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تعداد مقالات یافته شده: 250
ردیف عنوان نوع
1 Internet of Things-enabled Passive Contact Tracing in Smart Cities
ردیابی تماس غیرفعال با قابلیت اینترنت اشیا در شهرهای هوشمند-2022
Contact tracing has been proven an essential practice during pandemic outbreaks and is a critical non-pharmaceutical intervention to reduce mortality rates. While traditional con- tact tracing approaches are gradually being replaced by peer-to-peer smartphone-based systems, the new applications tend to ignore the Internet-of-Things (IoT) ecosystem that is steadily growing in smart city environments. This work presents a contact tracing frame- work that logs smart space users’ co-existence using IoT devices as reference anchors. The design is non-intrusive as it relies on passive wireless interactions between each user’s carried equipment (e.g., smartphone, wearable, proximity card) with an IoT device by uti- lizing received signal strength indicators (RSSI). The proposed framework can log the iden- tities for the interacting pair, their estimated distance, and the overlapping time duration. Also, we propose a machine learning-based infection risk classification method to char- acterize each interaction that relies on RSSI-based attributes and contact details. Finally, the proposed contact tracing framework’s performance is evaluated through a real-world case study of actual wireless interactions between users and IoT devices through Bluetooth Low Energy advertising. The results demonstrate the system’s capability to accurately cap- ture contact between mobile users and assess their infection risk provided adequate model training over time. © 2021 Elsevier B.V. All rights reserved.
keywords: بلوتوث کم انرژی | ردیابی تماس | اینترنت اشیا | طبقه بندی خطر عفونت | Bluetooth Low Energy | Contact Tracing | Internet of Things | Infection Risk Classification
مقاله انگلیسی
2 Disintegration testing augmented by computer Vision technology
آزمایش تجزیه با فناوری Vision کامپیوتری تقویت شده است-2022
Oral solid dosage forms, specifically immediate release tablets, are prevalent in the pharmaceutical industry. Disintegration testing is often the first step of commercialization and large-scale production of these dosage forms. Current disintegration testing in the pharmaceutical industry, according to United States Pharmacopeia (USP) chapter 〈701〉, only gives information about the duration of the tablet disintegration process. This infor- mation is subjective, variable, and prone to human error due to manual or physical data collection methods via the human eye or contact disks. To lessen the data integrity risk associated with this process, efforts have been made to automate the analysis of the disintegration process using digital lens and other imaging technologies. This would provide a non-invasive method to quantitatively determine disintegration time through computer algorithms. The main challenges associated with developing such a system involve visualization of tablet pieces through cloudy and turbid liquid. The Computer Vision for Disintegration (CVD) system has been developed to be used along with traditional pharmaceutical disintegration testing devices to monitor tablet pieces and distinguish them from the surrounding liquid. The software written for CVD utilizes data captured by cameras or other lenses then uses mobile SSD and CNN, with an OpenCV and FRCNN machine learning model, to analyze and interpret the data. This technology is capable of consistently identifying tablets with ≥ 99.6% accuracy. Not only is the data produced by CVD more reliable, but it opens the possibility of a deeper understanding of disintegration rates and mechanisms in addition to duration.
keywords: از هم پاشیدگی | اشکال خوراکی جامد | تست تجزیه | یادگیری ماشین | شبکه های عصبی | Disintegration | Oral Solid Dosage Forms | Disintegration Test | Machine Learning | Neural Networks
مقاله انگلیسی
3 بیوپلیمر: ماده ای پایدار برای کاربردهای غذایی و پزشکی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 22 - تعداد صفحات فایل doc فارسی: 48
پلیمرهای زیستی یک گروه پیشرو از مواد کاربردی مناسب برای کاربردهای با ارزش بالا هستند که مورد توجه محققان و متخصصان در رشته‌های مختلف قرار گرفته اند. برای درک جنبه های اساسی و کاربردی بیوپلیمرها برای رسیدگی به چندین مشکل پیچیده مرتبط با سلامت و رفاه مهم به تحقیقات بین رشته ای نیاز است. برای کاهش اثرات زیست محیطی و وابستگی به سوخت های فسیلی، تلاش زیادی برای جایگزینی پلیمرهای مصنوعی با مواد زیست تخریب پذیر، به ویژه آنهایی که از منابع طبیعی به دست می آیند، انجام شده است. در این راستا، بسیاری از انواع پلیمرهای طبیعی یا زیستی برای رفع نیازهای کاربردهای روزافزون توسعه یافته اند. این بیوپلیمرها در حال حاضر در مصارف غذایی مورد استفاده قرار می گیرند و به دلیل خواص منحصر به فردشان در حال گسترش در صنایع دارویی و پزشکی هستند. این بررسی بر روی کاربردهای مختلف پلیمرهای زیستی در صنایع غذایی و پزشکی تمرکز دارد و چشم انداز آینده را برای صنعت بیوپلیمر ارائه می دهد.
واژگان کلیدی: پلیمرهای زیستی | کاربردهای پزشکی و غذایی | مواد زیست تخریب پذیر | پلی ساکاریدهای میکروبی | کیتوزان
مقاله ترجمه شده
4 یک مدل ریاضی چند منظوره برای زنجیره تامین داروسازی با توجه به تراکم دارو در کارخانه‌ها
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 15 - تعداد صفحات فایل doc فارسی: 47
مدیریت زنجیره تامین ( SCM ) , به روش یکی از مسائل مهم در جنبه مدیریتی , نقش مهمی در مقابله با مسایل انسانی و مشکلات ایفا می‌کند . به دلیل برخی محدودیت‌ها ( به عنوان مثال , ظرفیت تولید و ظرفیت ذخیره‌سازی ) و خواسته ها( به عنوان مثال , کاهش هزینه و افزایش درآمد ) , مدیران زنجیره تامین همیشه به دنبال بهترین پاسخ به مقدار و نوع ارتباط بین سطوح مختلف SCM هستند . در تحقیقات آتی , یک زنجیره تامین دارو ( PSC ) با سه تابع هدف توسعه‌یافته , با هدف به حداقل رساندن هزینه‌های کلی , خواسته‌های برآورده نشده , و کاهش زمان انتظار در ورودی کارخانه . در تحقیقات آتی , موضوع کلی و تحقیقات در مدل‌سازی PSC و حل مساله مورد بحث قرار گرفته‌اند . سپس یک مدل برنامه‌ریزی غیرخطی با تحقیقات قبلی برای حل کاستی‌های موجود پیشنهاد شده‌است.
همچنین روش‌های تصمیم‌گیری چند هدفه برای انطباق با اهداف متناقض مدل به طور همزمان استفاده می‌شوند . سپس نرم‌افزار تجاری GAMS برای حل مشکل اندازه‌های مختلف به کار می‌رود . در نهایت ، تحلیل حساسیت گسترده و ارزیابی نتایج مورد بحث قرار می‌گیرد و پیشنهادهای توسعه آتی ارایه می‌شوند.
واژه های کاربردی : زنجیره تامین دارو | فسادپذیری | زمان‌بندی | فهرست | نظریه کیوینگ
مقاله ترجمه شده
5 Adverse Reaction Detection from Social Media based on Quantum Bi-LSTM with Attention
تشخیص واکنش نامطلوب از رسانه های اجتماعی بر اساس کوانتوم Bi-LSTM با توجه-2022
Drug combination is very common in the course of disease treatment. However, it inevitably increases the overall risk of adverse drug reactions (ADRs). It is very important to early and accurately detect and identify the potential ADRs for combined medication safety and public health. Social media is an important pharmacovigilance data source for ADR detection. But the data are complex, mass, clutter, highly sparse, so it is difficult to detect the ADR information from these data. Deep learning stands out in terms of increased accuracy. However, it takes a lot of training time and requires a lot of computing power. Quantum computing has strong parallel computing capability, and requires less computing power. By introducing attention mechanism and quantum computing into Bi-directional Long Short-Term Memory (Bi-LSTM), a quantum Bi-LSTM with attention (QBi-LSTMA) model is constructed for ADR detection from social media big data. QBi-LSTMA is composed of 6 variable component subcircuits (VQC) stacked. Under the condition that the main topology of Bi-LSTM remains unchanged, the biases of QBi-LSTMA in input gate, forgetting gate, candidate memory unit and output gate are removed to simplify the network structure, and the weight and active value qubits of the model are used to update the network weight. The performance of the proposed method is evaluated on the SMM4H dataset, comparing with one traditional ADR detection method and three deep learning based ADR detection approaches. The experiment results show that the proposed method has great potential in ADRs detection. To the best of our knowledge, this is the first time to investigate quantum computing to detect ADRs from social media big data.
INDEX TERMS: Social media big data | Adverse drug reactions (ADRs) | Bi-directional Long Short-Term Memory (Bi-LSTM) | Quantum Bi-LSTM with attention (QBi-LSTMA).
مقاله انگلیسی
6 Algebraic Attacks on Block Ciphers Using Quantum Annealing
حملات جبری به رمزهای بلوکی با استفاده از آنیل کوانتومی-2022
Drug combination is very common in the course of disease treatment. However, it inevitably increases the overall risk of adverse drug reactions (ADRs). It is very important to early and accurately detect and identify the potential ADRs for combined medication safety and public health. Social media is an important pharmacovigilance data source for ADR detection. But the data are complex, mass, clutter, highly sparse, so it is difficult to detect the ADR information from these data. Deep learning stands out in terms of increased accuracy. However, it takes a lot of training time and requires a lot of computing power. Quantum computing has strong parallel computing capability, and requires less computing power. By introducing attention mechanism and quantum computing into Bi-directional Long Short-Term Memory (Bi-LSTM), a quantum Bi-LSTM with attention (QBi-LSTMA) model is constructed for ADR detection from social media big data. QBi-LSTMA is composed of 6 variable component subcircuits (VQC) stacked. Under the condition that the main topology of Bi-LSTM remains unchanged, the biases of QBi-LSTMA in input gate, forgetting gate, candidate memory unit and output gate are removed to simplify the network structure, and the weight and active value qubits of the model are used to update the network weight. The performance of the proposed method is evaluated on the SMM4H dataset, comparing with one traditional ADR detection method and three deep learning based ADR detection approaches. The experiment results show that the proposed method has great potential in ADRs detection. To the best of our knowledge, this is the first time to investigate quantum computing to detect ADRs from social media big data.
INDEX TERMS: Social media big data | Adverse drug reactions (ADRs) | Bi-directional Long Short-Term Memory (Bi-LSTM) | Quantum Bi-LSTM with attention (QBi-LSTMA).
مقاله انگلیسی
7 Three-month follow-up effects of a medication management program on nurses’ knowledge
اثرات پیگیری سه ماهه یک برنامه مدیریت دارو در دانش پرستاران-2021
This quasi-experimental study examined the effects of a medication management program on nurses knowledge of medication management, three months after program completion. Fifty-seven nurses took a multiple-choice test both immediately after the program and three months later. Changes in test performance were assessed using McNemar’s test and generalized estimating equations for binary outcomes. Test results were generally consistent from immediately post-program to three months later, though four items differed significantly. From immediately post-program to three months later, fewer nurses correctly answered the items: documenting no medication administration (98.2 vs 86.6, p = 0.04); documenting opioid administration (56.1 vs 33.3, p = 0.01); and observation after opioid administration (35.1 vs 19.3, p = 0.08. Significantly more nurses correctly answered the item concerning the pharmacology of medication administered with food (64.9 vs 77.2, p = 0.09). We recom- mend both continuous medication management training and focusing on the correspondence between theory- based knowledge and clinical practice routines.
keywords: پرستار بیمارستان | برنامه آموزشی | مدیریت دارو | دانش | Hospital nurse | Education program | Medication management | Knowledge
مقاله انگلیسی
8 Empowering seizure management skills: Knowledge, attitudes, and experiences of school staff trained in administering rescue drugs in Northern Italy
توانمندسازی مهارت های مدیریت تشنج: دانش، نگرش ها و تجربیات کارکنان مدرسه ای که در مدیریت داروهای نجات در شمال ایتالیا آموزش دیده اند-2021
Purpose: The administration of rescue medication at school concerns students for which it may be essential, on doctor’s prescription, to take therapy during school hours. In this case, since the parents are absent, the first rescuer is necessarily the school staff, who should be properly trained because prolonged seizures can cause severe harm and even death.
Methods: Every year, the Local Health Unit ‘‘TO3” in Northern Italy, provides training for school staff to administrate rescue medication at school. From December 2019 to February 2020, the same questionnaire was administered to school staff trained for seizures at the end of the course, while the staff trained for other diseases completed it before the course.
Results: About 60% of the sample (N = 123) had been trained in seizure management at least once in their lifetime. Median knowledge score in subjects with no seizures training was 7 (Q25/Q75: 5/8), while it was 9 (Q25/Q75: 6/10) in subjects with seizures training (p < 0.001). The self-reported level of confidence in their skills to administer rescue medication was high in 10.2% of subjects not trained for seizures and in 62.9% of those trained (p < 0.001).
Conclusion: Results suggest that medical training for school staff increases knowledge scores and levels of self-confidence relating to the administration of rescue medication. Moreover, after the training, the school staff changed attitude toward seizures, no longer considering them a problem, and became more aware, less fearful, and more inclined to act in case of need, making school a better place for all students.
keywords: معلم | مدرسه | آموزش | اضطراری | نجات داروها | تشنج | Teacher | School | Training | Emergency | Rescue medication | Seizure
مقاله انگلیسی
9 The application of reusable learning objects (RLOs) in preparation for a simulation laboratory in medication management: An evaluative study
استفاده از اشیاء یادگیری قابل استفاده مجدد (RLOS) در آماده سازی یک آزمایشگاه شبیه سازی در مدیریت دارو: یک مطالعه ارزیابی-2021
To enhance the preparedness of undergraduate nursing and midwifery students to participate in the safe provision of medication administration on their clinical placements, an innovative blended learning strategy was designed and developed by the authors. The blended learning strategy included a suite of online reusable learning objects specific to medication management theoretical knowledge and psychomotor skills to prepare students for a 90-minute practical face to face simulation laboratory session. Students identified that the reusable learning objects had prepared them for the simulation laboratory session and was rated as a productive learning experience. The blended learning strategy implemented to teaching and learning medication management to undergraduate nursing and midwifery students can positively influence students’ acquisition of knowledge and psychomotor skills to safely administer medications prior to their practice placements in a clinical setting.
keywords: یادگیری تلفیقی | مدیریت دارو | اشیاء یادگیری قابل استفاده مجدد | شبیه سازی | Blended learning | Medication management | Reusable learning objects | Simulation
مقاله انگلیسی
10 Resident Opioid Prescribing Habits Do Not Reflect Best Practices in Post-Operative Pain Management: An Assessment of the Knowledge and Education Gap
عادت های تجویز داروهای ساکن، بهترین شیوه ها را در مدیریت درد پس از عمل منعکس نمی کنند: ارزیابی شکاف دانش و آموزش-2021
OBJECTIVE: To evaluate deficiencies in knowledge and education in opioid prescribing and to compare surgical resident opioid-prescribing practices to Opioid Prescribing Engagement Network (OPEN) procedure-specific guidelines.
DESIGN: Anonymous web-based survey distributed to all general surgery residents to evaluate prior education received and confidence in knowledge in opioid prescribing. The number of 5 milligram oxycodone tablets prescribed for common procedures was assessed and compared with OPEN for significance using Wilcoxon signed rank tests.
SETTING: General surgery residency program within large university-based tertiary medical center. PARTICIPANTS: Categorical general surgery residents of all postgraduate years.
RESULTS: Fifty-six of 72 (78%) categorical residents completed the survey. Few reported receiving formal education in opioid prescribing in medical school (32%) or residency (16%). While 82% of residents felt confident in opioid side effects, fewer felt the same with regards to opioid pharmacokinetics (36%) or proper opioid disposal (29%). Opioids prescribed varied widely with residents prescribing significantly more than recommended by OPEN in 9 of 14 procedures.
CONCLUSIONS: Tackling the evolving opioid epidemic requires a multidisciplinary approach that addresses prescribing at all steps of the process, starting with trainee education.
KEY WORDS: Opioid Epidemic | Opioid Prescribing Engagement Network | Surgical Education | Resident Education
COMPETENCIES: Patient Care, Medical Knowledge, Practice-Based Learning and Improvement
مقاله انگلیسی
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