Short-term Scale Research Articles

Learning self-regulation: an important soft skill for AYAs

Background: The ability to self-regulate plays a critical role in accomplishing the developmental tasks in the stages of late adolescence, early adulthood and general psychosocial wellbeing. Aims: As part of the Association of Adolescent and Child Care in India’s multicentric studies on youth behaviour, the current study aimed to understand the effect of self-regulation, total, short-term and long-term as measured by the ASRI in female college-going students. We also studied these scores in relation to sociodemographic factors such as gender, age, sibling status, along with other variables such as perceived control over one’s life on self-regulation abilities. Methods: A cross-sectional study was conducted using convenience sampling. Participants (n = 354) were in the age groups between 17 to 19 and 20 (late adolescence) to 21 years (young adults), pursuing B.A., BCom., or BSc. in a college in North India. The Adolescent Self-Regulatory Inventory (ASRI) was administered to participants in order to assess both short-term and long-term self-regulation. Permission: Ethical clearance for this project was given by AACCI’s Institutional Ethics Committee. tool used: Moilanen Adolescent Self-regulation Inventory ASRI. The Internal consistency (alpha) for the ASRI was 0.75 for short-term self-regulation scale and 0.80 for long-term self-regulation scales. Confirmatory factor analyses were performed to check for the inventory’s validity, two factors were used, short-term and long-term self-regulation, factors correlated 0.83. Statistical analysis: The data was analysed using the Jeffreys's Amazing Statistics Program (JASP 0.17.2.0). T-tests were conducted to study the effects of age, engagement in extracurricular activities, perceived internet and social media dependence, and substance use on the ASRI. One-way ANOVAs were conducted to determine the effects of sibling status, academic course, and perceived control over one’s life on the ASRI. Additionally, we also used the Kruskal-Wallis test, Mann-Whitney U test, Welch’s test and Levene’s test of equality of variances. The statistical significance of the calculated coefficients was considered at p<0.05. Results: The participants who self- perceived that they had control over their lives had higher scores for overall self-regulation (p=0.002), short-term (p=0.03) and long-term self-regulation (p=.0.004) on the ASRI compared to those who were not sure and those who did not believe that they had any control over their lives. Participants who self -perceived that they were dependent on social media had lower scores on short-term (p=0.01) and long-term self-regulation (p=0.01) on the ASRI compared to those who did not perceive themselves as being dependent on social media. Conclusion: Our sample showed that among all the variables we examined e.g., age sibling status, participation in extracurricular activities and tobacco, alcohol consumption , significant results were found for only two variables that accessed students’ self-perception (control over one’s life and social media usage). This suggests that at the stages of late adolescence and young adulthood, self-perception contributes to self-regulation abilities.

Linkages between intra-annual radial growth and photosynthetic production of four main species in a temperate forest in northeast China

The comprehension of the linkage between radial stem growth and photosynthesis is fundamental to gaining insight into the growth and developmental mechanisms of trees, as well as their adaptive capacity to future climate change. However, such crucial information is rarely available, particularly on short-term temporal scales. In this study, we continuously monitored the intra-annual radial stem growth of a non-porous species (Pinus koraiensis) and three ring-porous species (Quercus mongolica, Fraxinus mandshurica, Ulmus japonica) with monthly measurements in 2019 and weekly measurements from 2020 to 2022(second part in 2022) in a temperate forest of Northeast China based on microcore techniques. Concurrently, we estimated the daily photosynthetic production at the individual tree level using a photosynthetic production model developed by our research team. Subsequently, a generalized linear model was conducted to determine the temporal lag of intra-annual radial stem increment and photosynthetic production. Our results indicate significant differences in the initiation of radial growth among the observed years, while the cessation of growth remained consistent. Specifically, ring-porous species exhibited an earlier onset of radial growth compared to non-porous species, but no consistent pattern was observed for the cessation of growth across different wood types. Additionally, we found a strong coupling between radial growth increment and photosynthetic production without any day lag, particularly in different classes. Furthermore, a notable linear association was observed between relative intra-annual radial growth and photosynthetic production, further indicating a positive correlation between photosynthesis and radial growth. We concluded that the carbohydrates required for radial growth are at least partially derived from photosynthetic production produced on the same day when the leaves were available for photosynthesis. These findings enhance our understanding of the seasonal dynamics of radial growth at the cellular level and provide new insights into the relationship between radial growth and photosynthetic production on short-term temporal scales.

Spectral and Timing Study of the Newly Detected Ultraluminous X-Ray Sources in NGC 3585 Using Different Chandra Observations.

The present work aims to study the previously unstudied Ultraluminous X-ray sources (ULXs) in the galaxy NGC 3585 at its various epochs of Chandra observation. We report here the detection of two new ULXs viz. CXOUJ111306.0-264825 (X-1) and CXOUJ111325.3-264732 (X-2) with their bolometric luminosity > 1039erg s−1 in its various Chandra observations. X-1 was found to be a spectrally hard ULX in both the epochs where it was detected. However in the ULX, X-2, a slight hardening of the spectra was observed within a period of 17 years. Assuming isotropic emission and explained by disk blackbody model, the spectrally softer epoch of X-2 with an inner disk temperature, kTin ∼ 0.79 keV and bolometric luminosity ∼ 2.51 × 1039erg s−1 implies for X-2 to be powered by a compact object, necessarily a black hole of mass, MBH ∼ 44.85+82.11−25.92M⊙ accreting at ∼ 0.42 times the Eddington limit. The Lightcurve of X-1 and X-2 binned at 500s, 1ks, 2ks and 4ks has shown no signature of short-term variability in both the ULXs in kilo-seconds time scales. Overall, both the detected ULXs seem to be almost static sources both in long-term (years) as well as short-term (kilo-seconds) time scales with the presently available Chandra Observations.

Synthesis of Solar Production and Energy Demand Profiles Using Markov Chains for Microgrid Design

Uncertainties related to the energy produced and consumed in smart grids, especially in microgrids, are among the major issues for both their design and optimal management. In that context, it is essential to have representative probabilistic scenarios of these environmental uncertainties. The intensive development and massive installation of smart meters will help to better characterize local energy consumption and production in the following years. However, models representing these variables over large timescales are essential for microgrid design. In this paper, we explore a simple method based on Markov chains capable of generating a large number of probabilistic production or consumption profiles from available historical measurements. We show that the developed approach can capture the main characteristics and statistical variability of real data on both short-term and long-term scales. Moreover, the correlation between both production and demand is conserved in generated profiles with respect to historical measurements.

The influence of weathering processes on microtopographic changes of sandstone under simulated upper intertidal conditions from hourly to weekly scales

Micro-scale surface changes of bedrock on shore platform have been monitored with micro-erosion meters (MEMs) and show significant downwearing over several (≥2) years. At short-term temporal scale of hours to days, rock surface behaviors are reversible and do not generate net surface lowering. Therefore, there is a temporal gap between the two temporal scales of surface behaviors over which grain detachment must occur. In this study, an upper intertidal zone (2-h immersion with subsequent 2-week exposure) was simulated in the laboratory to investigate the fast erosion rate at high elevation identified from the Otway coast in south-eastern Australia using MEMs.During the experiments, the microtopography of rock surface was monitored 2-hourly during the daytime period (0−12 h) on Day 1 (beginning), 8 (middle) and 14 (end) of the 14-day simulation to understand the cumulative effect of short-term surface changes on rock weathering and erosion at longer timescales in order to provide insights into the role of rock surface dynamics in shore platform evolution. Through manipulation of the salt content of water as well as temperature and humidity in the controlled measures, the experiment was also designed to test the primary weathering process in driving short-term surface changes at this particular tide level. Results show the primary role of temperature-induced salt crystal volume change in generating the short-term rock surface movements on high tidal to supratidal rock. Although the cumulative effect of short-term surface behaviors on rock microtopography appeared to be observed over longer timescales, it still remains uncertain if this drives rock decay at the weekly scale. Future studies are required to monitor the rock surface over a sufficiently long period (up to months) to determine the temporal threshold of erosion and understand the linkage between short-term surface changes and rock decay.

Acidity in rainwater and airborne suspended particles in the southwestern coast of the East Sea (Sea of Japan): Their potential impact on seawater total alkalinity

Our understanding of the impact of atmospheric acid deposition on marine carbonate system remains limited, largely due to a lack of data regarding acidity present in atmospheric particles and precipitation. Previous research has relied on the electroneutrality-based ion balance method for indirect estimation of atmospheric acidity. In this study, atmospheric samples collected at a coastal site of South Korea were mixed with seawater to measure the change in seawater total alkalinity (ΔTAAPL) associated with atmospheric proton loading. For the precipitation samples, the measured ΔTAAPL and electroneutrality-based estimates showed a significant correlation. However, we did not observe similar results for the atmospheric particle samples. Furthermore, the decrease in oceanic TA due to ΔTAAPL was substantially smaller than that in dissolved inorganic carbon from concurrent nitrogen fertilization. Consequently, the adverse impact of acid deposition on ocean acidification or air-sea exchange of CO2 appears to be insignificant on a short-term scale.

Improved Giza pyramids construction algorithm for Modify the deep neural network-based method for energy demand forecasting

Accurate prediction of energy demand is crucial for improving services, reducing costs, and optimizing operations in energy systems. Deep neural networks (DNNs) have emerged as a popular method for energy demand forecasting. However, the performance of DNNs can be affected by data quality and hyperparameter selection. To address these concerns, this study proposes a novel energy demand forecasting technique that combines DNNs with an enhanced Giza pyramid construction methodology. The aim of this study is to provide a more reliable and effective approach for forecasting energy demand. The DNNs are employed to capture the complex relationships between input and output variables, while the Giza pyramids algorithm is utilized to optimal selection of hyperparameters of the network. Real-world energy demand data is used to evaluate the proposed approach, comparing it with state-of-the-art baseline models. The research methodology involves assessing the suggested approach using real-world energy demand information and conducting a comparative analysis with cutting-edge baseline models, including modified BP neural network (MBPNN), Neural Network based Genetic Algorithm (NNGA), and reinforcement learning and deep neural network (RLDNN). The IGPCA/CNN method outperforms other methods in energy prediction accuracy across short-term, medium-term, and long-term time scales. It achieves an MSE score of 0.564, lower than MBPNN, NNGA, and RLDNN. In medium-term prediction, it achieves an MSE score of 0.587, better than MBPNN, NNGA, and RLDNN. In long-term prediction, it achieves an MSE score of 0.629, lower than MBPNN and RLDNN. Further analysis and validation experiments are needed to ensure robustness and generalizability. Comparing the method with other state-of-the-art approaches can provide a comprehensive understanding of its superiority. The performance of the models is evaluated based on reliability and effectiveness in energy demand forecasting. The major conclusion of this study is that the proposed approach outperforms the initial models in accurately forecasting energy demand. The combination of DNNs and the improved Giza pyramid construction methodology results in enhanced performance, demonstrating superior reliability and effectiveness compared to other models. The study highlights the significance of accurate energy demand prediction for optimizing energy systems and reducing costs.

A dual-timescale dynamics model for evaluating long-term profiles of distributed energy resources under multi-market mechanisms

Distributed energy resources (DER), encompassing distributed renewable resources and distributed energy storage systems, are progressively emancipating themselves from reliance on subsidies and engaging in the realms of energy, green certificate, and capacity markets. However, the long-term profile of DERs under multiple market conditions remains uncertain, and traditional methods are inadequate in addressing the challenges posed by the non-linearity and non-convexity of various factors that affect investment and bidding behavior. Additionally, the varying time scales of clearing models across different markets require a quantitative approach that combines long and short-term scales to determine DER profitability under various critical boundaries. To address these challenges, this paper proposes a dual-timescale dynamics model. Firstly, the system dynamics (SD) method is employed to establish the interplay among various market entities, simulating capacity investment of generation units on a long-term scale. Secondly, the stochastic unit commitment (SUC) is modeled to verify the operating constraints of the system while simultaneously calculating the short-term market clearing results. These results are then fed back to the long-term simulation loop to evaluate the further profitability of DERs. Lastly, a price adjustment mechanism was devised to simulate the frequent occurrence of negative energy prices in high-proportion renewable energy systems, thereby enhancing the fidelity of simulated price data to real-world environments. Through a 20-year simulation, the proposed model quantitatively analyzes the impact of market mechanisms on DER profitability and the influence of DERs on market clearing results. The model has the potential to provide valuable analytical support for the design of DER-related market mechanisms and policy decisions.

How do regulatory environmental policies perform? A case study of China's Top-10,000 enterprises energy-saving program

Even before China's commitment to carbon neutrality and peak in September 2020, many programs had been implemented to save energy. One such program was the “Action Plan for Energy Conservation and Low-Carbon for Ten Thousand Enterprises” (hereafter Top-10,000 Enterprises Energy-Saving Program, or Top-10,000 Program, or Program) launched in 2011, which targets high energy users. The official assessment of this program, which began during China's 12th Five Year Plan period (2011–2015), claimed that it had achieved its goals. However, there has been no precise measurement of how much it has contributed to China's total energy conservation achievements and how enterprises responded to it. Employing the regression discontinuity design approach and Chinese taxation survey data, this study represents a pioneering attempt to assess the energy savings resulting from this program. Our study estimates that this program accounted for an average of 5.8% of nationwide energy savings. The primary methods employed by enterprises to save energy were enhancing energy efficiency and dynamically adjusting production scale. The results of our study suggest that regulatory environmental policies should not be hindered by short-term scale effects. Instead, further innovations are required to minimize the cost of energy savings.

Employing categorical boosting (CatBoost) and meta-heuristic algorithms for predicting the urban gas consumption

This study was conducted on the presentation of a method to improve the forecast of urban gas consumption based on the weather variables including temperature, pressure, humidity, wind speed and also the gas price. The diversity of input variables as well as investigating a short-term (daily) scale, led to creation complex and nonlinear relationships between the variables, which makes its solving difficult. To this end, the categorical boosting (CatBoost) method is combined with some meta-heuristic algorithms to create hybrid models. These meta-heuristic algorithms include Phasor Particle Swarm Optimization, Artificial Bee Colony, Battle Royale Optimizer, Grey Wolf Optimizer, Satin Bowerbird algorithm, and Fruit Fly Optimization Algorithm. During the network training, the K-Fold cross-validation has also been used to prevent overfitting. Finally, using an actual dataset, the performance of the proposed method is investigated. The results showed that the proposed method can predict the value of short-term urban gas consumption. The results showed that the hybrid Catboost-PPSO model had the best performance among all presented hybrid models. Therefore, using the PPSO algorithm to optimize the hyper-parameters of the CatBoost network is recommended for predicting gas consumption.

Long-Term Efficacy of Internet-Based Cognitive Behavioral Therapy Self-Help Programs for Adults With Depression: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Depression is a worldwide mental disorder and a leading cause of disability. Many people with depression do not want to take medication or have the motivation to seek psychotherapy treatment for many reasons. Guided internet-based self-help programs may be a promising solution for addressing these issues. This kind of intervention has proven to be effective in reducing depression symptoms on a short-term scale. However, as treatment often is a long-term rehabilitation process, it is important to examine not only the short-term effects of internet-based cognitive behavioral therapy (iCBT) self-help treatment but also the follow-up or long-term efficacy of this kind of intervention. This systematic review and meta-analysis aimed to identify studies that examined follow-up data ≥8 weeks after posttreatment measurements and thereby examined the long-term efficacy of iCBT self-help programs with minimal weekly guidance for people with depression. It aimed to analyze the long-term efficacy of iCBT treatments compared to control conditions as well as long-term efficacy within the iCBT treatment conditions. Additionally, it aimed to conduct subgroup analyses according to the follow-up time points for each outcome. Finally, it examined long-term improvements in quality of life. The Cochrane Collaboration Depression, Anxiety, and Neurosis Controlled Trials Register (CCDANCTR), grey literature, reference lists, and correspondence were used to search for published and unpublished randomized controlled trials (RCTs) that reported the long-term or follow-up efficacy of computer-based or iCBT self-help treatments for depression with minimal guidance of up to 10 min/wk. The search took place between 2015 and 2022 (October). The search resulted in a total of 2809 study abstracts, of which 15 studies (with 17 samples) met all inclusion criteria and were included in the long-term analysis. The results showed that the depression outcomes of all follow-up time points together in the treatment conditions were favored over the control conditions with a medium effect size of 0.43 (n=1689 participants; 9 RCTs; standardized mean difference [SMD] -0.43, 95% CI -0.67 to -0.20; P<.001). The analysis of long-term efficacy within the iCBT treatment conditions showed that the follow-up outcomes of the treatment groups were favored over the posttreatment outcomes with a small effect size of 0.20 (n=2196 participants; 17 RCTs; SMD 0.20, 95% CI 0.07-0.49; P=.003). Findings for improving quality of life also showed that the iCBT conditions were favored over the control conditions with a small effect size of 0.19 (n=1345 participants; 3 RCTs; SMD 0.19, 95% CI 0.08-0.30; P<.001). This systematic review and meta-analysis found that iCBT self-help interventions had a superior long-term efficacy for individuals with depressive symptoms compared to control groups. The within-group analysis of iCBT treatment conditions also showed statistically significant improvements in reducing depressive symptoms at follow-up compared to posttreatment measurements.

Identification of Impacts from Meteorology and Local and Transported Photochemical Generation on Ozone Trends in Changsha from 2018 to 2020

Ozone (O3) pollution in Hunan province has become the most important factor among the six common conventional pollutants (i.e., NO2, SO2, CO, O3, PM10, and PM2.5) in the atmospheric environment. Further investigation has indicated that the relevant studies of O3 are insufficient. Therefore, it is essential to clarify the key driving factors of O3 variations for government regulators. In this study, a combined method consisting of a generalized additive model (GAM), empirical orthogonal function (EOF), and absolute principal component scores (APCs) model was employed to identify and quantify the impacts of meteorology and local photochemical generation (local) and that transported from outside (nonlocal) on O3 variations from 2018-2020. Simultaneously, the driving factors of O3 annual values from 2018 to 2019 and from 2019 to 2020 in Changsha were analyzed. The results showed that O3 episodes were commonly caused by meteorology when the relative contribution from precursors was high, on the short-term time scale. Overall, on the temporal scale, meteorology and local were the driving factors for the increasing annual O3 from 2018 to 2019. Additionally, the contribution from meteorology, local, and nonlocal decreased from 2019 to 2020, leading to a lower level of O3 concentration in 2020. Geographically, the east, north, and south of Changsha were mainly affected by meteorology, local, and nonlocal, respectively. Throughout the three years, nonlocal exhibited a sustained decreasing trend, whereas the tendencies from meteorology and local varied by year and geography. Local contribution in the north of Changsha increased from 2018 to 2019, which was likely attributed to the increasing biogenic volatile organic compound emission (BVOCs), and it became lower in the south owing to the strengthened consumption by NOx. Impacts from meteorology on O3 in all sites were enhanced from 2018 to 2019. By contrast, local contribution decreased in the north and increased in the south with the decline in BVOC and NOx emissions from 2019 to 2020, when the meteorological impacts on O3 in the whole area became weak.

Transient interaction effects of temperature and light intensity on isoprene and monoterpene emissions from Schima superba and Phoebe bournei

Biogenic volatile organic compound (BVOC) (such as isoprene (ISO) and monoterpenes (MTs)) emissions from plants play a great role in the atmospheric chemistry. Now frequency of dramatic changes of weather such as transient temperature changing increases, most current studies focus on the effects of simulating climate change (long-term) on BVOC emissions. While studies of transient effects on that are less reported. This study aimed to identify the ISO and MT emissions and the related physiological processes in the short-term scale at different temperature (T) and light intensity (PAR), in seeding stage of Schima superba and Phoebe bournei belonging to typical subtropical tree species. The results showed that the ISO and MT emissions were significantly affected by T and PAR, either independently or interactively. With the increase of T and PAR, the ISO and MT emissions increased, with the maximum rates of ISO and MTs of 39.39 and 1042.35 pmol m−2 s−1 for S. superba under 40 °C × 500 μmol m−2 s−1 condition, while the maximum rates reached 18.73 and 6737.41 pmol m−2 s−1 at 30 °C × 1500 μmol m−2 s−1 for P. bournei. The increase of ISO and MT emissions with T and PAR increasing that was related to the promotion of Pn and gs in plants. Regarding MT components, the proportion of α-pinene decreased with T and PAR increasing, with the lowest ratios of 4.91 % and 21.16 % for S. superba and P. bournei under 40 °C × 1500 μmol m−2 s−1 condition. However, the proportion of β-pinene significantly increased, with the highest ratios of 67.42 % and 57.93 % for S. superba and P. bournei under 30 °C × 1500 μmol m−2 s−1 condition, which is attributed to differences in light tolerance between the two plants. Our study provides basis for evaluating the transient changes of environmental factors on BVOC emissions and optimizing regional BVOC emission models.

The Perturbation of Ozone and Nitrogen Oxides Impacted by Blue Jet Considering the Molecular Diffusion

This study investigated the diffusion impact on the chemical perturbation of NOx and O3 caused by the streamer and leader parts of a blue jet in the low stratosphere (18–30 km), using the coupling of a detailed stratospheric chemistry model and a typical diffusion model. The study found that diffusion significantly impacted the evolution of chemical perturbations at both short-term and long-term time scales after the blue jet discharge, with changes in NOx and O3 concentrations observed at different altitudes (18–28 km). At 18 km, the concentrations of NOx and N2O that account for diffusion start to decrease after 1 s, whereas those without diffusion remain at their peak concentrations. Meanwhile, O3 is slowly destroyed with less NOx, rather than dropping to an unrealistic low value immediately after the discharge without diffusion. The perturbation caused by the blue jet discharge disappears within a few tens of seconds at 18 km when molecular diffusion is considered. At 30 km, the chemical perturbation from four point sources was observed through changes in NO2 concentrations. However, the total concentration of NO2 perturbed by the streamer part discharge at the given surface was negligible when considering diffusion. Overall, this study provided a useful model tool for a more accurate assessment of the chemical effects of individual blue jets.

Probabilistic load margin assessment considering forecast error of wind power generation

The increasing integration of wind power in power systems necessitates the probabilistic assessment of various uncertain factors. In operational planning, modeling short-term scale uncertainties, i.e., wind power forecast errors, plays an important role. In this paper, according to the different forecast values, the corresponding probability distributions of wind power forecast errors are developed using a data-driven manner. Then, the polynomial chaos expansion surrogate is developed to facilitate the probabilistic load margin assessment considering wind power forecast errors. The effectiveness of the forecast error model is verified using the historical data of realistic wind power plants. The results show that the probability distributions of forecast errors vary with the level of forecast values. Moreover, the performance of the polynomial chaos expansion surrogate in estimating probabilistic load margin is validated in the IEEE 30-bus system. The results demonstrate that the versatile forecast error distributions significantly impact the characteristics of load margin. Moreover, the polynomial chaos expansion surrogate can accelerate the load margin assessment compared to the Monter Carlo simulation while retaining the same accuracy.

Human activities impact the propagation from meteorological to hydrological drought in the Yellow River Basin, China

Quantifying the role of human activities in the process of drought propagation from meteorological drought to hydrological drought is helpful in developing mitigation strategies and in prediction of hydrological drought. To quantify the role of human activities, we compared the results from using simulated natural and observed streamflow to calculate drought indices and drought propagation time. In addition, we compared different propagation detection methods for evaluating the spatiotemporal characteristics of drought propagation from meteorological to hydrological in the Yellow River Basin during the period 1962–2018. The results show: (1) The intensity of meteorological drought (characterized by the standardized precipitation evapotranspiration index) in upstream regions showed an upward trend on the short-term (1-month) scale. At the 6-month scale, human activities reduced hydrological drought events (characterized by the standardized streamflow index) at 93% of stations in the midstream and downstream reaches. (2) The proposed center of gravity method is more suitable for calculating the propagation time after the impact of human activities, while the onset method is suitable for early drought warning. (3) Human activities shortened the propagation time for the controlled area of about 58% of stations (11 out of 19) during the growing season and prolonged it for the controlled area of about 53% of stations (10 out of 19) during the nongrowing season in the Yellow River Basin.

Intelligent Short-Term Multiscale Prediction of Parking Space Availability Using an Attention-Enhanced Temporal Convolutional Network

The accurate and rapid prediction of parking availability is helpful for improving parking efficiency and to optimize traffic systems. However, previous studies have suffered from limited training sample sizes and a lack of thorough investigation into the correlations among the factors affecting parking availability. The purpose of this study is to explore a prediction method that can account for multiple factors. Firstly, a dynamic prediction method based on a temporal convolutional network (TCN) model was confirmed to be efficient for ultra-short-term parking availability with an accuracy of 0.96 MSE. Then, an attention-enhanced TCN (A-TCN) model based on spatial attention modules was proposed. This model integrates multiple factors, including related dates, extreme weather, and human control, to predict the daily congestion index of parking lots in the short term, with a prediction period of up to one month. Experimental results on real data demonstrate that the MSE of A-TCN is 0.0061, exhibiting better training efficiency and prediction accuracy than a traditional TCN for the short-term prediction time scale.

Analysis of short-term and local scale variations in fish community structure in Dachen Island waters

Understanding the changes in community structure of fishery resources in island reef waters is crucial for effective fishery resource management, as these waters constitute a critical link in maintaining the sustainable development of offshore fishery resources. The study analyzed the structure of the fish community in the waters surrounding Dachen Island by using data collected during four voyage surveys conducted between September 2020 and April 2021. The study utilized co-occurrence network analysis, multiple regression trees, and functional diversity analysis to investigate the short-term and local scale changes in fish community structure. The results showed that the fish community in the waters adjacent to Dachen Island can be categorized into two groups: annual and stage-use species. The stage-use species include Scoliodon laticaudus, Trichiurus lepturus, Tetraodontidae, and others, which are used during the summer and autumn stages of bait migration. Additionally, Dasyatidae and others, which used during the winter and spring stages of reproductive migration. Furthermore, the study found that the habitats dominated by nearshore rocky habitats and distant deep habitats formed the local scale variation in community structure in island waters. The integrated analysis of species α-diversity and functional diversity provided a basis for understanding the mechanisms of biodiversity action. Our study aims to provide a theoretical framework for the development of fishery resource management strategies based on the life cycle of fish, and to serve as a reference for the prioritization of key protection areas for fishery resources in island and reef environments. Overall, the study’s findings can promote the understanding of community structure dynamics in island waters. Additionally, these findings can offer valuable insights into the restoration of habitats and the implementation of marine ranching activities in Dachen Island. Moreover, the outcomes can serve as a theoretical foundation for the development of appropriate management and conservation strategies for fisheries resources.

A holistic and proactive approach to forecasting cyber threats

Traditionally, cyber-attack detection relies on reactive, assistive techniques, where pattern-matching algorithms help human experts to scan system logs and network traffic for known virus or malware signatures. Recent research has introduced effective Machine Learning (ML) models for cyber-attack detection, promising to automate the task of detecting, tracking and blocking malware and intruders. Much less effort has been devoted to cyber-attack prediction, especially beyond the short-term time scale of hours and days. Approaches that can forecast attacks likely to happen in the longer term are desirable, as this gives defenders more time to develop and share defensive actions and tools. Today, long-term predictions of attack waves are mostly based on the subjective perceptiveness of experienced human experts, which can be impaired by the scarcity of cyber-security expertise. This paper introduces a novel ML-based approach that leverages unstructured big data and logs to forecast the trend of cyber-attacks at a large scale, years in advance. To this end, we put forward a framework that utilises a monthly dataset of major cyber incidents in 36 countries over the past 11 years, with new features extracted from three major categories of big data sources, namely the scientific research literature, news, blogs, and tweets. Our framework not only identifies future attack trends in an automated fashion, but also generates a threat cycle that drills down into five key phases that constitute the life cycle of all 42 known cyber threats.

Dependence structure among rare earth and financial markets: A multiscale-vine copula approach

This paper examines the dynamic upper and lower tail dependence across rare earth metals, clean energy, gold, world equity, base metals, and crude oil markets at various time scales. Firstly, raw return series are decomposed into various time scales using the maximum overlapping discrete wavelet transform method, then the time-varying pairwise dependencies, accounting for the impact of the covariate (in our case, the rare earth stock index), are analysed using vine-copula. This so called multiscale-vine copula approach is applied to daily data from June 25, 2009 to October 7, 2022, covering the Covid-19 outbreak. The results show that, for raw returns, the rare earth market moderates the positive dependence between world equity and clean energy markets. At the short-term time scale, unlike other pairwise dependencies, rare earth eases the dependency between clean energies. During the Covid-19 pandemic period, the rare earth stock index significantly affects the correlation of the gold and oil markets and makes them more resilient to global health shocks. At the mid-term time scale, the impact of the rare earth index is more pronounced, for both the entire sample and during the Covid-19 outbreak, as the dynamic dependencies of most indices, such as clean energy-world equity, base metals-world equity, and crude oil-clean energy, significantly decline after accounting for the influence of rare earth metals. The main result at the long-term time scale is that the Covid-19 pandemic moderates the dependency of clean energy-gold even further when considering the impact of the rare earth stock index. In general, the rare earth stock index plays a significant role in easing the extent of dependency in the medium term during the entire sample and the pandemic. These findings provide some useful implications for heterogeneous investors and market participants operating at various time scales.