Search Results (2,338)

Public-sector sustainability and climate reporting increasingly address environmental exposure, governance, and financial effects, yet existing frameworks do not adequately disclose preparedness for low-probability, high-impact systemic risks whose probabilities, timing, thresholds, and transmission channels remain deeply uncertain. This article develops a Public-Sector Resilience Reporting Standard (PSRRS) as a pre-standard architecture for government preparedness disclosure. The design has three bounded objectives: diagnose cross-framework disclosure gaps, translate these gaps into a theoretically grounded capability-to-disclosure architecture, and demonstrate its analytical use through an illustrative Florida application and two hazard-neutral stress tests. The documentary corpus includes international sustainability and public-sector reporting standards, ISO and UNDRR resilience and continuity instruments, three Florida resilience documents, and peer-reviewed literature on resilience governance, decision-making under deep uncertainty, critical infrastructure interdependency, catastrophic uncertainty, climate-risk disclosure, public finance, climate-risk pricing, local-government credit risk, investor attention, and ransomware service disruption. A structured interpretive coding protocol classifies each framework as explicit, partial, or not explicit across nine disclosure dimensions; a codebook appendix identifies the assessment criteria, the a priori and inductively refined dimensions, and the validation boundaries. Florida is not treated as a basis for statistical or jurisdictional generalization. Instead, it illustrates how a comparatively developed resilience architecture may disclose statutory continuity, critical-asset data, project ranking, and output metrics while leaving systemic dependencies, adaptive triggers, long-horizon fiscal exposure, residual service risk, distributional effects, and assurance mechanisms insufficiently visible in the reviewed reporting corpus. AMOC and case-grounded cyber-fiscal stress tests show how the PSRRS shifts reporting from hazard inventories and funded projects toward auditable evidence of institutional capacity, adaptive readiness, and public-value protection. The article specifies mandatory, recommended, and optional clauses, evidence requirements, indicator examples, a disclosure index, a sample report structure, and a three-tier pilot conformity model. The contribution is conceptual and operational, but not yet a validated formal standard; cross-jurisdictional piloting, inter-rater coding, cost testing, assurance testing, and stakeholder consultation are identified as the next stage of standardization. Full article

Coastal marinas and high-traffic nearshore sites accumulate pollution from vessel movements, tourism, and shifting weather, yet routine monitoring rarely operates at the temporal resolution needed to catch emerging risks before they become acute. This study developed an AI-enabled decision support system and tested it across three European coastal sites over a 90-day window in summer 2025: an urban marina (Site A), a tourism marina (Site B), and a mixed-use port channel (Site C). A composite Water Quality Risk Index (WQRI), combining five normalised environmental and vessel-traffic stressor dimensions, fed a two-layer AI framework in which a gradient boosting model estimated short-term traffic-related stress and a random forest model classified next-day risk. Vessel traffic was heaviest at Site B, but water quality risk followed a different pattern: Site C returned the highest mean WQRI and logged the most hours under red alert despite intermediate traffic volumes, indicating that sustained moderate traffic mattered more than peak volume. Vessel intensity and WQRI were positively correlated at all three sites, most strongly at Site B, and the next-day random forest risk classifier, trained across all three sites, achieved strong discriminative performance (AUC 0.93). When the system indicated elevated risk, managers responded by deploying inspections, issuing traffic advisories and increasing monitoring activity. The pilot shows that connecting vessel tracking, environmental sensing, and ML-based classification into a single decision loop can move coastal pollution management from reactive to anticipatory. Full article

River regulation often leads to uniform conditions within the river channel, alters sediment dynamics, and contributes to the degradation of aquatic habitats. Deflectors are increasingly used as habitat-forming elements in river restoration projects. However, the interaction between hydraulic conditions, sediment inflow, deflector location, and surface roughness and their effects on the spatial extent of sediment removal remain insufficiently studied. Laboratory experiments were conducted in a flow channel using three geometrically identical deflectors arranged according to the configuration observed in the Flinta River in western Poland. The studies were conducted for the following combinations: three discharges (Q = 0.40, 0.64, and 1.70 dm³ s⁻¹), three water depths (h = 0.03, 0.06, and 0.09 m), three cumulative surrogate-sediment masses (R_um = 0.5, 1.0, and 1.5 kg), three dimensionless longitudinal positions (ξ = 0.21, 0.61, and 1.00), and two deflector roughness specifications—smooth or rough. The two-dimensional extent of the sediment-free zone was quantified based on aerial photographs using the normalized surface index A^∗. Dimensionless water depth and dimensionless discharge were the dominant factors χ > Q^∗ > R^∗ ≫ ξ, and their interaction $Q^{\ast }\times \chi$ constituted the strongest two-way effect, whereas sediment mass had a significant but secondary influence. Surface roughness did not independently affect the mean $A^{\ast }$ value but altered the spatial characteristics: longitudinal position was non-significant for smooth deflectors but became significant for rough deflectors, particularly through interactions between depth and location and between depth, location, and surface. These findings indicate that deflector roughness should not be specified as an isolated design parameter but should be selected jointly with the expected flow-depth regime and the longitudinal placement of successive structures. In practical terms, the results can support the preliminary design and positioning of habitat-forming deflectors intended to create or maintain spatially differentiated sediment-cleared zones in regulated channels, thereby contributing to more evidence-based and sustainable river restoration. Full article

Soil salinization has become a major global abiotic threat restricting sustainable viticulture, especially in coastal and inland saline–alkali zones. Unlike cereal crops mainly suffering from sodium toxicity, grapevine (Vitis vinifera L.) is a typical chloride-sensitive woody perennial, subjected to superimposed damages of osmotic stress, ionic imbalance and secondary oxidative injury under saline conditions which severely suppress vegetative growth and degrade berry quality. This review systematically summarizes the multi-layered physiological adaptive mechanisms of grapevine against salt stress, including ion homeostasis maintained by salt overly sensitive (SOS), Na⁺/H⁺ exchanger (NHX) and chloride channel (CLC) transporter families, active accumulation of osmoprotectants, synergistic enzymatic and non-enzymatic antioxidant systems, and phytohormone crosstalk networks formed by endogenous phytohormones (abscisic acid, ABA; jasmonic acid, JA; salicylic acid, SA; brassinosteroid, BR) and small signaling molecules. We further elaborate comprehensive molecular regulatory cascades governing salt tolerance, covering core functional genes for ion transport, master transcription factor families WRKY, MYB, APETALA2/Ethylene Response Factor (AP2/ERF), NAC, basic helix–loop–helix (bHLH) and emerging epigenetic regulatory layers mediated by deoxyribonucleic acid (DNA) methylation, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). In addition, we integrate four categories of field mitigation strategies for saline vineyards: germplasm improvement via salt-tolerant rootstock grafting, rhizosphere soil basal amendment, exogenous biostimulant regulation, and precision agronomic optimization. Current experimental systems do not fully recapitulate complex field combined-stress conditions, as most studies rely on laboratory single-salt stress simulation. Meanwhile, multi-omics, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) gene editing and high-throughput phenotyping tools provide promising approaches to deepen our understanding of grape salt tolerance. This review constructs a comprehensive theoretical framework linking physiological responses, molecular regulatory networks and practical field technologies, offering systematic theoretical references and technical guidance for salt-tolerant germplasm innovation and environmentally sustainable viticulture on saline soils. Full article

This paper addresses the real-time trajectory tracking problem for the Twin Rotor MIMO System (TRMS), a nonlinear, strongly coupled, open-loop unstable aerodynamic laboratory benchmark whose six-dimensional state space is only partially observable through pitch and yaw angle encoders. A High-Gain Observer (HGO) is designed to reconstruct the four unmeasured states, comprising angular velocities and rotor torques, from encoder measurements alone. Three observer-based backstepping control architectures are proposed and experimentally validated on the physical TRMS platform at a 1 kHz embedded sampling rate: (i) adaptive backstepping with tuning functions, which eliminates the over-parametrization inherent in conventional adaptive formulations through a single unified parameter update law; (ii) backstepping with online Brain Storm Optimization (BSO) of the design gains; and (iii) backstepping with online Artificial Bee Colony (ABC) gain optimization. All three architectures achieve stable 100 s trajectory tracking, whereas the conventional non-adaptive backstepping baseline diverges after 42 s due to progressive yaw-channel instability exceeding 4 rad. The BSO- and ABC-optimized controllers achieve the highest pitch-axis tracking precision (reducing pitch root-mean-square errors by 68% relative to the baseline), while the adaptive tuning functions architecture yields the best yaw-axis stability (0.2244 rad RMSE, a 91% reduction). The tuning functions architecture primarily resolves the yaw-channel instability caused by parametric over-parametrization, while the metaheuristic optimizers primarily improve pitch tracking precision through online gain refinement. Closed-loop stability is rigorously established via Lyapunov analysis and the nonlinear separation principle. The High-Gain Observer is directly validated on the two measured states through comparison of its pitch and yaw angle estimates against the incremental encoder signals over the full 100 s trial; the angular velocity and rotor torque estimates are only indirectly supported by the sustained stability of the closed loop, since no velocity or torque sensor is available on the rig. Comprehensive simulation and real-time experimental comparisons quantify the performance, robustness, and computational feasibility of each architecture under identical operating conditions. Full article

Limited access to quality seed constrains vegetable productivity and food security in Uganda, where informal seed systems remain central to farmers’ access to seed but are inadequately documented and largely unregulated. This study documented vegetable seed production and management practices, identified opportunities and constraints within informal seed systems, and examined the role of community seed banks in strengthening seed security. Using a mixed-method approach, quantitative surveys and key informant interviews were conducted with seed producers, seed traders, and community seed bank leaders across Uganda. Study respondents were predominantly men, with most respondents aged ≥35 years, despite women’s and youth’s primary role in vegetable production. Respondents reported key seed management constraints of seed storage pests, low-quality seeds, inconsistent market supply and price fluctuations, limited capital, and weak regulatory oversight. Seed treatment practices primarily consisted of sun-drying and plant-based pesticides, while seed quality assessment relied largely on experience and visual inspection, using cues such as seed color, size, and cleanliness, with limited use of standardized testing. Given that most vegetable growers obtain seeds through community-based seed channels, strengthening actors’ capacity to produce and supply high-quality seed is essential for sustaining vegetable production, improving farmer incomes, and enhancing food and nutrition security. Full article

Background: Resistant hypertension poses a significant therapeutic challenge, often leading to severe polypharmacy. Renal denervation (RDN) has re-emerged as a valuable adjunctive intervention for blood pressure control. Case Presentation: We report the case of a 64-year-old man (body mass index [BMI] 34 kg/m²) with long-standing resistant hypertension (RH), after previous percutaneous coronary intervention (PCI) to the left anterior descending artery, heart failure with preserved ejection fraction (HFpEF), and prior nephron-sparing surgery for clear cell renal carcinoma. Despite treatment with an extensive antihypertensive regimen encompassing nine pharmacological classes including diuretic therapy (angiotensin-converting enzyme inhibitor; calcium channel blocker, thiazide diuretic, β-blocker, α1-blocker, central α2-agonist, mineralocorticoid receptor antagonist, loop diuretic, long-acting nitrates), blood pressure remained severely uncontrolled on both home and office measurements. Persistent hypertension was accompanied by exertional dyspnoea and episodes of exertional chest discomfort. Following comprehensive evaluation and exclusion of secondary causes of hypertension, the patient underwent catheter-based renal denervation using the SymplicitySpyral™ (Medtronic) multi-electrode radiofrequency system. The procedure was associated with substantial and sustained improvement in blood pressure control, with mean 24 h ambulatory blood pressure measurements decreasing to 130/80 mmHg at six-month follow-up. Importantly, successful blood pressure reduction enabled major simplification of pharmacotherapy, including complete discontinuation of clonidine, loop diuretic therapy, and long-acting nitrates, together with marked dose reduction in doxazosin. Conclusions: This case illustrates the potential clinical utility of renal denervation in carefully selected patients with true resistant hypertension and pronounced sympathetic overactivity. Beyond achieving satisfactory blood pressure control, RDN may facilitate meaningful reduction in medication burden, potentially improving treatment adherence, quality of life, and long-term cardiovascular risk. Written informed consent was obtained from the patient for both the procedure and the publication of this case report. Full article

Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is essential for traffic flow analysis, infrastructure assessment, and dynamic urban planning. Moving vehicle detection in satellite video sequences is a basic task that turns raw imagery into useful traffic-state information, supporting these applications. Despite the advantages of satellite video data, detecting moving vehicles in practice remains a tough problem. Objects are extremely small and lack clear appearance details, while low local contrast makes them hard to separate from complex backgrounds. Satellite platform motion also introduces background misalignment and intensity fluctuations, resulting in missed detections and false alarms that hurt monitoring reliability. Furthermore, current methods do not fully exploit temporal motion cues or transform-domain priors, creating a performance bottleneck that restricts their practical use. To solve these problems, this paper proposes a Channel-Attentive Spatio-Temporal-Frequency Alignment (CASTFA) framework to effectively use and combine multi-dimensional features for moving vehicle detection in satellite videos, with the goal of providing high-quality traffic monitoring data to help smart city planning. Specifically, a State Space-Guided Temporal Compression (SSGTC) module first collects information along the time dimension with linear computational complexity, greatly reducing overhead while keeping motion cues that are critical for traffic-state estimation. The compressed temporal features are then processed with a multi-scale Haar wavelet transform to get hierarchical time-frequency representations that capture subtle motion dynamics across different frequency bands. At the same time, a pre-trained backbone network extracts multi-scale spatial features. To allow these different domains to work together, a Cross-Domain Feature Alignment (CDFA) mechanism aligns and combines spatial and time-frequency features through channel-attentive operations. Experimental results on the publicly available satellite video moving vehicle detection dataset show that the proposed CASTFA method consistently outperforms existing approaches, with better precision, recall, and F1-scores across diverse urban scenarios. These results show that CASTFA can provide reliable moving vehicle detection performance under difficult real-world conditions, supporting accurate traffic-flow monitoring and providing valuable geospatial intelligence for smart urban planning, transportation management, and sustainable city development. Full article

Geopolitical uncertainty represents a growing source of systemic risk that reshapes international markets, disrupts cross-border operations, and challenges firms’ ability to sustain financial performance. This research examines the mechanisms through which geopolitical instability relates to firm financial outcomes in Southeast Asian economies by assessing the mediating roles of supply chain resilience, currency volatility, and foreign investment confidence. Based on a quantitative cross-sectional design, data were collected from 308 firms across Southeast Asian Economies and analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that geopolitical risks significantly influence financial performance, with the strongest effects transmitted through financial channels. Currency volatility and foreign investment confidence emerge as critical mediators, demonstrating that exchange rate instability and investor risk perceptions substantially shape firm performance under geopolitical pressure. While supply chain resilience enhances firms’ capacity to adapt to external disruptions, its direct contribution to financial performance remains insignificant. The model explains 66.7% of the variance in financial performance, reflecting strong explanatory capability. These findings extend existing knowledge by integrating financial, operational, and institutional mechanisms to clarify how geopolitical disruptions propagate into firm-level outcomes. The results underscore the importance of financial preparedness, institutional effectiveness, governance quality, and adaptive capabilities in managing geopolitical uncertainty. Full article

The Mississippi River Delta region is of national and international relevance in terms of agriculture, energy, river navigation, and fisheries. Being one of the most engineered rivers in the world, the Mississippi River has been intensively altered over the past 150 years. The river alterations included the construction of dams, levees, diversions, channelization, spillway flood control systems, and many others. These engineering practices have significantly modified the natural hydrology and sediment dynamics of the river and its deltaic region. While these interventions have provided critical benefits such as flood protection, improved navigation, and economic development, they have also led to profound environmental and ecological consequences. The reduction in sediment delivery to the Mississippi River Delta has accelerated land loss, contributing to the disappearance of coastal wetlands at an alarming rate. The land loss has diminished critical habitats for wildlife, reduced storm surge protection for coastal communities, and disrupted the delta’s natural ability to adapt to fast subsidence. The long-term sustainability of the delta is further threatened by the compounding effects of climate change, including rising sea levels, increased storm intensity, and extreme precipitation and drought conditions. This paper examines the effects, consequences, and future risks of the major river engineering practices on the Mississippi River Delta and provides strategic recommendations that balance human needs with changing natural conditions to ensure sustainability. Specific recommendations include river diversion upstream of New Orleans, better strategies to deal with floods and droughts, strategic maintenance or removal of portions of levees, hybrid coastal-inland human migration, improved transportation connections between coast and inland, and better preparation for future ecosystem shifts. This review is needed because river engineering has made the Mississippi River Delta economically vital yet increasingly vulnerable to sediment loss, wetland collapse, saltwater intrusion, flooding, and population decline. By synthesizing these linked natural and human consequences, it provides a timely framework for rethinking delta sustainability under climate change. Full article

Posture transition behavior in sows approaching parturition provides an important physiological cue for farrowing prediction. However, manual monitoring is time-consuming, labor-intensive and difficult to sustain under nighttime production conditions, while existing machine vision approaches remain limited in their ability to represent continuous posture transitions in complex farm environments. Here, we propose an event-level posture transition recognition framework that integrates YOLOv11n with an RGB–Flow cross-modal temporal network. YOLOv11n is first used to detect basic sow postures at the frame level, after which candidate transition events are automatically generated and refined according to temporal state changes. For each event segment, RGB appearance features and optical-flow motion features are extracted to construct dual-branch spatio-temporal representations. We further develop a multi-scale cross-modal attention temporal network (MS-CMATNet) for event-level behavior classification. The network captures local temporal dynamics through a multi-scale module, enhances interactions between RGB and Flow representations through cross-modal attention, and improves feature discriminability and stability by incorporating temporal–channel attention blocks (TCBAM) and an auxiliary cross-modal consistency loss (AuxCross). Experiments show that MS-CMATNet achieves an Accuracy of 88.14%, a Macro-Recall of 84.04%, and a Weighted-F1 score of 87.66% under the fixed training/validation split, outperforming the compared machine learning models, deep temporal models, and representative temporal and cross-modal baselines. Repeated stratified cross-validation and paired t-tests further confirm that MS-CMATNet achieves statistically reliable improvements over most compared baselines, particularly in Macro-F1 and Weighted-F1. These findings demonstrate the potential of the proposed framework for automated farrowing prediction in smart livestock farming. Full article

Long-endurance unmanned aerial vehicle (UAV) missions beyond 72 consecutive flight hours face a reliability boundary at which thermal gyroscope drift drives the inertial navigation system (INS) position error into rapid nonlinear divergence at a predictable threshold t_c. This paper presents BAZ II, a simulation-validated multi-agent navigation system that extends the analytical BAZ (bifurcation-aware zonal navigation) framework. Its central idea is to treat communication quality as a planning resource and combine it with multi-agent collaboration, making the navigation cliff a manageable degradation event rather than a hard operating limit. Four contributions support this idea: a thermalhysteresis MEMS gyroscope drift model reproduces the analytical cliff in simulation and supplies its physical mechanism; a distributed collaborative simultaneous localization and mapping (SLAM) filter coupled to a stochastic continuous-time Markov chain (CTMC) interagent channel sustains GPS-denied localization within the operational accuracy budget; a 3D Gaussian process RF-aware model predictive controller (MPC) with cognitive radio frequency-hopping restores link availability under jamming, while an analytic hierarchy process (AHP)-weighted multi-objective communication cost improves latency and jitter at negligible signal-to-noise ratio cost; finally, the integrated controller executes within the onboard real-time budget of an NVIDIA Jetson Xavier NX. All results are obtained in simulation, with hardware-in-the-loop and field testing remaining as priority future work. Full article

Tea is a high-value agricultural sector whose sustainable development is closely tied to the livelihoods of numerous smallholders. Persistent rural labor outmigration has made it increasingly difficult for smallholder-dominated tea production to sustain a balance between quantity expansion and quality improvement. While experimental studies show that digital–intelligent technologies can improve tea yield and quality, whether their real-world diffusion contributes to a sustainable quantity–quality balance remains underexplored. This study proposes the quantity–quality equilibrium level of tea production (QQEL_TP) to measure this balance and constructs a regional digital–intelligent transformation (RDIT) index to capture the penetration of digital–intelligent technologies into local industries. Using panel data from China’s major tea-producing provinces from 2012 to 2023, this study applies a two-way fixed-effects model to examine the RDIT–QQEL_TP relationship. Results show that RDIT significantly improves QQEL_TP. Further analysis suggests that e-commerce development is a potential channel, while heterogeneity analysis indicates that the positive effect is more likely to materialize in provinces with a stronger digital–intelligent foundation, an existing Taobao village foundation, and a smaller tea industry scale. This study provides industry-level evidence on the role of digital–intelligent transformation in promoting agricultural sustainability, and offers policy implications for addressing production-side quantity–quality imbalances in other smallholder-dominated industries. Full article

Artificial intelligence, digital payments and platform-based services are reshaping the operating conditions of Africa’s informal economy and micro-enterprise sector. While mobile money, digital marketplaces, app-mediated logistics and algorithmic scoring systems are commonly presented as instruments of financial inclusion and enterprise modernisation, their effects on informal traders remain uneven, conditional and under-governed. This systematic review synthesises recent peer-reviewed evidence on AI-mediated digitalisation pathways for informal and micro-enterprises in Africa, with particular attention to mobile money, platform payments, app-based logistics, digital credit, algorithmic management and platform governance. Following PRISMA-informed systematic review procedures, this review analyses 60 peer-reviewed, DOI-bearing articles published between April 2022 and June 2026 through a mechanism–outcome synthesis approach. The final corpus was selected through database searching, duplicate removal, title-and-abstract screening, full-text eligibility assessment, quality appraisal and mechanism–outcome coding. The findings show that digitalisation can expand market access, reduce cash-handling risks, create transaction histories, strengthen customer reach, improve operational continuity and support household resilience. However, the same digital infrastructures may also intensify livelihood vulnerability through opaque scoring, unexplained account freezes, fee shocks, exclusionary verification procedures, algorithmic ranking losses, data extraction and weak dispute-resolution mechanisms. The review therefore argues that informal enterprise digitalisation should not be understood only as a technology adoption issue, but as a socio-technical systems governance challenge. The article contributes a governance-and-risk framework linking local infrastructure, platform and payment design, AI (artificial intelligence) mediation, adoption conditions and livelihood outcomes. It further identifies minimum policy and design protections required for inclusive and resilient participation, including transparent fees, proportionate verification, human appeal channels, explainable restrictions, data-use consent, timely settlement and contingency mechanisms during digital outages or erroneous flags. The review concludes that sustainable digital inclusion for African informal micro-enterprises depends not merely on access to digital tools, but on the fairness, transparency, recoverability and accountability of the systems through which traders participate. Full article

As an important policy tool for promoting green development, environmental regulation (ER) has exerted a profound impact on the employment sector. Using panel data from 106 prefecture-level and above cities in China’s Yangtze River Economic Belt (YREB) from 2011 to 2022, we examine the impact and mechanisms of ER on employment scale and structure. Empirical results reveal that the strengthening of ER has a significant inhibitory effect on employment growth, but a positive impact on the optimization of the employment structure. As regulatory intensity increases, the suppressive effect on employment scale diminishes, while structural optimization strengthens. Moreover, the effects vary across regions. The scale-inhibiting effect is more pronounced in the Upper-Middle Yangtze regions, in areas with initially low regulation intensity, and in large cities. Structural optimization is most significant in the Lower Yangtze regions and in small/medium-sized cities. We also find that ER indirectly changes employment through three channels: industrial structure upgrading (ISU), green innovation (GV), and foreign direct investment (FDI). Our findings underscore the necessity of balancing environmental stringency with social sustainability, and the conclusions of our study are applicable to policymakers in China and other economies. Full article