Search Results (1,037)

Rabies remains a preventable yet fatal zoonotic disease and a major public health concern in Bangladesh, which aims to eliminate dog-mediated human rabies by 2030. Despite free availability of post-exposure prophylaxis (PEP), adherence to the WHO-recommended PEP regimen remains low. This study assessed PEP compliance and identified determinants of regimen completion among animal-exposed patients. We conducted a hospital-based observational study using secondary data from 457 patients who initiated PEP at the National Rabies Prevention and Control Centre (NRPCC) in Dhaka, from February 2023 to July 2023. Sociodemographic, clinical, and exposure-related factors were analyzed to identify predictors of compliance. Only 17.1% of patients completed the full PEP regimen, including rabies immunoglobulin (RIG) administration for WHO Category III exposures where indicated. Higher adherence was observed among females, individuals aged ≥15 years, lower-income groups, and those residing within 10 km of the treatment center. Exposure-related factors including dog bites, multiple exposures, unprovoked incidents, and appropriate exposure care were also associated with improved compliance. Despite free access, PEP completion remains critically low. Targeted strategies, including decentralized PEP delivery, improved public awareness, and strengthened follow-up systems, are essential to improve adherence and support progress toward rabies elimination by 2030. Full article

Hierarchical Public Sector Organizations (PSOs), particularly military organizations, face persistent challenges in sustaining innovation due to structural rigidity, hierarchical control, and embedded resistance to change. While existing literature explains why innovation emerges and why it is resisted, significantly less attention has been devoted to understanding how innovation becomes institutionalized as a sustained organizational capability. This study addresses this gap by introducing the Bi-focal Innovation Contagion Model (BICM), an agent-based framework that conceptualizes innovation diffusion and resistance as a co-evolutionary “tug-of-war” between competing organizational forces. The model integrates top-down governance mechanisms and bottom-up innovation processes, capturing how heterogeneous actors interact within hierarchical systems to shape the diffusion, assimilation, and stabilization of innovation over time. Using the Israel Defense Forces (IDF) as an empirical source case, the model explores how Innovation Management Systems (IMS) may be designed to support the institutionalization of innovation as a self-sustaining organizational capability within hierarchical PSOs. Simulation results suggest that hybrid innovation architectures may better sustain innovation across varying leadership conditions. This occurs when centralized strategic coordination is combined with decentralized innovation activity and supported by mature innovation agents with sufficient centrality and hierarchical reinforcement. The findings highlight the critical role of IMS as an organizational architecture for achieving anti-fragility, enabling innovation dynamics to persist, adapt, and strengthen in the face of uncertainty, leadership turnover, and shifting strategic priorities. By integrating agent-based modeling with organizational theory, this study contributes a dynamic framework for understanding and designing sustainable innovation systems in hierarchical PSOs. Full article

The control of micro energy grids (MEGs) is characterized by volatility, uncertainty, and decentralization. Traditional power distribution algorithms, designed for centralized, dispatchable generators, are inadequate for MEG environments. Controllable load management provides peak shaving, load balancing, frequency regulation, and voltage stability, as well as fast balancing services for renewable energy grids in distributed power systems. A non-grid-tied inverter costs a fraction of its grid-tied counterpart for the same capacity. In the initial setting, one or more inverters are used. As the demand grows, more non-grid-tied inverters are added to the mix. Non-grid-tied inverters cannot be connected in parallel. There is no practical solution available in the market for the optimum utilization of this type of setting. Unlike a grid-tied microgrid, in non-grid-tied mode, a microgrid uses grid power only when needed, prioritizing renewable sources. This paper explores autonomous strategies for controlling and coordinating multiple renewable energy sources in MEG settings. It reviews and develops an algorithmic framework for optimal load distribution among multiple renewable sources, including solar photovoltaic (PV), wind turbines, and battery energy storage systems (BESSs). The proposed framework integrates resource forecasting, multi-objective optimization, and adaptive supervisory control to ensure stability, maximize renewable penetration, and minimize operational costs. Performance considerations, mathematical modelling, and potential implementation architectures are discussed. A hybrid approach, combining multiple algorithms, is therefore proposed. In this paper a real-life solution is proposed to a real-life problem. Full article

The widespread deployment of photovoltaic (PV) inverters with digital control and communication systems has increased the power grid’s attack surface, making it more vulnerable to cyberattacks. This creates a need for locally implementable attack-detection methods that do not disrupt inverter operation. This paper therefore proposes an irradiance-driven natural watermarking approach for decentralized detection of false data injection (FDI) attacks on inverter terminal measurements. The approach leverages irradiance-driven DC-link voltage variations to watermark the inverter outputs, generating a non-removable signature in the true measurements. The proposed method is evaluated using a real-time hardware-in-the-loop model of a three-phase grid-following PV inverter that captures PV-array and grid-connection dynamics. Implementation robustness is further assessed on a separate hardware grid-forming inverter testbed with non-idealized components. In the tested cases, the detection model identifies noise-injection and replay attacks within $15\mathrm{ms}$, while otherwise undetectable model-based attacks are revealed when DC-link voltage variations between 5% and 10% occur. These experimental results demonstrate that irradiance-driven natural watermarking can reveal FDI attacks without affecting normal inverter operation. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

The proliferation of large language model (LLM) agents has enabled increasingly complex multi-step automation; however, composing multiple agents into coherent systems introduces significant orchestration challenges that remain poorly documented. This survey examines LLM-based multi-agent orchestration from 2023 through early 2026 (literature cutoff: March 2026), with explicit attention to the evidence hierarchy used to interpret deployment claims. We propose a three-topology, one-adaptivity taxonomy—centralized, decentralized, and hierarchical coordination topologies, each optionally augmented with a dynamic–adaptive control axis—grounded in classical multi-agent systems theory and recent empirical evidence. We compare six leading frameworks (LangGraph, CrewAI, AutoGen/Microsoft Agent Framework, OpenAI Agents SDK, MetaGPT, and DSPy) along axes directly relevant to practitioners: state-management granularity, token-cost structure, failure-recovery options, and design philosophy. The emerging protocol stack is examined in terms of why MCP (agent-to-tool) and A2A (agent-to-agent) occupy complementary layers, how the ACP–A2A merger signals protocol convergence, and where ANP’s decentralized-discovery design fits. Production design considerations—state management, task planning, error handling, scalability, and security—are evaluated with reference to published benchmarks. Vendor-reported figures are marked † throughout and held to a documented evidence hierarchy, which separates them from peer-reviewed and government-evaluator measurements. We close by identifying eight open challenges and proposing a six-dimension evaluation framework for multi-agent coordination quality. This paper offers practitioners a decision framework covering taxonomy, framework selection, protocol adoption, and early operational pilots. Full article

As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has limitations in dynamic network environments. Robot Operating System 2 (ROS 2) achieves decentralized communication through the introduction of DDS. However, the single Data Distribution Service (DDS) mechanism remains inadequate for cross-network communication and high-performance local data exchange. Addressing the current issue in ROS communication research: the coexistence of multiple mechanisms without a unified analytical framework or guidance for selection. This paper systematically traces the evolution of the ROS communication architecture from centralized to distributed systems. It constructs a unified analytical framework covering two dimensions: communication models and data transmission paths. Crucially, to overcome the unreliability of cross-protocol comparisons based on heterogeneous literature, this paper designs and executes a set of unified benchmark experiments on a controlled testbed. These experiments systematically evaluate the performance of two mainstream DDS implementations (CycloneDDS and FastDDS) across five key metrics: latency, throughput, jitter, scalability, and packet loss rate under load. Additionally, a comprehensive comparative analysis of the performance of three transmission modes is conducted. Based on this comprehensive evaluation, this paper summarizes the performance characteristics of different mechanisms and further proposes an optimization-based middleware selection method for quantitative communication mechanism selection under different workload and application requirements. This paper provides a systematic reference for the design and optimization of ROS communication systems and offers guidance for promoting the application of multi-middleware collaborative architectures in robotic systems. Full article

The increasing integration of renewable energy sources into interconnected multi-area power systems (IMAPSs) has led to a significant reduction in synchronous inertia, making frequency regulation considerably more challenging. While existing studies have explored the use of integral sliding mode load frequency control (ISMLFC) schemes to stabilize area frequency and tie-line power flows in IMAPSs, these approaches predominantly rely on conventional two-phase sliding mode control. Such methods, however, have demonstrated notable limitations in maintaining the stability of IMAPSs under increasingly complex operating conditions. In addition, all the IMAPS state variables must be measured, which can cause difficulty in real IMAPS applications. Therefore, this study proposes a novel load frequency control (LFC) strategy that coordinates the single-phase sliding mode control and state observer methods to solve these above limitations. First, a dynamic IMAPS model with single phase sliding mode control based on state observer scheme is established under renewable resource uncertainties and load disturbances. Then, a novel linear matrix inequality (LMI) based on Lyapunov functional is constructed to analyze the stability of the IMAPS. Furthermore, the decentralized single-phase sliding mode load frequency control (DSPSMLFC) method is developed for the LFC of the ISMLFC. Finally, three testing scenarios are employed to verify the efficiency and advantage of the proposed DSPSMLFC approach in MATLAB/Simulink R2023a. The simulation results confirm that the proposed DSPSMLFC scheme can improve the LFC of the IMAPS under renewable resource uncertainties and load disturbances. Full article

Oxygen transfer enhancement in aquaculture was investigated using a low-cost fine-bubble spray system operated under controlled hydrodynamic conditions. Experiments were conducted under oxygen-depleted conditions (initial DO = 2.4 mg L⁻¹), and oxygen transfer kinetics were evaluated using the dynamic method. The dissolved oxygen concentration increased to 6.2 mg L⁻¹ within 1 h, corresponding to a net oxygen transfer of 9.55 ± 0.46 g. The volumetric mass transfer coefficient (kLa) was determined to be 1.44 h⁻¹ (R² = 0.97), while the specific oxygen transfer efficiency (SOTE) reached 76.4 ± 7.8 gO₂ kWh⁻¹. Dimensionless analysis (Re ≈ 2 × 10⁴, Sc ≈ 500, Sh ≈ 682) indicates a turbulent, convection-dominated transport regime. Biological observations showed a 43% increase in fish growth under spray-assisted conditions, indicating improved oxygen availability. The observed oxygen transfer enhancement was primarily associated with hydrodynamic interfacial area generation rather than diffusion-limited transport. The low-power configuration and simplified system design suggest potential applicability for decentralized aquaculture operations. The proposed approach also provides an engineering framework for evaluating low-cost aeration technologies under aquaculture operating conditions. Full article

As digitalization is widely used, Industrial Control Systems (ICSs) face severe cybersecurity challenges, where traditional defenses often lack real-time detection and immutable audit trails. Therefore, we propose a security alert platform that integrates blockchain, smart contracts, and homomorphic encryption. By leveraging the decentralized architecture of blockchain, the platform ensures the integrity and non-repudiation of operational logs. Concurrently, anomaly detection logic is embedded within smart contracts to enable an automated, real-time alerting mechanism. Furthermore, to preserve industrial data privacy, homomorphic encryption is employed, allowing the system to perform anomaly detection directly on encrypted data, thereby maintaining confidentiality throughout the data lifecycle. Preliminary analysis indicates that the proposed platform effectively enhances the resilience of ICS, strengthening both defense against unauthorized operations and post-incident forensic capabilities. Full article

The increasing integration of distributed energy resources (DERs), including photovoltaic systems, battery energy storage systems, electric vehicles, and flexible loads, is transforming modern power systems and creating new challenges for coordination, control, and cybersecurity. Conventional Virtual Power Plant (VPP) architectures typically rely on centralized energy management systems, which may face scalability limitations, communication bottlenecks, cybersecurity risks, and reduced resilience to failures. This paper presents a blockchain-enabled decentralized Virtual Power Plant framework for secure and autonomous coordination of distributed energy resources. The proposed architecture combines blockchain technology, smart contracts, IoT-based communication infrastructure, and decentralized energy management functions within a unified multi-layer coordination framework. Smart contracts automate energy scheduling, peer-to-peer transaction validation, and settlement processes, reducing dependence on centralized control entities. Lightweight blockchain consensus mechanisms are employed to improve scalability while limiting computational overhead. The effectiveness of the proposed framework is evaluated through simulation-based case studies involving decentralized DER coordination, peer-to-peer energy trading, and resilience assessment under node-failure conditions. Its performance is compared with that of a conventional centralized VPP architecture in terms of scalability, coordination reliability, communication overhead, transaction transparency, and fault tolerance. The results indicate that the decentralized framework improves operational resilience, coordination transparency, and scalability under increasing DER participation while maintaining satisfactory energy balancing performance. Although blockchain-based coordination introduces additional transaction latency, the proposed approach enhances cybersecurity, reduces dependence on centralized control structures, and provides a flexible foundation for future intelligent smart-grid applications. Full article

The increasing volume of digital health data generated through Electronic Health Records (EHRs), emergency care systems, and real-time monitoring technologies has intensified the need for secure cross-institutional healthcare analytics. However, privacy concerns, regulatory restrictions, institutional mistrust, and risks associated with centralized data aggregation continue to limit large-scale healthcare data sharing. This paper presents OPAL4Health, a governance-oriented and privacy-preserving distributed healthcare analytics framework grounded in the MIT Open Algorithms (OPAL) paradigm. The framework integrates federated analytics, blockchain-based auditability, explainable artificial intelligence (XAI), and institutional governance mechanisms within a unified computation-to-data healthcare ecosystem. Unlike conventional federated healthcare systems that primarily focus on decentralized computation alone, OPAL4Health emphasizes governance, transparency, auditability, and policy-aligned distributed analytics while preserving institutional data sovereignty. The privacy protections supported by OPAL4Health are primarily architecture-based and governance-oriented, relying on local institutional data retention, controlled query execution, and blockchain-auditable analytical workflows rather than formally provable cryptographic privacy guarantees. The framework was evaluated through a real-world urgent care pilot across seven hospitals in Riyadh, Saudi Arabia, using 184 anonymized patient cases collected between May 2015 and September 2016. Analytical findings identified a median onset-to-arrival delay of 285 min (95% Confidence Interval (CI): 270–302), low ambulance utilization (18.5%), and hospital bypass behavior in 42% of cases. Peak Emergency Department (ED) congestion periods were also identified. Scenario-based modeling projected potential long-term healthcare savings of approximately $602 million over 15 years through improved Emergency Medical Services (EMS) allocation and reduced disability-adjusted life years (DALYs). The findings demonstrate the feasibility of governance-oriented, privacy-preserving distributed healthcare analytics within OPAL4Health while generating actionable operational and policy-relevant insights without centralizing sensitive patient-level records. The proposed framework provides a transferable model for secure, transparent, and accountable digital health collaboration across healthcare ecosystems. Full article

Under the trend of intelligent transportation and connected vehicles, real-time control plays a vital role in improving bus system efficiency. Existing bus control strategies typically treat buses as homogeneous points and achieve system equilibrium by maintaining consistent headways. However, this simplification overlooks differences in dynamic responses and the evolution of powertrain lifespan arising from vehicle heterogeneity. It converts the sparse constraint problem, which is intended to ensure timely arrival, into a hard constraint on the vehicle trajectory over the entire time horizon, thereby excessively restricting individual optimal evolutionary paths and causing the optimization process to become trapped in a local optimum. To this end, this paper proposes SMATD3, a multi-agent cooperative control algorithm that accounts for vehicle heterogeneity. By adopting a centralized training and decentralized execution paradigm and avoiding the specification of a fixed inter-vehicle spacing target, the algorithm enables each vehicle to adaptively adjust its speed control strategy according to its own dynamic characteristics, thereby achieving the coordinated optimization of system equilibrium and individual objectives. The simulation results indicate that the proposed method can effectively suppress bus tailgating and achieve the coordinated multi-objective optimization of operational stability, passenger travel efficiency, energy consumption, and battery health. From a sustainability perspective, improved headway regularity and service reliability can enhance public transit attractiveness and support mode shift, while smoother energy use and reduced battery degradation lower lifecycle impacts. Full article

One to four million annual premature deaths are associated with household air pollution. This indoor pollution is mainly generated by traditional biomass cookstoves. Thus, solar cooking can significantly reduce this toll. Its proliferation would also mitigate deforestation pressures. Additionally, for developing countries, it would alleviate the fuel collection workload, mainly borne by women responsible for fuel collection. Electric cooking provides a clean and controllable alternative to thermal cookers for indoor food preparation, sterilization and heating. This study presents a minimal, off-grid photovoltaic solar cooker that operates without batteries and power electronics. Such a cooker constitutes a low-cost and high-reliability solution for electrically decentralized locations. The system encompassing the cooker is conceived as an accessible entry point for household-level photovoltaic (PV) adoption. So, it offers the potential to catalyze the uptake of clean-energy technologies and to support sustainable development. The proposed design dissipates PV power into heat using commercial positive temperature coefficient (PTC) resistors operating near their Curie temperature. A simplified theoretical model is formulated to easily estimate the thermal power and heat-transfer conductances required for achieving cooking temperatures. An instrumented prototype allows for characterizing the transient temperature evolution during controlled heating and cooling experiments in the laboratory, facilitating development in an initial step avoiding the PV panel. The results demonstrate that the minimal PV configuration is technically feasible, robust, and compatible with low-resource settings. This encourages its adoption in communities experiencing energy poverty. Full article

As cloud computing proliferates, outsourced data faces severe security threats, yet existing searchable encryption (SE) schemes rely on classical hardness assumptions, centralized trust authorities, and static access control, leaving critical gaps in quantum resistance, single-point-of-failure prevention, and dynamic permission management. To address these limitations, we propose BL-ABSE, a blockchain-enhanced, lattice-based attribute-based searchable encryption framework. BL-ABSE employs the Ring Learning With Errors (RLWE) problem as its security foundation and applies the Number Theoretic Transform (NTT) to reduce polynomial multiplication from $O\left(n^2\right)$ to $O(nlogn)$. To eliminate single-point trust risks, the framework further integrates a $(t,n)$ threshold key protocol across an edge-node consortium governed by Practical Byzantine Fault Tolerance (PBFT) consensus. A smart-contract-maintained on-chain revocation list enables permission withdrawal via a single blockchain transaction without re-encryption. Experimental evaluation demonstrates that commitment generation requires approximately 23 ms at $n=1024$, search latency scales linearly at roughly 29 µs per record, and revocation completes in approximately 2 s regardless of system scale. Formal security proofs under the quantum polynomial-time (QPT) adversary model reduce six security properties—index indistinguishability, query privacy, threshold key security, Byzantine fault tolerance, audit immutability, and revocation immediacy—to the hardness of RLWE and the Short Integer Solution (SIS) problems. To the best of our knowledge, BL-ABSE is the first framework to simultaneously achieve post-quantum security, attribute-based access control, decentralized key management, instant revocation, and immutable auditing within a single unified framework. We further conduct threshold parameter verification, end-to-end revocation latency decomposition, blockchain throughput stress testing, search-pattern leakage quantification, and communication/storage overhead analysis, providing a comprehensive evaluation of both performance and security trade-offs. We explicitly characterize the search-pattern leakage inherent in the deterministic commitment design as a correctness–privacy trade-off and discuss mitigation directions. Full article