Original Strategy Research Articles

Enhancing activity and stability of Fe[sbnd]N[sbnd]C catalysts through co incorporation for oxygen reduction reaction

FeNC single atom catalysts (SACs) have attracted great interest due to their highly active FeN4 sites. However, the pyrolysis treatment often leads to inevitable metal migration and aggregation, which reduces the catalytic activity. Moreover, due to the Fenton reaction caused by FeNC in alkaline and acidic solutions, the presence of Fe and peroxide in electrodes may generate free radicals, resulting in serious degradation of the organic ionomer and the membrane. Herein, we report an original strategy of introducing Co single atoms into FeNC catalysts, forming atomically dispersed bimetallic active sites (FeCoNC) and improving the activity and stability of the catalyst. Benefiting from this strategy, FeCoNC catalyst exhibits excellent oxygen reduction reaction (ORR) activity in alkaline media (E1/2 = 0.88 V) and in acidic media (E1/2 = 0.77 V). As the cathode of Zn-air battery (ZAB), FeCoNC shows an excellent peak power density of 142.8 mW cm−2 and a specific capacity of 806.6 mAh/gZn. This work provides a novel avenue to optimize and enhance the ORR performance of atomic dispersed FeNC catalysts.

Students as game designers and developers: developing cooperative strategy board games to teach team leadership skills

Purpose The purpose of this manuscript is to highlight how gamification is transforming recruitment, retention and training to resolve employee engagement challenges in the ever-changing workplace. In collaborating with game industry experts and designers, the authors taught students team leadership skills by demonstrating how to design and play their own original cooperative strategy games. In doing so, students learn what gamification is and how it can be used to train our multigenerational workforce; how designing board games teaches team leadership skills (e.g. communication, conflict management, power, decision-making); and how to develop cooperative strategy game elements (player characters, roles and actions) that motivate engaging gameplay with successful learning outcomes. Design/methodology/approach In this paper, the authors discuss the practical implications of a three-phase semester-long experiential learning experience, where game design demonstrates to students how employees can work collaboratively together in teams. Students’ developed storylines (i.e. strategic objectives) including motivation challenges, social media communication breakdowns, sabotage, global sustainability and other real-world challenges. In Phase 1, students learn about how companies are using gamification in training high-performing teams. In Phase 2, students play a cooperative strategy game Forbidden Island with their student teams. In Phase 3, students design (and play) their original cooperative strategy game, instructions booklet and build a facilitation guide. Findings Approximately, 400 students developed 48 original cooperative strategy games that can be used to teach team leadership skills. These student teams designed cooperative strategy board games – in which the entire team either wins or loses based on their control of valued resources and player actions as key decision-making points. The data and feedback indicate that the learning experience helped them practically consider team interdependence in making effective decisions, and in creating creative self-efficacy, resilience and self-confidence in their own leadership voice. In this manuscript, the authors focus on providing an overview and implementation plan for our semester-long experiential learning exercise. Originality/value This experiential exercise was implemented from 2019 to 2023 in different learning modalities (face-to-face, hybrid, 100% synchronous online learning) and during challenging times (prepandemic, pandemic and postpandemic learning environments). These varying experiences provided them with a challenge to persevere and learn about their own interpersonal skills and resilience in a creative engaging way with limited resources. Students developed 26 original games during 100% synchronous remote learning due to COVID. To the best of the authors’ knowledge, no other business school is working with gaming industry experts and game designers to teach team leadership to this degree during pre- and postpandemic environments.

Deep attributes: innovative LSTM-based seismic attributes

SUMMARY Seismic attributes are derived measures from seismic data that help characterize subsurface geological features and enhance the interpretation of subsurface structures: we propose to exploit the hidden layers of Long–Short Time Memory neural network predictions as possible new reflection seismic attributes. The idea is based on the inference process of a neural network, which in its hidden layers stores information related to different features embedded in the input data and which usually are not considered. Neural network applications typically ignore such intermediate steps because the main interest lies in the final output, which is considered as the exclusive exploitable feature of the process. On the contrary, here we analyse the possibility to exploit the intermediate prediction steps, hereafter referred as ‘deep attributes’ because they are produced by a deep learning algorithm, to highlight features and emphasize characteristics embedded in the data but neither recognizable by traditional interpretation, nor evident with classical attributes or multi-attribute approaches. Nowadays, classical signal attributes are numerous and used for different purposes; we here propose an original strategy to calculate attributes previously never exploited, which are potentially complementary or a good alternative to the classical ones. We tested the proposed procedure on synthetic and field 2-D and 3-D reflection seismic data sets to test and demonstrate the stability, affordability and versatility of the entire approach. Furthermore, we evaluated the performance of deep attributes on a 4-D seismic data set to assess the applicability and effectiveness for time-monitoring purposes and comparing them with the sweetness attribute.

Language Learning Strategies, Proficiency And Gender: The Case of Palestinian University Students

This case study, conducted at a Palestinian university, explored the language learning strategies employed by learners of English as a foreign language and the relationship with proficiency. Furthermore, since gender is often considered a major variable in learning success in some areas of the world, the study wished to explore the role of gender in strategy use and level of proficiency in the Palestinian context. The study included 109 (41 male, 68 female) students studying in English preparatory courses prior to entering the university. The data were gathered through an original 60-item language learning strategy questionnaire, modelled to some extent on the Strategy İnventory for Language Learning or SILL (Oxford, 1990) and constructed from material contributed by the students themselves. The results indicated that students reported medium to frequent strategy use, which was not significantly related to proficiency level, and there were only three significant gender differences according to strategy use, in favor of the females. This suggests that gender is not a salient influence in these students' strategy use or levels of proficiency. This article concludes by suggesting implications both for the immediate context of the study and also for environments beyond the location of the current study.

Abstract WMP96: When to Change? Altering Strategies in Mechanical Thrombectomy May Impact Reperfusion Rates

Introduction: Earlier and greater reperfusion in mechanical thrombectomy (MT) leads to better clinical outcomes. The timing and potential benefit of switching thrombectomy techniques after failed device passes remains unclear. Methods: Retrospective analysis of a prospective comprehensive stroke center database including patients with intracranial ICA, MCA M1/M2, or Basilar occlusions undergoing MT with either Stent-Retriever (SR), Contact Aspiration (CA) or Combined technique (CT) from 2015 to 2023. Our primary endpoint was complete/near-complete reperfusion (eTICI 2c-3) at pass #2 (after pass #1 failure). Secondary endpoints included 1) eTICI2b-3 at pass#2, and 2) eTICI2c-3 with pass #3 (following failure in two consecutive passes with the same technique). We used logistic or Firth regression to compare reperfusion rates and Kaplan-Meier analysis for cumulative rates. Results: Among 3130 MTs in the study period, 1810 met inclusion criteria. Median age [IQR] was 65 [55 - 76] years and 51.5% were male. Following a failed first pass, switching from CA to either CT or SR was associated with higher eTICI 2c-3 (aOR [95%CI] 2.8 [1.3 - 6.5] and 7.4 [1.4-54.8], respectively). Changing from CT or SR to any other technique was not associated with higher reperfusion. Secondary endpoint analysis for eTICI2b-3 yielded comparable results. For the third pass, switching to CT after two repeated failed SR or two repeated failed CA attempts was associated with higher eTICI 2c-3 as compared to repeating the original strategy (SR aOR 5.8 [1.4 - 33.3]; AC aOR 5.6 [1.1 - 41.2]). We did not observe benefit in switching to alternative technique following one or two consecutive passes with CT. After three repeated attempts with the same technique, CT led to greater eTICI 2c-3 as compared to SR and CA (83% vs. 72% vs. 64%, p < 0.001). Conclusions: While limited by small subgroup sample sizes, our study strengthens the hypothesis that early changes in MT technique may have an impact on reperfusion. Our data indicates that if CA fails on first pass, transition to SR or CT should be considered. After two failed consecutive SR or CA passes, transition to CT should be contemplated.

Improving Split Reporters of Protein-Protein Interactions through Orthology-Based Protein Engineering.

Protein-protein interactions (PPIs) can be detected through selective complementation of split fluorescent reporters made of two complementary fragments that reassemble into a functional fluorescent reporter when in close proximity. We previously introduced splitFAST, a chemogenetic PPI reporter with rapid and reversible complementation. Here, we present the engineering of splitFAST2, an improved reporter displaying higher brightness, lower self-complementation, and higher dynamic range for optimal monitoring of PPI using an original protein engineering strategy that exploits proteins with orthology relationships. Our study allowed the identification of a system with improved properties and enabled a better understanding of the molecular features controlling the complementation properties. Because of the rapidity and reversibility of its complementation, its low self-complementation, high dynamic range, and improved brightness, splitFAST2 is well suited to study PPI with high spatial and temporal resolution, opening great prospects to decipher the role of PPI in various biological contexts.

Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method

To enhance the positioning accuracy of standalone GNSS receivers in environments unable to provide precise ephemeris and clock offset, such as undeveloped forest areas that lack network communication and power supply, this study employed the Time Difference Carrier Phase (TDCP) technology to improve the positioning accuracy of Standard Point Positioning (SPP), where the Least-Squares (LS) and the extended Multi-Epoch Least Squares (MELS) method were applied in the position domain filtering for a single GNSS receiver and compare its performance with the existing observation domain filtering method. Firstly, the simulated data sets with various positioning accuracies were used to verify the effectiveness and convergence of the LS filtering methods. The results indicate that the LS filtering method produces a lower root mean square (RMS) error than the original strategy. Secondly, this study uses two kinematic GNSS data sets to evaluate the performance of the observation and position domain filtering, with an emphasis on the MELS method. The numerical experiment results show that the position domain LS filtering method outperforms the other two methods. The open environment experiments result shows that the positioning domain filtering method achieved positioning accuracies of 0.202 m, 0.843 m, and 2.036 m in the E, N, and U directions, respectively, with improvements of 68.0%, 21.6%, and 24.0%, compared to the original algorithm which achieved positioning accuracies of 0.631 m, 1.076 m, and 2.680 m. It also achieved improvements of 24.0%, 4.0%, and 18.3%, respectively, compared to the observation domain filtering method with positioning accuracies of 0.353 m, 0.886 m, and 2.526 m. The forest scenes experiments result shows that the positioning domain filtering method achieved positioning accuracies of 1.308 m, 1.375 m, and 2.133 m in the E, N, and U directions, respectively, with improvements of 42.4%, 36.2%, and 27.6%, compared to original algorithm which achieved positioning accuracies of 1.863 m, 1.873 m, and 2.722 m, and also achieved improvements of 27.0%, 19.4% and 10.6%, respectively, comparing to observation domain filtering method with positioning accuracies of 1.661 m, 1.642 m and 2.359 m. Moreover, the examination of the LS method results based on different epochs reveals that the filtering accuracy increases as more epochs are incorporated into the position domain integration and the enhancement value reaches a few millimeters.

A KMP-based interactive learning approach for robot trajectory adaptation with obstacle avoidance

Purpose Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model. Design/methodology/approach This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found. Findings Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy. Originality/value An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

Effective regeneration of deactivated Raney-Ni catalyst during multiphase hydrogenation of vegetable oil

Raney nickel is extensively used as a catalyst in the hydrogenation of vegetable oils. However, it deactivates over time and is known as a spent nickel catalyst, which is potentially hazardous to the environment. By contrasting different approaches, a straightforward and original strategy for regenerating spent nickel catalyst was developed by comparing various methods. The fresh, spent nickel catalyst, and treated catalyst samples were characterized using X-ray diffraction, Fourier transform infrared, atomic absorption spectroscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy, and color scheme analyses. The results showed that the catalyst deactivation was primarily due to oil deposition over the active sites, agglomeration of catalyst, and entrainment of nickel during hydrogenation. Using n-hexane as the solvent with a spent nickel catalyst-to-solvent ratio of 1:12 (g/mL), a 65 °C temperature, and a two-hour extraction time, ultrasonication-assisted solvent extraction of spent nickel catalyst proved to be the most effective and efficient process for regeneration.

To Transfer or Not? Analysis of the Original Equipment Manufacturer's Strategic Outsourcing Strategy in the Presence of the Supplier's Innovation Effort

Recently, a new trend has arisen in which many original equipment manufacturers (OEMs) outsourced to traditional low-cost manufacturing bases (e.g., China) have started to transfer production to new emerging regions with even lower costs (e.g., Southeast Asia). However, it is still unclear whether and when OEMs should transfer to the new environment, especially when taking the traditional supplier's initial innovation effort into consideration. To address this question, we consider a sourcing game where one OEM sources from a traditional low-cost supplier that can be either competitive (CS) or noncompetitive (NS) initially. Moreover, both of these types of suppliers have the ability to innovate by themselves. Driven by the increasing cost pressure, the OEM may transfer to a new supplier (LS) that has an even lower cost but suffers yield uncertainty. The equilibrium results show that the OEM is less likely to transfer when its brand value is large enough. Interestingly, we find that the competition between the CS and OEM does not always prompt the OEM to transfer. When the brand advantage is large enough and the traditional supplier's unit production cost for the OEM is moderate, the OEM should not transfer under the CS scenario but should transfer under the NS scenario. Finally, we find that when the OEM's brand advantage increases, the effectiveness of innovation in preventing the OEM from transferring decreases under the CS scenario, while it increases under the NS scenario.

Modelling a reinforced concrete arch bridge by means of the Discrete Macro-Element Model: the case of Giovanni XXIII bridge in Ragusa

Numerical modelling of reinforced concrete structures in the nonlinear field requires the adoption of sophisticated although robust models. When it comes to bridges, to obtain an accurate numerical model uniaxial elements should not be employed due to geometrical inconsistency. Two- or three-dimensional elements are more appropriate even though the explicit modelling of concrete and steel bars can make the simulations too cumbersome for many practical applications. In this study, the Discrete Macro-Element Model (DMEM), implemented in the software HiStrA, initially introduced for the nonlinear modelling of existing masonry structures and also applied in the case of masonry arch bridges, is extended for modelling reinforced concrete structures. The presence of rebars is explicitly accounted for by introducing their contribution in the interface elements. The original modelling strategy is here applied to the Giovanni XXIII bridge in Ragusa (Italy), a road reinforced concrete supported deck arch bridge located in the city centre. The structure is currently a case study identified within an accurate survey and permanent health monitoring installation designed within the MonVia Project funded by the Italian Ministry of Economic Development. The results reported in this paper are relative to numerical simulations where the mechanical parameters of the linear elastic model are calibrated to match the experimental dynamic properties of the structure while the nonlinear parameters are consistent with those reported in the original design of the bridge. Nonlinear static analyses are performed considering both operational and seismic load configurations. The results could be used to define the threshold levels to be considered for the health monitoring of the bridge.

Optimize the energy storage system with an artificial intelligence-based control strategy

Abstract Currently, energy storage systems adopt control strategies based on the crossover approach despite their limited generalization performance. To improve the control effect of the control strategy, the limitation of the SOC of the energy storage element is added. This adds smoothness to the system’s output power, enabling the energy storage element to distribute the power reasonably. Meanwhile, we propose and introduce a DPI control strategy based on PSO optimization into the control strategy of the second-order low-pass filtering method, thereby further enhancing and optimizing the original control strategy. Taking the investment cost of the energy storage system and the stable operation of the system as the objective functions, the constraint function is determined according to the parameters, the PSO algorithm is used to construct a two-layer optimization model of the energy storage system, and the FCEM is introduced to determine the objective weights. Based on the constructed model, an arithmetic example analysis of the energy storage system is carried out using artificial intelligence. Under the steady-state analysis of Buck mode, the low voltage side voltage adjustment time based on the PID control strategy is about 0.108s, and the low voltage side voltage adjustment time based on PSOPID is 0.032s, which is reduced by 0.070s compared with the PID control strategy. Also, when optimizing a system that generates electricity from renewable sources on its own, the two goals of minimizing the investment in the energy storage system and making sure it runs smoothly are met when the system’s fixed power is equal to 6.58 MW and its capacity is less than 267 MWh.

Extension of the D3 and D4 London dispersion corrections to the full actinides series.

Efficient dispersion corrections are an indispensable component of modern density functional theory, semi-empirical quantum mechanical, and even force field methods. In this work, we extend the well established D3 and D4 London dispersion corrections to the full actinides series, francium, and radium. To keep consistency with the existing versions, the original parameterization strategy of the D4 model was only slightly modified. This includes improved reference Hirshfeld atomic partial charges at the ωB97M-V/ma-def-TZVP level to fit the required electronegativity equilibration charge (EEQ) model. In this context, we developed a new actinide data set called AcQM, which covers the most common molecular actinide compound space. Furthermore, the efficient calculation of dynamic polarizabilities that are needed to construct CAB6 dispersion coefficients was implemented into the ORCA program package. The extended models are assessed for the computation of dissociation curves of actinide atoms and ions, geometry optimizations of crystal structure cutouts, gas-phase structures of small uranium compounds, and an example extracted from a small actinide complex protein assembly. We found that the novel parameterizations perform on par with the computationally more demanding density-dependent VV10 dispersion correction. With the presented extension, the excellent cost-accuracy ratio of the D3 and D4 models can now be utilized in various fields of computational actinide chemistry and, e.g., in efficient composite DFT methods such as r2SCAN-3c. They are implemented in our freely available standalone codes (dftd4, s-dftd3) and the D4 version will be also available in the upcoming ORCA 6.0 program package.

Key issues and solutions affecting efficiency and stability of perovskite/heterojunction tandem solar cells

Efficient and stable perovskite/heterojunction tandem solar cells (PTSC) are a direction of joint exploration in both academia and industry. Achieving efficient solar energy utilization by assembling structural layers with different bandgaps in an optical sequence is the original design strategy for PTSC. Through the reasonable distribution of the absorption spectra of each layer, the photoelectric conversion efficiency (PCE) of PTSC can theoretically be increased to more than 40%. At present, the efficiency advantage of small-area PTSC is well-established, but there are still many challenges in the commercialization of solar cell efficiency and stability. Therefore, in this work, the two-terminal (2T) and four-terminal (4T) stacking methods are regarded as the main structural routes, and the optimal design of the key structural layers of PTSC, bandgap adjustment, additive regulation, optimization of interlayer transport, and optimization of the module interconnection and encapsulation methods are focused on. Based on the existing research results, the key problems and solutions affecting the efficiency and stability of PTSC are summarized and outlooked, aiming to provide directional solutions to the key problems in the structural design of PTSC. In addition, from the application perspective, it is proposed that before the stability problem of the perovskite is fundamentally solved, the 4T PTSC is more likely to achieve product iteration and industrial efficiency improvement, with the expectation of taking the lead in commercialization. This work emphasizes the popularization and practical application of commercialization, with a perspective that is more in line with the market trend and close to the industrial demand, and is expected to provide an important reference for the commercialization of PTSC in the academic circles.

Superhydrophobic MOF/polymer composite with hierarchical porosity for boosting catalytic performance in an humid environment.

The poor hydrostability of most reported metal-organic frameworks (MOFs) has become a daunting challenge in their practical applications. Recently, MOFs combined with multifunctional polymers can act as a functional platform and exhibit unique catalytic performance; they can not only inherit the outstanding properties of the two components but also offer unique synergistic effects. Herein, an original porous polymer-confined strategy has been developed to prepare a superhydrophobic MOF composite to significantly enhance its moisture or water resistance. The selective nucleation and growth of MOF nanocrystals confined in the pore of PDVB-vim are closely related to the structure-directing and coordination-modulating properties of PDVB-vim. The resultant MOF/PDVB-vim composite not only produces superior superhydrophobicity without significantly disturbing the original features but also exhibits a novel catalytic activity in the Friedel-Crafts alkylation reaction of indoles with trans-β-nitrostyrene because of the accessible sites and synergistic effects.

Plasma-assisted fabrication of ultra-dispersed copper oxides in and on C-rich carbon nitride as functional composites for the oxygen evolution reaction.

Significant efforts have been continuously devoted to the mastering of green catalysts for the oxygen evolution reaction (OER), whose sluggish kinetics prevents a broad market penetration of water splitting as a sustainable route for large-scale hydrogen production. In this extensive scenario, carbon nitride (CN)-based systems are in focus thanks to their favorable characteristics, and, whereas graphitic CN has been largely investigated, the potential of amorphous carbon nitride (a-CNx) systems remains almost entirely unexplored. In this regard, our study presents a novel two-step plasma-assisted route to a-CNx systems comprising ultra-dispersed, i.e. "quasi-atomic" CuxO (x = 1, 2). The target materials were fabricated using an original strategy consisting in the magnetron sputtering of a-CNx on conducting glasses at room temperature, followed by functionalization with low CuxO amounts by radio frequency (RF)-sputtering, and final annealing under an inert atmosphere. The tailoring of the CuxO co-catalyst content and spatial dispersion, as well as the overall composite features as a function of preparative conditions, enabled a direct modulation of the resulting OER performances, rationalized based on the formation of p-n CuxO/a-CNx heterojunctions. The amenable and scalable synthesis approach underscores the practicality of this method to develop (photo)electrocatalysts synergistically integrating the advantages of both constituents, yielding low-cost, green, and stable functional platforms that could contribute to the broader adoption of sustainable energy solutions.

Original Strategy for Verbatim Collecting Knowledge from Mostly-Illiterate and Secretive Experts: West Africa Traditional Medicine’s Case

Original Strategy for Verbatim Collecting Knowledge from Mostly-Illiterate and Secretive Experts: West Africa Traditional Medicine’s Case

Business Strategy and Competitive Advantage of Companies

The purpose of this paper is to identify and describe the role of culture and strategic choices in achieving competitive advantage for companies. The research methodology uses a systematic literature review that includes international journals that discuss strategic management. From several journals, an analysis was carried out and a common thread was drawn to get a scientific understanding related to the topic and title of this article. The results of the study indicate that in doing business across countries, it is very necessary to understand the right culture so that the company's presence can be accepted by the community. Companies can achieve competitive advantage by applying the right strategy to the right industry structure or by combining strategies and resources. Companies can implement strategies as needed and do not have to imitate all strategies, for example, companies in Japan only apply Porter's two strategies, the most of which are overall cost advantages, while the second is differentiation, while others use the original Japanese strategy. Keywords: Business strategy, competitive advantage, and company

Modulation of Cytosolic Phospholipase A2 as a Potential Therapeutic Strategy for Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder lacking any curative treatment up to now. Indeed, actual medication given to the patients alleviates only symptoms. The cytosolic phospholipase A2 (cPLA2-IVA) appears as a pivotal player situated at the center of pathological pathways leading to AD and its inhibition could be a promising therapeutic approach. A cPLA2-IVA inhibiting peptide was identified in the present work, aiming to develop an original therapeutic strategy. We targeted the cPLA2-IVA using the phage display technology. The hit peptide PLP25 was first validated in vitro (arachidonic acid dosage [AA], cPLA2-IVA cellular translocation) before being tested in vivo. We evaluated spatial memory using the Barnes maze, amyloid deposits by MRI and immunohistochemistry (IHC), and other important biomarkers such as the cPLA2-IVA itself, the NMDA receptor, AβPP and tau by IHC after i.v. injection in APP/PS1 mice. Showing a high affinity for the C2 domain of this enzyme, the peptide PLP25 exhibited an inhibitory effect on cPLA2-IVA activity by blocking its binding to its substrate, resulting in a decreased release of AA. Coupled to a vector peptide (LRPep2) in order to optimize brain access, we showed an improvement of cognitive abilities of APP/PS1 mice, which also exhibited a decreased number of amyloid plaques, a restored expression of cPLA2-IVA, and a favorable effect on NMDA receptor expression and tau protein phosphorylation. cPLA2-IVA inhibition through PLP25 peptide could be a promising therapeutic strategy for AD.

A Multi-Strategy Improved Sooty Tern Optimization Algorithm for Concrete Dam Parameter Inversion

A original strategy for optimizing the inversion of concrete dam parameters based on the multi-strategy improved Sooty Tern Optimization algorithm (MSSTOA) is proposed to address the issues of low efficiency, low accuracy, and poor optimizing performance. First, computational strategies to improve the traditional Sooty tern algorithm, such as chaos mapping to improve the initial position of the population, a new nonlinear convergence factor, the LIMIT threshold method, and Gaussian perturbation to update the optimal individual position, are adopted to enhance its algorithmic optimization seeking ability. Then, the measured and finite element data are combined to create the optimization inversion fitness function. Based on the MSSTOA, the intelligent optimization inversion model is constructed, the inversion efficiency is improved by parallel strategy, and the optimal parameter inversion is searched. The inversion strategy is validated through test functions, hypothetical arithmetic examples, and concrete dam engineering examples and compared with the inversion results of the traditional STOA and other optimization algorithms. The results show that the MSSTOA is feasible and practical, the test function optimization results and computational time are better than the STOA and other algorithms, the example inversion of the elastic modulus is more accurate than the traditional STOA calculation, and the results of the MSSSTOA inversion are reasonable in the engineering example. Compared with other algorithms, the local extremes are skipped, and the time consumption is reduced by at least 48%. The finite element hydrostatic components calculated from the inversion results are well-fitted to the statistical model with minor errors. The intelligent inversion strategy has good application in concrete dam inverse analysis.