordinary cokriging method has been applied, in which the auxiliary variable is spatially correlated with the primary one and it is known exhaustively. Fifteen sampling densities are extracted from the target population of the primary variable, which are compared with the simulated values after performing coestimation. The obtained results follow a potential function that indicates the mean global error (MGE) based on the sampling density percentage (SDP) ( M G E = 1.2366 · S D P − 0.224 ). [ABSTRACT FROM AUTHOR]

Some companies improve their production performance using manufacturing or operations models. In the last decade these models have come to be known as "X" production systems (XPS), or company-specific production systems. XPS systems have been oriented mainly to implement lean manufacturing and continuous improvement principles, but have shown little progress in terms of sustainability principles. The emergence of databases (DBs), big data, and business intelligence (BI) systems have enabled the creation of system panels to measure performance and manage business processes. These panels also allow assessment of the implementation of the principles, methods, and tools of the XPS. The effectiveness of these systems requires sufficient IT infrastructure and an acceptable quality of data, but the key performance indicator (KPI) panels of XPSs often lack consistent and acceptable data. In other cases, the IT infrastructure does not cover the needs of the personnel managing the operations, which limits the progress of XPSs. This paper documents the positive experience of one company in jointly auditing and improving data quality and IT infrastructure, which better aligned its XPS with sustainability objectives. It explores, using an empirical case study where the researchers were actively involved, the possibility of complementing the KPI panel system of XPS assessment systems with the principles of sustainability, data quality, and IT infrastructure. The paper describes the development and testing of an assessment tool based on those principles, and offers a number of results that may benefit similar small- and medium-sized companies (SMEs). [ABSTRACT FROM AUTHOR]

The decline of the labor share of income over the last few decades has been documented for many developed economies. A declining labor share is associated with rising income inequality, which raises obvious economic and social concerns. Although several explanations for this fact have been provided in the literature, they usually rely on elastic substitution between private capital and labor, which is generally not supported by the empirical literature. We argue in this paper that the fall in the labor share is potentially associated with the decline in public investment ratios, which have also been observed in most developed economies in the last few decades. We use a calibrated small-scale macroeconomic model to show how a negative public investment shock can have a sizeable negative effect on the labor share. Two assumptions are key in this result: that public capital directly augments private capital in production and that the elasticity of substitution between private capital and labor is smaller than one. We argue that both assumptions are plausible in practice. Our results suggest that, to promote long-run sustainable and inclusive growth, governments should increase the fraction of output devoted to public investment. [ABSTRACT FROM AUTHOR]

Assessment of two buffered QuEChERS (quick, easy, cheap, effective, rugged, and safe) versions (i.e., citrate and acetate) modified by including methanol to recover the residues of three cyclohexanedione oxime (CHD) herbicides and three of their byproducts from agricultural soil was performed. In this context, a full second-order face-centered factorial experimental design was developed to quantify the influences of the main five variables (i.e., extraction time, water content, soil weight, and extraction solvent volume and composition) on the target compound recoveries. The fitting equations satisfactorily described the extraction process behavior. The mathematical models also showed the most influencing independent variables (i.e., extraction solvent composition and soil weight). Handling simpler expressions was possible with the acetate QuEChERS but not with the citrate QuEChERS. The recoveries of the CHD residues were close to 100% after performing the extraction under suitable conditions. Furthermore, dispersive solid-phase extraction (dSPE) clean-up steps were assessed to reduce the matrix effect in mass spectrometry. In this sense, the citrate QuEChERS in combination with the PSA + C18 clean-up step was the best option for the extraction of CHD residues. [ABSTRACT FROM AUTHOR]