Using the IBM SPSS SW Tool with Wavelet Transformation for CO₂ Prediction within IoT in Smart Home Care

Predictive value of blood biomarkers in elderly patients with non-small-cell lung cancer

Aim: Whether GRHL1 can be considered as a potential biomarker for screening non-small-cell lung cancer (NSCLC) is still uncertain. We aimed to investigate the value of circulating blood GRHL1 on detecting NSCLC in an older population. Materials & methods: Diagnostic models from 351 older patients with NSCLC were constructed to assess the predictive value of blood GRHL1 on distinguishing NSCLC. Results: We observed that GRHL1 (odds ratio: 3.25; 95% CI: 1.70-6.91; p < 0.001) maintained a strong relationship with an elevated rate of NSCLC after adequate clinical confounding factors were controlled for. Importantly, serum GRHL1 (area under the curve: 0.725; 95% CI: 0.708-0.863; p < 0.001) had a good predictive value. Conclusion: This is the first time that circulating GRHL1 has been shown to have good value for early detection of NSCLC in an elderly population.

Using Interoperability between Mobile Robot and KNX Technology for Occupancy Monitoring in Smart Home Care

It is important for older and disabled people who live alone to be able to cope with the daily challenges of living at home. In order to support independent living, the Smart Home Care (SHC) concept offers the possibility of providing comfortable control of operational and technical functions using a mobile robot for operating and assisting activities to support independent living for elderly and disabled people. This article presents a unique proposal for the implementation of interoperability between a mobile robot and KNX technology in a home environment within SHC automation to determine the presence of people and occupancy of occupied spaces in SHC using measured operational and technical variables (to determine the quality of the indoor environment), such as temperature, relative humidity, light intensity, and CO2 concentration, and to locate occupancy in SHC spaces using magnetic contacts monitoring the opening/closing of windows and doors by indirectly monitoring occupancy without the use of cameras. In this article, a novel method using nonlinear autoregressive Neural Networks (NN) with exogenous inputs and nonlinear autoregressive is used to predict the CO2 concentration waveform to transmit the information from KNX technology to mobile robots for monitoring and determining the occupancy of people in SHC with better than 98% accuracy.

A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

The study deals with detection of the occupation of Intelligent Building (IB) using data obtained from indirect methods with Big Data Analysis within IoT. In the area of daily living activity monitoring, one of the most challenging tasks is occupancy prediction, giving us information about people's mobility in the building. This task can be done via monitoring of CO2 as a reliable method, which has the ambition to predict the presence of the people in specific areas. In this paper, we propose a novel hybrid system, which is based on the Support Vector Machine (SVM) prediction of the CO2 waveform with the use of sensors that measure indoor/outdoor temperature and relative humidity. For each such prediction, we also record the gold standard CO2 signal to objectively compare and evaluate the quality of the proposed system. Unfortunately, this prediction is often linked with a presence of predicted signal activities in the form of glitches, often having an oscillating character, which inaccurately approximates the real CO2 signals. Thus, the difference between the gold standard and the prediction results from SVM is increasing. Therefore, we employed as the second part of the proposed system a smoothing procedure based on Wavelet transformation, which has ambitions to reduce inaccuracies in predicted signal via smoothing and increase the accuracy of the whole prediction system. The whole system is completed with an optimization procedure based on the Artificial Bee Colony (ABC) algorithm, which finally classifies the wavelet's response to recommend the most suitable wavelet settings to be used for data smoothing.

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

The Current Situation and Learning Strategies of Foreign Students in Chinese Learning Following Entrepreneurial Psychology

Based on entrepreneurial psychology, the current situation of foreign students' use of learning strategies in Chinese learning is explored, the overall situation of learning strategies in this process is analyzed, and the relationship between foreign students' use of learning strategies and various factors are obtained through the designed questionnaire survey. First, a questionnaire suitable for the research respondents is designed to investigate the current situation of foreign students' use of learning strategies in Chinese learning; second, 200 questionnaires are distributed, and 195 questionnaires are recovered, with a recovery rate of 97.5%. After the invalid questionnaire is excluded, the effective rate is 95%; furthermore, the reliability of the questionnaire data is analyzed by SPSS25 software, and Cronbach's α coefficient is 0.869, which proves that the questionnaire has high reliability; finally, the overall situation of foreign students' use of learning strategies in Chinese learning is analyzed from the aspects of their majors, their levels of Chinese proficiency, Chinese learning time, age and personality. The results show that the frequency of using cognitive strategies in learning Chinese is the highest, with a score of 3.689; There is a positive correlation between the use of learning strategies and the degree of proficiency of Chinese; Among them, the foreign students who have studied for 2-3 years use learning strategies the most frequently, and the students aged 28-32 use learning strategies the most frequently in the Chinese level test 4. This study provides new ideas for foreign students' Chinese teaching and has a certain reference for foreign students' Chinese teaching strategies.

Human Activity Classification Using Multilayer Perceptron

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.

Indirect Recognition of Predefined Human Activities

The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO₂, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

Prevalence and Antimicrobial Susceptibility Patterns of Bacterial Pathogens in Urinary Tract Infections in University Hospital of Campania "Luigi Vanvitelli" between 2017 and 2018

Urinary tract infections (UTIs) are the most common and expensive health problem globally. The treatment of UTIs is difficult owing to the onset of antibiotic-resistant bacterial strains. The aim of this study was to define the incidence of infections, identify the bacteria responsible, and identify the antimicrobial resistance profile. Patients of all ages and both sexes were included in the study, all admitted to University Hospital of Campania “Luigi Vanvitelli”, between January 2017 and December 2018. Bacterial identification and antibiotic susceptibility testing were performed using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) and Phoenix BD. Among the 1745 studied patients, 541 (31%) and 1204 (69%) were positive and negative for bacterial growth, respectively. Of 541 positive patients, 325 (60%) were females, while 216 (39.9%) were males. The largest number of positive subjects was recorded in the elderly (>61 years). Among the pathogenic strains, 425 (78.5%) were Gram-negative, 107 (19.7%) were Gram-positive, and 9 (1.7%) were Candida species. The most isolated Gram-negative strain is Escherichia coli (E. coli) (53.5%). The most frequent Gram-positive strain was Enterococcus faecalis (E. faecalis) (12.9%). Gram-negative bacteria were highly resistant to ampicillin, whereas Gram-positive bacteria were highly resistant to erythromycin.

A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning

The performance of the brake system is directly related to the safety and reliability of the mine hoist operation. Mining the useful fault information in the operation of a mine hoist brake system, analyzing the abnormal parts and causes of the equipment, and making accurate early prediction and diagnosis of hidden faults are of great significance to ensure the safe and stable operation of a mine hoist. This study presents a fault diagnosis method for hoist disc brake system based on machine learning. First, the monitoring system collects the information of the hoist brake system, extracts the fault features, and pretreats it by SPSS (Statistical Product and Service Solutions). This work provides data support for fault classification. Then, due to the complex structure of the hoist brake system, the relationship between the fault factors often has a significant impact on the fault. Considering the correlation between the fault samples and the attributes of each sample data, the C4.5 decision tree algorithm is improved by adding Kendall concordance coefficient, and the improved algorithm is used to train the sample data to get the decision tree classification model. Finally, the fault sample of the hoist brake system is trained to get the algorithm model, and then the fault diagnosis rules are generated. The state of the brake system is judged by classifying the data. Experiments show that the improved C4.5 decision tree algorithm takes the relativity of conditional attributes into account, has a higher diagnostic accuracy when processing more data, and has concise and clear fault classification rules, which can meet the needs of hoist fault diagnosis.

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO₂ concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO₂ concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO₂ concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

This article introduces a new way of using a fibre Bragg grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a smart home (SH). CO₂ sensors are used to determine the CO₂ concentration of the monitored rooms in an SH. CO₂ sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO₂ prediction, by means of an artificial neural network (ANN) with a scaled conjugate gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO₂ concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO₂ prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO₂ prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the adaptive filtration with an LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

Novel Proposal for Prediction of CO2 Course and Occupancy Recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.