Search Results (1,080)

With the large-scale integration of electric vehicles (EVs), distributed charging scheduling has become a key enabler for coordinated charging management. However, its reliance on information exchange makes it susceptible to cyberattacks, including False Data Injection (FDI), Denial of Service (DoS), and replay attacks. Such attacks may compromise privacy and corrupt or interrupt communication, leading to incorrect consensus prices and degraded scheduling performance. To mitigate these threats, this paper proposes a distributed resilient charging scheduling strategy. Specifically, anomalous nodes are detected through neighbor-based observations, while a belief-degree-based trust mechanism is employed to isolate low-trust nodes and suppress attack propagation. In addition, an individual price resetting mechanism is developed to restore convergence to the optimal price of the remaining EVs following node isolation. Simulations on communication networks with 5 to 100 EVs show that, under all three attacks, the compromised node is detected at the second and isolated at the third consensus iteration after attack onset, no healthy node is falsely isolated, and the remaining fleet converges to the optimum of the reduced scheduling problem with a price deviation below $8.2\times {10}^{-3}$. A buffered detection envelope extends these guarantees to asynchronous communication, heterogeneous time-varying delays, packet losses, and intermittent attacks: in 245 randomized stress runs on 5-to-100-EV networks, every attacker is isolated, no healthy node is isolated outside the harshest composite scenario, and the final price deviation remains below $1.1\times {10}^{-2}$. Extensive simulations under representative cyberattack scenarios verify the effectiveness and robustness of the proposed strategy within the stated assumptions. Full article

Accurate photovoltaic (PV) power forecasting is essential for enhancing grid stability, optimizing energy management, and facilitating the large-scale integration of renewable energy resources. Although deep learning techniques have demonstrated promising results in PV forecasting, their predictive performance is highly dependent on effective hyperparameter optimization. Furthermore, studies dedicated to field-deployed perovskite photovoltaic systems operating under semi-arid desert climatic conditions remain limited. To address this gap, this study proposes an Adaptive Hybrid Grey Wolf Optimizer–Sparrow Search Algorithm (AH-GWOSSA) deep learning framework for single-step-ahead (5 min lead time) and multi-step power forecasting of a perovskite PV system installed in Mosul, Iraq. A real-world dataset comprising 21,456 valid daytime observations (filtered for solar irradiance > 5.0 W/m²) was collected between November 2025 and April 2026 and partitioned strictly chronologically (70% training, 10% validation, 20% held-out testing). The proposed framework was benchmarked against Persistence, an unoptimized Base LSTM, Standard GRU, CNN-LSTM, standalone GWO, SSA, Random Search, and an ablation Fixed-Weight Hybrid under an equivalent evaluation budget of 160 candidate network trainings. Over 30 independent optimization runs, AH-GWOSSA achieved the lowest mean RMSE of 15.55 W (std. 0.34 W), an MAE of 8.79 W (std. 0.25 W), and the highest mean R² of 0.885 (std. 0.008). Non-parametric Wilcoxon signed-rank testing across 30 paired run-wise RMSE values confirmed a statistically significant improvement over the unoptimized Base LSTM ($W=78.0$, $p=1.8\times {10}^{-6}$ at $\alpha =0.01$). Multi-horizon evaluations (5, 15, 30, 60, and 120 min) demonstrated consistent superiority over Persistence and Base LSTM, with RMSE improvements of up to 39.8% at the 30 min horizon. The findings demonstrate that adaptive hybrid metaheuristic optimization provides a competitive framework for ultra-short-term perovskite PV forecasting, with RMSE improvements of up to 39.8% over Persistence at the 30 min horizon, offering a solid foundation for future smart-grid and battery storage management applications. Full article

This study develops a techno-economic sizing and energy-management framework based on the Hippopotamus Optimization Algorithm (HOA) for hydrogen-integrated autonomous Hybrid Renewable Energy Systems (HRES) for rural electrification. A representative remote community in Tabuk, Saudi Arabia, is investigated using 11 years of NASA POWER satellite-derived meteorological data. The modelled community is constructed from a synthesised connected-load inventory representing approximately 600 households and 3000 residents; accordingly, the results represent a simulation-based planning case study rather than a validated design for a specific settlement. Seven configurations combining photovoltaic generation, wind turbines, battery storage, hydrogen production and storage, fuel cells, and diesel generation are evaluated considering Total Net Present Cost, CO₂ emissions, and Loss of Power Supply Probability (LPSP), with a Demand Response Management System (DRMS) incorporated into the framework. The three objectives are combined using a weighted-sum scalar formulation, complemented by a hard-constrained formulation for reliability. HOA is benchmarked against Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Walrus Optimizer (WO), and Osprey Optimization Algorithm (OOA) under a common budget of 10,000 objective-function evaluations per run and 10 independent runs. Under the constrained formulation, six of the seven configurations satisfy LPSP ≤ 5% within the investigated sizing bounds, with costs of energy (COE) ranging from $0.1046/kWh for PV/wind/battery to $0.1357/kWh for wind/battery/diesel; the fully renewable PV/wind/hydrogen configuration is feasible at $0.1195/kWh. Only the wind-free configuration fails to satisfy both imposed constraints because of the 30% diesel-energy limit rather than reliability. Evaluation over the eleven individual meteorological years shows that all designs violate the 5% reliability criterion in every year, reaching 2.0–2.9 times the design LPSP because hour-of-year averaging removes prolonged low-resource periods. Re-optimization against the worst observed year increases COE by 33–63% and storage capacity by factors of three to five, with hydrogen storage in the fully renewable configuration increasing from 10 to 75.4 kg. Sensitivity analysis identifies wind availability as the dominant economic parameter, with a 20% wind-speed reduction increasing COE by 36.1%. The DRMS reduces the peak-to-average ratio by 20.0% for the assumed evening-peaking profile, whereas no reduction is obtained for an afternoon-peaking profile consistent with measured Saudi residential demand. These findings demonstrate that meteorological and demand-profile representation materially affects autonomous HRES sizing and should be explicitly considered when interpreting techno-economic optimization results. Full article

Cloud–edge continuums are driving the shift of cloud-native applications from centralized data centers to latency-, mobility-, privacy-, and energy-saving applications. The serverless architecture provides an attractive “Function-as-a-Service” (FaaS) model in this transition, as it is driven by events, elastic and fine-grained, and controlled by the platform. But introducing a mix of heterogeneous edge nodes, fog/MEC resources, regional clouds, and hyperscale data centers creates a seemingly simple FaaS deployment problem to solve with a set of multi-objective orchestration challenges: runtime selection, autoscaling, cold start mitigation, placement, migration, workflow coordination, state management, trust, cost, energy, and carbon. In this article, we provide an extensive critical review of serverless functions in cloud–edge environments. While some surveys are narrowly focused on aspects of autoscaling, offloading, IoT, or security, the review brings together architectural evolution, runtime mechanisms, platform ecosystems, governance issues, sustainability issues, and emerging applications using AI. It builds a multidimensional taxonomy ranging from runtime systems, autoscaling, cold start mitigation, function placement, and offloading/migration, to workflow orchestration, state and data management, intelligent scheduling, security, sustainability, and industrial serverless platforms. It also presents a built-in conceptual model that connects application needs, runtime environment, orchestration intelligence, governance policies, and system-level results. The synthesis reveals that cloud–edge serverless systems need accountable placement, state-aware workflows, reproducible benchmarking, trustworthy orchestration, and carbon-aware lifecycle control, which can be achieved only by going beyond latency and elasticity. The paper ends with research directions on adaptive, interoperable, explainable, and sustainable serverless systems on the cloud–edge continuum. Full article

This study explores the robust connections between camel production, economic growth, and climatic conditions in Saudi Arabia using annual time-series data. The Johansen cointegration test was applied to assess long-run associations and validate the VAR framework, along with VAR diagnostic tests involving impulse response functions (IRFs), forecast error variance decomposition (FEVD), and forecast evaluation, which were used to analyze the predictive performance of the variables. The results show no evidence of a long-run equilibrium relationship among the studied variables. The VAR results indicate significant short-run dynamic interactions among temperature, camel production, and economic growth. Temperature changes exhibit strong self-adjustment and are also affected by camel meat production. Raw camel milk production shows a significant dynamic response to past economic growth, while camel meat production and economic growth exhibit relatively limited short-run feedback within the VAR model. The diagnostic results indicate that camel production is principally motivated by their own historical shocks. Forecast evaluation confirms the model’s strong predictive accuracy for camel production and economic variables. The findings highlight the importance of sustainable camel management and climate-resilient production strategies. These will sustain food systems and support the Sustainable Development Goals (SDGs), notably SDG, 2SDG 12, and SDG 13. Full article

Mathematical biology spans multiple computational regimes—from ordinary differential equation models of gene regulatory networks to stochastic simulation of chemical kinetics and reaction-diffusion models of spatial pattern formation—each imposing distinct computational demands on the software infrastructure that executes them. The choice of programming language for implementation of these mathematical models carries quantitative performance consequences that have not been systematically measured across the range of models employed in contemporary mathematical biology. This study provides a computational performance evaluation across ten languages (Python, Julia, Rust, C, C++, C#, F#, Go, Java, and R) and six mathematical biology modeling regimes: deterministic ODE integration, stochastic chemical kinetics, parameter-space exploration, reaction-diffusion spatial modeling, agent-based discrete simulation, and Bayesian parameter inference. Execution time, peak memory footprint, cyclomatic complexity, type-conversion density, and the semantic alignment between data structures and biological state representation were recorded under a uniform experimental protocol. Under a unified hand-coded Dormand–Prince 5(4) integrator, native implementations outperform managed-runtime counterparts by nearly two orders of magnitude for ODE integration, a differential that shrinks sharply once library-delegated solvers are removed from the comparison; the gap contracts below $55\times$ when stochastic kinetics shift the bottleneck from arithmetic throughput to branch resolution. In memory-bandwidth-limited reaction-diffusion modeling, native and just-in-time implementations converge to within a few percent. Agent-based models expose a distinct regime-dependent overhead: immutable-by-default collection semantics impose disproportionate cost during mutation-intensive computation. Peak memory varies by roughly two orders of magnitude across languages, directly affecting deployment density for large-scale simulation. Code-structural measurements confirm that algorithmic form enforces a floor on cyclomatic complexity, irrespective of language, while type strictness and mutation semantics generate substantial differences in per-line cognitive load. These results provide a quantitative foundation for computational tool selection in mathematical biology, challenging universal language recommendations and supporting choices grounded in the algorithmic character of each modeling regime. Full article

Thermal management is important for ensuring the safety and reliability of energy storage batteries. Conventional single-mode cooling methods fail to maintain efficiency and temperature uniformity under variable operating conditions. This study presents a phase change material (PCM)-coupled liquid-cooling strategy with an operation condition adaptive control scheme for large-capacity energy storage batteries. A battery management system numerical model with paraffin/expanded composite PCM and a parallel-channel liquid cold plate was developed. The thermal management performance of this system was comprehensively validated under different coolant flow rates, inlet temperatures, PCM melting points, and latent heats, followed by a quantitative sensitivity analysis to identify dominant influencing factors. Based on the parametric analysis, an adaptive cooling strategy was proposed: passive PCM cooling is adopted at low charge/discharge rates, while a delayed liquid-cooling activation strategy is implemented at a high rate to fully use PCM latent heat and reduce energy consumption. Results denote that this system reduces the battery surface maximum temperature by 5.1 °C and the maximum temperature difference by 2.62 °C compared with liquid cooling alone. The coolant flow rate exerts the greatest influence on both the temperature difference and the maximum temperature through the sensitivity analysis, followed by the PCM melting point, coolant temperature, and latent heat. Under the delayed cooling strategy, the total liquid-cooling runtime during continuous operation is reduced by 1720 s, with the number of cooling cycles decreasing from eight to three. This work provides an engineering-based, operation-adaptive thermal management solution that significantly enhances the temperature control effectiveness, energy efficiency, and operational safety of energy storage power stations under real-world variable working conditions. Full article

AI and machine learning deployments in regulated decision contexts face an intolerance for inadmissible outputs (“hallucinations” when the model is generative) that current explainability methods address only after the fact. Bounded AI denotes prevention by architectural design. This study establishes five conditions (C1–C5) under which a bounded artificial intelligence (AI) architecture transfers from one regulated decision domain to another. Conditions C1 through C4 adapt or combine previously established principles. The most significant contribution is the discrete joint-state topology condition, C5, for which no precedent was found in this role. The claim is that these five conditions are jointly necessary for the closure property to survive an architectural transfer—while sufficiency is not claimed. Two of the five conditions are structural prerequisites governing whether the architecture’s operators can be constructed in a destination at all. The remaining three provide warrant conditions governing whether it is the appropriate instrument or not. In existing runtime-assurance architectures, the constraint acts after inference, on the output of the learned component. In contrast, the pattern developed in this paper reverses the assurance steps via a deterministic-first/learned-second approach. Namely, the assurance architecture acts before inference on the input domain: a deterministic filter admits only rule-compliant objects, and the trigger fires non-discretionarily on joint-state cell occupancy rather than on the learned score. The architecture’s domain-neutral type signatures are formalized, and three structural transfers are developed in depth: predictive maintenance, energy-grid management, and credit underwriting, each concluding with a closure proof. All three transfers remain conceptual and report no deployment outcomes. The proofs are conditional on three stated premises that establish soundness with respect to a rule set rather than a safety case. The strongest evidence of transferability is a market-surveillance destination classified as admissible in advance and later realized on a live venue. C5 is what discriminates the transferability. It is shown that the autonomous-vehicle perception case satisfies C1 through C4, but fails C5 because the required distinctions are absent from the representation at every granularity—which is a failure that no additional compute can resolve. The architecture becomes domain-neutral through the act of transfer, not before it. Full article

Reliable state-of-charge (SOC) estimation is essential for lithium-ion battery management, yet parameter drift, operating-profile variation, and sensor faults can compromise observer consistency. This study presents a reproducible constrained FFRLS-EKF framework in which online second-order RC parameter updates are subjected to resistance, capacitance, and time-constant feasibility constraints before being scheduled in the EKF. Estimator residuals and parameter variations are then reused for exploratory concurrent fault analysis. Because the dynamic driving-cycle datasets do not provide independently measured continuous reference SOC, SOC RMSE/MAE is not reported for DST, FUDS, UDDS, US06, or BJDST; Coulomb counting is treated only as a non-independent trajectory reference because it also contributes to the FFRLS regression target. A separate 21-checkpoint HPPC validation, with reference labels withheld from the estimator, yields SOC RMSE/MAE values of 2.24/1.76 percentage points for the constrained adaptive method, compared with 2.50/2.04 percentage points for the fixed EKF. A 270-run robustness study varies fault magnitude, onset time, voltage-noise level, and initial SOC. The results identify physical projection as the dominant stabilizing mechanism, with adaptive forgetting providing secondary transient-memory adjustment. An additional 243-run two-fault stress test shows that residual-sensitivity decoupling is not universally identifiable: exact-pair recovery degrades as noise increases and remains strongly dependent on the operating profile and fault pair. Accordingly, the concurrent fault module is presented as a transparent diagnostic baseline rather than a universally validated fault-isolation method. Full article

Framework and runtime selection for containerized microservices are usually guided by request-level benchmarks, yet deployment-facing costs often dominate operational expenditure in Kubernetes environments. This study empirically evaluates four such infrastructure characteristics: container image size, startup time, idle resource consumption, and horizontal scaling latency. Eight microservice framework configurations spanning three execution models are evaluated: JVM (Spring Boot, Spring WebFlux, Quarkus, and Ktor), GraalVM Native image (Quarkus variants, including distroless and UPX-compressed images), and Rust (Actix Web). All metrics are collected technology-agnostically at the container level via cAdvisor and Kubernetes lifecycle events. Three trade-off profiles emerged during the research: Rust achieves a 2.95 MiB idle memory footprint (a 69:1 ratio versus Spring Boot on a working-set basis, or 21:1 on the more conservative proportional-set-size basis) through garbage-collector-free memory management. GraalVM Native image variants start 1.2–1.7× faster and consume up to 1.9× less memory than their JVM equivalents, although this memory advantage is not uniform: the standard reactive Native image consumes more idle memory (115.0 MiB) than the corresponding JVM variant (98.0 MiB). A UPX compression paradox is identified and explained at the kernel level: compression shrinks images by approximately 2.5:1 yet inflates idle memory to 215–229 MiB, above JVM baselines, because decompression into private anonymous memory defeats shared page mapping. Scale-up latency (1.8–3.9 s) is governed by per-instance startup rather than framework-exclusive lifecycle optimizations, partially refuting one of four research hypotheses. The findings yield context-dependent selection guidance and a fully reproducible benchmark suite. All measurements were obtained on a single-node bare-metal K3s cluster, the primary metrics characterize the idle state of a minimal no-operation service, and the only load applied is a single fixed-rate validity check at 100 requests per second. The reported values therefore constitute lower-bound, deployment-facing infrastructure costs rather than predictions of behavior under production business workloads, multi-node topologies, or managed cloud substrates. Full article

City-scale microscopic traffic simulation is increasingly used for policy evaluation, operational planning, and disruption analysis, where repeated scenario runs must retain vehicle-level trajectories rather than only aggregate traffic indicators. This requirement creates an efficiency–consistency trade-off: single-node simulators preserve centralized state ownership but become inefficient for million-vehicle tasks, whereas distributed execution reduces runtime but may disrupt vehicle updates at partition boundaries. Cross-partition movement can cause trajectory breaks, duplicate or missing updates, and inconsistent local interaction states if boundary events, vehicle context, and update ownership are not coordinated. To address this problem, this study proposes DiCoSim, a distributed coordination framework for boundary-consistent large-scale microscopic traffic simulation. DiCoSim integrates incremental spectral-clustering partitioning, spatio-temporal event aggregation, and acknowledgment-controlled state handoff to coordinate workload balance, boundary communication, and vehicle handoff. Experiments on a 483 km² Tianjin network with 1.5 million agents show that DiCoSim achieved a $14.49\times$ strong-scaling speedup on 16 compute nodes while maintaining close agreement with centralized execution. For the fixed boundary-crossing evaluation cohort, the trajectory interruption rate was reduced to 0.06%. In addition, a single 72 h continuous high-load run achieved 99.96% availability. These results indicate that, under the tested Tianjin conditions, coordinated boundary management supports efficient million-agent microscopic simulation while maintaining vehicle-state continuity across partitions. Full article

Battery prognosis is a critical component of battery management systems, enabling the prediction of end of life (EoL) and remaining useful life (RUL). However, obtaining sufficiently large labelled datasets for data-driven prognosis is challenging because battery ageing experiments are time-consuming and expensive. To address this data scarcity, we propose a conditional generative adversarial network (GAN) framework for targeted synthetic battery-data generation, in which degradation regime is explicitly used as conditioning information. The framework generates samples from three degradation regions—early, pre-knee, and post-knee—and is investigated using two representations of the same underlying battery data: direct time series and Gramian Angular Fields (GAFs). The generated data are evaluated using representation-specific quantitative metrics together with qualitative distributional analyses. As an additional validation of synthetic-data utility, GAN-generated samples are incorporated as unlabelled data in a Mean Teacher semi-supervised EoL prediction framework. Across 10 matched random-seed runs, augmentation reduces the mean EoL prediction error for both representations. For the time-series workflow, MAE and RMSE decrease by 6.37% and 3.66%, respectively, while the GAF-based workflow shows larger reductions of 15.93% and 16.20%. The improvements in both metrics are statistically significant for the GAF-based workflow, and after augmentation no statistically significant difference is detected between the aggregate EoL prediction errors of the GAF and time-series-based models. These findings demonstrate the potential of targeted conditional GANs for battery-data augmentation and highlight GAF-based generation as a promising complementary approach to conventional time-series-based augmentation for battery prognosis. Full article

The You Only Look Once (YOLO) has been widely adopted in aquaculture monitoring and management due to its real-time performance and deployment flexibility. The recently introduced YOLO26 architecture incorporates Non-Maximum Suppression (NMS)-free end-to-end inference and is optimized for deployment on resource-constrained CPU-based devices, making it particularly relevant for edge deployment in commercial aquaculture applications. Nevertheless, its performance, operational efficiency, and deployment suitability compared with previous YOLO generations remain largely unvalidated in aquaculture-specific scenarios. This study benchmarks YOLO26 against three Ultralytics predecessors (YOLOv5u, YOLOv8, and YOLO11) across nano, small, and medium model scales for the detection of fish mortality, a critical indicator of fish population health and welfare, in recirculating aquaculture systems (RAS). Twelve model variants were evaluated for detection accuracy, training efficiency across seven dataset sizes, and inference performance on both high-performance NVIDIA A100 GPUs and the resource-constrained, CPU-only Raspberry Pi 5 edge device. All models achieved comparable performance on the full dataset, with mAP50 varying by only 1.25 percentage points across three independent training runs, indicating minimal influence of architectural generation on final mortality detection accuracy when sufficient training data are available. However, notable differences emerged in data efficiency and deployment performance. YOLOv8 demonstrated the strongest training efficiency, achieving 90% mAP50 with only 400 training images, whereas YOLO26 nano and small variants required 1000 images to reach comparable accuracy. In contrast, YOLO26 exhibited advantages during edge deployment, with YOLO26n achieving the highest inference speed on the Raspberry Pi 5 at 7.84 ± 0.13 FPS across three benchmark sessions, while YOLOv5mu outperformed all contemporary medium-scale architectures on CPU-based hardware. These results demonstrate that architectural novelty alone is an insufficient criterion for model selection. The findings support a deployment-oriented framework in which training data availability, target hardware, and inference requirements collectively inform model selection for aquaculture applications. Full article

With the escalating frequency of sophisticated zero-day attacks, overcoming the critical limitations of signature-based Intrusion Detection Systems (IDSs) has become paramount. This study proposes a hybrid multi-layered intrusion detection framework combining traditional machine learning, Deep Neural Architectures (DenseNN), and ensemble methods to evaluate zero-day resilience within cloud-level backend connectivity interfacing EV and V2X management ecosystems. Using the comprehensive CSE-CIC-IDS2018 benchmark as a surrogate environment, a code-executed Leave-One-Attack-Out (LOAO) cross-validation protocol across 13 distinct attack families was implemented to assess unseen-attack-family generalization within the benchmark to unseen threats. Furthermore, Explainable Artificial Intelligence (XAI) auditing, utilizing SHapley Additive exPlanations (SHAP) and Integrated Gradients, was integrated to inspect decision boundaries and resolve feature-attribution failure modes. Critically, the audit identified an artifact-driven data leakage caused by the Timestamp and identifier features, demonstrating that models learned temporal schedules rather than behavioral network signatures. Re-executing all experiments post-leakage removal quantified performance drops across all classifiers (e.g., Gaussian NB dropping by up to 20.88 percentage points in accuracy (at the 60% training ratio; 18.30 points at the 80% ratio)). Under standard binary classification metrics, tree ensembles (Random Forest and Extra Trees) achieved high in-distribution detection (F1 > 0.95) with rapid inference latency (≈0.05–−0.07 ms/sample). However, the rigorous LOAO evaluation revealed a substantial generalization penalty on truly unseen zero-day families (e.g., SQL Injection and Infiltration), where simpler linear models demonstrated broader generalization robustness (mean LOAO F1 = 0.397) compared with complex tree-ensemble models. By rectifying dataset leakage and benchmarking deployment trade-offs (training runtime, throughput, and memory footprint), this study delivers actionable, transparent guidelines for deployment-oriented IDS evaluation in dynamic network infrastructures. Full article

Climate change influences crop prices through its impacts on agricultural production, costs, consumer and producer behavior, and the market equilibrium. Therefore, this study aims to examine the dynamic relationship between climate variability and date palm producer prices in Saudi Arabia utilizing annual time-series data from 1991 to 2024 and applying a Vector Autoregression (VAR) method to investigate dynamic responses over shorter and longer horizons. The analysis incorporates impulse response functions (IRFs), forecast error variance decomposition (FEVD), and historical decomposition (HD). The empirical findings indicate that date palm producer prices exhibit strong persistence and are predominantly explained by their own innovations in the short run. At horizon 10, date-palm producer-price innovations account for 94.82% of the forecast-error variance, while climatic variables jointly account for approximately 5.18%. Although climate factors’ shocks exert measurable short-run effects on producer prices, these responses diminish over time, indicating that the market effectively absorbs climate-related changes while maintaining persistence. However, FEVD and HD results reveal that the contribution of temperature and relative humidity to producer price fluctuations increases steadily over time, highlighting the growing importance of climate variability in shaping long-term market dynamics. The findings highlight the relevance of considering climate-smart agricultural practices, drought-tolerant date-palm varieties, and climate adaptation and risk-management strategies in the date-palm sector. While these measures were not directly evaluated in the present model, they may contribute to strengthening the sector’s resilience to climatic variability, supporting price stability, and promoting sustainable agricultural development. Their specific effects on price volatility and food security, however, require further empirical investigation. Full article