Data Sources Research Articles

Pengaruh Perubahan Kurikulum Pendidikan Terhadap Motivasi Belajar Siswa

This research aims to investigate the impact of curriculum changes on students' learning motivation at the secondary education level. The curriculum is a key element in the education system that can influence the learning process and student achievement. The data collection technique uses qualitative with a literature study approach. The data source used is a literature study, the data analysis technique used is a comparative study of the literature and identifying the impacts of main curriculum changes on student learning motivation, both positive and negative impacts. The research results show that curriculum changes can have varying impacts on student learning motivation. Positive impacts can occur when curriculum changes include refreshing learning materials, a more interactive teaching approach, clear educational goals, fair evaluation, and challenges that are appropriate to students' ability levels.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Feeding global aquaculture.

The growth of animal aquaculture requires ever more feed. Yet, fish and crustacean farming is argued to be sustainable because wild fish use is low and has improved over time. Here, accounting for trimmings and by-products from wild fish in aquaculture feed, and using four different sources of industry-reported feed composition data, we find ratios of fish inputs to farmed outputs of 0.36 to 1.15-27 to 307% higher than a previous estimate of 0.28. Furthermore, a metric that incorporates wild fish mortality during capture and excludes unfed systems raises the wild fish mortality-to-farmed fish output ratio to 0.57 to 1.78. We also evaluate terrestrial ingredients in aquaculture feeds. Widely cited estimates of declines in wild fish use from 1997 to 2017 entailed a trade-off of more than fivefold increase in feed crops over the same period. Our assessment challenges the sustainability of fed aquaculture and its role in food security.

Key Drivers of Consumption, Conceptual, Sensory, and Emotional Profiling of Cheeses Based on Origin and Consumer Familiarity: A Case Study of Local and Imported Cheeses in Greece

The origin of a product, consumer familiarity, and purchasing identity are factors that affect the perception of cheese consumption. The present study aims at identifying consumers’ conceptualizations and attitudes towards local Greek cheeses of the North-Aegean Sea islands, such as Ladotyri, Graviera, Kasseri, Kaskavali, Melichloro, and Kalathaki, some of which have a Protected Designation of Origin (PDO) status, as opposed to cheeses of non-Greek origin, such as Cheddar, Regatto, and Gouda. Sensory and emotional attributes of local, local PDO, and imported cheeses, as well as drivers associated with consumers’ choice and acceptance above and beyond their sensory attributes, were studied using three methods: (a) flash profile to gain insight into the sensory positioning of products and description of samples; (b) qualitative analysis of focus groups to pinpoint consumer knowledge, preference, and consumption criteria; and (c) a new methodology for natural language processing and sentiment analysis of social media posts to determine consumer conceptualizations. Social media posts have proven to be a valuable source of linguistic and cultural data for cheeses. Local cheeses, including PDO products, were found to be linked to village life and family gatherings, home, tradition, and childhood memories, with saltiness and hardness being their main sensory attributes. Imported cheeses were linked to fast food, pizza, and snacking, with elasticity and gumminess as prominent sensory qualities. The main criteria for purchase were intended usage and versatility, taste and texture, price, and familiarity. The findings provide key sensory attributes, information about consumer purchasing criteria, and relevant vocabulary for the promotion of cheeses as agri-food and gastronomic identity key products.

Pancasila as State Ideology and Pillar of Religious Harmony in Indonesian Society

Religious diversity in a country is natural, but even so, an arrangement within the country is needed to accommodate differences in religion and belief. Without a fair arrangement, this can make the state less stable and cause religious conflicts in a country.This article examines Pancasila as the state ideology in forming religious tolerance. This research is a qualitative research with a literature study approach, namely by using books, documents, journals and other related sources as sources of research data. This study discussed about Pancasila as state ideology and religion, Pancasila Philosophy in Islamic Views, and Pancasila as an ideology forming religious tolerance. While data analysis is carried out by grouping, sorting and then looking for themes that are in accordance with the focus of research for later interpretation. Results of studi show;1) Pancasila as the state ideology has been fully supported by religious normativity. 2) The values and norms contained in Pancasila have compatibility with the values that become the teachings of Islam. and 3) Pancasila plays an important role in creating religious tolerance, mutual respect and respect for adherents of other religions, and not forcing beliefs on other parties.

Dual-view jointly learning improves personalized drug synergy prediction.

Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples. We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and make complementary contributes to the final prediction of drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies. Source code and data are available at https://github.com/LiHongCSBLab/JointSyn. Supplementary data are available at Bioinformatics online.

Ancient Fishing Strategies for the Extinct Thicktail Chub (Gila crassicauda) in the Sacramento-San Joaquin Delta

ABSTRACT Now extinct, Thicktail chub (Gila crassicauda) was endemic to central California and was a key component of local fisheries before the 1800s. Little is known about their biology, behavior, ecology, or role in precontact Native American fisheries. Archaeological sites contain large numbers of thicktail chub bones and represent a key source of data to fill our considerable gap in knowledge. We developed regression equations to convert thicktail chub skeletal elements to estimates of standard length (SL). Bones from two contemporaneous precontact Late Period (ca 700 -200 cal BP) sites, CA-CCO-138, and CA-CCO-647, in the Sacramento-San Joaquin Delta, show that precontact thicktail chub achieved a maximum standard length (SL) of 306 mm. Bone size distributions suggest spearing was the most likely capture technique, likely during a spring-summer nearshore fishery. The results contribute vital information to thicktail chub historical ecology and their key role in precontact Indigenous fisheries.

Variables Influencing Participants’ Choice of Training Institutions for Training Program

This study compares variables influencing Participants’ choice of training institutions for participant program in Kathmandu Valley. Choice of training institutions for training program is taken as dependent variable. Costs, locations, academic career goal, training institutions affiliations and environment are independent variables. The primary sources of data are utilized to reach the opinions of respondents with respect to choice of training institution for training program in Kathmandu Valley. The survey is based on one hundred respondents. To obtain the purpose of the study, structured questionnaires are framed. The regression models are projected to trial the significance and impact of variables affecting choice of the training institutions for training program in Kathmandu Valley. The result shows that location, environment and Training Academic affiliation are positively connect to choice of training institutions for training program. It signifies that better the environment and Training Academics affiliation higher would be the choice of liked training institutions. The result also shows that more proper the location of the training institutions, greater is the chance of selecting the training institutions for training program. The regression result shows the cost, environment and training institutions affiliation have positive impact on choice of training institutions for training program. In the same manner, the beta coefficients for carrier goal and locations have significant positive impact of the selecting training institutions for training program, which is significant at 5 percent level of significance. Overall, the selecting of training institutions for training program in Kathmandu Valley is affected by the carrier goals followed by the location.

Efficacy and safety of sucroferric oxyhydroxide versus sevelamer carbonate: A systematic review and meta-analysis.

Phosphate binders are commonly used in patients receiving kidney replacement therapy (KRT), aiming to reduce and maintain serum phosphorus. Chronic kidney disease-mineral and bone disorder has been linked to reduced lifespan and worsened quality of life. This study aims to examine the efficacy and safety of sucroferric oxyhydroxide versus sevelamer carbonate in patients receiving KRT. The data sources examined were MEDLINE (PubMed), Scopus, and the Cochrane Central Register of Controlled Clinical Trials with a search deadline of October 2023. We examined randomized controlled trials that compared sucroferric oxyhydroxide versus sevelamer carbonate in the adult population receiving KRT. We performed a meta-analysis combining the data from trials, using R-studio. Inclusion criteria were met by five randomized trials. There was no statistically significant difference in the reduction of serum phosphorus between the two groups (MD: -0.07 mmol/L, 95% CI-random effects: -0.15 to 0.02). In the same line, a non-statistically significant difference was observed in serum i-PTH reduction between the two drugs (MD = -1.53 mg/dL, 95% CI = (-4.45, 1.4), p = 0.26, random effects model). No statistically significant difference was observed in all adverse events between the two groups (odds ratio: 1.11, 95% CI: 0.65-1.88, random effects model). Further analysis of gastrointestinal adverse events revealed that sevelamer carbonate increases gastrointestinal adverse events by up to 60% (odds ratio: 1.60, 95% CI: 1.31-1.97, common (fixed) effect model). This meta-analysis of randomized trials showed that both drugs, sucroferric oxyhydroxide and sevelamer equally and effectively controlled serum phosphorus levels, whereas sucroferric oxyhydroxide revealed a better profile in terms of gastrointestinal adverse events. Sucroferric oxyhydroxide is a valuable option for patients receiving KRT when sevelamer carbonate is more difficult to tolerate.

Analysis of Factors Affecting Participation of Smallholder Farmers in Wheat Cluster Farming: The Case of Gasera District, Bale Zone, Oromia Region, Ethiopia

Agriculture is the main means of livelihood in Africa. Wheat is one of the globally produced and marketed cereal crops which cover 15% of the total sowing areas of cereal crops in the world. This study focused on the analysis of factors affecting smallholder farmer’s participation in wheat cluster farming in Gasera District of Bale zone. A multi-stage sampling method was employed for this study. Simple random sampling technique was used to select 259 sample respondents in three selected Kebeles out of 24 total kebeles in the woreda. Primary data sources were generated using an interview schedule, five Focus Group Discussions, and eight key informant interviews. Besides, secondary data were obtained from literature and the Woreda Agricultural Office. Both descriptive statistics and econometric models were employed to analyze the collected data. The descriptive statistics results showed that from the total sampled households, 169 (65.3%) were participant and, 90 (34.7%) were non-participant smallholder farmers of wheat cluster farming technology. According to the probit regression model, education status, wheat land, livestock size, frequency of extension contact, credit access, availability of fair price, market information, agricultural input access, training access and membership in a cooperative had a positive influence on farmers’ decisions to participate in wheat cluster farming. Therefore, the local community, Woreda Agriculture Office and research institutes need to expand cluster farming technology to increase wheat production and policies aiming at increasing farmers’ awareness of producing wheat in cluster to increase yield of farmers.

Building a community-driven bioinformatics platform to facilitate Cannabis sativa multi-omics research.

The web portal can be accessed at www.icgrc.info.

Causes and Effects of Academic Staff Turnover Intentions on Organizational Performance: Comparative Studies on Ambo and Haramaya Universities

The purpose of this study was to conduct a comparative analysis of the causes of academic staff turnover intentions and their impact on the performance of Ambo and Haramaya Universities. The study primarily focused on factors such as socio-demographic characteristics, job satisfaction, compensation, work environment, and leadership style as determinants of academic staff turnover. A stratified sampling method was used to determine the sample population, while a convenience sampling technique was employed to collect data from the sample. Out of a total population of 1,672 academic staff members, a sample size of 143 was determined using Yamane's sampling formula. Of the 143 distributed questionnaires, 133 were returned, with 10 remaining uncollected. Both primary and secondary data sources were utilized, with primary data gathered through questionnaires. The research was designed as a descriptive and explanatory study to both describe relevant statistical data and explain the causes and effects observed in the study. Data were analyzed using descriptive statistics, including frequencies, percentages, and numerical values, as well as inferential statistics, such as correlation analyses and multiple regression models. University performance was measured through various indicators, including stakeholder engagement and satisfaction, revenue generation from business entities, quality of education, research and community services, the expansion of universities with modern facilities, and the number of academic programs offered. The findings revealed that the causes of academic staff turnover have a positive relationship with university performance, while turnover itself has a negative relationship with university performance. This implies that if universities effectively manage the causes of academic staff turnover, their overall performance is likely to improve.

Linguistic Strategies in Political Discourse: Hedges and Boosters in the 2024 US Presidential Debate

This study examines the strategic use of hedges and boosters in the first US presidential debate of June 26, 2024, between Joe Biden and Donald Trump. These linguistic devices are crucial in shaping political discourse by modulating confidence and authority. Hedges introduce ambiguity and caution, while boosters amplify certainty and assertiveness. Using the official debate transcript as the primary data source, this analysis employs a qualitative content analysis to identify and categorize these strategies. It highlights distinct usage patterns: Joe Biden frequently uses hedges to express caution and acknowledge complexities, while Donald Trump relies heavily on boosters to project unwavering confidence and decisiveness. Biden’s strategic use of hedges allows him to navigate contentious issues with a balanced tone, while Trump’s emphasis on boosters strengthens his assertive stance and enhances his perceived authority. This research fills a gap in existing literature by specifically analyzing these linguistic devices within the context of presidential debates. The findings provide insights into how both candidates use hedges and boosters to influence voter perceptions and address rhetorical challenges, contributing to a deeper understanding of political communication strategies and their effects on audience reception.

J-Curve For Indonesia’s Trade Balance with Non-Linier ARDL

The performance of Indonesia’s Balance of Payments (BOP), especially for the trade balance/current account, which recorded a surplus and adequate foreign exchange reserves, controlled political conditions, and attractive yields, can impact the exchange rate. This improvement in the exchange rate further encouraged the entry of foreign investors, which caused the Indonesian economy to continue to grow when the world experienced a financial crisis. This study analyzes asymmetric models to prove the Marshall Lerner Condition of Indonesia with trading partners. Using NARDL methods, this study uses monthly data from 2005 to 2021 from the International Federal Reserve (IFS), a data source for foreign trade activities. The result of this study is a J curve in Indonesia’s trade pattern with its trading partners, formed in the United States, Singapore, Vietnam, and Japan. For the nonlinear ARDL method, the J curve is formed in trading partners Netherlands, Germany, Korea, Singapore, United Kingdom, Vietnam, and Japan. The implementation of Indonesia’s exchange rate policy should be followed by a policy that can suppress the exchange rate against inflation because if this policy is not followed, it will not significantly impact Indonesia’s trade performance in the long run.

Site Selection of Large Shopping Malls in Wanbailin District of Taiyuan City

The construction and site selection of shopping malls are directly related to their commercial operation effectiveness and market competitiveness. Some scholars have conducted extensive research in this field, particularly achieving notable results in terms of influencing factors, methodologies, and technological applications for site selection, yet there are still some deficiencies. Traditional site selection methods primarily rely on qualitative analysis and empirical judgment. However, with technological advancements, Geographic Information System technology has gradually been applied to shopping mall site selection. GIS technology enables the integration of multiple data sources, spatial analysis, and visualization, providing a scientific basis for site selection decisions. This article utilizes ArcGIS software to construct an application model for shopping mall site selection in Wanbailin District. Through spatial analysis, eligible areas are identified, achieving a scientific and visual approach to site selection. By considering the main factors affecting the location of large shopping malls in Wanbailin District and using ArcGIS as the basic platform, an application model is built to find the optimal location for building shopping malls. The results show that introducing GIS into mall location planning improves efficiency and accuracy while providing a reliable basis for scientific location.

Increasing expenditures on home- and community-based services: Do home care workers benefit?

To examine the association of Medicaid home- and community-based services (HCBS) expenditures on the home care workforce. We use two national, secondary data sources from 2008 to 2019: state-level Medicaid HCBS expenditures and the American Community Survey, in which we identify direct care workers in the home (i.e., home care workers), defined as nursing, psychiatric, and home health aides or personal care aides working in home health care services, individual and family services, and private households. Our key explanatory variable is HCBS expenditures per state per year. To estimate the association between changes in Medicaid HCBS expenditures and the workforce size, hourly wages and hours worked, we use negative binomial, linear, and generalized ordered logit regression, respectively. All models include demographic and socioeconomic characteristics, the number of potential HCBS beneficiaries (individuals with a disability and income under the federal maximum income eligibility limits), indicators for minimum wage and/or overtime protections for direct care workers, wage pass-through policies, and state and year fixed effects. We exclude states with incomplete reporting of expenditures. States' HCBS expenditures increased between 2008 and 2019 after adjusting for inflation and the number of potential HCBS beneficiaries. Yet, home care workers' wages remained stagnant at $11-12/h. We find no association between changes in Medicaid HCBS expenditures and wages. For every additional $1 million in Medicaid HCBS expenditures, the expected number of workers increases by 1.2 and the probability of working overtime increased (0.0015% points; p < 0.05). Results are largely robust under multiple sensitivity analyses. We find no evidence of a statistically significant relationship between changes in state-level changes in Medicaid HCBS expenditures and worker wages but do find a significant, but small, association with hours worked and workforce size.

Data fusion method for aircraft surveillance in flight zone based on Trans-Attention

Aiming at the problem of low monitoring accuracy and position jump of single monitoring source of aircraft in the flight zone, a method of aircraft monitoring data fusion based on Transformer and attention mechanism is proposed. Firstly, the encoder structure of Transformer is used to extract features of each monitoring source data respectively, and then weight values are assigned to different monitoring sources through the attention mechanism. Finally, regression calculation is performed through the fully connected network to obtain the final fusion result. The monitoring data of the surface surveillance radar and the broadcast automatic dependent surveillance system are selected as the fusion source, and the multi-point positioning data is used as the true label. The experimental results show that this method effectively reduces the monitoring error of a single monitoring source, and the fusion effect is better than the long short-term memory network, recurrent neural network and extended Kalman filter fusion method based on the attention mechanism, and the mean absolute error is improved by 2.20%、14.32%and respectively 33.94%.

A systematic review of the extant literature on the Tanzanian e-government services for economic development

This paper investigates the existing Tanzanian e-government services for economic development through a systematic review of the literature. The main objective is to examine researchers’ articles on e-government services towards achieving the Tanzanian economic growth as well as the current issues and trends on e-government globally. Specifically, this study aims to answer three research questions on: what are the functional e-government services in Tanzania? What is the level of Tanzanian e-government service performance? And what are the factors influencing Tanzanian e-government service performance for economic development? A systematic literature search was conducted on four aggregate data sources, which included Google Scholar, ResearchGate, CORE, and Refseek. A combination of search key terms was employed to obtain the data sources from 2015 to 2022. One hundred and five articles were found by the combined search and screened, resulting to eighteen articles that were recommended for the study. Content and Microsoft Excel computer application were used to analyse the screened data sources. The findings reveal that, a number of functional services is offered and considered potential for revenue collection and contribute to the nation's economic development. Nevertheless, Tanzania has improved and dynamically advanced to the transaction level of e-government implementation where the majority of the citizens interact for services and information. However, factors related to the activeness of the platforms, accessibility, usability, infrastructure, security issues, privacy, and technical support services among others, were identified to influence the performance of Tanzanian e-government services and thus call attention to the government in addressing these issues.

Factors Influencing Teachers’ Turnover from Government Secondary Schools in Makete District, Tanzania

This study investigated about factors influencing teachers’ turnover from government secondary schools in Makete District, Tanzania. The study employed the descriptive design. The design was chosen because it is the best for fact finding studies. While the number of schools in Makete district was 120, only five schools were selected through simple random sampling. The target sample included 16 participants: five heads of schools, 10 teachers and one District Education Officer. The study used interviews as source of data. The researchers used a smart phone to record participants’ responses during interview sessions and then, the responses were transcribed verbatim. The study analyzed the data according to themes. The study concludes that there are various factors that cause teachers’ turnover from Secondary schools in Makete District. Such reasons include low salary, lack of teachers’ houses, misunderstanding at work place, heavy workload, lack of support from school administration and delayed promotion. In order to curb these, challenges, the government needs to recruit more teachers to meet the requirement of teachers in government secondary schools, especially in rural areas. There is a need to build teachers’ houses in order to accommodate them in quality houses. Finally, the government needs to provide special allowances for teachers working in remote areas like Makete.

FV-DLV3+: a light-weight flooded vegetation extraction method with attention-based DeepLabv3+

ABSTRACT Climate change and human activities have led to a decline in wetland vegetation and sharp reduction of wetland area, which has caused considerable damage to the hydrological cycle and ecological functions of many wetlands. Flooded vegetation (FV) is an important wetland landscape, and strengthening the research on methods of the extracting FV using remote sensing or aerial photos plays an important role in monitoring changes to wetlands. This paper used optical satellite and synthetic aperture radar satellite images as data sources, construct a set of feature variable sets suitable for FV extraction; in addition, a deep learning method based on the semantic segmentation model DeepLabv3+, FV-DLV3+, is proposed for FV extraction. The method adopts the lightweight MobileNetV2 model as the backbone feature extraction network, introduces the SegFormer network to make up for the feature information loss caused by MobileNetV2, and further captures more effective features and suppresses the interference of the background through an attention mechanism to enhance the ability to extract feature boundaries, thereby improving the accuracy of FV extraction. Taking the wetland of Poyang Lake as the experimental area, Landsat 8 and Sentinel-1A satellite data were selected as the data sources to produce a deep learning dataset, and U-Net, DeepLabv3+, Swin-Unet, Swin Transformer, and CVTNet were selected for comparison when conducting model training and validating experiments. The results show that FV-DLV3+ has an excellent effect on FV extraction of the Poyang Lake wetland, which effectively reduces the computational complexity of the process while improving extraction accuracy; the mean intersection over union of this paper’s method was higher than the five above-mentioned methods, which was 12.93% and 5.85% higher than that of DeepLabv3+ and CVTNet, respectively.