Search Results (185)

Exploring the interrelationships and influencing factors of the multifunctionality of cultivated land is crucial for achieving its multifunctional protection and sustainable use. In this paper, we take the Min River basin as a case study to construct a multifunctional evaluation system based on “agricultural production, social security, ecological service, and cultural landscape” using multi-source data. We analyze the spatial and temporal characteristics of the multifunctionality of cultivated land through kernel density estimation (KDE) and visual mapping. Subsequently, we assess the trade-off strength between the multifunctional aspects of cultivated land using the root mean square error (RMSD). Finally, we identify the drivers of the multifunctional trade-off intensity of cultivated land and analyze their influencing mechanisms using Geographic Detectors. The results show that (1) from 2010 to 2020, the multifunctional structure of cultivated land in the study area underwent significant changes: the levels of agricultural production, social security, and ecological service functions first increased and then decreased, while the levels of cultural landscape function and comprehensive function continued to increase. The spatial distribution is characterized, respectively, by “high in the east and low in the west”, “high in the west and low in the east”, “high in the north and low in the south”, “high in the whole and sporadically low in the northeast”, and “high in the middle and low in the surroundings”. (2) During the study period, the trade-off strengths related to social security functions increased, while the trade-off strengths of the remaining multifunctional pairs of cultivated land showed a weakening trend, with high values of trade-off strengths among functions particularly prominent in the Nanping Municipal District. (3) Both natural and human factors significantly affect the multifunctional trade-off strength of cultivated land. Among the specific factors, elevation, slope, average annual temperature, and per capita GDP are the key factors influencing the strength of the trade-offs between functions. The results of this study provide empirical support for enriching the understanding of the multifunctionality of cultivated land and offer a decision-making basis for promoting the differentiated management of cultivated land resources and the synergistic development of its multifunctionality. Full article

Pairs trading is a short-term speculation trading strategy based on matching a long position with a short position in two assets in the hope that their prices will return to their historical equilibrium. In this paper, we focus on identifying opportunities where mean reversion will happen quickly, as the commission costs associated with keeping the positions open for an extended period of time can eliminate excess returns. To this end, we propose the use of the local Hurst exponent as a signal to open trades in the cryptocurrencies market. We conduct a natural experiment to show that the spread of pairs with anti-persistent values of Hurst revert to their mean significantly faster. Next, we verify that this effect is universal across pairs with different levels of co-movement. Finally, we back-test several pairs trading strategies that include $H<0.5$ as an indicator and check that all of them result in profits. Hence, we conclude that the Hurst exponent represents a meaningful indicator to detect pairs trading opportunities in the cryptocurrencies market. Full article

The Military Internet of Things (MIoT) has emerged as a new research area in military intelligence. The MIoT frequently has to constitute a federation-capable IoT environment when the military needs to interact with other institutions and organizations or carry out joint missions as part of a coalition such as in NATO. One of the main challenges of deploying the MIoT in such an environment is to acquire, analyze, and merge vast amounts of data from many different IoT devices and disseminate them in a secure, reliable, and context-dependent manner. This challenge is one of the main challenges in a federated environment and forms the basis for establishing trusting relationships and secure communication between IoT devices belonging to different partners. In this work, we focus on the problem of fulfillment of the data-centric security paradigm, i.e., ensuring the secure management of data along the path from its origin to the recipients and implementing fine-grained access control mechanisms. This problem can be solved using innovative solutions such as applying attribute-based encryption (ABE). In this work, we present a comprehensive solution for secure data dissemination in a federated MIoT environment, enabling the use of distributed registry technology (Hyperledger Fabric), a message broker (Apache Kafka), and data processing microservices implemented using the Kafka Streams API library. We designed and implemented ABE cryptography data access control methods using a combination of pairings-based elliptic curve cryptography and lightweight cryptography and confirmed their suitability for the federations of military networks. Experimental studies indicate that the proposed cryptographic scheme is viable for the number of attributes typically assumed to be used in battlefield networks, offering a good trade-off between security and performance for modern cryptographic applications. Full article

The transformation of ecosystem structure leads to changes in ecosystem services (ESs) and their relationship. However, most research in this area has focused on particular scales and timeframes, often overlooking the significance of spatial and temporal variations. Therefore, we used the equivalent value method to evaluate seven typical ESs in the Yangtze River Delta (YRD) between 2000 and 2020: food production (FP), water supply (WS), climate regulation (CR), environmental purification (EP), soil conservation (SC), biodiversity maintenance (BM), and aesthetic landscape (AL). We further employed the Spearman correlation coefficient and bivariate Moran’s I to evaluate the relationship of ESs and their spatial heterogeneity at grid, township, county and city scales. Our results show that (1) All ESs except AL exhibited a fluctuating upward trend from 2000 to 2020, resulting in a total increase in ecosystem service (ES) value of RMB 650.63 billion. (2) Approximately 70% of the ES pairs demonstrated a synergistic relationship, with the exception of FP and other ESs, which mainly showed a trade-off. (3) With the scale increased from grid to city level, the degree of trade-off between FP and other ESs strengthened at different levels, while the synergy degree of among other ESs gradually decreased. (4) The relationship between ESs demonstrated strong spatial heterogeneity, with FP and other ESs exhibiting trade-offs primarily in the northern and southern YRD, while other ES pairs exhibited mostly synergy in these regions. This study provides scientific information for governments to optimize land use distribution and improve ESs. Full article

Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m², significantly higher than the 2514.06 ± 691.59 µGym² obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts. Full article

In algorithmic trading, “overfitting” often arises during parameter optimization. To avoid scenarios where in-sample performance is excellent but out-of-sample performance fails to meet expectations, appropriate parameter combinations are crucial for enhancing the robustness of a strategy. To find the appropriate parameter combinations, we identified “parameter plateaus” in the three-dimensional space generated by strategy performance metrics. These plateaus represent parameter combinations where the surrounding performance metrics are relatively similar, reducing the risk of drastic performance drops. Utilizing the four parameter selection methods designed in this study (weighted selection, Standard Deviation Selection, Island Area Selection, and Island Volume Selection), we selected parameter combinations in-sample and validated them out-of-sample, complemented by “rolling window analysis” for long-term profitability stability. We used historical backtesting data from the Taiwan Stock Index Futures, covering the period from 1 January 2000 to 31 December 2022. The data were paired with trading strategies developed based on the moving average technical indicator. Through the four parameter selection methods and the system backtesting approach using rolling windows, we identified parameter combinations in-sample and then validated them out-of-sample. The results showed that the performance metrics improved by more than 50% over those generated using traditional optimal point selection methods, demonstrating the superiority of the parameter selection methods proposed in this study. Full article

Karst ecosystems have become complex social–ecological systems (SESs) as a result of the interventions of large-scale ecological restoration programs, and the ecosystem services (ESs) that provide regional well-being can, to some extent, be described as social–ecological system services (S–ESs). Understanding the relationships among multiple S–ESs and exploring their drivers are essential for effective ecological management in karst areas, especially in regions differently affected by ecological engineering programs. Taking South China Karst (SCK) as a study area, we first identified two regions as comparative boundaries, namely significant engineering impact regions (SEERs) and non-significant ecological engineering impact regions (NEERs). Then we used ES assessment models, Spearman correlation, and optimal parameter geographic detector to identify the supply capacity, trade-offs/synergies, and their drivers of six types of S–ESs in SEERs and NEERs. The findings included: (1) SEERs were predominantly concentrated in the central and southern SCK regions, accounting for 33.98% of the total SCK area, with the most concentrated distribution observed in Guizhou and Guangxi. (2) Within the entire SCK, six S–ESs maintained a relatively stable spatial distribution pattern over time, with the most pronounced increase in soil conservation and a slight decrease in water retention, and the S–ES hotspots were more concentrated within the SEERs. (3) Most S–ES pairs within SEERs were optimized synergistically, with lower trade-off intensity and higher synergy intensity compared to NEERs. (4) S–ES pairs were affected by the interactions between the natural and socio-economic factors, with land use changes playing a crucial role, and natural factors were difficult to predict but cannot be ignored. Based on the results, we propose different SES sustainable development suggestions, with a view to providing theoretical support for the optimization of SES functions and the consolidating of integrated ecological construction. Full article

While the effects of immigrants on aggregate trade flows have been extensively examined, the role of immigrants in shaping trade in value added (TiVA) remains underexplored. Employing a panel dataset covering 38 Organization for Economic Co-operation and Development (OECD) member host countries and 64 immigrant home countries spanning 2000–2018 and estimating a random intercept and random slope mixed-effects model, we find that immigrants play a significant role in enhancing the value added from their home countries that is embedded in their host countries’ exports to the world. We document these effects at the aggregate level and across sectors (i.e., manufacturing, agriculture, and services). There is, however, considerable variation in the influence of immigrants on TiVA across country pairs. Our findings highlight that immigrants significantly enhance trade sophistication by promoting specialization and upward movement in the value chain, yielding economic benefits for their home and host countries. Full article

This research proposes a novel arbitrage approach in multivariate pair trading, termed the Optimal Trading Technique (OTT). We present a method for selectively forming a “bucket” of fiat currencies anchored to cryptocurrency for monitoring and exploiting trading opportunities simultaneously. To address quantitative conflicts from multiple trading signals, a novel bi-objective convex optimization formulation is designed to balance investor preferences between profitability and risk tolerance. We understand that cryptocurrencies carry significant financial risks. Therefore this process includes tunable parameters such as volatility penalties and action thresholds. In experiments conducted in the cryptocurrency market from 2020 to 2022, which encompassed a vigorous bull run followed by a bear run, the OTT achieved an annualized profit of 15.49%. Additionally, supplementary experiments detailed in the appendix extend the applicability of OTT to other major cryptocurrencies in the post-COVID period, validating the model’s robustness and effectiveness in various market conditions. The arbitrage operation offers a new perspective on trading, without requiring external shorting or holding the intermediate during the arbitrage period. As a note of caution, this study acknowledges the high-risk nature of cryptocurrency investments, which can be subject to significant volatility and potential loss. Full article

Background: Fold change is a common metric in biomedical research for quantifying group differences in omics variables. However, inconsistent calculation methods and inadequate reporting lead to discrepancies in results. This study evaluated various fold-change calculation methods aiming at a recommendation of a preferred approach. Methods: The primary distinction in fold-change calculations lies in defining group expected values for log ratio computation. To challenge method interchangeability in a “stress test” scenario, we generated diverse artificial data sets with varying distributions (identity, uniform, normal, log-normal, and a mixture of these) and compared calculated fold-changes to known values. Additionally, we analyzed a multi-omics biomedical data set to estimate to what extent the findings apply to real-world data. Results: Using arithmetic means as expected values for treatment and reference groups yielded inaccurate fold-change values more frequently than other methods, particularly when subgroup distributions and/or standard deviations differed significantly. Conclusions: The arithmetic mean method, often perceived as standard or picked without considering alternatives, is inferior to other definitions of the group expected value. Methods using median, geometric mean, or paired fold-change combinations are more robust against violations of equal variances or dissimilar group distributions. Adhering to methods less sensitive to data distribution without trade-offs and accurately reporting calculation methods in scientific reports is a reasonable practice to ensure correct interpretation and reproducibility. Full article

This article presents a new methodology for designing a robust, decentralized control structure that considers stochastic parametric uncertainty and uses a multi-objective approach. This design tunes the loop pairing and controller to be implemented. The proposed approach obtains the optimal and nearly optimal controllers relevant to the nominal scenario. Once obtained, the robustness of these solutions is analyzed. This methodology is compared with a traditional approach for selecting the most robust control pairings. The traditional approach obtains lightly robust controllers, i.e., the most robust controllers with an acceptable performance for the nominal scenario, and it obtains trade-offs between robustness and nominal performance. However, the traditional approach has a high computational cost because it is necessary to consider uncertainty in the optimization stage. The proposed approach mathematically guarantees the acquisition of at least one neighbor controller for each existing lightly robust controller. Therefore, this approach obtains solutions similar to lightly robust solutions with a significantly lower computational cost. Furthermore, the proposed approach provides the designer with more diversity and interesting solutions that are not lightly robust. The different approaches are compared using an example of a multi-variable process with two alternative control structures. The results show the usefulness of the proposed methodology. Full article

Rural areas provide ecosystem services (ESs) to urban metropolitan regions. These services are threatened by the constant pressure of urbanisation and new interest in rural development. This has heightened the conflict between environmental concerns and developmental needs, thereby presenting significant land management and rural planning challenges. Employing a quantitative measurement and optimisation framework, we investigate six representative ES variables to assess planning strategies that can address this contradiction. We used a suburban rural area around Nanjing, China, as our study area. We collected spatial data from 2005 to 2020 at two scales (village level and 500 m grid) to map ESs, quantify interactions (trade-offs and synergies among ES bundles), and identify the social, ecological, and landscape drivers of rural change. Based on this, rural planning strategies for optimising ESs at different scales have been proposed. Our findings include (1) spatial heterogeneity in the distribution of ESs, (2) the identification of seven synergistic and eight trade-off pairs among ESs, (3) a spatial scale effect in suburban rural areas, and (4) the spatial trade-offs/synergies of ESs exhibiting a ‘Matthew effect’. The identification of key trade-offs and synergistic ES pairs and the categorisation of ES bundles form the basis for a multi-scale hierarchical management approach for ESs in the region. By examining the commonalities and variations in drivers across diverse scales, we established connections and focal points for spatial planning. We use these findings to propose spatial planning and landscape policy recommendations for rural suburban areas on multiple scales. This study aims to provide a comprehensive and detailed spatial optimisation strategy for rural areas that can help contribute to their revitalisation. Full article

Uncovering the trade-offs and synergy relationship of multiple ecosystem services (ESs) is important for scientific ecosystem management and the improvement of ecological service functions. In this study, we investigated the spatiotemporal changes of four typical ES types (i.e., water yield (WY), carbon storage (CS), soil conservation (SC), and habitat quality (HQ)) from 2001 to 2020 in the Han River Basin (HRB). Meanwhile, the trade-offs and synergies between paired ESs and the socioecological drivers of these ESs were further explored. The results showed that grassland, cropland, and bare land decreased by 12,141.3 km², 624.09 km², and 22.1 km² during the study period, respectively, which can be attributed to their conversion to forests in the HRB. Temporally, the WY, CS, and SC all showed a continuously increasing trend. Spatially, WY and HQ exhibited bipolar clustering characteristics, with WY exhibiting low-value clustering in the upstream and high-value clustering in the downstream, while CS showed the clustering characteristics of a scattered distribution of cold and hot spots from 2001 to 2020. The spatial patterns of aggregation locations in CS and HQ were relatively similar, with clusters of higher ES values mainly distributed in the western and central regions and clusters of lower ES values mainly located in the eastern and southeastern regions, while the aggregation of WY was spatially concentrated. Overall, the CS showed a significant positive correlation with HQ, but a significant negative correlation with WY. Spatially, WY and HQ, CS, and SC showed a substantial trade-off relationship in the northwest and southeast parts of the study area, while HQ, CS, and SC mainly exhibited a synergistic relationship in most parts of the study area. Slope and temperature had high influencing factor coefficients on multiple ESs; the mixed effect of terrain and natural factors was significantly greater than the impact of a single factor on ESs, and terrain factors played an essential role in the changes in ESs. The findings can provide technical and theoretical support for integrated scientific ecosystem management and sustainable development at the local scale. Full article

Forecasting returns in financial markets is notoriously challenging due to the resemblance of price changes to white noise. In this paper, we propose novel methods to address this challenge. Employing high-frequency Brazilian stock market data at one-minute granularity over a full year, we apply various statistical and machine learning algorithms, including Bidirectional Long Short-Term Memory (BiLSTM) with attention, Transformers, N-BEATS, N-HiTS, Convolutional Neural Networks (CNNs), and Temporal Convolutional Networks (TCNs) to predict changes in the price ratio of closely related stock pairs. Our findings indicate that a combination of reversion and machine learning-based forecasting methods yields the highest profit-per-trade. Additionally, by allowing the model to abstain from trading when the predicted magnitude of change is small, profits per trade can be further increased. Our proposed forecasting approach, utilizing a blend of methods, demonstrates superior accuracy compared to individual methods for high-frequency data. Full article

In the face of advancing quantum computing capabilities posing significant threats to current cryptographic protocols, the need for post-quantum cryptography has become increasingly urgent. This paper presents a comprehensive analysis of the performance of various post-quantum cryptographic algorithms specifically applied to digital signatures. It focuses on the implementation and performance analysis of selected algorithms, including CRYSTALS-Dilithium, Falcon, and SPHINCS+, using the liboqs library. Performance tests reveal insights into key pair generation, file signing, and signature verification processes. Comparative tests with the well-known and popular RSA algorithm highlight the trade-offs between security and time efficiency. The results can help to select secure and efficient ciphers for specific 5G/6G services. Full article