Hasil untuk "Balance of trade"

Yu-Shan Han, Sheng-Lun Zhao, Chu Chen et al.

This study presents SEAF-Net, a lightweight and efficient detection network designed for low-contrast and highly dynamic underwater environments. Built upon YOLOv11n, SEAF-Net introduces three complementary structural enhancements: (1) Omni-Dimensional Dynamic Convolution (ODConv) to improve adaptive modeling of multi-scale and directional texture variations; (2) SimA-SPPF, which embeds the SimAM attention mechanism into the SPPF module to enable neuron-level saliency reweighting and effective suppression of complex background interference; and (3) GhostC3k2 to reduce redundant computation while preserving sufficient representational capacity. Evaluated on a standardized 13-class underwater fish dataset under a unified training and evaluation protocol, SEAF-Net achieves 6.1 GFLOPs, 92.683% Precision, 88.459% Recall, 93.333% mAP50, 73.445% mAP, and a 90.522% F1-score. Compared with the YOLOv11n baseline, SEAF-Net improves F1-score and Recall by 0.510% and 0.575%, respectively, while reducing computational cost by approximately 6%, demonstrating a favorable accuracy–efficiency trade-off under lightweight constraints. Ablation results further confirm that SimA-SPPF plays a dominant role in background suppression, ODConv consistently enhances deformation and directional texture modeling, and GhostC3k2 effectively controls computational overhead without degrading detection accuracy. To assess deployment feasibility, additional test set evaluations were conducted under deployment-oriented conditions using resource-limited hardware, yielding an F1-score of 88.54%. This result confirms that the proposed model maintains stable detection performance and robustness beyond training and validation stages. Overall, SEAF-Net provides an effective balance of accuracy, efficiency, and robustness, offering practical support for low-carbon, scalable, and sustainable intelligent aquaculture monitoring and underwater ecological assessment in real-world environments.

Hiba Farhat, Ayman Kassem

Len Chapman’s early photographs of Port Rashid serve as an entry point into the narrative of port construction and its profound impact on global commerce and urban landscapes. These images, capturing the nascent stages of development, bear witness to the monumental transformations spurred by the construction of new ports and the modernization of historical ones in the UAE. The rapid economic and urban growth in the UAE, exemplified by projects like Port Rashid, reflects broader shifts in Gulf port geopolitics and global trade dynamics. The emergence of megaports and the era of containerization have reshaped maritime infrastructure and trade routes. This gave rise to unintended consequences, such as the disruption of coastal urban fabrics and the emergence of residual unsolved spaces. Recent recovery and rehabilitation processes like “districtification,” museumification, and cultural reuse raise critical questions about long‐term sustainability, public access preservation, and achieving a balance between passive and active engagement with port proximities. This article delves into specific case studies, including the Al Hamra ghost village, Al Shindagha, and MIZA Abu Dhabi to explore the impacts of maritime capitalism on urban and cultural landscapes. These sites reveal the challenges of balancing economic growth with sustainable urban recovery efforts, including “districtification,” museumification, and cultural reuse. The analysis underscores the complexities of ensuring long‐term sustainability, public access, and active engagement in port‐adjacent areas. By adopting descriptive and comparative methodologies, this research aims to deepen the understanding of how maritime commerce shapes coastal urban areas in the UAE. The study contributes to discussions on sustainable urban development and heritage preservation, emphasizing the need for mindful urban planning in the Gulf’s rapidly evolving and economically competitive landscape.

Mohadeseh Ghavami pour sereshkeh , Amirreza Mahmoudi

With the development of technology, artificially intelligent beings have been used in almost all areas of our daily lives. It is expected that these beings, which are currently at the service of humans and facilitate their work, will reach human level in the future and be able to perform some professions. These developments also bring with them numerous legal and criminal questions. What is the legal status of artificially intelligent beings, who is criminally liable in the crimes that arise from their use, and what is their role in the criminal proceedings, are among the questions that need to be answered. The purpose of this study is to provide a general assessment of these questions by considering legal regulations. The study methodology is descriptive-analytical. In this study, we have reached the conclusion that AIs are in the position of “property”, cannot be held liable for crimes resulting from their use, and although they have an important contribution to the criminal justice process, they cannot replace the subjects of the proceedings (judge, prosecutor, lawyer). In this context, the current legal regulations are largely capable of resolving the problems that have arisen. However, if AIs are to reach the status of “humans” as fully independent and self-aware entities, fundamental changes will be needed in our legal system. 1. Introduction The term “Fourth Industrial Revolution,” which refers to the digitalization of the production chain, the coordination of machines, humans, infrastructure, and the development of intelligent systems, was first introduced in 2011 at an exhibition held in Hanover, Germany. With the Fourth Industrial Revolution, it is predicted that the use of industrial robots will increase rapidly and automation will completely replace production processes, digital technologies will have significant impacts on efficiency, productivity, income distribution, and the environment, and half of the world’s trade will be conducted using the interaction of intelligent entities. With significant advances in information technology, we are witnessing the increasing expansion of applications of artificial intelligence entities in various fields. Artificially intelligent entities are widely used in almost all areas, including military operations, the industrial sector, the service sector, insurance services, medicine, and law. Translation programs, speech recognition systems, facial recognition systems, unmanned aerial vehicles used in military operations, and software that makes legal decisions by examining litigation data are only a small part of the areas where artificial intelligence is actively used. The increasing use of artificial intelligence saves time, labor, and money. However, the increasing presence of artificial intelligence in our daily lives also brings with it numerous legal challenges. The evolution and progress in the field of law, which can be considered a set of rules regulating human behavior, occur much slower than technological advances. For this reason, it is difficult to constantly update legal regulations in line with technological advances; As a result, questions such as the definition of artificial intelligence, the legal status of artificial intelligence, and the responsibility for the results of decisions based on predictions and inferences of artificial intelligence still await answers. In the first part of this study, the concept of artificial intelligence, its types, and its historical developments are explained to provide a conceptual assessment of the subject. In the second part, the views existing in the legal doctrine on the legal status of artificial intelligence are examined, and in the third part, the assessment of artificial intelligence from the perspective of criminal law is analyzed. Finally, in the conclusion section, possible challenges in the criminal law status of artificial intelligence with the increasing use of artificial intelligent entities and related solutions and suggestions will be addressed. Definition and History of Artificial Intelligence In studies related to technological progress, the term artificial intelligence, which is called “Artificial Intelligence” in English, is frequently used. However, a single and universally accepted definition of artificial intelligence has not yet been provided. John McCarthy, who coined the term artificial intelligence, defines it as “the engineering and science of building intelligent machines.” Humans can use their intelligence to solve problems they encounter. Artificial intelligence is a human-made machine that, depending on its level of development, acquires the ability to solve problems and is able to imitate human behavior. The definition of artificial intelligence, which Sebastian Tran, director of the Stanford University Artificial Intelligence Laboratory, has called “understanding something complex and making appropriate decisions,” is now recognized as a comprehensive and accepted definition in this field. Some authors consider artificial intelligence as a machine and define it as “the ability of a machine to learn from experience, adapt to new stimuli, and perform tasks similar to humans.” In line with the explanations provided, artificial intelligence is defined as follows: "Artificial intelligence refers to the imitation of human mental processes such as perception, reasoning, learning, and problem-solving by machines." The goal of artificial intelligence technology, which is the simulation of intelligence similar to human intelligence, is to create a robotic system consisting of machines, computers, and software that acts similarly to human intelligence, understands and classifies events, learns, evaluates, makes decisions based on the results of the evaluation, and implements the decision made. Just as there is disagreement over the definition of artificial intelligence, there is no consensus on the definition of a robot. Neil M. Richards, a professor of law, defines a robot as: “Biologically non-living systems that are created to perform both physical and mental activities”; therefore, it has been emphasized that just as there is no single definition for artificial intelligence, there is no complete consensus among experts on its definition for a robot. This definition emphasizes two characteristics of robots: 1. Being biologically non-living; 2. The ability to perform physical and mental activities. 2. Methodology This research has been conducted using a descriptive-analytical method and its main goal is to examine the theoretical foundations, analyze the legal status, and identify challenges related to the criminal liability of artificial intelligence systems. Also, with a comparative look at the legal systems of other countries, an attempt is made to provide solutions to strengthen Iran's legal frameworks in this field. The required information has been collected through the library method and the sources used include specialized books, scientific articles, research reports, domestic and international laws, and official documents related to the research topic. In this research, data collected from various sources have been examined using comparative analysis, content analysis, and a critical approach. In the comparative analysis, the Iranian legal system has been compared with the laws and regulations of leading countries such as the European Union, the United States, and Japan to determine its strengths and weaknesses. Content analysis was used to examine and interpret the data from the perspective of legal arguments, and in a critical approach, the limitations and shortcomings of the Iranian legal system in facing the challenges of artificial intelligence were examined. This research was conducted in several stages: first, the background of the subject was studied and previous research and articles on the criminal liability of artificial intelligence were analyzed. Then, the existing legal situation was examined and domestic and international laws related to artificial intelligence were analyzed. Next, theoretical and practical challenges related to the criminal liability of artificial intelligence systems were identified, and finally, legal reforms and practical solutions appropriate to the conditions of Iran were proposed. This approach, which is a combination of comparative law methods and legal analysis, allows for the examination of laws and the presentation of reform proposals based on global experiences and helps the researcher to reach a comprehensive understanding of the current situation and provide practical solutions for amending laws and strengthening the Iranian legal system in this area. 3. Results and Discussion Artificial intelligence as a technological phenomenon has created numerous challenges in the field of criminal law. Studies show that in the current situation, these systems are considered more as “objects” than “persons” from a legal perspective. However, the expansion of the use of artificial intelligence and its possibility of achieving a level of self-awareness and decision-making autonomy requires a review of the fundamental concepts of criminal law. One of the important results of this research is that artificial intelligence systems in their current state lack will, awareness, and criminal intent and cannot be directly considered criminally liable. For this reason, in the event of a crime related to these systems, criminal liability is attributed to their users, programmers, or producers. This shows that current laws are mainly designed for the liability of human individuals and are inadequate in the face of the complexities of artificial intelligence systems. A comparative analysis of the legal systems of other countries showed that some countries, including the European Union, have addressed these challenges with the concept of “electronic personality.” In this approach, advanced AI systems are granted legal personality that allows them to be held accountable within a specific framework. However, this idea poses several philosophical and operational challenges and requires further studies and the development of detailed laws in Iranian criminal law. Another issue examined in this study is the role of AI in facilitating judicial processes. The findings show that AI can serve as an efficient tool in collecting evidence, analyzing data, and predicting the outcomes of lawsuits. However, there are concerns about algorithmic bias, privacy, and the potential for violating individuals’ fundamental rights, which require the development of detailed regulatory regulations to prevent the misuse of this technology. Another conclusion of the study is that with the increasing use of AI in self-driving cars and other complex devices, the likelihood of crimes such as accidents caused by defects in algorithms increases. In these cases, the current Iranian laws lack comprehensive provisions for determining criminal liability, and there is a need to more precisely define self-driving cars and determine the liability of individuals involved in the design, production, and use of these devices. Finally, the present study shows that the current Iranian laws, despite covering some aspects of the use of AI, are not sufficient to manage the emerging challenges of this technology. It is suggested that to address these shortcomings, a comprehensive legal framework be developed in which new concepts such as “electronic personality,” “producer liability,” and “joint liability” are considered. Also, the establishment of a specialized regulatory body to manage the legal challenges of AI seems necessary. These measures can help create a balance between exploiting the capabilities of AI and protecting fundamental human rights. 4. Conclusions and Future Research The advancement of AI technology and the expansion of its application have created complex issues in legal systems. The present study showed that AI in its current state lacks independent will and consciousness and cannot be recognized as criminally responsible. As a result, criminal liability for crimes related to it falls on users, producers, and programmers. However, if AI achieves the level of self-awareness, a review of legal concepts will be necessary. Current Iranian laws are mainly based on traditional approaches and are not sufficient to face the challenges of AI. In leading countries, concepts such as electronic personality have been proposed that could be the basis for AI liability in the future, but they need to be adapted to national legal structures. Also, AI has great potential to improve judicial processes, but risks such as algorithmic bias and privacy violations must also be managed. Future research could focus on exploring the possibility of granting legal personality to AI, developing regulations for self-driving cars, managing algorithmic biases, and conducting more comprehensive comparative studies to help develop more rigorous legal frameworks to manage the legal challenges posed by this technology

Qi Lu, Yonghao Chen, Xiangang Cao et al.

Fully mechanized mining involves high energy consumption, particularly during cutting and transportation. Scraper conveyors, crucial for coal transport, face energy efficiency challenges due to the lack of accurate dynamic coal flow models, which restricts precise energy estimation and optimization. This study constructs dynamic coal flow and scraper conveyor energy efficiency models to analyze the impact of multiple variables on energy consumption and lump coal rate. A dynamic coal flow model is developed through theoretical derivation and EDEM simulations, validated for parameter settings, boundary conditions, and numerical methods. The multi-objective optimization model for energy consumption is solved using the NSGA-II-ARSBX algorithm, yielding a 33.7% reduction in energy consumption, while the lump coal area is reduced by 27.7%, indicating a trade-off between energy efficiency and coal fragmentation. The analysis shows that increasing traction speed while decreasing scraper chain and drum speeds effectively lowers energy consumption. Conversely, simultaneously increasing both chain and drum speeds helps to maintain lump coal size. The final optimization scheme demonstrates this balance—achieving improved energy efficiency at the cost of increased coal fragmentation. Additional results reveal that decreasing traction speed while increasing chain and drum speeds results in higher energy consumption, while increasing traction speed and reducing chain/drum speeds minimizes energy use but may negatively affect lump coal integrity.

Jing Jiang, Hui Zhao, Jun Zhang et al.

Abstract Land management practices play a crucial role in balancing agricultural production and ecosystem services. This study evaluated the trade-offs between provisioning ecosystem services (crop yields) and other key ecosystem services including regulating services (water yield, soil conservation, carbon sequestration) and supporting services (biodiversity) under three land management scenarios in the Loess Plateau of China. An integrated assessment framework combining biophysical models, economic valuation, and trade-off analysis was applied. The results showed significant trade-offs between provisioning ecosystem services (agricultural production) and regulating and supporting ecosystem services (water yield, soil conservation, carbon sequestration, and biodiversity). The ecological restoration scenario maximized regulating and supporting services but reduced agricultural output by 15%, while the sustainable intensification scenario increased agricultural production by 15% with moderate ecosystem service provision. The business-as-usual scenario showed intermediate performance for both. Trade-offs were driven by land use intensity, landscape configuration, biogeochemical cycles, and hydrological processes. We propose strategies including sustainable intensification practices, landscape multifunctionality enhancement, ecosystem-based adaptation, participatory land-use planning, and improved monitoring systems. These findings highlight the need for integrated approaches that balance food production and environmental sustainability, providing insights for land management policies aligned with UN Sustainable Development Goals.

Huaqu Zeng, Zhen Li, Zongyou Chen et al.

<h4>Purpose</h4>To evaluate the impact of monitor units (MUs) optimization on volumetric modulated arc therapy (VMAT) plan for nasopharyngeal carcinoma (NPC).<h4>Methods</h4>Twenty-one NPC patients were retrospectively analyzed. Dual-arc VMAT plan were designed using photon optimization algorithms without the monitor unit objective (MUO) tool, denoted as the base plan. Each base plan was re-optimized with the MUO tool with the Maximum MU parameter set to 30% of the base plans' total MUs and Strength parameters set to 50, 80, and 100, generating plans S50, S80, and S100. Target and organ-at-risk (OAR) dose distributions, MUs, beam delivery time, and gamma passing rates were compared between re-optimized and base plans. Statistical analysis was performed using SPSS 17.0 (paired t-tests; significance: P < 0.05).<h4>Results</h4>Plan S100 reduced target PCTV2 D98% by >4% (relative to the base plan) in four patients. Plan S80 reduced target PGTV and PGTVnd Dmax and target PCTV2 D98% for >3% but <4% in two patients, while other target dose parameters changed by <2%. Compared to the base plan, all re-optimized plans increased the brainstem Dmax (P < 0.05), though the maximum increase was < 1.5%. Plan S50 reduced both parotid glands D50% and Dmean (P < 0.001), while plan S80 reduced both parotids Dmean and the left parotid D50% (P < 0.001). Conversely, S100 increased both parotids D50% and Dmean and the spinal cord Dmax (P < 0.05). Plan S80 and S100 increased the thyroid V40 (P < 0.05). MU reductions averaged 5.1% (S50), 21.4% (S80), and 30.9% (S100), with consistent beam delivery times (~2.5 minutes). Gamma passing rates improved sequentially from the base plan to S50, S80, and S100.<h4>Conclusion</h4>MU optimization in NPC VMAT planning effectively reduces MUs and enhances delivery accuracy (improved gamma passing rates). While target coverage and OAR sparing were generally maintained, higher MUO strengths (e.g., S100) may necessitate careful consideration of dosimetric trade-offs. Moderate MUO settings (e.g., S80) offer a favorable balance between MU reduction and plan fidelity.

Siyuan Shen, Feng Liu, Hanyang Wang et al.

Recent advances in self-supervised models have led to effective pretrained speech representations in downstream speech emotion recognition tasks. However, previous research has primarily focused on exploiting pretrained representations by simply adding a linear head on top of the pretrained model, while overlooking the design of the downstream network. In this paper, we propose a temporal shift module with pretrained representations to integrate channel-wise information without introducing additional parameters or floating-point operations per second. By incorporating the temporal shift module, we developed corresponding shift variants for 3 baseline building blocks: ShiftCNN, ShiftLSTM, and Shiftformer. Furthermore, we propose 2 technical strategies, placement and proportion of shift, to balance the trade-off between mingling and misalignment. Our family of temporal shift models outperforms state-of-the-art methods on the benchmark Interactive Emotional Dyadic Motion Capture dataset in fine-tuning and feature-extraction scenarios. In addition, through comprehensive experiments using wav2vec 2.0 and Hidden-Unit Bidirectional Encoder Representations from Transformers representations, we identified the behavior of the temporal shift module in downstream models, which may serve as an empirical guideline for future exploration of channel-wise shift and downstream network design.

ZHANG Chaozheng, SUN Xiaoyu, ZHANG Han et al.

[Objective] This study was aimed to reveal the spatio-temporal characteristics of supply-demand balance of ecosystem services, and further to explore the dual effects and influencing mechanisms of land use change on the supply-demand balance of ecosystem services. [Methods] Taking the middle reaches of the Yangtze River as the research area and seleting 2000— 2018 as the research period, the quantitative matrix of supply-demand of ecosystem services was employed to analyze the spatio-temporal characteristics of the supply-demand balance of ecosystem service, and the ecological contribution rate of land use change was introduced to analyze the influencing mechanisms of land use change on the supply-demand balance of ecosystem services. [Results] (1) During the study period, the supply-demand balance of ecosystem services in the middle reaches of the Yangtze River had been continuously deteriorating, which was mainly caused by the large-scale expansion of construction land and the large-scale reduction of cultivated land and forest land, resulting in the decrease of supply capacity and the increase of consumption demand of ecosystem services. (2) The relationship between different supply of ecosystem services had changed from trade-off to synergy in time scale, and the synergy between different demand and supply-demand balance of ecosystem services had been further enhanced on the time scale, but the trade-off or synergy between supply, demand, and supply-demand balance was highly heterogeneous on the spatial scale. (3) Land use change in the MRYR had dual effects on the supply-demand balance of ecosystem services, and the deterioration effect was significantly larger than the improvement effect, but the types of land use change that cause the improvement and deterioration of supply-demand of ecosystem services and their sub-services had temporal heterogeneity. [Conclusion] Promote types of land use change that could improve the supply-demand balance of ecosystem services; promote types of land use change that are compatible with key ecosystem services; and curb types of land use change that would worsen the supply-demand balance of ecosystem services, in order to increase the sustainability of ecosystem services and improve the quality of the ecological environment.

Louis Yat Hin Chan, Gunnar Rø, Jørgen Eriksson Midtbø et al.

Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.

Y. Qu, Jiayang Wu, Yuning Zhang et al.

We theoretically investigate and optimize four-wave mixing (FWM) in silicon nitride (SiN) waveguides integrated with 2D layered graphene oxide (GO) films. Based on extensive previous measurements of the material parameters of the GO films, we perform detailed analysis on the influence of device parameters including waveguide geometry, GO film thickness, length, and coating position, on the FWM conversion efficiency (CE) and conversion bandwidth (CB). The influence of dispersion and photo-thermal changes in the GO films is also discussed. Owing to the strong mode overlap between the SiN waveguides and the highly nonlinear GO films, FWM in the hybrid waveguides can be significantly enhanced. We obtain good agreement with previous experimental results and show that by optimizing the device parameters to balance the trade-off between Kerr nonlinearity and loss, the FWM CE can be improved by as much as ∼20.7 dB and the FWM CB can be increased by ∼4.4 folds, relative to the uncoated waveguides. These results highlight the significantly enhanced FWM performance that can be achieved in SiN waveguides by integrating 2D layered GO films.

S. Ovshinsky, M. Fetcenko, J. Ross

Serge Assaad, Shuxi Zeng, Chenyang Tao et al.

A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. In this work, we discuss the pitfalls of these strategies - such as a steep trade-off between achieving balance and predictive power - and present a remedy via the integration of balancing weights in causal learning. Specifically, we theoretically link balance to the quality of propensity estimation, emphasize the importance of identifying a proper target population, and elaborate on the complementary roles of feature balancing and weight adjustments. Using these concepts, we then develop an algorithm for flexible, scalable and accurate estimation of causal effects. Finally, we show how the learned weighted representations may serve to facilitate alternative causal learning procedures with appealing statistical features. We conduct an extensive set of experiments on both synthetic examples and standard benchmarks, and report encouraging results relative to state-of-the-art baselines.

I. Sánchez-Vicente, María Guadalupe Fernández-Espinosa, O. Lorenzo

Abstract Plants are sessile organisms that need to complete their life cycle by the integration of different abiotic and biotic environmental signals, tailoring developmental cues and defense concomitantly. Commonly, stress responses are detrimental to plant growth and, despite the fact that intensive efforts have been made to understand both plant development and defense separately, most of the molecular basis of this trade-off remains elusive. To cope with such a diverse range of processes, plants have developed several strategies including the precise balance of key plant growth and stress regulators [i.e. phytohormones, reactive nitrogen species (RNS), and reactive oxygen species (ROS)]. Among RNS, nitric oxide (NO) is a ubiquitous gasotransmitter involved in redox homeostasis that regulates specific checkpoints to control the switch between development and stress, mainly by post-translational protein modifications comprising S-nitrosation of cysteine residues and metals, and nitration of tyrosine residues. In this review, we have sought to compile those known NO molecular targets able to balance the crossroads between plant development and stress, with special emphasis on the metabolism, perception, and signaling of the phytohormones abscisic acid and salicylic acid during abiotic and biotic stress responses.

L. Linsi, Daniel Mügge

Abstract Official international economic statistics are generally considered accurate and meaningful gauges of cross-border flows of trade and capital. Most data users also assume that the quality of the underlying data keeps improving over time. Through an extensive review of the national accounting literature, archival research, two dozen interviews with high-level statisticians, and a series of data quality tests, we evaluate this common view for the primary source of data on trade and capital flows: the International Monetary Fund’s Balance of Payments (BOP) Statistics. Our assessment paints a less rosy picture: reported figures are far less accurate than they are typically imagined to be and often do not correspond to the theoretical concepts with which users associate them. At the same time, measurement quality deteriorates over time as the transnationalization of economic production gradually undermines the validity of BOP statistics. Our findings raise serious questions about the widespread use of these numbers, with their deceptive pretense to accuracy, in scholarly research and public debate about the international political economy.

Melinda Boyers, Francesca Parrini, Norman Owen-Smith et al.

Abstract Southern Africa is expected to experience increased frequency and intensity of droughts through climate change, which will adversely affect mammalian herbivores. Using bio-loggers, we tested the expectation that wildebeest (Connochaetes taurinus), a grazer with high water-dependence, would be more sensitive to drought conditions than the arid-adapted gemsbok (Oryx gazella gazella). The study, conducted in the Kalahari, encompassed two hot-dry seasons with similar ambient temperatures but differing rainfall patterns during the preceding wet season. In the drier year both ungulates selected similar cooler microclimates, but wildebeest travelled larger distances than gemsbok, presumably in search of water. Body temperatures in both species reached lower daily minimums and higher daily maximums in the drier season but daily fluctuations were wider in wildebeest than in gemsbok. Lower daily minimum body temperatures displayed by wildebeest suggest that wildebeest were under greater nutritional stress than gemsbok. Moving large distances when water is scarce may have compromised the energy balance of the water dependent wildebeest, a trade-off likely to be exacerbated with future climate change.

Cuong D. Dao, Behzad Kazemtabrizi, Christopher J. Crabtree et al.

Abstract Maintenance is essential in keeping wind energy assets operating efficiently. With the development of advanced condition monitoring, diagnostics and prognostics, condition‐based maintenance has attracted much attention in the offshore wind industry in recent years. This paper models various maintenance activities and their impacts on the degradation and performance of offshore wind turbine components. An integrated maintenance strategy of corrective maintenance, imperfect time‐based preventive maintenance and condition‐based maintenance is proposed and compared with other traditional maintenance strategies. A maintenance simulation programme is developed to simulate the degradation and maintenance of offshore wind turbines and estimate their performance. A case study on a 10‐MW offshore wind turbine (OWT) is presented to analyse the performance of different maintenance strategies. The simulation results reveal that the proposed strategy not only reduces the total maintenance cost but also improves the energy generation by reducing the total downtime and expected energy not supplied. Furthermore, the proposed maintenance strategy is optimised to find the best degradation threshold and balance the trade‐off between the use of condition‐based maintenance and other maintenance activities.

Michelle Rogan, M. L. Mors

Zhineng Hu, Ya-zhen Chen, Liming Yao et al.

Eszter Wirth, Juan M. Ramírez-Cendrero

In this article, it is traced the presence of the Dutch disease (DD) in the Norwegian economy, which entails the analysis of both its manifestations and links to policies. After systematizing the oil industry’s configuration throughout the period 1970-2018, the impact of the oil sector on the rest of the economy is outlined in conjunction with the Norway’s dependency on the former industry. Specifically, no clear resource reallocation effects of the DD can be observed, although there are certain traces of its spending effects. Moreover, despite the relatively low oil dependency ratios in comparison with other oil producers, the whole of the Norwegian economy shows strong linkages to the oil sector and the trade balance has suffered from increasing deficits related to manufactured products with higher technology content. Based on these results, the faint manifestations of the DD in Norway are diagnosed, which enables us to offer contributions in terms of development policies for oil-rich countries.

Ans Vercammen, Caroline Park, Robyn Goddard et al.

With increasing demand for large-scale data and effective, wide-spread action, conservation volunteers can play an important role in tackling the global biodiversity crisis. Cost-cutting pragmatism aside, the recruitment of volunteers into diverse roles within conservation organisations also responds to growing public concern for the environment and demand for 'meaningful' engagement in people's pastime. Here we argue that this auspicious premise of a win-win transaction fails to acknowledge a range of emerging ethical issues regarding the management of unpaid workers. This lack of critical examination frustrates the development of solutions that are effective for conservation and fair to volunteers. We focus our attention on three archetypal cases—citizen science, voluntourism and unpaid internships—to highlight the complex and value-laden trade-offs that need to be negotiated to ethically manage unpaid work in conservation. We identify opportunities to redress the balance between volunteer needs and conservation goals. Ultimately, we hope to stimulate further, more open debate on the effective and fair use of the available labour force in conservation.