Consumption Data Research Articles

Pembuatan Barcode Kemasan Produk Pada CV. Kuliner Makmur Sejahtera Kota Padang

Rendang, a traditional Minangkabau dish from West Sumatra, Indonesia, has gained international recognition and has inspired many entrepreneurs to promote this cuisine globally. One such entrepreneur is CV. Kuliner Makmur Sejahtera (KMS), which produces rendang under the brand Katuju. To enhance operational efficiency and support global market expansion, KMS requires an improved inventory management system. The primary challenge is the difficulty in monitoring inventory and tracking products without a barcode system. This community service program aims to address this issue by implementing barcode technology on Katuju's product packaging. The activities include designing, creating, and printing barcodes, as well as training KMS staff on how to use them. The process starts with observation and interviews to identify needs, followed by barcode design and production using an online barcode generator, and testing the barcode functionality with a scanning system. The results indicate that the community service team successfully trained KMS in barcode creation and printing, and integrated barcodes into product packaging. Evaluation through questionnaires showed participant satisfaction with the program and readiness to apply the barcode system to their products. This program is expected to improve inventory management, data accuracy, and support CV. KMS's global market expansion by providing tools necessary for efficient product tracking and valuable consumer data collection.

Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting

To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecasting models. This study seeks to enhance electricity load forecasting by proposing a hybrid model that combines Sorted Coefficient Wavelet Decomposition with Long Short-Term Memory (LSTM) networks. This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. Various models, including Stacked LSTM, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), were compared and optimized using grid search with cross-validation on consumption data from Lome, a city in Togo. The results indicate that the ConvLSTM model outperforms its counterparts based on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and correlation coefficient (R2) metrics. The ConvLSTM model was further refined using wavelet decomposition with coefficient sorting, resulting in the WT+ConvLSTM model. This proposed approach significantly narrows the gap between actual and predicted loads, reducing discrepancies from 10–50 MW to 0.5–3 MW. In comparison, the WT+ConvLSTM model surpasses Autoregressive Integrated Moving Average (ARIMA) models and Multilayer Perceptron (MLP) type artificial neural networks, achieving a MAPE of 0.485%, an RMSE of 0.61 MW, and an R2 of 0.99. This approach demonstrates substantial robustness in electricity load forecasting, aiding stakeholders in the energy sector to make more informed decisions.

Enhancing sustainable healthcare practices through energy-efficient wireless body area networks

This paper explores how the integration and performance of Wireless Body Area Networks (WBANs) contribute to sustainable healthcare practices. WBANs play a crucial role in reducing the environmental footprint of healthcare systems by providing continuous monitoring that can prevent unnecessary hospitalizations and reduce the consumption of disposable medical supplies. Additionally, the design of WBANs using environmentally friendly materials, low-power devices, and recyclable components is discussed to optimize the sustainability of healthcare practices. To enhance infrastructure, services, and quality of life, new technologies have been integrated to improve WBANs. Patients' essential health data may be continuously and instantly collected thanks to these networks, and healthcare providers can receive the data right away for quick analysis and action. WBANs have healthcare benefits, but there are also major drawbacks. Technologies that can combine low power consumption, minimal delay, and high enough data rates are needed for WBANs in order to support a variety of medical applications. The effectiveness of the LoRaWAN and IEEE 802.15.6 technologies in WBAN is assessed in this paper. With the use of seven crucial metrics which are throughput, arrival rate, delay, energy consumption, residual energy, network lifetime, and packet delivery ratio (PDR), this paper seeks to study which technology is appropriate with WBANs using NS3 as a simulation program. With network parameters, 1000 seconds, and 50 nodes, the paper evaluated the performance of each technology. The results show that IEEE 802.15.6 is superior in terms of throughput, PDR, and arrival rate whereas LoRaWAN is superior in terms of energy consumption, residual energy, network lifetime, and delay.

Association between Higher Intake of Flavonols and Lignans and Better Mood: Evidence from Dietary and Biomarker Evaluation in Healthy Individuals.

The aim of this study is to investigate associations between (poly)phenol consumption, circulating (poly)phenol metabolites, and mood states in healthy individuals. The study included 333 healthy individuals. Mood state was assessed with the Positive and Negative Affect Schedule questionnaire. Dietary (poly)phenol intake wasestimated matching food consumption data collected using aFood Frequency Questionnaire(FFQ) with a comprehensive in-house (poly)phenol database. A total of 102 (poly)phenol metabolites werequantified in fasting plasma and 24h urine samples by Liquid Chromatography-Mass Spectrometry using a validated method. A higher intake of lignans, flavanones, and flavonols estimated from FFQs was associated with positive mood after adjusting for age and sex (β: 0.118 to 0.134). A total of 11 urinary (poly)phenol metabolites, including lignan and flavonol metaboliteswere associated with less negative mood (β: -0.387 to -0.205). No association was found between mood and plasma (poly)phenols. A higher consumptionof lignans flavanones and flavonols isassociated with a better mood, while certain urinary metabolites are associated with less negative mood. The lack of associations between fasting plasma (poly)phenols and mood may be due to their transient nature incirculation compared with 24 h urinary metabolites, which reflect longer-term exposure.

Improvement of Drug Stock Management Using ABC-VED Analysis in a University Hospital

Objective: Hospitals allocate approximately 35% of their budgets to pharmaceutical and material stocks in order to provide uninterrupted health services. For this reason, hospital managers spend a lot of time and effort in order to keep a minimum level of stock so as not to disrupt health service production and thus to better manage the limited resources of the enterprise. The aim of this study is to group the drugs according to their financial value and vital importance. In this way, it is aimed to perform stock management in the least costly, errorfree and effective way. Method: In the study, data corresponding to one-year (2021) consumption of a total of 1327 items of drugs were analyzed. The total cost is approximately 83 million ₺. In this study, an ABC-VED cross-analysis was performed on the one-year drug consumption data of a university hospital with 853 beds in order to provide an example of a more controlled inventory structure. Results: According to the results, it is seen that the ratio of drugs in the first category, that is, drugs that are both high cost and vital for the patient, to the total drug item is 38%. The cost of these drugs constitutes 87% of the total cost. Conclusion: Taking into account the classifications put forward in the study, strict monitoring of a small number of drugs and regular weekly counts will make drug stock management much easier for managers.

Nutritional Status and Food Habits of Child Labor in Rangpur City, Bangladesh

Background: In Bangladesh, the harsh reality of child labor persists, with over 8 million children engaged in such work. This not only deprives them of their childhood but also has severe implications for their mental and physical health. This study seeks to investigate the daily food intake and nutritional status of child laborers in Rangpur City, shedding light on the broader health impacts of child labor in this region. Methods: The study employed a structured questionnaire and anthropometric measurements to assess the socioeconomic status and dietary habits of participants. Pretesting ensured questionnaire accuracy, while daily and weekly recall methods captured comprehensive food consumption data. Socioeconomic inquiries included income, education, employment, and family details, providing a holistic understanding of participants' circumstances. Results: The survey highlights that most working children are aged 12-14, predominantly employed in mechanics and sales, working long hours under precarious conditions for low wages, primarily to support their families. Despite some attending primary school and receiving employer-provided meals and medical aid, these children struggle with inadequate education and nutrition, and a high incidence of workplace accidents. Conclusion: Child laborers have often poorer nutritional status than the average person. They are not provided with a proper diet. Consequently, they are dealing with various health problems.

Consuming Habits Fast Food and the incidence of obesity in elementary school students

Obesity has become a global pandemic throughout the world and is declared the largest chronic health problem, making it a major public health problem, including for school-aged children. The aim of this research is to identify the relationship between consumption fast food and the incidence of obesity in elementary school students. Design approach cross-sectional with a student population in one of the elementary schools in Manado City. The number of respondents was 162 people with the criteria being aged 6 years and over and having consent from their parents, taken using techniques quota sampling. Fast food consumption data was collected using a paper-based questionnaire, while the incidence of obesity was measured by BMI. Data were analyzed using the Chi-square test with significance values p ≤ 0.005. The research results showed a significant relationship between consumption frequency fast food with the incidence of obesity in students (p < 0.001). It is recommended for schools to provide school canteens that comply with health standards and for parents to prepare a healthy breakfast for children

K-PCD: A new clustering algorithm for building energy consumption time series analysis and predicting model accuracy improvement

Clustering algorithms are often applied to building energy consumption data analysis to mine representative patterns of building energy usage. This paper proposes a new clustering algorithm: K-PCD, which is particularly suitable for building energy consumption time series. K-PCD utilizes a Pearson correlation coefficient-based distance measure (PCD), and a novel centroid calculation method that takes into account both the PCD and the traditional Euclidean distance (ED) between time series. This study also proposes a new clustering validity index (CVI) tailored to the predictability of building energy consumption: Energy Prediction Clustering Performance Index (EPCPI). The K-PCD and EPCPI are practically analyzed and validated using one year of data from 29 real buildings. The results indicate that comparing with traditional clustering algorithms, the K-PCD achieves better clustering results. This EPCPI index thoroughly explores building operation patterns, and after adding clustering labels, it can maximize the prediction accuracy of the Back Propagation Neural Network (BPNN) model for building energy consumption.

The Importance of Protecting E-Commerce Consumer Personal Data

Technological advances offer various conveniences in human daily activities in order to meet their needs. One of the daily human activities that has changed due to the presence of technology is online buying and selling through e-commerce media. Shopping with e-commerce is people's choice because it saves time and costs. However, behind this convenience, attention needs to be paid to the security of consumer data on e-commerce platforms. This article will discuss the importance of protecting personal data from e-commerce consumers. This article was written using a normative type research method with a statutory approach and a conceptual approach. The legal materials used in this research are primary legal materials and secondary legal materials. The technique of collecting legal materials in this research uses literature studies (library research). The legal problem analysis technique used in this research is deductive analysis technique. The results of this research are that personal data security is very necessary in shopping activities in e-commerce, because it is related to consumer satisfaction. When consumers feel safe and are not wary of shopping on e-commerce, they will make shopping on e-commerce a priority compared to shopping conventionally.

Comparative Analysis of K-Nearest Neighbors and Decision Tree Methods in Determining Students’ Purchase Interest in MacBook Laptops

In the context of increasingly competitive technology markets, companies need to know consumer preferences accurately to optimize product offerings and increase sales. Two classification methods that are often used in data mining, namely K-Nearest Neighbors and Decision Tree, have their own advantages and disadvantages. This study proposes a solution that involves processing student data using both classification methods to identify the most accurate and effective method for identifying purchase intentions. This study aims to compare the performance of the two methods in determining student purchase intentions for MacBook laptops. The research methodology includes collecting data from 100 students covering various factors such as user experience, design and portability, technical specifications, price, and security. This data is then classified using the K-Nearest Neighbors and Decision Tree methods. Furthermore, a confusion matrix is used to provide a more detailed picture of the performance of the two methods. The results of the study show that the Decision Tree method has a higher accuracy (91%) compared to K-Nearest Neighbors (88%). In addition, Decision Tree excels in other metrics such as precision (87.18% vs. 85.71%), recall (89.47% vs. 85.71%), specificity (91.94% vs. 89.66%), and F1-Score (88.31% vs. 85.71%). The decision tree also has a higher NPV value and lower FPR and FNR rates than K-Nearest Neighbors, indicating that it is superior in avoiding misclassification. The study's conclusion is that the Decision Tree method is more effective and accurate than K-Nearest Neighbors in determining students' purchase intentions for MacBook laptops. The decision tree shows better performance in almost all evaluation metrics, making it a more reliable method to use in consumer data analysis. The results of this study are expected to help companies choose a more appropriate and effective analysis method for their marketing strategies, as well as provide a basis for further research in the field of consumer purchase intention classification.

Industrial water consumption index: A new bridge between water consumption and socioeconomic development

Regional water consumption is closely linked to socioeconomic development trends; however, studies that analyze and evaluate socioeconomic development trends based on water consumption data are lacking. This study established a new general index, the industrial water consumption index (IWCI), using several variables from water consumption data through the entropy weight method and tested the index in Jiaxing City, China. The results showed that the relationship between water consumption and socioeconomic development varied depending on different industrial divisions. The IWCI, established based on water consumption data was highly sensitive to changes in socioeconomic development, especially rapid changes. Compared to the raw water consumption data, the IWCI presented a better performance in evaluation indicators for the selected several divisions, with a Pearson correlation coefficient ranging from 0.70 to 0.85 and a normalized mutual information ranging from 0.31 to 0.45. Overall, the IWCI provides an effective way to evaluate socioeconomic development trends by relying solely on water consumption data, particularly for industrial divisions where water plays an important role in production. The index allows managers to quickly assess and analyze socioeconomic development trends and develop corresponding response measures.

Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction

Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction

Private commercial companies sharing health-relevant consumer data with health researchers in sub-Saharan Africa: an ethical exploration

ABSTRACT Sharing large digital-first datasets, including for purposes for which they were not originally intended, is a hallmark of the “big data revolution”. Through their routine operations, private commercial companies collect massive amounts of diverse data from their customers, some of which may interest those working in the public sector, such as health researchers. Researchers and government agencies worldwide have been increasingly using data from commercial entities (such as Google, Microsoft, Apple, Facebook/Meta, Twitter/X and Amazon, among others) to generate health-related insights. This article explores ethical issues raised by the practice of commercial companies sharing consumer data with third parties for the purposes of promoting health in the sub-Saharan African (SSA) context. First, as an illustrative example, it examines some of the ways telecommunication (telecom) companies in SSA shared mobility data from cellphone users with public health researchers during the COVID-19 pandemic. Second, it examines a recent debate about the ethical responsibilities of companies that collect, process and share user-generated data, drawing implications for the SSA context. Finally, since this is a relatively understudied subject, we point out some areas where future conceptual and empirical work could contribute to the development of relevant ethics guidance and regulatory governance in SSA.

Consumer interaction and privacy: the impact of in-feed lead generation ads on self-disclosure intention

PurposeAmidst the growing emphasis on privacy protection, this study aims to investigate how online interaction introduced in Lead Generation Ads (LGAs) affects consumers' Self-Disclosure Intention (SDI), particularly in the context of the Chinese advertising market.Design/methodology/approachThis research employs two scenario-based online surveys to analyse in depth the impact of LGAs on consumers' SDI. The first study collects valid feedback from 220 consumers through an online questionnaire to assess the direct effect of perceived interactivity on SDI. The second study, using an experimental design with a sample of 265 participants, further explores the mediating roles of perceived control and perceived vulnerability in the relationship between perceived interactivity and SDI and examines the moderating effect of privacy invasion experience (PIE).FindingsThis study reveals the significant and positive influence of perceived interactivity on SDI, with perceived control acting as a mediator that enhances this effect. Conversely, perceived vulnerability weakens the positive impact of perceived interactivity on SDI. Additionally, we explore the moderating role of PIE and find that it significantly influences the relationship between perceived interactivity and SDI. These findings underscore the importance of considering consumer privacy sensitivity, particularly in the design of interactive marketing strategies and within highly interactive advertising environments.Research limitations/implicationsOur research uncovers consumer privacy attitudes and behaviours in the Chinese market, providing insights into its unique dynamics of privacy and information disclosure. However, the geographical and cultural specificity of our study may limit its generalizability. Future studies should expand into various cultural and market contexts, considering the impact of digital technologies on consumer interactions and information disclosure, thereby enhancing the depth and applicability of global marketing strategies.Practical implicationsAdvertising platforms should explore online interactive communication methods to enhance consumers' perceived interactions and alleviate privacy concerns. Also, platforms should be designed with system security in mind to prevent the leakage and misuse of consumer data, thus increasing consumers' SDI.Social implicationsThe study provides insights for marketers on designing more effective and privacy-sensitive online advertising strategies in the Chinese market. Understanding the factors influencing consumers’ willingness to share personal information can lead to more effective engagement in digital marketing campaigns.Originality/valueBy integrating interactivity theory with privacy computing theory, this research provides a new perspective on the role of online interaction in consumer privacy protection and information disclosure decisions. The findings not only enrich the theoretical frameworks of interactive marketing and privacy protection but also offer empirical support for marketing practitioners in regard to designing interactive advertising strategies, especially balancing consumer privacy protection with the enhancement of shopping intentions.

Continuously Learning Prediction Models for Smart Domestic Hot Water Management

Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and continual learning algorithms to personalize DHW predictions of household occupants’ behavior. Such models, alongside a control system that decides when to heat, enable the development of a heat-pumped-based smart DHW production system, which can heat water only when needed and avoid energy loss due to the storage of hot water. Deep learning models, and attention-based models particularly, can be used to predict time series efficiently. However, they suffer from catastrophic forgetting, meaning that when they dynamically learn new patterns, older ones tend to be quickly forgotten. In this work, the continuous learning of DHW consumption prediction has been addressed by benchmarking proven continual learning methods on both real dwelling and synthetic DHW consumption data. Task-per-task analysis reveals, among the data from real dwellings that do not present explicit distribution changes, a gain compared to the non-evolutive model. Our experiment with synthetic data confirms that continual learning methods improve prediction performance.

Virtualization and energy management optimization of high speed computer network data centers based on optical switching and network technology

With the development of cloud computing and big data technology, the scale and complexity of data centers are increasing, and the thermal energy consumption management of computer processors can effectively improve the energy efficiency of data centers and reduce operating costs. Therefore, this study discusses the application of high-speed computer network architecture based on optical switching and network technology in data center virtualization. By constructing a high-speed computer network model based on optical switching technology, virtualization technology is used to dynamically schedule and manage resources, combined with real-time monitoring system, and the energy consumption data of the processor is collected. Using the data analysis method, the energy consumption performance under different load conditions is evaluated and the optimization strategy is proposed. The experimental results show that the network architecture using optical switching technology significantly reduces the latency of data center and improves the resource utilization. In the thermal management of the processor, dynamic resource scheduling successfully reduces the energy consumption and effectively maintains the stability and performance of the system. As a result, data center virtualization solutions based on optical switching and networking technologies can significantly optimize the power management of computer processors and improve overall energy efficiency.

FINANCIAL MARKET’S ROLE IN DETERMINING THE VALUE OF INTERNATIONAL BUSINESS INTANGIBLE ASSETS

As humanity moves into the era of digital technologies and the knowledge economy, the importance of intangible assets in the global economy and international business has been increasingly recognised. Amid the forced virtual communication resulting from the lingering effects of the COVID-19 pandemic, as well as the social conflicts and cyber-attacks associated with Russia's hybrid war in Ukraine, the value of intangible assets such as reputation, investor and customer loyalty, consumer data, branding, intellectual expertise and copyrights has taken on new importance and relevance. The purpose of the research is to improve the conceptual and methodological foundations for determining the nature and value of intangible assets of leading international companies, in particular, their undisclosed components, based on the use of financial market data. Research methodology. The study is based on systems analysis, analysis and synthesis, statistical and graphical methods. These methods were used primarily to identify the structure of the intangible assets of leading international companies, highlighting the interrelationships between the formation of the gap between the external (financial market) and internal (balance sheet) value of companies' shares and the value of their intangible assets. The publication supports the assumption that the identified gap between the market capitalisation and shareholder's equity of an individual company represents the undisclosed part of intangible assets that is not available in the company's official statements. This gap includes such components as reputation, trust and loyalty of customers and investors, brand strength, etc. Given the dynamic development of the international business sphere, the critical importance of flexibility, speed of decision-making, and the need for investors to obtain the most up-to-date information about the company's potential, including the value of its intangible assets, the proposed approach can serve as an effective decision-making tool and an alternative means of determining the real value of a company's intangible assets.

Analysis of Workload of Corn Seed Planting Equipment Operators Integrated with Fertilization Equipment

Introduction: The use of corn seed planting tools integrated with fertilizing tools requires a lot of energy because workers act as controllers, so that it often causes physical and mental workloads. Workloads that are too heavy and exceed work capacity can cause fatigue. This fatigue will eventually cause pain. Objective: The objective of this study is to investigate the physical workload of operators in operating corn seed planting tools integrated with fertilizing tools in supporting SDGs. Method: The methodology used in this study includes pulse observations carried out 5 times on each operator for 3 working days. The pulse before and after implantation was measured by measuring the pulse on the wrist artery with the 10-beat method. Results and Discussion: The results showed that the worker's pulse can describe the workload as a manifestation of muscle movement when doing work. The greater the muscle activity, the greater the fluctuation of the pulse movement that occurs. The operator's workload is in the light to medium category. Oxygen consumption of workers is influenced by the intensity of the work done. Based on oxygen consumption data, it is known that the workload of this corn seed planting tool is still relatively low because workers still feel comfortable and safe while doing planting work. Research Implications: The practical and theoretical implications of this study prove that the greater the muscle activity, the greater the fluctuation of the pulse rate movement that occurs. Workers' oxygen consumption is influenced by the intensity of the work being done. Originality/Value: This study contributes that the worker's pulse rate indicates workload as a manifestation of muscle movement while working. The workload of this corn seed planter is still relatively low because workers still feel comfortable and safe when using the corn seed planter.

Analisis Efektivitas Sistem Kendali Otomatis PJU Berbasis IoT Menggunakan Mikrokontroler ESP32 dengan Metode Regresi Linier

Public street lighting (PJU) is essential infrastructure for nighttime security. This research develops an automatic PJU control system based on IoT using light and motion sensors with an ESP32 microcontroller. The system allows for PJU lamp control via a website, providing high flexibility for users. The ESP32 microcontroller is connected to a PIR sensor to detect motion. When the sensor detects movement, the intensity of the PJU lamps is increased, and when no movement is detected, the light intensity is reduced to save energy. Users can control the PJU lamps from any internet-connected device. Testing results show an increase in energy efficiency, with an average power consumption reduction of 13.77 watts and an efficiency increase of 42.67%. An R-squared value of 0.629 indicates that the model is quite good at explaining the variability in power consumption data. This system can automatically turn on street lights and be monitored in real-time, And hopes to contribute to the development of smarter and more efficient street lighting systems.

The geography of healthcare: Mapping patient flow and medical resource allocation in China

The misallocation of medical resources leads to interregional patient flow in search of better healthcare. Using out-of-pocket medical expenditure data and a delineating method, this paper identifies spatial clusters of medical services in China based on patient flow across cities. Our findings indicate that healthcare resources are more concentrated in northern China, while southern China is divided into several large healthcare clusters at the same threshold. The provincial capital and economically significant cities are more likely to serve as medical cluster centers. We further apply the gravity model to examine the effects of healthcare disparity on cross-city medical expenditure. The results reveal that geographic disparities in high-quality medical resources encourage remote healthcare-seeking behavior, and the shorter the distance between locations, the higher the level of medical consumption. Patients are inclined to seek medical services within their own province and within specific medical clusters identified through delineation methods. This effect is more pronounced among patients from non-central cities. This study highlights healthcare inequality by examining cross-regional medical expenditure, providing valuable insights for future healthcare policy.