Solution Framework Research Articles

Finely Pore‐Engineered Ultra‐Stable Cluster–Cluster‐type Metal–Organic Frameworks with Shifting One‐Step Ethylene Purification

AbstractOne‐step ethylene (C2H4) purification from multicomponent mixtures to obtain polymerized grade C2H4 is vital in industry but still difficult to achieve. Herein untramicropore engineering is demonstrated in unique cluster–cluster‐type metal–organic framework (CCMOF) adsorbents (SNNU‐107‐X, X = OH/Cl/F) with shifting one‐step ethylene purification from the ternary mixture. Triangular prismic [Cd8(TAZ)9] and hollow hexagonal prismic [Cd42(TAZ)42] clusters (TAZ = tetrazolate) connect each other directly without inter‐cluster linkers to generate SNNU‐107‐X CCMOFs with fixed hexagonal large pore and alterable quadrilateral small pore. It is curious that these unique CCMOFs can keep their 3D porous frameworks in aqueous solution with pH = 2–13 for 30 days and even in boiling water for 15 days. Regulated by terminal coordinated anions (OH/Cl/F) located in quadrilateral pores, SNNU‐107‐X exhibits controllable and shifting ethylene adsorption and separation performance. In particular, SNNU‐107‐F can separate 99.999% C2H4 from C2H2/CO2/C2H4 ternary mixture with a productivity of 274.4 L kg−1 by one‐step process superior to almost all advanced materials. The time‐dependent kinetic adsorption, in situ FT‐IR spectra, DFT, and GCMC calculations indicate that the shifting ethylene separation of SNNU‐107‐X is ascribed to a thermodynamic and dynamic coupling effect modulated by single terminal anions in the ultramicropore space.

Advancing non-linear PDE's solutions: modified Yang transform method with Caputo-Fabrizio fractional operator

Abstract Nonlinear partial differential equations have a crucial rule in many physical processes. In this paper, a novel approach is used to study nonlinear partial differential equations of fractional order, which is named as Modified Yang Transform (MYT) method. This approach combines Yang transform with the Adomian decomposition method. The fractional order is considered in the Caputo-Fabrizio sense. Convergence analysis of the modified Yang transform to nonlinear fractional order partial differential equations is presented. Additionally, a solution framework for the solution of nonlinear partial differential equation is carried out and some examples are provided to highlight the application of the current method. To illustrate that how the solution behaves for various fractional orders, 2D and 3D graphs are plotted. Various tables are also provided to show the difference between exact and approximate solutions and the values are compared with other methods in the literature. Results and discussion sections are included for each example to explain the graphs, tables and their results.

The Molecular Additive N-Acetyl-L-Phenylalanine Delays the Crystallization and Suppresses the Phase Impurity for Achieving Triple-Cation Perovskite Solar Cells with Efficiency Over 25.

The crystallization process plays an important role in the formation of high-quality perovskite film for achieving efficient perovskite solar cells (PSCs), especially in the formation of mixed-cation perovskite film, as there are normally more phase impurities than in pure CH(NH2)2PbI3 (FAPbI3) film. Herein, a molecular additive strategy, i.e., introducing non-planar molecule N-acetyl-L-phenylalanine (APO) into the lead iodide (PbI2) precursor solution, is proposed to modulate crystallization kinetics and inhibit the generation of phase impurities of metastable pretreated perovskite film. The delayed crystallization process promotes a sufficient reaction between organic salts solution and inorganic Pb-I framework, and perovskite phase decomposition is prevented by forming strong hydrogen bonds between ─NH and I, resulting in the formation of uniform film with large-size crystal grains and high-purity crystalline phase. Ultimately, the target PSC devices achieve an impressive power conversion efficiency (PCE) of 25.05%, which is among the highest values of triple-cation (FAMACs) PSCs. Meanwhile, PSC modules with 10.8 cm2 obtain a PCE of 20.35%. Furthermore, the unencapsulated PSCs retain 94% of the initial efficiency after 40 days of storage under ambient conditions with 20% RH and also yield superior operational stability under light soaking at maximum power point tracking (MPPT) in nitrogen (N2) atmosphere.

Online penetration trajectory planning using blind areas of network radar system for an unmanned combat aerial vehicle

PurposeA densely distributed network radar system compensates for the disadvantages of sparse radars and poses a significant threat to low-altitude penetration by an unmanned combat aerial vehicle (UCAV). Unlike previous studies, this paper aims to consider radar blind areas and proposes a rapid online method for planning low-altitude penetration paths.Design/methodology/approachFirst, the optimization problem coupling digital elevation map (DEM), radar detection probability model and nonholonomic UCAV kinematic model is established. Second, an online solution framework of penetration path planning is constructed. An intervisibility method and map scaling are proposed to generate a detection probability map (DPM). Through completeness and consistency analysis, an adaptive hybrid A* algorithm with fast local replanning strategy is proposed to search a path that takes into account time-consuming, detection probability under nonholonomic constraints. Finally, three scenarios of multiple known, pop-up and vanished static radars are simulated using C++. The computational performance is compared and analyzed.FindingsThe results showed that the proposed online method can generate low-detection-probability penetration paths within subseconds.Originality/valueThis paper provides a new online method to plan UCAV penetration trajectory in military and academic contexts.

Omnichannel Strategy in Cloud-Based Contact Centers: A Blueprint for Success

This comprehensive technical article examines the evolution and implementation of omnichannel contact center solutions, focusing on the transition from traditional siloed systems to integrated cloud platforms. The article investigates the architectural framework, implementation challenges, and strategic advantages of modern contact center solutions, with particular emphasis on platforms like Genesys Cloud. Through detailed case studies and performance metrics, the research demonstrates how organizations across various sectors have achieved significant improvements in customer satisfaction, operational efficiency, and cost reduction through systematic implementation of omnichannel strategies. The article provides insights into emerging technologies, scalability considerations, and best practices for successful contact center transformation, while highlighting the critical role of AI-driven solutions and microservices architecture in enhancing customer experience and operational excellence.

(In)Harmonious transition from an intent to an action: emotional correlates of susceptibility to control and authorship

AbstractThe harmonious transition from intention to action is a positive experience associated with a sense of authorship over one’s own actions. Its opposite is the presence of an internal control that ensures action. According to the assumptions of self-determination theory, a person’s development is aimed at achieving harmony between intention and action. This constitutes autonomy, the second key element of which is self-reflection. The results of our study conducted on a sample of 778 people do not support the content-focused concept of autonomy. Based on the literature on self-regulation, we assumed that the transition from intention to action might be related to the anxiety, pride, curiosity and compassion. The results of our study confirm these expectations and suggest that a structural approach may offer a better understanding of the transition form intention to action than a content theory, thus providing a theoretical framework for practical solutions.

Crop-raiding by wildlife and cropland abandonment as feedback from nature-based solutions: lessons from case studies in China and Nepal

Abstract Conservation efforts under the nature-based solutions (NbS) framework aim at better management of ecosystems and improvement of human well-being. Policies targeting forest-based livelihoods align well with the NbS principles, but their social-ecological outcomes are often confounded by complex human-environment interactions. In this study, we identify one major feedback effect of the ecosystem dynamic on people’s livelihoods based on datasets collected from two study areas in China and Nepal. Our methodology integrates satellite remote sensing, household surveys, and statistical models to investigate households’ cropland abandonment decisions under the influence of crop-raiding by wildlife. Results show that cropland parcels that have experienced crop-raiding are more likely to be abandoned in the following years. The more damage the crops have suffered on a given parcel, the more likely it is that the parcel will be abandoned. Parcels in proximity to natural forests, farther away from the house location, and with poorer access to paved roads bear a higher risk of being abandoned. These effects are robust and consistent after controlling for multiple parcel features and household characteristics at different levels and using the dataset from each study area separately. We conclude that policymakers need to consider this undesirable feedback of the ecological system to the livelihoods of local people to better achieve co-benefits for ecosystems and human society.

Convolution tensor decomposition for efficient high-resolution solutions to the Allen–Cahn equation

This paper presents a convolution tensor decomposition based model reduction method for solving the Allen–Cahn equation. The Allen–Cahn equation is usually used to characterize phase separation or the motion of anti-phase boundaries in materials. Its solution is time-consuming when high-resolution meshes and large time scale integration are involved. To resolve these issues, the convolution tensor decomposition method is developed, in conjunction with a stabilized semi-implicit scheme for time integration. The development enables a powerful computational framework for high-resolution solutions of Allen–Cahn problems, and allows the use of relatively large time increments for time integration without violating the discrete energy law. To further improve the efficiency and robustness of the method, an adaptive algorithm is also proposed. Numerical examples have confirmed the efficiency of the method in both 2D and 3D problems. Orders-of-magnitude speedups were obtained with the method for high-resolution problems, compared to the finite element method. The proposed computational framework opens numerous opportunities for simulating complex microstructure formation in materials on large-volume high-resolution meshes at a deeply reduced computational cost.

AI+AR based Framework for Guided Visual Diagnosis of Equipment

Automated solutions for effective support services, such as failure diagnosis and repair, are crucial to keep customer satisfaction and loyalty. However, providing consistent, high quality, and timely support is a difficult task. In practice, customer support usually requires technicians to perform onsite diagnosis, but service quality is often adversely affected by limited expert technicians, high turnover, and minimal automated tools. To address these challenges, we present a novel solution framework for aiding technicians in performing visual equipment diagnosis. We envision a workflow where the technician reports a failure and prompts the system to automatically generate a diagnostic plan that includes parts, areas of interest, and necessary tasks. The plan is used to guide the technician with augmented reality (AR), while a perception module analyzes and tracks the technician’s actions to recommend next steps. Our framework consists of three components: planning, tracking, and guiding. The planning component automates the creation of a diagnostic plan by querying a knowledge graph (KG). We propose to leverage Large Language Models (LLMs) for the construction of the KG to accelerate the extraction process of parts, tasks, and relations from manuals. The tracking component enhances 3D detections by using perception sensors with a 2D nested object detection model. Finally, the guiding component reduces process complexity for technicians by combining 2D models and AR interactions. To validate the framework, we performed multiple studies to:1) determine an effective prompt method for the LLM to construct the KG; 2) demonstrate benefits of our 2D nested object model combined with AR model.

Integrating AI with Chemical Engineering for Rapid Biomaterial Development in Health Applications

This research presents an innovative approach to healthcare biomaterial development by integrating AI with chemical engineering methodologies. This study combines AI’s predictive power with the precision of chemical engineering to accelerate the discovery and optimization of biomaterials tailored for health applications while focusing on biocompatibility, adaptability, and stringent biomedical functionality. By using machine learning frameworks and advanced simulation software, the project developed predictive models that streamline biomaterial synthesis. This reduced experimental cycles by up to 40% and shortened development timelines by over 30%. Through neural networks and hybrid AI models, material properties such as degradation rate, strength, and biocompatibility can be accurately predicted, which minimizes in vivo testing and accelerates the transition from laboratory research to clinical trials. Protocols optimized through AI-driven insights enhance biomaterial production’s reproducibility and scalability while reducing material costs by 20%, which supports economically viable synthesis methods for large-scale applications. This research contributes a customizable AI framework for health-focused biomaterial solutions and impacts areas such as regenerative medicine, drug delivery, and implantable devices. Future directions include integrating real-time clinical feedback into AI models to improve safety and adaptability, thus advancing sustainable and personalized approaches to biomaterial development. This interdisciplinary framework demonstrates how AI-enhanced chemical engineering workflows can yield adaptive biomaterials suited to specific biomedical needs and offers unprecedented precision in material selection. The model’s high predictive accuracy in biocompatibility screening, achieving over 90%, enables focused experimental validation while reducing the need to rely on animal studies, which fosters ethical research practices. The sustainability impact of this project is significant, as AI-driven optimization reduces resource consumption and waste and aligns with eco-friendly biomaterial development practices. Ultimately, this research paves the way for rapid, efficient, and sustainable innovations in healthcare materials and sets a foundation for future advancements in personalized medical applications.

Explainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics

AbstractOptimizing costs and profits while meeting customer demand is a critical challenge in the development of perishable supply chains. Customer-centric demand forecasting addresses this challenge by considering customer characteristics when determining inventory levels. This study proposes a solution framework comprising two steps: (a) segmentation using customer characteristics and (b) demand forecasting for each segment using transparent and responsible artificial intelligence techniques. We employed k-means, hierarchical clustering, and explainable AI (XAI) to segment, model, and compare several machine-learning techniques for demand forecasting. The results showed that support vector regression outperformed the autoregressive models. The results also showed that the two-step segmentation and demand forecasting process using hierarchical clustering and LSTM outperforms (Weighted average RMSE across segments = 61.57) the conventional single-step unsegmented forecasting process (RMSE overall data = 238.18). The main implication of this study is the demonstration of XAI in enhancing transparency in machine learning and an improved method for reducing forecasting errors in practice, which can strengthen the supply chain resilience for perishable products.

A Nature-based Solutions Framework for Embedding Climate Change Mitigation and Adaptation into Urban Land Use Plans through Strategic Environmental Assessment (SEA).

Climate change impacts comprise a particular challenge for authorities when reconciling the implications of land use planning decisions. Whilst Strategic Environmental Assessment (SEA) is typically applied to the development of urban land use plans, the selection of mitigation and adaptation strategies for climate change impacts can have knock-on effects on nature. However, Nature-based Solutions (NbSs) could provide an innovative means of addressing climate change mitigation and adaptation without these knock-on effects. The main aim of this research is therefore to propose a conceptual framework for embedding NbSs into the main stages of the SEA process to potentially enhance climate change mitigation and adaptation in urban land use planning. This is achieved through a systematic literature review of academic and grey literature sources, with subsequent content analysis. This study demonstrates the value of matching these manifold NbS approaches to climate change impacts potentially addressed in SEA process stages and suggests how this might be achieved in practice focusing on urban land use plans.

3-Dimensional numerical analysis of geosynthetic double-encased annulus stone columns

A geosynthetic double-encapsulated annulus stone column technique is proposed in this study to overcome limitations associated with conventional stone columns, such as limited lateral load-bearing capacity and potential issues related to aggregate dispersion in soft soils. Geosynthetic double-encapsulated annulus stone columns are consistent with conventional stone columns besides being confined in and around the surrounding soil. The performance of geosynthetic double-encapsulated annulus stone columns is primarily dependent upon their outer-to-inner diameter ratio. For this reason, comprehensive 3-dimensional numerical studies were carried out to evaluate the optimum outer-to-inner diameter ratio of the annulus stone column. The results suggest that the ultimate load-carrying capacity of an annulus stone column increases with an increasing ratio of outer to inner diameter until an optimum value. In addition, the double encapsulation enhances confinement, improving shear stiffness and reducing lateral bulging. However, the load-carrying capacity was substantially reduced beyond the critical outer-to-inner diameter ratio. A simple analytical model extending the theory of thin cylinders is also introduced to estimate the accumulated stresses and strains in the geosynthetic encasement. The proposed model operates within a simplified framework of elastic solutions that facilitate practising engineers to design and optimize geosynthetic encasements for enhanced structural performance.

A frequency-independent absorption function surrogate for perfectly matched layer in exterior acoustics

In many engineering applications, the solution of acoustic wave problems in the infinite domain is required over a broad frequency range with densely sampled increments. In order to achieve efficient numerical simulations via a spatial discretization, e.g. finite element method, additional artificial absorbing boundaries are necessary to truncate the computational domain into appropriate bounded sizes. One of the most commonly used non-reflecting techniques to attenuate propagating waves is known as the perfectly matched layer. However, the system matrices arising from the finite element treatment of the Helmholtz equation in the absorbing layers are frequency-dependent, implying that they must be formed and inverted at each frequency of interest. Such a procedure is rather troublesome for frequency sweeps. To address this, a surrogate of perfectly matched layers is proposed, which enables the corresponding system matrices to be independent of the frequency. Moreover, it avoids the use of a relatively large computational domain and relatively thick enclosed layers at low frequencies, thus improving the ability of perfectly matched layers across the entire frequency range. After that, an adaptive projection-based model order reduction scheme is further developed to reduce the computational complexity of exterior acoustic systems. A robust error indicator based on the relative error of two constructed reduced order models is accordingly introduced. The performance of the present solution framework is discussed and compared with other implementation strategies, in the context of multi-frequency solution of two-dimensional test models with single or multiple scatterers.

A coupled scaled boundary finite element and phase-field algorithm for seismic loading

Seismic-induced damage, integral to the safety evaluations of major engineering projects, persists as a key focus of research worldwide. Based on the Scaled Boundary Finite Element Method (SBFEM) and the Phase-Field Method (PFM), a coupled algorithm tailored for reciprocal loading was introduced in this paper, integrating strategies including "closure constraints," "numerical threshold strategy," and "subdomain block integration." Adopting object-oriented principles, a universal coupling solution framework has been built and seamlessly embedded within GEODYNA, a self-developed finite element software system. The accuracy was validated through rigorous benchmark tests. The entire process of crack initiation, propagation, and dynamic opening-closing cycles in the Koyna concrete gravity dam was reproduced. Furthermore, the effect of mesh size and computational timestep on the structural seismic response, crack localization, and the extent of damage in the dam were explored. The outcomes demonstrated that the SBFEM-PFM coupling algorithm performs effectively and meets the engineering precision criteria for seismic evaluations and reinforcement analyses of crucial structures.

Gamification in the Design of Virtual Patients for Swedish Military Medics to Support Trauma Training: Interaction Analysis and Semistructured Interview Study.

This study explores gamification in the design of virtual patients (VPs) to enhance the training of Swedish military medics in trauma care. The challenges related to prehospital trauma care faced on the battlefield require tailored educational tools that support military medics' education and training. The aim of the study is to investigate how to design VPs with game elements for Swedish military medics to support learning in military trauma care. By understanding the reasoning and perceptions of military medics when interacting with VPs, this study aims to provide insights and recommendations for designing VPs with game elements that are specifically tailored to their needs. The study involved 14 Swedish military medics of the Home Guard-National Security Forces participating in a tactical combat care course. Participants interacted with 3 different VP cases designed to simulate military trauma scenarios. Data were collected through think-aloud sessions and semistructured interviews. The data were analyzed using interaction analysis, structured by the unawareness, problem identification, explanation, and alternative strategies or solutions (uPEA) framework, and reflexive thematic analysis to explore participants' reasoning processes and perceptions and identify possible game elements to inform the VP design. Mapping the military medics' reasoning to the uPEA framework revealed that study participants became more creative after making a mistake followed by feedback and after receiving a prompt to make a new decision. The thematic analysis revealed 6 themes: motivation, "keep on trying"; agency in interaction with VPs; realistic tactical experience; confidence, "I know that the knowledge I have works"; social influence on motivation; and personalized learning. Participants suggested that game elements such as scoring; badges; virtual goods; progress bars; performance tables; content unlocking; hints; challenge; control; imposed choice; narrative; avatars; sensation; randomness; difficulty adapting; competition; leaderboards; social pressure; progression; and renovation can promote engagement, motivation, and support confidence in decision-making. Gamification in the design of VPs represents a promising approach to military medical training, offering a platform for medics to practice medical and tactical decision-making in a risk-free environment. The insights gained by the study may encourage designing VPs with game elements, as well as including possibly wrong decisions, their consequences, and relevant feedback, that may support military medics' reflections and decision-making.

Pathwise Stochastic Control and a Class of Stochastic Partial Differential Equations

In this article, we study a stochastic optimal control problem in the pathwise sense, as initially proposed by Lions and Souganidis in [C. R. Acad. Sci. Paris Ser. I Math., 327 (1998), pp. 735-741]. The corresponding Hamilton-Jacobi-Bellman (HJB) equation, which turns out to be a non-adapted stochastic partial differential equation, is analyzed. Making use of the viscosity solution framework, we show that the value function of the optimal control problem is the unique solution of the HJB equation. When the optimal drift is defined, we provide its characterization. Finally, we describe the associated conserved quantities, namely the space-time transformations leaving our pathwise action invariant.

Order dispatch problem of the inter-city or inter-district ridesplitting service

This study formulates the order dispatch problem of the inter-city/inter-district ridesplitting service as an integer linear programming, and a solution framework that combines Lagrangian relaxation (LR) and alternating direction methods of multipliers is constructed. Specifically, LR problem is constructed by relaxing coupling constraints; based on this, the augmented Lagrangian relaxation (ALR) problem is constructed by adding a quadratic penalty term. Then, the ALR problem can be decomposed into a series of interdependent vehicle route subproblems by using linearisation technique and block coordinate descent method. The upper bound of the model is generated by ALR and a heuristic, and the solution quality can be evaluated by the lower bound provided by LR. Finally, numerous experiments are conducted to verify the effectiveness of proposed algorithms. The results show that the proposed algorithm could achieve an average cost saving of 10% and an average detour reduction of over 20% than the existing algorithm.

Multi-Stage Rolling Grid Expansion Planning for Distribution Networks Considering Conditional Value at Risk

Existing single-stage planning and multi-stage non-rolling planning methods for distribution networks have problems such as low equipment utilization efficiency and poor investment benefits. In order to solve the above problems, this paper firstly proposes a multi-stage rolling planning method for distribution networks based on analyzing the limitations of the existing planning methods, which divides the planning cycle of the distribution network into multiple planning stages, and makes rolling amendments to the planning scheme of each stage according to the latest information during the planning cycle. Then, a multi-stage rolling planning model of distribution network taking into account conditional value at risk is established with the objective of minimizing the total investment and operation cost of the distribution network. On the one hand, the users’ electricity bill is taken into account in the objective function, and the necessity of this part of the benefits is demonstrated. On the other hand, the conditional value at risk is used to quantify the uncertainty of the operation cost in the process of the expansion planning of the distribution network, which reduces the operation cost risk of the distribution network. Next, this paper uses the rainflow counting method to characterize the capacity decay characteristics of energy storage in the distribution network, and proposes an iterative solution framework that considers energy storage capacity decay to solve the proposed model. Finally, the proposed method is applied to an 18-node distribution network planning case. This confirms that the multi-stage rolling planning method could improve the investment benefits and reduce the investment cost by approximately 27.27%. Besides, it will increase the total cost by approximately 2750 USD in the case if the users’ electricity bill is not taken into account. And the maximum capacity of energy storage may decay to 87.6% of the initial capacity or even lower during operation, which may cause the line current to exceed the limit if it is not taken into account.

Robust and accurate numerical framework for multi-dimensional fractional-order telegraph equations using Jacobi/Jacobi-Romanovski spectral technique

This paper presents a novel spectral algorithm for the numerical solution of multi-dimensional fractional-order telegraph equations, a critical model used to capture the combined effects of diffusion and wave propagation. The core innovation of this work is the application of Jacobi-Romanovski polynomials as the basis functions for spectral discretization. These polynomials offer unique advantages, including the ability to handle nonstandard domains and boundary conditions, making them particularly suitable for partial differential equation (PDE) applications. A comprehensive error analysis is conducted, providing deep insights into the convergence rates and factors affecting the accuracy of the numerical solutions. Extensive numerical experiments further demonstrate the superior performance of the proposed spectral algorithm in solving a wide range of multi-dimensional fractional-order telegraph equation models. The results show a significant improvement in accuracy and computational efficiency compared to traditional numerical methods, such as finite difference or finite element techniques. This research advances the field of computational science by offering a robust, efficient, and versatile numerical framework for the precise solution of complex multi-dimensional PDEs.