Proposal and evaluation of a solar‐based polygeneration system: Development, exergoeconomic analysis, and multi‐objective optimization

Azizi, Saeid; Nedaei, Navid; Yari, Mortaza

doi:10.1002/er.8084

Cited by 19 publications

(6 citation statements)

References 66 publications

(99 reference statements)

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“…The IoB is a structure composed of several key components that work together to influence and shape human behavior [43]. These components include behavioral psychology, which provides insights into the factors that shape human behavior, such as motivation and social influence; behavioral analysis, which uses data and analytics to understand human behavior and develop personalized interventions; usage data, which are collected and analyzed from various sources such as social media, mobile apps, and IoT devices to identify patterns and trends; the IoT, which is the network of interconnected devices that collect and exchange data and is used by the IoB to collect data about individuals' behavior and provide personalized interventions; products and services that are developed to influence human behavior, such as mobile apps that encourage healthy habits and smart home systems that optimize energy usage based on individual behavior; and user experience, which aims to provide a seamless and intuitive user experience that encourages individuals to engage with technology and adopt desirable behaviors, incorporating elements of gamification to make behavioral change more engaging and enjoyable [44,45]. Figure 1 depicts the IoB structure, which is associated with several factors.…”

Section: Iob Architecture Definition Usagesmentioning

confidence: 99%

“…Figure 1 depicts the IoB structure, which is associated with several factors. which aims to provide a seamless and intuitive user experience that encourages individ uals to engage with technology and adopt desirable behaviors, incorporating elements o gamification to make behavioral change more engaging and enjoyable [44,45].…”

Section: Iob Architecture Definition Usagesmentioning

confidence: 99%

“…which aims to provide a seamless and intuitive user experience that encourages individuals to engage with technology and adopt desirable behaviors, incorporating elements of gamification to make behavioral change more engaging and enjoyable [44,45]. Figure 1 depicts the IoB structure, which is associated with several factors.…”

Section: Various Features Of Iob For the Healthcare Sectionmentioning

confidence: 99%

“…In this way, behavior alternation apps could be suggested [59]. Also, the IoB has several applications in healthcare, including health monitoring, healthcare insurance, evaluating patients' activities, mask identification, illness supervision, fitness monitoring, healthcare improvement, examining physiology, evaluating health situations, and personalized medication and treatment [45]. IoB technology can be used to build health-monitoring smartphone apps that evaluate heart rates, food intake, sleep patterns, and blood sugar levels [60].…”

Section: Iob Utilization For Healthcare Practicesmentioning

confidence: 99%

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The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.

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Section: Iob Architecture Definition Usagesmentioning

confidence: 99%

Section: Iob Architecture Definition Usagesmentioning

confidence: 99%

Section: Various Features Of Iob For the Healthcare Sectionmentioning

confidence: 99%

Section: Iob Utilization For Healthcare Practicesmentioning

confidence: 99%

See 2 more Smart Citations

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Guo et al applied multi-objective optimization methods to the field of cloud computing and optimized multi-objective task scheduling 22 . Azizi et al applied multi-objective optimization methods to a novel poly-generation system based on a solar power tower for power generation, optimizing the exergetic efficiency of the system 23 . Nedaei et al applied multi-objective optimization methods to an innovative multigeneration system based on a heliostat solar field to obtain the optimum operating conditions and achieve a desirable performance level 24 .…”

Section: Introductionmentioning

confidence: 99%

A multi-constraint and multi-objective optimization layout method for a mine water inrush monitoring network

Zhao

et al. 2023

Sci Rep

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Mine water inrush can cause property losses and casualties, but current theoretical and technological approaches cannot accurately predict such events. Through the networked deployment of water level sensors along a mine roadway, a mine water inrush monitoring network was developed, and a multi-constraint and multi-objective optimal deployment method was established. By setting practical constraints of the mining area, water inrush risk level, and installation at specified locations, and considering two objective functions of minimum total cost and minimum average monitoring time, a mathematical model was established. The non-dominated sorting genetic algorithm II (NSGA-II) was designed to solve the model. The method temporally and spatially optimized the network, which was then verified in the Beiyangzhuang coal mine in north China. The average response time of the monitoring network was 916 s using only 28 water level sensors. The higher the water inrush risk level, the shorter the monitoring network response time. Under the 2, 3, and 4 risk levels, the network’s response time to simulated water inrush accidents was less than 3000, 2100, and 900 s, respectively. The multi-constraint and multi-objective optimization layout method further enhanced the effectiveness of the network, providing a novel system for the early warning of mine water inrush.

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Towards an environmentally friendly power and hydrogen co-generation system: Integration of solar-based sorption enhanced gasification with in-situ CO2 capture and liquefaction process

Khosravi,

Khoshbakhti Saray,

Neshat

et al. 2023

Chemosphere

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Proposal and evaluation of a solar‐based polygeneration system: Development, exergoeconomic analysis, and multi‐objective optimization

Cited by 19 publications

References 66 publications

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

A multi-constraint and multi-objective optimization layout method for a mine water inrush monitoring network

Towards an environmentally friendly power and hydrogen co-generation system: Integration of solar-based sorption enhanced gasification with in-situ CO2 capture and liquefaction process

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