Innovative Trends Research Articles

Non Parametric Trend Analysis of Tea Cultivation in India

With the second-largest producer and the highest consumer in the world, tea is one of the most important beverage crops in India. Considering the importance of tea industry to India’s national economy, a trend analysis of tea cultivation has been done here. Using data on the acreage, production, and productivity of tea in India from 1960 to 2021, and following a non-parametric approach Modified Mann-Kendall test has been used to detect the trend in the data. Addition to this, Sen’s slope estimator and innovative trend analysis have been performed to analyze the magnitude of trend whereas the Pettitt Mann-Whitney test has been used to detect the single change point. The findings of tests revealed that the area, production and productivity of tea in India have significantly increasing trend over the years.

Analysis of Changes in Water Flow after Passing through the Planned Dam Reservoir Using a Mixture Distribution in the Face of Climate Change: A Case Study of the Nysa Kłodzka River, Poland

Climate change and extreme weather events have the potential to increase the occurrences of flooding and hydrological droughts. Dam reservoir operation can mitigate or aggravate this impact. This study aims to evaluate the influence of the planned Kamieniec Ząbkowicki dam reservoir on the flow patterns of the Nysa Kłodzka river in the context of changing hydrological conditions and climate change. In the study, a 40-year observational series of hydrological data was used to simulate changes in water flow through the river valley in a numerical model. This simulation was conducted both for the natural river valley and for the same river valley but with the added reservoir dam. Flow simulations revealed that dam operation increased downstream flow values, reducing variability in extreme high-flow events. Addition, the mixture log-normal distribution shows that the operation of the dam resulted in a reduction in the variability of both low flows and extreme high-flow events. Furthermore, the model illustrates that moderate-flow conditions remain relatively stable and similar before and after dam construction. The Mann–Kendall trend test, Sen slope trend test and Innovative Trend Analysis indicated that the dam had a significant impact on flow trends, reducing the negative trend. This hydrotechnical structure stabilizes and regulates flows, especially in response to climate-induced changes. These findings highlight the effectiveness of the dam in mitigating flood risk and supporting water resource management. It is essential to consider the role of the dam in adapting to changing hydrological conditions influenced by climate change. For practical application, efficient flow regulation by reservoir administration is crucial.

Trend Assessment by the Mann-Kendall Test and the Innovative Trend Analysis Method (North-West Algeria)

Trend Assessment by the Mann-Kendall Test and the Innovative Trend Analysis Method (North-West Algeria)

Innovative trend analysis for evaluation of groundwater storage in Baitarani River Basin

Innovative trend analysis for evaluation of groundwater storage in Baitarani River Basin

Quantitative exploration of the innovative trend method for evapotranspiration and its sensitivity to climatic variables: The case study of Southeast Vietnam

Quantitative exploration of the innovative trend method for evapotranspiration and its sensitivity to climatic variables: The case study of Southeast Vietnam

Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region

Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.

Integrated statistical and graphical non-parametric trend analysis of annual and seasonal rainfall in the Shire River Basin, Malawi

This study integrates purely statistical methods of Mann–Kendall (MK) and Spearman rho (SMR) with statistical-graphical methods of Onyutha trend (OT) test and innovative trend analysis (ITA) to examine annual and seasonal rainfall variations at 12 stations across the Shire River Basin (SRB) during 1976–2005. The results reveal a general decreasing trend for annual rainfall throughout the basin. At seasonal scale, the following trends were observed: an increase for the December-January–February (DJF) season, especially in the southern portion of the basin; a decrease for the March–April-May (MAM) and June-July–August (JJA) seasons; and inconclusive results for the September–October-November (SON) season. Despite nearly all time series indicating consistent trend direction as established by the four tests, the ITA identified the most significant rainfall patterns on both annual and seasonal basis. The performance abilities for the MK, SMR, and OT tests demonstrated the closest agreement at the verified significant level. In addition to the monotonic trend results obtained statistically, sub-trends are visually distinguished using the graphical features of the OT and ITA approaches. For the former, changes are seen as step jumps in the mean of the data, and for the latter, trends regarding high and low rainfall clusters are evaluated, hence offering more details regarding rainfall variability, such as the SRB’s sensitivity to both floods and droughts. Thus, the completely different aspects offered by the visually oriented methods complement the purely monotonic trend detection methods.

Assessment of the Spatiotemporal Changes in the Extreme Precipitation Climate Indices over the Chungcheong Region of South Korea during 1973–2020

This study analyzed the changes and trends in twelve extreme precipitation-based climate indices obtained using daily data from 10 synoptic stations in the Chungcheong region of South Korea during the 1973–2020 period. The climate indices were used to assess the trends in the extreme precipitation characteristics of duration, frequency, and intensity using the innovative trend analysis (ITA) method. The results of the ITA were further compared with two other non-parametric test methods such as Mann–Kendall (MK) and Spearman’s rho (SR). The results showed that most stations exhibited significant increasing trends in all the investigated climate indices at a 95% confidence level as indicated by the ITA method, with only a few stations indicating significant decreasing trends in R95p, R99p, Rx3day, and Rx5day. The sub-trend analysis further revealed the dominance of neutral behavior around the low-value cluster, especially for the extreme precipitation duration. At the same time, increasing trends dominate the high-value cluster at most stations. Meanwhile, only R10mm, R99p, and R95p exhibited monotonic trends in the Boeun and Seosan stations, respectively. Further, the ITA exhibited superior performance over the MK and SR methods by indicating the presence of more significant trends in the climate indices at most stations. The distribution of the extreme precipitation indices for duration, frequency, and intensity indicate the pronounced risk of flood conditions around the north–central and some parts of southern regions, while the western region indicates a potential drought risk, which could greatly impact the water resources and consequently agricultural activities in the study area. The results of this study provide essential information for addressing the climate-related problems of water resource management and agriculture in the study area and other related climatic regions.

Analysis of Policy Applications of Artificial Intelligence in Education

Along with the continuous improvement of data processing and application capabilities, artificial intelligence has gradually penetrated into all areas of human production and life, driving human society into the age of artificial intelligence. At present, AI education is the trend of education innovation and development. The rise of artificial intelligence brings hope for the innovation and development of the education field. As an important pillar for the realization of a new country of science and technology and a strong country of talents, the state pays special attention to the requirements for the transformation of high-quality education by artificial intelligence when formulating policies and plans. The birth of artificial intelligence brings more possibilities for the modernization of education, and plays a solid foundation and promotes innovation in the fields of basic education, higher education and vocational education. The purpose of this paper is to analyze the application of the policy of Artificial Intelligence + Education in the field of education. It explains the strategic significance of the rise of AI in education, and explores the necessary means to collaboratively manage the risks of AI in education.This study will rationally analyze the AI educational applications and explore targeted risk avoidance strategies to safeguard the deep integration of AI and education.

Research on the Spatial-Temporal Disparity and Convergence Characteristics of Innovation and Economic Development in China: Based on Functional Data Analysis

Unbalanced and insufficient development is a prominent problem in China’s period of economic transformation, and an accurate grasp of the current situation and evolutionary trend of innovation and economic development is of great significance in finding a solution and promoting regional coordinated development. This paper introduces the framework of functional data analysis to the study of both innovation and economic development in China, examining its spatial dynamic distribution and convergence characteristic in greater depth. Empirical results revealed the following. (1) Significant regional differences exist in the absolute level of China’s innovation development, with a balanced improvement in the development speed and growth potential. Significant differences exist in the absolute level and speed of economic development, but not in the potential energy of development as reflected by acceleration. (2) The σ(t) function of nationwide and regional innovation development shows a downward trend. There is σ(t) convergence in economic development nationwide and in the northeastern, eastern, and western regions, but the regional disparities in economic development within the six central provinces have not yet been effectively mitigated. (3) Nationwide and regional innovation development are in a state of β(t) convergence. There is significant β(t) convergence in the nationwide economic development, with a transition point from divergence to convergence in the northeastern, eastern, and western regions. However, the β(t) function of economic development in the central region shows a region-wide divergence. The findings of this paper have important policy implications for recognizing the balance and disparities in innovation and economic development among regions in China and promoting coordinated regional development.

A worthy example of service to the cause (to mark the centenary of B. T. Panov’s birth)

The paper considers the research and pedagogical activities of Boris Trofimovich Panov (1923–2007), a famous Russian linguo-methodologist of the mid-to-late twentieth century. The relevance of the study is due to the scientist’s significant contribution to the development of Russian language teaching methodology. Moreover, B. T. Panov’s developments can be effectively used in accordance with the innovative trends of the modern educational process and the requirements of the Federal State Educational Standard. His most significant teaching materials and study guides which address the theoretical and practical issues of teaching Russian in secondary schools are considered from a modern perspective. The research results are based on the analysis of scientific and scientific-methodological sources, the descriptive method, and methodological experience generalisation. The paper concludes that B. T. Panov’s methodological writings remain relevant since mastering new things is impossible without reliance on traditional methodological knowledge that ensures the continuity of scientific development.

The nexus between circular economy innovation, market competitiveness, and triple bottom lines efficiencies among SMEs: evidence from emerging economies

Recently, the trend of circular economy innovation (CEI) drive SMEs to initiate more sustainable practices to attain market competitiveness but rare attention has been paid in emerging economies. Hence, this study plans to explore the impacts of CEI on the triple bottom lines (TBL) efficiencies in the light of market competitiveness among the production SMEs in emerging economies. The study complied data by using a survey-based technique in Pakistan, Malaysia, and China. With a sample of 306 for each segment, data were evaluated with PLS-SEM to clarify results. The findings reveal that CEI has positive significant effects on the market competitiveness and TBL efficiencies among the production SMEs in emerging economies. The findings also clarify that market competitiveness mediates the relationship between CEI and the TBL efficiencies. The findings elaborate the theoretical foundations for environmental-based production SMEs to formulate more sustainable strategies in the light of CEI to gain market competitiveness. It also clarifies the understandings of policy makers and environmental regulators by providing a novel precursor to frame the environment-based TBL guidelines for SMEs. It adds to the UN sustainability agenda by elevating the role of CEI as a novel domain among emerging economies grounded on the resource-based view theory.

Application of the ITA approach to analyze spatio-temporal trends in monthly maximum rainfall categories in the Vu Gia-Thu Bon, Vietnam

This study aims to investigate the trend behavior of monthly maximum in daily rainfall categories in the Vu Gia-Thu Bon river basin located in central Vietnam. Daily maximum rainfall series from 12 rainfall stations for the period 1979–2018 were utilized to characterize six categories of the intensity of daily maximum rainfall: light (0–4 mm/day, category A), mild-moderate (4–16 mm/day, category B), moderate-heavy (16–32 mm/day, category C1), heavy (32–64 mm/day, category C2), heavy-torrential (64–128 mm/day, category D1), and torrential (≥ 128 mm/day, category D2). The new approach of the Innovative Trends Analysis was then applied to the six classified categories. The results revealed that category B had a dominant increasing trend (32% of rain events) for all the stations in January (5.85%) and February (3.44%). In March and April, category A was dominant with 45% and 20%, respectively. In July, category C1 was dominant with 25%, while in August and September, category C2 prevailed over all stations with 45% (all stations) and 20%, respectively. The categories D1 and D2 were observed at all stations in December and November, with 26% and 31% of events, respectively. These results indicate an increasing trend in the categories B, C1, C2, and D1.

Advancements in Flame-Retardant Systems for Rigid Polyurethane Foam.

The amplified employment of rigid polyurethane foam (RPUF) has accentuated the importance of its flame-retardant properties in stimulating demand. Thus, a compelling research report is essential to scrutinize the recent progression in the field of the flame retardancy and smoke toxicity reduction of RPUF. This comprehensive analysis delves into the conventional and innovative trends in flame-retardant (FR) systems, comprising reactive-type FRs, additive-type FRs, inorganic nanoparticles, and protective coatings for flame resistance, and summarizes their impacts on the thermal stability, mechanical properties, and smoke toxicity suppression of the resultant foams. Nevertheless, there are still several challenges that require attention, such as the migration of additives, the insufficient interfacial compatibility between flame-retardant polyols or flame retardants and the RPUF matrix, and the complexity of achieving both flame retardancy and mechanical properties simultaneously. Moreover, future research should focus on utilizing functionalized precursors and developing biodegradable RPUF to promote sustainability and to expand the applications of polyurethane foam.

Outlook of the Trend of Technological Innovation on China's Employment Industry

This paper focuses on the impact of technological development on the job market, covering a wide range of areas including primary, secondary and tertiary industries. In the context of technological development, some repetitive and low-skilled jobs will be replaced by automation, but at the same time, more high-skilled and high-value-added jobs will be created. The paper also analyzes the role of technological innovation in driving the job market in China and provides a vision of the future job market - a future where work patterns will become increasingly flexible, including freelance and remote work; where vocational training and education will place more emphasis on practice and innovation; and where the job market will become more international and diverse, requiring people with cross-cultural communication skills and multilingual skills.

Spatio‐temporal analysis of trends in annual maximum rainfall in the North‐West of Algeria: Comparative analysis of recent and old non‐parametric methods

AbstractThis article examines the spatial variability of extreme precipitation trends in northwestern Algeria (Macta) and compares the results obtained from the four recent and old non‐parametric methods. A dataset of annual maximum precipitation consisting of 41 observation years (1970–2010) and 41 rain gauges was used. The results of the four old and new methods used to detect trends, Mann–Kendall (MK), Bravais–Pearson (BP), Spearman (SR) and innovative trend analysis (ITA), show good agreement. They revealed that a decrease in the trend of annual maximum precipitation was detected during the first period (1970–1992) with −44% (MK), −61% (BP), −68% (SR) and −76% (ITA). On the other hand, in the second period (1993–2010), a total shift occurred in which a significant increase in annual maximum precipitation trends was observed with +63% (BP), +34% (MK and SR) and +93% (ITA). These results show the ability of ITA to detect partial trends that the other three tests do not allow. Our results allow decision‐makers to properly design adaptation strategies in the face of the intensification of these extreme events.

Environmental commitments and Innovation in China's corporate landscape: An analysis of ESG governance strategies

This study delves into the nexus between corporate ESG commitments—with a spotlight on environmental considerations—and innovation trends in China's corporate sector, leveraging data from Bloomberg's extensive database encompassing over 5102 companies. Our objective was to discern if and how environmental components within the ESG framework serve as precursors to a company's innovative inclinations. Adopting a quantitative methodology, we employed Bayesian Linear Regression and Neural Networks to unearth patterns. Key findings reveal that companies with pronounced environmental commitments within their ESG strategies are not only more innovative but also align more closely with global sustainability benchmarks. Moreover, the role of transparent governance processes in bolstering innovation was evident, highlighting the significance of corporate accountability. The research further underscores the synergy between strategic diversification and innovation, suggesting that an optimal balance in diversification strategies augments a firm's innovative prowess. By integrating traditional accounting insights with cutting-edge data analytics, our study offers a holistic perspective on the environmental and financial ramifications of ESG-driven innovations. This research holds profound implications for academia, industry stakeholders, and policymakers, emphasizing the strategic role of environmental commitments in shaping sustainable and innovative corporate trajectories.

Flash-flood susceptibility modelling in a data-scarce region using a novel hybrid approach and trend analysis of precipitation

ABSTRACTRecently, the incidence of heavy rainfall events and associated flash floods have encouraged us to investigate long-term trends in extreme rainfall and flash flood vulnerability mapping. Thus, in this study, a hybrid model was designed by integrating weight of evidence and Naïve Bayes (WOE-NB) to identify areas in Uttarakhand prone to flash floods, and we compared its ability with that of AdaBoost. Furthermore, the significance of long-term rainfall trends was evaluated using Mann–Kendall, modified Mann-Kendall, and innovative trend analysis (ITA), and extreme rainfall events were examined for 51 years (1970–2020). Results showed the WOE-NB and AdaBoost had acceptable goodness of fit (area under the curve = 0.969 and 0.973, respectively). Moreover, ITA can identify some important patterns based on on-trend results that other tests cannot. The return period revealed about 97.54% of the flash floods were caused by normal rainfall, with 2.45% being caused by severely abnormal rainfall.

Trend detection of annual precipitation of Karnataka, India during 1951–2020 based on the innovative trend analysis method

Trend detection of annual precipitation of Karnataka, India during 1951–2020 based on the innovative trend analysis method

Combined use of graphical and statistical approaches for rainfall trend analysis in the Mae Klong River Basin, Thailand

Abstract The main purpose of this paper was to investigate the monthly, seasonal, and annual rainfall variability in the Mae Klong River Basin in Thailand using the Mann–Kendall (MK) test, Sen's slope method, Spearman's Rho (SR) test, and the innovative trend analysis (ITA) method. The monthly rainfall data of eight stations for the period 1971–2015 were used for trend analysis. Datasets with significant serial correlation were corrected by the trend-free pre-whitening (TFPW) approach for statistical methods. The MK test showed increasing rainfall trends for five out of eight stations in the dry season while 50% of stations indicated increasing trends in the wet season. On an annual scale, 75% of the stations exhibited increasing rainfall trends. The results of the SR test were in line with the MK test for seasonal and annual rainfall. The ITA method showed comparable findings with those of the statistical methods. For the entire basin, trend analysis found increasing rainfall on both seasonal and annual scales by all the tests. The findings of this study could benefit water supply and management, drought monitoring, agricultural production activities, and socioeconomic development in the Mae Klong River Basin in the future.