Original Time Research Articles

On the formation of a new world order

The thesis of the formation of a new world order is widespread in both Russian and foreign political discourse, but it needs to be substantiated. The rejection of the ‘old' world order also requires clarification: what exactly is unacceptable and what should be radically restructured. Since the end of the WWI, the original time marker of the existing world order, the question of its renewal has come up five or six times. Today, as always, it is important to assess the changes in the balance of power on the world stage. They affect (i) the main actors, their roles, activities, algorithms of behaviour; (ii) the tools at their disposal that can be used to interact with the international environment; (iii) new coalition rapprochements and new cleavages in the international arena; (iv) new global and regional balances (regarding military, economic, resource, scientific, technical and other potentials). The question is whether this results in the accumulation of a new quality by the international system, which requires its radical renewal, or whether the system is able to absorb changing realities, adapting to them and improving the existing algorithms of its own functioning. The time factor can work not only to accelerate new trends, but also to promote the inertia of the existing ones. In some areas (globalization, the role of multilateral institutions, arms control) the resources possessed by the existing international system by no means seem to be exhausted. Finally, the abstractly formulated idea of the need for a new world order does not clarify some problems of an existential nature: (i) the hierarchy of the international system and the consequences of its ‘softening,' (ii) the unequal availability of various elements of stability/power (technology, information, resources, financial instruments, human capital, movement of people, social stability, etc.) for different states and societies, (iii) the correlation of internal problems and relations with the outside world, (iv) the phenomenon of sovereign particularism. As topics for further discussion, briefly formulated criteria for sustainability, effectiveness, maturity and adaptive capabilities of the world order are proposed.

Seismicity of a relic slab: space–time cluster analysis in the Vrancea Seismic Zone

The Vrancea Seismic Zone (VSZ) is an atypical intermediate-depth earthquake nest located in the South-East Carpathians in Romania, often regarded as a relic slab sinking into the mantle. The origin of the slab and the earthquakes it produces are debated because brittle failure is unlikely to occur at mantle depths. Cluster types and statistical properties of seismicity can vary with deformation regime, fluid content and temperature. Here we investigate the spatial–temporal properties of earthquakes recorded in the VSZ from a high-quality local catalogue, identify and classify earthquake clusters, placing constraints on the debated nature of the Vrancea slab, its coupling with the overlying crust, and its potential for triggering crustal earthquakes. We use the nearest-neighbour distance to estimate the correlation between pairs of earthquakes, considering the origin time, magnitude, b-value and fractal dimension of the observed seismicity with Mw ≥ 2.5 from the 1977–2023 period. With this approach, we find a small percentage of clustered seismicity (8%), suggesting that background seismicity dominates. We identified 12 conventional mainshock-dominated sequences and 17 swarms with relatively small (0.92) and large (1.73) b-values, respectively, that may reflect differences in stress or the presence of fluids in the crustal volume. The aftershock decays—p-values of 0.99 for the large-event clusters and 1.42 for the swarms—may reflect typical and, respectively, fast stress relaxations. Swarm clusters are predominantly localised within the crust, whereas conventional mainshock sequences lack foreshocks and prevail in the subcrustal domain, occasionally triggering aftershocks at shallower depths. This partitioning suggests higher heat flow and fluid-modulated seismicity in the crust, as opposed to brittle failure conditions, triggered by slab dynamics in the mantle, including abrupt stress release without preceding signals. The spread of aftershocks from subcrustal large mainshocks to shallower depths indicate a stress transfer process where the descending Vrancea slab influences seismic activity in the overlying crust.Graphical

Study on the Impact of LDA Preprocessing on Pig Face Identification with SVM.

In this study, the implementation of traditional machine learning models in the intelligent management of swine is explored, focusing on the impact of LDA preprocessing on pig facial recognition using an SVM. Through experimental analysis, the kernel functions for two testing protocols, one utilizing an SVM exclusively and the other employing a combination of LDA and an SVM, were identified as polynomial and RBF, both with coefficients of 0.03. Individual identification tests conducted on 10 pigs demonstrated that the enhanced protocol improved identification accuracy from 83.66% to 86.30%. Additionally, the training and testing durations were reduced to 0.7% and 0.3% of the original times, respectively. These findings suggest that LDA preprocessing significantly enhances the efficiency of individual pig identification using an SVM, providing empirical evidence for the deployment of SVM classifiers in mobile and embedded systems.

Anomaly detection in multidimensional time series for water injection pump operations based on LSTMA-AE and mechanism constraints

Addressing the issues of inadequate information exchange among subsequences in the operational time series of water injection pumps, leading to low accuracy and high false alarm rates in anomaly detection, this paper proposes a multidimensional time series anomaly detection method for water injection pump operations, leveraging Long Short-Term Memory Autoencoder augmented with Attention Mechanism (LSTMA-AE) and mechanistic constraints. The LSTMA-AE framework encompasses three primary modules: a Time Feature Extraction Module (Encoder), an Attention Layer, and a Data Reconstruction Module (Decoder). The Encoder captures temporal dependencies and features within the input sequences, mapping the input data into a higher-dimensional space. The Attention Layer, embedded within the hidden state computation, dynamically adjusts the contribution of each timestep’s input information to the hidden state, thereby enhancing the extraction of vital information while ignoring irrelevant data. The Decoder is responsible for reconstructing the latent representations generated by the Encoder back into the original time series data. By utilizing LSTMA-AE, we aim to improve the accuracy of anomaly detection, while simultaneously employing mechanistic constraints to mitigate false alarm rates. Experimental results demonstrate that this approach significantly outperforms methods such as polynomial interpolation, random forest, and LSTM-AE in terms of anomaly detection accuracy on field datasets from oilfields, accompanied by a notably lower false alarm rate.

Survival modelling of smartphone trigger data in crowdsourced seismic monitoring: with applications to the 2023 Pazarcik and 2019 Ridgecrest earthquakes

Abstract Crowdsourced smartphone-based earthquake early warning systems have recently emerged as reliable alternatives to more expensive solutions based on scientific instruments. For example, during the deadly 2023 Pazarcik event in Turkey, the system implemented by the Earthquake Network citizen science initiative provided up to 58 s of warning to people exposed to life-threatening ground shaking. We develop a statistical methodology based on a survival mixture cure model that provides full Bayesian inference on epicentre, depth, and origin time, and we design a tempering Markov chain Monte Carlo algorithm to account for the multi-modality of the posterior distribution. The methodology is applied to data collected by the Earthquake Network during three seismic events, including the 2023 Pazarcik and 2019 Ridgecrest earthquakes.

HA-SCINet based Carbon Emission Prediction of the Typical Park

Background: With the rapid economic growth and the accelerated process of industrialization, the production activities of enterprises within parks have significantly increased, leading to a continuous rise in carbon emissions. Under the context of the "dual carbon" goals, studying the prediction of carbon emissions in typical parks holds significant practical importance. It is not only a key measure to address climate change but also an important pathway to achieve sustainable development. Objective: In order to predict the carbon emissions of the typical park more accurately, we propose a carbon emissions prediction model HA-SCINet. Methods: The model uses a recursive downsampling-convolution-interaction architecture. In each layer, the long-term dependence in time series data is extracted by HyperAttention. Then through the L-layer SCI-Block of the binary tree structure, the down-sampling interactive learning extracts both short-term and long-term dependencies. These extracted features are merged and reorganized, and added to the original time series to generate a new sequence with enhanced predictability. Finally, employ a fully connected network for forecasting the enhanced sequence. The carbon emission data of the typical park serve as input, leading to higher accuracy prediction results through the Stacked K-layer stacked HA-SCINet. Results: The mean square error (MSE) and mean absolute error (MAE) of HA-SCINet prediction model are 0.0819 and 0.204 respectively, outperforming the mainstream Dlinear and Nlinear models Conclusion: The experimental results show that the devised model outperforms in predicting carbon emissions, and is better suited for forecasting carbon emissions within the context of the typical park.

Progressive Bounded Error Piecewise Linear Approximation with Resolution Reduction for Time Series Data Compression

Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to reduce the data size and thus save energy. When a certain level of data accuracy is sacrificed, lossy compression technologies can achieve better compression ratios. However, different applications may have different requirements for data accuracy. Instead of keeping multiple compressed versions of a time series w.r.t. different error bounds, HIRE hierarchically maintains a tree, where the root records a constant function to approximate the whole time series, and each other node records a constant function to approximate a part of the residual function of its parent for a particular time period. To retrieve data w.r.t. a specific error bound, it traverses the tree from the root down to certain levels according to the requested error bound and aggregates the constant functions on the visited nodes to generate a new bounded error compressed version dynamically. However, the number of nodes to be visited is unknown before the tree traversal completes, and thus the data size of the new version. In this paper, a time series is progressively decomposed into multiple piecewise linear functions. The first function is an approximation of the original time series w.r.t. the largest error bound. The second function is an approximation of the residual function between the original time series and the first function w.r.t. the second largest error bound, and so forth. The sum of the first, second, …, and m-th functions is an approximation of the original time series w.r.t. the m-th error bound. For each iteration, Swing-RR is used to generate a Bounded Error Piecewise Linear Approximation (BEPLA). Resolution Reduction (RR) plays an important role. Eight real-world datasets are used to evaluate the proposed method. For each dataset, approximations w.r.t. three typical error bounds, 5%, 1%, and 0.5%, are requested. Three BEPLAs are generated accordingly, which can be summed up to form three approximations w.r.t. the three error bounds. For all datasets, the total data size of the three BEPLAs is almost the same, with the size used to store just one version w.r.t. the smallest error bound and significantly smaller than the size used to keep three independent versions. The experiment result shows that the proposed method, referred to as PBEPLA-RR, can achieve very good compression ratios and provide multiple approximations w.r.t. different error bounds.

Origin of the only myrmecomorphic stink bug, Pentamyrmex spinosus (Hemiptera: Pentatomidae), in the radiation era of ants (Hymenoptera: Formicidae)

AbstractMyrmecomorphy is the most common type of Batesian mimicry in arthropods. In the true bugs (Insecta: Hemiptera: Heteroptera), myrmecomorphy has been recorded in at least seven superfamilies, but not among the stink bugs (Pentatomoidea) until 2014. The only known species that exhibits a high degree of myrmecomorphy during both the adult and nymphal stages within this group, Pentamyrmex spinosus, was reported based on a single specimen, yet little was known beyond its morphological description. In this study, the biology of this species is reported as living in bamboo (Poaceae: Bambusoideae), in close proximity with a highly similar ant species, Polyrhachis dives (Hymenoptera: Formicidae). Further, the phylogenetic position and the origin time of this stink bug species were inferred in the context of Pentatomoidea. The dated phylogeny indicates that Pentamyrmex spinosus is the sister group of Phyllocephalinae, a specialized grass‐feeding subfamily in Pentatomidae, and diverged about 42.7 Ma (52.6–33.2 Ma), roughly synchronous with the radiation of the crown group of Polyrhachis ants (42.0–33.0 Ma) and slightly after the early radiation of bamboos (66.9–24.9 Ma). Our results suggest that the origin of this myrmecomorphic stink bug was probably driven by the rapid diversification of spiny ants and bamboo. In addition, our results also provide a reference framework for the phylogenetic and taxonomic systems of Pentatomoidea and Pentatomidae.

The Temporal Difference and Timelessness in Kant and Heidegger

Abstract I spell out two theses, one shared by Kant and Heidegger, the other Kant’s alone: (1) there is a difference between “within-time-ness” (Innerzeitigkeit) and original or pure time (the temporal difference); (2) the temporal difference is articulated by a self-conscious act not bound by time. While each agrees that the “time-less” original or pure time has limits within which particular temporal determinations have their significance, Kant goes further in asserting that the pure ‘I’ must cognize the determinate boundaries of original or pure time if objective cognition is to be possible. The ‘I’ thus rejects time as a horizon of its own significance. An understanding of this argument suggests why Kant’s position is not as obviously susceptible to Heidegger’s criticism: not only does Kant positively argue that the ‘I’ is atemporal, but the content of this argument tells us why it is impossible for the ‘I’ to be ‘extant’ or a superadded representation in need of unification with temporality. Finally, it suggests that the dispute between Kant’s and Heidegger’s philosophical orientations rests not on whether temporality has been understood, but on how the ultimate conditions of intelligibility relate to their limits.

Early–middle Permian Mediterranean gorgonopsian suggests an equatorial origin of therapsids

Therapsids were a dominant component of middle–late Permian terrestrial ecosystems worldwide, eventually giving rise to mammals during the early Mesozoic. However, little is currently known about the time and place of origin of Therapsida. Here we describe a definitive therapsid from the lower–?middle Permian palaeotropics, a partial skeleton of a gorgonopsian from the island of Mallorca, western Mediterranean. This specimen represents, to our knowledge, the oldest gorgonopsian record worldwide, and possibly the oldest known therapsid. Using emerging relaxed clock models, we provide a quantitative timeline for the origin and early diversification of therapsids, indicating a long ghost lineage leading to the evolutionary radiation of all major therapsid clades within less than 10 Myr, in the aftermath of Olson’s Extinction. Our findings place this unambiguous early therapsid in an ancient summer wet biome of equatorial Pangaea, thus suggesting that the group originated in tropical rather than temperate regions.

Optimizing Traffic Light Timing Using Graph Theory: A Case Study at Urban Intersections

Purpose of the study: This study aims to optimize traffic light timing at the Usman Salengke-Poros Malino-K.H. Wahid Hasyim intersection using a graph theory approach. By modeling compatible traffic flows and calculating optimal signal durations, the study seeks to reduce congestion, minimize delays, and improve traffic efficiency. Methodology: This study utilized manual traffic volume data collection methods with direct field observations at the Usman Salengke-Poros Malino-K.H. Wahid Hasyim intersection. It employed Webster's method for optimal cycle calculation and MATLAB software for simulation. Tools included measuring tapes (Stanley), stopwatches (Casio), and data sheets for recording traffic flow. Surveys captured vehicle types and peak hour volumes. Main Findings: The optimal traffic light cycle duration was calculated as 95 seconds, reducing the original cycle time of 128 seconds. Peak traffic volume was observed at 1,383 pcu/hour (Usman Salengke North). The green light duration increased for Usman Salengke North to 39 seconds and for Poros Malino to 28 seconds. Total average vehicle waiting time decreased by 33.3%, with improved throughput by 20%. Novelty/Originality of this study: This study introduces a practical application of graph theory for optimizing traffic light timing, using compatible flow modeling to simplify intersection analysis. Unlike adaptive systems requiring expensive technology, this approach relies on manual traffic data, offering cost-effective solutions. It advances existing knowledge by providing a simplified, scalable method for reducing congestion and enhancing traffic efficiency in urban settings.

Another Look at Dependence: The Most Predictable Aspects of Time Series

Serial dependence and predictability are two sides of the same coin. The literature has considered alternative measures of these two fundamental concepts. In this article, we aim to distill the most predictable aspect of a univariate time series, that is, the one for which predictability is optimized. Our target measure is the mutual information between the past and future of a random process, a broad measure of predictability that takes into account all future forecast horizons, rather than focusing on the one-step-ahead prediction error mean square error. The most predictable aspect is defined as the measurable transformation of the series that maximizes the mutual information between past and future. This transformation arises from the linear combination of a set of basis functions localized at the quantiles of the unconditional distribution of the process. The mutual information is estimated as a function of the sample partial autocorrelations, using a semiparametric method that estimates an infinite sum by a regularized finite sum. The second most predictable aspect can also be defined, subject to suitable orthogonality restrictions. Finally, we illustrate the use of the most predictable aspect for testing the null hypothesis of no predictability and for point and interval prediction of the original time series.

Recent Origin of a Range-Restricted Species With Subsequent Introgression in its Widespread Congener in the Phyteuma spicatum Group (Campanulaceae).

Understanding the causes of restricted geographic distributions is of major interest to evolutionary and conservation biologists. Inferring historical factors has often relied on ad hoc interpretations of genetic data, and hypothesis testing within a statistical framework under different demographic scenarios remains underutilised. Using coalescent modelling on RAD-sequencing data, we (i) test hypotheses about the origin of Phyteuma gallicum (Campanulaceae), a range-restricted endemic of central France sympatric with its widespread congener Ph. spicatum, and (ii) date its origin, irrespective of its mode of origin, to test the hypothesis that the restricted range is due to a recent time of origin. The best supported model of origin is one of a dichotomous split of Ph. gallicum, confirmed as distinct species, and the Central European Ph. nigrum with subsequent gene flow between Ph. gallicum and Ph. spicatum. The split of Ph. gallicum and Ph. nigrum is estimated at 45-55,000 years ago. Coalescent modelling on genomic data not only clarified the mode of origin (dichotomous speciation instead of a previously hypothesised hybridogenic origin) but also identified recency of speciation as a sufficient, although likely not the sole, factor to explain the restricted distribution range. Coalescent modelling strongly improves our understanding of the evolution of range-restricted species that are frequently of conservation concern, as is the case for Ph. gallicum.

Key stage of evolution of endemism in Gaoligong Shan after the late Pliocene

AbstractIn the Mountains of Southwest China (MSC), accelerated diversification triggered by mountain uplift and monsoon intensification in the Miocene is widely revealed, but less is known about the stage after the Pliocene. Thus, the tempo‐spatial evolution of the endemism of Gaoligong Shan (GLGS), a young and isolated mountain with the highest biodiversity in the MSC, was determined. Temporal patterns were inferred by the stem ages of the endemic species and spatial patterns were inferred through ancestral area reconstructions and the distributions of the sister taxa. Temporally, the calibrated origin times of 114 phylogenetically well resolved endemic species (75 plants, 33 animals and six fungi) ranged from the early Miocene to the Holecene. Spatially, 82 centric origin species (mean = 3.08 Ma) were significantly younger than 32 eccentric origin species (6.84 Ma). Eccentric origins accumulated smoothly while centric origins burst after the late Pliocene, especially after the Pleistocene (n = 52). In centric origins, most of the sister taxa showed sympatric (n = 47) or allopatric (n = 23) distributions in the GLGS. The eccentric origins were mainly dispersed from the southern lower latitude region (n = 20). Principle component analysis indicated that niche conservatism exerted greater contribution to the speciation of the endemic species. Uplift of the GLGS and monsoon intensification during the late Pliocene accelerated the formation of its endemism. The “Species pump” effect of the Pleistocene climatic fluctuations is further revealed. Speciation triggered by geological isolation by mountain and river barriers through niche conservatism exceeds adaptive evolution.

La medición de la renta de la tierra minera en Chile y sus cursos de apropiación (1940–2017)

Abstract In this paper, we present the foundations and results for a new rent database on mining land in Chile (1940–2017), which takes into account not only the surplus profits of the sector, but also the different mechanisms in which this land is appropriated by other social actors. The results are weighted in relation to the whole national economy, which is why an original time series of the general rate of profits and its components, surplus-value and total advance capital, is also provided. In this paper, we posit a methodological foundation based on Marx's developments and a critical review of the existing statistics and previous measures. The results are original as they are the first long-run time series of mining land rent which considers the main appropriation mechanisms by different social actors. In turn, it shows that previous studies underestimate the weight of mining land rent in the Chilean economy, particularly when the prices of copper are rising. In addition, the results make it possible to pose new questions regarding the development of the national specificity considered. As a result of this new evidence, we indicate specific determinations of the different political cycles in Chilean national life, showing the historical persistency of mining land rent beyond changes in its appropriation forms and, therefore, stepping outside of the import substitution industrialization and neoliberalism dichotomy, which dominates the long-run economic historiography in this country.

Air Quality Prediction Based on Singular Spectrum Analysis and Artificial Neural Networks.

Singular spectrum analysis is a powerful nonparametric technique used to decompose the original time series into a set of components that can be interpreted as trend, seasonal, and noise. For their part, neural networks are a family of information-processing techniques capable of approximating highly nonlinear functions. This study proposes to improve the precision in the prediction of air quality. For this purpose, a hybrid adaptation is considered. It is based on an integration of the singular spectrum analysis and the recurrent neural network long short-term memory; the SSA is applied to the original time series to split signal and noise, which are then predicted separately and added together to obtain the final forecasts. This hybrid method provided better performance when compared with other methods.

An innovative approach for FMCW radar vital sign monitoring with removal of respiratory harmonics

Frequency Modulated Continuous Wave (FMCW) radar shows broad research potential in monitoring vital signs. However, the harmonic components generated by the human respiratory process overlap with the frequency band of the heartbeat signal, posing challenges to precisely monitoring the heartbeat signal. To address this issue, this paper introduces the singular spectrum analysis method, which accurately identifies and removes respiratory harmonics by adding a sine signal with the same frequency as the respiratory harmonics to the original time series. Furthermore, the paper introduces energy entropy and the Pearson correlation coefficient to comprehensively decompose physiological signals to optimize the traditional variational mode decomposition algorithm, proposing the EE-VMD and PCC-VMD algorithms. Experimental results demonstrate that the SNR increased, and the accuracy of measuring respiratory rate and heartbeat rate has significantly improved. The results presented in this paper not only advance theoretical methodologies for the monitoring of human vital signs but also underscore the practical applicability of FMCW radar in this domain.

Performance Test of Pilot Earthquake Early Warning System in Western Java, Indonesia

This study assesses the performance of a pilot Earthquake Early Warning (EEW) system set up in Java's western region in Indonesia. The Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) developed the pilot EEW system based on the Earthworm Platform, in response to local seismic activity. The system aims to deliver early alerts for seismic occurrences by leveraging the seismic network set up by BMKG. In this paper, we proposed EEW suited to Indonesia, using to identify events and to estimate earthquake parameters and integrated these into the Earthworm Platform using accelerograph data from the BMKG network. The pilot EEW system was tested on real-time scenarios in 27 events in the western part of Java from October 2022 to May 2023. Based on real-time testing, the systems proved to be quick (6 seconds after origin time in the 2022 Cianjur earthquake) and accurate in providing earthquake parameters, such as origin time, hypocenter, and magnitude compared to the BMKG catalog (Note: Epicenter errors are less than 30 km, with more than half of the errors falling under 10 km; the majority of events have origin times calculated within a margin of about 2 seconds, and a magnitude error between 0.3 and 0.6). These results offer valuable insights into the operational capabilities of the pilot EEW system in western Java and its capacity to issue accurate early earthquake warnings in Indonesia.

A designed predictive modelling strategy based on data decomposition and machine learning to forecast solar radiation

Consistent with the United Nations Sustainable Development Goal 7, the design and optimization of clean energy resources (e.g., solar energy) is a highly motivating task for all researchers globally who continue to build research synergies that can tackle the future likelihood of energy crises due to socioeconomic and strategic environmental policy. In this paper, a weekly solar radiation (SR) forecasting model is designed using a robust local mean decomposition (RLMD) technique unified with a random forest (RF) algorithm to generate a fully optimised hybridized RLD-RF model that has a promising capability to forecast the SR values. In the first stage of model design, the RLMD, a frequency resolution method, is applied to decompose the original SR time series into amplitude modulation subseries (AMs), frequency modulation subseries (FMs), and the low-frequency product functions (PFs) to reveal the internal structure of the model construction data to incrementally optimize the RLD-RF model where only PFs were used. Subsequently, the statistically significant lagged subseries at a week ahead forecasting horizon (t – 1) of the low-frequency PFs with residual components are extracted individually, via partial autocorrelation function (PACF), to capture the historical behaviour of frequency-resolved SR component in order to build a robust modelling framework. Consequently, the random forest (RF) algorithm is employed to forecast each of the subseries using PACF-based lagged inputs to construct a fully optimised hybrid RLMD-RF predictive model. RLMD-RF is benchmarked against a baseline RF, M5tree, and multiple linear regression (MLR), Artificial neural network (ANN) and Gaussian process regression (GPR) algorithms, including their hybridized counterparts (i.e., RLMD-M5tree, RLMD-MLR, RLMD-ANN, and RLMD-GPR) using statistical score metrics in the independent testing phase. The results generated at test sites in Queensland State, Australia that have high solar energy potential confirm that the RLMD-RF method can produce quality predictions of weekly solar radiation against the benchmarking comparison models. For instance, RLMD-RF for Barcaldine are higher in terms (EWI= 0.938, ENS= 0.878) against RLMD-MLR (EWI = 0.845, ENS = 0.705), the RLMD-M5tree (EWI = 0.836, ENS = 0.684), RLMD-ANN (EWI = 0.836, ENS = 0.715), RLMD-GPR (EWI = 0.839, ENS = 0.716), the RF (EWI = 0.720, ENS = 0.564), the M5tree (EWI = 0.692, ENS = 0.522), the MLR (EWI = 0.683, ENS = 0.508), the ANN (EWI = 0.708, ENS = 0.519) and the GPR (EWI = 0.708, ENS = 0.520). Similarly, the RLMD-RF also outperformed in Rockhampton, Clermont, and Lockyer Valley stations as compared to other models. This research establishes the practical usefulness of hybridised RLMD-RF modelling framework for accurate SR forecasting and advocates its possible consideration in renewable and sustainable energy production and monitoring systems that can aid in decision-making by energy utilities and stakeholders (e.g., climate and energy policy experts).

Influence of different geographical origins and bottle storage times on the chemical composition, antioxidant activity and sensory properties of cabernet sauvignon wine

To investigate the effects of origin and storage time on wine quality, HPLC and HS-SPME-GC–MS were employed to analyze the chemical compositions and VOCs of Cabernet Sauvignon wines from four sub-regions in Xinjiang and with five storage times. Results showed that most of the organic acids were reduced and the anti-oxidant activity decreased during storage. Wines from different regions exhibited differences in the contents of gallic acid, (+)-catechin and (−)-epicatechin. A total of 68 VOCs were identified, with esters and alcohols being the most dominant VOCs. In total, nine VOCs with OAV > 1 were detected in all samples, impacting wine flavor. Cabernet Sauvignon wines were differentiated using PCA and OPLS-DA. 15 and 17 chemical markers were screened based on VIP > 1 for wine of different sub-regions and bottle storage times, respectively. This research provides a data reference for quality characterization of wines obtained from different sub-regions and bottle storage times in Xinjiang.