Seasonal Data Research Articles

Assessing wild turkey productivity before and after a 14-day delay in the start date of the spring hunting season in Tennessee.

Ten state wildlife management agencies in the United States, including six within the Southeast, have delayed their spring wild turkey (Meleagris gallopavo) hunting seasons since 2017 by five or more days to address concerns related to the potential effects of hunting on wild turkey seasonal productivity. One hypothesis posits that if the spring hunting season is too early, there may be insufficient time for males to breed hens before being harvested, thus leading to reduced seasonal productivity. We conducted an experiment to determine whether delaying the wild turkey hunting season by 2 weeks in south-middle Tennessee would affect various reproductive rates. In 2021 and 2022, the Tennessee Fish and Wildlife Commission experimentally delayed the spring hunting season to open 14 days later than the traditional date (the Saturday closest to 1 April) in Giles, Lawrence, and Wayne counties. We monitored reproductive rates from 2017 to 2022 in these three counties as well as two adjacent counties, Bedford and Maury, that were not delayed. We used a Before-After-Control-Impact design to analyze the proportion of hens nesting, clutch size, hatchability, nest success, poult survival and hen survival with linear mixed-effect models and AIC model selection to detect relationships between the 14-day delay and reproductive parameters. We detected no relationship (p > .05) between the 14-day delay and any individual reproductive parameter. In addition, recruitment (hen poults per hen that survived until the next breeding season) was very low (<0.5) and did not increase because of the 14-day delay. The traditional Tennessee start date had been in place since 1986 while the turkey harvest increased markedly until about 2006 and more recently stabilized. Our data indicate that moving the start of the hunting season from a period just prior to peak nest initiation to 2 weeks later, to coincide with a period just prior to peak nest incubation initiation, resulted in no change to productivity or populations in wild turkey flocks in south-middle Tennessee.

Seasonal variability in the otolith and somatic size relationship of Japanese anchovy larvae: Counter effects of somatic growth and temperature

Otolith microstructure analysis has played an essential role in understanding the recruitment mechanisms of fish. Despite the fundamental assumption of a close correlation between otolith size and somatic size in growth back-calculations, numerous studies have shown that somatic growth and temperature have negative and positive effects on otolith size at the same somatic size, respectively. However, few studies have investigated the effects of somatic growth and temperature on otolith and somatic size relationships at intra- and inter-cohort levels in the same study framework. The present study investigated the effects of somatic growth and temperature on the seasonal variability in the otolith and somatic size relationship in Japanese anchovy Engraulis japonicus larvae collected from multiple seasonal cohorts throughout the year in the Kii Channel, Japan. The otolith and somatic size relationships were strong within each seasonal cohort sample (R2 = 0.771–0.949), but the relationship became weaker when all the seasonal data were pooled (R2 = 0.571). Relative otolith size at a given somatic size (ROS) was negatively related to somatic growth rates within each seasonal cohort sample (i.e., at the intra-cohort level), but the relationship became weak when all the data were pooled. On the contrary, sea temperature had positive effects on ROS, which would explain the seasonal variability in the otolith and somatic size relationship. The multiple regression analysis showed that temperature influenced ROS almost six times more than somatic growth. Overall, the present study demonstrated that an uncoupling in the otolith size–somatic size relationship was generated by the counter effects of somatic growth and temperature. The observed counter effects should be taken into account in estimating past larval growth for larvae obtained from different environmental conditions.

Reliability-driven time-of-use tariffs for efficient plug-in electric vehicle integration

Demand response (DR) plays a crucial role in balancing electricity supply and demand, but existing studies on price-based DR for plug-in electric vehicle (PEV) load management lack comprehensive considerations. Therefore, this study designs reliability-driven time-of-use (TOU) tariffs for the integration of PEVs, considering various factors such as random component failures, the stochastic characteristics of non-dispatchable components, and fluctuations in load patterns and uncertainties. The proposed framework employs advanced stochastic simulation models to assess generation facility reliability amidst extensive PEV deployment in three steps. First, individual components, including conventional generation capacity, renewable energy sources, and demand, as well as PEV load models, are developed. Then, a system reserve margin assessment is carried out via the generation of seasonal synthetic time series data. During this step, innovative techniques, including box-plot methods and expert rules, are used to simplify the representation of the system reserve margin at each hour. Finally, optimized TOU pricing schedules for PEVs are generated. These pricing hours are initially categorized as off-peak, mid-peak, or on-peak, streamlining scenario analysis and aligning PEV charging profiles with predefined pricing schedules for grid reliability evaluation. Case studies have shown that the proposed method can improve the reliability indices by effectively mitigating the impacts.

A novel seasonal grey prediction model with fractional order accumulation for energy forecasting

To accurately predict sequence data with seasonal characteristics, we combine data restart technology and fractional order accumulation into a novel seasonal grey model (FSGM (1,1, α)). The particle swarm optimization (PSO) algorithm is used to solve the fractional order and background value coefficients of the model, and the effectiveness of FSGM (1,1, α) is verified using three cases. Finally, we use FSGM (1,1, α) to predict quarterly electricity generation in Beijing and Henan Province and quarterly petroleum coke production in China from 2023 to 2027. The research results indicate that, first, FSGM (1,1, α) is reasonable and effective and has the ability to accurately capture the dynamic trend of seasonal data. Second, compared with the grey model (GM (1,1)), seasonal grey model (SGM (1,1)), data grouping grey model (DGGM (1,1)), data grouping seasonal model (DGSM (1,1)), and data grouping seasonal time model (DGSTM (1,1)), which have seasonal characteristics, FSGM (1,1, α) can better fit the original data, achieve higher prediction accuracy, and perform better. Third, from 2023 to 2027, it is predicted that there will be no significant change in Beijing's electricity generation, and the current stable trend will be maintained. Both the power generation in Henan Province and the petroleum coke production in China will steadily increase to a certain extent, with obvious seasonal cyclical fluctuations. Notably, the power generation and petroleum coke production in Henan Province in the fourth quarter of 2027 will increase by 11.50 % and 10.93 %, respectively, compared to those in the fourth quarter of 2023.

Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula

Climate change is altering global ocean phenology, the timing of annually occurring biological events. We examined the changing phenology of the phytoplankton accumulation season west of the Antarctic Peninsula to show that blooms are shifting later in the season over time in ice-associated waters. The timing of the start date and peak date of the phytoplankton accumulation season occurred later over time from 1997 to 2022 in the marginal ice zone and over the continental shelf. A divergence was seen between offshore waters and ice-associated waters, with offshore bloom timing becoming earlier, yet marginal ice zone and continental shelf bloom timing shifting later. Higher chlorophyll a (chl a) concentration in the fall season was seen in recent years, especially over the northern continental shelf. Minimal long-term trends in annual chl a occurred, likely due to the combination of later start dates in spring and higher chl a in fall. Increasing spring wind speed is the most likely mechanism for later spring start dates, leading to deeper wind mixing in a region experiencing sea ice loss. Later phytoplankton bloom timing over the marginal ice zone and continental shelf will have consequences for surface ocean carbon uptake, food web dynamics, and trophic cascades.

Remote Midlatitude Control of Rainfall Onset at the Southern African Tropical Edge

Abstract Rainfall over the southern African tropical boundary is highly variable, particularly around the onset of the summer rains. However, understanding of the causes of variability in onset timing is poor. A lack of observational data in the region is compounded by the complexity of climate dynamics and variability at the interface of the tropics and the midlatitudes. The key to onset over the region is the Congo air boundary (CAB), a distinguishing circulation feature present in September and declining in frequency until November. The CAB is crucial to early season rainfall; precipitation is broadly inhibited when the CAB is present, while its breakdown allows large-scale rainfall and onset over central southern Africa. This work identifies a remote midlatitude influence on the timing of CAB breakdown using the high-resolution reanalysis dataset ERA5. Propagating and (upstream) breaking low-wavenumber Rossby waves in the Southern Hemisphere westerly jet are shown to be related to &gt;70% of breakdown events. Breakdown is forced by 1) the replacement of subsidence over eastern southern Africa with synoptically forced ascent and 2) wind field modification that leads to positive 850-hPa continental convergence anomalies and enhanced moisture advection from the Congo basin and Indian Ocean. Upper-level anticyclonic vorticity tendency induced by enhanced convection slows Rossby wave propagation aloft, contributing to wave breaking and the persistence of conditions favorable to convection. The identification of this midlatitude influence on CAB breakdown reveals new potential sources of predictability for onset and demonstrates the significant equatorward extent of midlatitude influences to the tropical edge. Significance Statement This study aims to improve our understanding of the causes of variability in the start date of the summer rainy season in central southern Africa. The research reveals that certain upper-level waves in the Southern Hemisphere westerly jet are associated with patterns of weather that force a large-scale transition from dry pre-onset conditions to widespread rainfall in a time scale of 2–5 days, and that these waves account for a large proportion of such transitions. This finding is of interest to subseasonal weather forecasters and may help to improve the long-range forecasting of onset date.

Biodiversity and the Community Structure of Chromista Cavalier-Smith, 1981 in Nyong and Kienke River Mouths (South-Cameroon)

A survey was undertaken from March to June 2014 on the biodiversity and the community structure of Chromista Cavalier-Smith, 1981 in Nyong and Kienke River mouths (South-Cameroon). In each river, raw waters were collected from upstream to downstream at four sites. Cells were counted using the Malassez cells procedure and species were identified. A total of 10427.1x10&amp;lt;sup&amp;gt;5&amp;lt;/sup&amp;gt; cells corresponded to three phyla, eight classes, 23 orders, 32 genera and 40 species (24 freshwater species (60.0% of total species richness and total collection respectively), three marine species (7.5% and 2.4% of the total species richness; and total collection respectively), and one brackish water specialist in Kienke (2.5% and 5.1%), 13 tolerant species (32.5% and 32.6%)). The trophic diatom index revealed undisturbed conditions with no or little alteration of human origin and a low organic pollution (oligotrophic or mesotrophic state) (Nyong: TDI=52.7; Kienke: TDI=69.7; pooled assemblage: TDI=65.0). A low species richness was detected (richness ratio in Nyong: d=0.008; Kienke: d=0.003; pooled rivers: d=0.004), a high species diversity (Shannon index close to maximum) (Nyong: H’=2.742 and H’&amp;lt;sub&amp;gt;max&amp;lt;/sub&amp;gt;=2.996; Kienke: H’=2.685 and H’&amp;lt;sub&amp;gt;max&amp;lt;/sub&amp;gt;=2.996; pooled rivers: H’=3.245 and H’&amp;lt;sub&amp;gt;max&amp;lt;/sub&amp;gt;=3.689), a very low dominance by a few species (Berger-Parker index close to 0) (Nyong: I&amp;lt;SUB&amp;gt;BP&amp;lt;/SUB&amp;gt;=0.156; Kienke: I&amp;lt;SUB&amp;gt;BP&amp;lt;/SUB&amp;gt;=0.175; pooled rivers: I&amp;lt;SUB&amp;gt;BP&amp;lt;/SUB&amp;gt;=0.134), and Hill’s ratio were close to 1 (Nyong: Hill=0.819; Kienke: Hill=0.803; pooled rivers: Hill=0.722). The community was highly even with a high value of the Pielou’s evenness close to 1 (Nyong: J=0.915; Kienke: J=0.896; pooled rivers: J=0.880). Two useful species and one harmful species to fish were rare in Kienke. Species exhibited in Kienke and pooled data in rainy season, a positive global net association while it was negative in Nyong. Assemblage fitted Preston’s model in Nyong with a high environmental constant in the dry season (m’=1.469), low constant in the rainy season (m’=0.947) and the pooled seasons (m’=0.853). In Kienke constants were low (dry season: m’=0.574; rainy season: m’=0.566; pooled seasons: m’=0.581) suggesting a evolved community in less disturbed environments where the majority of species showed moderate abundances. In the dry season, the pooled assemblage functionned on the basis of maintaining a complex information network (close to ecological balance) developed at spatio-temporal scales (ZM model) and it presented a low force of regeneration (fractal dimension of the distribution of individuals among species (1/γ)=0.925&amp;lt;1). The evolved oligotrophic state (close to natural balance) of the chromists’ community should be preserved and protected and the studied rivers classified as reference.

Sustainable practices impact and planting date on yield of sorghum (Sorghum bicolor L. Moench)

Environmental stress affects the yield of sorghum. This impact can be reduced by seed stimulation technique and determining the appropriate planting date. An experiment was conducted in the spring and fall seasons of 2022. Randomized complete block design with split-plot arrangement in four replications was used. Planting dates (spring season: February 15th, March 1st, 15th, April 1st, 15th; fall season: June 15th, July 1st, 15th, August 1st, 15th) were assigned to the main plots. Seed stimulation treatments (banana peel extract 35% + citric acid 100 mg L−1 and soaking in distilled water only) were applied to the subplots. The interaction treatment of soaking with banana peel extract + citric acid and the planting date of April 15th showed superiority in traits of head grain number (1743) and total grain yield (6.135 tons ha−1) during the spring season. Seed stimulation treatment with banana peel extract + citric acid on July 1st planting date outperformed in traits of plant dry weight (398.8 g), head grain number (4582), total grain yield (10.82 tons ha−1), and biological yield (67.8 tons ha−1) during the fall season. It can be concluded that seed stimulation with banana peel extract + citric acid improved yield characteristics. Early planting date in the spring season (February 15th, March 1st, or March 15th) or the delay in the fall season (August 1st or August 15th) resulted in less efficient plant performance. This is closely related to the nature of the climatic conditions accompanying crop growth from planting to harvesting. Also, using of agricultural applications related to the use of natural materials could helping hand sustainable agriculture. It can be recommended to soak seeds with banana peel extract + citric acid and adopt the planting date of April 15th for the spring season and July 1st for the fall season to enhance grain yield.

SARIMA with Sliding Window Implementation for Forecasting Seasonal Demand Data

Demand forecasting is an essential part of business process management. A comparison of methods is needed to get the best model to provide good forecasting results. Difficulties in meeting consumer demands and predicting these requests using demand data at companies CV. ABCD is the main problem in this research. The SARIMA and decomposition methods are used for comparison and search for the best model before forecasting. SARIMA with a windowing size of 56, indicating the smallest MAPE value of 3,91%. The value &lt;10%, so it can be said to produce an excellent forecasting value. Forecasting results with SARIMA show a meeting between actual and forecasting data in 2022. Therefore, it can be said the forecasting results for 2023 and 2024 can be used as a reference for the company CV. ABCD to meet customer demand and avoid stock shortages.

LQ45 Stock Portfolio Optimization by considering Return Predictions using the Holt Winter Method

A portfolio represents a selection of assets owned by individuals or groups with the goal of generating profit. Stocks are among the various forms of investments available. Investors typically consider two key factors: expected returns and associated risks. Portfolio optimization endeavors to maximize returns while minimizing risks. With the progression of time, portfolio optimization strategies increasingly incorporate return predictions using machine learning techniques. In this study, the Holt-Winter method is employed to forecast stock prices and returns, known for its accuracy in estimating seasonal time series data. Previous research primarily utilized the Mean- Variance method for portfolio optimization, but the outcomes were deemed unsatisfactory. Therefore, this study adopts a novel approach by integrating the Mean-Variance model with forecasting. Performance testing of the optimal portfolio emphasizes sensitivity, aiming for substantial average returns, minimal standard deviation, and a high Sharpe ratio. Comparative analysis with the LQ45 index portfolio reveals that the sensitivity-driven portfolio exhibits superior performance, yielding higher values in terms of average returns, standard deviation, and Sharpe ratio.

Unveiling Water Allocation Dynamics: A Text Analysis of 25 Years of Stakeholder Meetings

Abstract Managing water resources in regions with high climate variability and frequent extreme weather events poses challenges for policymakers. To facilitate water allocation in these cases, participatory and collaborative decision-making approaches have become common. However, the evaluation of these approaches is hindered by the lack of structured methods and data to understand them. To address this knowledge gap, we propose a novel methodology that leverages text data to identify key topics, conflicts, and influential actors that shape water allocation dynamics. Our methodology is tested using records of 1020 water basin committee meetings held between 1997 and 2021 across twelve basin committees in Ceará, Brazil – a region known for its extensive history of droughts that have impacted water governance. To uncover key water management issues discussed during these meetings, we employed a three-step topic modeling framework: (1) sentence embedding, (2) dimensionality reduction, and (3) sentence clustering. Furthermore, we used entity recognition, dependency parsing, and network graphs to identify powerful actors influencing these meetings and, ultimately, the decisions taken. Our findings revealed stakeholders’ heightened concern for urban water supply over agricultural demand during droughts. We found that “reservoir operation” was the most recurring topic, especially in basins where the strategic reservoirs are located. Discussions related to “climate information” became significantly more important over time, which indicates that water allocation decisions are increasingly based on the seasonal forecast and data on oceanic indices provided by the meteorology agency. Despite the presence of local users in the committees, governmental representatives dominated the discussions and were central in all river basins. In conclusion, our proposed approach harnesses existing text data to uncover spatiotemporal patterns related to participatory water allocation. This study opens new avenues for investigating water governance using text-based analysis.

Comparison of SARIMA, Bagging Exponential Smoothing with STL Decomposition and Robust STL Decomposition for Forecasting Red Chili Production

Time series analysis enables the identification of trends and patterns in data, allowing for the development of forecasting models that predict future values. One effective approach for forecasting seasonal time series data is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Bagging Exponential Smoothing with STL Decomposition (BES-STL) is an ensemble machine learning method aimed at enhancing forecasting accuracy. STL Method, which stands for Seasonal-Trend decomposition using Loess, is utilized to decompose time series data into three components, namely trend, seasonal, and remainder components. In the remainder component, the process of bootstrap aggregation (bagging) with Moving Block Bootstrapping (MBB) is used to obtain synthetic data, followed by averaging the value by month from the entire series as the forecast results. A comparative analysis was conducted using the SARIMA, BES-STL, and BES-RSTL models. The optimal model, with the lowest MAPE and RMSE, is then implemented to predict national red chili production. The results indicate that the SARIMA(1,1,1)(0,1,1)12 model has the best performance with a MAPE of 7.06 and a RMSE of 95,473. The top-performing model is utilized to forecast data from January to December 2022. Additionally, the forecasted results are compared to the actual data, resulting in a highly precise MAPE of 5.39.

Are winter conditions impacting annual organic production in the northern Adriatic? Verifications and future projections

Primary production in the northern Adriatic (NAd) reaches its yearly peak in the winter with high-intensity variations from year to year. According to the hypothesis, the intensity of local winter primary production, controlled by the degree of the spreading of Po River waters across the NAd, reflects on the annual secondary production of the ongoing year. The hypothesis is evaluated here based on the new data set and extends from 2018 to 2020, referring additionally to 2017 data which are already published. Data collected in 2017 and 2020 support the hypothesis, pointing to the large organic outputs after highly productive winters. Despite the lack of seasonal data for 2018 and 2019, large annual production was deducted by large abundances of the allochthonous gelatinous zooplankton species – Mnemiopsis leidyi. Numerical models show that in 2018–2020 the NAd was mostly “separated” from the rest of the Adriatic Sea by a northern branch of a large cyclonic gyre with high salinity water (from central Adriatic and/or Kvarner Bay) entering the NAd along the eastern (Istrian) coast. Such a circulation system could favour the spreading of the Po River waters across the NAd, inducing high primary production in winter, at the beginning of the yearly pelagic cycle, with a subsequent retention/accumulation of organic matter produced in the following months in the area. Using climate projections of temperature and salinity and the associated circulation and following the observed biological relations, a prediction of the organic matter production in the NAd can be obtained. With increased horizontal density gradients in future winters, an intensification of transversal motions across the NAd is expected. Thus, the retention of the Po waters with higher winter production in the NAd may be predicted. Following the hypothesis, a higher annual organic production and a probable higher occurrence of gelatinous plankton in the east of the NAd are expected.

Influence of the sand-clay ratio of the burial material of forensic targets on ground-penetrating radar (GPR) responses–comparison of dry and rainy season data

This study aims to assess the impact of the sand-clay ratio on the detection of simulated forensic targets resembling human burial evidence using ground-penetrating radar (GPR). To achieve this goal, we established a controlled site consisting of four experimental graves (SEPs), with each SEP representing a distinct burial scenario. The project’s objectives encompass evaluating how varying the clay soil content influences the detection of swine targets using 2D, quasi-3D, and 4D GPR imaging techniques. We observed signal attenuation in the GPR data during the rainy season and after reflection from larger targets, primarily due to the conductive behavior of the soft tissues within the targets. We achieved more precise results in an SEP that was 85% sand and 15% clay material, regardless of seasonality. We obtained better results in the dry season due to the greater penetrability of electromagnetic waves and more explicit reflections. In comparison, we achieved more precise results for SEP03, which was 70% sand and 30% clay material. In conclusion, controlled sites are invaluable tools for geophysical investigations, as they provide a controlled environment where we can meticulously adjust various parameters, leading to a more precise and insightful analysis of the results. By systematically manipulating factors such as the soil composition, target depth, and environmental conditions, researchers can effectively calibrate their instruments and methodologies, enhancing the accuracy and reliability of their findings. Therefore, controlled sites not only facilitate comprehensive data collection but also serve as essential platforms for refining and validating geophysical techniques, ultimately advancing the field of geophysical forensics.

Analysis of Rainfall Trend in North Interior Karnataka (NIK) Meteorological Subdivision of Karnataka by Non-Parametric Methodologies

The present study analyzed the trend in the month-wise, seasonal and annual rainfall employing three non-parametric tests viz. Mann-Kendall (M-K), Modified Mann-Kendall (MM-K) tests and Sen’s slope estimator for North Interior Karnataka (NIK) meteorological subdivision of Karnataka using 60 years (1960-2019) rainfall data. The M-K test results revealed that there is no monotonic trend in all data sets which indicates that there may exist the influence of serial correlation in the rainfall data. The MM-K test was applied to rainfall data of the NIK subdivision, the result of the test revealed that January, February, March and August months have a monotonic increasing trend whereas April, May, July and September months have a monotonic decreasing trend whereas June, October, November and December have no monotonic trend in monthly rainfall data. The winter season has a monotonic increasing trend, monsoon and post-monsoon seasons have a monotonic decreasing trend, and the pre-monsoon season has no monotonic trend in seasonal rainfall data. The MM-K test statistic (Tau) value for the NIK (-0.12) subdivision was found to be significant and negative indicating that there is a monotonic decreasing trend. Larger negative Sen’s slope values for NIK (-1.40) subdivision indicate the high decreasing rate of change of rainfall for annual rainfall data. Therefore, to know the trend in rainfall data most accurately in the presence of outliers and serial correlation, the Modified Mann-Kendall (MM-K) test and Sen’s slope estimator are recommended instead of Mann-Kendall (M-K) and Sen’s slope estimator.

The implication of spatiotemporal rainfall distribution on the estimation of areal rainfall characteristics in Upper Erer Sub-basin, Ethiopia

Abstract The distribution of rainfall is not uniform in various regions of the world. Rainfall study is used to recognize the characteristics, duration, and variability of temporal and spatial rainfall distributions. In Ethiopia, the annual and seasonal rainfall distributions are variable in space and time. This study is focused on the implication of spatiotemporal rainfall distribution on the areal rainfall characteristics evaluation under the Upper Erer Sub-basin, located in Eastern Ethiopia. In the study area, average annual rainfall amount for Dire Dawa, Harar and Haramaya, Girawa, and Gursum stations are found to be 647, 816, 801, 958, and 840 mm with coefficients of variation of 23, 20, 20, 19, and 31%, respectively. However, the rain gauges here are sparsely distributed. The rainfall occurrence and distribution at various gauging stations have been found to vary significantly both temporally and spatially. The rainfall occurrence and distribution at various gauging stations had been found to vary significantly both temporally and spatially. The spatiotemporal rainfall distribution found in the stations was assessed using a joint probability of rain days approach. The result indicated joint probability of rain days estimation approach under monthly time step has better performance than daily, decadal, and seasonal data. The joint probability approach is used along with rainfall amount under monthly rainfall for areal rainfall estimation assessment in rainfall–runoff modeling.

Resource selection by Sarda cattle in a Mediterranean silvopastoral system.

Knowledge of how grazing cattle utilize heterogeneous landscapes in Mediterranean silvopastoral areas is scarce. Global positioning systems (GPS) to track animals, together with geographic information systems (GIS), can relate animal distribution to landscape features. With the aim to develop a general spatial model that provides accurate prediction of cattle resource selection patterns within a Mediterranean mountainous silvopastoral area, free-roaming Sarda cows were fitted with GPS collars to track their spatial behaviors. Resource selection function models (RSF) were developed to estimate the probability of resource use as a function of environmental variables. A set of over 500 candidate RSF models, composed of up to five environmental predictor variables, were fitted to data. To identify a final model providing a robust prediction of cattle resource selection pattern across the different seasons, the 10 best models (ranked on the basis of the AIC score) were fitted to seasonal data. Prediction performance of the models was evaluated with a Spearman correlation analysis using the GPS position data sets previously reserved for model validation. The final model emphasized that watering point, elevation, and distance to fences were important factors affecting cattle resource-selection patterns. The prediction performances (as Spearman rank correlation scores) of the final model, when fitted to each season, ranged between 0.7 and 0.94. The cows were more likely to select areas lower in elevation and farther from the watering point in winter than in summer (693 ± 1 m and 847 ± 13 m vs. 707 ± 1 m and 635 ± 21 m, respectively), and in spring opted for the areas furthest from the water (963 ± 12). Although caution should be exercised in generalizing to other silvopastoral areas, the satisfactory Spearman correlations scores from the final RSF model applied to different seasons indicate resource selection function is a powerful predictive model. The relative importance of the individual predictors within the model varied among the different seasons, demonstrating the RSF model's ability to interpret changes in animal behavior at different times of the year. The RSF model has proven to be a useful tool to interpret the spatial behaviors of cows grazing in Mediterranean silvopastoral areas and could therefore be helpful in managing and preserving ecosystem services of these areas.

Detecting Change in a Volatile Curve United State Stock Market (US SM) with the Use of Automated Decomposition for Time Series Components

The main reason for this investigation is to use manual process of identification of time series components with two types of automated decomposition for time series known as automated BFTSC (break for time series components) and, automated GFTSC (Group for time series components) in detecting change in a volatile curve united states stock market (US SM). In identification of components of time series present in the seasonal data of US stock market. The data US Stock Market was a monthly data from January 2001 until December 2018 and a total of 18 years. The stock market of US is also available as a secondary data at the DataBank of University Utara Malaysia Library. The weaknesses of BFAST were corrected by the extension of BFAST to BFTSC and GFTSC. Both were created to capture the cyclical and irregular components that were not captured by BFAST technique and it was included in the methodology of this study. BFTSC and GFTSC were considered to provide a combined image of all the four components of time series while GFTSC had additional advantage of providing equations to the components automated. Evaluation using simulation data and empirical data vindicated the accuracy of BFTSC and GFTSC based on linear trend less volatile data. They are effective and better than BFAST because it was able to identify 100% of the data with the basic four time series components monthly. Both techniques detects 99 % of the entire components in the time series data in a linear trend data.

KMT-2023-BLG-0416, KMT-2023-BLG-1454, KMT-2023-BLG-1642: Microlensing planets identified from partially covered signals

Aims. We investigate the 2023 season data from high-cadence microlensing surveys with the aim of detecting partially covered shortterm signals and revealing their underlying astrophysical origins. Through this analysis, we ascertain that the signals observed in the lensing events KMT-2023-BLG-0416, KMT-2023-BLG-1454, and KMT-2023-BLG-1642 are of planetary origin. Methods. Considering the potential degeneracy caused by the partial coverage of signals, we thoroughly investigate the lensing-parameter plane. In the case of KMT-2023-BLG-0416, we have identified two solution sets, one with a planet-to-host mass ratio of q ~ 10−2 and the other with q ~ 6 × 10−5, within each of which there are two local solutions emerging due to the inner-outer degeneracy. For KMT-2023-BLG-1454, we discern four local solutions featuring mass ratios of q ~ (1.7−4.3) × 10−3. When it comes to KMT-2023-BLG-1642, we identified two locals with q ~ (6 − 10) × 10−3 resulting from the inner-outer degeneracy. Results. We estimate the physical lens parameters by conducting Bayesian analyses based on the event time scale and Einstein radius. For KMT-2023-BLG-0416L, the host mass is ~0.6 M⊙, and the planet mass is ~(6.1−6.7) MJ according to one set of solutions and ~0.04 MJ according to the other set of solutions. KMT-2023-BLG-1454Lb has a mass roughly half that of Jupiter, while KMT-2023-BLG-1646Lb has a mass in the range of between 1.1 to 1.3 times that of Jupiter, classifying them both as giant planets orbiting mid M-dwarf host stars with masses ranging from 0.13 to 0.17 solar masses.

Coccolith-calcite Sr/Ca as a proxy for transient export production related to Saharan dust deposition in the tropical North Atlantic

Atmospheric dust deposition can modulate the earth’s climate and atmospheric CO2 through fertilising the ocean (nutrient source) and by accelerating the biological carbon pump through fuelling the ballasting process. To distinguish the biogeochemical effects of Saharan dust with respect to fertilization and ballasting, and to gain a broader perspective on the coccolith calcite Sr/Ca in relation to the drivers of coccolith export production, we determined the coccolith-Sr/Ca from a one-year (2012–2013) time-series sediment trap record in the western tropical North Atlantic (M4—49°N/12°W). High Sr/Ca were linked to enhanced export production in the upper part of the photic zone, most notably under windier, dry, and dustier conditions during spring. Attenuated Sr/Ca in the autumn probably reflect a combination of lower Sr-incorporation by dominant but small-size placolith-bearing species and the presence of “aged” coccoliths rapidly scavenged during a highly productive and usually fast export event, likely added by (wet) dust ballasting. Higher Sr/Ca observed in the large coccolith size fractions support the existing notion that larger-sized coccolithophores incorporate more Sr during calcification under the same environmental conditions. The presence of the abnormally Sr-rich species Scyphosphaera apsteinii is also shown in the separated large fraction of our Sr/Ca seasonal data.