Structural Aspects and Prediction of Calmodulin-Binding Proteins

Calmodulin (CaM) is an important intracellular protein that binds Ca²⁺ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM’s ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca²⁺ concentration through interactions with CaM.     

Farmers' preferences for adopting on-farm concentration of raw milk: Results from a discrete choice experiment in Germany

Milk concentrates are used in the manufacturing of dairy products such as yogurt and cheese or are processed into milk powder. Processes for the nonthermal separation of water and valuable milk ingredients are becoming increasingly widespread at farm level. The technical barriers to using farm-manufactured milk concentrate in dairies are minimal, hence the suspicion that the practice of on-farm raw milk concentration is still fairly uncommon for economic reasons. This study, therefore, set out to investigate farmers' potential willingness to adopt a raw milk concentration plant. The empirical analysis was based on discrete choice experiments with 75 German dairy farmers to identify preferences and the possible adoption of on-farm raw milk concentration. The results showed that, in particular, farmers who deemed the current milk price to be insufficient viewed on-farm concentration using membrane technology as an option for diversifying their milk sales. We found no indication that adoption would be impeded by a lack of trustworthy information on milk processing technologies or capital.     

High lithium storage of Co3O4 enabled by integrating hollow and porous carbon scaffolds

The Co₃O₄, as a potential anode of lithium-ion batteries, has gained considerable attention because of high theoretical capacity. However, the Co₃O₄ is suffering from serious structure deterioration and rapid capacity fading due to its bulky volume change during cyclic charge/discharge process. Herein, to stabilize the lithium storage performance of the Co₃O₄ nanoparticles, a characteristic carbon scaffold (HPC) integrating hollow and porous structures has been fabricated by a well-designed method for the first time. The ultrafine Co₃O₄ nanoparticles are cleverly anchored on the HPC (HPC@Co₃O₄) and hence achieve significantly improved electrochemical properties including high capacity, improved reaction kinetics and outstanding cycle stability, showing high capacity of 1084.7 mAh g^-1 after 200 cycles at 200 mA g^-1 as well as 681.4 mAh g^-1 after 300 cycles at 1000 mA g^-1. The HPC@Co₃O₄ therefore shows good promising for application in advanced lithium-ion battery anodes. The results of the systematically material and electrochemical characterizations indicate that the synergistic effects of ultrafine Co₃O₄ nanoparticles and well-designed HPC scaffolds are responsible for the outstand performance of the HPC@Co₃O₄ anode. Moreover, this work can enrich the understanding and development of stable and high-performance metal oxide-based lithium-ion battery anodes for advanced lithium storage.     

Cubic B-splines method for Hall and ion slip impacts on unsteady MHD rotating flow past a vertical moving porous plate

The heat and mass transfer of unsteady magnetohydrodynamic (MHD) flow of Newtonian fluid with Hall current and ion-slip currents due to vast possible engineering applications is very important in areas like power generators, MHD accelerators, refrigeration coils, electric transformers, and heating elements. A quality-based research on Hall and ion-slip consequences on the rotating unsteady MHD flow past an infinite perpendicular moving absorbent plate have not been performed. Therefore, the Hall and ion-slip consequences on rotating unsteady MHD flow past an infinite perpendicular moving absorbent plate have not been performed. The similarity transformations are engaged to transfer the governing partial differential equations within favor of the scheme of nonlinear ordinary differential equations and elucidated numerically making use of cubic B-splines collocation mechanism. The influences of felicitous parameters on basic equations are remarked on through graphical profiles. Even though the computational estimations of frictional forces, Nusselt number, and Sherwood number for various parameters are distributed in tabular format and exchanged of views comparatively.     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

Cu-template-dependent synthesis of PtCu nanotubes for oxygen reduction reactions

The development of electrocatalysts with high activity and durability for oxygen reduction reaction (ORR) in acidic electrolyte environments remains a serious challenge for clean and efficient energy conversion. Synergistic effects between Pt and inexpensive metals, the d band center of Pt and catalyst morphology could adjust the adsorption and desorption of oxygen intermediates by the Pt. All the factors affect the catalytic performance of Pt-based nanocrystals. Here, we prepared Cu@PtCu₃ NWs with an average diameter of 74.9 nm for Cu and about 10 nm PtCu₃ layer. After etching, the Cu@PtCu₃ nanowires is transformed into PtCu nanotube structure, due to the removal of copper from the surface and interior. PtCu NTs for ORR shows excellent activities and durability due to the integration of structural advantages and synergistic effects. Notably, the mass activity and specific activity of PtCu NTs (0.105 A mg^?1_Pt and 0.230 mA cm^?2_Pt) are 2.0 and 3.8 times higher than that of commercial Pt/C (0.053 A mg^?1_Pt and 0.06 mA cm^?2_Pt). The etching process to change the morphology of the catalyst and alter the electronic structure of the catalyst is expected to be useful for the design of future structured Pt-based alloy nanocatalysts.     

Surface Reconstruction Engineering with Synergistic Effect of Mixed-Salt Passivation Treatment toward Efficient and Stable Perovskite Solar Cells

Surface passivation treatment is a widely used strategy to resolve trap-mediated nonradiative recombination toward high-efficiency metal-halide perovskite photovoltaics. However, a lack of passivation with mixture treatment has been investigated, as well as an in-depth understanding of its passivation mechanism. Here, a systematic study on a mixed-salt passivation strategy of formamidinium bromide (FABr) coupled with different F-substituted alkyl lengths of ammonium iodide is demonstrated. It is obtained better device performance with decreasing chain length of the F-substituted alkyl ammonium iodide in the presence of FABr. Moreover, they unraveled a synergistic passivation mechanism of the mixed-salt treatment through surface reconstruction engineering, where FABr dominates the reformation of the perovskite surface via reacting with the excess PbI₂. Meanwhile, ammonium iodide passivates the perovskite grain boundaries both on the surface and top perovskite bulk through penetration. This synergistic passivation engineer results in a high-quality perovskite surface with fewer defects and suppressed ion migration, leading to a champion efficiency of 23.5% with mixed-salt treatment. In addition, the introduction of the moisture resisted F-substituted groups presents a more hydrophobic perovskite surface, thus enabling the decorated devices with excellent long-term stability under a high humid atmosphere as well as operational conditions.     

Effect of a sweeping air stream and gas–phase aspect ratio of an isothermal Stefan diffusion column on the experimental estimation of binary gas diffusivities

The Stefan column consists of liquid A evaporating into an inert/stagnant gas B with a sweeping B stream at the top. It was designed to estimate binary gas diffusivities, D_AB’s, but “end effects” such as gas mixing at the top and interfacial curvature have been either ignored or uncorrelated to the operational settings. This study’s hypothesis is that gas mixing at the top and the gas–phase aspect ratio affect D_AB estimation in the acetone (A)-ambient air (B) system at 50?°C. The sweeping stream Reynolds number (Re) and the gas–phase aspect ratio (AR?=?initial gas phase height to column internal diameter) were the variables tested. Isothermal evaporation-diffusion experiments were conducted in which the temporal interfacial descent was tracked. The settings were 492 ≤ Re ≤ 5378 and AR between 5 and 15. A 1D transport model allowed determination of the experimental diffusivity, D_AB,exp, by nonlinear regression. For Re < 600, the D_AB,exp errors relative to D_AB,CE (predicted by the Chapman–Enskog kinetic theory for low-density gases) were small and unrelated to AR, while for Re > 600 the errors increased considerably with Re and were inversely proportional to AR. This study is the first to relate the column’s operational settings to the D_AB estimation errors. The column should be operated at low sweeping gas Re and large AR for accurate D_AB,exp’s. The low Re region deserves further study, while the present transport model may have to be replaced by computational fluid dynamics simulations to account for the multidimensional gas flow patterns.