LCA metrics: Durability, performance, and recyclability. The BROAD Group has conducted research into the use of stainless steel to improve the safety and durability of high rise buildings. The research has proven the unique properties of austenitic and duplex stainless steels in particular, including the “B-CORE” sandwich structure stainless steel construction material developed by the BROAD Group, make stainless steel excellent for use in all structural load bearing members of buildings.
The opinion dynamics, which utilizes mathematical and physical models to investigate the spreading of opinions in social networks, has drawn attention of researchers in related fields these years, on its excellent performance in evaluating and predicting the public mind on various general issues. In this paper, inspired by the Spiral of Silence (SOS) theory that shows people tend to alter their opinion to be confirmed with the dominant one considering the fear to be isolated, a novel Dynamical Weight Hegselmann-Krause (DWHK) model is proposed. Specifically, the variation of interpersonal influence as opinion spread in the community is considered, represented by dynamic weight in networks according to the SOS theory. Extensive experiments in different types of networks show the effectiveness and efficiency of the proposed model, considering the crowds corresponding to specific kinds of the spiral of silence.
In patients’ medical service consumption behavior, patients’ choice of medical institution is an important link, which determines patients’ medical quality and medical cost, and even further affects the distribution of medical resources in the whole health service market. Patients may have problems such as high knowledge barrier and information redundancy in the process of choosing hospitals. Nowadays, with the continuous development of machine learning, the recommendation system using graph neural network has achieved good results in solving this kind of information overload problem. Therefore, we mainly focus on the application of the recommendation system in the process of patients choosing hospitals. Here we complete the construction of the initial data set through data simulation, and then we train and debug the six graph neural network recommendation system models. In addition, we propose a new comprehensive index to improve the traditional index, which is difficult to better represent the model performance. In the future, we plan to apply this research to our smart medical big data cloud platform. On the one hand, the cloud platform will provide a more solid data basis for our model; on the other hand, we can provide personalized medical recommendation services for platform users by using the recommendation system.
At present, the world is being faced with the challenge of an aging population, correlating to a growing number of seniors with dementia. With this uptick in persons with dementia (PWD), managing dementia-induced agitation, a behavior present in 90 precent of PWD characterized by physical aggression, verbal outburst, or other troubling behavior, is a pressing issue [1]. Caregiver burden associated with agitation is one of the leading causes a community-based PWD is institutionalized [2], so by supporting PWD-caregiver dyads, we improve individual quality of life and relieve stress placed on the global healthcare system. Use of AI technology, big data, and integrated networks of wearable and ambient sensors has enabled continuous monitoring of dementia care. However, most methods focus on data collection at the early stages of dementia. More research is needed on how novel technologies can empower PWD and their caregivers to take action to manage agitation and support them in the long term as symptoms progress. Moreover, current methods have not taken full advantage of the information obtained and do not provide personalized care. In this paper, we use the Universal Village (UV) perspective to evaluate the current status of smart technologies with the potential for use in preventing and mitigating agitation while providing support to the caregiver. We conduct evaluations based on the framework of a closed feedback control loop: data acquisition, communication, decision making, and action. We propose that a robust PWD agitation management system should take into consideration the interaction between the smart healthcare system and other seven smart city subsystems: smart home, intelligent transportation, urban planning and crowd management, smart energy management, smart city infrastructure, smart response system for city emergency, smart environmental protection and smart humanity, and also study how managing agitation would be affected by four major impacting factors of smart cities: information flow, material cycle, lifestyle, and community. This systematic study will help us explore in depth the complicated dynamic relationship between multiple impacting factors and propose a UV-oriented, integrated, resilient, inclusive, and sustainable development framework design. As such, the novel framework will improve PWD quality of life and reduce the care burden for formal and informal caregivers through continuous, unobtrusive monitoring, life-long agitation management throughout different stages of dementia, PWD-caregiver dyad-specific guidance, preventive healthcare, and timely treatment.
Pedestrian tracking studies have been facilitated by a large amount of surveillance apparatus in the city while also raising public privacy concerns. In this paper, we propose X-Tracking, a privacy-aware pedestrian tracking paradigm designed for vision systems in Smart City. It allows low-cost compatibility with existing surveillance architecture. To protect entities’ privacy, X-Tracking uses video pre-processing with desensitization so that identity information is unexposed to the tracking algorithm. We implement system-level privacy protection by redesigning the tracking framework that decouples all services based on a single responsibility principle. Then, we elaborate on the roles, behaviors, and protocols used in the new system and illustrate how the paradigm strikes a favorable balance between privacy protection and convenience services. Furthermore, we propose a new tracking task that aims to track humans in masking surveillance video. It is comparable to previous tracking tasks but considering the target with a distorted appearance poses new challenges for visual tracking. Finally, we evaluate the baseline algorithm on the task with a demo dataset.
