Shinpaku shingō no jikan oyobi shūhasū ryōiki no supāsusei ni motozuku aratana hisesshokugata shinpakusū suiteihō

Purpose: This study aims to analyze green supply chain management (GSCM) and green marketing strategies (GMS) to green purchasing intentions (GPI). This study conducts on craft SMEs in the Special Region of Yogyakarta, Indonesia. Design/methodology/approach: This study uses primary data which is obtained through questionnaires. The unit of analysis in this study is organizations and individuals. The sampling technique is purposive sampling, with the criteria of SMEs that conduct environmentally friendly production processes and consumers who have ever bought green products. Data analysis uses structural equation modeling. Findings: The results of the data analysis show that there is an influence of green supply chain management on green marketing strategy, and there is an influence of green marketing strategy on green purchase intention. Research limitations/implications: This study is limited by relatively small sample size. The sample is only environmentally oriented SMEs. Large companies that are also environmentally friendly have not been included as samples in this study, so the results of this study only generalized to SMEs. Future research should accommodate these two types of companies, namely SMEs and companies, so that it can be easier to generalize the findings and allow different tests of GSCM to be applied to SMEs and large companies. This study only analyzed GSCM from two dimensions, namely GP and GCC. Other variables that can be used to explain GSCM are internal environmental, green information systems, eco-design and packaging. Practical implications: GSCM can be started with conducts the right GP and always coordinating with consumers which related to green products. GP (green purchasing) and GCC (green consumer cooperation) as GSCM elements have a strong association in predicting the success of a green marketing strategy. It is expected that SMEs should pay attention to the raw material purchase, so that the problem of environmentally friendly raw materials can be truly obtained to enter the production process and produce environmentally friendly products. Originality/value: This study analyzes the relationship between GSCM practices and organizational performance in the green marketing and business strategiescontext, where there is still a scarcity of studies in this context. Besides that, there is an increase in awareness of green operations and green marketing in Asia, but the relevant studies in Asian countries have not been conducted much, especially in Southeast Asia. The result of this study proves that the GSCM model can increase value along the supply chain by emphasizing green supply chain management and green marketing.Peer Reviewe

An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

Epilepsy is one of the most common neurological diseases globally, affecting around 50 million people worldwide. Fortunately, up to 70 percent of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The scalp-based EEG test, despite being the gold standard for diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled professionals for operation, and is discomforting for users. In this paper, we propose EarSD, a novel lightweight, unobtrusive, and socially acceptable ear-worn system to detect epileptic seizure onsets by measuring the physiological signals from behind the user's ears. EarSD includes an integrated custom-built sensing, computing, and communication PCB to collect and amplify the signals of interest, remove the noises caused by motion artifacts and environmental impacts, and stream the data wirelessly to the computer or mobile phone nearby, where data are uploaded to the host computer for further processing. We conducted both in-lab and in-hospital experiments with epileptic seizure patients who were hospitalized for seizure studies. The preliminary results confirm that EarSD can detect seizures with up to 95.3 percent accuracy by just using classical machine learning algorithms

Brain Tumour Classification Using Deep Features from Structural MRI

C

Interference motion removal for Doppler radar vital sign detection using variational encoder-decoder neural network

The treatment of interfering motion contributions remains one of the key challenges in the domain of radar-based vital sign monitoring. Removal of the interference to extract the vital sign contributions is demanding due to overlapping Doppler bands, the complex structure of the interference motions and significant variations in the power levels of their contributions. A novel approach to the removal of interference through the use of a probabilistic deep learning model is presented. Results show that a convolutional encoder-decoder neural network with a variational objective is capable of learning a meaningful representation space of vital sign Doppler-time distribution facilitating their extraction from a mixture signal. The approach is tested on semi-experimental data containing real vital sign signatures and simulated returns from interfering body motions. It is demonstrated that the application of the proposed network enhances the extraction of the micro-Doppler frequency corresponding to the respiration rate

Nature inspired method for noninvasive fetal ECG extraction

This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women's body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with sequential analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The databases used to test the algorithms are available on a server at the generalist repositories (figshare) integrated with Matonia et al. (Sci Data 7(1):1-14, 2020). The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%. The obtained results show that the fHR related parameters were determined accurately for most of the records, outperforming the other state-of-the-art approaches. The poorer quality of certain signals have caused deviation from the estimated fHR for certain records in the databases. The proposed algorithm is compared with certain well established algorithms, and has proven to be accurate in its fECG extractions.Web of Science121art. no. 2015

Signal Processing Contributions to Contactless Monitoring of Vital Signs Using Radars

Vital signs are a group of biological indicators that show the status of the body’s life-sustaining functions. They provide an objective measurement of the essential physiological functions of a living organism, and their assessment is the critical first step for any clinical evaluation. Monitoring vital sign information provides valuable insight into the patient's condition, including how they are responding to medical treatment and, more importantly, whether the patient is deteriorating. However, conventional contact-based devices are inappropriate for long-term continuous monitoring. Besides mobility restrictions and stress, they can cause discomfort, and epidermal damage, and even lead to pressure necrosis. On the other hand, the contactless monitoring of vital signs using radar devices has several advantages. Radar signals can penetrate through different materials and are not affected by skin pigmentation or external light conditions. Additionally, these devices preserve privacy, can be low-cost, and transmit no more power than a mobile phone. Despite recent advances, accurate contactless vital sign monitoring is still challenging in practical scenarios. The challenge stems from the fact that when we breathe, or when the heart beats, the tiny induced motion of the chest wall surface can be smaller than one millimeter. This means that the vital sign information can be easily lost in the background noise, or even masked by additional body movements from the monitored subject. This thesis aims to propose innovative signal processing solutions to enable the contactless monitoring of vital signs in practical scenarios. Its main contributions are threefold: a new algorithm for recovering the chest wall movements from radar signals; a novel random body movement and interference mitigation technique; and a simple, yet robust and accurate, adaptive estimation framework. These contributions were tested under different operational conditions and scenarios, spanning ideal simulation settings, real data collected while imitating common working conditions in an office environment, and a complete validation with premature babies in a critical care environment. The proposed algorithms were able to precisely recover the chest wall motion, effectively reducing the interfering effects of random body movements, and allowing clear identification of different breathing patterns. This capability is the first step toward frequency estimation and early non-invasive diagnosis of cardiorespiratory problems. In addition, most of the time, the adaptive estimation framework provided breathing and heart rate estimates within the predefined error intervals, being capable of tracking the reference values in different scenarios. Our findings shed light on the strengths and limitations of this technology and lay the foundation for future studies toward a complete contactless solution for vital signs monitoring