Multiple Methods Research Articles

HOLT-WINTER METHOD FOR FORECASTING LIQUID ALUMINUM SULFATE USAGE FOR PROBABILISTIC INVENTORY MODELING Q WITH ERLANG DISTRIBUTION

Water is a natural resource important for life and daily activities. Water distributed by the Regional Drinking Water Company (PDAM) should include a coagulation process using liquid aluminum sulfate as a coagulant before it can be consumed. Therefore, this research aims to predict the need for liquid aluminum sulfate in PDAM from 2023 to 2024 using Holt-Winter's method. It also aims to evaluate the optimum liquid aluminum sulfate chemical inventory policy using Q probabilistic inventory model with Normal and erlang probabilistic distributions in PDAM. The data was obtained from Tirta Musi PDAM in Palembang City, Indonesia. The results of forecasting liquid aluminum sulfate demand level data with the Holt-Winter multiplicative method provide the smallest MAPE value. The erlang probability distribution assumption has been met through the Kolmogorov Smirnov test method. The erlang probabilistic inventory model provides a more optimal policy solution than the normal probabilistic inventory model, with minimum total cost and higher service level.

Effect of bone marrow niche on hematopoietic stem cells.

Hematopoietic stem cells (HSCs) reside in a milieu that supports their functions, differentiation, and survival. This niche consists of several types of cells, including mesenchymal stem/stromal cells, endothelial cells, osteoblasts, megakaryocytes, macrophages, adipocytes, lymphoid cells, and nerve fibers. The interactions between these cells and HSCs have a role in HSC fate. Several studies have focused on identification of the biological and cellular mechanisms contributing to the establishment of this niche. However, the exact mechanisms of the interaction between HSCs and the bone marrow niche have not been elucidated yet. Unraveling these mechanisms would help in the design of effective methods for maintenance and multiplication of HSCs in clinical settings, in addition to establishment of novel therapies for hematopoietic diseases. The current review summarizes the effects of the niche cells on HSC function and underlying mechanisms of interplay between HSCs and their niche.

Topography‐dependent eikonal solver in tilted transverse isotropic media with anelliptical factorization

AbstractIncorporating anisotropy and complex topography is necessary to perform traveltime tomography in complex land environments while being a computational challenge when traveltimes are computed with finite‐difference eikonal solvers. Previous studies have taken this challenge by computing traveltimes in transverse isotropic media involving complex topography with a finite‐difference eikonal equation solver on a curvilinear grid. In this approach, the source singularity, which is a major issue in eikonal solvers, is managed with the elliptical multiplicative factorization method, where the total traveltime field is decomposed into an elliptical base traveltime map, which has a known analytical expression and an unknown perturbation field. However, the group velocity curve can deviate significantly from an ellipse in anellipitically anisotropic media. In this case, the elliptical base traveltime field differs significantly from the anelliptical counterpart, leading to potentially suboptimal traveltime solutions, even though it helps to mitigate the detrimental effects of the source singularity. To overcome this issue, we develop a more accurate topography‐dependent eikonal solver in transverse isotropic media that relies on anelliptical factorization. To achieve this, we first define the coordinate transform from the Cartesian to the curvilinear coordinate system, which provides the necessary framework to implement the topography‐dependent transverse isotropic finite‐difference eikonal solver with arbitrary source and receiver positioning. Then, we develop a semi‐analytical method for the computation of the topography‐dependent anelliptical base traveltime field. Finally, we efficiently solve the resulting quadratic elliptical equation using the fast sweeping method and a quartic anelliptical source term through fixed‐point iteration. We assess the computational efficiency, stability and accuracy of the new eikonal solver against the solver based on elliptical factorization using several transverse isotropic numerical examples. We conclude that this new solver provides a versatile and accurate forward engine for traveltime tomography in complex geological environments such as foothills and thrust belts. It can also be used in marine environments involving complex bathymetry when tomography is applied to redatumed data on the sea bottom.

The Effects of Different Plant Growth Regulators and Nutrient Solutions on Leaf Bud Propagation in Different Cassava Varieties

Cassava mosaic virus is a growing threat to cassava cultivation, and the rapid propagation of disease-free cassava stems offers an alternative approach to producing planting material. This study investigated the effects of different plant growth regulators and nutrient media on leaf bud propagation among different cassava varieties. A 3 x 4 factorial with a randomized complete block design (RCBD) was used with three replications. Three cassava varieties, Kasetsart 50 (KU50), Rayong 9 (RY9), and Huay Bong 60 (HB60) were assigned as factor A, and four different media, i.e., control treatment (distilled water), indole-3-butyric acid (IBA) at 500 mg/L, Semi-Autotrophic Hydroponic (SAH) nutrient solution, and IBA+SAH, were assigned as factor B. Data were recorded for growth and survival traits at 30 days. The results showed that the RY9 variety exhibited superior growth parameters, whereas the KU50 and HB60 varieties showed a higher survival rate. Applying IBA, alone or with SAH, negatively affected cassava plantlet growth and survival rate. On the other hand, applying SAH alone yielded results comparable to the control treatment, significantly differing from the IBA and IBA+SAH treatments. This study also noted that IBA applications enhanced root traits in the RY9 variety but not in other varieties. The HB60 variety outperformed the survival rates and dry shoot weights of RY9 and KU50 under SAH treatment. This finding can be used to select an effective method for leaf bud multiplication in response to the demand for disease-free cassava planting materials.

MODELING AND FORECASTING OF TOURISM INCOME: THE CASE OF TURKEY

This study aims to identify the optimal forecasting model for predicting Türkiye's tourism income, a crucial factor for economic planning and development. This study employs different forecasting techniques, including the seasonal Autoregressive Integrated Moving Average (SARIMA), the additive and multiplicative Holt-Winters methods, the Exponential Smoothing State Space (ETS), Artificial Neural Networks (ANNs) and seasonal-trend decomposition procedure based on the loess (STL)-ANN hybrid model and evaluates their performance. The methodology involves analyzing monthly tourism income data from January 2012 to December 2023, incorporating additional economic indicators such as the economic confidence index, number of visitors, consumer price index, industrial production index, and USD exchange rate, which serve as input for ANN models. The findings reveal that ANNs, particularly the model that incorporates tourism income alongside other economic indicators, outperform traditional models with the lowest Mean Absolute Scaled Error (MASE) and Root Mean Squared Scaled Error (RMSSE). Specifically, the ANN model with additional predictors demonstrates the highest forecasting accuracy. These results suggest that advanced machine learning techniques provide superior predictive capabilities compared to traditional linear models. The study underscores the importance of integrating complex models to achieve more accurate forecasting, offering valuable insights for policymakers and practitioners in the tourism sector.

Combined population attributable fractions for dementia are dependent on strong assumptions of the form of interactions between risk factors: an analysis of data from the Canadian Longitudinal Study on Aging

AbstractBackgroundSeveral recent studies have used communality weights to calculate a combined population attributable fraction (PAF) of modifiable risks factors for dementia. Other research has suggested this method may be underestimating the true PAF. We applied multiple methods to calculate the combined PAF of 12 modifiable risk factors using record‐level, national Canadian data.MethodWe used baseline data (2011‐2015) from the comprehensive cohort of the Canadian Longitudinal Study on Aging (CLSA). We measured the 12 risk factors identified in the Lancet Commission on dementia prevention, intervention, and care. We calculated a combined PAF of these risk factors using the independent multiplicative method of Barnes and Yaffe (BY‐M), the additive and multiplicative methods of Welberry (W‐A and W‐M), and communality‐adjusted multiplicative method of Norton (N‐M). We also implemented a novel method (J‐MA) to mix multiplicative and additive interactivity based on the number of risk factors present. All methods incorporated sample weights and used the risk ratios from the Lancet Commission.ResultThere were 30,097 baseline participants in the CLSA ranging in age from 45 to 85 years (median 62). Combined PAFs varied widely: 78.5%(W‐M), 75.9% (BY‐M), 59.8% (J‐MA), 58.2% (W‐A), and 43.3% (N‐M). The BY‐M and W‐M methods resulted in unrealistically high combined risk ratios (RR >90) in individuals with many risk factors. The other methods produced broadly realistic results. Despite a nominally multiplicative form, N‐M produced results consistent with sub‐additive interactivity and makes arbitrary analytic choices. The J‐MA method also uses an arbitrary function to blend multiplicative and additive interactions. The W‐A approach made no assumption other than additive interactivity.ConclusionAssumptions regarding how risk factors for dementia interact highly influence combined PAFs. All multiplicative methods produce either unrealistic results or require arbitrary analytic choices. The additive method makes no arbitrary assumptions but may not reflect the actual form of interactions between risk factors. Not enough is known on how modifiable risk factors for dementia interact to estimate combined PAFs with confidence. When reporting PAFs for dementia a range of possible values, including one assuming additive interactions, should be provided and the assumptions behind each value clearly stated.

Dynamic Matrix Multiplication Using Reinforcement Learning

Efficient matrix multiplication is a core computational task in machine learning, deep learning, and scientific computing, as it significantly impacts the performance of model training and inference. This project explores the use of reinforcement learning (RL) to dynamically select the optimal matrix multiplication algorithm based on input matrix characteristics. We compare traditional matrix multiplication methods, including Strassen’s algorithm, Winograd’s algorithm, and tiled matrix multiplication, and implement a Q- learning-based agent to choose the most efficient algorithm adaptively. Our RL-driven approach enables real-time algorithm selection, balancing computational speed and resource usage across different matrix types and sizes. Experimental results demonstrate the system's ability to reduce execution time by selecting algorithms suited to specific scenarios, providing insights into algorithm efficiency and adaptability for various data sizes. Furthermore, this research suggests practical optimization strategies for handling large-scale computations in machine learning applications, including using parallel processing, GPU acceleration, and parameter tuning to minimize computational bottlenecks. This project underscores the potential of integrating RL for adaptive computation within AI workflows, offering a scalable solution for enhancing matrix operation efficiency in data-intensive applications

Applications of Vedic Mathematics in Computer Science

The convergence of ancient wisdom and modern computation is an intriguing frontier explored in this research paper, which investigates the potential application of Vedic Mathematics in the domain of Computer Science. Vedic Mathematics, originating from ancient Indian texts known as the Vedas, offers a distinct approach to mathematical problem - solving that emphasizes mental agility, pattern recognition, and elegant algorithms. This paper delves into the historical context of Vedic Mathematics, tracing its origins and key principles, and highlights its relevance in contemporary Computer Science. The intersection of Vedic Mathematics and Computer Science is illuminated through the identification of shared principles, such as pattern recognition and optimization. The paper explores specific Vedic Mathematics techniques that have the potential to enhance computational efficiency, including the "Nikhilam" method for subtraction and the "Vertically and Crosswise" method for multiplication. Case studies are presented to demonstrate the practical application of Vedic Mathematics techniques in Computer Science, spanning areas such as data processing, algorithmic optimization, and error detection and correction. However, this integration is not without its challenges and ethical considerations. The paper addresses concerns about accuracy and reliability, particularly when integrating unconventional mathematical techniques into modern computational paradigms. Cultural sensitivity and proper acknowledgment of the origins of Vedic Mathematics are paramount, as is the need to ensure that the techniques are accessible to a diverse audience. The ethical implications of cross - cultural knowledge exchange are also explored, emphasizing the importance of collaboration and respect. Looking ahead, the paper outlines potential research directions and implications for education in Computer Science. The integration of Vedic Mathematics offers opportunities for developing novel algorithms, enhancing problem - solving skills, and fostering cross - cultural understanding. Collaborations between mathematicians and computer scientists are highlighted as a promising avenue for innovation in algorithmic design. In conclusion, the fusion of Vedic Mathematics and Computer Science represents a dynamic interplay of ancient traditions and modern advancements. As we navigate the complexities of integration, we embark on a journey that transcends disciplinary boundaries, fosters cultural appreciation, and holds the potential to reshape computational methodologies in the digital age.

EVALUATION OF MAIZE HYBRID IN MULTI-LOCATIONAL TRIAL USING GGE BIPLOT AND AMMI MODEL

This study was executed to assess the twenty-one single cross maize hybrids with three local checks (BHM-12, BHM-13 and BHM-14) in five locations of Bangladesh for its adaptability and stability and also to study the genotypes and environment interaction. Yield data of twenty-four maize hybrids was analyzed through the additive main effects and multiplicative interaction (AMMI) and GGE biplot methods. Considering the grain yield, bi~1 and S2 di~0 value, it was observed that G10 and G16 were the higher yielding and suitable across the environments. On the other hand, G20 and G14 were higher yielding but were responsive to favorable environments. Among the five locations, the environment of Hathazari was the poorest, whereas Barishal and Dinajpur were the most favorable environments for maize production. When hybrids were compared with ideal genotypes, it was observed that G14, G16, G10, and G20 were closed to the ideal genotypes so that they can be more desirable than other tested hybrids. The AMMI biplot indicated that G24, G16, G13, G17 and G14 were positioned adjacent to the biplot’s origin which indicated their stability in performance across environments. Finally, stability analysis with the help of GGE and AMMI statistics identified two hybrids G14 and G16 that could be used as reference for future crop improvement program.

Soil health assessment of dressing and smelting slag field based on heavy metal pollution-buffer-fertility three aspects

The soil health of heavy metals in dressing and smelting slag field varies soil physicochemical properties. This study proposed a new soil health index based on heavy metal pollution-buffer-fertility for dressing and smelting slag field. Consequently, spatial distribution of soil physicochemical properties and heavy metals were varied, and correlated to each other. Soil buffer function and fertility played a much more important role in soil health in the dressing and smelting slag field located in Gejiu city, which can result in that soil health indexes were higher than those in Huili county, although the soil heavy metal pollution in the former was severer than that in the latter. Maximum values of soil health indexes for dressing and smelting slag field in Gejiu city were 3.84, 0.61, and 1.75 corresponding to additive, multiplicative, and maximum value composite methods, which were higher than those in Huili county with 2.25, 0.61, and 0.17. The former’s high value is concentrated in southeastern regions and low value in some western areas, the latter’s high value occurred in southeastern districts and low value in northwestern places. So this study unveils a novel perspective on the soil health consequences associated with soil heavy metal pollution-buffer-fertility three aspects.

A Short-Circuit Current Calculation Model for Renewable Power Plants Considering Internal Topology

With the large-scale integration of renewable energy into the grid, traditional short-circuit current (SCC) calculation methods for synchronous generators are no longer applicable to inverter-based non-synchronous machine sources (N-SMSs). Current SCC calculation methods for N-SMSs often use a single-machine multiplication method, which tends to overlook the internal variability of N-SMSs within power plants, leading to low calculation accuracy. To address this issue, this paper first derives an analytical expression for SCC in grid-connected inverters under low voltage ride through (LVRT) control strategies. Then, a single-machine steady-state SCC calculation model is proposed. Based on the classification of N-SMSs, a practical SCC calculation model for renewable power plants is introduced, balancing accuracy and computational speed. The feasibility of the model is validated through simulations. The proposed method enables simple calculations to obtain the steady-state voltage and SCC at the machine terminal, offering strong engineering practicality.

Conceptual study of subcritical assembly with neutron generator for reactor physics experiments and neutron utilization

In Japan, critical assemblies are regulated under the “Act on the Regulation of Nuclear Source Material, Nuclear Fuel Material and Reactions” which provides guidelines for the construction and the operation of nuclear facilities: the core configurations of nuclear experiments are constrained accordingly. In addition, the nuclear facilities are required to comply with strict administrations if the nuclear facilities have more than the certain amount of nuclear fuels. On the other hand, subcritical assemblies are not subjected to the nuclear reactor regulation because the subcritical assemblies are not classified as nuclear reactors. We propose the subcritical assembly with a pulsed accelerator neutron source, which uses nuclear fuels below the regulation amount to ease maintenance and to widen experimental capabilities. Conceptual study of such system was carried out to calculate the deviation between reactivity difference in critical state and that in subcritical state by using the MCNP 6.2 code and the JENDL 4 library. Neutron source multiplication method and pulsed neutron source method were performed. The deviation can be estimated around 10 % by setting detectors in appropriate positions. Additionally, neutron radiography was studied as a simple application. The geometrical shapes of sample rods were seen clearly.

On a Stable Multiplicative Calculus-Based Hybrid Parallel Scheme for Nonlinear Equations

Fractional-order nonlinear equation-solving methods are crucial in engineering, where complex system modeling requires great precision and accuracy. Engineers may design more reliable mechanisms, enhance performance, and develop more accurate predictions regarding outcomes across a range of applications where these problems are effectively addressed. This research introduces a novel hybrid multiplicative calculus-based parallel method for solving complex nonlinear models in engineering. To speed up the method’s rate of convergence, we utilize a second-order multiplicative root-finding approach as a corrector in the parallel framework. Using rigorous theoretical analysis, we illustrate how the hybrid parallel technique based on multiplicative calculus achieves a remarkable convergence order of 12, indicating its effectiveness and efficiency in solving complex nonlinear equations. The intrinsic stability and consistency of the approach—when applied to nonlinear situations—are clearly indicated by the symmetry seen in the dynamical planes for various parameter values. The method’s symmetrical behavior indicates that it produces accurate findings under a range of scenarios. Using a dynamical system procedure, the ideal parameter values are systematically analyzed in order to further improve the method’s performance. Implementing the aforementioned parameter values using the parallel approach yields very reliable and consistent outcomes. The method’s effectiveness, reliability, and consistency are evaluated through the analysis of numerous nonlinear engineering problems. The analysis provides a detailed comparison with current techniques, emphasizing the benefits and potential improvements of the novel approach.

Green spectrophotometric approaches applied to tertiary mixture for management of common cold and COVID-19 symptoms

Recently, cold and cough dosage forms have gained significant attention due to their use in the supportive protocols for managing COVID-19 symptoms. In this study, a pharmaceutical formulation containing Paracetamol (PAR), Guaifenesin (GUA), and Phenylephrine hydrochloride (PHE) was investigated for spectral resolution and quantification using advanced spectrophotometric methods. The spectra of these components were significantly overlapped and present in their combined tablet in a challenging ratio of 250:100:5 for PAR, GUA, and PHE, respectively. The established approaches were employed for the simultaneous determination of these drugs in their pharmaceutical formulation without interference from matrix excipients. The study involved various manipulation steps, allowing each component in the combination to be analyzed by more than one approach. Integrating these methods with smart mathematical techniques, the issue of spectral data overlap was resolved without the need for preliminary separation steps. The developed methods are dual wavelength, first derivative, derivative ratio, ratio difference, constant center coupled with spectrum subtraction, and constant multiplication paired with spectrum subtraction. The proposed methods were linear over the concentration range of 3.0–35.0 μg/mL for GUA and 3.0–30.0 μg/mL for PHE. While the PAR ranges for the first derivative and constant multiplication methods were 2.5–35.0 μg/mL and 2.5–25.0 μg/mL, respectively. Excellent linearity of the suggested methods was demonstrated by the high correlation coefficients (R2), ≥ 0.9998 for all the tested compounds. These methodologies were validated according to ICH guidelines. Validation results demonstrated excellent accuracy, with recovery percentages ranging from 98 to 102 %, and precision, with RSD values less than 2 %. The obtained results were statistically compared with the official ones using F-test, Student’s t-test, and one-way ANOVA, revealing no significant differences. The proposed methods are accurate, green, smart, fast, and cost-effective. Their compliance with Green Analytical Chemistry principles was evaluated and compared to a published method using various tools to enable a more holistic evaluation from different perspectives. The promising results revealed that the investigated methods are superior green alternatives for routine analysis of the cited drugs in laboratories with limited resources and without access to expensive instruments.

Evaluation of the implementation of Main Distribution Material (MDU) spare part procurement at seven customer service unit (ULP) in Semarang are using ABC analysis and time series forecasting

Evaluation of the Implementation of Main Distribution Material (MDU) Spare Part Procurement at Seven Customer Service Units (ULP) in Semarang Area using ABC Analysis and Time Series Forecasting (Case Study: PT PLN (Persero) UP3 Semarang) Time series forecasting is a method used to predict the number of future needs in a time series. This research was conducted at the company PT PLN (Persero) UP3 Semarang, where the author tried to deal with the company's problems related to the procurement of Main Distribution Material (MDU) which often occurs overstock or lack of stock. The author uses ABC analysis to classify goods based on value ratings ranging from the highest value so that three parts with group A are obtained for forecasting, kWH meters, 2 × 10 mm2 PWR cables, and 4A MCBs. Forecasting is done with four methods, the Multiplicative Decomposition method, the Holt-Winters method, the Moving Average method, and the Exponential Smoothing method. From the results of the error calculation, it is obtained that the Holt-Winters method obtains the lowest percentage error with results of 10% - 20% or is good to use for forecasting. The comparison between the forecasting results and the overall actual demand is above 70%, which means that the forecasting results are close to the actual demand that occurs, thus increasing the accuracy of the Holt-Winters forecasting method. Suggestions for improvement that can be given to the company are to apply the Holt-Winters forecasting method in forecasting MDU spare part inventory at PT PLN (Persero) UP3 Semarang.

COMPARING OF ARTIFICIAL NEURAL NETWORK AND MULTIPLICATIVE HOLT WINTERS EXPONENTIAL SMOOTHING METHODS IN FORECASTING DEMAND

General Background: Fluctuations in raw material orders pose significant challenges for business operators, especially during peak seasons like holidays and the new year, often resulting in shortages or excess inventory. Specific Background: This study focuses on forecasting demand for wallet products from UMKM Pengerajin Dompet Khas Tanggulangin (PDKT) by comparing two forecasting methods: Artificial Neural Networks (ANN) and the Multiplicative Holt-Winters method, which is tailored for seasonal data. Knowledge Gap: While existing literature recognizes the effectiveness of various forecasting techniques, there is limited comparative analysis of ANN and Holt-Winters specifically in the context of UMKM wallet production, highlighting the need for empirical validation. Aims: This research aims to identify the most accurate forecasting method to optimize raw material usage and production planning. Results: The findings indicate that the ANN method yields a superior Root Mean Square Error (RMSE) of 14.249, compared to 93.436 for the Holt-Winters method, establishing its higher predictive accuracy. Novelty: The study contributes to the field by providing a comparative analysis of forecasting methods tailored to the specific context of UMKM, demonstrating the efficacy of ANN over traditional methods. Implications: These results suggest that adopting ANN for demand forecasting can significantly enhance inventory management and production efficiency for PDKT MSMEs, ultimately leading to better resource allocation and reduced operational costs.

A NOVEL CONCEPT: THE PRODUCT OF TWO NEGATIVELY DIRECTED NUMBERS IS A NEGATIVELY DIRECTED NUMBER THOUGH THE NEGATIVE OF A NEGATIVE NUMBER IS A POSITIVE NUMBER

In this paper, the author proved that the product of two negatively directed numbers is a negatively directed number. This article is the outcome of previously published (2021-2024) ten (10) articles of this author. It is true that the negative of a negative number is a positive number. It has been done by applying the inversion process to a negative number. The process of inversion does not satisfy the basic concept of multiplication. Multiplication is defined as the adding of a number concerning another number repeatedly. So, the process of inversion does not comply with the fundamental concept of multiplication. According to the Theory of Dynamics of Numbers there exist three types of numbers: (1) Neutral or discrete numbers (2) Positively directed numbers (3) Negatively directed numbers. In general, we use four types of operators: addition (+), subtraction (-), multiplication (x), and division (÷) in mathematical calculations. Besides these, we use the negative sign (-) as an inversion operator. The positive sign (+) and negative sign (-) also represent the direction of neutral or discrete numbers. In this paper, the author introduced Fermat's Last Theorem: xn + yn = zn for n = 2 in Bhattacharyya's Theorem to prove that the product of two negatively directed numbers is a negatively directed number using the concept of the Theory of Dynamics of Numbers. In this paper, the author framed new laws of multiplication and inversion. Also, the author has given a comparative study between the conventional method of multiplication and the present concept of multiplication citing some practical examples. The author has become successful in finding the root of a quadratic equation in real numbers even if the discriminant, b2 – 4ac < 0 without using the concept of the imaginary number and also in determining the radius of a circle even if g2 + f2 < c, in real number without using the concept of complex numbers. With one example the author proved that this theorem is applicable in ‘Calculus’ also.

Temporal dynamics of land use land cover (LULC) changes and assessment of environmental and climatic parameters using remote sensing and GIS: A case study in Limboti Reservoir, Loha Taluka, Maharashtra, India

In this study, multispectral satellite data from Landsat 4-5 TM and Landsat 8 OLI/TIRS was used to analyse Land Use Land Cover (LULC) change detection and to retrieve environmental parameters. The LULC change detection was done between 2007 and 2021. This study used the supervised classification-maximum likelihood approach in ArcGIS 10.3 software to detect LULC in Loha Taluka, Maharashtra. The taluka was classified into four major LULC classes, viz. water, vegetation, settlement and barren-land. Changes in LULC directly or indirectly will also induce a change in the environmental parameters. Environmental parameters (water surface temperature, chlorophyll-a concentration and total suspended solids) were analysed for Limboti Reservoir. Data were predicted for next two years (2022 and 2023) using multiplicative seasonal decomposition and Holt-Winter’s multiplicative method and trends of each parameter were analysed using Mann-Kendall trend method. For both years, vegetative land was the most extensive LC in Loha Taluka, accounting for more than 60% of the total land area. However, throughout these years, urbanisation was rampant and vegetative land was converted to settlement. In these years, open water resources such as reservoirs, lakes and rivers covered a very small percentage of the total area, which is a serious threat to the ecosystem. As a result, proper water body management is essential; otherwise, these resources will be destroyed and will no longer be able to contribute to the area’s socioeconomic growth. Keywords: Change detection, Chlorophyll content, Land use land cover, Water quality, Water surface temperature

PERBANDINGAN METODE GARISMATIKA DENGAN METODE PERKALIAN BERSUSUN UNTUK MENINGKATKAN KEMAMPUAN BERHITUNG PERKALIAN PESERTA DIDIK KELAS IV SDN SERANG 9

This study aims to compare the use of the Garismatika method with the traditional column multiplication method in improving multiplication calculation skills among fourth-grade students at Sekolah Dasar Negeri Serang 09. The research method is quantitative with a nonequivalent control group design. The study sampled fourth-grade students, with each class consisting of 26 students. The results show that students who used Garismatika method demonstrated better improvement in multiplication skills compared to those who used the column multiplication method. The improvement in multiplication calculation skills is supported by hypothesis data obtained from the post-test results between the experimental and control groups, with a t-test value of 2.10 and a t-table value of 2.06. Additionally, the N-Gain data in the experimental group was 0.56, compared to 0.41 in the control group.

Effectiveness of Japanese Multiplication Method on Improving Grade Two Malaysian Students’ Multiplication Computation Fluency

Effectiveness of Japanese Multiplication Method on Improving Grade Two Malaysian Students’ Multiplication Computation Fluency