Heterogeneous multirobot systems show great potential in complex tasks requiring coordinated hybrid cooperation. However, existing methods that rely on static or task‐specific models often lack generalizability across diverse tasks and dynamic environments. This highlights the need for generalizable intelligence that can bridge high‐level reasoning with low‐level execution across heterogeneous agents. To address this, a hierarchical multimodal framework that integrates a prompted large language model (LLM) with a fine‐tuned vision‐language model (VLM) is proposed. At the system level, the LLM performs hierarchical task decomposition and constructs a global semantic map, while the VLM provides semantic perception and object localization, where the proposed GridMask significantly enhances the VLM's spatial accuracy for reliable fine‐grained manipulation. The aerial robot leverages this global map to generate semantic paths and guide the ground robot's local navigation and manipulation, ensuring robust coordination even in target‐absent or ambiguous scenarios. The framework is validated through extensive simulation and real‐world experiments on long‐horizon object arrangement tasks, demonstrating zero‐shot adaptability, robust semantic navigation, and reliable manipulation in dynamic environments. To the best of our knowledge, this work presents the first heterogeneous aerial–ground robotic system that integrates VLM‐based perception with LLM‐driven reasoning for global high‐level task planning and execution.

The rapid development of financial technologies significantly transforms the architecture of digital financial services and reshapes the role of traditional banks within the financial system. Under conditions of wartime economy and increased digital dependence, there is a growing need for comprehensive academic analysis of the impact of fintech companies on the banking sector in Ukraine. The purpose of this article is to provide a comprehensive analysis of the impact of fintech companies on the transformation of digital financial services in the Ukrainian banking sector, considering market, regulatory, and risk-related factors. The study is based on an integrated theoretical and empirical research design that examines structural changes in the fintech ecosystem, digital service channels, and bank business models. A systemic approach is applied to interpret digital transformation as a holistic process. The analysis covers the period from 2020 to 2024. Particular attention is paid to the interaction between banks and fintech companies within hybrid cooperation models. The main research hypothesis assumes that fintech companies act as a system-forming driver of digital financial services transformation rather than merely an innovative supplement to traditional banking services. The methodological framework includes methods of analysis and synthesis, structural and dynamic analysis, comparative analysis, content analysis of regulatory documents, and methods of classification and generalization. The compound annual growth rate is used to assess fintech market development dynamics. The empirical base is formed using open statistical and analytical data from relevant institutions. As a result of the study, key directions of digital financial services transformation under the influence of fintech companies are identified. Structural shifts within the fintech ecosystem and the growing importance of infrastructure and platform-based solutions are revealed. The impact of fintech companies on bank business models and digital service channels is characterized. Major risks associated with digitalization of financial services are systematized. Regulatory features of digital transformation are generalized. The theoretical contribution of the study lies in the advancement of scientific approaches to analyzing digital transformation in financial services. The practical value consists in the applicability of the findings for banking institutions and regulatory authorities. The originality of the research lies in the comprehensive consideration of fintech companies as a driver of systemic transformation of digital financial services. Future research should focus on quantitative assessment of fintech impact on banking financial stability. This article is empirical in nature.

Abstract Drawing on the findings from the contributions to this Special Issue, this article maintains that, fifteen years after Lisbon, a debate on the law and politics of delegated rulemaking in the European Union (EU) is as relevant as ever. That is because the functional and procedural integration of the Member States in the EU multilevel and multi-jurisdictional legal system, supplanted by unprecedented challenges for supranational crisis management in the recent years, often led to unpredictable and often controversial configurations of delegated rulemaking. One may thus conclude that, fifteen years later, the constitutional framework put in place in the Lisbon Treaty has not been able to effectively constrain the normative development that evolved from delegated rulemaking, which often followed a different path. Hence the Lisbon Treaty provides only a partial blueprint for delegated rulemaking, leaving out rulemaking by EU agencies, regulatory frameworks and private parties, as well as increasingly composite and hybrid cooperation formulas involving EU and national executive actors. The future of EU delegated rulemaking will very much depend on the capacity to reach consensus on important meta-principles and political conceptions, such as democratisation and parliamentarisation, shaping the normative spaces generated within the sui generis dynamic European model of governance.

Hybrid work has become increasingly popular in the aftermath of the Covid-19 pandemic. Despite its popularity, many organisations still strive to find an answer to "how to do hybrid cooperation right''. Enabling collaboration across digital and physical workspaces, participants, and practices is a great challenge, as it inevitably introduces asymmetric relations, incongruences in frames of reference, and misaligned ecosystems and technical infrastructures. The paper investigates approaches used to manage asymmetries in hybrid work settings and their impact on cooperation. Through a multi-sited ethnographic study, we reveal a prevalent reliance on mimicking tools and practices native to fully physical or fully digital settings in the hybrid space. These mimicking practices often arise because the hybrid work setting is viewed through a deficit lens, whereby it is perceived as lacking certain elements (e.g., body, voice, mobility) and access to modalities from especially the physical setting, thereby necessitating the need for compensation work. To provide vocabulary to conceptualize and articulate this type of practice, we introduce the concept compensation work, referring to work that is carried out to offset a deficiency or absence that has been identified, aiming to restore and re-establish a familiar state that has vanished. While compensating through mimicking is intuitive, such approaches assume that our known practices and interactions from the physical workspace can indeed be translated to another context, neglecting the fact that changing the medium inevitably impacts the message. Thus, cooperative practices and interactions taking place in the physical workspace do not remain the same when unfolding in the hybrid space. Finally, we suggest that in order to design technologies and practices that support hybrid cooperation we first need to acknowledge the hybrid workspace as a distinct "third space'' next to the fully remote and fully physical workspace. This includes following an experience-driven approach to hybrid cooperation, encouraging the design of innovative interactions that extend beyond merely compensating for asymmetries and leveraging the unique capabilities and affordances of these settings.

Abstract Human-computer interaction as a coordinating element between human and machine is used in many different ways. Due to their digital processes, countless industries are dependent on an effective intermeshing of humans and machines. This often involves preparatory work or sub-processes being carried out by machines, which humans initiate, take up, continue, finalise or check. Tasks are broken down into sub-steps and completed by humans or machines. Aggregated cooperation conceals the numerous challenges of hybrid cooperation in which communication and coordination must be mastered in favour of joint decision-making. However, research into human-computer interaction can also be thought of differently than a mere aggregation of humans and machines. We want to propose a nature-inspired possibility that has been successfully practising the complex challenges of joint decision-making as proof of successful communication and coordination for millions of years. Collective intelligence and the processes of self-organisation offer biomimetic concepts that can be used to rethink socio-technical systems as a symbiosis in the form of a human-computer organism. For example, the effects of self-organisation such as emergence could be used to exceed the result of an aggregation of humans and machines as a future social anthropology 4.0 many times over.

This paper introduces a novel metaheuristic algorithm named the opposition-based cooperation search algorithm with Nelder–Mead (OCSANM). This enhanced algorithm builds upon the cooperation search algorithm (CSA) by incorporating opposition-based learning (OBL) and the Nelder–Mead simplex search method. The primary application of this algorithm is the design of a fractional-order proportional–integral–derivative (FOPID) controller for a buck converter system. A comprehensive evaluation is conducted using statistical boxplot analysis, nonparametric statistical tests and convergence response comparisons to assess the algorithm’s performance and confirm its superiority over CSA. Furthermore, the FOPID-controlled buck converter system based on OCSANM is compared with two top-performing algorithms: one using a hybridized approach of Lévy flight distribution with simulated annealing (LFDSA) and the other employing the improved hunger games search (IHGS) algorithm. This comparison encompasses transient and frequency responses, performance indices and robustness analysis. The results reveal the notable advantages of the proposed OCSANM-based system, including 25.8% and 8.7% faster rise times, 26% and 8.8% faster settling times compared with the best-performing approaches, namely LFDSA and IHGS, respectively. In addition, the OCSANM-based system exhibits a 34.7% and 9.6% wider bandwidth than the existing approaches-based systems. Incorporating voltage and current responses of the buck converter’s switched circuit with the OCSANM-based FOPID controller further underscores the algorithm’s effectiveness. To provide a comprehensive assessment, the paper also compares the proposed approach’s time and frequency domain responses with those of 17 other state-of-the-art approaches attempting to control buck converter systems similarly. These findings affirm the effectiveness of the OCSANM in designing FOPID controllers for buck converter systems.

This research introduces a novel metaheuristic algorithm, OCSAPS, representing an upgraded cooperation search algorithm (CSA) version. OCSAPS incorporates opposition-based learning (OBL) and pattern search (PS) algorithms. The proposed algorithm's application aims to develop a fractional order proportional-integral-derivative (FOPID) controller tailored for a buck converter system. The efficacy of the proposed algorithm is assessed by statistical boxplot and convergence response analyses. Furthermore, the performance of the OCSAPS-based FOPID-controlled buck converter system is benchmarked against CSA, Harris hawk optimization (HHO), and genetic algorithm (GA). This comparative analysis encompasses transient and frequency responses, performance indices, and robustness analysis. The outcomes of this comparison highlight the distinctive advantages of the proposed approach-based system. Moreover, the proposed approach's performance was compared with six other approaches used to control buck converter systems similarly regarding both time and frequency domain responses. Overall, the findings underscore the efficacy of the OCSAPS algorithm as a robust solution for designing FOPID controllers in buck converter systems.

As non-renewable resources are limited and the overall CO2 emissions need to be reduced drastically, there is an increasing need in making the energy supply more sustainable. This leads to a needed change in the traditional energy system, in which decentralized local energy resources must be better integrated. In order to face the challenges for a future energy management, the consideration of the domestic level is gaining more attention. In this paper, we aim to get insights into the potential value of local cooperations and focus on economic and control issues as to whether and how the domestic level can participate in upcoming solutions. We introduce a possible setup for a virtual economic unit representing a hybrid cooperation of domestic energy participants, whereby the objective of this cooperation is to realize the maximum possible savings for the community with specified individual contracts between the participants and assuming that participants continue to have additional contracts with their energy service provider. We propose a deterministic linear optimization model for determining optimal energy load profiles of the participants with the external suppliers and energy exchange between participants in a virtual economic unit. To efficiently solve this model and get an exact assignment of supply and demand, we present a maximum saving flow algorithm taking into account the underlying bipartite structure of this problem. The solution achieved is specified as a peer-to-peer allocation between the participants involved and provides insights into the aspects that determine the concrete assignment. It also has the advantage of leading to a robust solution within the collaboration case studies for a basic set-up demonstrate the impact of this approach on the economic potential of aggregating local generation and demand at the same time.

Abstract This study presented the possibility of the hybrid cooperation between continuous wave laser (CW) laser and vertical cavity surface emitting laser (VCSEL) laser operation performance signature for upgrading ultra high reach optical fiber systems. The total estimated lighted power spectrum configuration is simulated with wavelength ranges from 1.543 to 1.555 μm. Total estimated lighted power spectrum configuration is studied with spectral time variations. Total estimated lighted power spectrum is indicated and measured after fiber system length. The total electrical modulated signal amplitude spectrum variations are demonstrated with threshold value after receiver side. The total signal/noise modulated spectrum signal configuration is clarified with time after receiver side. Total estimated electronic power spectrum is measured and tested after receiver system. Also, the total estimated electronic power spectrum configuration is demonstrated with spectral frequency ranges from 200 to 800 GHz.

Retracted: Analysis of the 3D Application Evaluation System of Landscape Based on Hybrid Cooperation of VR and AI

SRv6 can provide hybrid cooperation between a centralized network controller and network nodes. IPv6 routers maintain multi-hop ECMP-aware segments, whereas the controller establishes a source-routed path through the network. Since the state of the flow is defined at the ingress to the network and then is contained in a specific packet header, called Segment Routing Header (SRH), the importance of such a header itself is vital. Motivated by the need to study and investigate this technology, this paper discusses some security-related issues of Segment Routing. A SRv6 capable experimental testbed is built and detailed. Finally, an experimental test campaign is performed and results are evaluated and discussed

In recent years, one of the key postulates in the European Union’s policy has become the development of renewable energy sources. In order to achieve the desired synergy effect, the idea of combining two selected sources of energy appeared. This article presents a technical and economic analysis of a hybrid connection of a ground source heat pump with a photovoltaic installation. Taking into account the heat demand of the building, a ground heat pump with a catalog nominal heating power of 25 kW was selected. This article presents the problem of the economic profitability of using a hybrid combination of a heat pump and photovoltaic panels in domestic hot water and central heating systems. The justification for the use of such heat sources in these installations is due to global trends and the gradual departure from conventional energy sources such as oil or gas boilers. This paper presents the economic and ecological results of using the pump heat connected together with photovoltaic panels. In the economic analysis, with the assumed installation costs related to the use of the considered heat pump and PV, two parameters commonly used in the investment analysis (static and dynamic) were used, namely, the simple payback period and the net present value of the investment. For the adopted assumptions, the usable area of the facility and the number of years of use were indicated, at which the investment in question is competitive with other alternative investment interest methods and will start to bring tangible benefits. The performed analysis also has measurable environmental benefits in the form of a reduction in carbon dioxide emissions at the level of 2893 kg/year into the atmosphere. The presented solution will help future investors understand the investment profitability mechanism for their households.

In response to the negative effects caused by structures of the dominant agricultural system and new market opportunities, increasing food supply structures have re-emerged in the urban-rural context of industrialized countries in recent years. These food supply structures often accompany new forms of hybrid cooperation models, including actors and institutions that have not shared resources previously. They form new alliances for sustainable transformation in the agri-food sector. Simultaneously, discourse has arisen in science and practice about the sustainability potential of such hybrid cooperation, referring to a lack of critical systematization and the necessity for creating an assessment concept. From the latter, one could draw conclusions about the transformative potential of such cooperation models and their potential to serve as blueprints for other regions. In this conceptual paper, a classification approach derived from social enterprise literature is elaborated, extended, and evaluated, to design a classification of new hybrid cooperation models that allow comparisons between regions and are sensitive to their dynamics. We show in an application how the classification approach, considering the dimensions “actors”, “resources”, and “actions”, serves to discover patterns in the development of short food-supply chain practices, identifying individual transition paths and, thus, making statements about their sustainability and challenges.

On SRv6 Security

The traditional urban landscape planning and design method cannot integrate the design into the whole planning scene and cannot express the planning effect of multiple schemes and large scenes in an efficient, intuitive, and coordinated way. With the development of computer science and technology, virtual reality technology based on AI is playing an important role in many aspects of urban landscape planning and design. In this paper, on the basis of 3D visualization landscape planning and design, 3D visualization urban landscape modeling is carried out using VR combined with the constructed deep neural network (DNN) to establish a 3D database and virtual scene model of the landscape, and 3D representation of the virtual scene landscape is realized. The designed solution allows the user to grab the object and release it into the workspace. If any placement is needed, the user can pick up the layer again and move it to a new location. Finally, the simulation experiment and its result analysis were conducted to effectively analyze the site of the real model and its matching rate, to realize the integration of the 3D image, 3D stereo, and system integration technology, and to finally complete the landscape design task.

Electric spring (ES) as a novel concept in power electronics has been developed for the purpose of dealing with demand-side management. In this paper, to conquer the challenges imposed by intermittent nature of renewable energy sources (RESs) and other uncertainties for constructing a secure modern microgrid (MG), the hybrid distributed operation of ESs and electric vehicles (EVs) parking lot is suggested. The proposed approach is implemented in the context of a hybrid stochastic/robust optimization (HSRO) problem, where the stochastic programming based on unscented transformation (UT) method models the uncertainties associated with load, energy price, RESs, and availability of MG equipment. Also, the bounded uncertainty-based robust optimization (BURO) is employed to model the uncertain parameters of EVs parking lot to achieve the robust potentials of EVs in improving MG indices. In the subsequent stage, the proposed non-linear problem model is converted to linear approximated counterpart to obtain an optimal solution with low calculation time and error. Finally, the proposed power management strategy is analyzed on 32-bus test MG to investigate the hybrid cooperation of ESs and EVs parking lot capabilities in different cases. The numerical results corroborate the efficiency and feasibility of the proposed solution in modifying MG indices.

Reliable load time series forecasting plays an important role in guaranteeing the safe and stable operation of modern power system. Due to the volatility and randomness of electricity demand, the conventional forecasting method may fail to effectively capture the dynamic change of load curves. To satisfy this practical necessity, the goal of this paper is set to develop a practical machine learning model based on feature selection and parameter optimization for short-term load prediction. In the proposed model, the ensemble empirical mode decomposition is used to divide the original loads into a sequence of relatively simple subcomponents; for each subcomponent, the support vector machine is chosen as the basic predictor where the real-valued cooperation search algorithm (CSA) is used to seek the best model hyperparameters, while the binary-valued CSA is set as the feature selection tool to determine the candidate input variables; finally, the aggregation of all the submodules’ outputs forms the final forecasting result. The presented method is assessed by short-term load data from four provincial-grid dispatching centers in China. The experiments demonstrate that the proposed model can provide better results than several conventional models in short-term load prediction, while the emerging CSA method is an effective tool to determine the parameter combinations of machine learning method.

In order to improve the positioning accuracy of indoor and dense obstacles, a positioning algorithm based on maximum likelihood recursive estimation and cubature Kalman filter is proposed for the positioning technology based on the received signal strength. The algorithm consists of two steps: initial position estimation and mobile location. Firstly, according to the principle of triangulation, the possible target region of mobile terminal is determined and the region is divided into smaller possible target region step by step. Then, based on the hybrid cooperation of the received signal strength and time of arrival, the nonlinear CKF filter is used to realize the mobile location. Finally, MATLAB is used to simulate the algorithm, and the simulation results show that the proposed method has better positioning performance even in the shadow area.

In this letter, we propose a novel hybrid cooperation scheme in the context of heterogeneous sub-6 GHz/millimeter-wave cellular networks, where users are classified either as cell-center or cell-edge users. Using stochastic geometry tools, we propose an analytical framework to investigate the achieved performance of our proposed scheme. Specifically, analytical expressions for the moments of the conditional success probability are derived and a simple approximation of the meta-distribution is calculated, leveraging the moment-matching method with the Beta-distribution. Our results show that the proposed scheme is beneficial for the cell-edge users, and therefore, the overall network performance is significantly enhanced.

<p>In this letter, we propose a novel hybrid cooperation scheme in the context of heterogeneous sub-6 GHz/millimeter-wave cellular networks, where users are classified either as cell-center or cell-edge users. Using stochastic geometry tools, we propose an analytical framework to investigate the achieved performance of our proposed scheme. Analytical expressions for the moments of the conditional success probability are derived and a simple approximation of the meta-distribution is calculated, leveraging the moment-matching method with the Beta-distribution. Our results show that the cooperation technique is beneficial for the cell-edge users and that the proposed scheme significantly improves the overall network performance.</p>