Ni/WC-Mo2C ternary-interface electrocatalyst for bidirectional optimization of HER and OER

ABSTRACTAn underwater glider is a type of autonomous profiling instrument platform used for gathering data to explore the ocean. Having a neutrally buoyant glider hull is one way to improve the glider's endurance with a passive compensation for buoyancy change. This article applies the bi-directional evolutionary structural optimization (BESO) method to the optimization of an underwater glider hull, based on two materials. Firstly, the method for determining the glider's neutral buoyancy is carried out. Secondly, the optimization problem is defined and the optimization procedure is presented. In the BESO procedure, the original design area elements with low strain energy are iteratively switched from high-value materials to low-value materials until a prescribed fraction is reached. Finally, an optimal underwater glider design is generated and the result demonstrates a reasonable material distribution of the neutrally buoyant glider hull. A 26.4% buoyancy adjustment is achieved and the mass of the glider is decreased by 31%.

The accuracy of seismic demand models in seismic vulnerability analysis of structures or components mainly depends on the seismic intensity measures (IMs) and engineering demand parameters (EDPs). This paper proposes a novel method to obtain the optimal seismic demand model for the seismic vulnerability analysis of bridges. The method obtains the IM‐EDP combination by matching all IMs and EDPs within a wide range one by one, considering the contribution of multiple IM parameters to the seismic response of the structure and avoiding the blindness of EDP selection. The IM is determined by calculating Pearson correlation coefficient and partial correlation coefficient, controlling the correlation between EDP and IM (or IMs) to a minimum to reduce the multicollinearity within the vector IMs and avoid ill‐conditioned models. The optimal seismic demand model is obtained by inspecting the scatter plot and residual plot of suboptimal seismic demand models determined from all combinations by guaranteeing efficiency and sufficiency. The efficiency of seismic demand models is guaranteed by controlling the root mean square error (RMSE) and the coefficient of determination (R2). The sufficiency of models is guaranteed by controlling the slope of fitted line. A continuous rigid frame bridge with double thin‐walled piers is used as a case study and a dynamic time‐history analysis is performed to obtain the seismic vulnerability of bridge with the proposed method. The results show that the proposed method is feasible and ideally suited for optimizing seismic demand model.

Cities are in a period of rapid urban development and high-rise buildings are constantly emerging. The characteristics of a fire in a high-rise building are the rapid spread of the fire, the difficulty of fighting the fire, and the difficulty of evacuation. Intelligent fire evacuation requires dynamic planning of paths in fire field, it is necessary to automatically adjust the evacuation route in the building according to the real-time information of the fire. In this paper, an improved bidirectional ant colony algorithm is proposed to optimize fire evacuation routes. In order to improve the global search capability of the algorithm, a bidirectional search strategy with the A* algorithm is designed for the ant colony algorithm, the blindness of the algorithm is reduced in the initial search, the pheromone update strategy is improved, and the convergence speed of the algorithm is increased. The fire scene information is combined with the steering penalty coefficient to improve the algorithm’s evaporation coefficient, heuristic function and transition probability, avoid the risk of falling into the local optimum, improve the search efficiency of the algorithm and the smoothness of the path, and effectively avoid areas affected by the fire. The effectiveness of the algorithm is verified by simulation.

Cocrystallization-driven bidirectional optimization tactic towards realizing the perfect combination of clinical drugs milrinone with furosemide at the molecular level

With the rapid increase in the number of electric vehicles (EVs), vehicle-to-grid (V2G) technology plays a vital role in reducing the burden on the power system. This technology optimizes network load distribution through a two-way charging mechanism and effectively alleviates network load fluctuations. However, potential negative impacts on EV battery life should also be a cause for concern. Furthermore, the technology does not fundamentally change the charging behavior of electric vehicles. Against this background, this study proposes a multi-objective optimization strategy to adapt electricity price policy to network load fluctuations to control charging behavior. This strategy optimizes battery attenuation, charging costs, and network load fluctuations, aiming to alleviate network load fluctuations while completely solving user concerns about charging and battery maintenance costs. Simulation analysis has verified the effectiveness of this model in reducing grid load fluctuations and balancing user costs.

The article presents a formal analysis in the framework of bi-directional optimality theory of the free choice, ignorance and indifference implicatures conveyed by the use of indefinite expressions or disjunctions. Ignorance is expressed by standard means of epistemic logic. To express indifference we use Groenendijk and Stokhof's question meanings. To derive implicature, Grice's conversational maxims, and an additional principle expressing preferences for minimal models, are formulated as violable constraints used to select optimal candidates out of a set of alternative sentence-context pairs. The implicatures of an utterance of ?are then defined as the sentences which are entailed by any optimal context for ?(but not by ?itself). Entailment is defined in a version of update semantics where contextual updates are derived by competition among contexts. Free choice and other modal implicatures of disjunctions and indefinites will follow, but also scalar implicatures and exhaustification.

Chapter 3 investigated ways in which languages convey negation by means of an expression that corresponds to the first-order logic connective ¬. In English this would be not. Negation can also be attracted to other expressions in the sentence, particularly indefinites in argument or adjunct position (Section 1). Negative attraction creates negative indefinites. The class of negative indefinites includes both negative quantifiers (English nobody, nothing, nowhere, never) and n-words. Negative attraction is extended to multiple indefinites under negation in Section 2. Sentences involving a range of negative indefinites raise problems for the principle of compositionality of meaning, because some languages assign a double negation reading to such a sequence, and others a single negation reading. Chapter 1 argued that the compositionality problem cannot be solved in the lexicon, and exploited the polyadic quantifier analysis proposed by de Swart and Sag (2002) to offer a grammatical analysis. The argumentation is briefly summarized in Section 3, in preparation of the typology of double negation and negative concord languages. The polyadic quantifier analysis developed by de Swart and Sag (2002) works well for French because it displays ambiguities between single and double negation readings in sentences that combine two negative expressions. However, in most other languages there is a strong bias toward either the double negation or the negative concord reading. Section 4 builds a bidirectional optimality theory (OT) built on top of the polyadic quantifier analysis in order to account for the systematic contrast between negative concord and double negation languages. In negative concord languages, the functional motivation that favors marking of ‘negative variables’ in the syntax wins out. Double negation languages value first-order iteration in the semantics. Languages that display ambiguities have an overlapping range of constraints in a stochastic extension of the model developed in Chapter 6 (Section 3). As Section 4 emphasizes, the bidirectional setup is essential, for syntactic and semantic variation go hand in hand. Section 5 returns to the relation between negative concord and negative polarity, and offers a diachronic perspective while Section 6 concludes the chapter.

This paper develops a bi-directional prediction approach to predict the production parameters and performance of differential fibers based on neural networks and a multi-objective evolutionary algorithm. The proposed method does not require accurate description and calculation for the multiple processes, different modes and complex conditions of fiber production. The bi-directional prediction approach includes the forward prediction and backward reasoning. Particle swam optimization algorithms with K-means algorithm are used to minimize the prediction error of the forward prediction results. Based on the forward prediction, backward reasoning uses the multi-objective evolutionary algorithm to find the reasoning results. Experiments with polyester filament parameters of differential production conditions indicate that the proposed approach obtains good prediction results. The results can be used to optimize fiber production and to design differential fibers. This study also has important value and widespread application prospects regarding the spinning of differential fiber optimization.

The model and algorithm for Vehicle Routing and Scheduling Problem (VRSP) in picking up and delivering customers to airport service were presented in this paper. VRSP is an extension of the class Vehicle Routing Problem (VRP) with more resource constraints. Mathematical models are described for the VRSP with minimizing the cost based on low carbon emissions. The Bi-directional Optimization Heuristic Algorithm (BOHA) is developed for this model. The results verified the effectiveness of the model and Algorithm. The proposed model and algorithm can provide quantitative support and help the air ticket company decision-maker develop economical, punctual shuttle service with high satisfaction.

According to the optimal assertions approach of Benz and van Rooij (2007), conversational implicatures can be calculated based on the assumption that a given signal was optimal, i.e. that it was the sender's best choice if she assumes, purely hypothetically, a particular naive receiver interpretation behavior. This paper embeds the optimal assertions approach in a general signaling game setting and derives the notion of an optimal signal via a series of iterated best responses (cf. Jager, 2007). Subsequently, we will compare three different ways of interpreting such optimal signals. It turns out that under a natural assumption of expressibility (i) the optimal assertions approach, (ii) iterated best response and (iii) strong bidirectional optimality theory (Blutner, 1998, 2000) all prove equivalent. We then proceed to show that, if we take the iterated best response sequence one step further, we can account for M-implicatures (Horn's division of pragmatic labor) standardly in terms of signaling games.

Cross-domain fault diagnosis of marine diesel engines based on stepwise diffusion and iterative bidirectional optimization

IntroductionSepsis, a life-threatening and costly condition, necessitates early detection for effective management. However, diagnostic and therapeutic delays are common. Furthermore, sepsis complexity and dynamics challenge existing early warning systems, impeding timely intervention.MethodsIn this study, we introduce the Bayes-Optimized Boosting-Mamba Tab Model (BOBM), a novel deep learning-based algorithm that incorporates a bidirectional optimization learning mechanism within an ensemble framework. We designed dynamic agent models to enhance computational efficiency and adaptability across diverse clinical scenarios. Additionally, the model architecture was optimized for different temporal phases to enable a more precise estimation of the timing and progression of sepsis risk. To improve interpretability, we implemented SHapley Additive exPlanations value analysis, providing clinicians with actionable insights into potential patterns of sepsis progression.ResultsThe experimental evaluation demonstrated that our model attained the highest AUC (AUC: 0.86–0.95) in the MIMIC-IV dataset across different temporal windows prior to sepsis onset, confirming its adaptability and robust generalizability across various prediction timeframes. Furthermore, the model demonstrated superior performance (AUC: 0.978–0.982) when validated on two independent large-scale external datasets.ConclusionIn contrast to traditional prediction approaches, our model extends the potential intervention timeframe significantly, while incorporating dynamic monitoring capabilities spanning the 7- to 28-day window before clinical suspicion, thereby establishing a foundation for regional sepsis surveillance systems.

Resource limitation and predation mortality are major determinants of microbial population dynamics, and optimization for either aspect is considered to imply a trade-off with respect to the other. Adaptation to these selective factors may, moreover, lead to disadvantages at rich growth conditions. We present an example of a concomitant evolutionary optimization to both, substrate limitation and predation in an aggregate-forming freshwater bacterial isolate, and we elucidate an underlying genomic mechanism. Bacteria were propagated in serial batch culture in a nutrient-restricted environment either with or without a bacterivorous flagellate. Strains isolated after 26 growth cycles of the predator-prey co-cultures formed as much total biomass as the ancestor at ancestral growth conditions, albeit largely reallocated to cell aggregates. A ~273 kbp genome fragment was lost in three strains that had independently evolved with predators. These strains had significantly higher growth yield on substrate-restricted media than others that were isolated from the same treatment before the excision event. Under predation pressure, the isolates with the deletion outcompeted both, the ancestor and the strains evolved without predators even at rich growth conditions. At the same time, genome reduction led to a growth disadvantage in the presence of benzoate due to the loss of the respective degradation pathway, suggesting that niche constriction might be the price for the bidirectional optimization.

The oxygen evolution reaction (OER) performance of ruthenium-based oxides strongly correlates with the electronic structures of Ru. However, the widely adopted monometal doping method unidirectionally regulates only the electronic structures, often failing to balance the activity and stability. Here, we propose an "elastic electron transfer" strategy to achieve bidirectional optimization of the electronic structures of Sr, Cr codoped RuO2 catalysts for acidic OER. The introduction of electron-withdrawing Sr intrinsically activates the Ru sites by increasing the oxidation state of Ru. Simultaneously, Cr acts as an electron buffer, donating electrons to Ru in the presence of Sr in the as-prepared catalysts and absorbing excess electrons from Sr leaching during the OER. Such a bidirectional regulation feature of Cr prevents overoxidation of Ru and maintains its high oxidation state during the OER. The optimal Ru3Cr1Sr0.175 catalyst exhibits a low overpotential (214 mV @ 10 mA cm-2) and excellent stability (over 300 h).

Bidirectional optimization of strong–weak adsorption pairs in amorphous-crystalline heterostructure via built-in electric field towards efficient hydrogen evolution reaction