d-Allulose production via a simplified in vitro multienzyme cascade strategy: Biosynthesis and crystallization

d-Allulose is a rare sugar with multiple physiological functions whose application has been restricted by low conversion rate, weak thermostability of the used enzymes, and complex production. In this study, a simplified multienzyme cascade strategy was designed for biosynthesis of d-allulose from d-glucose, and a detailed investigation of the crucial enzyme allulose 6-phosphate phosphatase was conducted. Under optimized conditions, the yield of d-allulose reached 4.12 ± 0.14 and 80.20 ± 3.20 mmol/L using 10.0 and 200.0 mmol/L d-glucose as substrate, respectively. Furthermore, the influence of various factors on the cooling crystallization of d-allulose was investigated, and the crystallization process was optimized. Consequently, a crystallization recovery rate of 77.43% ± 2.50% and a purity of 98.70% ± 0.05% were achieved. Moreover, the obtained crystals of d-allulose were characterized by XRD, TGA-DSC, and optical microscopy

Experimental validation of a predictive energy management strategy for agricultural fuel cell electric tractors

Efficiency and durability are critical challenges for the large-scale adoption of fuel cell powertrains in agricultural tractors due to the demanding nature of farm-related tasks. This study presents a novel predictive energy management strategy that uses forecasts of the average load of the farming cycle to achieve stationary fuel cell operation, thus limiting degradation caused by load cycling, high-power, and low-power operation. The recurrent characteristics exhibited by agricultural duty cycles allow the calculation of highly accurate average load prediction, which can be sufficient for a predictive energy management strategy to reduce degradation without hindering fuel consumption. As a result, this forecast is utilized in conjunction with a straightforward and real-time-capable energy management strategy, which foresees the operation of the fuel cell system at the level of the average load. The work is structured into two main contributions: model-based design of the energy management strategy, and experimental validation on a fuel cell powertrain testbed. Three typical agricultural duty cycles were studied to evaluate the benefits of the proposed strategy, demonstrating that energy management strategies can mitigate fuel cell degradation without compromising system efficiency. In particular, the predictive energy management strategy offers the potential to mitigate the negative impact of tractor operational idling time at the headland turns through the reduction of fuel consumption, thereby enhancing overall tractor performance. The experimental powertrain setup stands as an innovative contribution within the research landscape concerning fuel cell powertrains for tractors and validates the benefits of the proposed strategy. Furthermore, the conducted testbed analyses assume significance in highlighting the effects of the energy management strategy on the physical fuel cell operating factors and emphasizing the comprehensive benefits across the powertrain system, particularly with regard to the thermal management of the fuel cell system, where the prevention of derating is achievable through the implementation of the proposed energy management approach. Overall, the findings of this study have important implications for the development of sustainable and efficient farming procedures through the adoption of fuel cell technology in agricultural machinery

Flow in an Alveolated Duct during Pulsate Bi-level Ventilation

PBLV provides enhanced oxygenation and carbon dioxide removal compared to BLV. The physical reason behind the improvement is not clear. This work provides insight into the fluid flow behavior in a singular alveolus during PBLV and BLV

Preference Explanation and Decision Support for Multi-Objective Real-World Test Laboratory Scheduling

Complex real-world scheduling problems often include multiple conflicting objectives. Decision makers (DMs) can express their preferences over those objectives in different ways, including as sets of weights which are used in a linear combination of objective values. However, finding good sets of weights that result in solutions with desirable qualities is challenging and currently involves a lot of trial and error. We propose a general method to explain objectives' values under a given set of weights using Shapley regression values. We demonstrate this approach on the Test Laboratory Scheduling Problem (TLSP), for which we propose a multi-objective solution algorithm and show that suggestions for weight adjustments based on the introduced explanations are successful in guiding decision makers towards solutions that match their expectations. This method is included in the TLSP MO-Explorer, a new decision support system that enables the exploration and analysis of high-dimensional Pareto fronts

Optimization-Based Development of a Causal, Cascaded, Map-Based Energy Management Strategy for Hybrid Electric Vehicles with Multiple Control Variables

The objective of this research is a generally valid and modular methodology for developing a causal, fuel-optimized, model-free energy management strategy (EMS), in-cluding a gear shift strategy, for hybrid electric vehicles (HEVs) with multiple electric machines (EMs) and possibly multiple transmissions. Therefore, this paper presents a novel methodology for the optimization-based development of a causal, cascaded, map-based EMS for HEVs with multiple control variables (MCVs), based on related research on Pontryagin's minimum principle (PMP), equivalent consumption minimization strategy (ECMS), and map-based EMS approaches. The proposed methodology is applicable to any hybrid powertrain topology, as well as battery electric vehicles (BEVs) with multiple EMs and possibly multiple transmissions. The EMS is mathemat-ically described as an optimal control problem in order to compute optimal control maps (OCMs) based on multi-criteria optimization considering soft and hard constraints. The EMS is defined as a multi-stage decision-making process and is therefore implemented as a cascaded logic. This enables offline calculated OCMs to be manipulated by downstream additional sub-optimal rules for system and gear state transitions at each decision level. The EMS can be implemented as either a non-predictive or predictive strategy. To demonstrate the functionality of the proposed methodology, it is applied to a P24-HEV as an example, and simulation results are presented and discussed. Finally, recommendations for future work are provided

The impact of urban street green transformation on subjective well-being and evaluation of the location: A case study in Vienna, Austria

Urban green landscapes, such as street-and ground-level greenery, are essential for urban populations, enabling frequent and spontaneous interactions with nature in cities. While many cities have increased their green infrastructure and landscapes, their impact on well-being and environmental evaluations needs to be studied more. In the present study, we conducted a field experiment that directly addressed this aspect. Specifically, on two urban streets in Vienna (Austria), we conducted the same structured field experiment during two different periods, during March and May/June in 2022, resulting in different levels of greenery in two urban streets. We aimed to study if and how varying quantities of greenery in urban street landscapes influence subjective well-being in terms of subjective feelings of stress and affective mood, as well as the restorative potential of the locations. Our results showed that, unlike the often-reported positive impact of urban green spaces, the varying amount of greenery on the streets did not positively affect the well-being or the restorative potential of the locations. The results highlight that simply implementing greenery might not be sufficient to induce positive effects. Instead, more intense and dense greenery would be necessary to achieve the desired outcomes