Future Prediction Research Articles

Exogenous moisture deficit fuels drought risks across China

Intensifying droughts under climatic warming are of widespread concern owing to their devastating impacts on water resources, societies and ecosystems. However, the effects of exogeneous drivers on regional droughts remain poorly understood. Using the Lagrangian method, atmospheric reanalysis data and climate projections from the Coupled Model Inter-comparison Project phase 6 (CMIP6), we show how exogenous precipitation minus evaporation (PME) deficit drives droughts across China. More specifically, we demonstrate that four distinct trajectories of such exogenous PME deficit fuel regional droughts. Three of these trajectories relate to oceanic PME deficit originating from the North Atlantic, eastern Bering Sea and Indian Ocean, and one trajectory characterizes exogenous terrestrial PME deficit from the Siberian Plateau. We show that during 1980–2020, droughts induced by exogenous PME deficit account for 45% of all droughts that occurred in China’s coastal region, and for 7% of all droughts in the northwestern regions. Under climate scenario SSP245 (SSP585), limiting warming to 1.5 °C compared to 2 °C above pre-industrial levels could avoid 60% (84%) of exogenous drought exposure. This would in turn reduce population exposure by 40% (49%), and economic exposure by 73% (66%). Our study unravels how exogenous PME deficit drives droughts in China, underscoring the role that external drivers have on regional droughts and associated future prediction.

Social feedback biases emerge during recall but not prediction and shift across the development of social anxiety.

Memory is a reconstructive process that can result in events being recalled as more positive or negative than they actually were. While positive recall biases may contribute to well-being, negative recall biases may promote internalizing symptoms, such as social anxiety. Adolescence is characterized by increased salience of peers and peak incidence of social anxiety. Symptoms often wax and wane before becoming more intractable during adulthood. Open questions remain regarding how and when biases for social feedback are expressed and how individual differences in biases may contribute to social anxiety across development. Two studies used a social feedback and cued response task to assess biases about being liked or disliked when retrieving memories vs. making predictions. Findings revealed a robust positivity bias about memories for social feedback, regardless of whether memories were true or false. Moreover, memory bias was associated with social anxiety in a developmentally sensitive way. Among adults (study 1), more severe symptoms of social anxiety were associated with a negativity bias. During the transition from adolescence to adulthood (study 2), age strengthened the positivity bias in those with less severe symptoms and strengthened the negativity bias in those with more severe symptoms. These patterns of bias were isolated to perceived memory retrieval and did not generalize to predictions about social feedback. These results provide initial support for a model by which schemas may infiltrate perceptions of memory for past, but not predictions of future, social events, shaping susceptibility for social anxiety, particularly during the transition into adulthood.

Data-driven dynamic pricing and inventory management of an omni-channel retailer in an uncertain demand environment

In recent years, omni-channel retailing has become immensely popular among both retailers and consumers. In this approach, retailers often leverage their brick-and-mortar stores to fulfill online orders, leading to the need for simultaneous decision-making on replenishment and inventory rationing. This inventory strategy presents significant complexities in traditional dynamic pricing and inventory management problems, particularly in unpredictable market environments. Therefore, we have developed a dynamic pricing, replenishment, and rationing model for omni-channel retailers using a two-level partially observed Markov decision process to visualize the dynamic process. We propose to use a deep reinforcement learning algorithm, called Maskable LSTM-Proximal Policy Optimization (ML-PPO), which integrates the current observations and future predictions as input to the agent and uses the invalid action mask to guarantee the allowable actions. Our simulation experiments have demonstrated the ML-PPO's efficiency in maximizing retailer profit and service level, along with its generalized ability to tackle dynamic pricing and inventory management problems.

Application of MaxEnt modeling to evaluate the climate change effects on the geographic distribution of Lippia javanica (Burm.f.) Spreng in Africa.

Lippia javanica is a typical indigenous plant species mostly found in the higher elevation or mountainous regions in southern, central, and eastern Africa. The ongoing utilization of the species for ethnobotanical applications and traditional uses, coupled with the changing climate, increases the risk of a potential reduction in its geographic distribution range in the region. Herein, we utilized the MaxEnt species distribution modelling to build the L. javanica distribution models in tropical and subtropical African regions for current and future climates. The MaxEnt models were calibrated and fitted using 286 occurrence records and six environmental variables. Temperatures, including temperature seasonality [Bio 4] and the maximum temperature of the warmest month [Bio 5], were observed to be the most significant determinants of L. javanica's distribution. The current projected range for L. javanica was estimated to be 2,118,457 km2. Future model predictions indicated that L. javanica may increase its geographic distribution in western areas of the continent and regions around the equator; however, much of the geographic range in southern Africa may shift southwards, causing the species to lose portions of the northern limits of the habitat range. These current findings can help increase the conservation of L. javanica and other species and combat localized species loss induced by climate change and human pressure. We also emphasize the importance of more investigations and enhanced surveillance of traditionally used plant species in regions that are acutely susceptible to climate change.

Time Series Modeling of Tuberculosis Cases in India from 2017 to 2022 Based on the SARIMA-NNAR Hybrid Model.

Tuberculosis (TB) is still one of the severe progressive threats in developing countries. There are some limitations to social and economic development among developing nations. The present study forecasts the notified prevalence of TB based on seasonality and trend by applying the SARIMA-NNAR hybrid model. The NIKSHAY database repository provides monthly informed TB cases (2017 to 2022) in India. A time series model was constructed based on the seasonal autoregressive integrated moving averages (SARIMA), neural network autoregressive (NNAR), and, SARIM-NNAR hybrid models. These models were estimated with the help of the Bayesian information criterion (BIC) and Akaike information criterion (AIC). These models were established to compare the estimation. A total of 12,576,746 notified TB cases were reported over the years whereas the average case was observed as 174,677.02. The evaluating parameters values of RMSE, MAE, and MAPE for the hybrid model were found to be (13738.97), (10369.48), and (06.68). SARIMA model was (19104.38), (14304.15), and (09.45) and the NNAR were (11566.83), (9049.27), and (05.37), respectively. Therefore, the NNAR model performs better with time series data for fitting and forecasting compared to other models such as SARIMA as well as the hybrid model. The NNAR model indicated a suitable model for notified TB incidence forecasting. This model can be a good tool for future prediction. This will assist in devising a policy and strategizing for better prevention and control.

Latitudinal pronounced variations in tintinnid (Ciliophora) community at surface waters from the South China Sea to the Yellow Sea: Oceanic-to-neritic species shift, biotic-abiotic interaction and future prediction

The oceanic-to-neritic species shift of microzooplanktonic tintinnids and their interaction with relevant abiotic variables are two crucial processes in the marine ecosystem. However, these processes remain poorly documented in China's marginal seas. In the summer of 2022, we investigated the community structure of pelagic tintinnids in surface waters from the South China Sea (SCS) to the Yellow Sea (YS), passing through the East China Sea (ECS). A number of 58 species from 23 genera were identified, with 36 and 22 species belonging to oceanic and neritic genera, respectively. The abundance proportion of oceanic and neritic genera exhibited a decreasing and increasing trend, respectively, from the SCS to YS. Furthermore, four distinctive tintinnid community groups were classified based on cluster analysis using tintinnid species and abundance data, and the position of southern Taiwan Strait was identified as the “Shift Point” for oceanic-to-neritic species dominance. The top two tintinnid species in each group showed distinct variations in body size. Additionally, multivariate biotic-abiotic statistical analyses revealed that temperature determined tintinnid species richness, while temperature, salinity, Si(OH)4, and Chl a determined tintinnid abundance. Our study provides a substantial foundation for recognizing the oceanic-to-neritic species shift of tintinnids in the China's marginal seas, and highlights the role of biotic-abiotic factors in driving biogeochemical fluxes and the potential response of microzooplankton to future climate change.

Reduction potential of ammonia emissions and impact on PM2.5 in a megacity of central China

Ammonia control has attracted attention due to the possibility for fine particles (PM2.5) mitigation. Based on past decade ammonia emissions assessments and future predictions, this study seasonally evaluated the ammonia emissions reduction potential in 2025 and 2030 in Wuhan, a Central China megacity, according to the short-term and long-term predictable policies. Furthermore, combined with the reduction potential, PM2.5 components observation and thermodynamic model, the effectiveness of implementing ammonia emission control to reduce PM2.5 by 2025 and 2030 was explored seasonally. Results indicated that the total ammonia emissions are expected to decrease by 19.6–33.9% in 2025 and 2030 under positive reduction scenarios, or increase by 8.9–11.7% in the absence of any intervention. Livestock holds the largest potential for reducing ammonia emissions accounting for 46.4–52.5% of the total. Improvement of human excrement management in rural regions also contributes a 35–37% potential. Despite the implementation of exhaust requirements, ammonia emissions from vehicles in 2030 are expected to continue to increase by 55.3% and 23.5% under the regular (S1) and enhanced (S2) reduction strategy scenarios, respectively. Seasonally, the most potential source of ammonia reduction in spring, summer and fall remains livestock. While in winter, non-agricultural sources dominate the reduction potential. Further results indicated that by ammonia control is expected to decrease PM2.5 concentration up to 5% (less than 1 μg m−3) in 2025–2030. Despite the better effectiveness in winter, ammonia control won't be an effective way to reduce PM2.5 in Central China in future, from the management policies and areal ammonia-rich conditions.

WGCNA combined with machine learning to find potential biomarkers of liver cancer.

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

Early intervention and adding effective doses of EGb761 like Ginkgo extract slow down dementia progression: insights to the neurovascular unit.

Dementia is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deterioration. Pharmacological interventions play a crucial role in managing dementia symptoms and potentially slowing down disease progression. This study aimed to investigate the impact of pharmacological interventions, including acetylcholinesterase inhibitors (AChEIs), memantine, and Gingko extract, on the progression of dementia, with a specific focus on mild cognitive impairment (MCI), Alzheimer's disease (AD), and non-Alzheimer dementias. A total of 547 participants out of 3,547 cases in a specific dataset followed by the same author, including healthy controls, individuals with MCI, AD, and non-Alzheimer dementias, were included in this study. The follow-up duration was up to 211 months, allowing for a minimum 3 visits comprehensive assessment of disease progression. The treatment approaches included AChEIs, memantine, and combination therapy, with variations in the starting time for these treatments based on the dementia type. The use of AChEIs and memantine showed efficacy in improving cognitive function and overall function in individuals with MCI, AD, and non-AD dementias. Combination therapy EGb761 like Gingko extract with AChEIs and/or Memantine demonstrated a slower progression compared to AChEIs alone in individuals with prodromal dementia (MCI) and AD. The starting time for memantine and combination therapy was earlier in non-AD dementia cases compared to AD dementia cases and prodromal dementia. Pharmacological interventions, particularly the use of AChEIs and memantine, can have a positive impact on cognitive function and overall function in individuals with dementia. The combination of AChEIs with EGb761 like Gingko extract may provide additional benefits in slowing down disease progression in AD cases. Early recognition and accurate classification of MCI subtypes are crucial, and the use of EGb761 like Gingko extract is recommended for symptomatic treatment. Future personalized risk predictions based on biomarker constellations may further enhance the multi-target treatment approaches of MCI and different dementia types.

Design of graphene-based terahertz absorber and machine learning prediction model

In today's revolution of artificial intelligence, machine learning (ML) has come up with providing most rapid, accurate solution towards many complicated design problems. In this paper, a regression-based machine learning model has been developed, trained, and tested to forecast the performance of the Terahertz absorber. Different parameter variations of the dual circular ring Graphene FSS absorber viz., periodicity, thicknesses and radius of circular rings have been performed to attain maximum absorption of 99.9 % at center frequency 10.5 THz and bandwidth 1 THz (10.0 THz – 11.0 THz). The efficiency of nine different regression models (Keras regressor, Histogram gradient regressor, adaboost regressor, gradient boosting regressor, random forest regressor, decision tree regressor, k neighbours regressor, ridge regression, and linear regression) were tested against accurately predicting the absorption values and their performances were compared using R2 score and RSME. The study yielded very good R2 scores (near to 1.0) in case of random forest regression, thereby, demonstrating its effectiveness for future absorptivity prediction. The developed model considerably reduces time and effort in accurate prediction of the design parameters for THz absorber for any given absorptivity.

Rendering protein mutation movies with MutAmore

BackgroundThe success of AlphaFold2 in reliable protein three-dimensional (3D) structure prediction, assists the move of structural biology toward studies of protein dynamics and mutational impact on structure and function. This transition needs tools that qualitatively assess alternative 3D conformations.ResultsWe introduce MutAmore, a bioinformatics tool that renders individual images of protein 3D structures for, e.g., sequence mutations into a visually intuitive movie format. MutAmore streamlines a pipeline casting single amino-acid variations (SAVs) into a dynamic 3D mutation movie providing a qualitative perspective on the mutational landscape of a protein. By default, the tool first generates all possible variants of the sequence reachable through SAVs (L*19 for proteins with L residues). Next, it predicts the structural conformation for all L*19 variants using state-of-the-art models. Finally, it visualizes the mutation matrix and produces a color-coded 3D animation. Alternatively, users can input other types of variants, e.g., from experimental structures.ConclusionMutAmore samples alternative protein configurations to study the dynamical space accessible from SAVs in the post-AlphaFold2 era of structural biology. As the field shifts towards the exploration of alternative conformations of proteins, MutAmore aids in the understanding of the structural impact of mutations by providing a flexible pipeline for the generation of protein mutation movies using current and future structure prediction models.

Visualization with Prediction Scheme for Early DDoS Detection in Ethereum.

Blockchain technologies have gained widespread use in security-sensitive applications due to their robust data protection. However, as blockchains are increasingly integrated into critical data management systems, they have become attractive targets for attackers. Among the various attacks on blockchain systems, distributed denial of service (DDoS) attacks are one of the most significant and potentially devastating. These attacks render the systems incapable of processing transactions, causing the blockchain to come to a halt. To address the challenge of detecting DDoS attacks on blockchains, existing visualization schemes have been developed. However, these schemes often fail to provide early DDoS detection since they typically display only past and current system status. In this paper, we present a novel visualization scheme that not only portrays past and current values but also forecasts future expected system statuses. We achieve these future predictions by utilizing polynomial regression with blockchain data. Additionally, we offer an alternative DDoS detection method employing statistical analysis, specifically the coefficient of determination, to enhance accuracy. Through our experiments, we demonstrate that our proposed scheme excels at predicting future blockchain statuses and anticipating DDoS attacks with minimal error. Our work empowers system managers of blockchain-based applications to identify and mitigate DDoS attacks at an earlier stage.

An Analytical Study Predicting Future Conditions and Application Strategies of Concrete Bridge Pavement Based on Pavement Management System Database

South Korea is implementing various policies to address the aging of infrastructures and improve road infrastructure management. Moreover, numerous research projects aiming at the development of necessary technologies for the proper implementation of these policies are underway. This study specifically aims to overcome existing problems in bridge pavement maintenance, such as the inaccuracy of future condition predictions and the selection of incorrect evaluation indicators. Our goal is to provide a new approach for the improved management of the bridge pavement management system (BPMS). To address the issues of accuracy in future condition prediction and evaluation indicator selection within the existing maintenance system, we utilized particle filtering, a Kalman filter method among machine learning techniques. This method allows for the prediction of future conditions, based on the nonlinearly collected bridge pavement conditions within BPMS. Furthermore, we proposed a systematic bridge pavement management strategy. This strategy utilizes traffic volume (ESALs; equivalent single axle loadings), a factor that can influence the future condition of bridge pavement, in correlation with the future condition predicted through particle filtering within BPMS.

Comparison of the air change per hour measured over four seasons in the residential buildings of the urban, rural, and industrial areas of South Korea: K-IOP Study

Air infiltration, calcuated by air changes per hour (ACH) is a key factor in assessing the potential amount of air borne pollutants moving from outdoor into indoor spaces. We measured the natural ACH through fall, winter, spring, and summer (2 weeks/season), in 81 nonsmoking elderly houses located in urban (n = 29), industrial (n = 26) and rural (n = 26) areas from 2021 to 2022 consecutively. Indoor CO2 data measured at dawn (01:00 to 05:00 a.m.) over four season was used to estimate the ACH. Moreover, morning ACHs were also calculated from the CO2 level monitored in the morning, when the levels were dropped significantly with opening a window during fall, winter, and spring. The ACH at dawn over the four seasons ranged from 0.02 to 0.03,while that in the morning with opening a window was 0.2 to 0.3. Our multivariate regression models demonstrated that the difference in CO2 concentration during dawn was positively associated with the ACH change after adjusting for seasonality. In addition, after controlling for the study area, ACH was approximately 50 % higher during summer than during the other seasons (p < 0.05). This study elucidates the seasonal and regional distributions of ACH; a determination of these patterns may further contribute to future simulation or prediction studies assessing the associations among indoor air quality, activity patterns and ventilation practices among Korean elderly population.

In medias res: the diminishing of historical continuity in modern thought

Innovation and future predictions are discussed as the main goals of modern technology. Living in this empirical, modern world set on the future has the possibility of diminishing the value of historical continuity as observation and outcome-based theory take precedence over contemplation and tradition. It is proposed that the forgetfulness of modernity creates a stilted perception of time and thought which results in a dissonance between the perceiving subject and their surroundings. This is exacerbated by digital media as it mostly frames information as an attractive or trending source of amusement rather than as a possibility for edification. The result of this dissonance between the subject and their surroundings and the influence of digital media can be seen in the thoughtless or repetitive action and the abdication of action altogether in favour of escape from reality. This is problematised in as far constructive action for the well-being of the individual cannot be sustained in terms of the current engagement with digital technology.&#x0D; &#x0D; Drawing on thinkers such as Connerton (1989, 2009), Davidson (2004), Habermas (1987, 1989), a discussion follows regarding how the diminishing historical continuity in thought can lead to the manipulation and a lack of rationality discussed in Horkheimer’s and Adorno’s culture industry thesis. To adopt the approach of continuity as embodied in the phrase in medias res(in the midst of things) in interpretation, rather than an observation assuming novel activity, may bring an alternative consideration for how modern technology (specifically the digital) can be used to assist the individual in taking contextual action rather than trying to escape action altogether while reframing the potential of digital technology towards a constructive, achievable standing rather than resigning it to a problematic system of distraction and degradation of thought.&#x0D;

Evaluating polygenic risk scores for predicting cardiometabolic traits and disease risks in the Taiwan Biobank

Type 2 diabetes (T2D) and hypertension are common comorbidities and, along with hyperlipidemia, serve as risk factors for cardiovascular diseases. This study aimed to evaluate the predictive value of polygenic risk scores (PRSs) on cardiometabolic traits related to T2D, hypertension, and hyperlipidemia and the incidence of these three diseases in Taiwan Biobank samples. Using publicly available, large-scale Genome-Wide Association Studies (GWAS) summary statistics, we constructed cross-ethnic PRSs for T2D, hypertension, body mass index (BMI), and 9 quantitative traits typically used to define the three diseases. A composite PRS (cPRS) for each of the 9 traits was constructed by aggregating the significant PRSs of its genetically correlated traits. The associations of each 9 trait at baseline as well as the change of trait values during a 3 to 6-year follow-up period with its cPRS were evaluated. The predictive performances of cPRSs in predicting future incidences of T2D, hypertension, and hyperlipidemia were assessed. The cPRSs had significant associations with baseline and changes of trait values in 3-6 years and explained a higher proportion of variance for all traits than individual PRSs. Furthermore, models incorporating disease-related cPRSs, along with clinical features and relevant trait measurements achieved area under the curves (AUC) values of 87.8%, 83.7%, and 75.9% for predicting future T2D, hypertension, and hyperlipidemia in 4 to 6 years, respectively. This study revealed the complex genetic correlation structures of quantitative traits associated with the three diseases and underscores the potential of PRSs to improve future prediction models for T2D, hypertension, and hyperlipidemia.

Genuine Dirac Half-Metals in Two-Dimensions.

When spin-orbit coupling (SOC) is absent, all proposed half-metals with twofold degenerate nodal points at the K (or K') point in 2D materials are classified as "Dirac half-metals" owing to the way graphene is utilized in the earliest studies. Actually, each band crossing point at the K or K' point is described by a 2D Weyl Hamiltonian with definite chirality; hence, it should be a Weyl point. To the best of its knowledge, there have not yet been any reports of a genuine (i.e., fourfold degenerate) 2D Dirac point half-metal. In this work, using first-principles calculations, it proposes for the first time that the 2D d0 -type ferromagnet Mg4 N4 is a genuine 2D Dirac half-metal candidate with a fourfold degenerate Dirac point at the S high-symmetry point, intrinsic magnetism, a high Curie temperature, 100% spin polarization, topology robust under the SOC and uniaxial and biaxial strains, and spin-polarized edge states. This work can serve as a starting point for future predictions of intrinsically magnetic materials with genuine 2D Dirac points, which will aid the frontier of topo-spintronics research in 2Dsystems.

H4MER: Human 4D Modeling by Learning Neural Compositional Representation with Transformer.

Despite the impressive results achieved by deep learning based 3D reconstruction, the techniques of directly learning to model 4D human captures with detailed geometry have been less studied. This work presents a novel neural compositional representation for Human 4D Modeling with transformER (H4MER). Specifically, our H4MER is a compact and compositional representation for dynamic human by exploiting the human body prior from the widely used SMPL parametric model. Thus, H4MER can represent a dynamic 3D human over a temporal span with the codes of shape, initial pose, motion and auxiliaries. A simple yet effective linear motion model is proposed to provide a rough and regularized motion estimation, followed by per-frame compensation for pose and geometry details with the residual encoded in the auxiliary codes. We present a novel Transformer-based feature extractor and conditional GRU decoder to facilitate learning and improve the representation capability. Extensive experiments demonstrate our method is not only effective in recovering dynamic human with accurate motion and detailed geometry, but also amenable to various 4D human related tasks, including monocular video fitting, motion retargeting, 4D completion, and future prediction. The video demos are in the project homepage: https://boyanjiang.github.io/H4MER/.

Faculty perceptions and use of e-learning resources for medical education and future predictions

Purpose: This study aims to investigate medical faculties’ perceptions and current practice of using e-learning resources, needs and suggestions for more effective use of such resources, and future directions of e-learning in medical education.Methods: This descriptive study was conducted on full-time faculty members who were registered users of the e-learning portal of the consortium of Korean medical schools. Participants were invited to an online survey containing 45 items that addressed their perceptions and use of e-learning resources, and their predictions of future use. Descriptive analysis and reliability analysis were conducted as well as a thematic analysis of qualitative data.Results: Ninety faculty members from 31 medical schools returned the questionnaires. Participants positively perceived e-learning resources and that they predicted their use would become increasingly popular. Still, only half of the respondents were using e-learning resources for teaching and agreed that they were willing to share their e-learning resources. Our study illustrates several barriers inhibit faculty use and sharing of e-learning resources, and a need for a more comprehensive, better-organized resource repository. Participants also pointed out the needs for more resources on multimedia assessment items, clinical videos, and virtual patients.Conclusion: Our study sheds light on medical faculty needs for institutional support and faculty development programs on e-learning, and institutional policies that address faculty concerns regarding ownership, intellectual property rights, and so forth on creating and sharing such resources. Collaborations among medical schools are suggested for creating a better organized around learning outcomes and more comprehensive repository of resources.

SOLAR CYCLE STRUCTURE AND STRUCTURE OF ACTIVITY CYCLES AT LATE TYPE STARES

It is shown that the use of a description of the solar cycle that takes into account the odd zonal harmonic of the solar magnetic field allows us to deepen our knowledge of two important aspects of solar activity: first, to clarify and expand predictions for the near future of the evolution of the cyclic activity of the Sun, second, to formulate a program for monitoring the spectrophotometric characteristics of the radiation of stars like the Sun, aimed at obtaining new information about their magnetic fields.