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iRestroom : A smart restroom cyberinfrastructure for elderly people
iRestroom: زیرساخت سایبری سرویس بهداشتی هوشمند برای افراد مسن-2022 According to a report by UN and WHO, by 2030 the number of senior people (age over 65) is
projected to grow up to 1.4 billion, and which is nearly 16.5% of the global population. Seniors
who live alone must have their health state closely monitored to avoid unexpected events (such as
a fall). This study explains the underlying principles, methodology, and research that went into
developing the concept, as well as the need for and scopes of a restroom cyberinfrastructure
system, that we call as iRestroom to assess the frailty of elderly people for them to live a
comfortable, independent, and secure life at home. The proposed restroom idea is based on the
required situations, which are determined by user study, socio-cultural and technological trends,
and user requirements. The iRestroom is designed as a multi-sensory place with interconnected
devices where carriers of older persons can access interactive material and services throughout
their everyday activities. The prototype is then tested at Texas A&M University-Kingsville. A Nave
Bayes classifier is utilized to anticipate the locations of the sensors, which serves to provide a
constantly updated reference for the data originating from numerous sensors and devices installed
in different locations throughout the restroom. A small sample of pilot data was obtained, as well
as pertinent web data. The Institutional Review Board (IRB) has approved all the methods. keywords: اینترنت اشیا | حسگرها | نگهداری از سالمندان | سیستم های هوشمند | یادگیری ماشین | IoT | Sensors | Elder Care | Smart Systems | Machine Learning |
مقاله انگلیسی |
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Is the Internet of Things a helpful employee? An exploratory study of discourses of Canadian farmers
آیا اینترنت اشیا یک کارمند مفید است؟ بررسی اکتشافی گفتمان های کشاورزان کانادایی-2022 The increasing global population and the growing demand for high-quality products have called
for the modernization of agriculture. “Internet of Things” is one of the technologies that is pre-
dicted to offer many solutions. We conducted a discourse analysis of 19 interviews with farmers in
Ontario, Canada, asking them to describe their experience of working with IoT and related
technologies. One main discourse with two opposing tendencies was identified: farmers recognize
their relationship with IoT and related technology and view technology as a kind of “employee”,
but some tend to emphasize (1) an optimistic view which is discourse of technology is a “Helpful
Employee”; while others tend to emphasize (2) a pessimistic view which is a discourse of tech-
nology is an “Untrustworthy Employee”. We examine these tendencies in the light of the literature
on organizational behavior and identify potential outcomes of these beliefs. The results suggest
that a farmer’s style of approaching technology can be assessed on a similar scale as managers’
view of their employees and provide a framework for further research. keywords: فناوری اینترنت اشیا | کشاورزی | تحلیل گفتمان | سبک استفاده از تکنولوژی | Internet of things technology | Agriculture | Discourse analysis | Style of use of technology |
مقاله انگلیسی |
3 |
Plant leaf disease detection using computer vision and machine learning algorithms
تشخیص بیماری برگ گیاه با استفاده از بینایی کامپیوتری و الگوریتم های یادگیری ماشین-2022 Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recom-
mended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to
the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind
the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in
plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing
with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the
samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farm-
ers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to
256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means
clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted
using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis
and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally,
the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM),
Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is
tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples. keywords: شبکه های عصبی کانولوشنال | تبدیل موجک گسسته | تجزیه و تحلیل مؤلفه های اصلی | نزدیکترین همسایه | بیماری برگ | Convolutional Neural Networks | Discrete Wavelet Transform | Principal Component Analysis | Nearest Neighbor | Leaf disease |
مقاله انگلیسی |
4 |
An exploration of local rules to map spawning processes to regular hardware architectures
کاوشی در قوانین محلی برای نگاشت فرآیندهای تخم ریزی به معماری های سخت افزاری معمولی-2022 This thesis presents an exploration of population growth via simulation in software to ascertain if a massively parallel hardware system can manage applications running within.
Task execution happens dynamically and is controlled by the growth mechanism implementing efficient mapping in simulation.
Algorithms that provide population simulation models are often inspired by those
evidenced in biology and in particular those of cellular automata and L-systems. These
algorithms are of particular interest due to their complexity and self-replication and
recent research has shown that it is the refinement of the biological methodology that
has resulted in their complexity. Further to this, adaptation of the design has moved the
algorithm on towards being able to organize and build itself from a single cell. A growth
model is utilized in software systems to provide production of meaningful data. The
development of bio-inspired software is constrained by using contemporary processor
architectures. |
مقاله انگلیسی |
5 |
Wireless Sensor Network coverage optimization based on Yin–Yang pigeon-inspired optimization algorithm for Internet of Things
بهینه سازی پوشش شبکه حسگر بی سیم بر اساس الگوریتم بهینه سازی الهام گرفته از کبوتر یین یانگ برای اینترنت اشیا-2022 As an important technology of Internet of Things (IoT), wireless sensor network (WSN) has
the problem of low coverage caused by uneven nodes distribution. Aiming at the problem,
a WSN coverage optimization method based on the Yin–Yang pigeon-inspired optimization
algorithm (Yin–YangPIO) is proposed. Firstly, the good point set is introduced into initialization
phase which makes pigeon population more evenly distributed in the solution space; then, Yin–
Yang-pair optimization algorithm (YYPO) and pigeon-inspired optimization algorithm (PIO) are
combined, and different strategies are used in the map and compass operator and the landmark
operator to improve the optimization ability; later on, the opposition-based learning is added to
PIO to expand the search range; finally, several functions are selected to prove the optimization
ability of the Yin–YangPIO. Through three sets of WSN coverage optimization experiments with
different parameters, the effectiveness of the proposed method in WSN coverage optimization
is demonstrated.
Keywords: Wireless sensor network | Pigeon-inspired optimization algorithm | Yin–Yang-pair optimization algorithm | Opposition-based learning | Internet of Things | شبکه حسگر بی سیم | الگوریتم بهینه سازی الهام گرفته از کبوتر | الگوریتم بهینه سازی جفت یین یانگ | یادگیری مبتنی بر مخالفت | اینترنت اشیا |
مقاله انگلیسی |
6 |
AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics
AgroLens: یک معماری مزرعه هوشمند کمهزینه و سبز پسند برای پشتیبانی از تشخیص بیماریهای برگ در زمان واقعی-2022 Agriculture is one of the most significant global economic activities responsible for feeding the
world population of 7.75 billion. However, weather conditions and diseases impact production
efficiency, reducing economic activity and the food sovereignty of economies worldwide. Thus,
computational methods can support disease classification based on an image. This classification
requires training Artificial Intelligence (AI) models on high-performance computing resources,
usually far from the user domain. State of the art has proposed the concept of Edge Computing
(EC), which aims to bring computational resources closer to the domain problem to decrease
application latency and improve computational power closer to the client. In addition, EC has
become an enabling technology for Smart Farms, and the literature has appropriated EC to
support these applications. However, predominantly state-of-the-art architectures are dependent
on Internet connectivity and do not allow diverse real-time classification of diseases based on
crop leaf on mobile devices. This paper sheds light on a new architecture, AgroLens, built with
low-cost and green-friendly devices to support a mobile Smart Farm application, operational
even in areas lacking Internet connectivity. Among our main contributions, we highlight the
functional evaluation of AgroLens for AI-based real-time classification of diseases based on leaf
images, achieving high classification performance using a smartphone. Our results indicate that
AgroLens supports the connectivity of thousands of sensors from a smart farm without imposing
computational overhead on edge-compute. The AgroLens architecture opens up opportunities
and research avenues for deployment and evaluation for large-scale Smart Farm applications
with low-cost devices.
keywords: بیماری گیاهی | مزرعه هوشمند | اینترنت اشیا | یادگیری عمیق | سبز پسند| Plant disease | Smart Farm | Internet of Things | Deep learning | Green-friendly |
مقاله انگلیسی |
7 |
Biometric indices of eleven mangrove fish species from southwest Bangladesh
شاخص های بیومتریک یازده گونه ماهی حرا از جنوب غربی بنگلادش-2021 Biometric indices, i.e. i) length-weight relationships (LWRs), ii) form factor (a3.0), iii) length-frequency distributions (LFDs), and iv) condition factors (relative KR and Fulton’s KF) are considered to be very cru- cial in the assessment of fishery studies as they provide information on fish population growth and coastal habitat well-being. The study of biometric indices of mangrove fish has, however, received little attention. Our research investigates the LFDs, LWRs, a3.0, KR and KF of 395 individuals from nine families (Latidae, Engraulidae, Gobiidae, Mugilidae, Synbranchidae, Schilbeidae, Scatophagidae, Plotosidae, and Terapontidae). The LFDs showed that the lowest total length (TL) was 4.57 cm for Stolephorus tri, and highest TL was 56.20 for Monopterus cuchia. The LWRs showed that the b (allometric coefficient) values ranging from 2.01 (Plotosus canius) to 3.29 (Terapon jarbua), appeared as highly significant (P < 0.001). Moreover, the KR values ranged from 0.80 to 1.36, which indicate a good state of health of the population. Our findings could be useful in updating the FishBase (online database) and tracking mangrove fish spe- cies sustainably.© 2021 National Institute of Oceanography and Fisheries. Hosting by Elsevier B.V. This is an open accessarticle under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Length-weight relationship | Growth | Form factor | Condition | FishBase |
مقاله انگلیسی |
8 |
Improvements in biometric health measures among individuals with intellectual disabilities: A controlled evaluation of the Fit 5 program
بهبود اقدامات سلامت بیومتریک در افراد دارای معلولیت فکری: ارزیابی کنترل شده از برنامه Fit 5-2021 Background: Individuals with intellectual disabilities (ID) have poorer health statuses compared to the general population. Actions are needed to address health disparities and promote healthy lifestyles among individuals with ID. Moreover, studies that consider program settings for this population are needed. Objective: The Special Olympics health program, Fit 5, was evaluated to assess effectiveness in improving health measures for individuals with ID. The settings of programs’ implementation were also considered. Methods: Four Special Olympics basketball teams participated as an intervention group, and three teams as a control group, in a study of the Fit 5 program that was implemented during, and as part of, a regular 8-week basketball season. Resting heart rate (RHR) and blood pressure, and height and weight to calculate Body Mass Index (BMI), were measured before and after the program. Differences in pre- and post-measures were compared between the two groups with paired samples t-tests and ANCOVA. Results: Participants in the intervention group had significantly greater improvements in resting systolic and diastolic blood pressures (p ¼ 0.02 and 0.03, respectively) and RHR (p ¼ 0.003). BMI increased for both groups; however, the increase in the intervention group was significantly less (p ¼ 0.006). The Special Olympics setting was considered familiar and supportive and effectively reached individuals with ID. Conclusion: The Fit 5 program positively impacts RHR and blood pressure, and could help reduce extents of BMI increases, in individuals with ID when implemented in a common setting. Further investigation of the impact of Fit 5 and similar programs in additional settings is warranted.© 2020 Elsevier Inc. All rights reserved. Keywords: Intellectual disability | Health promotion | Physical activity | Health risk factors | Program settings |
مقاله انگلیسی |
9 |
Accounts of NGO performance as calculative spaces: Wild Animals, wildlife restoration and strategic agency
حساب های عملکرد سازمان های غیر دولتی به عنوان فضاهای محاسباتی: حیوانات وحشی، ترمیم حیات وحش و آژانس استراتژیک-2021 Whereas corporations typically share a common primary objective of generating profits for their
owners, non-governmental organisations (NGOs) principally pursue a panoply of various social
and/or ecological objectives. Accordingly, an NGO’s performance in pursuit of its objectives can
rarely be accounted for in straightforwardly comparable quantitative terms. How then can an
NGO instead construct qualitative accounts of its performance that show how it makes a differ-
ence in pursuit of its objectives? This paper examines qualitative accounts of performance against
an objective to restore wildlife, which are included in the annual reports of a large conservation
NGO. These accounts are conceptualised as being calculative spaces, configured by framing work
being done within these accounts. Analysis of this framing work finds that these accounts identify
a performance object (i.e. specific wild animal populations), establish relations that seemingly
affect this performance object (i.e. threats to wild animal populations and actions to conserve
these populations), and attribute the NGO with agency to make a difference to this performance
object (i.e. as a strategic actor directing and co-ordinating wildlife restoration). Thus, this paper
demonstrates that seeing quantitative and qualitative accounts of organisational performance in
the same conceptual terms creates conditions of possibility for developing a fuller understanding
of an organisation’s calculations of its own capacity to act upon society. keywords: سازمان غیر دولتی | مسئوليت | کادر بندی | محاسبات | گفتمان | حفاظت | NGO | Accountability | Framing | Calculation | Discourse | Conservation |
مقاله انگلیسی |
10 |
Factors Relating to Nurses’ Knowledge and Attitudes Regarding Pain Management in Inpatients
عوامل مرتبط با دانش و نگرش پرستاران در مورد مدیریت درد در بیماران بستری-2021 Purpose: To describe factors associated with nurses’ attitudes or lack of knowledge regarding pain
management in adult inpatients.
Design: Transverse descriptive survey-based study. Methods: This was a transverse descriptive survey-based study. The population was obtained through nonprobability convenience sampling. The Knowledge and Attitudes Survey Regarding Pain was made available to 470 nurses at a tertiary level hospital. Associations were sought with the unit where assigned, years of experience, specific training on pain, and postgraduate education. Results: The sample included 134 nurses with a mean age of 41.6 ± 10.8 years; 87% were women, 64% worked rotating shifts, 64% had more than 10 years of experience, and 31% had specific training in pain management. The greatest number of correct responses was obtained from nurses with specific training in pain management (p ¼ .001) and nurses who worked in units of surgical hospitalization (p ¼ .004). The lack of training was associated with a deficit in knowledge and inadequate attitudes about pain management. In nurses with less than 10 years of experience, worse results were observed in knowledge, whereas the unit of work was decisive in the results about attitude (p < .05). Conclusions: Among the nurses surveyed, some knowledge gaps were detected, as were certain inappropriate attitudes, associated with lack of training, lack of experience, and being assigned to specific hospitalization units. |
مقاله انگلیسی |