Process Configuration Research Articles

A miniaturized feedstocks-to-fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass.

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a "one-pot" process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

Breaking the Passivation Effect for MnO2 Catalysts in Li−S Batteries by Anion‐Cation Doping

AbstractTransition metal oxides (TMOs) are recognized as high‐efficiency electrocatalyst systems for restraining the shuttle effect in lithium‐sulfur (Li−S) batteries, owing to their robust adsorption capabilities for polysulfides. However, the sluggish catalytic conversion of Li2S redox and severe passivation effect of TMOs exacerbate polysulfide shuttling and reduce the cyclability of Li−S batteries, which significantly hinders the development of TMOs electrocatalysts. Here, through the anion‐cation doping approach, dual incorporation of phosphorus and molybdenum into MnO2 (P,Mo‐MnO2) was engineered, demonstrating effective mitigation of the passivation effect and allowing for the simultaneous immobilization of polysulfides and rapid redox kinetics of Li2S. Both experimental and theoretical investigations reveal the pivotal role of dopants in fine‐tuning the d‐band center and optimizing the electronic structure of MnO2. Furthermore, this well‐designed configuration processes catalytic selectivity. Specifically, P‐doping expedites rapid Li2S nucleation kinetics by minimizing reaction‐free energy, while Mo‐doping facilitates robust Li2S dissolution kinetics by mitigating decomposition barriers. This dual‐doping approach equips P,Mo‐MnO2 with robust bi‐directional catalytic activity, effectively overcoming passivation effect and suppressing the notorious shuttle effect. Consequently, Li−S batteries incorporating P,Mo‐MnO2‐based separators demonstrate favorable performance than pristine TMOs. This design offers rational viewpoint for the development of catalytic materials with superior bi‐directional sulfur electrocatalytic in Li−S batteries.

Breaking the Passivation Effect for MnO2 Catalysts in Li-S Batteries by Anion-Cation Doping.

Transition metal oxides (TMOs) are recognized as high-efficiency electrocatalyst systems for restraining the shuttle effect in lithium-sulfur (Li-S) batteries, owing to their robust adsorption capabilities for polysulfides. However, the sluggish catalytic conversion of Li2S redox and severe passivation effect of TMOs exacerbate polysulfide shuttling and reduce the cyclability of Li-S batteries, which significantly hinders the development of TMOs electrocatalysts. Here, through the anion-cation doping approach, dual incorporation of phosphorus and molybdenum into MnO2 (P,Mo-MnO2) was engineered, demonstrating effective mitigation of the passivation effect and allowing for the simultaneous immobilization of polysulfides and rapid redox kinetics of Li2S. Both experimental and theoretical investigations reveal the pivotal role of dopants in fine-tuning the d-band center and optimizing the electronic structure of MnO2. Furthermore, this well-designed configuration processes catalytic selectivity. Specifically, P-doping expedites rapid Li2S nucleation kinetics by minimizing reaction-free energy, while Mo-doping facilitates robust Li2S dissolution kinetics by mitigating decomposition barriers. This dual-doping approach equips P,Mo-MnO2 with robust bi-directional catalytic activity, effectively overcoming passivation effect and suppressing the notorious shuttle effect. Consequently, Li-S batteries incorporating P,Mo-MnO2-based separators demonstrate favorable performance than pristine TMOs. This design offers rational viewpoint for the development of catalytic materials with superior bi-directional sulfur electrocatalytic in Li-S batteries.

Techno-economic analysis and optimisation of Piperazine-based Post-combustion carbon capture and CO2 compression process for large-scale biomass-fired power plants through simulation

This study aims to investigate a cost-effective and energy-efficient amine-based post-combustion carbon capture (PCC) process for large-scale supercritical biomass-fired power plants (SC BFPP). Thus, we have quantified the energy and economic performance of the PCC process with different configurations and solvents. Three process configurations which included the standard configuration, the absorber intercooler (AIC) with the advanced flash stripper (AFS) and the AIC, AFS and side stream extraction (SSE) were simulated in Aspen Plus® V11 using 30 wt% and 40 wt% piperazine (PZ) as solvent. In addition to this, CO2 compression trains using the heat pump (HP) and supercritical CO2 cycle (s-CO2) were also simulated. Sensitivity analysis of the PCC process was carried out to investigate the impact of important parameters on the energy performance of the process. Furthermore, energy analysis shows that a minimum energy consumption of 2.78 GJ/tCO2 was achieved with the PCC process using 40 wt% PZ, a combination of the AIC, AFS and SSE for capture and s-CO2 for compression. This achieved a significant energy saving of 1.01 GJ/tCO2 compared with the standard PCC process using 30 wt% monoethanolamine that is used as the benchmark in this study. The economic analysis results showed that the minimum CO2 capture cost of 55.70 $/tCO2 was achieved using the AIC-AFS-SSE-sCO2 configuration and 40 wt% PZ as solvent. This represents a 19.5 % reduction in cost compared with the standard 40 wt% PZ process with a cost of 69.18 $/tCO2. The optimisation of the PZ-based PCC process was carried out to determine the optimal solvent concentration with the minimum carbon capture cost. It was found that the optimal PZ concentration for the PCC process based on standard and AFS configurations were 37.5 wt% and 32.5 wt%, respectively. The optimisation of the stripper pressure for the minimum carbon capture cost was conducted. As a result, compared with the standard PCC process using 40 wt% PZ, the energy consumption and CO2 capture cost of the optimised process at the suggested pressure of 7 bar were reduced by 41.6 % and 32.4 %, respectively.

Unraveling the alternative process configurations for more environmentally friendly Maleic Anhydride production

This study aims to propose and evaluate alternative configurations for producing maleic anhydride (MA) through the n-butane oxidation pathway to enhance overall environmental friendliness. Seven alternative configurations, which vary in the number of non-adiabatic reactors and heat exchange methods, as well as the stripping agents used for recovering MA in the purification section, are rigorously designed and comprehensively compared. Among the proposed configurations, we have found that the one utilizing single reactor with co-current cooling and water as the stripping agent (Scheme 2) shows the most promise. It has an optimal yield of 70.0 %, an energy efficiency of 97.52 %, and exergy efficiency of 44.92 %. A cradle-to-gate life cycle impact assessment determines a Global Warming Potential (GWP) of 0.091 kg-CO2eq/kg-MA from this scheme. In contrast, a techno-economic evaluation determines a minimum required selling price (MRSP) of 0.965 USD/kg-MA from this scheme. All these indicators are superior compared to those documented in the existing literature, suggesting a higher level of environmental friendliness. Subsequently, a suitable control strategy is proposed for the optimized Scheme 2 and evaluated by rejecting various kinds of feed disturbances (i.e., ± 10 % change in feed flowrate, ± 5 % change in molten salt flowrate, 1 % and 2 % of i-butane as feed impurity, and catalyst deactivation up to 40 %). Enhanced controllability is achieved by regulating the flowrate ratio between the flashed liquid and the n-butane (FL/NBU), which promptly indicates the reaction conversion during production. Based on the findings, we have concluded that overall environmental friendliness can be significantly enhanced with moderate modifications in the process configuration, which are easily feasible in the industry.

Exergy analysis of process configurations for phenol recovery from wastewater

Wastewater with high phenol content, primarily generated by the petrochemical, coal, oil, and gas industries, must be treated before disposal to meet global regulations and prevent environmental harm. Liquid-liquid extraction is a well-studied method employed to address this need. Despite the extensive focus on to energy performance, exergy analysis has not yet been incorporated. Therefore, this study aims to compare four process configurations by integrating an exergetic approach with economic and emissions analyses to quantify thermodynamic efficiency, total annual cost, and CO2 emissions. Process simulations were conducted using Aspen Plus V.12, and the economic analysis was performed using the Aspen Process Economic Analyzer. The input streams of all configurations were 100 ton/h of wastewater containing 275 kg/h of phenol and 266 kg/h of hydroquinone, with pure methyl iso-butyl ketone (MIBK) as the makeup solvent. Extraction and mixing exhibited the lowest exergy destruction, while distillation accounted for approximately 55 % of the total exergy destruction. Configurations that incorporated direct medium-pressure steam input resulted in greater exergy destruction, leading to reduced exergy efficiency, but a lower total annual cost. The most economical configuration was achieved by heat integration of De Dietrich® process with low-pressure steam (C-IE lps), benefiting from exergy analysis insights. The exergy efficiency of this configuration increased from 33 % to 38 %, and the total annual cost decreased by 183 thousand USD per year compared to the initial De Dietrich® process (configuration C). Similarly, CO2 emissions were reduced to the lowest level, reaching 4.87 kg CO2 /h.

Integration of energy storage and determination of optimal solar system size for biomass fast-pyrolysis

This study presents a model and analysis of a heliostat field collector (HFC) system integration with fast-pyrolysis of biomass and determination of the optimal solar system size for this integrated system. Given the intermittent nature of solar energy, an auxiliary heater and a thermochemical energy storage system (TCES) are included. Four cases of HFC integration with the fast-pyrolysis process have been studied: 1) low solar radiation, 2) sufficient solar radiation, 3) high solar radiation, and 4) no solar radiation with available stored energy in TCES. The solar energy system was modeled and calculated using the Engineering Equation Solver (EES) software, while the fast-pyrolysis process and the TCES were simulated using the Aspen Plus software. A thermodynamic and economic analysis has been conducted to estimate the share of solar energy for different process configurations. Economic calculations have been conducted for three different heliostat filed areas: 4000, 8000, and 12000 m2. Solar fraction, investment and operational costs, as well as total cost were calculated for these three heliostat field areas. The results indicate that the optimum heliostat field area for the studied biomass pyrolysis plant is 8000 m2 and the average solar fraction of the required energy in summer is 0.39 and while it is 0.34 for the whole year. Simulation results considering this optimized heliostat filed area indicate that 6.27 t/h of bio-oil is produced from 10 t/h of hybrid poplar biomass. Implementing this solar-assisted system reduces CO2 emissions, increases efficiency of the system and lowers thermal energy requirement for the fast-pyrolysis process from 6MW to 3.99MW.

PRODUCTION OPTIMIZATION USING PETRI NETS

The work finds purpose in the development of automating fibrous bag palletizing solutions. Using Petri nets, representation and studying of the entire process in a simple manner was possible, from bag apprehension until the final palletization of the filled bags – which allowed the analysis of system performance. Using PIPE, different process configurations were laid out and with FlexSim 2024 a virtual representation of the production line was made.

High-performance Multi-stage Baffled A2O Treatment Process for Domestic Sewage on Plateaus

At high altitudes, the low air pressure, low atmospheric oxygen content, and cryogenic environment during the cold season greatly limit the treatment efficiency of wastewater treatment plants (WWTPs). A novel pilot-plant configuration of the multi-stage baffled A2O wastewater treatment process was proposed and tested in Xizang. Different operational conditions involving at different influent loads and at low temperatures (10.0–11.0°C) were tested. When the influent flowrate increased to 4 m3∙d−1, the hydraulic retention time (HRT), internal and external reflux ratio, dissolved oxygen (DO), and aeration demands (gas to water ratio) all decreased to 34.2 h, 3.5/7, a stable 2.0–2.5 mg∙L−1, and 17.5, respectively. The effluent chemical oxygen demand (COD), total nitrogen (TN), ammonia nitrogen (NH+4−N), and total phosphorus (TP) all met the requirements of Class 1 Grade A of the China National Municipal Wastewater Discharge Standards (GB 18918-2002). The contribution of denitrifying phosphorus removal (DPR) to the removal of both nitrogen and phosphorus was over 50%. The alpha diversity and abundance of the top genera in the microbial community structure were both higher than the plateau WWTP. The reaction activity of the DPR process was significantly enhanced via the increased abundance of key functional genes within the metabolism pathway of ammonia-oxidizing bacteria (AOB) and nitrogen-oxidizing bacteria (NOB). The special multi-stage baffled structure featured a strategy of high sludge storage that improved the system tolerance for low temperatures and ensured favorable and stable performance for nitrogen and phosphorus removal at low temperatures. A short, periodic, and cyclically intermittent operation mode, with each cycle lasting only 20 minutes, effectively inhibited filamentous bacteria sludge bulking, resulting in a sludge volume index (SVI) that decreased to within 120 mL∙g−1 during the first 15 days of system start-up. A long sludge retention time (SRT) with no sludge discharging over 169 days and reduced aeration demands contributed to lower operation costs. The investigation revealed that the system had a high capacity for storing sludge phosphorus, possessing a TP content within a range of 23.45–28.99 mg∙g−1. This study provides a feasible solution for efficiently and economically treating wastewater in high-altitude areas.

Adaptively remeshed multiphysical modeling of resistance forge welding with experimental validation of residual stress fields and measurement processes

Welding processes used in the production of pressure vessels impart residual stresses in the manufactured component. Computational modeling is critical to predicting these residual stress fields and understanding how they interact with notches and flaws to impact pressure vessel durability. In this work, we present a finite element model for a resistance forge weld and validate it using laboratory measurements. Extensive microstructural changes, near-melt temperatures, and large localized deformations along the weld interface pose significant challenges to Lagrangian finite element modeling. The proposed modeling approach overcomes these roadblocks in order to provide a high-fidelity simulation that can predict the residual stress state in the manufactured pressure vessel; a rich microstructural constitutive model accounts for material recrystallization dynamics, a frictional-to-tied contact model is coordinated with the constitutive model to represent interfacial bonding, and adaptive remeshing is employed to alleviate severe mesh distortion. An interrupted-weld approach is applied to the simulation to facilitate comparison to displacement measures. Several approaches are employed for residual stress measurement in order to validate the finite element model: neutron diffraction, the contour method, and the slitting method. Model-measurement comparisons are supplemented with detailed simulations that reflect the configurations of the residual-stress measurement processes themselves. The model results show general agreement with experimental measurements, and we observe some similarities in the features around the weld region. Factors that contribute to model-measurement differences are identified. Finally, we conclude with some discussion of the model development and residual stress measurement strategies, including how to best leverage the efforts put forth here for other types of related weld problems.

Structured pruning of neural networks for constraints learning

In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity in applications such as cancer treatment, algorithmic configuration, and chemical process optimization. This integration often uses Mixed Integer Programming (MIP) formulations to represent the chosen ML model, that is often an Artificial Neural Networks (ANNs) due to their widespread use. However, ANNs frequently contain a large number of parameters, resulting in MIP formulations impractical to solve. In this paper we showcase the effectiveness of a ANN pruning, when applied to models prior to their integration into MIPs. We discuss why pruning is more suitable in this context than other ML compression techniques, and we highlight the potential of appropriate pruning strategies via experiments on MIPs used to construct adversarial examples to ANNs. Our results demonstrate that pruning offers remarkable reductions in solution times without hindering the quality of the final decision, enabling the resolution of previously unsolvable instances.

La publicitación de los feminicidios de madres buscadoras en México

This article analyzes the publicizing of the femicides of searching mothers from the perspective of sociology of critique. To this end, it tests an actantial model of complaints through a digital ethnography based on public texts, with the purpose of understanding the slow process of configuration of this social problem as a public problem. The work analyzes this type of feminicide, whose execution pattern is related to the field of organized violence, through a strategy focused on the justifications of the actors involved. In this logic, it reconstructs the stories and narratives of the political and social actors involved in the process, describes relationships between experts and victims in the complaint and access to justice, and provides elements for understanding the meaning that social and political actors give to their experiences of this borderline social problem. The article closes with some considerations about the challenges in researching the structural effects of this type of violence in the field of social sciences and multi-localized public action at a national and subnational scale.

Optimization of Power to Gas system with cooled reactor for CO2 methanation: Start-up and shut-down tests with Ru-based and Ni-based kinetics

The dynamic behavior of a methanation process is analyzed in the context of Power to Gas (PtG) technology, to convert carbon dioxide from different sources and hydrogen produced via electrolysis with renewable energy to synthetic methane. The process configuration consists of a single cooled plug flow reactor (PFR) where both ruthenium and nickel-based kinetic models are tested in the Aspen Dynamics simulation environment. In particular, dynamic analyses are performed to study the start-up and shut-down phases and respect injection parameters in the existing gas network. The process consists of three main sections: conditioning, reaction and separation. After the development and optimization of the control system, the start-up and shut-down tests are carried out, to study the trend of composition for the injection in the gas network. The plant size is set according to the following values: 700 Nm3/h of hydrogen and 175 Nm3/h of carbon dioxide, which correspond to the methanation reaction stochiometric ratio. Different cases are considered in both the start-up and shut-down tests, by changing the GHSV values and the catalyst adopted. Moreover, in the shut-down test two different rates are considered to evaluate the behavior of the system.

Integrating use of sugarcane bagasse with sugarcane juice for efficient l-lactic acid production through fed-batch simultaneous saccharification and fermentation: Process configurations and kinetic analysis

The present study investigated the utilization of sugarcane juice (SJ) and sugarcane bagasse (SB) for L-lactic acid (L-LA) production by a newly isolated Enterococcus gallinarum TSKKU D-6. SJ was first investigated as a single substrate, yielding 43.9 ± 2.9 g-LA/L, with a fermentation efficiency of 72.0 ± 0.1 %, based on sucrose content in SJ. On the other hand, simultaneous saccharification and fermentation (SSF) of SB as a single substrate yielded 68.2 ± 2.7 g-LA/L, with a fermentation efficiency of 74.1 ± 0.0 %, based on cellulose content in SB. SSF of SJ and SB as co-substrates was subsequently carried out to increase the acid production, where SJ was first fermented, followed by the addition of SB to enhance LA titer. Effects of mixing and SB addition strategy on the kinetics and economic feasibility of L-LA production were investigated using a bioreactor equipped with a single and dual impellers, as well as the use of dual impellers under fed-batch SSF. The use of single and dual impellers gave similar L-LA titers of 95.9 ± 1.4 and 102.6 ± 0.6 g/L, respectively. Nevertheless, the use of dual impellers shortened the processing time, enhancing LA productivity from 0.37 to 1.12 g/(L·h). As a result, the economic yield and productivity were improved by 7.1 % and 187.5 %, respectively. Better still, performing fed-batch SSF with the use of dual impellers further improved the economic productivity by 8.7 %. This study demonstrated that integrating use of SB as a low-cost substrate considerably enhanced the acid production and lessened the use of SJ. It was also demonstrated that mixing, and process configuration are critical factors determining the process performance.

Configuración de una red LAN utilizando Python scripting para generar redes definidas por software (SDN).

Network management is an essential task in modern organizations, where the complexity of technological infrastructures demands efficient and accurate solutions. With network devices from leading manufacturers such as Cisco and Mikrotik. In response to these challenges, automating the configuration of network devices has gained popularity as an approach to improve efficiency and reduce errors. Python, a programming language widely adopted for its versatility and ease of use, has become a key tool for this automation. Within this ecosystem, Netmiko emerges as a specialized library that facilitates automated interaction with network devices using SSH. Netmiko allows network administrators to write scripts that can connect to Cisco and Mikrotik devices, execute configuration commands, and retrieve information about the network status. This approach not only speeds up the configuration process, but also ensures consistency and accuracy in deploying configurations, especially in multi-device environments. As networks continue to grow in complexity, the adoption of tools such as Netmiko becomes essential to maintaining the operability and security of network infrastructures.

ANALYSIS OF APPROACHES TO DESIGN ONTOLOGICAL MODELS OF AN ADAPTIVE SOFTWARE SYSTEM

The paper analyzes approaches to the design of ontological models of adaptive software systems. To evaluate the quality of the projected ontologies, specialized groups of metrics are used, which allow us to determine the shortcomings in the structure and functioning of the model. An analysis of the use of ontological models in the process of designing and developing adaptive software systems has been carried out. It was established that the ontological approach allows for a more effective presentation of the subject area, which improves the software configuration process. The use of ontological models allows flexible responses to changes in user requirements without the need to directly update the source code of the software. The process of designing ontologies based on classical and abstract approaches is presented. It was determined that the use of a classical approach to the design of ontological models could complicate the adaptation process due to the need to change the structure and reconfiguration of the system. To solve this problem, an abstract approach is proposed that ensures the dynamism of the adaptation process, without the need for a complete reconfiguration of the system. A set of specialized metrics is defined that allows for the analysis of the ontological model of the software system, the evaluation of its syntactic and semantic quality, compliance with rules and interoperability, and the possibility of expansion. A comparative analysis of abstract and classical approaches to the design of ontological models was carried out based on defined structural metrics, as well as metrics of the scheme and filling of the knowledge base. The obtained values of structural and hierarchical metrics confirm the quality and effectiveness of the proposed approaches. According to the defined metrics, the absence of cycles and entanglement in the ontological model indicates the correctness of the construction of the structure from the point of view of ergonomics and the possibility of further changing the structure and filling of the ontological model. The analyzed values of the schema metrics and the filling of the knowledge base demonstrate the better effectiveness of the abstract approach in the process of determining the software configuration, which provides faster and more correct processing of ontological SWRL rules during adaptation.

Performance analysis of refuse‐derived fuel gasification plant with carbon capture and storage for power, heating, and hydrogen production

AbstractAmong various waste‐to‐energy technologies, gasification is one of the most promising, because of high efficiency, feedstock flexibility, and carbon capture potential. This case study is focused on comprehensive analysis of integrated gasification combined cycle‐based plant with refuse‐derived fuel (RDF) as feedstock and carbon capture. As there are hardly any studies focused on simulation of waste gasification with carbon capture, most of which are lacking important process specifics, this study addresses existing research gap. Process flowsheets are developed in detail according to literature data for various process configurations and simulated in AspenPlus software, while obtained results on material and energy balance were used for estimation of plant efficiency and performance indicators. Waste generation data in Novi Sad, Serbia, were used for determination of RDF flowrate. Configurations include different syngas cleaning pathways, final products (power, heating, and hydrogen) and co‐gasification with coal. Cogeneration increases overall plant efficiency from 27%–36% (power production only) to 63%–76%. High net hydrogen efficiencies, around 58%, compensate lower power and thermal energy production in hydrogen‐based configurations. Overall, co‐gasification produces better results due to higher feedstock heating value. Obtained results will be used in further research for environmental and economic evaluation to provide multi‐level assessment of proposed processes.

Thermo-kinetic synergy in separating dimethyl carbonate/methanol/water mixtures using ionic liquids-based mixed solvents

The low selectivity of traditional organic solvents and the limited mass transfer efficiency caused by the high viscosity of ionic liquids (ILs) pose challenges in separating of dimethyl carbonate (DMC)/methanol/water mixtures. Therefore, a novel mixed solvent is proposed with a mass ratio of methyl salicylate to 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide of 6:4. The synergistic effects of thermodynamic, kinetic, and economic performance using the mixed solvent are investigated. Comparisons of overall efficiency and total annual cost (TAC) for four process configurations using direct and indirect extractive distillation sequences to produce industrial and battery-grade DMC products are conducted. The results indicate that direct extractive distillation outperforms the indirect sequence, reducing TAC by 6.1–14.3 % for producing industrial-grade DMC and 6.6–15.8 % for battery-grade DMC. Additionally, using mixed solvents in direct extractive distillation is more effective than using pure solvents, enhancing mass transfer coefficients by 2.8–20.9 % for industrial-grade DMC and 1.8–17.8 % for battery-grade DMC. This approach also reduces TAC by 3.2–3.6 % for industrial-grade DMC and 4.7–5.7 % for battery-grade DMC.

Beyond the Whole: Reduced Empathy for Masked Emotional Faces Is Not Driven by Disrupted Configural Face Processing.

Sharing of emotional states is reduced for individuals wearing face coverings, but the mechanism behind this reduction remains unknown. Here, we investigated if face occlusion by masks reduces empathy by disrupting configural processing of emotional faces. Participants rated their empathy for happy and neutral faces which were presented in upright or inverted orientation and wore opaque, clear, or no face masks. Empathy ratings were reduced for masked faces (opaque or clear) as well as for inverted faces. Importantly, face inversion disrupted empathy more for faces wearing opaque masks relative to those wearing clear or no masks, which stands in contrast to the predictions generated by the classic configural processing models. We discuss these data within the context of classic and novel configural face perception models, and highlight that studying inverted occluded faces presents an informative case worthy of further investigation.

Riding the waves of uncertainty: Towards strategic agility in medicine supply systems

AbstractWe investigate how organizations embedded in a supply system collectively respond to risks and seize opportunities arising from crisis events under shifting forms of uncertainty. Using the United Kingdom (UK) medicine supply system as the research context, we explore how decision‐makers navigated the effects of an event with knowable implications (UK's European Union exit, 2016–2020) followed by an event with unknowable implications (COVID‐19 global pandemic, 2020–2021). We adopt a longitudinal case research design that incorporates causal loop diagramming, to understand the system's responses. We find that learning evolves as crisis events unfold, changing from surface (know‐what) to deep (know‐why and ‐how) and at the highest level, it is transcendent. Transcendent learning entails understanding system effects into the future (i.e., beyond the past and present) and in relation to other supply systems (i.e., beyond the UK system). Capabilities to absorb, avoid, and accelerate away from shocks are developed sequentially as learning changes. We contribute to prior research by developing a theory of system‐level strategic agility and the adaptation processes that underpin it. The latter hinge on dynamic resource (re)allocation and the continuous (re)configuration of processes, protocols, regulations, and structures.