Limited Information Exchange Research Articles

Clinical coordination in cancer diagnosis: healthcare stakeholders’ viewpoints in Latin America

Abstract Background Delays in diagnosis contribute to increasing cancer mortality rates in Latin American countries due to, among others, highly fragmented healthcare systems. The objective of this study, part of a larger research project (EquityCancer-LA), was to analyze cross-level coordination during cancer diagnosis and factors influencing it from healthcare stakeholders’ perspectives in Chile, Colombia, and Ecuador. Methods This descriptive-interpretative qualitative study was based on 114 semi-structured individual interviews in 2022-23 using a criterion sample of health professionals from primary care (PC) (n = 40) and secondary care (SC) (n = 44) as well as managers and policy makers (n = 30) from two public healthcare networks in each country. Sample reached saturation. A thematic analysis was conducted first by country and then comparatively, guided by the theoretical framework of clinical coordination. Results In all countries, informants highlighted limited cross-level exchange of information and limited coordination of clinical management, delays in referral, and long waiting times after referral, leading to diagnostic delays. Factors hindering coordination differed across countries: structural (insufficient personnel, access to diagnostic tests in PC); organizational (limited/inadequate coordination mechanisms, e.g. shared clinical records; poor working conditions); and health professional characteristics (limited knowledge and skills among PC doctors). Direct feedback was emphasized to facilitate coordination of cancer diagnosis: virtual joint clinical conferences in Chile, co-location of PC and SC in Colombia, and informal networks (based on personal contacts) in Ecuador. Conclusions The findings underscore the critical coordination problems contributing to delays in cancer diagnosis. Addressing these challenges requires strategic interventions aimed at strengthening primary care and fostering cross-level coordination. Key messages • Cancer diagnosis in Latin America suffers from coordination deficits due to structural, organizational, and professional factors. • Strengthening primary care and cross-level coordination is needed.

Decentralized distributionally robust chance-constrained operation of integrated electricity and hydrogen transportation networks

Hydrogen energy offers a promising pathway to decarbonizing future energy systems. Renewable energy-based hydrogen production and road transportation-based hydrogen delivery have strengthened the coupling between the power distribution network (PDN) and the hydrogen transportation network (HTN). In this article, we propose a decentralized distributionally robust chance-constrained (DRCC) method for the integrated electricity and hydrogen transportation network (IEHTN) to foster synergies between PDN and HTN. First, an optimal scheduling framework is developed to coordinate hydrogen production and transportation in the IEHTN, where multiple operational states of water electrolyzers and the time-constrained tube trailer routing are modeled. Second, a data-driven DRCC approach is proposed to model the uncertain behaviors of renewable power generation and hydrogen demand. A conditional value-at-risk approximation is then employed to convert the DRCC problem to a tractable mixed-integer linear program that can be solved by the off-the-shelf solver. Third, an alternating direction method of multipliers (ADMM) based solving scheme is designed to solve the problem in a distributed manner with limited information exchange between PDN and HTN. To address the non-convexity brought by binary variables in the ADMM, a tractable alternating optimization procedure (AOP) strategy is utilized to guarantee the convergence of solution. Finally, case studies are conducted on an IEHTN composed of the modified IEEE 33-bus PDN and 52-node HTN to verify the validity of the proposed method.

Coordination of medium-voltage distribution networks and microgrids based on an aggregate flexibility region approach

The large-scale integration of distributed energy resources (DERs) presents operational challenges for medium-voltage distribution networks (MVDNs) and microgrids (MGs) because the conventional centralized scheduling framework lacks of an effective information exchange mechanism for coordinating the scheduling between MVDNs and MGs while supporting privacy concerns. The present work addresses these issues by leveraging a convex hull-based aggregate flexibility region (AFR) associated with the scheduling capability of massive DERs to coordinate the scheduling between an MVDN and MGs efficiently with very limited information exchange. Specifically, the AFR associated with the DERs in an MG is constructed based on the convex hull fitting method, and coordinated scheduling is facilitated by introducing safety operation constraints for the MVDN based on the AFRs of the MGs. Moreover, the coordination model is transformed into a readily solvable quadratically constrained quadratic programming problem by applying second-order cone transformations. The results of numerical computations applied to an IEEE 33-bus test system demonstrate that the obtained AFRs effectively characterize the flexible scheduling capability of DERs, and the proposed coordinated scheduling mechanism between the MVDN and MGs reduces the network losses and voltage deviations of the MVDN, while preserving information privacy.

Investigating Innovation Diffusion in Gender-Specific Medicine: Insights from Social Network Analysis

The field of healthcare is characterized by constant innovation, with gender-specific medicine emerging as a new subfield that addresses sex and gender disparities in clinical manifestations, outcomes, treatment, and prevention of disease. Despite its importance, the adoption of gender-specific medicine remains understudied, posing potential risks to patient outcomes due to a lack of awareness of the topic. Building on the Innovation Decision Process Theory, this study examines the spread of information about gender-specific medicine in online networks. The study applies social network analysis to a Twitter dataset reflecting online discussions about the topic to gain insights into its adoption by health professionals and patients online. Results show that the network has a community structure with limited information exchange between sub-communities and that mainly medical experts dominate the discussion. The findings suggest that the adoption of gender-specific medicine might be in its early stages, focused on knowledge exchange. Understanding the diffusion of gender-specific medicine among medical professionals and patients may facilitate its adoption and ultimately improve health outcomes.

Joint Service Caching, Resource Allocation and Task Offloading for MEC-Based Networks: A Multi-Layer Optimization Approach

To provide reliable and elastic Multi-access edge computing services, one feasible solution is to federate geographically proximate edge servers to form a logically centralized resource pool. Optimization of such systems, however, becomes challenging. In this paper, we study the problem of maximizing users' QoE in a MEC-based network, through jointly optimizing service caching, resource allocation and task offloading decisions. We formulate a mixed-integer nonlinear programming (MINLP) problem for the task and establish its NP-hardness. To tackle it efficiently, we propose a novel two-stage algorithmic solution based on approximation and decomposition theory. The proposed algorithm achieves high system performance while at the same time, ensures all constraints from different layers are satisfied. Meanwhile, the structure of the algorithm also fits the multi-layer optimizing feature, making it suitable to be implemented at different layers. In addition, we propose a distributed and online version of our mechanism with very limited information exchange between MEC servers, and further demonstrate how the cost of service switches from real MEC systems can be incorporated into our framework. We evaluate our mechanisms through simulations with both synthetic and real-world traces, and results indicate they are effective as compared to representative baseline algorithms.

Decentralized stabilization of large-scale linear parameter varying systems

In stabilization of a Large-Scale System (LSS), the decentralized nature of the controller is a significant issue, because centralized controllers are difficult and impractical for real-time implementation. The designing procedure for decentralized controllers should guarantee the stability of the overall LSS and at the same time, allow limited information exchange in the LSS. In this paper, a decentralized controller for a nonlinear LSS modeled by a Linear Parameter Varying (LPV) model is designed. The controller design procedure is formulated as a convex feasibility problem which can be solved by finding a feasible answer to some Linear Matrix Inequalities (LMIs). The solution to this feasibility problem assures a fully decentralized controller where information exchange among local controllers is forbidden and, only data transfer among each controller and its corresponding subsystem is allowed. In the proposed approach, the Lyapunov function of the LSS equipped with local controllers is considered as the sum of the Lyapunov functions of all subsystems. Then, the stability conditions are derived to assure the stability of the LSS. After designing a decentralized controller to ensure LSS stability, the same approach is exploited for H∞ and H2 performance improvement of the LSS in the presence of disturbances. To verify the efficacy of the designed controller, a large-scale power system which is a practical example is considered, and the proposed approach is applied on it. The simulation results prove the appropriateness of the designed local controllers.

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

Transformative Potential: A Comprehensive Analysis of China-Pakistan Agricultural Cooperation under CPEC

Given the persistent concern of global food security, particularly in developing nations where agricultural sectors faces various challenges, the increasing collaboration between China and Pakistan under China-Pakistan Economic Corridor (CPEC) presents an interesting case study for agricultural cooperation. This research explores the partnership, examining its evolution, potential, and inherent challenges. The analysis maps out the growth of bilateral trade between China and Pakistan from 2011 to 2019, analyzing policy frameworks, and explores the diverse fields of cooperation, including infrastructure, product processing, machinery, and information exchange. While highlighting promising prospects, it identifies challenges such as low enterprise competitiveness, limited information exchange, and political risks, all considered in the context of CPEC and the Belt and Road Initiative (BRI). It emphasizes the transformative potential of China-Pakistan agricultural cooperation under CPEC, underscoring the importance of comprehensive planning and strategic approaches to maximize mutual benefits in the domain of international agricultural collaboration.

The Social Fabric of Watershed Management: Comparison of Citizen-Based and Agency-Based Organizations

This research offers an exploration of the social networks within two distinct watershed groups in the Hudson River, New York State, USA: citizen-based and agency-based organizations. Through a social network analysis of their operations and interactions, this study unveils the complex dynamics and roles of individual nodes in facilitating nine types of connections, such as political and financial, within these networks. The citizen-based organization demonstrated denser and more cohesive networks, suggesting robust relationships and enhanced resilience and adaptability. In contrast, the agency-based organization exhibited more hierarchical networks. This study employs both network-level and node-level analyses to examine the social networks within watershed groups. Our network-level analysis focuses on metrics such as density, average degree, and hierarchy, while our node-level analysis examines clustering coefficients and influence. It also explores ego networks through an analysis of their density and the effective size of structural holes. Our finding is that the social networks of the two groups are quite distinct, and there is limited exchange of information and resources between them. However, we discovered that effective communication among a few well-connected individuals (e.g., those with high influence values) within each group can enhance the effectiveness and resilience of these networks. These analyses aim to provide a detailed understanding of the social dynamics within regional watershed groups.

Generating Talking Face With Controllable Eye Movements by Disentangled Blinking Feature.

In virtual reality, talking face generation is committed to using voice and face images to generate real face speech videos to improve the communication experience in the case of limited user information exchange. In a real video, blinking is an action often accompanied by speech, and it is also one of the indispensable actions in real face speech videos. However, the current methods either do not pay attention to the generation of eye movements, or cannot control the blinking in the generated results. To this end, this article proposes a novel system which produces vivid talking face with controllable eye blinks driven by the joint features including identity feature, audio feature, and blink feature. In order to disentangle the blinking action, we designed three independent features to individually drive the main components in the generated frame, namely the facial appearance, mouth movements, and eye movements. Through the adversarial training of the identity encoder, we filter out the information of the eye state from the identity feature, thereby strengthening the independence of the blinking feature. We introduced the blink score as the leading information of the blink feature, and through training, the value can be consistent with human perception to form a complete and independent control of the eyes. Experimental results on multiple datasets show that our method can not only reproduce real talking faces, but also ensure that the blinking pattern and time are fully controllable.

Opinion Dynamics with Limited Information

We study opinion formation games based on the famous model proposed by Friedkin and Johsen (FJ model). In today’s huge social networks the assumption that in each round agents update their opinions by taking into account the opinions of all their friends is unrealistic. So, we are interested in the convergence properties of simple and natural variants of the FJ model that use limited information exchange in each round and converge to the same stable point. As in the FJ model, we assume that each agent i has an intrinsic opinion s_i in [0,1] and maintains an expressed opinion x_i(t) in [0,1] in each round t. To model limited information exchange, we consider an opinion formation process where each agent i meets with one random friend j at each round t and learns only her current opinion x_j(t). The amount of influence j imposes on i is reflected by the probability p_{ij} with which i meets j. Then, agent i suffers a disagreement cost that is a convex combination of (x_i(t) - s_i)^2 and (x_i(t) - x_j(t))^2. An important class of dynamics in this setting are no regret dynamics, i.e. dynamics that ensure vanishing regret against the experienced disagreement cost to the agents. We show an exponential gap between the convergence rate of no regret dynamics and of more general dynamics that do not ensure no regret. We prove that no regret dynamics require roughly varOmega (1/varepsilon ) rounds to be within distance varepsilon from the stable point of the FJ model. On the other hand, we provide an opinion update rule that does not ensure no regret and converges to x^* in tilde{O}(log ^2(1/varepsilon )) rounds. Finally, in our variant of the FJ model, we show that the agents can adopt a simple opinion update rule that ensures no regret to the experienced disagreement cost and results in an opinion vector that converges to the stable point x^* of the FJ model within distance varepsilon in textrm{poly}(1/varepsilon ) rounds. In view of our lower bound for no regret dynamics this rate of convergence is close to best possible.

Amalgamating TSO and DSO Energy Markets Through Minimal Data Exchange-Based Framework

Integration of distributed energy resources (DERs) in the distribution system (DS) raises the need to have a trading platform for their energy products. The evolution of distribution system operators (DSOs) facilitates the participation of DERs in the energy market at DS level. To utilize the potential of DERs in DS at the transmission level, an iterative coordinated market operations (CMO) scheme for a day-ahead energy market is proposed in this paper. In the proposed scheme, coordination between the transmission system operator and DSOs is established with limited exchange of information to realize an efficient and economical market framework. The communication procedure and time interval for data exchange during various intermediate operations, and convergence criteria of the proposed iterative scheme are defined. The main goal is to accommodate CMO into the existing market clearing mechanism with minimal changes. Case studies on three test systems demonstrate the viability of proposed scheme and the improvement in overall economics in comparison to the conventional method of market clearing as practiced or proposed in literature.

Multi-region dynamic economic dispatch with active power real-time balance constraint

In this paper, a multi-region dynamic economic dispatch model with power balance constraint is constructed to seek the actual reserve demand and reasonable reserve allocation method of the system by relying on the dynamic response process of the units to the power balance of the interconnected system. For this kind of dynamic optimization problem with differential equation constraints, a discrete-optimization rolling solution strategy based on scale-optimization is proposed. First, in the discrete part of the dispatching model, basing on the correlation between the continuous quantities discrete scale and the active real-time balancing effect of the dispatching scheme, a continuous quantities discrete scale optimization strategy is proposed to maintain the safe and economic operation of the system; then, for the convergence problem of the solution of the dispatching model after discretization, this paper introduces the idea of decentralized coordination optimization and the operator splitting quadratic programming (OSQP), and through the limited information exchange between the regional grid and the coordinator, the consistent coordination of consistent variables is achieved. The simulation results verify the effectiveness of the model and the solution strategy.

Collaborative optimal scheduling of integrated energy system considering carbon trading

In order to further realize the zero carbon transition of the energy system, this paper proposes a robust optimal scheduling model for transmission and distribution collaboration considering carbon trading. In this model, the multi energy equipment in the integrated energy system is modeled for transmission and distribution coordination. The long-distance power transmission network is an electricity gas coupling network, and the energy hub is the park distribution network. The nonconvex dynamic power flow network is relaxed by using the method of second-order cone programming, and the ADMM method is used to ensure that the optimal energy coordination is obtained between regions under the limited information exchange, so as to ensure user privacy. The validity of the scheduling model is proved by the case study.

Joint Data Transmission and Energy Harvesting for MISO Downlink Transmission Coordination in Wireless IoT Networks.

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs. For this, we derive a multi-objective optimization (MOO) formulation to obtain the optimal beamforming pattern (BP) and power splitting ratio (PR), and we propose a fractional programming (FP) model to find the solution. To tackle the nonconvexity of FP, an evolutionary algorithm (EA)-aided quadratic transform technique is proposed, which recasts the nonconvex problem as a sequence of convex problems to be solved iteratively. To further reduce the communication overhead and computational complexity, a distributed multi-agent learning-based approach is proposed that requires only partial observations of the channel state information (CSI). In this approach, each BS is equipped with a double deep Q network (DDQN) to determine the BP and PR for its UE with lower computational complexity based on the observations through a limited information exchange process. Finally, with the simulation experiments, we verify the trade-off between SE and EH, and we demonstrate that, apart from the FP algorithm introduced to provide superior solutions, the proposed DDQN algorithm also shows its performance gain in terms of utility to be up to 1.23-, 1.87-, and 3.45-times larger than the Advantage Actor Critic (A2C), greedy, and random algorithms, respectively, in comparison in the simulated environment.

Some power allocation algorithms for cognitive uplink satellite systems

Cognitive satellite communication (SatCom) is rapidly emerging as a promising technology to overcome the scarcity of the exclusive licensed band model in order to fulfill the increasing demand for high data rate services. The paper addresses power allocation methods for multi-operator multi-beam uplink satellite communication systems co-existing with a Ka-band terrestrial network, using cognitive radio paradigm. Such a scenario is especially challenging because of (i) the coexisting multiple SatCom operators over the cognitive band need to coordinate the use of their resources under limited inter-operator information exchange, and (ii) nonlinear onboard high power amplifier (HPA) which leads to nonlinear interference between users and beams. In order to tackle the first challenge, we propose distributed power allocation algorithms including the standard Alternate Direction Multiplier Method (ADMM); Regarding the HPA nonlinear impairment, we propose nonlinear-aware power allocation based on Signomial Programming. The proposed solutions outperform state-of-the-art in both cases.

INTERNATIONAL WATER LAWS AND THEIR POTENTIAL TO ACHIEVE INTEGRATED MANAGEMENT OF THE INDUS BASIN

This article examines the legal framework governing water management in the Indus basin and identifies its gaps and limitations. The author suggests that adopting international water laws, such as the United Nations Convention on the Law of the Non-Navigational Uses of International Watercourses, could provide a more comprehensive and impartial legal structure for managing water resources in the region. Additionally, the author argues that implementing an integrated management approach involving India and Pakistan could create a favourable opportunity for sustainable development based on limited territorial sovereignty, consultation, notification, and information exchange principles. Overall, the article proposes solutions to the legal challenges of managing water resources in the Indus Basin, intending to achieve sustainable development and equitable utilization of water resources while minimizing conflicts between the two riparian states.

Grid Impact Aware TSO-DSO Market Models for Flexibility Procurement: Coordination, Pricing Efficiency, and Information Sharing

This paper proposes five market models for the procurement of flexibility by transmission (TSO) and distribution system operators (DSOs), based on several TSO-DSO coordination schemes, including a disjoint distribution, disjoint transmission, common, fragmented, and multi-level market. The properties of these models are then analyzed. In particular, the common market is first proven to be more efficient than the other market models. Then, different methods are proposed to adequately price TSO/DSO interface flows, when procuring cross-grid flexibility. It is then shown that, when interface flows are optimally priced, the fragmented and multi-level market solutions converge to those of the common market, reaching optimal efficiency. To prevent the need for any network information sharing among system operators in the different coordination schemes, decomposition methods based on bi-level programming and the alternating direction method of multipliers (ADMM) are proposed. A developed case study, considering an interconnected transmission-distribution system, corroborates the mathematical findings by highlighting the greater efficiency of the common market, the effect of adequate interface pricing on reducing procurement costs, and the capability of the decomposition methods to reach optimal market solutions with limited information exchange.

The Effects of Information on the Formation of Migration Routes and the Dynamics of Migration.

Most models of migration simply assume that migrants somehow make their way from their point of origin to their chosen destination. We know, however, that-especially in the case of asylum migration-the migrant journey often is a hazardous, difficult process where migrants make decisions based on limited information and under severe material constraints. Here we investigate the dynamics of the migration journey itself using a spatially explicit, agent-based model. In particular we are interested in the effects of limited information and information exchange. We find that under limited information, migration routes generally become suboptimal, their stochasticity increases, and migrants arrive much less frequently at their preferred destination. Under specific circumstances, self-organised consensus routes emerge that are largely unpredictable. Limited information also strongly reduces the migrants' ability to react to changes in circumstances. We conclude, first, that information and information exchange is likely to have considerable effects on all aspects of migration and should thus be included in future modelling efforts and, second, that there are many questions in theoretical migration research that are likely to profit from the use of agent-based modelling techniques.

Distributed Baseline Load Estimation for Load Aggregators Based on Joint FCM Clustering

Residential customer baseline load (CBL) estimation is critical to Demand Response (DR) implementation. The accurate estimation of baseline load is challenging because of the complexity and uncertainty of customer behavior. In addition, conventional methods are based on centralized datasets, which may be unrealistic considering the customer privacy issues and the geographical distribution of data storage under multiple load aggregators (LA). This paper proposes a fully distributed framework for CBL estimation based on joint Fuzzy C-Means (FCM). The fuzzy membership matrix is utilized to capture the load features. LAs can acquire the joint clustering result through limited information exchange in a privacy-preserving way. At last, the CBL is calculated through an inverse process of clustering. Case studies have been conducted utilizing the dynamic Time of Use trail data from Low Carbon London project. Compared with widely used averaging methods, the proposed method significantly improves the estimation accuracy.