Search Results (95)

The rapid advancement of underwater wireless communication technologies is critical to unlocking the full potential of marine resource exploration and environmental monitoring. This paper reviews recent progress in three primary modalities: underwater acoustic communication, radio frequency (RF) communication, and underwater optical wireless communication (UWOC), each designed to address specific challenges posed by complex underwater environments. Acoustic communication, while effective for long-range transmission, is constrained by ambient noise and high latency; recent innovations in noise reduction and data rate enhancement have notably improved its reliability. RF communication offers high-speed, short-range capabilities in shallow waters, but still faces challenges in hardware miniaturization and accurate channel modeling. UWOC has emerged as a promising solution, enabling multi-gigabit data rates over medium distances through advanced modulation techniques and turbulence mitigation. Additionally, bio-inspired approaches such as electric field communication provide energy-efficient and robust alternatives under turbid conditions. This paper further examines the practical integration of these technologies in underwater platforms, including autonomous underwater vehicles (AUVs), highlighting trade-offs between energy efficiency, system complexity, and communication performance. By synthesizing recent advancements, this review outlines the advantages and limitations of current underwater communication methods and their real-world applications, offering insights to guide the future development of underwater communication systems for robotic and vehicular platforms. Full article

Promoting the sustainable development of virtual water trade is of great significance to safeguarding China’s water resource security and balanced regional economic growth. This study analyzes the virtual water trade network among 31 Chinese provinces based on multi-regional input–output tables from 2012, 2015, and 2017, using total trade decomposition, social network analysis, and exponential random graph models. The key findings are as follows: (1) The total virtual water trade volume remains stable, with Xinjiang, Jiangsu, and Guangdong as the core regions, while remote areas such as Shaanxi and Gansu have lower trade volumes. The primary industry dominates, and it is driven by simple value chains. (2) Provinces such as Xinjiang, Heilongjiang, and Jiangsu form the network’s core. Network density and symmetry increased from 2012 to 2015 but declined slightly in 2017, with efficiency peaking and then dropping, and the clustering coefficient decreased annually. Four economic sectors exhibit distinct interactions: frequent two-way flows in Sector 1, significant inflows in Sector 2, prominent net spillovers in Sector 3, and key brokers in Sector 4. (3) The network evolved from a core-periphery structure with weak ties to a stable, heterogeneous, and resilient system. (4) Influencing factors, such asper capita water resources, economic development, and population, significantly impact trade. Similarities in economic levels, population, and water endowments promote trade, while spatial distance has a limited effect, with geographic proximity showing a significant negative impact on long-distance trade. Full article

Avian influenza viruses (AIVs) pose significant risks to occupational populations engaged in poultry farming, livestock handling, and live poultry market operations due to frequent exposure to infected animals and contaminated environments. This review synthesizes evidence on AIV exposure patterns and risk factors through a comprehensive analysis of viral characteristics, host dynamics, environmental influences, and human behaviors. The main routes of transmission include direct animal contact, respiratory contact during slaughter/milking, and environmental contamination (aerosols, raw milk, shared equipment). Risks increase as the virus adapts between species, survives longer in cold/wet conditions, and spreads through wild bird migration (long-distance transmission) and live bird trade (local transmission). Recommended control measures include integrated animal–human–environment surveillance, stringent biosecurity measures, vaccination, and education. These findings underscore the urgent need for global ‘One Health’ collaboration to assess risk and implement preventive measures against potentially pandemic strains of influenza A viruses, especially in light of undetected mild/asymptomatic cases and incomplete knowledge of viral evolution. Full article

Background/Objectives: Concussions have been associated with deficits in attentional control. The current work examined whether attentional correlates could be enhanced following acute aerobic exercise in those with a history of concussion (CH). Methods: EEG was collected as participants completed a flanker task to evoke stimulus-locked (N2, P3) and response-locked error-related (ERN, Pe) ERPs, before and after participants completed a bout of acute aerobic exercise at moderate intensity. Conflict was modulated with distance (close/far) and congruency (incongruent/congruent) of the distractors relative to the targets. Results: CH individuals had reduced accuracy in high-conflict conditions, with improvements following exercise. No differences were observed in attentional cognitive control across the four conditions (close/far congruent, close/far incongruent); however, reduced interference control was shown in far conditions, when compared to close conditions. When compared to non-concussed controls, increased accuracy with increased response time in individuals with a concussion history was likely attributed to the speed–accuracy trade-off. Close conditions highlighted a decreased Pe amplitude in CH individuals (as opposed to the active controls), suggesting CH individuals may present with challenges when evaluating an error with working memory. Conclusions: The findings demonstrated acute exercise improved accuracy among CH individuals, and performance monitoring is impacted negatively long term following a concussion. Full article

This study explores the provenance of white marble architectural elements from Roman Philippopolis, with a particular focus on the Eastern Gate complex. By determining the origin of the marble, we aim to elucidate economic, social, and urban dynamics related to material selection and trade networks. The investigation examines the symbolic significance of prestigious marble in elite representation and highlights the role of quarry exploitation in the region’s economic and technological development. The Eastern Gate, a monumental ensemble integrated into the city’s urban fabric, was primarily constructed with local Rhodope marble, alongside imported materials such as Prokonnesian marble. Analytical methods included petrographic examination, chemical analysis of trace elements (Mn, Mg, Fe, Sr, Y, V, Cd, La, Ce, Yb, and U), and stable isotope analysis (δ¹⁸O, δ¹³C). Statistical evaluations were performed for each sample (37 in total) and compared with a comprehensive database of ancient quarry sources. The results underscore the dominance of local materials while also indicating selective use of imports, potentially linked to symbolic or functional criteria. The findings support the hypothesis of local workshop activity in the Asenovgrad/Philippopolis area and shed light on regional and long-distance marble trade during the Roman Imperial period, reflecting broader economic and cultural interconnections. Full article

Accurate path loss prediction is essential for optimizing Long-Range Wide-Area Network (LoRaWAN) performance. Previous studies have employed various Machine Learning (ML) models for path loss prediction. However, environmental factors such as temperature, humidity, barometric pressure, and particulate matter have been largely neglected. This study bridges this gap by evaluating the performance of five boosting ML models—AdaBoost, XGBoost, LightGBM, GentleBoost, and LogitBoost—under dynamic environmental conditions. The models were compared with theoretical models (Log-Distance and Okumura-Hata) and existing studies that employed the same dataset based on metrics such as RMSE, MAE, and R². Furthermore, a detailed performance vs. complexity analysis was conducted using metrics such as training time, inference latency, model size, and energy consumption. Notably, barometric pressure emerged as the most influential environmental factor affecting path loss across all models. Bayesian Optimization was applied to fine-tune hyperparameters to improve model accuracy. Results showed that LightGBM outperformed other models with the lowest RMSE of 0.5166 and the highest R² of 0.7151. LightGBM also offered the best trade-off between accuracy and computational efficiency. The findings show that boosting algorithms, particularly LightGBM, are highly effective for path loss prediction in LoRaWANs. Full article

High-density cities have obvious characteristics of compact urban spatial form and intensive land use in terms of spatial environment, and have always been a topic of academic focus. As a typical coastal historical district, the Macau Peninsula pier district (mainly the Macau Inner Harbour) has a high building density and a low average street width, forming a vertical coastline development model that directly converses with the ocean. This area is adjacent to Macau’s World Heritage Site and directly related to the Marine trade functions. The distribution pattern of cultural heritage linked by the ocean has strengthened Macau’s unique positioning as a node city on the Maritime Silk Road. This text is based on the theory of urban development, integrates spatial syntax and POI analysis techniques, and combines the theories of waterfront regeneration, high-density urban form and post-industrial urbanism to integrate and deepen the theoretical framework, and conduct a systematic study on the urban spatial characteristics of the coastal area of the Macau Peninsula. This study found that (1) Catering and shopping facilities present a dual agglomeration mechanism of “tourism-driven + commercial core”, with Avenida de Almeida Ribeiro as the main axis and radiating to the Ruins of St. Paul’s and Praça de Ponte e Horta, respectively. Historical blocks and tourist hotspots clearly guide the spatial center of gravity. (2) Residential and life service facilities are highly coupled, reflecting the spatial logic of “work-residence integration-service coordination”. The distribution of life service facilities basically overlaps with the high-density residential area, forming an obvious “living circle + community unit” structure with clear spatial boundaries. (3) Commercial and transportation facilities form a “functional axis belt” organizational structure along the main road, with the Rua das Lorchas—Rua do Almirante Sérgio axis as the skeleton, constructing a “functional transmission chain”. (4) The spatial system of the Macau Peninsula pier district has transformed from a single center to a multi-node, network-linked structure. Its internal spatial differentiation is not only constrained by traditional land use functions but is also driven by complex factors such as tourism economy, residential migration, historical protection, and infrastructure accessibility. (5) Through the analysis of space syntax, it is found that the core integration of the Macau Peninsula pier district is concentrated near Pier 16 and the northern area. The two main roads have good accessibility for motor vehicle travel, and the northern area of the Macau Peninsula pier district has good accessibility for long and short-distance walking. Full article

This paper presents a bio-inspired rigid–flexible continuum robot driven by flexible shaft tension–torsion synergy, tackling the trade-off between actuation complexity and flexibility in continuum robots. Inspired by the muscular arrangement of octopus arms, enabling versatile multi-degree-of-freedom (DoF) movements, the robot achieves 6-DoF motion and 1-DoF gripper opening and closing movement with only six flexible shafts, simplifying actuation while boosting dexterity. A comprehensive kinetostatic model, grounded in Cosserat rod theory, is developed; this model explicitly incorporates the coupling between the spinal rods and flexible shafts, the distributed gravitational effects of spacer disks, and friction within the guide tubes. Experimental validation using a physical prototype reveals that accounting for spacer disk gravity diminishes the maximum shape prediction error from 20.56% to 0.60% relative to the robot’s total length. Furthermore, shape perception experiments under no-load and 200 g load conditions show average errors of less than 2.01% and 2.61%, respectively. Performance assessments of the distal rigid joint showcased significant dexterity, including a 53° grasping range, 360° continuous rotation, and a pitching range from −40° to +45°. Successful obstacle avoidance and long-distance target reaching experiments further demonstrate the robot’s effectiveness, highlighting its potential for applications in medical and industrial fields. Full article

Maritime transport carries over 80% of global trade by volume and remains the most energy-efficient mode for long-distance goods movement. However, the sector contributes approximately 3% of global Greenhouse Gas (GHG) emissions, a share that could rise to 17% by 2050 without effective regulation. In response, the International Maritime Organization (IMO) has introduced initial and short-term measures to enhance energy efficiency and reduce emissions. In 2023, IMO Strategy expanded on these efforts with medium-term measures, including Market-Based Mechanisms (MBMs) such as a GHG levy, a feebate system, and fuel intensity regulations combined with carbon pricing. This study evaluates the economic and environmental impacts of these measures using an integrated computational simulation model that combines Ocean Engineering and Economics. Our results indicate that all proposed measures support the IMO’s intermediate emission reduction targets through 2035, cutting absolute emissions by more than 50%. However, economic impacts vary significantly across regions, with most of Africa, Asia, and South America experiencing the greatest adverse effects on GDP and trade. Among the measures, the GHG levy exerts the strongest pressure on economic activity and food prices, while a revised fuel intensity mechanism imposes lower costs, particularly in the short term. Revenue redistribution mitigates GDP losses but does so unevenly across regions. By leveraging a general equilibrium model (GTAP) to capture indirect effects often overlooked in prior studies, this analysis provides a comprehensive comparison of policy impacts. The findings underscore the need for equitable and pragmatic decarbonization strategies in the maritime sector, contributing to ongoing IMO policy discussions. Full article

Maritime transportation is responsible for most global trade and is generally considered more environmentally efficient compared to other modes of transport, particularly for long-distance trade. With increasingly stringent emission regulations, however, accurately quantifying emissions and identifying their key determinants has become essential for effective environmental management. This study introduced a structured and comparative statistical modeling framework for ship-based emission modeling using gross tonnage (GT) as the primary predictor variable, due to its strong correlation with emission levels. Emissions for hydrocarbon (HC), carbon monoxide (CO), particulate matter with an aerodynamic diameter of less than 10 μm (PM10), carbon dioxide (CO₂), sulfur dioxide (SO₂), nitrogen oxides (NO_x), and volatile organic compounds (VOC) were estimated using a bottom-up approach based on emission factors and formulas defined by the U.S. Environmental Protection Agency (EPA), using data from 38,304 vessel movements through the Bosphorus in 2021. These EPA-estimated values served as dependent variables in the modeling process. The modeling framework followed a three-step strategy: (1) outlier detection using Rosner’s test to reduce the influence of outliers on model accuracy, (2) curve fitting with 12 regression models representing four curve types—polynomial (e.g., linear, quadratic), concave/convex (e.g., exponential, logarithmic), sigmoidal (e.g., logistic, Gompertz, Weibull), and spline-based (e.g., cubic spline, natural spline)—to capture diverse functional relationships between GT and emissions, and (3) model comparison using difference performance metrics to ensure a comprehensive assessment of predictive accuracy, consistency, and bias. The findings revealed that nonlinear models outperformed polynomial models, with spline-based models—particularly natural spline and cubic spline—providing superior accuracy for HC, PM₁₀, SO₂, and VOC, and the Weibull model showing strong predictive performance for CO and NO_x. These results underscore the necessity of using pollutant-specific and flexible modeling strategies to capture the intricacies of maritime emission dynamics. By demonstrating the advantages of flexible functional forms over standard regression techniques, this study highlights the need for tailored modeling strategies to better capture the complex relationships in maritime emission data and offers a scalable and transferable framework that can be extended to other vessel types, emission datasets, or maritime regions. Full article

Identifying blueberry characteristics such as the wax bloom is an important task that not only helps in phenotyping (for novel variety development) but also in classifying berries better suited for commercialization. Deep learning techniques for image analysis have long demonstrated their capability for solving image classification problems. However, they usually rely on large architectures that could be difficult to implement in the field due to high computational needs. This paper presents a small (only 1502 parameters) Bayesian–CNN ensemble architecture that can be implemented in any small electronic device and is able to classify wax bloom content in images. The Bayesian model was implemented using Keras image libraries and consists of only two convolutional layers (eight and four filters, respectively) and a dense layer. It includes a statistical module with two metrics that combines the results of the Bayesian ensemble to detect potential misclassifications. The first metric is based on the Euclidean distance (${\mathcal{L}}_2$) between Gaussian mixture models while the second metric is based on a quantile analysis of the binary class predictions. Both metrics attempt to establish whether the model was able to find a good prediction or not. Three experiments were performed: first, the Bayesian–CNN ensemble model was compared with state-of-the-art small architectures. In experiment 2, the metrics for detecting potential misclassifications were evaluated and compared with similar techniques derived from the literature. Experiment 3 reports results while using cross validation and compares performance considering the trade-off between accuracy and the number of samples considered as potentially misclassified (not classified). Both metrics show a competitive performance compared to the state of the art and are able to improve the accuracy of a Bayesian–CNN ensemble model from $96.98\%$ to $98.72\pm 0.54\%$ and $98.38\pm 0.34\%$ for the ${\mathcal{L}}_2$ and $r^2$ metrics, respectively. Full article

Strategically important objects, such as dams, tunnels, bridges, and others, require long-term structural health monitoring programs in order to preserve their structural integrity with minimal downtime, financial expenses, and increased safety for civilians. The current study focuses on developing a damage detection methodology that is applicable to the long-term monitoring of such structures. It is based on the identification of resonant frequencies from operational modal analysis, removing the effect of environmental factors on the resonant frequencies through support vector regression with optimized hyperparameters and, finally, classifying the global structural state as either healthy or damaged, utilizing the Mahalanobis distance. The novelty lies in two additional steps that supplement this procedure, namely, the nonlinear estimation of the relative effects of various environmental factors, such as temperature, humidity, and ambient loads on the resonant frequencies, and the selection of the most informative resonant frequency features using a non-parametric neighborhood component analysis algorithm. This methodology is validated on a wooden two-story truss structure with different artificial structural modifications that simulate damage in a non-destructive manner. It is found that, firstly, out of all environmental factors, temperature has a dominating decreasing effect on resonance frequencies, followed by humidity, wind speed, and precipitation. Secondly, the selection of only a handful of the most informative resonance frequency features not only reduces the feature space, but also increases the classification performance, albeit with a trade-off between false alarms and missed damage detection. The proposed approach effectively minimizes false alarms and ensures consistent damage detection under varying environmental conditions, offering tangible benefits for long-term SHM applications. Full article

With the intensification of global resource competition, the issue of timber supply has escalated from an economic concern to a significant strategic challenge. This study focuses on the evolution of disruption resilience in the global trade network for wood forest products, aiming to reveal the patterns of resilience dynamics under disruption risks by simulating underload cascading failure phenomena. The study provides theoretical support for enhancing the security and stability of the global wood forest product supply chain. Utilizing global trade data from the UN Comtrade Database 2023, a directed weighted complex network model was constructed, spanning upstream, midstream, and downstream sectors, with trade intensity distances serving as edge weights. By developing an underload cascading failure model, the evolution of disruption resilience was simulated under various disruption scenarios from 2002 to 2023, and the long-term impacts of critical node failures on network performance were analyzed. The results demonstrate significant spatiotemporal heterogeneity in the disruption resilience of the global wood forest product trade network. The upstream network exhibits improved resilience in total node strength but reduced global efficiency. The midstream network shows marked volatility in resilience due to external shocks, such as the global financial crisis, while the downstream network remains relatively stable. Simulations reveal that failures in core nodes (e.g., China, the United States, and Germany) disproportionately degrade global efficiency and node strength, with node centrality metrics positively correlated with network performance loss. This study elucidates the evolutionary mechanisms of disruption resilience in the wood forest product trade network under risk propagation, offering actionable insights for optimizing network robustness and supply chain stability. It is recommended that policymakers promote green supply chain initiatives, accelerate afforestation projects, and enhance domestic timber self-sufficiency to reduce reliance on imported timber, thereby strengthening node resilience and fostering sustainable forest resource utilization for economic and environmental benefits. Full article

Circular economy theorists and advocates typically describe traditional market economies as linear “take, make, use and dispose” systems. Various policy interventions, from green taxes to extended producer responsibility, are therefore deemed essential to ensure the systematic (re)introduction of residuals, secondary materials and components in manufacturing activities. By contrast, many nineteenth- and early twentieth-century writers documented how the profit motive, long-distance trade and actors now largely absent from present-day circularity discussions (e.g., waste dealers and brokers) spontaneously created ever more value out of the recovery of residuals and waste. These opposite assessments and underlying perspectives are perhaps best illustrated in the nineteenth classical liberal and early twenty-first century interventionist writings on circularity of Fellows, members and collaborators of the near tricentennial British Royal Society for the Encouragement of Arts, Manufactures and Commerce. This article summarizes their respective contributions and compares their stance on market institutions, design, intermediaries, extended producer responsibility and long-distance trade. Some hypotheses as to the sources of their analytical discrepancies and current beliefs on resource recovery are then discussed in more detail. A final suggestion is made that, if the analysis offered by early contributors is more correct, then perhaps the most important step towards greater circularity is regulatory reform (or deregulation) that would facilitate the spontaneous recovery of residuals and their processing in the most suitable, if sometimes more distant, locations. Full article

This study sheds light on the origin and trade routes of early red coral artifacts found in Xinjiang, primarily dating to the Han and Jin dynasties. The red coral relics examined, excavated from the Shengjindian cemetery of the Western Han Dynasty in Turpan, offer critical insights into the material’s provenance and its introduction to this pivotal region along the ancient Silk Road. Advanced gemological, mineralogical, and geochemical analyses—utilizing computed tomography (CT), laser Raman spectroscopy, and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS)—has revealed distinctive features. These include red coloration, a waxy luster, concentric ring structures in cross-section, and calcareous composition, identifying the coral as Sardinian (Corallium rubrum), likely originating from the western Mediterranean region. The findings carry significant archaeological implications. Red coral first appears in the archaeological record in Xinjiang during the Western Han period, facilitated by the thriving Silk Road trade and the expanding influence of Buddhist culture. This study not only confirms the Mediterranean origin of these artifacts but also highlights their integration into the cultural and economic networks of ancient Xinjiang, underscoring the significance of early long-distance trade and cultural exchange. Full article