Incoming Data Research Articles

The Role of Affordability on the Adoption of Residential Point-of-Use Drinking Water Filtering Systems in China

Access to clean drinking water is fundamental to human health, but a significant portion of China’s population lacks this essential resource due to low water quality. Point-of-use (POU) water filtering systems, offering ease of installation and maintenance, have emerged as a viable solution for providing clean drinking water in China. However, despite their advantages, the adoption rate remains below 20%. This study investigates whether and how price affordability affects the adoption of residential POU water filtering systems in China. In doing so, we conduct a quantitative analysis of the national POU water filtering systems sales and household income data from 2007 to 2022 in China. Our results show that the ratio of the initial purchase price to per capita disposable income and the adoption rate of POU systems in China are strongly positively correlated. Our findings shed light on potential pathways to facilitating their wider adoption, not only in China but also in other emerging countries.

Investigating The Influence of Online Training and Material Mastery on Buddhist Pre-marriage Guiding Skills

This research aims to investigate the influence of online training and material mastery partially and simultaneously on Buddhist pre-marriage guiding skills. This research used a quantitative approach with a correlational survey design. The population were 235 Buddhist pre-marriage guiding facilitators. The sampling technique was purposive sampling; 148 respondents were taken as samples, which was determined using the Yamane formula. This research used questionnaires as instruments with 120 items using Likert scale assessments, which were distributed to respondents after passing the validity and reliability test. Incoming data was tabulated in Excel and processed using SPSS 29. The techniques used to analyze the data were descriptive statistics and multiple linear regression Analysis, as well as the T-test, F test and Coefficient Determination test after the classical assumption test was carried out. This research showed that online training and material mastery had a positive and significant influence partially and simultaneously on the Buddhist pre-marriage guiding skills; this research indicates that the facilitators have good guiding skills in providing Buddhist pre-marriage guidance. This research also indicated that facilitators could follow the online training model well because they could use information technology to follow the learning process holistically and continuously. This research also indicated that the facilitators mastered the pre-marriage guidance material well because they know and understand the material, apply it, analyze it, evaluate it, and create or compile, summarize, combine, and formulate Buddhist pre-marriage guidance material.

The unequal conversion of intended redistribution into factual redistribution in Europe and its impact on social inequalities between families

Redistribution is one of the key elements of contemporary social order, and research has dealt with it in various ways. Most welfare state research mixes in its analysis the regulations on redistribution and actual outcomes. However, to adequately understand redistribution, one needs to distinguish between regulations and outcomes. Measuring unequal conversion in terms of deviations between redistributions as regulated and redistributive outcomes shows how far a redistribution corresponds to what has been officially agreed upon in societies and who is actually better or worse off in the end. Empirically focusing on family-related redistribution, our analysis was based on European Union Statistics on Income and Living Conditions data and the European microsimulation tool EUROMOD. First, we assumed that differences between regulations and outcomes vary depending on the family type. Thus, we compared the actual and simulated disposable household income for various family types. Second, we assumed that differences between regulations and outcomes vary by country. Consequently, we analysed a variety of European countries. Our findings showed that there are substantial differences between regulations on redistribution and outcomes. These differences increase income inequalities between families. This held true for all of our study countries, although to a different degree.

Ready-to-eat cereal consumption is associated with improved nutrient intakes and diet quality in Canadian adults and children across income levels.

Results from observational studies suggest ready-to-eat cereal (RTEC) consumption is associated with higher diet quality. In the United States, studies have shown that RTEC is an important contributor to nutrient intakes across income levels. However, it is unknown if this association varies by income level in the Canadian population. Given its affordability, RTEC may represent an important source of nutrients for lower-income individuals. This study evaluated the association of RTEC consumption with nutrient intakes and diet quality across household income levels in Canadian adults and children. Income and dietary data from 24 h dietary recalls were obtained from the 2015 Canadian Community Health Survey (CCHS)-Nutrition in 6,181 children (2-18 years) and 13,908 adults (19+ years). Diet quality was assessed with a modified Nutrient Rich Food Index (NRF) 9.3. Income levels were stratified into low, middle, and high based on family size, and data were analyzed by RTEC consumption and income level using multivariate linear regression adjusted for energy, age, and sex. Diet quality was greater in adult and child RTEC consumers across all household income levels. Children and adults consuming RTEC also had higher nutrient intakes, including shortfall nutrients such as calcium, dietary fiber, iron, magnesium, and vitamin D. RTEC provided <10% of energy intake, <4% of saturated fat intake, and <9% of total sugar intake across all ages and income levels, while also providing one-third of daily iron intake and at least 10% of daily intake of dietary fiber, thiamin, folate, and vitamin B6. RTEC consumption was associated with improved nutrient intakes and diet quality in adults and children across household income levels. Nutrient dense and affordable food choices, such as RTEC, may be a helpful strategy to improve the diet quality of Canadians, particularly those with a lower household income.

Designing an Information System for Recording Incoming and Outgoing Goods at the Warehouse of the Engineering Faculty, Universitas Sebelas Maret, Surakarta

The Faculty of Engineering, Universitas Sebelas Maret (UNS) in service activities for students requires consumables stored in the warehouse of the Faculty of Engineering, UNS. Every month a document recapitulation is carried out to control the circulation that occurs in the warehouse of the UNS Faculty of Engineering. The document recap system still uses the manual method, namely by recording incoming and outgoing goods to the ledger. Data from the general ledger is copied into the Microsoft Excel application to create a circulation report. The current manual system still has human errors that often occur, such as missing or tucked records of incoming and outgoing goods. This has an impact on when recapitulating the results of the information data obtained is less accurate. Based on the description of the background above, it can be formulated the problem to be solved in this study is "How to design an Information System for Recording Incoming and Outgoing Goods in the Warehouse of the Faculty of Engineering UNS". Limitations The problem that the author discusses in the study focuses more on the process of recapitulating incoming and outgoing goods data, as well as making reports on the circulation of goods within the UNS Faculty of Engineering. The method used to design an information system for recording goods data uses the Software Development Life Cycle (SDLC) with the Waterfall model. The programming language used to design an information system for recording data items is PHP Native and MySQL database as a data storage medium. With the design of this item data recording information system, it is hoped that it will make it easier for the head of the warehouse in terms of ordering goods in accordance with the stock data in the warehouse of the Faculty of Engineering, UNS.

Aplikasi Praktis Metode Pengembangan Cepat (RAD) Dalam Sistem Manajemen Penjualan Dan Pengembalian Barang Berbasis Web

In the process of recording sales transactions at Toko Kurnia Jaya always use a manual typewriter so that it is not effective and inaccurate in recording sales transactions, and sales notes are still manual on paper. Another problem is in the process of returning goods, if the goods purchased by the buyer are not appropriate. From these problems, it is formulated how to build a web-based application for recording sales transactions and returning goods. The data collection method that the author uses in completing this report is the method of observation, interviews and library studies. The system development model that the author uses is the RAD model, the programming language that the author uses is PHP and MySql as a database and tested using the Blackbox testing method. With the application of processing sales transactions and returning goods, it can facilitate the search for goods data and more quickly find information about the price of goods and make it easier to manage transaction data, both knowing the total sales results, returning goods and recording incoming goods data.

A Survey of Incremental Deep Learning for Defect Detection in Manufacturing

Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedures to dynamically update model-based detection methods that use sequential streaming during the training phase. This paper reviews how new process, training or validation information is rigorously incorporated in real time when detection exceptions arise during inspection. In particular, consideration is given to how new tasks, classes or decision pathways are added to existing models or datasets in a controlled fashion. An analysis of studies from the incremental learning literature is presented, where the emphasis is on the mitigation of process complexity challenges such as, catastrophic forgetting. Further, practical implementation issues that are known to affect the complexity of deep learning model architecture, including memory allocation for incoming sequential data or incremental learning accuracy, is considered. The paper highlights case study results and methods that have been used to successfully mitigate such real-time manufacturing challenges.

Food Security: The impact of migrants and remittances in Sri Lanka

The objective of this paper is to show that migrants and the subsequent remittances influence household food consumption in different ways. This paper contributes to the literature by developing an analytical framework to analyse how migrants and remittances need to be looked at separately in the migration/food security nexus. The multivalued treatment effect is used to analyse the model using the Sri Lankan household income and expenditure data (2016/17). The findings of this study shed light on areas where the government and policymakers should focus on improving the impact of migration and remittances on food security in Sri Lanka. The study's findings show that migrants and remittances affect food expenditure, calorie consumption dietary diversity in two distinct ways. Both migrants and remittances positively impact food expenditure and calorie consumption. However, remittances promote less healthy food consumption, while migrants promote healthy food consumption. Migrants and remittances jointly affect households to purchase and consume calories from expensive food. Remittances positively affect food diversity in terms of both expenditure and calorie consumption. However, migrants positively affect only expenditure-based food diversity.

Online Unsupervised Domain Adaptation via Reducing Inter- and Intra-Domain Discrepancies.

Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain on cross-domain object recognition by reducing a distribution discrepancy between the source and target domains (interdomain discrepancy). Prevailing methods on UDA were presented based on the premise that target data are collected in advance. However, in online scenarios, the target data often arrive in a streamed manner, such as visual image recognition in daily monitoring, which means that there is a distribution discrepancy between incoming target data and collected target data (intradomain discrepancy). Consequently, most existing methods need to re-adapt the incoming data and retrain a new model on online data. This paradigm is difficult to meet the real-time requirements of online tasks. In this study, we propose an online UDA framework via jointly reducing interdomain and intradomain discrepancies on cross-domain object recognition where target data arrive in a streamed manner. Specifically, the proposed framework comprises two phases: classifier training and online recognition phases. In the former, we propose training a classifier on a shared subspace where there is a lower interdomain discrepancy between the two domains. In the latter, a low-rank subspace alignment method is introduced to adapt incoming data to the shared subspace by reducing the intradomain discrepancy. Finally, online recognition results can be obtained by the trained classifier. Extensive experiments on DA benchmarks and real-world datasets are employed to evaluate the performance of the proposed framework in online scenarios. The experimental results show the superiority of the proposed framework in online recognition tasks.

The effect of the implementation of the international code of marketing of breast-milk substitutes on child mortality in Ghana and Tanzania.

The International Code of Marketing of Breast-Milk Substitutes is an important instrument to protect and promote appropriate infant and young child feeding and the safe use of commercial milk formulas. Ghana and Tanzania implemented the Code into national legislation in 2000 and 1994, respectively. We aimed to estimate the effects of the Code implementation on child mortality (CM) in both countries. The countries analysed were Ghana and Tanzania. For CM and HIV rates, data from the Institute for Health Metrics and Evaluation from up to 2019 were used. Data for income and skilled birth rates were retrieved from the World Bank, for fertility from the World Population Prospects, for vaccination from the Global Health Observatory and for employment from the International Labour Organization. We used the synthetic control group method and performed placebo tests to assess statistical inference. The primary outcomes were CM by lower respiratory infections, mainly pneumonia, and diarrhoea and the secondary outcome was overall CM. One-sided inference tests showed statistically significant treatment effects for child deaths by lower respiratory infections in Ghana (P = 0·0476) and Tanzania (P = 0·0476) and for diarrhoea in Tanzania (P = 0·0476). More restrictive two-sided inference tests showed a statistically significant treatment effect for child deaths by lower respiratory infections in Ghana (P = 0·0476). No statistically significant results were found for overall CM. The results suggest that the implementation of the Code in both countries had a potentially beneficial effect on CM due to infectious diseases; however, further research is needed to corroborate these findings.

Multi-objective Machine Learning for control performance assessment in PID control loops

Control Performance Assessment (CPA) is a critical endeavor in industrial processes, ensuring optimal functioning of control systems. Traditionally, CPA has been addressed through solutions using some control performance indicators. Nowadays, the integration of data science and machine learning has emerged as a viable alternative, particularly in classification tasks related to CPA. That is, in a binary classification scheme, the goal is to predict whether incoming data from the control loop belongs to class 0 or 1, representing the absence or presence of an anomaly (performance degradation). In such a case, a trade-of between false positives and false negatives should be obtained, via the training phase of a given supervised machine learning structure for example. Usually, this is a conflicting trade-of, where multi-objective optimization techniques in the training phase of such learners could bring interesting results. In this paper, we explore the usability of multi-objective optimization training in machine learning, for control performance assessment classification. A database describing 30 control performance indicators (features) in a PID control loop is used. The obtained results indicate that the proposed approach could bring interesting applications to improve the performance of CPA classification systems.

Community Farm monitoring toolkit: Design and implementation of a Low-cost tool for sustainable community based agricultural projects in Africa

Nowadays sustainable agriculture exploits new technologies such as internet of things (IoT) mixed with community engagement and participation that represent a great potential for addressing sustainability goals at large. This paper presents a low-cost community-based control system for remote monitoring of surrounding parameters and other sustainability indicators such as quantity of yearly harvests, yearly local employments and so on with the use of sensors. The hardware system includes an air quality sensor, an air humidity and temperature sensor, a soil moisture sensor, a real time clock providing the time and an SD card to record the collected data. The designed tool gathers both data from sensors and incoming data from community through SMS which are therefore processed and forwarded to a specific online dashboard to support community decision-making, thus forming the community control system feedback loop. Experimental results from the considered agricultural community project show that the system works well and could be used in other similar and/or larger scale projects.

Prediction error minimization as a common computational principle for curiosity and creativity.

We propose expanding the authors' shared novelty-seeking basis for creativity and curiosity by emphasizing an underlying computational principle: Minimizing prediction errors (mismatch between predictions and incoming data). Curiosity is tied to the anticipation of minimizing prediction errors through future, novel information, whereas creative AHA moments are connected to the actual minimization of prediction errors through current, novel information.

The lorenz-extended rohde function for asymmetric lorenz curve (Application in income data)

The lorenz-extended rohde function for asymmetric lorenz curve (Application in income data)

An Empirical Study on the Relationship between Farmers' Income Increase and Primary Industry in Zhanjiang City

This article briefly introduces the 25 themes of the "No. 1 Document" of the CPC Central Committee. Based on the data of farmers' income, agriculture, forestry, animal husbandry and fishery, and agriculture, animal husbandry and fishery service in Zhanjiang City, Guangdong Province from 1978 to 2021, this paper conducted an empirical study on the relationship between the primary industry and farmers' income in Zhanjiang City. In the empirical analysis, first of all, the stationarity test of the data was carried out, the EG co-integration modeling was carried out, the stepwise regression method was used to eliminate multicollinearity, and the generalized difference method was used to eliminate sequence correlation, and the optimized model was obtained. The results show that the total output value of agriculture and fishery has no significant effect on the increase of farmers' income in Zhanjiang City. Forestry, animal husbandry, agriculture, forestry, animal husbandry and fishery services have a significant impact on farmers' income. The total output value of forestry, animal husbandry and agriculture and the service industry of forestry, animal husbandry and fishery increased by 1%, and the income of farmers increased by 0.429%, 0.240% and 0.619%, respectively. The impact of agriculture and fishery on per capita disposable income in Zhanjiang rural areas is lagging, with a maximum lag of 9 years. Accordingly, the corresponding policy suggestions are put forward.

Signed expressions spotting and recognition in an assistive technology system

An algorithm for processing the continuous stream of incoming video data to detect and recognize signed expressions is proposed. The aim is to improve an assistive technology system. The method is based on the dynamic flow of expression models between a repository of inactive models and pools of active and completed models. When modeling signed expressions, so-called visemes are used. These are units smaller than words - the equivalent of phonemes known from speech recognition. Visemes are extracted by clustering descriptors of the holistic skeletons. The sequential-simultaneous nature of gestures is considered, and the modeling is carried out in four parallel channels related to the position and shape of both hands. The conditions for activation, deactivation, and recognition of the model as completed are formulated, assuming asynchronous changes in individual channels. The method is successfully tested on a demanding dataset recorded by potential users.

Artificial Rabbit Optimizer with deep learning for fall detection of disabled people in the IoT Environment

&lt;abstract&gt; &lt;p&gt;Fall detection (FD) for disabled persons in the Internet of Things (IoT) platform contains a combination of sensor technologies and data analytics for automatically identifying and responding to samples of falls. In this regard, IoT devices like wearable sensors or ambient sensors from the personal space role a vital play in always monitoring the user's movements. FD employs deep learning (DL) in an IoT platform using sensors, namely accelerometers or depth cameras, to capture data connected to human movements. DL approaches are frequently recurrent neural networks (RNNs) or convolutional neural networks (CNNs) that have been trained on various databases for recognizing patterns connected with falls. The trained methods are then executed on edge devices or cloud environments for real-time investigation of incoming sensor data. This method differentiates normal activities and potential falls, triggering alerts and reports to caregivers or emergency numbers once a fall is identified. We designed an Artificial Rabbit Optimizer with a DL-based FD and classification (ARODL-FDC) system from the IoT environment. The ARODL-FDC approach proposes to detect and categorize fall events to assist elderly people and disabled people. The ARODL-FDC technique comprises a four-stage process. Initially, the preprocessing of input data is performed by Gaussian filtering (GF). The ARODL-FDC technique applies the residual network (ResNet) model for feature extraction purposes. Besides, the ARO algorithm has been utilized for better hyperparameter choice of the ResNet algorithm. At the final stage, the full Elman Neural Network (FENN) model has been utilized for the classification and recognition of fall events. The experimental results of the ARODL-FDC technique can be tested on the fall dataset. The simulation results inferred that the ARODL-FDC technique reaches promising performance over compared models concerning various measures.&lt;/p&gt; &lt;/abstract&gt;

Comparative Study of Design Models for Shear Strengthening of RC Beams with NSM FRP

This study investigates the accuracy of the existing design models for shears-strengthened reinforced concrete (RC) beams with near-surface mounted (NSM) fibre-reinforce polymer (FRP) rods and laminates. Comparative studies have been conducted on the predicted shear contributions of NSM FRP materials in the strengthened beams using state-of-the-art existing design models. To assess the accuracy of these models, the predictions were compared with the experimental results on 131 test specimens from 24 studies. The results of this study can be used for standard committees to choose the most precise models for their corresponding design standard code or guidelines. From the results of this study, it can be concluded that mechanics-based models proposed by Mofidi et al. (2023) and Bianco et al. (2014) were superior when compared to other existing models in most measured metrics. The models produced by regressions of data or neural networks only performed well under the statistical parameters for which they were fitted. Such models may not perform well when compared with the data that was not used to calibrate the models or when assessed by a metric that they are not calibrated with. On the other hand, for the mechanics-based models, due to the presence of the principles of shear mechanics and bonding in the development of such models, the mechanics-based models can perform to a satisfactory level with existing and incoming experimental test data and through different statistical test parameters.

Economic benefit analysis of enterprises from multi-dimensional perspective based on K-means clustering

Abstract Economic benefit is the basic starting point of all economic activities that further developing financial advantages is helpful for improve the market seriousness of companies. The development of enterprises lessens work utilisation and acquires greatest advantages with the base speculation. Therefore, in this paper, the evaluation indices of economic benefit in enterprises are classified and the analysis model of economic benefit based on K-means clustering is established. According to the principal component analysis method, multi-dimensional analysis is conducted on X Company’s orders, income and expenditure data, loan amount and other aspects, which can help the senior leaders to scientifically manage and control the economic situation of the company, and make a better scientific forecast for future development.

Identifying Latent Groups in Spatial Panel Data Using a Markov Random Field Constrained Product Partition Model

Understanding the heterogeneity over spatial locations is an important problem that has been widely studied in many applications such as economics and environmental science. In this paper, we focus on regression models for spatial panel data analysis, where repeated measurements are collected over time at various spatial locations. We propose a novel class of nonparametric priors that combines Markov random field (MRF) with the product partition model (PPM), and show that the resulting prior, called by MRF-PPM, is capable of identifying the latent group structure among the spatial locations while efficiently utilizing the spatial dependence information. We derive a closed-form conditional distribution for the proposed prior and introduce a new way to compute the marginal likelihood that renders efficient Bayesian inference. We further study the theoretical properties of the proposed MRF-PPM prior and show a clustering consistency result for the posterior distribution. We demonstrate the excellent empirical performance of our method via extensive simulation studies and applications to a US precipitation data and a California median household income data study.