Collection Of Consumption Data Research Articles

An auto-encoder bio medical signal transmission through custom convolutional neural network

The transmission of biomedical signals in real-time is extremely difficult and necessitates the use of cloud and internet of things (IoT) infrastructure. Security is also an important consideration, however, to achieve this, a reconstruction method is developed where the entire signal is fed as an input, just the primary portion, the entire signal is taken then encoded, and then deliver to the destination. It is unlocked using a reconstruction technique without any signal attenuation. The key difficulty is how to manage the sensor network once the input is prepared for transmission. This has problems with extremely high network energy consumption and accurate data collection. The accuracy of data reconstruction through is improved by compressive sensing. The lifespan of the network as a whole could be extended, in this study; the proposed proposed system convolutional neural network (PS-CNN) is an integrated model that combines feature selection and auto-encoder. In order to produce the most useful features for particular tasks, our algorithm can eventually separate the appropriate task units from the irrelevant tasks.

Sustainable and self-sufficient social home through a combined PV‑hydrogen pilot

Utilizing surplus renewable energy to produce and store green hydrogen can help meet energy demands during periods of low renewable energy production, and thus contributing greatly to decarbonize emission-intensive sectors, such as the residential and building sector. In this context, this manuscript reports the design, construction and operation of a hybrid PV‑hydrogen demonstrative pilot (PVHyP) for the 100% electrical self-sufficiency of a social housing in the town Novales (Spain). The work addresses i) the collection of real consumption data from the dwelling, ii) the design considering real characteristics and market availability of the devices, iii) final balance of plant implementation, and iv) the energy management strategy followed. The implemented control system allows the facility to be tracked and operated remotely. Besides, this flagship implementation in the SUDOE region (Spain, Portugal and southwest of France) has achieved the electrical independence from the grid under diverse climate conditions, eliminating 2260 kg of CO2 emissions associated to electricity consumption, saving almost 15,200 kWh of primary energy during two years of monitoring. Thus, around 1170 € where avoided in electricity bills (100% savings), resulting on multiple benefits for social dwellers in risk of energy poverty.

Integrating Technology and Heritage Design for Climate Resilient Courtyard House in Arid Region

This research has investigated the sustainability and climate resilience of courtyard houses of adobe architecture in the UAE. It analyzed design effectiveness in terms of power consumption, CO2 emissions, thermal comfort, and daylight use, employing simulations to assess building structures and construction systems. Adopting a three-phase mixed-methods approach, the study began with a literature review on courtyard house design, construction, and environmental performance, emphasizing sustainable design and passive ventilation. The second phase involved a case study of a UAE courtyard house (Al Midfa), including site visits, interviews, and energy consumption and CO2 emission data collection. The final phase used building energy simulation software to model energy performance and evaluate passive ventilation's role in reducing energy consumption and CO2 emissions, with simulation results validated against real-world data. Advanced Sefaira simulations with the Energy Plus Engine identified one out of seven modified models (M5) as exceptionally thermally efficient, influencing the architectural design of the Al Midfa house. To transform the Al Midfa house into a sustainable climate-resistant structure, the research suggested retrofitting with new glazing and insulation on the inside of external walls and on the roof surface at a combined U-value of 0.4 W/m2to enhance energy efficiency without altering the exterior. A notable innovation was the use of injected cellulose insulation in wall systems, combining efficient insulation with architectural aesthetics, signifying a shift towards energy-efficient interior modifications. The study's findings contribute to the evolution of traditional house designs toward climate change resilience and a sustainable future. Doi: 10.28991/CEJ-2024-010-03-018 Full Text: PDF

The advantages and comparison of resistive touch screens and capacitive touch screens

ATouch screens are an indispensable component in electronic products nowadays, and resistive and capacitive touch screens are the most common and widely used touch screens in touch screens. However, there is still a lack of intuitive literature to explain the differences and advantages between capacitive and resistive touch screens. This article provides a partial overview of the principles and structures of resistive and capacitive touch screen technologies and finally analyzes their respective advantages and disadvantages. The resistive touch screen is mainly divided into four-wire resistive touch screens and five-wire resistive touch screens. Its advantage over capacitive touch screens is that the process is more mature, and the product cost is lower. However, capacitive touch screens, due to their different working principles from resistive touch screens, can achieve multi-point touch, which is a significant feature. In capacitive touch screens, mutual capacitive projection capacitive touch screens have become the main favored technology in the touch screen industry due to their low power consumption, stable signal, and fast data collection speed. They are also an essential direction for the future development of touch screens.

Characterization of College Students’ Consumption Behavior for Ideological and Political Education - Based on ADDIE Model

Abstract As a specific social group, the consumption behavior of college students not only reflects their economic ability but also contains deep-seated values and attitudes toward life. With the development of the social economy and the change in consumption concepts, the consumption patterns and psychology of college students have diversified. This paper deeply analyzes the characteristics of college students’ consumption behavior and the value of their ideological and political education. It also adopts the ADDIE model to systematically study college students’ consumption behavior. The improved K-means clustering algorithm allows us to classify the consumption behavior of 29,635 students and analyze their changing trends by collecting consumption data. The average monthly consumption of most students is approximately 800 to 1000 yuan, and the frequency of consumption is mainly 60-75 times per month. Students with different consumption levels have different needs and responses when it comes to ideological and political education. The paper proposes targeted strategies for ideological and political education to guide students in establishing correct consumption concepts and cultivating rational consumption behavior. The study effectively enhances the research on college students’ consumption behavior and the practical effect of ideological and political education through the application of the ADDIE model, providing support for the overall development of college students.

Soil moisture forecasting for precision irrigation management using real-time electricity consumption records

The installation of the smart electricity meters (SEMs) to the high-density pumping wells in the North China Plain (NCP) provided a method to control the groundwater withdraw. The record in real-time electricity consumption from SEMs also provided an alternative method to forecast soil moisture to make irrigation decisions to improve irrigation efficiency. The irrigation timing and the irrigation amount could be obtained on-line from the SEMs based on the coefficients obtained for electricity-to-water conversion (E-Wc). The real-time recordings in the irrigation timing and amounts were further used to forecast the next irrigation timing and amount based on the water rights per area of land, the lower limit of soil water contents for different crops at their different growing stages and online meteorological data. A smartphone app was developed based on this framework. The running of the app to simulate soil water contents based on the soil water balance equation was supported by databases on soil texture, crop coefficients, water rights, E-Wc and threshold soil water content values as well as the real-time collection of electricity consumption and meteorological data. The simulated real-time soil water contents were shown on the mobile phone screen and could be compared with the threshold values for making irrigation decisions for users. The accuracy in forecasting the soil water contents using the methods developed in this framework was tested under different irrigation schedules for winter wheat and summer maize during 2018–2019 and 2021–2022, and the app was tested for three locations starting from sowing winter wheat in October 2022 to April in 2023. The results showed that the app could be easily run, and precise real-time soil water contents were simulated. With the application of the pumping well renovation project in the NCP, most of the pumping wells were installed with the SEMs, and using SEMs for groundwater pumping control and irrigation decisions should be possible.

Healthy Food Education Affects Underweight Adolescent's Knowledge and Intakes of Energy, Macro and Micro Nutrients

One of the causes of underweight is an imbalance between nutritional intake and needs. The condition often occurs in teenagers and in Indonesia there are 8.1%. This study aims to investigate the impact of healthy diet education delivered by peers on the level of knowledge; energy, macro and micro nutrients intake among underweight adolescents at state Senior High School 1 in Singosari, Malang Regency. This study conducted on 33 undeweight adolescent’s girls selected based on inclusion criteria. Data collection was carried out before and after the intervention. Adolescents’ nutritional knowledge was collected by asking questions using Google forms and food consumption data collection was carried out by direct interviews using a 24-hour food recall form. The intervention took the form of education and guidance from peer tutors when the subjects consumed snacks and lunch at school, which was carried out 14 times, 2 times a week during 8 weeks. The effect of providing education on the level of knowledge; Energy, carbohydrate, protein, fat and zinc intake were analyzed using the paired sample t test, while vitamin C and iron intake used the Wilcoxon test (95% CI; α = 0.05).The results showed significant improvements in the level of knowledge intake of Energy, Protein, Fat, Carbohydrates as well as Iron and Zinc (p-value <0.05), while the increase in vitamin C intake was not significantly different (p-value >0.05). These findings demonstrated the potential of providing healthy diet education by peers to increase knowledge, energy, macro and micro nutrient intake among underweight adolescent girls.

A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption

The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download.

"CAPABILITIES OF NB-IoT TECHNOLOGY FOR SOLVING TASKS OF MONITORING, ACCOUNTING AND COLLECTING DATA ON ELECTRICITY CONSUMPTION "

"Problems with accounting, collection and correct readings of electricity meters are of great importance when preparing and invoicing for electricity, especially in neighborhoods with private households, where the process of monitoring electricity consumption is not always provided for various reasons beyond the control of the consumer of services. The development of modern information technologies, including mobile communications using the technology of ΝΒ-ΙοΤ, allows us to solve problems related to improving the efficiency of monitoring, accounting, and data collection of electricity consumption in microdistricts not only quickly and efficiently, but also increase the management efficiency of the process of collecting meter readings, as well as reducing the costs of the Service Provider for transport services, personnel and other items of expenditure. The article presents an analysis of various brands of metering devices used in the Urker microdistrict and the use of metering devices with the support of technology ΝΒ-ΙοΤ. A simulation model of access to metering devices using the technology of ΝΒ-ΙοΤ is shown. The issues related to the causes and problems of electricity theft are touched upon. The article will be useful to students, doctoral students, engineers, anyone who is related to automation and control of technological processes and productions, as well as anyone who deals with the problems of implementing the ASUE, IoT technology. "

Impact of COVID-19 pandemic waves on health-care worker hand hygiene activity in department of medicine and ICU as measured by an automated monitoring system.

Hand hygiene (HH) compliance among health-care workers is important for preventing transmission of infectious diseases. To describe health-care worker hand hygiene activity in ICU and non-ICU patients' rooms, using an automated monitoring system (AMS), before and after the onset of the COVID-19 pandemic. At the Intercommunal Hospital of Créteil, near Paris, France, alcohol-based hand sanitizer (ABHS) consumption in the Department of Medicine (DM) and ICU was recorded using an AMS during four periods: before, during, and after the first wave of the COVID-19 pandemic, and during its second wave. From 1st February to 30th November 2020, in the DM, the mean number of doses per patient-day for each of the four periods was, respectively, 5.7 (±0.3), 19.4 (±1.3), 17.6 (±0.7), and 7.9 (±0.2, P<0.0001). In contrast, ICU ABHS consumption remained relatively constant. In the DM, during the pandemic waves, ABHS consumption was higher in rooms of COVID-19 patients than in other patients' rooms. Multivariate analysis showed ABHS consumption was associated with the period in the DM, and with the number of HCWs in the ICU. An AMS allows real-time collection of ABHS consumption data that can be used to adapt training and prevention measures to specific hospital departments.

A household-scale life cycle assessment model for understanding the food-energy-water nexus

The household is an important locus of decision-making regarding food, energy, and water (FEW) consumption. Changes in household FEW consumption behaviors can lead to significant reductions in environmental impacts, but it can be difficult for consumers to compare the relative impacts of their consumption quantitatively, or to recognize the indirect impacts of their household consumption patterns. We describe two novel tools designed to address this problem: A hybrid life cycle assessment (LCA) framework to translate household consumption of food, energy, and water into key environmental impacts including greenhouse gas emissions, energy use, and water use; and a novel software application called HomeTracker that implements the framework by collecting household FEW data and providing environmental impact feedback to households. We explore the question: How can a life cycle assessment-based software application facilitate collection and translation of household consumption data to meaningful environmental impact metrics? A case study in Lake County, Illinois is presented to illustrate use of the HomeTracker application. Output data describing environmental impacts attributable to household FEW consumption in the study area are shown in order to illustrate key features and trends observed in the case study population. The framework and its associated output data can be used to support experimental research at the household scale, allowing for examination of what users purchase and consume over an extended period of time as well as increased understanding of household behavior trends and environmental impacts, and as future work.

The Potential of Condiments, Seasonings, and Bouillon Cubes to Deliver Essential Micronutrients in Asia: Scenario Analyses of Iodine and Iron Fortification.

Micronutrient deficiencies are still highly prevalent in Asia. Fortification of cooking aids, such as condiments (fish and soy sauces), seasonings, and bouillon cubes, may be an additional strategy to improve micronutrient intake. The current study evaluated the potential impact of iodine and iron fortification of cooking aids on micronutrient intake in Asian countries. A systematic literature search was performed to collect consumption data from different countries in Asia. Data from 18 studies in nine Asian countries were included. Scenario analyses were performed using different fortification levels based on regulations and literature. Mean intake of cooking aids ranged from 3.2-15.9 g/day for condiments and 0.4-11.7 g/day for seasonings and bouillon cubes. When replacing salt with iodized salt (30 µg of iodine/g of salt), iodine intake would increase by 13-119 µg/day for soy and fish sauces (9-80% of the Nutrient Reference Value (NRV)), and 5-83 µg/day for bouillon cubes and seasonings (4-56% of the NRV). Fortification with iron 0.5 mg/g food product for condiments or 1 mg/g food product for bouillon cubes and seasonings improved iron intake for soy and fish sauces by 1.6-8.0 mg/day (11-57% of the NRV), and for bouillon cubes and seasonings by 0.4-5.6 mg/day (3-40% of the NRV). These results indicate that, depending on the consumption pattern, fortification of cooking aids can be a suitable strategy to increase intake of micronutrients.

Design and key technology of the energy consumption management system for the liquid cooling data center

AbstractIn view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. Particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.

Design of a Standard and Programmatically Accessible Interface for Smart Meters to Allow Monitoring Automation of the Energy Consumed by the Execution of Computer Software

Software has become more computationally demanding nowadays, turning out high-performance software in many cases, implying higher energy and economic expenditure. Indeed, many studies have arisen within the IT community to mitigate the environmental impact of software. Collecting and measuring software’s power consumption has become an essential task. This paper proposes the design of a standard interface for any currently available smart meter, which is programmatically accessible from any software application and can collect consumption data transparently while a program is executed. This interface is structured into two layers. The former is a driver that provides an OS-level standard interface to the meter, while the latter is a proxy offering higher-level API for a concrete programming language. This design provides many benefits. It makes it possible to substitute the meter for a different device without affecting the proxy layer. It also allows the presence of multiple proxy implementations to offer a programmatic interface to the meter for several languages. A prototype of the proposed interface design has been implemented for a concrete smart meter and OS to demonstrate its feasibility. It has been tested with two experiments. Firstly, its correct functioning has been validated. Later, the prototype has been applied to monitor the execution of a high-performance program, a machine learning application to select the most relevant features of electroencephalogram data.

Life cycle assessment of extraction of edible oil from mustard seeds: A case study of an oil industry

Life cycle assessment (LCA) has been used to quantify the potential environmental impact of a process. The present study assessed the environmental hotspots of mustard oil extraction process from mustard seeds using a life cycle assessment (LCA) method. This study applies the LCA methodology to assessing environmental hotspots in the entire chain of the oil extraction and comparing the environmental impact of each process at the stages of extraction. The primary inventory data collection of industrial processes, inputs and energy consumption of oil extraction was collected by visiting the plant units and interviewing of plant manager and engineers. A gate to gate system boundary condition was used for this study. The LCA analysis includes the seed + seed cleaning machine, seed conveyor, kohlu plant, expeller, pumping and filtration. The LCA was performed using the Gabi software, and the CML 2001 technique was employed for the impact assessment. Environmental effect categories such as global warming potential (GWP), eutrophication, ozone depletion, and abiotic depletion were identified. Results of this study indicate that the seed + seed cleaning machine and oil filteration & packaging are the primary contributor to GWP, HP, POCP, and HTP. The seed + seed cleaning machine and oil filteration & packaging processes are serving as bottlenecks when it comes to meeting their environmental indicators.

Imputation for sub-sampling in Indonesian National Socioeconomic Survey

Collecting consumption and expenditure data might result in some measurement problems, such as potential recall bias. In addition, the respondent burden is another issue as a consequence of the interview lasting for hours. Consumption and expenditure data in Indonesia is collected through the National Socioeconomic Survey (Susenas). Indonesia is a country with many factors that can influence how long an interview may take, especially when collecting consumption and expenditure data, so deliberate sub-sampling and imputation need to be considered. The focus of this study is to look at the possibility of using sub-sampling of expenditure data and imputing the deliberately missing data using a standard method of missing data imputation (mice), a multilevel approach (jomo), and two machine learning approaches (missRanger and miceRanger). The results show that only mice with reasonable imputation results, in particular when breaking down by some categories. Although missRanger is the fastest, it has a large bias compared to the actual data.

Determination of Amygdalin in Apricot Kernels and Almonds Using LC-MS/MS.

Cyanogenic glycosides are secondary metabolites in plants. In almonds and apricot kernels, amygdalin is an abundant cyanogenic glycoside. Upon consumption, amygdalin is enzymatically metabolized into hydrogen cyanide. Depending on the number of kernels consumed and the amygdalin concentration, ingestion of amygdalin-containing kernels may result in adverse effects. To better understand the US marketplace, the development and validation of analytical methods to reliably measure amygdalin in apricot kernels and almonds is needed to support the collection of occurrence and consumption data in retail products. The aim of this study was to develop and validate a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the quantitation of amygdalin in apricot kernels and almonds following the U.S. Food and Drug Administration (FDA). Foods Program Guidelines for the Validation of Chemical Methods, 3rd Edition. Apricot kernels and almonds were cryogenically homogenized and extracted using methanol containing an internal standard (IS), geniposide, followed by filtration, dilution, and LC-MS/MS analysis. Matrix effects were minimized using dilution. Quantitation was achieved using an external, solvent-based calibration. The amygdalin response was linear (r2 > 0.99) over a range of 0.05-50 µg/mL. The recovery of amygdalin spiked at 10-10 000 µg/g in sweet apricot kernels, raw almond, and dry-roasted almond ranged from 90 to 107% with RSDs ≤6%. The method limit of detection and limit of quantitation was 0.8 and 2.5 ng/g, respectively. Amygdalin concentrations in 18 market samples ranged from 2 to 24 000 µg/g. Corresponding estimates of cyanide concentration ranged from 0.2 to 1420 µg/g. Method performance meets the acceptance criteria defined by FDA guidelines and is fit for purpose for the analysis of amygdalin in apricot kernels and almonds. An LC-MS/MS method is developed for the quantification of amygdalin in apricot kernels and almonds.

Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia

The paucity of reliable, timely household consumption data in many low- and middle-income countries have made it difficult to assess how global poverty has evolved during the COVID-19 pandemic. Standard poverty measurement requires collecting household consumption data, which is rarely collected by phone. To test the feasibility of collecting consumption data over the phone, we conducted a survey experiment in urban Ethiopia, randomly assigning households to either phone or in-person interviews. In the phone survey, average per capita consumption is 23 percent lower and the estimated poverty headcount is twice as high than in the in-person survey. We observe evidence of survey fatigue occurring early in phone interviews but not in in-person interviews; the bias is correlated with household characteristics. While the phone survey mode provides comparable estimates when measuring diet-based food security, it is not amenable to measuring consumption using the ‘best practice’ approach originally devised for in-person surveys.

Occurrence of pharmaceuticals in the wastewater of a Greek hospital: Combining consumption data collection and LC-QTOF-MS analysis

In this article we applied drug consumption approach and chemical analysis in parallel to investigate the concentrations of a large number of pharmaceuticals in different streams of a General Hospital. Drugs consumption data was collected during two periods (Period 1, 2) and the predicted environmental concentrations (PECs) were estimated for the wastewater of a building housing specific medical services (Point A) and for the entire hospital (Point B). Hospital wastewater samples (HWW) samples were also collected from these points and periods and the measured environmental concentrations (MEC) were determined using UHPLC-ESI-QTOF-MS/MS. According to consumption data, the highest number of drugs was consumed in the departments of Hematology, Intensive Care Unit, Cardiology, Internal Medicine, and Oncology, while the number of active substances used in the hospital was 413 (Period 1) and 362 (Period 2). For most substances, much higher PEC and MEC values were found at the HWW of Point A indicating that on-site treatment of this stream could be examined in the future. The application of wide-scope target analysis allowed the quantification of 122 compounds, while 21 additional substances were identified using suspect screening. The highest mean concentrations in Period 1 were found for acetaminophen (1100 μg/L) and rifaximin (723 μg/L), while in Period 2 for iopromide (458 μg/L) and acyclovir (408 μg/L). Among the detected compounds, 19 metabolites were determined. Atenolol acid, 1-hydroxy-midazolam and clopidogrel carboxylic acid were quantified at concentrations much higher than parent compounds indicating the importance of metabolites' monitoring in HWW. Calculation of PEC/MEC ratio for 36 pharmaceuticals showed sufficient correlation of these values for 19 % to 33 % of the substances depending on the examined period and sampling point. The parallel collection of drugs consumption data and chemical analysis give a thorough picture of the substances present in HWW and their main sources, facilitating decision-making for their better management.

Application of Neural Network–Based Modeling for Leak Localization in Water Mains

Water distribution network (WDN) leakage is estimated to account for more than 20% of potable water in the United States. Because existing leakage detection practices are mainly laborious or require excessive budget allocation, novel model-based techniques have become prevalent by employing smart meters. Specifically, incorporating machine learning and metaheuristic optimization methods into such model-based approaches to predict water pipeline leakage is one of the state-of-the-art approaches. This paper presents a predictive leakage detection approach by leveraging advanced metering infrastructure (AMI) and cyber-monitoring data. Primarily, the synchronous collection of consumption and monitoring data (i.e., flow and pressure) constitutes the proposed predictive tool in a combinative framework of genetic algorithms (GA) and particle swarm optimization (PSO). The time-consuming EPANET 2.2 simulation toolkit in MATLAB is circumvented by a set of trained artificial neural networks (ANNs). Sensitivity analyses are conducted to investigate the sensitivity of the proposed leakage prediction approach to multiple parameters. Ultimately, the number and location of the flow and pressure monitoring stations are optimized using maximum sensor availability thresholds. Mean absolute percentage error (MAPE) is employed to measure the model’s accuracy. The proposed predictive tool offers insightful horizons on how considerable background leakage can be detected and prioritized without using labor-intensive, costly methods.