Reversal Of Trend Research Articles

Recent Cereal Phenological Variations under Mediterranean Conditions

This study analyzes the temporal patterns of rainfed cereal phenology extracted from the GIMMS NDVI3g dataset in the main cereal-growing regions under a Mediterranean climate in Spain, Portugal, France and Italy during the period 1982–2022. The series before and after the beginning of the 21st century were analyzed separately. Phenological parameters were extracted using the modified dynamic threshold method, and their trends were analyzed. Correlation analyses were performed to study the relationships among these parameters and to analyze the influence of hydroclimatic variables on the start (SOS) and end (EOS) of the growing season. Results showed a temporal reversal in phenological trends between both study periods, coinciding with the global warming hiatus. In the first period (1982–2002), SOS and EOS advanced (−7.5 and −3.1 days, respectively), and the length of growing season (LOS) increased. However, during the second stage (2003–2022), SOS and EOS were delayed (7.5 and 1.7 days, respectively), and LOS decreased. Similar dynamics were observed for the influence of the hydroclimatic variables on SOS and EOS, stronger in the first period and weaker in the second. This study provides valuable information on the phenological dynamics of rainfed cereals that may be useful for their management and planning in climate change scenarios.

Regional divergent evolution of vegetation greenness and climatic drivers in the Sahel-Sudan-Guinea region: nonlinearity and explainable machine learning

IntroductionThe vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region.MethodsThis study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework.ResultsThe study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region.

Long-term variations and trends of tropospheric and ground-level NO2 over typical coastal areas

Tropospheric and ground-level levels of nitrogen dioxide (NO2) may have changed due to environmental policies and technological changes. Here, multiyear ground-based measurement (2014–2022) and satellite-derived retrieval (October 2004 to May 2023) datasets of NO2 concentrations are used to study the long-term ground-level and tropospheric NO2 patterns and trends at four time scales (interannual, quarterly, monthly, and hourly) over the three regions with the highest global NO2 concentrations. Several tropospheric NO2 hotspots are observed over industrialized and highly populated coastal zone areas. Over the past 18 years, these hotpots have generally decreased due to the implementation of strong and effective environmental protection policies. A significant trend reversal was observed in 2011 in East-central China. The maximum values of tropospheric NO2 occurred in winter over East-central China, Western Europe and East-central USA, with concentrations of 13.010, 4.243 and 4.149 × 1015 molecules cm−2, respectively. The Bohai Sea exhibited disproportionately high tropospheric NO2 levels, suggesting it as a significant contributor to regional nitrogen pollution. Ground-level NO2 shows similar temporal patterns to tropospheric concentrations, with over 82.8 % of sites in East-central China exceeding 30 μg m−3, compared to 12.8 % in Western Europe and 3.8 % in East-central USA. The analysis of diurnal variations in ground-level NO2 concentrations shows a significantly decrease during the daytime compared to nighttime, reflecting that the peak occurs at approximately 08:00 and 20:00 LT, while the lowest concentrations at around 12:00 LT. These insights clarify the impacts of environmental policies on NO2 levels and identify critical areas for targeted pollution control, enhancing our understanding of NO2 dynamics in high-pollution regions globally.

Anthropogenic forcings reverse a simulated multi-century naturally-forced Northern Hemisphere Hadley cell intensification

The Hadley circulation plays a central role in determining the location and intensity of the hydrological cycle in tropical and subtropical latitudes. Thus, the human-induced historical and projected weakening of the Northern Hemisphere Hadley circulation has considerable societal impacts. Yet, it is currently unknown how unparalleled this weakening is relative to the response of the circulation to natural forcings in past centuries. Here, using state-of-the-art climate models, we show that in contrast to the recent and future human-induced Hadley circulation weakening, natural forcings acted to intensify the circulation by cooling the climate over the last millennium. The reversal of a naturally-forced multi-centennial trend by human emissions highlights their unprecedented impacts on the atmospheric circulation. Given the amplifying effect of natural forcings on the Hadley circulation, our analysis stresses the importance of adequately incorporating natural forcings in climate model projections to better constrain future tropical climate changes.

Chlorogenic acid attenuates tet (X)-mediated doxycycline resistance of Riemerella anatipestifer.

The increasing resistance of R. anatipestifer has posed a significant threat to the poultry industry in recent years. The tet gene is the primary determinant of tetracycline resistance in numerous bacteria, and the enzyme modification gene tet(X) is predominantly detected in tetracycline-resistant R. anatipestifer strains. In this study, we evaluated the susceptibility of both the standard strain and clinical isolates of R. anatipestifer to doxycycline. And the expression levels of tet(X), tet(A), and tet(O) genes were detected. To assess drug susceptibility, shuttle plasmids were constructed to transfer the tet(X) gene into the standard strain of R. anatipestifer followed by treatment with chlorogenic acid. The results revealed that the minimum inhibitory concentration of doxycycline for the standard strain was 0.25μg/mL, whereas it exceeded 8μg/mL for the clinical isolates. Furthermore, there was a significant upregulation observed in expression levels of tet(X), tet(A), and tet(O) genes among induced strains. Interestingly, when transferring the tet(X) gene into the standard strain, its sensitivity to doxycycline decreased; however, MIC values for chlorogenic acid remained consistent between both standard and drug-resistant strains of R. anatipestifer. Moreover, we made a surprising discovery that screening passage with chlorogenic acid resulted in increased sensitivity of R. anatipestifer to doxycycline. Further analysis demonstrated a reversal in expression trends among three differentially expressed genes within induced drug resistance group after intervention with chlorogenic acid. The main objective behind this study is to investigate both killing effect exerted by chlorogenic acid on drug-resistant R. anatipestifer as well as its regulatory impact on drug resistance genes. This will provide novel insights and theoretical basis towards development of chlorogenic acid as a promising drug for treatment and control of drug resistance in R. anatipestifer.

Fiscal Supermultiplier and Endogenous Money in the United States: The COVID-19 Pandemic vs. the Global Financial Crisis*

ABSTRACT The paper examines different real-monetary dynamics and policy responses during the COVID-19 pandemic (C19P) and the Global Financial Crisis (GFC) in the United States through the lens of the Sraffian Supermultiplier model and the endogenous money approach. On the one hand, the C19P triggered a relatively deeper contraction in GDP, but its recovery was relatively faster and more robust. The ‘V’ shaped recovery from the C19P significantly contrasted the ‘L’ shaped trajectory of the post-GFC period. On the other hand, the money supply (M2) grew relatively more, and the monetary base expanded relatively less during the C19P. To explain these different real-monetary dynamics, a theoretical framework is developed and analyzed empirically for the United States. The theoretical model and the empirical analysis suggest that money supply is endogenously determined by creditworthy demand derived from the multiplier-accelerator effect of non-capacity generating autonomous demand, in which government spending plays a key role. Therefore, the absence of full hysteresis after the C19P shock had nothing to do with a traditional neoclassical trend reversal mechanism but with the fiscal supermultiplier effect of the extraordinary pandemic-related government spending.

Behavioral Finance: The Impact of Investor Expectation on Financial Decision-Making

Behavioral finance challenges traditional financial theories by questioning the assumption of investor rationality, highlighting the influence of cognitive biases, emotions, and psychological factors on decision-making. This deviation from conventional models like the Efficient Market Hypothesis recognizes the complexity of human decision-making in financial contexts. The field focuses on key phenomena: loss aversion, market anomalies, the framing effect, and the endowment effect. Loss aversion reveals a stronger reaction to losses than equivalent gains, influencing overly cautious decisions. Market anomalies, such as momentum and trend reversals, challenge efficient market expectations. The framing effect shows how decision-making is swayed by information presentation. The endowment effect impacts perceived asset value, affecting financial decisions. These biases significantly impact investor behavior, potentially leading to suboptimal portfolio choices. Recognizing these biases is crucial for making informed financial decisions. Behavioral finance research has profound implications, emphasizing the integration of behavioral insights with traditional models. Future studies should explore cumulative behavioral effects, conduct cross-cultural analyses, and investigate diverse market conditions for more effective investment strategies and regulations. Understanding global financial behavior through behavioral finance is essential for comprehensive insights into market dynamics and decision-making processes.

Elucidating the changing particulate matter pollution and associated health effects in rural India during 2000–2019

Atmospheric pollution is a serious problem in many countries, including India, and it is generally considered as an urban issue. To fill the knowledge gap about particulate pollution and its adverse health effects in rural India for well-informed region-specific policy interventions, we present new insights on the rural pollution of India in terms of PM2.5. Here, we analyse PM2.5 pollution and its associated health burden in rural India using satellite and reanalyses data for the period 2000–2019. We observe a gradual and consistent rise of atmospheric pollution in rural areas of India. The highest PM2.5 levels are observed in Indo-Gangetic Plain (IGP) during winter and post-monsoon seasons (107.0 ± 17.0 and 91.0 ± 21.7 μg/m3, respectively). A dipole reversal in seasonal trends between winter and post-monsoon seasons is found for black carbon (BC) and organic carbon (OC) in the rural IGP. The rural North West India (NWI) experiences elevated PM2.5 concentrations due to dust storms, while the rural hilly region (HR) in the Himalaya remains the least polluted region in India. The highest PM2.5 associated cardiopulmonary mortality in 2019 is observed in the rural IGP districts (1000–5100), whereas the highest mortality due to lung cancer at district level accounts for 10–60 deaths. The highest mortality attributed to PM2.5 is observed in districts of Uttar Pradesh, Bihar, West Bengal, Punjab, Haryana and Rajasthan. The priority-wise segregation of states as per World Health Organisation (WHO) Interim targets (ITs), as assessed in this study, might be helpful in implementation and development of policies in phases. We, therefore, present the first detailed study on the PM2.5 pollution in rural India, and provide valuable insights on its distribution, variability, sources and associated mortality, and emphasize the need for addressing this issue to protect public health.

Ignoring Plant Physiological Responses to Elevated CO2 Will Overestimate Terrestrial Vertebrate Biodiversity Loss Under Global Climate Change

AbstractRising CO2 is accelerating Earth warming and drying by altering the radiation budget (CO2 radiative effect), and it is commonly thought that future biodiversity will be greatly threatened. However, elevated CO2 also modifies plant physiology including photosynthesis rate and water use efficiency (CO2 physiological effect), the impacts of which have been usually ignored. By coupling machine learning techniques and Earth system models, this study considered both of the two effects and simulated terrestrial vertebrate biodiversity changes under moderate CO2 emission pathway. Our results show that the CO2 radiative effect positively impacts terrestrial vertebrate biodiversity at high latitudes but has a negative impact at low latitudes, whereas the physiological effect generally has a positive impact that peaks near the equator. Between 45°N and 45°S, 15% of areas with diminishing terrestrial vertebrate biodiversity due to radiative effect experience a reversal in trend when physiological effect is also considered; species migration pressure is also mitigated. Additionally, the protection priority rankings of 36 biodiversity hotspots need to be readjusted with the consideration of CO2 physiological effect, for which the impacts on terrestrial vertebrate biodiversity are important and varied greatly over space. Overall, our findings highlight the critical impacts of vegetation physiology on terrestrial vertebrate biodiversity in response to elevated atmospheric CO2, which can provide a reference for resource allocation and biodiversity conservation policymaking.

Use of tobacco cigarettes, e-cigarettes, and shishas among children and adolescents: Results of the "Präventionsradar" from 2016 to 2023

To measure trends in the use of various smoking products among children and adolescents from 2016 to 2023. The data is based on seven waves of the "Präventionsradar", a school-based epidemiological study in grades 5 to 10. The primary endpoints of the study were the lifetime and monthly prevalence of the use of tobacco cigarettes, e-cigarettes, shisha and the use of at least two of these smoking products (combined use). Prevalence estimates are based on logistic regression models. The analysis was based on 94,127 questionnaires. The sex ratio was balanced (49% female), mean age was 13 years (SD=1.8). In 2022/2023, the lifetime prevalence of smoking tobacco cigarettes was 18.8% (95% CI 18.1-19.5), of e-cigarettes 23.5% (95% CI 22.8-24.3), of shisha 14.0% (95% CI 13.4-14.7) and combined use 19.0% (95% CI 18.3-19.7). The monthly prevalence of smoking tobacco cigarettes was 5.9% (95% CI 5.5-6.4), of e-cigarettes 7.0% (95% CI 6.5-7.4), of shisha 3.2% (95% CI 2.8-3.5) and combined use 4.8% (95% CI 4.4-5.2). The following trends in lifetime prevalence have emerged since 2016: tobacco cigarettes (-3.0 percentage points), e-cigarettes (+1.8 percentage points), shisha (-9.2 percentage points), combined use (-2.7 percentage points). In the years of the COVID-19 pandemic, youth smoking decreased and rose again post-COVID with the exception of shisha. The use of smoking products in adolescence occurs frequently. Over the observational period, a trend reversal towards e-cigarettes as the most popular product among children and young people is most likely. In addition, the combined use of several smoking products has become a common pattern of consumption. Consistent preventive measures are required in order to achieve the goal of a smoke-free society in 2040.

The Intraday Heterogeneity and Risk Pricing Reversal Between Day and Night: Evidence from China

ABSTRACT Using a large panel of high-frequency trading data, this article investigates the intraday heterogeneity of risk pricing process in the Chinese stock market. Our research reveals that the financial assets in China exhibit intraday beta variation: high/low market risk level assets, with beta values above/below unity, tend to have downward/upward sloping beta trajectories across the intraday trading periods. We confirm a trend reversal of the risk-return relation between day and night in the Chinese market: the assets’ intraday returns are negatively correlated with their market risk level, whereas their overnight returns are positively correlated with their risk level. Further investigation shows a connection between the intraday beta variation and the day-night reversal anomaly, and the risk-return reversal between day and night can be partially explained by the intraday beta variation of financial assets. After controlling for the intraday beta variation, the risk-return reversal anomaly between day and night becomes less pronounced in the Chinese market.

Improving tuberculosis case detection through contact risk stratification by Xpert MTB/RIF Ultra and spatial parameters: Evaluation of an innovative active case finding strategy in Mozambique (Xpatial-TB)

Prompt diagnosis is critical for tuberculosis (TB) control, as it enables early treatment which in turn, reduces transmission and improves treatment outcomes. We investigated the impact on TB diagnosis of introducing Xpert Ultra as the frontline diagnostic test, combined with an innovative active-case finding (ACF) strategy (based on Xpert Ultra semi-quantitative results and spatial parameters), in a semi-rural district of Southern Mozambique. From January-December 2018 we recruited incident TB-cases (index cases, ICs) and their household contacts (HCs). Recruitment of close community contacts (CCs) depended on IC´s Xpert Ultra results, and the population density of their area. TB-contacts, either symptomatic or people living with HIV, were asked to provide a spot sputum for lab-testing. Trends on TB case notification were compared to the previous years and to those of two districts in the south of the Maputo province (control area), using an interrupted time series analysis with and without control (CITS/ITS). A total of 1010 TB ICs (37.1% laboratory-confirmed) were recruited; 3165 HCs and 4730 CCs were screened for TB. Eighty-nine additional TB cases were identified through the ACF intervention (52.8% laboratory-confirmed). The intervention increased by 8.2% all forms of TB cases detected in 2018. Xpert Ultra trace positive results accounted for a high proportion of laboratory confirmations in the ACF cohort (51.1% vs 13.7% of those passively diagnosed). The Number Needed to Screen to find a TB case differed widely among HCs (55) and CCs (153). During the intervention period, a reversal of the previous negative trend in lab-confirmed case notifications was observed in the district. However, the CITS model did not show any statistically significant difference compared to the control area. Paediatric population benefited the most from the ACF strategy and HCs screening seemed an effective intervention to find microbiological confirmed cases in early stages of the disease.

Mueller matrix-based characterization of cervical tissue sections: a quantitative comparison of polar and differential decomposition methods.

Quantitative optical polarimetry has received considerable recent attention owing to its potential for being an efficient diagnosis and characterizing tool with potential applications in biomedical research and various other disciplines. In this regard, it is crucial to validate various Mueller matrix (MM) decomposition methods, which are utilized to extract and quantify the intrinsic individual polarization anisotropy properties of various complex optical media. To quantitatively compare the performance of both polar and differential MM decomposition methods for probing the structural and morphological changes in complex optical media through analyzing their intrinsic individual polarization parameters, which are extracted using the respective decomposition algorithms. We also intend to utilize the decomposition-derived anisotropy parameters to distinguish among the cervical tissues with different grades of cervical intraepithelial neoplasia (CIN) and to characterize the healing efficiency of an organic crystal. Polarization MM of the cervical tissues with different grades of CIN and the different stages of the self-healing crystal are recorded with a home-built MM imaging setup in the transmission detection geometry with a spatial resolution of . The measured MMs are then processed with both the polar and differential MM decomposition methods to extract the individual polarization parameters of the respective samples. The derived polarization parameters are further analyzed to validate and compare the performance of both the MM decomposition methods for probing and characterizing the structural changes in the respective investigated optical media through their decomposition-derived intrinsic individual polarization properties. Pronounced differences in the decomposed-derived polarization anisotropy parameters are observed for cervical tissue sections with different grades of CIN. While a significant increase in the depolarization parameter is obtained with the increment of CIN stages for both the polar [ for CIN grade one (CIN-I) and for CIN grade two (CIN-II))] and differential ( for CIN-I and for CIN-II) decomposition methods, a trend reversal is seen for the linear diattenuation parameter , indicating the structural distortion in the cervical morphology due to the CIN disease. More importantly, with the differential decomposition algorithm, the magnitude of the derived parameter decreases from 0.26 to 0.19 with the progression of CIN, which was not being probed by the polar decomposition method. Our results demonstrate that the differential decomposition of MM holds certain advantages over the polar decomposition method to characterize and probe the structural changes in the cervical tissues with different grades of CIN. Although the quantified individual polarization parameters obtained through both the MM decomposition methods can be used as useful metrics to characterize various optical media, in case of complex turbid media such as biological tissues, incorporation of the differential decomposition technique may yield more efficient information. Also, the study highlights the utilization of MM polarimetry with an appropriate decomposition technique as an efficient diagnostic and characterizing tool in the realm of biomedical clinical research, and various other disciplines.

Gain reversal in the translocation dynamics of a semiflexible polymer through a flickering pore

We study the driven translocation of a semiflexible polymer through an attractive extended pore with a periodically oscillating width. Similar to its flexible counterpart, a stiff polymer translocates through an oscillating pore more quickly than a static pore whose width is equal to the oscillating pore’s mean width. This efficiency quantified as a gain in the translocation time, highlights a considerable dependence of the translocation dynamics on the stiffness of the polymer and the attractive nature of the pore. The gain characteristics for various polymer stiffness exhibit a trend reversal when the stickiness of the pore is changed. The gain reduces with increasing stiffness for a lower attractive strength of the pore, whereas it increases with increasing stiffness for higher attractive strengths. Such a dependence leads to the possibility of a high degree of robust selectivity in the translocation process.

Abstract 58: Socioeconomically Driven Reversal of Mortality Trends Among Patients With Primary Intracerebral Hemorrhage During the COVID-19 Pandemic: An Analysis of Nationally Representative Data

Background: Nationwide data demonstrating the impact of the COVID-19 pandemic on hemorrhagic stroke outcomes are lacking. Methods: We used the National Inpatient Sample (2016-2020) to identify adults (>=18 years) with primary intracerebral hemorrhage (ICH) or subarachnoid hemorrhage (SAH). We fit segmented logistic regression models to evaluate the differences in the rates of in-hospital mortality between the pre-pandemic (January 2016-February 2020) and pandemic periods (March 2020-December 2020). Multivariable logistic models were used to evaluate the differences in mortality between patients admitted from April to December 2020, with and without COVID-19, and those admitted during a similar period in 2019. Stratified analyses were conducted among patients residing in low and high-income zip codes and among patients with extreme loss of function (E-LoF) and those with minor to major loss of function (MM-LoF). Results: Overall, 309,965 ICH patients (mean age [SD]: 68[14.8], 47% female, 56% low-income) and 112,210 SAH patients (mean age [SD]: 60.2[15.4], 62% female, 55% low-income) were analyzed. Pre-pandemic, ICH mortality was decreasing by ≈1% per month (adjusted odds ratio, 95% confidence interval: 0.99, 0.99-1.00). However, during the pandemic, the overall ICH mortality increased by ≈2% per month (1.02, 1.00-1.02), and by ≈4% per month among low-income patients (1.04, 1.01-1.07). However, there was no change in trend among high-income patients during the pandemic (1.00, 0.97-1.03). Comorbid COVID-19 was significantly associated with higher odds of mortality, overall (ICH: 1.83, 1.33-2.51; SAH: 2.76, 1.68-4.54), and among patients with MM-LoF (ICH: 2.15, 1.12-4.16; SAH: 5.77, 1.57-21.17). Patients with E-LoF and comorbid COVID-19 had similar mortality rates with the 2019 cohort. Conclusion: Sustained efforts are needed to address socioeconomic disparities in healthcare access, quality, and outcomes during public health emergencies.

The oceanic mixed layer changes along with the state transition of the Beaufort Gyre

Recent state transition of the Beaufort Gyre has drawn great interest in the Arctic research community, but how the upper ocean hydrographic structure varies with this transition remains poorly understood. The upper ocean mixed layer plays an important role in climatic and ecological processes. Therefore, we analyze the Ice-Tethered Profiler (ITP) observations over the last two decades (2004–2022) to investigate the long-term trend of the mixed layer in the Arctic Ocean’s Beaufort Gyre (BG) from an observational perspective. Results show that the linear trend of the BG surface mixed layer depth (MLD) before and after 2015 has changed significantly, characterized by the vanishing or even reversal of the significant deepening trend. This transition is most pronounced in winter. The BG winter mixed layer is significantly cooler, saltier and denser in the mid-transition period (2013–2017) compared to the pre-transition period (2004–2012), but becomes significantly warmer, fresher and lighter in the post-transition period (2018–2022). The transition feature of the depth of maximum buoyancy frequency in the upper BG is similar to that of MLD, while this maximum decreases significantly in both the mid- and post-transition period when compared to their previous period. The deepening signal of MLD is propagated eastward, which coincides with the recent transition of BG position and freshwater distribution. Mechanism analysis further reveals that the reversal of winter MLD trend before and after 2015 may be due to changes in surface wind stirring and Ekman pumping. This study extends the investigation of the recent state transition of BG considering the upper hydrographic structure.

Amphetamine-type drug-related fatalities in the context of 8years of methamphetamine use measured by wastewater analysis in South Australia.

Methamphetamine is the illicit stimulant of choice in Australia. Countless initiatives have been employed to reduce methamphetamine use and drug-related harm. Wastewater analysis (WWA) has been used effectively as an objective measure of drug use at a population level and can be compared to indicators of harm, such as the number of drug-related fatalities. This paper attempts to describe methamphetamine use in the context of changes in levels measured in wastewater in South Australia whilst recognising considerable interventions over an 8-year period. Validated liquid chromatography-mass spectrometry methods were used to determine methamphetamine (and MDMA) levels in wastewater over an 8-year interval. The number of drug-induced deaths and driver fatalities involving methamphetamine (and MDMA) was presented and described in the context of changes in use measured by WWA. A rise in methamphetamine use according to WWA was evident up to 2017, followed by a gradual decrease to 2020 back to 2015 levels. Both driver fatalities and drug-induced deaths correlated well with use measured by WWA over the 8-year period. Multiple initiatives to curb supply, distribution and harm within the state and nationally have been implemented. The decrease in methamphetamine use after 2017 suggests that timely interventions have successfully reduced overall drug use and has led to fewer fatalities. This study shows that the response to increasing methamphetamine use in South Australia has resulted in a reversal of the upward trend in consumption and fewer drug-related fatalities.

Mechanisms of Stock Selection and Its Capital Weighing in the Portfolio Design Based on the MACD-K-Means-Mean-VaR Model

When designing a stock portfolio, investors must select stocks with different characteristics and increasing price trends and weigh each capital. Both are fundamental to diversifying loss and profit. Therefore, the mechanisms that accommodate both are needed. Based on this, this research aims to design a stock selection and capital weighing mechanism using the MACD-K-means-Mean-VaR model. The moving average convergence–divergence (MACD) is used to analyze stock buying time, providing trend, momentum, and potential price reversal insights. Then, stocks with increasing price trends are clustered using K-means, a grouping simple pattern data method based on specific characteristics. The best stocks from each cluster are capital weighted using the mean value at risk (mean-VaR), a portfolio optimization model adjusting loss possibility to the investor’s acceptance tolerance. The mechanism is then applied to Indonesia’s 100 stock index data to analyze variable sensitivities and compare it with another model. The application reveals that all variables significantly impact portfolio return mean and VaR, suggesting the need for clustering and analyzing stock price movements in stock portfolio design. This research academically develops a portfolio design mechanism by clustering stocks and analyzing price movement trends. It enables investors to practically diversify and choose stocks with increasing price trends, reducing losses and increasing profit opportunities.

Learning and processing framework using Fuzzy Deep Neural Network for trading and portfolio rebalancing

Trading strategies are an interesting topic of financial research. Moving Average Convergence Divergence (MACD) indicator is susceptible to performing worse than expected in unstable financial markets. This paper first presents a data-driven Interpretable Fuzzy Deep Neural Network (IFDNN) that provides insight into neural network inferences using fuzzy logic. Fuzzy rules are induced from the inference process of Neural Networks. Next, a learning and processing framework is proposed using IFDNN to detect trend reversals by forecasting look-ahead prices. IFDNN not only learns the drifts and shifts in market patterns, but also provides traders an option to dive into the reasoning behind why Neural Networks predict certain values. Genetic Algorithms are used to optimise trading parameters of the proposed framework. The proposed framework can perform portfolio rebalancing. The effectiveness of the framework is evaluated on three financial market indexes. The whipsaw effects cause frequent entrances and exits from the market. In this paper, a custom percentage oscillator is implemented to avoid this issue. The performances of the proposed framework using f-MACD are compared with those of the vanilla MACD. Two types of Reinforcement Learning models, Advantage Actor Critic and Deep Deterministic Policy Gradient are incorporated into the proposed framework with results compared.

Enhancing stock market trend reversal prediction using feature-enriched neural networks

According to several previous studies, neural network-based stock price predictors perform better for plunging patterns of stock prices than normal stock price patterns. Focusing on this issue, this study proposes a novel method that uses a neural network-based stock price predictor to predict the upward trend-reversal of the plunging market itself. To achieve more consistent prediction results for plunging patterns, newly designed input features are added to improve the performance of traditionally used neural network-based predictors. The statistics of the prediction scores for past plunging markets and analyzed, and the results are used to predict the upward trend-reversal in the plunging market that occurred during the test period. We demonstrate the superiority of the proposed method through the simulation results of 3-year trading on KOSDAQ, a representative stock market in South Korea.