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Succinic acid (SA) is one of the top platform chemicals with huge applications in diverse sectors. The presence of two carboxylic acid groups on the terminal carbon atoms makes SA a highly functional molecule that can be derivatized into a wide range of products. The biological route for SA production is a cleaner, greener, and promising technological option with huge potential to sequester the potent greenhouse gas, carbon dioxide. The recycling of renewable carbon of biomass (an indirect form of CO2), along with fixing CO2 in the form of SA, offers a carbon-negative SA manufacturing route to reduce atmospheric CO2 load. These attractive attributes compel a paradigm shift from fossil-based to microbial SA manufacturing, as evidenced by several commercial-scale bio-SA production in the last decade. The current review article scrutinizes the existing knowledge and covers SA production by the most efficient SA producers, including several bacteria and yeast strains. The review starts with the biochemistry of the major pathways accumulating SA as an end product. It discusses the SA production from a variety of pure and crude renewable sources by native as well as engineered strains with details of pathway/metabolic, evolutionary, and process engineering approaches for enhancing TYP (titer, yield, and productivity) metrics. The review is then extended to recent progress on separation technologies to recover SA from fermentation broth. Thereafter, SA derivatization opportunities via chemo-catalysis are discussed for various high-value products, which are only a few steps away. The last two sections are devoted to the current scenario of industrial production of bio-SA and associated challenges, along with the author's perspective. [ABSTRACT FROM AUTHOR]

Electrochemical hydrogen compression is a promising alternative to conventional mechanical compression due to low maintenance costs and high one-stage compression ratios. The typically employed planar systems, however, are intrinsically difficult to operate in high differential pressure environments. Tubular systems are inherently advantageous for high differential pressure systems but require innovative membrane electrode assembly development. This study unveils the first tubular EHC featuring a membrane electrode assembly supported by a stainless steel 3D-printed porous anode. The membrane electrode assembly for gas-phase electrochemistry is produced by spray coating the porous anode with Pt/C as the catalyst and joining the anode with a tubular catalyst-coated membrane. Electrochemical characterization demonstrates the functionality of the tubular EHC at current densities up to 60 mAcm − 2 with a cell potential of 200 mV under non-pressurized conditions. Moreover, a pressure difference of 2 bar is achieved at 60 mAcm − 2 within the first 60 min in continuous mode. By demonstrating the proof-of-principle for the first tubular EHC, this work paves the way for new research avenues in electrochemical process engineering, offering a broad spectrum of applications, from enhancing EHC technologies to advancing electrochemical CO 2 reduction. • Tubular membrane electrode assembly for electrochemical hydrogen compression. • Metal 3D-printed tubular electrode with tunable porosity. • Electrochemical characterization of tubular module. • A maximum hydrogen compression of 2 bar at current densities up to 60 mA/cm2. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper aims to develop an integrated Quality–Cost–Environmental (QCE) indicator for the selection of photovoltaic panels (PVps) offered to customers, considering the following criteria: (i) quality satisfaction; (ii) cost-effectiveness; and (iii) environmental impact throughout the life cycle. The proposed QCE indicator was developed within a framework that incorporated an FAHP (Fuzzy Analytic Hierarchy Process), cost-effectiveness analysis (CEA), and life cycle assessment (LCA). The model test confirmed its effectiveness in choosing a PVp which combines environmental friendliness throughout its entire life cycle with satisfactory quality and a reasonable purchase price for customers. The proposed model can be utilized by individuals, businesses, and public entities for the selection of high-quality, cost-efficient, and environmentally friendly PVps—thereby promoting sustainable development. [ABSTRACT FROM AUTHOR]

Modelling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a phenomenon, one is interested, e.g., in so-called T-optimal designs. These consist of the (design) points from a generally continuous design space at which the models deviate most from each other under the condition that they are best fitted to those points. Thus, the T-criterion represents a bi-level optimization problem, which can be transferred into a semi-infinite one but whose solution is very unstable or time consuming for non-linear models and non-convex lower- and upper-level problems. If one considers only a finite number of possible design points, a numerically well tractable linear semi-infinite optimization problem arises. Since this is only an approximation of the original model discrimination problem, we propose an algorithm which alternately and adaptively refines discretizations of the parameter as well as of the design space and, thus, solves a sequence of linear semi-infinite programs. We prove convergence of our method and its subroutine and show on the basis of discrimination tasks from process engineering that our approach is stable and can outperform the known methods. [ABSTRACT FROM AUTHOR]

P-type doping in selected areas of gallium nitride (GaN) using magnesium (Mg)-ion implantation and subsequent ultra-high-pressure annealing (UHPA) are investigated to improve the performance of vertical GaN power devices. UHPA allows a high-temperature process without decomposition of the GaN surface and virtually complete activation of the implanted Mg ions in GaN. In the present paper, we provide an overview of recent challenges in making UHPA more realistic as an industrial process. Instead of UHPA at more than 1400 °C for a short duration, prolonged UHPA at 1300 °C demonstrates a comparable acceptor activation of Mg-ion-implanted GaN. This can reduce the annealing pressure to approximately 300 MPa and enlarge the processable wafer diameter. The second challenge is controlling the doping profiles in the lateral and vertical directions. We demonstrate fine patterning of the p-type regions, which indicates the limited lateral diffusion of Mg through UHPA. However, controlling the vertical doping profile is challenging. The nitrogen vacancies formed by ion implantation reduce the effective acceptor concentration near the surface, which can be compensated for by sequential nitrogen ion implantation. Defect-assisted Mg diffusion to the deeper region causes a redistribution of the Mg atoms and should be considered in the design of a device. Such anisotropic diffusion of Mg to the c-axis has potential applications in the fabrication of unique vertical device structures such as super junctions. [ABSTRACT FROM AUTHOR]

Air Taylor bubbles in a millichannel filled with water are characterized by an elongated shape, a bullet-shaped nose (head), and a comparatively flat tail. Many experimental and numerical investigations have been performed in the past. Yet, most of them consider Taylor bubbles in a straight channel with constant cross section. The effect of a local change in the channel geometry on both the bubble shape and the flow fields on each side of the gas–liquid interface is, however, difficult to predict. In this work, we present experimental data obtained in a vertical millichannel, where the flow is moderately obstructed by a constriction, whose ratio ranges from 10% to 36%. We find that the Taylor bubble takes an equilibrium position for downward liquid flow with 264.36 < Re < 529.67 and 264.36 < Re < 728.29 for 10.17% and 18.06% constriction ratios, respectively. In this area, an empirical correlation characterizing the bubble head is provided. Other flow regimes, such as bubble breakup, co- and countercurrent configurations, are identified and shown in the form of a regime map. The results, besides their relevance in process engineering, exhibit high reproducibility and will serve as a reference for future interface-resolving two-phase flow simulations. [ABSTRACT FROM AUTHOR]

In this era of industrial revolution and diminishing petroleum reserves, alternative options (woody biomass) for building next-generation fuels need to be critically explored. Here, process engineering aspects of 5-hydroxymethylfurfural (5-HMF) from Bambusa bamboo in ionic liquid ([Bmim]Cl) in the presence of a catalyst, CrCl3, in batch reactor have been explored in great detail. 5-HMF upon hydrogenation liberates 2,5-dimethylfuran (DMF), which is a liquid fuel. To maximize the production of platform chemical-5-HMF, effects of different mixing speeds (150–1200 rpm) and temperatures (120–160 °C) were captured on the yield of 5-HMF and glucose. A highest 5-HMF yield (25%) was obtained at 120 °C and 1200 rpm within a reaction time of 6 h. Moreover, a kinetic analysis of transformation of biomass into 5-HMF was carried out using curve fitting to estimate kinetic constants k1, k2, and k3. Mixing at asymptotic limits, i.e., no mixing (0 rpm) and very high mixing (1200 rpm), enables us to devise mixing regimes: 0–400 rpm, mass transfer–limited regime; 400–1100 rpm, intermediate regime; and > 1100 rpm, reaction-limited regime. Thus, tremendous improvement in reaction rate constants (k1, k2, k3) was observed when operated at higher mixing conditions (reaction-limited regime). Mixing limitation for this IL-based system can be eliminated by operating the reactor above 800 rpm, whereas lower temperature restricts conversion of 5-HMF to LA and FA. Hence, higher mixing speeds, i.e., > 1100 rpm, and lower temperature, i.e., ≈ 120 °C, can be suggested as the optimum operating conditions for maximization of platform chemical-5-HMF in the catalytic conversion of Bambusa bamboo in batch reactor. [ABSTRACT FROM AUTHOR]

The advancement of quality and environmentally sustainable materials and products made from them has improved significantly over the last few years. However, a research gap is the lack of a developed model that allows for the simultaneous analysis of quality and environmental criteria in the life-cycle assessment (LCA) for the selection of materials in newly designed products. Therefore, the objective of the research was to develop a model that supports the prediction of the environmental impact and expected quality of materials and products made from them according to the design solution scenarios considering their LCA. The model implements the GRA method and environmental impact analysis according to the LCA based on ISO 14040. The model test was carried out for light passenger vehicles of BEV with a lithium-ion battery (LiFePO4) and for ICEV. The results indicated a relatively comparable level of quality, but in the case of the environmental impact throughout the life-cycle, the predominant amount of CO2 emissions in the use phase for combustion vehicles. The originality of the developed model to create scenarios of design solutions is created according to which the optimal direction of their development in terms of quality and environment throughout LCA can be predicted. [ABSTRACT FROM AUTHOR]

Advancing the adoption of lithography‐based ceramic manufacturing (LCM) for structural applications requires understanding how processing parameters impact the printed material's flaw population and failure behavior. Exploratory studies on LCM alumina have mixed results regarding print parameter influence on strength and past fractography is insufficiently complete to guide process engineering for better performance. We perform four‐point flexural strength characterization and comprehensive fractography of LCM alumina to evaluate the impact of processing parameters including orientation, layer height, and curing energy on strength‐limiting defects. Characteristic strengths for fully dense, high‐purity LCM alumina are near or above conventionally processed alumina within the manufacturable range of print parameters studied in this work. Conversely, the Weibull modulus varies greatly (m = 3.8–19.5) depending on print parameters. The dominant strength‐controlling defects all have a printing‐based origin, but vary between delamination, surface texture, and bubbles depending on print orientation. Lowest performance results from alignment of the print axis along the bar length, yielding a low Weibull modulus associated with weak delamination flaws that are not appreciably improved by varying print layer height or curing energy. [ABSTRACT FROM AUTHOR]

L-cysteine is a proteogenic amino acid with many applications in the pharmaceutical, food, animal feed, and cosmetic industries. Due to safety and environmental issues in extracting L-cysteine from animal hair and feathers, the fermentative production of L-cysteine offers an attractive alternative using renewable feedstocks. Strategies to improve microbial production hosts like Pantoea ananatis, Corynebacterium glutamicum, Pseudomonas sp., and Escherichia coli are summarized. Concerning the metabolic engineering strategies, the overexpression of feedback inhibition-insensitive L-serine O-acetyltransferase and weakening the degradation of L-cysteine through the removal of L-cysteine desulfhydrases are crucial adjustments. The overexpression of L-cysteine exporters is vital to overcome the toxicity caused by intracellular accumulating L-cysteine. In addition, we compiled the process engineering aspects for the bioproduction of L-cysteine. Utilizing the energy-efficient sulfur assimilation pathway via thiosulfate, fermenting cheap carbon sources, designing scalable, fed-batch processes with individual feedings of carbon and sulfur sources, and implementing efficient purification techniques are essential for the fermentative production of L-cysteine on an industrial scale. [ABSTRACT FROM AUTHOR]

Production scheduling is a critical factor to enhancing productivity in manufacturing engineering and combinatorial optimization research. The complexity and dynamic nature of production systems necessitates innovative solutions. The Job Shop Flexible Programming Problem (FJSP) provides a realistic environment for production, where processing times are variable and uncertain, and multiple objectives need optimization. To solve the Multi-Objective Flexible Fuzzy Job Shop problem with partial flexibility (P-MOFfJSP), this paper proposes a hybrid metaheuristic approach that combines the Teaching–Learning-based Optimization (TLBO) algorithm with a Genetic Algorithm. The proposed algorithm of Adaptive TLBO (TLBO-A) uses two genetic operators (mutation and crossover) with an adaptive population reconfiguration strategy, ensuring solution space exploration and preventing premature convergence. We have evaluated the TLBO-A algorithm's performance on benchmark instances commonly used in programming problems with fuzzy variables. The experimental analysis indicates significant results, demonstrating that the adaptive strategy improves the search for suitable solutions. The proposed algorithm (TLBO-A) exhibits low variations (around 11%) compared to the best mono-objective heuristic for the fuzzy makespan problem, indicating its robustness. Moreover, compared with other heuristics like traditional TLBO, the variations decrease to around 1%. However, TLBO-A stands out as it aims to solve a multi-objective problem, improving the fuzzy makespan, and identifying good results on the Pareto frontier for the fuzzy average flow time, all within this low variation margin. Our contribution addresses the challenges of production scheduling in fuzzy time environments and proposes a practical hybrid metaheuristic approach. The TLBO-A algorithm shows promising results in solving the P-MOFfJSP, highlighting the potential of our proposed methodology for solving real-world production scheduling problems. [ABSTRACT FROM AUTHOR]

Steroid hydroxylations belong to the industrially most relevant reactions catalysed by cytochrome P450 monooxygenases (CYP450s) due to the pharmacological relevance of hydroxylated derivatives. The implementation of respective bioprocesses at an industrial scale still suffers from several limitations commonly found in CYP450 catalysis, that is low turnover rates, enzyme instability, inhibition and toxicity related to the substrate(s) and/or product(s). Recently, we achieved a new level of steroid hydroxylation rates by introducing highly active testosterone‐hydroxylating CYP450 BM3 variants together with the hydrophobic outer membrane protein AlkL into Escherichia coli‐based whole‐cell biocatalysts. However, the activity tended to decrease, which possibly impedes overall productivities and final product titres. In this study, a considerable instability was confirmed and subject to a systematic investigation regarding possible causes. In‐depth evaluation of whole‐cell biocatalyst kinetics and stability revealed a limitation in substrate availability due to poor testosterone solubility as well as inhibition by the main product 15β‐hydroxytestosterone. Instability of CYP450 BM3 variants was disclosed as another critical factor, which is of general significance for CYP450‐based biocatalysis. Presented results reveal biocatalyst, reaction and process engineering strategies auguring well for industrial implementation of the developed steroid hydroxylation platform. [ABSTRACT FROM AUTHOR]

One of the global ecological problems is the excessive carbon dioxide emissions generated by vehicles in the transport sector, including passenger transport. Therefore, the objective of this investigation was to develop a model that supports the prediction of vehicle variants that will be satisfactory to the customer in terms of: (i) quality level and (ii) environmental impact throughout the life cycle. This model was developed with the following techniques: TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), LCA (Life Cycle Assessment), SMARTER (Specific, Measurable, Achievable, Relevant, and Time-bound), Pareto–Lorenz, and the Multi-Criteria Decision Method rule (7 ± 2). A model test was carried out for production variants of the electric vehicle BEV (battery electric vehicle) for which the quality level and life cycle assessment were estimated. Vehicle quality levels ranged from 0.15 to 0.69, with a weight of 0.75. However, vehicle life cycle scores were estimated in the range of 0.25 to 0.57, with a weight of 0.25. Ultimately, the level of the vehicles' LCA ranged from 0.18 to 0.62. As a result, it was shown that on the basis of various modifications of the quality level of vehicle variants and the corresponding environmental impacts throughout their life cycle, it is possible to predict the vehicle variant that is most satisfactory for the customer and, at the same time, environmentally friendly. The originality of the model relies on supporting the making of sustainable design decisions and the planning of vehicle improvement actions according to customer expectations. Therefore, the model can be used to analyse different types of vehicles by producers and dealers of these products. [ABSTRACT FROM AUTHOR]

In the field of new-energy systems, the use of spent lithium-ion batteries is being rationalized as a prospective industry. However, with the progress of technology, the exploration of strategies that are efficient, green, and energy-saving has become the major issue for recycling batteries. Recently, the mechanochemical activation (MCA) process has been proposed as a method for reducing leaching time and temperature, further tackling the excessive usage of corrosive chemical agents in hydrometallurgical techniques. Herein, we present a co-grinding agent of spent LiCoO2 (S-LCO) and Na2SO3 (reduction properties) to facilitate the physical and chemical transformations of the S-LCO, thereby enhancing the leaching process. MCA can refine particle size, enhance its hydrophilicity, induce lattice distortion and refine lattice structure, and thereby the surface energy and surface negative charge are increased, further improving the reaction activity of S-LCO. Meanwhile, Na2SO3 promotes the isomorphic substitution between Na+ and Li+ and reduces Co3+ to Co2+. Because of this superiority, this novel MCA shortens leaching time and reduces leaching temperature, as well as improves leaching efficiency and reduces the use of acidic solvents. Practically, the cell with regenerated LiCoO2 exhibits excellent cycling performance and rate capability (a high capacity retention of 84.54% after 120 cycles at 0.2 C), which sheds some light on the interface process engineering of a co-grinding agent for recycling spent lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Diatoms are prominent and highly diverse microalgae in aquatic environments. Compared with other diatom species, Phaeodactylum tricornutum is an "atypical diatom" displaying three different morphotypes and lacking the usual silica shell. Despite being of limited ecological relevance, its ease of growth in the laboratory and well‐known physiology, alongside the steady increase in genome‐enabled information coupled with effective tools for manipulating gene expression, have meant it has gained increased recognition as a powerful experimental model for molecular research on diatoms. We here present a brief overview of how over the last 25 years P. tricornutum has contributed to the unveiling of fundamental aspects of diatom biology, while also emerging as a new tool for algal process engineering and synthetic biology. [ABSTRACT FROM AUTHOR]

PVC is widely used in packaging, electrical insulation, and medical devices due to its versatility owing to its resistance, incombustible and barrier properties as well as affordable cost. In the present study, bibliometric and co-occurrence analyses are proposed to identify trends, gaps, future directions, and challenges regarding process system engineering (PSE) applied to the production process of PVC using VOSviewer as a tool for analyzing the data obtained from SCOPUS. A mapping of different topics alluding to simulation of PVC production was provided to gain a better insight into the development of the topic and its progression. The findings indicate that the literature on this topic falls into five different clusters: modeling and simulation of PVC production, process control and optimization, and optimization strategies of the process. From a co-occurrence study we identified that mathematics and statistics applied to polymer chemistry, separation phenomena, and polymer production are the main areas of interest for further research. The trends suggest that Monte Carlo and numerical simulation can contribute to a deeper understanding of PVC's properties and behavior. In addition, the focus on plastics and microplastics reflects concerns about the environmental impact. A bibliometric study evidenced that PSE provides the tools for improvement in PVC production processes by employing advanced process engineering techniques. Modelling and new algorithms for simulation methods of continuous polymerization processes are important to enhance accuracy and efficiency across various applications. The study also proposes a research agenda for future researchers working in the field of the use of PSE applied to the PVC production process. [ABSTRACT FROM AUTHOR]

As we stand on the cusp of the 30th anniversary of the International Journal of Industrial Engineering: Theory, Applications and Practice (we call it "the Journal"), it is a moment to celebrate its remarkable journey and acknowledge the vital roles played by its authors and reviewers. From its inception, the Journal has evolved into a leading international journal renowned for its commitment to publishing high-quality research that has significantly impacted the field of industrial and manufacturing engineering. The Journal's scope encompasses a broad spectrum of industrial engineering disciplines. This comprehensive approach ensures that the Journal remains at the forefront of industrial engineering research and continues to serve as a valuable resource for both scholars and practitioners in the field. [ABSTRACT FROM AUTHOR]

In recent years a substantial effort has been devoted to the development of allogeneic cell-based immunotherapies with the aim of reducing costs and improving the accessibility of such treatments. NK cells have the potential to target a wide range of cancer cells without causing graft-versus-host disease (GvHD), making them an attractive option as off-the-shelf therapies for cancer treatment. Translating from laboratory production of NK cells to biomanufacturing technology for off-the-shelf products requires bioreactor scale-up and process optimization which will likely involve integrating biological understanding of NK cells with modern sensors for in-line monitoring of key process variables that impact NK cell proliferation and function. The development of NK biomanufacturing can benefit from the tremendous amount of knowledge accumulated from the biomanufacturing of therapeutic proteins. A quality by design (QbD) approach can be applied in NK cell biomanufacturing even in its early stages of process development. Identifying and characterizing critical quality attributes (CQA) and critical process parameters (CPPs) will help in developing a robust and efficient manufacturing process. Cell therapy offers the potential for curative treatment of cancers. Although T cells have been the predominantly used cell type, natural killer (NK) cells have attracted great attention owing to their ability to kill cancer cells and because they are naturally suitable for allogeneic applications. Upon stimulation by cytokines or activation by a target cell, NK cells proliferate and expand their population. These cytotoxic NK cells can be cryopreserved and used as an off-the-shelf medicine. The production process for NK cells thus differs from that of autologous cell therapies. We briefly outline key biological features of NK cells, review the manufacturing technologies for protein biologics, and discuss their adaptation for developing robust NK cell biomanufacturing processes. [ABSTRACT FROM AUTHOR]

Process-induced strain engineering is an effective method of crafting the strain state in 2D materials. Much like how it has been used in the fabrication of Si-based electronics, stressed thin films are deposited onto van der Waals-bonded 2D systems where relaxation of the stressor layer causes strain transfer into the 2D materials. This type of strain engineering can be used on a device-by-device level and be controlled for strain magnitude, compression or tension, uniaxiality or biaxiality, and directionality relative to crystal structure by varying film stress or geometry. One critical question in translating this technique to 2D materials is how temperature and time stable this strain engineering process is. In this work, we explore these factors through Raman spectroscopic mapping and photoluminescence spectroscopy ranging in temperatures from 293 to 4 K. It is shown that strain engineering with thin film stressors is equally persistent at all temperatures examined and time stable for a period of at least 14 months (the period of observation). These results suggest that process-induced strain engineering may be used to tune any number of interesting low-temperature properties in 2D materials and that any devices engineered in this way will have long-term stability for applications in electronics, optoelectronics, and beyond. [ABSTRACT FROM AUTHOR]

Regularization regression learning scheme finds its powerful applications in many areas, such as Computer science, manufacturing engineering, economic decision making, etc. In this paper, the indefinite kernel based coefficient dependent regularization algorithm for block-wise streaming data is proposed, and learning performance of this algorithm is studied by bounding the learning error. Our learning scheme works in an online and weighted average manner. The total error is decomposed into weighted average of local variance error and weighted average of local bias error. By the kernel decompose and integral operator method, satisfied error bound and learning rates are derived. Our error analysis shows that mild growth of the sizes of data block and the underregularization strategy can guarantee the convergence of the learning scheme. [ABSTRACT FROM AUTHOR]

Well-characterized and scalable downstream processes for the purification of biologics are extremely demanding for delivering quality therapeutics to patients at a reasonable price. Erythropoietin (EPO) is a blockbuster biologic with diverse clinical applications, but its application is limited to financially well-off societies due to its high price. The high price of EPO is associated with the technical difficulties related to the purification challenge to obtain qualified products with a cost-effective defined process. Though there are reports for the purification of EPO there is no report of a well-characterized downstream process with critical process parameters (CPPs) that can deliver EPO consistently satisfying the quality target product profile (QTPP), which is a critical regulatory requirement. To advance the field, we applied the quality by design (QbD) principle and design of experiment (DoE) protocol to establish an effective process, which is scalable up to 100× batch size satisfying QTPP. We have successfully transformed the process from static mode to dynamic mode and validated it. Insignificant variation (p > 0.05) within and between 1×, 10×, and 100× batches showed that the process is reproducible and seamlessly scalable. The biochemical analysis along with the biofunctionality data ensures that the products from different scale batches were indifferent and comparable to a reference product. Our study thereby established a robust and scalable downstream process of EPO biosimilar satisfying QTPP. The technological scheme presented here can speed up the production of not only EPO but also many other life-saving biologics and make them available to the mass population at a reduced cost. [ABSTRACT FROM AUTHOR]

Infant formula (IF) requires further optimization as there are still differences in health consequences between human milk (HM) and IF. The present review addresses the challenges and future opportunities inherent in the production of more biomimetic and sustainable IF. After presenting the targets, limitations and challenges for IF optimization, process innovations that could contribute to designing the next-generation of IF are discussed. The final section describes how such improvements should be addressed by means of a more systemic approach. Gaps in our knowledge of the compounds and structures in HM and their effects on digestion and health still exist, rendering the biomimicry of HM more difficult. Overall, optimizing IF is complex and requires trade-offs between synergistic and conflicting objectives, which include HM biomimicry, safety, functionality, ingredient sourcing as well as environmental, economic and social sustainability issues. Process innovations and optimized technological routes, including minimal processing, offer opportunities to implement new ingredients and improve the preservation of IF compounds, while ensuring microbial safety and addressing several pillars of sustainability through energy costs or reductions in gas emissions. Given the complexity of producing biomimetic and sustainable IF, a multi-objective optimization strategy is proposed, reliant on a multidisciplinary approach, where nutrition and process engineering would play pivotal roles with assistance from other disciplinaries such as biochemistry, microbiology, pediatric medicine, data and consumer sciences and public health. This rethinking of IF production should be driven by a multidisciplinary, non-profit consortium involving the entire value chain. • Lack of knowledge still exist regarding human milk compounds and structures. • Alternative processes open new avenue for infant formula (IF) optimization. • Multi-objective optimization will allow sustainable and biomimetic IF production. • Multi-disciplinary collaboration throughout the whole value chain is required. [ABSTRACT FROM AUTHOR]

The new demands for sustainable operation in the chemical industry due to increasing environmental regulations and agreements have generated the need to adapt existing processes to more intelligent production. The plastics sector is in a complex position due to its contribution to economic development and the climate crisis. Therefore, environmental assessment has become an important tool due to the benefits it provides by quantifying the environmental performance of processes, allowing it to balance operational and environmental needs. Polyvinyl chloride (PVC) is one of the most globally used polymers thanks to its resistance, flexibility, and cost-effectiveness. The polymer is synthetized by suspension polymerization, which is characterized by high productivity and controllability. However, it presents problems associated with intensive energy consumption and the emission of toxic substances and greenhouse gases. Therefore, an environmental assessment of the suspension PVC production process was performed using the waste reduction algorithm (WAR). The potential environmental impact (PEI) was quantified using the generation rate and the output velocity for four cases and three different fuels. It was found that the process transforms raw materials with high impacts, such as VCM, into substances with lower PEI, such as PVC. However, the process has a high generation of PEI due to the effects of energy consumption (−2860, −2410, 3020, and 3410 for cases 1–4, respectively). The evaluation of the toxicological impacts shows that the ATP category is the only one that presents a positive generation value (75 PEI/day); the product contributes to the formation and emission of impacts. The atmospheric categories showed that the energy consumption of the process is the most critical aspect with a contribution of 91% of the total impacts emitted. The AP and GWP categories presented the highest values. It was determined that the most suitable fuel is natural gas; it has lower impacts than liquid and solid fuels (coal). Additionally, it can be concluded that the PVC production process by suspension is environmentally acceptable compared to the polyethylene or polypropylene processes, with output impacts 228 and 2561 times lower, respectively. [ABSTRACT FROM AUTHOR]

Input‐output data sets are ubiquitous in chemical process engineering. We introduce a real‐time interactive navigation framework that provides several capabilities to the decision maker (DM). Once a surrogate model is trained the DM can perform what‐if analyses in both input and output spaces by manipulating sliders. An approximated convex hull spanned in the input space supports both a reliable surrogate prediction and a navigation close to the data set. The framework has been tested on data sets obtained with a flowsheet simulator modeling a real steam methane reforming process. [ABSTRACT FROM AUTHOR]

Data collection in process industry yields a variety of data types. To maintain the knowledge about the data and to enable finding and reusing them (FAIR data), a common description with metadata is necessary. In the project KEEN, ProMetaS – the Process Engineering/Industry Metadata Schema – was developed. It defines various metadata categories adhering to available metadata standards. In this article, ProMetaS, its implementation into the KEEN data platform, and its application in two use cases is described. [ABSTRACT FROM AUTHOR]

In engineering courses with a large number of students, fail and dropout rates are usually high even in courses that can be expected to be interesting to the students. Gamification tools have arisen as an interesting way to motivate the students for a real continuous assessment, increasing the student's attendance and performance. In this paper, a gamification approach based on a Kahoot tool, Moodle activities, and commemorative badges is proposed within a manufacturing course. The designed activities let the students compete among themselves to get the maximum number of points, which will be converted into extra grades that are added to the final exam grade. The gamification experience has been proven to be highly positive according to students' and instructors' perceptions, and the average increase of pass rate and course attendance with respect to previous years have increased from 22% to 34% and from 38% to 66%, respectively. [ABSTRACT FROM AUTHOR]

WO3 thin films with (002)-preferred orientation showing superior electron storage capacity were prepared using a vacuum evaporation system. The electron storage capacity of the WO3 photoelectrode can be improved by controlling the crystallographic orientation of the WO3 crystals. In photoelectrochemical water splitting, the TiO2/WO3 photoelectrode exhibited a significantly higher increment in current density than the pristine WO3 photoelectrode. The charging and discharging capacities of the preferentially oriented WO3 layer can be derived in full value to introduce a TiO2 layer with a porous morphology. Moreover, because a difference in the crystal facet results in a slight change in the energy of the valence and conduction bands, it can be speculated that better separation of electron–hole pairs is achieved, leading to exposure of the (002) dominant crystal facet of WO3. [ABSTRACT FROM AUTHOR]

The improvement of machine tools, and therefore, of industrial production, requires high accuracy of machining while adapting to the different dimensions of the workpieces and to the machines themselves. As a result, the improvement of testing procedures and the analysis of positioning accuracy results represent an important research task in modern manufacturing engineering. The paper presents the results of the research carried out with the aim of determining the influence of the choice of parameters for standardized testing of positioning accuracy on the measurement results with reference to the characteristics of the machines from the point of view of the size of the workspace and the machines themselves. In this way, it is possible to choose the appropriate test parameters of the machine tools depending on their geometrical characteristics and test conditions and within the existing standards. [ABSTRACT FROM AUTHOR]

The idea of sustainable development enforces the pro-environmental design and production of products. It also refers to products producing green energy. The current situation in the world, mainly in Europe, further intensifies these works. The new products occurring in this dynamic market are rarely known by customers. In such a case, they have the problem of proper selection based on their own needs. Hence, the purpose is to develop a method to support the customers during their choice of product. In this methodical study, the qualitative and environmental criteria and also price of purchase were simultaneously included. This method was developed using integrated selected techniques, e.g., brainstorming (BM), the SMARTER method, rule 7 ± 2, questionnaire with Likert scale, AHP method, PROMETHEE II method, and matrix data analysis. The results from the test of method allowed the development this method for possible selection of a product according to individual customer expectations but also supported by the knowledge and experience of experts. The method is dedicated to customers but also enterprises aspiring to simultaneously develop their own products. The originality of this work is the integration of the main criteria for the selection of the product by the customer, i.e., quality, impact on natural environment, and price. The novelty of the study is the ranking of selecting products by considering qualitative and environmental criteria, limiting the customer's necessary knowledge about the analyzed products to determine the weights of criteria, and visualization of the qualitative–environmental relationship. The model test was carried out for popular solar collectors. [ABSTRACT FROM AUTHOR]

Morula nuts were characterized for several physical and mechanical properties relevant to food process engineering. Mean moisture content of the nuts which had been naturally dried under the sun was 7.65% (w.b.). Mean nut length, width and thickness were, respectively, 25.64, 21.99 and 18.03 mm, while the derived geometric mean and equivalent diameters were 21.62 and 21.72 mm, respectively. Mean surface and projected areas were 1 475.74 and 445.02 mm², respectively, while mean nut volume was 5 371.90 mm³. Mean apparent density of the nuts was 811.30 kg m-3 while bulk density and bulk porosity were determined as 476.43 kg m-3 and 41.28%, respectively. Morula nuts are generally spherical, with mean sphericity of 84.40%, and aspect and flakiness ratios of 0.86, and 0.83, respectively. Mean angle of repose against a plywood surface was 19.13o while coefficient of friction against a galvanized iron surface was 0.24. Peak force upon mechanical compression in a texture analyser was 2 738.33 N, while energy expended during compression was 3 284.49 Nmm, with mean hardness being 774.02 N m m-1 and tensile strength recorded as 6.60 MPa. It may be concluded from these results that morula is a tough, moderately sized spherical nut whose density is lower than that of water, having high bulk porosity and low frictional properties. [ABSTRACT FROM AUTHOR]

Improving product quality while making decisions remains a challenge. The objective of this research was to develop a model that supports the precise enhancement of product quality through comprehensive analysis of possibilities, product incompatibilities, root causes, and recommended improvement actions. The model incorporated various tools and methods such as the SMARTER method, expert team selection, brainstorming, Ishikawa diagram, 5M+E rule, FAHP, and FTOPSIS methods. The study demonstrated that integrating quality management tools and decision-making methods into a unified model enables the accurate prioritization of activities for product quality management. This integrated approach represents the novelty of this research. The model was evaluated using a mechanical seal made of 410 alloy. The research findings can be valuable to enterprises seeking to enhance product quality at any stage of production, particularly for modified or new products. [ABSTRACT FROM AUTHOR]

Ensuring the expected quality of materials is still a challenge, mainly in order to precisely plan improvement actions that allow for stabilization of the production process. Therefore, the purpose of this research was to develop a novel procedure to identify critical causes of material incompatibility—the causes that have the largest negative impact on material deterioration, and the natural environment. The main originality of this procedure is developing a way to coherent analyse the mutual influence of the many causes of incompatibility of any material, after which the critical causes are identified and a ranking of improvement actions to eliminate these causes is created. A novelty is also developed in the algorithm supporting this procedure, which can be realized in three different ways to solve this problem, i.e.; by considering the impact of material incompatibility on: (i) the deterioration of the material quality; (ii) the deterioration of the natural environment; and (iii) simultaneously the deterioration of the quality of the material and the natural environment. The effectiveness of this procedure was confirmed after tests on 410 alloy, from which a mechanical seal was made. However, this procedure can be useful for any material or industrial product. [ABSTRACT FROM AUTHOR]

Large‐scale synthesis of GM1, an important ganglioside in mammalian cells especially those in the nervous system, is needed to explore its therapeutic potential. Biocatalytic production is a promising platform for such a purpose. We report herein the development of process engineering and glycosyltransferase improvement strategies to advance chemoenzymatic total synthesis of GM1. Firstly, a new short route was developed for chemical synthesis of lactosylsphingosine from the commercially available Garner's aldehyde. Secondly, two glycosyltransferases including Campylobacter jejuni β1–4GalNAcT (CjCgtA) and β1–3‐galactosyltransferase (CjCgtB) were improved on their soluble expression in E. coli and enzyme stability by fusing with an N‐terminal maltose binding protein (MBP). Thirdly, the process for enzymatic synthesis of GM1 sphingosines from lactosylsphingosine was engineered by developing a multistep one‐pot multienzyme (MSOPME) strategy without isolating intermediate glycosphingosines and by adding a detergent, sodium cholate, to the later enzymatic glycosylation steps. Installation of a desired fatty acyl chain to GM1 glycosphingosines led to the formation of target GM1 gangliosides. The combination of glycosyltransferase improvement with chemical and enzymatic process engineering represents a significant advance in obtaining GM1 gangliosides containing different sialic acid forms by total chemoenzymatic synthesis in a short route and with high efficiency. [ABSTRACT FROM AUTHOR]

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model. [ABSTRACT FROM AUTHOR]

The article deals with the development of stable and effective color concepts for aqueous personal care products with natural extracts. It is pointed out that colors convey information and can influence us. Natural plant-based dyes are becoming increasingly popular, but pose a challenge in terms of stability. The article proposes a method for testing stability and aligning product effectiveness and durability for plant-based dyes. It also addresses consumer acceptance of product and packaging design. The authors of the article are Iana Ekimova, Doris Schicker, and Arielle Springer from the Fraunhofer Institute for Process Engineering and Packaging IVV in Freising, Germany. [Extracted from the article]

Thin-film flows are frequently used in process engineering and are therefore of great interest in research. In most cases, waves occur which have a positive effect on the material and heat transport and significantly influence the dynamics of the film flow. A combination of µ-particle image velocimetry and confocal chromatic distance measurement is used to measure phase-averaged flow velocities in naturally developed film waves. A trigger signal is sent to the PIV system and a recording starts when a wave reaches the wave crest threshold. This makes it possible to trigger the µPIV system depending on the film thickness. The focus of this work is on the triggering of the wave crest. The functionality and quality of the measurements depend on the correct positioning of the confocal chromatic sensor, with the light spot at the level of the camera section. Simultaneously, the minimum, average, and maximum film thickness, as well as the wave amplitude and wave frequency, are recorded. Film flow is investigated on a glass plate at various angles (5°, 10°, 15°) of inclination, Reynolds numbers (30-200), and measurement positions. The results are compared with the theoretical solutions according to Nusselt. In addition, the influence of the tilt angle and the measuring position is explained. The measuring position, in particular, has a non-negligible influence since the characteristics of the film flow are largely dependent on the streamwise measuring position. Compared to the theoretical solutions according to Nusselt, the velocities and film thicknesses are significantly greater due to the presence of the waves. With increasing inclination angle and streamwise measuring position, the measured mean and maximum velocities of the film flow increase. They almost double when the inclination angle increases from 5° to 15°. When comparing the velocities depending on the measuring position (300–500 mm), it is noticeable that the velocities increase between 5 and 15%, depending on the angle, due to wave development and gravity. [ABSTRACT FROM AUTHOR]

Due to their ability to self-assemble into complex structures, block copolymers are of great interest for use in a wide range of future applications, such as self-healing materials. Therefore, it is important to understand the mechanisms of their structure formation. In particular, the process engineering of the formation and transition of the polymer structures is required for ensuring reproducibility and scalability, but this has received little attention in the literature. In this article, the influence of the addition rate of the selective solvent on the homogeneity of self-assembled vesicles of polystyrene-block-polyacrylic acid is demonstrated, as well as the influence of the reaction time and the mixing intensity on the morphology of the polymer structures. For example, it was demonstrated that the higher the mixing intensity, the faster the transition from micelle to vesicle. The experimental results are further supported by CFD simulations, which visually and graphically show an increase in shear rate and narrower shear rate distributions at higher stirring rates. Furthermore, it was demonstrated that the vesicle size is not only kinetically determined, since flow forces above a critical size lead to the deformation and fission of the vesicles. [ABSTRACT FROM AUTHOR]

Big data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. Initially, an overview of big data content, key characteristics, and related topics are presented. The paper also highlights a systematic review of available big data techniques and analytics. The available big data analytics tools and platforms are categorized. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data. [ABSTRACT FROM AUTHOR]

[Display omitted] • Lycopene is a natural red compound with potent antioxidant activity. • A breakthrough in increasing the yield of lycopene synthesis is needed. • Conventional fermentation enhanced the yield of lycopene by optimizing strains, additives, and culture conditions. • Nowadays, genetic engineering, protein engineering, and metabolic engineering have been applied to lycopene production. • Current lycopene production strategies can provide useful reference for metabolic engineering of lycopene production in plants. Lycopene is a natural red compound with potent antioxidant activity that can be utilized both as pigment and as a raw material in functional food, and so possesses good commercial prospects. The biosynthetic pathway has already been documented, which provides the foundation for lycopene production using biotechnology. Although lycopene production has begun to take shape, there is still an urgent need to alleviate the yield of lycopene. Progress in this area can provide useful reference for metabolic engineering of lycopene production utilizing multiple approaches. Using conventional microbial fermentation approaches, biotechnologists have enhanced the yield of lycopene by selecting suitable host strains, utilizing various additives, and optimizing culture conditions. With the development of modern biotechnology, genetic engineering, protein engineering, and metabolic engineering have been applied for lycopene production. Extraction from natural plants is the main way for lycopene production at present. Based on the molecular mechanism of lycopene accumulation, the production of lycopene by plant bioreactor through genetic engineering has a good prospect. Here we summarized common strategies for optimizing lycopene production engineering from a biotechnology perspective, which are mainly carried out by microbial cultivation. We reviewed the challenges and limitations of this approach, summarized the critical aspects, and provided suggestions with the aim of potential future breakthroughs for lycopene production in plants. [ABSTRACT FROM AUTHOR]

Sustainability is at the heart of continuous product improvement, where social, environmental, and economic considerations are key. This is a difficult task, especially in the area of the following economic and environmental changes, as well as changes in customer expectations. Additionally, it has become crucial to make product decisions that will be based on their entire life cycle (LCA). Although these aspects of sustainability and LCA are popular, bringing them together in one analysis remains challenging. Therefore, the article's objective was to develop a simplified model that supports decisionmaking, taking into account the criteria of sustainable development and life cycle assessment (LCA). The originality is the proposed model, which operates based on a qualitative indicator (product quality level), an environmental indicator (the product's impact on the environment throughout its life cycle) and a price indicator (purchase price of the product). These indicators are created using the TOPSIS method and the modified ACJ-E method. As a result, a product ranking is created. A novelty is the combination of Life Cycle Assessment (LCA) criteria with quality criteria and the actual purchase price of the product. The model test was carried out on the example of CATERPILLAR mini excavators. However, the model can be dedicated to any company that wants to make decisions in accordance with the idea of sustainable development. [ABSTRACT FROM AUTHOR]

Since China announced that it will strive to achieve carbon peak by 2030 and carbon neutrality by 2060, it has become the focus of the whole society. The implementation of carbon peaking and carbon neutrality goals requires a range of revolutionary technologies and involves an array of key scientific questions. A great deal of research has focused on the development of new concepts and innovative technologies on carbon science and sustainable development. NSR recently interviewed Professor Jing-Hai Li about the topic of the future of carbon neutrality science and technology from a scientific research perspective. Professor Li is an academician of the Chinese Academy of Sciences (CAS) at the Institute of Process Engineering, CAS. He is a scientist who has been working on exploring complex systems in chemical engineering by multi-scale methodology for more than 30 years. He proposed the concept of Mesoscience, a new interdisciplinary discipline. [ABSTRACT FROM AUTHOR]

The idea of sustainable development (SD) forces companies to combine the quality development of products with the simultaneous care of the natural environment. These actions should start with the product design process. The aim of the study was to create a modified method of Quality Function Development (QFD-CE), which will support the design of new products or improve the existing products on the market. In the proposed method (QFD-CE), the method integrates techniques such as: SMARTER method, brainstorming (BM), the method of selecting a team of experts, kinship diagram, fixed sum scale, and Likert scale. A novelty compared to the traditional QFD methodology is that design goals are set not only based on customer expectations, but also considering the impact on the natural environment. The originality of this proposition comes to the practical inclusion of including sustainability development criteria. The proposed method can be used in companies that design new products and are focused on caring for the natural environment. The QFD-CE method test method was performed for photovoltaic panels (PV). As part of the proposed QFD-CE method, the sequence of design activities was determined so that they meet customer expectations and can be simultaneously implemented according to the idea of SD. This method can be used for any product, mainly those that have a significant impact on the natural environment. [ABSTRACT FROM AUTHOR]

Improving products and production processes is necessary to ensure the competitiveness of the organisation. As part of these improvements, the popular approach is to use the FMEA method (Failure Mode and Effect Analysis). In the traditional FMEA approach, only the qualitative aspect is included, i.e., the analysis of the quality level of products or processes, its possible incompatibilities, and then proposing improving actions for them. It seems insufficient in times of expansion of the idea of sustainable development and dynamically changing customer requirements. Hence, the purpose of the research is to develop a fuzzy QE-FMEA method to simultaneously analyze hazards for product quality and the natural environment. This method will be based on a fuzzy decision environment. The main elements of originality of the developed method are: (i) extension of the characteristics of the selection of ratings for indicators with triangular fuzzy numbers and the development of a new characteristics of the selection of ratings for the environmental impact indicator, (ii) development of a selection matrix for the qualitative-environmental indicator (QE) according to the rules of triangular fuzzy numbers, (iii) determination of the method of estimating the value of the threat priority, additionally considering the qualitative-environmental indicator (RQE). The complement of research is developed procedure of the Fuzzy QE-FMEA method. It was shown that it is possible to include the effects of incompatibilities (effects of defects occurring in products or processes), which were simultaneously assessed considering the importance and impact on the natural environment. This method will be useful for any company for analysing defects of any products or processes mainly with significant impact on the natural environment. [ABSTRACT FROM AUTHOR]

Here, we report an ultracompact angular displacement sensor based on the Talbot effect of optical microgratings. Periodic Talbot interference patterns were obtained behind an upper optical grating. By putting another grating within the Talbot region, the total transmission of the two-grating structure was found to be approximatively in a linear relationship with the relative pitch angle between the two gratings, which was explained by a transversal shift of the Talbot interference patterns. The influence of the grating parameters (e.g., the grating period, the number of grating lines and the gap between the two gratings) was also studied in both a simulation and an experiment, showing a tunable sensitivity and range by simply changing the grating parameters. A sensitivity of 0.19 mV/arcsec was experimentally obtained, leading to a relative sensitivity of 0.27%/arcsec within a linear range of ±396 arcsec with the 2 μm-period optical gratings. Benefitting from tunable properties and an ultracompact structure, we believe that the proposed sensor shows great potential in applications such as aviation, navigation, robotics and manufacturing engineering. [ABSTRACT FROM AUTHOR]

Multivariate batch time-series data sets within Semiconductor manufacturing processes present a difficult environment for effective Anomaly Detection (AD). The challenge is amplified by the limited availability of ground truth labelled data. In scenarios where AD is possible, black box modelling approaches constrain model interpretability. These challenges obstruct the widespread adoption of Deep Learning solutions. The objective of the study is to demonstrate an AD approach which employs 1-Dimensional Convolutional AutoEncoders (1d-CAE) and Localised Reconstruction Error (LRE) to improve AD performance and interpretability. Using LRE to identify sensors and data that result in the anomaly, the explainability of the Deep Learning solution is enhanced. The Tennessee Eastman Process (TEP) and LAM 9600 Metal Etcher datasets have been utilised to validate the proposed framework. The results show that the proposed LRE approach outperforms global reconstruction errors for similar model architectures achieving an AUC of 1.00. The proposed unsupervised learning approach with AE and LRE improves model explainability which is expected to be beneficial for deployment in semiconductor manufacturing where interpretable and trustworthy results are critical for process engineering teams. [ABSTRACT FROM AUTHOR]

Herbal remedies are in most cases still manufactured with traditional equipment installations and processes. Innovative chemical process engineering methods such as modeling and process intensification with green technology could contribute to the economic and ecologic future of those botanicals. The integration of modern unit operations such as water-based pressurized hot water extraction and inline measurement devices for process analytical technology approaches in traditional extraction processes is exemplified. The regulatory concept is based on the quality-by-design demand for autonomous feed-based recipe operation with the aid of digital twins within advanced process control. This may include real-time release testing to the automatic cleaning of validation issues. Digitalization and Industry 4.0 methods, including machine learning and artificial intelligence, are capable of keeping natural product extraction manufacturing and can contribute significantly to the future of human health. [ABSTRACT FROM AUTHOR]

Machine health monitoring of rotating mechanical systems is an important task in manufacturing engineering. In this paper, a system for analyzing and detecting mounting defects on a rotating test rig is developed. The test rig comprises a slender shaft with a central disc, supported symmetrically by oscillating ball bearings. The shaft is driven at constant speed (with tiny variations) through a timing belt. Faults, such as the translation of the central disc along the shaft, the disc eccentricity, and defects on the motor reducer position or timing belt mounting position, are imposed. Time and frequency domain features, extracted from the vibration signal, are used as predictors in fault detection. This task is modeled as a multi-class classification problem, where the classes correspond to eight health states: one healthy and seven faulty. Data analysis, using unsupervised and supervised algorithms, provides significant insights (relevance of features, correlation between features, classification difficulties, data visualization) into the initial dataset, a balanced one. The experiments are performed using classifiers from MATLAB and six feature sets. Quadratic SVM achieves the best performance: 99.18% accuracy for the set of all 41 features extracted from X and Y accelerometer axes, and 98.93% accuracy for the subset of the 18 most relevant features. [ABSTRACT FROM AUTHOR]

With the advent of the coronavirus (COVID-19) pandemic in 2020, the consumer goods and services sectors in Brazil sought to meet the growing demand virtually. E-commerce in the country grew 47%, corresponding to the highest increase in 20 years. The purpose of this article is to demonstrate the feasibility of applying the Superiority and Inferiority Ranking Method for selecting the best website hosting service company, among several alternatives and criteria. It should be noted that there is a wide variety of companies in this segment, making the selection complex when without the aid of a decision-making method. A hybrid multicriteria decision support method was used, the Simple Aggregation of Preferences Expressed by Ordinal Vectors -Multi Decision Makers (SAPEVO-M) and the Superiority and Inferiority Ranking (SIR). The SAPEVO-M assists in determining the weight vector and the SIR favors the ordering of the alternatives. The alternatives and criteria were selected based on relevance and importance, respectively, for the authors. The solution found was satisfactory, since the most preferred company, LocaWeb, has the highest value for the preference criterion, storage, however, there is a great disparity between the best and worst alternative. [ABSTRACT FROM AUTHOR]

Undersaturated oil viscosity is one of the most important PVT parameters to be measured and/or predicted in a fluid sample. Since direct experimental measurements are expensive and time-costly, prediction methods are essential. In this work, viscosity data from more than five hundred fluid reports are utilized, and all correlation methods available in the literature and implemented in commercial software for reservoir and production engineering calculations, including fracked systems, are evaluated against the dataset. The results of this work are intended to set up workflows that give insight as to which method should be selected when running flow simulations, with emphasis on complex simulations such as in the case of EOR. The developed workflows provide the optimal choice of the viscosity correlation for the case of distinct viscosity ranges, as well as when overall performance is sought. A surprising result is that one of the oldest known correlations from the literature gives the best results (minimizes average absolute relative error) when tested against this large dataset. This may be attributed to the high locality that alternative correlations exhibit, which makes them generalize poorly. [ABSTRACT FROM AUTHOR]

Emotion classification is a research field that aims to detect the emotions in a text using machine learning methods. In traditional machine learning (TML) methods, feature engineering processes cause the loss of some meaningful information, and classification performance is negatively affected. In addition, the success of modelling using deep learning (DL) approaches depends on the sample size. More samples are needed for Turkish due to the unique characteristics of the language. However, emotion classification data sets in Turkish are quite limited. In this study, the pretrained language model approach was used to create a stronger emotion classification model for Turkish. Well-known pretrained language models were fine-tuned for this purpose. The performances of these fine-tuned models for Turkish emotion classification were comprehensively compared with the performances of TML and DL methods in experimental studies. The proposed approach provides state-of-the-art performance for Turkish emotion classification. [ABSTRACT FROM AUTHOR]