Higher Average Values Research Articles

The Mapping Students' Critical Thinking Ability on Vibration and Wave Material

Students' critical thinking skills are very important in facing the challenges of the 21st century. The purpose of this research is to determine the level of students' critical thinking. The benefit of this research is to obtain test results related to students' critical thinking abilities. The methodology in this study used a survey method involving 75 students who were taken randomly. The instrument used is in the form of critical thinking questions which are distributed via gform. The data obtained was analyzed using SPSS. Based on the survey results it is known that the number of subjects is 75 respondents consisting of 48 women and 27 men. Based on the survey results, it is also known that the average student's critical thinking skills are in the good category with a score that can be explained in detail, namely giving simple explanations (8.0), building basic skills (7.9), making inferences (7.8). provide further explanation (7.6) and set strategies and techniques in solving science questions (7.9). In conclusion, the construct that has the highest average value is to provide a simple explanation with an average value of 8.0 and the lowest is to provide a further explanation of 7.6. The recommendation from this study is that students' critical thinking skills still need to be improved and teachers can use the instruments that have been used.

Analyzing Milk Caramel Candy with the Addition of Emprit Ginger and Secang Wood Extract

This study added Emprit ginger extract and Secang wood extract to milk caramel candy. Adding Emprit ginger extract and Secang wood extract was expected to add nutritional value to milk caramel candy. The objectives of this study include (1) knowing the effect of adding Emprit ginger extract and Secang wood extract on the sensory quality of milk caramel candy, (2) finding out what people like, and (3) knowing the nutritional content in the form of water content and flavonoid content. The independent variable in this study was the addition of Emprit ginger extract and Secang wood extract, which were not the same, namely samples A (5%), B (7.5%), and C (10%). The data collection method in this study is to use subjective assessments, namely sensory tests and favorability tests, as well as objective assessments, namely laboratory tests of water and flavonoid content. The analytical method used in sensory tests is simple linear regression. The results showed an influence on the sensory quality of the experimental milk caramel candy in terms of color, aroma, texture, and taste. In the preference test sample, A has the highest average value in all aspects, and the sample with the lowest average value is sample C. The results of laboratory tests of water content in sample K were 2.91%, sample A was 4.26%, sample B was 5.62%, and sample C was 7.08%. Laboratory test results of flavonoid content of sample K are 0%, sample A as much as 0.16%, sample B as much as 0.24%, and sample C as much as 0.31%.

Wide-temperature-range thermoelectric n-type Mg3(Sb,Bi)2 with high average and peak zT values

Mg3(Sb,Bi)2 is a promising thermoelectric material suited for electronic cooling, but there is still room to optimize its low-temperature performance. This work realizes >200% enhancement in room-temperature zT by incorporating metallic inclusions (Nb or Ta) into the Mg3(Sb,Bi)2-based matrix. The electrical conductivity is boosted in the range of 300–450 K, whereas the corresponding Seebeck coefficients remain unchanged, leading to an exceptionally high room-temperature power factor >30 μW cm−1 K−2; such an unusual effect originates mainly from the modified interfacial barriers. The reduced interfacial barriers are conducive to carrier transport at low and high temperatures. Furthermore, benefiting from the reduced lattice thermal conductivity, a record-high average zT > 1.5 and a maximum zT of 2.04 at 798 K are achieved, resulting in a high thermoelectric conversion efficiency of 15%. This work demonstrates an efficient nanocomposite strategy to enhance the wide-temperature-range thermoelectric performance of n-type Mg3(Sb,Bi)2, broadening their potential for practical applications.

An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping

Data-driven flood susceptibility modeling is an efficient way to map the spatial distribution of flood likelihood. The quality of the flood susceptibility model relies on the learning technique and the data used for learning. The performance of learning techniques has been extensively examined. However, to date, the impact of data sampling strategies has received limited attention. Random sampling is widely favored because of its ease of use. It treats flood-related data as tabular and excludes their spatial dimensions. Flood occurrence is typically uneven over space. Therefore, non-flood sampling should not be completely random. To represent the impact of the spatial dimension, this study proposed a new sampling approach based on spatial dependence, called inverse-occurrence sampling. It selects more non-flood data in low-risk areas than in high-risk areas. The new sampling approach was compared with random and stratified sampling, using six machine learning techniques in two urban areas in Guangzhou, China, with distinct flood mechanisms, that is, Tianhe (flood density 1.5/km2, clustered distribution, average slope 9.02°, downtown district) and Panyu (flood density 0.15/km2, random distribution, average slope 4.55°, suburban district). Learning techniques include support vector machine (SVM), random forest (RF), artificial neural networks (ANNs), convolutional neural networks (CNNs), CNN-SVM, and CNN-RF. The main findings of this study were as follows: (1) Sampling approaches had a greater impact on model performance than learning techniques in terms of area under the receiver operating characteristic curve (AUC). The AUC variations caused by learning techniques ranged from 0.04 to 0.09. Meanwhile, the AUC variations caused by sampling approaches were between 0.15 and 0.22, all larger than 0.1. (2) The new sampling approach outperformed that of the other two sampling approaches for high average AUC values and small AUC variations. The outperformance is robust in regard to multiple learning techniques and different flooding mechanisms. AUCs in the inverse group had a narrower range (0.14–0.18 in Tianhe and 0.35–0.39 in Panyu) than in the random group (0.24–0.28 in Tianhe and 0.43–0.53 in Panyu) and the stratified group (0.23–0.30 in Tianhe and 0.42–0.48 in Panyu). (3) The most accurate learning technique for AUC was CNN-RF, followed by SVM, CNN-SVM, RF, CNN, and ANN. (4) ANN- and CNN-based models tended to produce polarized patterns in flood susceptibility maps, contradicting the ascending order of flood density with increasing susceptibility levels. Flood density outliers tended to appear in the models derived using RF and CNN-RF. Finally, the newly proposed sampling approach is suggested to be applied to flood susceptibility mapping to reflect the impact of spatial dependence.

THE USE OF PRO-GEAR INNOVATION IN IMPROVING THE UNDERSTANDING OF PROBABILITY TOPICS OF STATISTICS COURSES IN THE DEPARTMENT OF COMMERCE, PORT DICKSON POLYTECHNIC

The purpose of this study is to identify students' interest in and understanding of the probability topic in the Statistics courses after engaging in a demonstration learning session using the Pro-Gear innovation. The research methodology used is a descriptive and experimental method. A total of 42 first-semester Diploma in Marketing students were separated into two equal groups, A and B. Group A experienced demonstration learning that utilized Pro-Gear innovation, while Group B experienced conventional learning that lacked Pro-Gear innovation. The average score from the End of Chapter (EOC) evaluation of both groups was used to assess student understanding. The level of interest of students is analyzed using the survey form distributed to Group A, which went through demonstration learning using Pro-Gear innovation. Based on the average EOC score analysis, group A had a higher average score than group B. The average difference in EOC score is 7.7%. This shows that the level of understanding of a group of students who utilize Pro-Gear innovation is more advanced than that of a group that does not use Pro-Gear innovation. Meanwhile, the survey form analysis reveals a high average mean value for the eight items investigated. Thus, demonstration learning using the Pro-Gear innovation can enhance the understanding and interest of students in the probability topic.
 Keywords: Demonstration; Innovation; Probability

The Influence of Content Marketing and E-WOM on Customer Satisfaction through Brand Image in Content Creation Companies (Content Booster) in Malang City

The type of research used in this research is quantitative research. In this study the results obtained from the assistance of the SmartPLS Version 4 2023 program show that of the 4 variables tested, the one with higher results has a significant value, namely the E-WOM Variable (X2). with a total average obtained of 4.62 from several indicators in which it supports the highest score, namely the Valence of Opinion indicator of 4.73 where from the questionnaire questions distributed by customers the focus focuses on positive and negative opinions of a customer product packaged by Content Booster as Content Marketing which can affect the level of trust and review ratings in content distributed through E-WOM media. After getting the results from the descriptive research and hypothesis testing that have been described in this study, we can conclude as follows: 1. The overall results of the research using the help of the Smart PLS 4 2023 program show that what dominates in this study is the E-WOM variable with an average value 4.62 which in the assessment has a very good and high meaning. Supported by the high average value of each indicator, namely Intensity 4.0, Valence of Opinion 3.96, Content 3.86, Tone 3.94 and Timing 4.02 we can conclude that from the acquisition of data processing the use of E-WOM is currently very much needed in managing business and marketing. In this case E-WOM cannot stand alone, it is still supported by several variables, namely Content Marketing, Brand Image, in obtaining Customer Satisfaction in this multimedia business.

On winding reconstruction method for six-phase motor with single-phase winding open-circuit faults

Single-phase winding fault in six-phase permanent magnet synchronous motor (PMSM) will cause problems with reduced torque performance. For this problem, this paper analyses the reasons for the increase of torque ripple under single-phase winding open-circuit fault condition taking an 18-slot/10-pole (18S10P) six-phase surface mounted permanent magnet synchronous motor (SPMSM) as an example. Then, a torque performance improvement method by redistributing healthy phase windings to reconstruct the stator magnetic field is proposed in this paper. The proposed method not only can significantly reduce torque ripple caused by winding open-circuit faults but also can maintain a high average torque value. Moreover, this method is suitable for lots of six-phase motors with different pole-slot combinations. At last, considering the significant impact of harmonic contents in stator magnetic field on torque performance, an optimization method for armature current is proposed, which can decrease the torque ripple further. The simulation results by finite element analysis (FEA) show that the proposed method has significant superiority in motor fault operation.

Enhancing Practicality of Web-Based Mobile Learning in Operating System Course: A Developmental Study

Web-based Mobile Learning can enhance the learning experience in various educational contexts. However, in operating system courses, practical challenges arise when implementing a web-based mobile learning platform, which impacts the effectiveness and accessibility of learning materials for students. To overcome these challenges, this research and development (R&D) aims to improve the practicality of web-based mobile learning in operating system courses. The research adopts a systematic 4D (Define, Design, Develop, Disseminate) model to identify and explore strategies to optimize the practicality of the platform. Data collected from lecturers and students showed a high average value of practicality, 88.33% and 88.35%, respectively. This research contributes to improving the practical aspects of web-based mobile learning, thereby enhancing students’ learning experience and outcomes in the context of operating system courses.

Vegetation Dynamics of Sub-Mediterranean Low-Mountain Landscapes under Climate Change (on the Example of Southeastern Crimea)

In the context of a changing environment, understanding the interaction between vegetation and climate is crucial for assessing, predicting, and adapting to future changes in different vegetation types. Vegetation exhibits high sensitivity to external environmental factors, making this understanding particularly significant. This study utilizes geospatial analysis techniques, such as geographic information systems, to investigate vegetation dynamics based on remote sensing data and climatic variables, including annual air temperature, annual precipitation, and annual solar radiation. The research methodology encompasses data collection, processing, and analysis, incorporating multispectral imagery and multilayered maps of various parameters. The calculation of the normalized difference vegetation index serves to evaluate changes in vegetation cover, identify areas experiencing variations in green biomass, and establish strategies for the future development of different vegetation types. During the period from 2001 to 2022, the average normalized difference vegetation index value in the Southeastern Crimea region amounted to 0.443. The highest average values were recorded in the year 2006, reaching a magnitude of 0.469. Conversely, the lowest values were observed in the years 2001–2002, constituting 0.397. It has been ascertained that an overarching positive trend in the evolution of NDVI values from 2001 to 2022 is apparent, thus implying a notable augmentation in vegetative biomass. However, adversarial trends manifest in discrete locales adjacent to the cities of Sudak and Feodosia, along with the coastal stretches of the Black Sea. Correlation analysis is employed to establish relationships between vegetation changes and climatic indicators. The findings contribute to our understanding of the vulnerability of various vegetation types and ecosystems in the Southeastern Crimea region. The obtained data provide valuable insights for the development of sustainable vegetation resource management strategies and climate change adaptation in the region.

Microscopic Video-Based Grouped Embryo Segmentation: A Deep Learning Approach.

The primary aim of this research is to enhance the utilization of advanced deep learning (DL) techniques in the domain of in vitro fertilization (IVF) by presenting a more refined approach to the segmentation and organization of microscopic embryos. This study also seeks to establish a comprehensive embryo database that can be employed for future research and educational purposes. This study introduces an advanced methodology for embryo segmentation and organization using DL. The approach comprises three primary steps: Embryo Segmentation Model, Segmented Embryo Image Organization, and Clear and Blur Image Classification. The proposed approach was rigorously evaluated on a sample of 5182 embryos extracted from 362 microscopic embryo videos. The study's results show that the proposed method is highly effective in accurately segmenting and organizing embryo images. This is evidenced by the high mean average precision values of 1.0 at anintersection over union threshold of 0.5 and across the range of 0.5 to 0.95, indicating a robust object detection capability that is vital in the IVF process. Segmentation of images based on various factors such as the day of development, patient, growth medium, and embryo facilitates easy comparison and identification of potential issues. Finally, appropriate threshold values for clear and blur image classification are proposed. The suggested technique represents an indispensable stage of data preparation for IVF training and education. Furthermore, this study provides asolid foundation for future research and adoption of DL in IVF, which is expected to have a significant positive impact on IVF outcomes.

Prediction of diabetes disease using an ensemble of machine learning multi-classifier models

Background and objectiveDiabetes is a life-threatening chronic disease with a growing global prevalence, necessitating early diagnosis and treatment to prevent severe complications. Machine learning has emerged as a promising approach for diabetes diagnosis, but challenges such as limited labeled data, frequent missing values, and dataset imbalance hinder the development of accurate prediction models. Therefore, a novel framework is required to address these challenges and improve performance.MethodsIn this study, we propose an innovative pipeline-based multi-classification framework to predict diabetes in three classes: diabetic, non-diabetic, and prediabetes, using the imbalanced Iraqi Patient Dataset of Diabetes. Our framework incorporates various pre-processing techniques, including duplicate sample removal, attribute conversion, missing value imputation, data normalization and standardization, feature selection, and k-fold cross-validation. Furthermore, we implement multiple machine learning models, such as k-NN, SVM, DT, RF, AdaBoost, and GNB, and introduce a weighted ensemble approach based on the Area Under the Receiver Operating Characteristic Curve (AUC) to address dataset imbalance. Performance optimization is achieved through grid search and Bayesian optimization for hyper-parameter tuning.ResultsOur proposed model outperforms other machine learning models, including k-NN, SVM, DT, RF, AdaBoost, and GNB, in predicting diabetes. The model achieves high average accuracy, precision, recall, F1-score, and AUC values of 0.9887, 0.9861, 0.9792, 0.9851, and 0.999, respectively.ConclusionOur pipeline-based multi-classification framework demonstrates promising results in accurately predicting diabetes using an imbalanced dataset of Iraqi diabetic patients. The proposed framework addresses the challenges associated with limited labeled data, missing values, and dataset imbalance, leading to improved prediction performance. This study highlights the potential of machine learning techniques in diabetes diagnosis and management, and the proposed framework can serve as a valuable tool for accurate prediction and improved patient care. Further research can build upon our work to refine and optimize the framework and explore its applicability in diverse datasets and populations.

Karakteristik Organoleptik Dendeng Batokok dengan Bahan Pengasap dan Lama Pengasapan yang Berbeda

The objectiveof this study was to studythe characteristics of dendeng batokok that made with different duration of fumigationand the use of coconut shell, medang sawdust and rice husk as smoke material. Smokedduration was conducted during each 30, 60 and 90 minutes. Parameters observed during research were organoleptic attributes (colour, texture, aroma and taste). Theexperimental design used complete randomized design (CRD) with three replications. Nonparametric data of organoleptic tests processed statistically by Kruskal-Wallis method. Data processing using the program Minitab 15 version. The results ofthis study indicatethat; 1) the selected of dendeng batokok with the highest average value is 60 minutes for coconut shell smoking during, 90 minutes for medang sawdust smoking medang and 90 minutes for rice husk smoking, (2) the use of beef and buffalo meat with smoking coconut shell, medang sawdust and rice husk showed no real difference on organoleptic characteristics.

FedScore: A privacy-preserving framework for federated scoring system development

ObjectiveWe propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. Materials and methodsThe FedScore framework includes five modules: federated variable ranking, federated variable transformation, federated score derivation, federated model selection and federated model evaluation. To illustrate usage and assess FedScore’s performance, we built a hypothetical global scoring system for mortality prediction within 30 days after a visit to an emergency department using 10 simulated sites divided from a tertiary hospital in Singapore. We employed a pre-existing score generator to construct 10 local scoring systems independently at each site and we also developed a scoring system using centralized data for comparison. ResultsWe compared the acquired FedScore model’s performance with that of other scoring models using the receiver operating characteristic (ROC) analysis. The FedScore model achieved an average area under the curve (AUC) value of 0.763 across all sites, with a standard deviation (SD) of 0.020. We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models. ConclusionThis study demonstrates that FedScore is a privacy-preserving scoring system generator with potentially good generalizability.

The combined effect of highly variable DC current – Simulated from solar irradiance data – And electrolyte quality on zinc electrowinning

This paper is the second in a series of two papers focusing on the applicability of highly variable direct current (DC) for the production of high quality cathodes by electrowinning. Different current profiles were simulated from solar irradiance data and then applied to electrowinning cell using a precision programmable power supply. The simulated DC current was applied to the zinc electrowinning process, which was chosen as a case study due to its intensive energy consumption. The present work discusses the combined effect of highly variable current with the concentration of magnesium ions (Mg2+) in the zinc sulfate electrolyte. The impact of this impurity is relevant due to the complex mineralogy of the zinc ores available for processing (i.e., magnesium is one of the major elements contained in low-grade silicate ores). The results showed that even over a wide range of applied current densities (highest average value = 697 A m−2; highest peak value = 1486 A m−2), the average values of current efficiency calculated for the process, according to the different types of solar irradiance variability, are comparable. The lowest value (90.3% for intermittent day) could be explained by high amplitude variations in the intensity of the current density, which enhances the hydrogen evolution rate. The average values of current efficiency decreased with increasing impurity concentration in the electrolyte; decreases of 0.8–2.0% units in the average value of current efficiency were calculated at 15 g L−1 Mg2+. At this concentration, an increase of 2.1–6.8% in the average values of specific energy consumption was calculated, which was explained by the observed decrease in current efficiency and a slight increase in the cell voltage due to a decrease in the electrolyte conductivity. The deposits formed in the presence of 15 g L−1 and 30 g L−1 Mg2+ in the electrolyte showed a relatively greater number of pores, which, however, did not cause them to fracture or break during stripping.

The Effect of Medicine Ball Throw Exercise on Volleyball Servicing Ability in Pusri Palembang Athletes

This experiment used a pretest-posttest control group design technique. The Quasi Experimental Design or Group Pretest Posttest Design was employed in this investigation. Athlete Pusri Palembang provided the sample for this study, which included 20 athletes. In the investigation, the retrieval procedure samples This approach employs random sampling. The findings of the study support the concept. Because the p0,000 value is less than 0.05, this indicates that medicine ball throw workouts have a substantial effect on Service Ability for Volleyball Games. Where posttest results have a higher average value than pretest scores. It is possible to infer that there is an affect technique Medicine Ball Throw exercise against Service Ability for Volleyball Games in Volleyball Club Athletes Pusri Palembang at the alpha level of 0.05 with a 95% confidence level.

Electronic, nonlinear optical, UV–vis and NBO analysis of methyl methacrylate for optoelectronic and optical applications: DFT study and impact of conformation

Using Density Functional Theory (DFT) and time-dependent DFT (TD-DFT), we studied a compound widely used in daily life, namely: Methyl Methacrylate (MMA) monomer in its two stable isomers, cis-MMA and trans-MMA. The impact of conformation on the optical and electronic properties of MMA was highlighted. Structural parameters and thermodynamic properties were also assessed, and allowed to discuss the stability and reactivity of this compound. In addition, analysis of optoelectronic and electronic properties, global reactivity descriptors, natural bond orbital (NBO), and hole and electron reorganization energies was performed. The absorption and fluorescence properties, as well as the linear and nonlinear optical (NLO) properties of MMA were also investigated. The findings show that MMA is an insulator due to its wide band gap of 6.20 eV. It also exhibits good optoelectronic properties with high average electric field values of about 5.90 × 109 Vm−1 for cis-MMA and 5.42 × 109 Vm−1 for trans-MMA and high electric displacement values of at least 14.65 × 10-2 cm−2 for cis-MMA and 14.33 × 10-2 cm−2 for trans-MMA, suggesting a potential use for the design of piezoelectric and pyroelectric materials. In addition, trans-MMA has a dielectric constant close to that of good insulators; while cis-MMA exhibits a dielectric behaviour close to that of polymeric substances. Further, these materials are thermodynamically stable in its two conformations, with a good reactivity which can lead to good ability of polymerization. The analysis of the UV–vis spectra revealed that both forms of MMA absorb and emit mainly in the UV and that the Stokes shift of MMA is low, reducing its potential use in devices such as solar cells. At NLO level, MMA isomers exhibit weak optical properties such as second- and third-order optical susceptibility and cannot suitable for second and third harmonic generation; however, the conformation enhances second-order NLO response by at least 2 times when moving from cis- to trans-MMA, whereas the third-order response is almost unaffected. In addition, the conformation also affects the electronic and optoelectronic properties of MMA.

Investigation of coastal ammonium aerosol sources in the Northwest Pacific Ocean

Determining the magnitude and origins of nitrogen (N) deposition in the open ocean is vital for understanding how anthropogenic activities influence oceanic biogeochemical cycles. Excess N in the North Pacific Ocean (NPO) is suggested to reflect recent anthropogenic atmospheric deposition from the Asian continent, changes in nutrient dynamics due to marine N-fixation, and/or lateral transport of nutrients. We investigate the impact of anthropogenic and marine sources on reactive N deposition in the NPO, with a focus on ammonium (NH4+), an important bioavailable nutrient, using aerosol samples (n = 108) collected off the coast of China (Changdao Island). This study site is used as a proxy for continental emissions that can be exported and subsequently deposited to the ocean. The NH4+ concentration of aerosol samples varied seasonally (p < 0.05), with a higher average value in winter (2.8 ± 1.1 μg/m3) and spring (1.9 ± 0.8 μg/m3) compared to autumn (0.7 ± 0.6 μg/m3) and summer (1.4 ± 0.4 μg/m3). The isotopic composition of aerosol NH4+ varied seasonally, with higher averages in spring (13.3 ± 7.9‰) and summer (15.6 ± 6.2‰) compared to autumn (3.2 ± 2.5 ‰) and winter (3.8 ± 11.4 ‰). These seasonal patterns in the isotopic composition of NH4+ are investigated based on correlations of aerosol chemical species, seasonal shifts in transport patterns, partitioning of ammonia/ammonium between the gas and particle phase, and continental versus marine sources of ammonia. We find that anthropogenic activities, mainly agricultural practices (e.g., volatilization, fertilizer, animal husbandry), are the primary sources of NH4+ deposited to the NPO.

A Holocene lacustrine record of variations in chemical weathering intensity in the Mu Us Desert, China

The Mu Us Desert is an ideal location to study environmental changes during the Late Quaternary, because of its unique characteristics. The Abaoyan (ABY) profile, a typical aeolian-lacustrine profile located at the eastern edge of the desert, was investigated in this study. A basic chronological framework was established based on a combination of radiocarbon dating by accelerator mass spectrometry and in situ sedimentary phase identification. Furthermore, changes in regional chemical weathering intensity since the Holocene were evaluated through comprehensive analysis of the collected samples in terms of grain size, loss on ignition (LOI), chromaticity, geochemical element contents, and soil micromorphology. The results showed that the ABY profile was under the influence of primary and moderate chemical weathering. Regional paleoclimatic environmental changes could be divided into four stages. During Stage I (before 12.6 ka BP; pre-Holocene), the ABY profile was dominated by aeolian sand, showing a coarse average grain size, low LOI, and high chromaticity values, which may have indicated a dry and cold period. During Stage II (12.6–10.3 ka BP; early Holocene), the ABY profile was dominated by lacustrine sediments, indicating a shallow lake water environment with strong chemical weathering and a warm and humid climate. During Stage III (10.3–4.2 ka BP), the profile was dominated by sandy peat deposits with a relatively large proportion of clay particles suggesting that the intensity of chemical weathering was relatively strong in the region during this period and the climate was mainly warm and humid. During Stage IV (after 4.2 ka BP), the sediment was dominated by aeolian sand and secondary loess, the chromaticity increased, the LOI reduced, and the regional chemical weathering intensity was weaker. The change in chemical weathering intensity in this region was inferred to be a positive response to the Holocene East Asian monsoon circulation.

Trends in weather-related marine cargo insurance claims: A South African perspective

Background: Marine cargo insurance covers the loss or damage to goods while in transit. It is an important supply chain risk management tool for organisations involved in international trade, especially in regions where cargo insurance penetration is low, and protection gaps are high. However, marine cargo insurance faces new challenges because of increasing frequency and severity of extreme weather events. This study focuses on the case of South Africa: a developing country with a strategic role in connecting Africa with the global market.Objectives: The research analyses the trends, differences, and seasonality of weather-related marine cargo insurance claims in South Africa from 2013 to 2022.Method: Statistical and general claims analysis was performed on a master dataset of 17 727 claims to answer the three research questions related to the research objectives.Results: The results show that weather-related marine cargo insurance claims have been increasing over the past decade, creating challenges and disruptions to the supply chain network in South Africa. Furthermore, this research shows that weather-related claims have higher average values and follow a seasonal pattern compared with non-weather-related events on supply chains in South Africa.Conclusion: This research reveals the growing impact of weather-related events in South Africa.Contribution: It provides practical and theoretical implications for supply chain managers and insurers to manage weather-related risks more effectively and proactively contributes to the literature by offering empirical insights from a developing country perspective, and provides considerations for future work.

Utilization of Green Spinach Juice In Making Rolled Omelet

Dadar roll is one of the traditional Indonesian snacks commonly found in traditional markets. Spinach contains lots of Vitamins A and C and a little Vitamin B. Spinach also contains important mineral salts such as calcium, phosphorus, and iron. The research method used is experimental research with the experimental design carried out is a Complete Randomized Design with 3 treatments. The highest average value is indicated by treatment C in terms of color, aroma, texture, and taste. Treatment C is made from rolled omelet with 100gr green spinach + 550cc water (600cc spinach juice). It can be implied that the best C treatment is preferred by the panelists. With an assessment in terms of color, treatment C is the most preferred sample with a mean value of 4.80. In terms of aroma, treatment C is the most preferred sample with a mean value of 4.56. In terms of texture, treatment C is the most preferred sample with a mean value of 4.24. And in terms of taste, treatment C is the most preferred sample with a mean value of 4.28.