expiration time
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With the development of Internet e-commerce channels, online shopping platforms have become the main channel for consumers to buy nearly expired food. Date labels, as one of the main external clues, play a decisive role in nearly expired food purchasing. Therefore, based on attention-related theory, this study attempts to explore the influence exerted by different time frames on consumers’ willingness to buy and its mechanism. The results show that compared with the date, consumers have a higher willingness to buy nearly expired food when the expiration time is framed by delay. More specifically, compared to the date, the delay causes the individual to have a longer time perception, thus more preference for nearly expired food. Meanwhile, the mediating effect of time perception is moderated by food type. The conclusion of this research is helpful to expand the theoretical framework of time frames and related fields on nearly expired food, as well as provide practical guidance for marketers to effectively promote nearly expired food.

Abstract Gmail’s confidential mode enables a user to send confidential emails and control access to their content through setting an expiration time and passcode, pre-expiry access revocation, and prevention of email forwarding, downloading, and printing. This paper aims to understand user perceptions and motivations for using Gmail’s confidential mode (GCM). Our structured interviews with 19 Gmail users at UNC Charlotte show that users utilize this mode to share their private documents with recipients and perceive that this mode encrypts their emails and attachments. The most commonly used feature of this mode is the default time expiration of one week, and the least used feature is the pre-expiry access revocation. Our analysis suggests several design improvements.

Ventilator merupakan alat kesehatan yang paling dibutuhkan di masa pandemi ini. Berbagai institusi telah berusaha mengembangkan ventilator, akan tetapi banyak yang terkendala dengan hasil pengujian dan kalibrasinya yang tidak sesuai. Hal ini dikarenakan ketidaktahuan terhadap metode pengujian dan kalibrasi ventilator yang sesuai standar. Oleh karena itu, dikembangkanlah platform Platform Online Simulasi Virtual Pengujian dan Kalibrasi Ventilator Berbasis Browser. Platform ini kedepannya akan digunakan oleh institusi pengembang ventilator untuk melakukan pembelajaran dan meningkatkan kompetensi melalui pelatihan terkait pengujian dan kalibrasi ventilator. Penelitian ini dibatasi dengan menggunakan beberapa ruang lingkup pengujian yaitu tidal volume, minute volume, breath rate, I:E ratio, PEEP, inspiration time, dan Expiration time. Tahapan metode penelitian yang akan dilakukan mulai dari studi literatur, produk, library, algoritma, pengumpulan data ventilator dan gas flow analyzer, dilanjutkan dengan desain sistem, antarmuka pengguna, dan elemen visual, kemudian dilakukan pengembangan sistem dan antarmuka pengguna, lalu dilakukan pengujian fungsi, dan pengalaman pengguna. Hasil pengujian menunjukan seluruh fungsi 100% terlaksana dengan baik dan pengalaman pengguna 38% menunjukkan setuju dengan kriteria pengalaman pengguna yang terdiri dari Daya Tarik, Efisiensi, Perspicuity, Ketergantungan, Stimulasi, Novelty, Kepercayaan, Estetika, Adaptabilitas, Kegunaan, Penggunaan Intuitif, Nilai, Konten yang Dapat Dipercaya, Kualitas Isi, Haptics, dan Akustik

Abstract The Lapindo mudflow in the Porong area, Sidoarjo Regency has been 14 years old since it first occurred on May 29, 2006. The biggest loss from the Lapindo mudflow is the damage to infrastructure and residents’ homes which has an impact on the slowing down of Sidoarjo’s economy. One of the efforts made by the government is to establish a National Action Plan (RAN) for disaster risk reduction (PRB) as an effort to achieve the Sustainable Development Goals (SDGs). Considering that the Lapindo mudflow is still active and the expiration time cannot be estimated, a long-term management strategy is needed in settlements around the location that are adaptive to the existence of the disaster. The research was carried out using qualitative-descriptive methods. Data collection techniques are carried out by interview, observation and documentation study. The data obtained is then matched with the indicators for assessing the Sustainable Development Goals (SDGs). The results of the research will provide insights in the form of settlement strategies carried out by local governments and communities as part of sustainable development in accordance with the conditions of their respective regions.

This study investigated the usefulness of ventilator parameters in the prediction of development and outcome of acute respiratory distress syndrome (ARDS) in postoperative patients with esophageal or lung cancer on admission to the surgical intensive care unit (SICU). A total of 32 post-operative patients with lung or esophageal cancer from SICU in a tertiary medical center were retrospectively analyzed. The study patients were divided into an ARDS group (n = 21) and a non-ARDS group (n = 11). The ARDS group contained the postoperative patients who developed ARDS after lung or esophageal cancer surgery. The ventilator variables were analyzed in this study. Principal component analysis (PCA) was performed to reduce the correlated ventilator variables to a small set of variables. The top three ventilator variables with large coefficients, as determined by PCA, were considered as sensitive variables and included in the analysis model based on the rule of 10 events per variable. Firth logistic regression with selective stepwise elimination procedure was performed to identify the most important predictors of morbidity and mortality in patients with ARDS. Ventilator parameters, including rapid shallow breath index during mechanical ventilation (RSBIv), rate pressure product of ventilation (RPPv), rate pressure volume index (RPVI), mechanical work (MW), and inspiration to expiration time ratio (IER), were analyzed in this study. It was found that the ARDS patients had significantly greater respiratory rate (RR), airway resistance (Raw), RSBIv, RPPv, RPVI, positive end-expiratory pressure (PEEP), and IER and significantly lower respiratory interval (RI), expiration time (Te), flow rate (V˙), tidal volume (VT), dynamic compliance (Cdyn), mechanical work of ventilation (MW), and MW/IER ratio than the non-ARDS patients. The non-survivors of ARDS had significantly greater peak inspiratory pressure above PEEP (PIP), RSBIv, RPPv, and RPVI than the survivors of ARDS. By using PCA, the MW/IER was found to be the most important predictor of the development of ARDS, and both RPPv and RPVI were significant predictors of mortality in patients with ARDS. In conclusion, some ventilator parameters, such as RPPv, RPVI, and MW/IER defined in this study, can be derived from ventilator readings and used to predict the development and outcome of ARDS in mechanically ventilated patients on admission to the SICU.

The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. Methods: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. Results: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all p < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(−2.31, 2.34) vs. −0.02(−2.19, 2.16), expiration time −0.19(−2.35, 1.98) vs. −0.24(−2.36, 1.89), and inter-breath intervals −0.19(−2.73, 2.35) vs. −0.25(2.76, 2.26). The inspiratory:expiratory ratios were −0.14(−1.43, 1.16) vs. −0.14(−1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(−2.51, 2.96) vs. 0.29(−2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). Conclusion: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates.