تأثیر آموزش شناختی مبتنی بر واقعیت مجازی بر کودکان مبتلا به اختلال طیف اوتیسم
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 17
واقعیت مجازی (VR) یک محیط مصنوعی است که از طریق محرک های حسی که یک رایانه ایجاد میکند، تجربه می شود. قابلیت VR برای شبیه سازی واقعیت دسترسی به درمان های روانی را تا حد زیادی افزایش میدهد. برای تحلیل تأثیر آموزش ذهنی مبتنی بر VA روی کودکان مبتلا به اختلال طیف اوتیسم (ASD) مدل آموزش مداخله زود هنگام و بازخورد هوشمند جزئی ایجاد شد. برای کمک به شدت علائم و اثربخشی درمان آموزش ذهنی مبتنی بر VA در کودکان مبتلا به ASD ، از لیست بررسی رفتار اوتیسم (ABC) ، مقیاس درجهبندی اوتیسم دوران کودکی (CARS) و مقیاس رفتار اوتیسم Clancy (کابین) استفاده شد. نتایج نشان داد که آموزش ذهنی مبتنی بر VA برای کودکان مبتلا به ASD بسیار جذاب بود؛ آموزش ذهنی مبتنی بر VA علائم معمول (اختلال ارتباط اجتماعی ، تاخیر گفتاری، کم توجهی و رفتار جدی) کودکان مبتلا به ASD را به طور چشمگیری بهبود بخشید و طی 4 هفته پس از درمان لیست بررسی رفتار اوتیسم (ABC)، مقیاس درجهبندی اوتیسم دوران کودکی (CARS) و مقیاس رفتار اوتیسم را بهبود بخشید (کابین ها). داده ها حاکی از آن است که آموزش ذهنی مبتنی بر VA ممکن است روش خوبی برای درمان کودکان مبتلا به ASD باشد.
کلمات کلیدی : اختلال طیف اوتیسم | آموزش روانی کودکان | واقعیت مجازی | مداخله
|مقاله ترجمه شده|
A dynamic classification unit for online segmentation of big data via small data buffers
واحد طبقه بندی پویا برای تقسیم آنلاین داده های بزرگ از طریق بافر داده های کوچک-2020
In many segmentation processes, we assign new cases according to a model that was built on the basis of past cases. As long as the new cases are “similar enough” to the past cases, segmentation proceeds normally. However, when a new case is substantially different from the known cases, a reexamination of the previously created segments is required. The reexamination may result in the creation of new segments or in the updating of the existing ones. In this paper, we assume that in big and dynamic data environments it is not possible to reexamine all past data and, therefore, we suggest using small groups of selected cases, stored in small data buffers, as an alternative to the collection of all past data. We present an incremental dynamic classifier that supports real-time unsupervised segmentation in big and dynamic data environments. In order to reduce the computational effort of unsupervised clustering in such environments, the suggested model performs calculations only on the relevant data buffers that store the relevant representative cases. In addition, the suggested model can serve as a dynamic classification unit (DCU) that can act as an autonomous agent, as well as collaborate with other DCUs. The evaluation is presented by comparing three approaches: static, dynamic, and incremental dynamic.
Keywords: Incremental dynamic classifier | Dynamic segmentation | Incremental data analysis | Cluster analysis | Classification | Big data
Applying emergy and decoupling analysis to assess the sustainability of China’s coal mining area
استفاده از تحلیل اضطراری و جداسازی برای ارزیابی پایداری منطقه استخراج زغال سنگ چین-2020
The sustainable development of coal mining area continues to be one of the most topical issues in the world. Taking Shainxi Province as a case, this study applies emergy and decoupling analysis to build a multi-index sustainability evaluation system and constructs an emergy decoupling index to investigate the sustainability of a coal mining area in China during 2006e2015. It overcomes the problem of the unification of the traditional evaluation index system and integrates the influence of economic development, resources, the environment, and energy. The study finds that the coal mining area still depends on its coal resources. The sustainability of the coal mining area is still at a low level, and it is not sustainable in the long term. The economic growth still has a strong negative decoupling from the environmental loss. Energy management system and circular economic system should be built to improve the coal mining area’s sustainability. In the long run, the coal mining industry should gradually be abandoned. Based on China’s growing energy consumption, the findings of this study may not only serve as a reference for management to improve the sustainability of the coal mining areas but also to address China’s energy shortage problem.
Keywords: Sustainability | Emergy analysis | Decoupling | Coal mining area
Extending Fitts’ law in three-dimensional virtual environments with current low-cost virtual reality technology
گسترش قانون Fitts در محیط های مجازی سه بعدی با فناوری واقعیت مجازی کم هزینه فعلی-2020
Virtual reality (VR) interfaces require users to perform three-dimensional reaching and pointing movements to interact with objects positioned within the users arms reach. However, there has been limited work that has evaluated the applicability of established models of human motor control to model performance of these tasks in 3D virtual reality environments using current low-cost technologies. In this study, a 3D discrete pointing task using the Oculus Rift system was used to explore potential influences on movement in VR and to account for these influences in a new formulation of Fitts’ law. Target size and distance from the starting point of movement were systematically varied to generate a broad range of index of difficulty (ID) values. Target locations were specified using a spherical coordinate system in which inclination angle corresponded to the pitch of the movement axis with respect to the starting point of movements and azimuth angle corresponded to the roll of the movement axis with respect to the horizontal plane. In line with previous work, we observed that target size, radial distance, and inclination angle had a significant effect on movement time. The effect of inclination angle varied with target size, which suggests that target size affected depth estimation. Significant target characteristics and interaction effects were used to develop an extended Fitts’ law model, which accounted for 64.5% of the variation in movement times. Comparisons to other Fitts’ law models revealed that models accounting for the effects of target depth improved predictive power relative to the traditional Fitts’ law formulation. Together, these findings support the value of extending Fitts’ law models to account for domain-specific constraints in VR environments. We discuss these results in the context of previous work examining HMD display deficiencies and discrete 3D pointing tasks, and suggest several directions for future work.
Keywords: Fitts’ law | Virtual reality | Oculus Rift | Depth perception | Stereoscopic display
Optimal carbon storage reservoir management through deep reinforcement learning
مدیریت بهینه ذخیره مخزن کربن از طریق یادگیری تقویتی عمیق-2020
Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decisionmaking problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.
Keywords: Reinforcement learning | Multistage decision-making | Deep autoregressive model | Deep Q network | Surrogate modeling | Markov decision process | Geological carbon sequestration
Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network
به سمت کنترل بهینه واحدهای مدیریت هوا با استفاده از یادگیری تقویتی عمیق و شبکه عصبی بازگشتی -2020
A new generation of smart stormwater systems promises to reduce the need for new construction by enhancing the performance of the existing infrastructure through real-time control. Smart stormwater systems dynamically adapt their response to individual storms by controlling distributed assets, such as valves, gates, and pumps. This paper introduces a real-time control approach based on Reinforcement Learning (RL), which has emerged as a state-of-the-art methodology for autonomous control in the artificial intelligence community. Using a Deep Neu- ral Network, a RL-based controller learns a control strategy by interacting with the system it controls - effectively trying various control strategies until converging on those that achieve a desired objective. This paper formulates and implements a RL algorithm for the real-time control of urban stormwater systems. This algorithm trains a RL agent to control valves in a distributed stormwater system across thousands of simulated storm scenarios, seeking to achieve water level and flow set-points in the system. The algorithm is first evaluated for the control of an individual stormwater basin, after which it is adapted to the control of multiple basins in a larger watershed (4 km 2 ). The results indicate that RL can very effectively control individual sites. Performance is highly sensitive to the reward formulation of the RL agent. Generally, more explicit guidance led to better control performance, and more rapid and stable convergence of the learning process. While the control of multiple distributed sites also shows promise in reducing flooding and peak flows, the complexity of controlling larger systems comes with a number of caveats. The RL controller’s performance is very sensitive to the formulation of the Deep Neural Network and requires a significant amount of computational resource to achieve a reasonable performance en- hancement. Overall, the controlled system significantly outperforms the uncontrolled system, especially across storms of high intensity and duration. A frank discussion is provided, which should allow the benefits and draw- backs of RL to be considered when implementing it for the real-time control of stormwater systems. An open source implementation of the full simulation environment and control algorithms is also provided.
Keywords: Real-time control | Reinforcement learning | Smart stormwater systems
Developing entrepreneurial competences in biotechnology early career researchers to support long-term entrepreneurial career outcomes
توسعه صلاحیت های کارآفرینی در محققان حرفه ای اولیه بیوتکنولوژی برای حمایت از نتایج شغلی کارآفرینانه طولانی مدت-2020
This paper explores how early career biotechnology researchers develop entrepreneurial competences through participation in a bespoke entrepreneurship education competition and whether this affects their longer-term entrepreneurial actions. Specifically, we discuss the pedagogy and evaluate the short- and long-term impact of a long-running entrepreneurship competition, where biotechnology doctoral and postdoctoral researchers address societal and environmental challenges through hypothetical new venture creation. We present evidence regarding the efficacy of this experiential education, where online mentoring is blended with a team-based residential competition utilising inspirational speakers, practitioner support and peer learning in encouraging ECRs to consider commercialising their research. We conclude that long-term entrepreneurial career outcomes can be fostered through tailored short-term interventions.
Keywords: Entrepreneurship | SET | STEM | Entrepreneurship education | Evaluation | Commercialisation | Biotechnology | careers
Democratization of AI, Albeit Constrained IoT Devices & Tiny ML, for Creating a Sustainable Food Future
دموکراتیک سازی هوش مصنوعی ، دستگاه های محدود IoT و Tiny ML ، برای ایجاد آینده غذایی پایدار-2020
Abstract—Big Data surrounds us. Every minute, our smartphone collects huge amount of data from geolocations to next clickable item on the ecommerce site. Data has become one of the most important commodities for the individuals and companies. Nevertheless, this data revolution has not touched every economic sector, especially rural economies, e.g., small farmers have largely passed over the data revolution, in the developing countries due to infrastructure and compute constrained environments. Not only this is a huge missed opportunity for the big data companies, it is one of the significant obstacle in the path towards sustainable food and a huge inhibitor closing economic disparities. The purpose of the paper is to develop a framework to deploy artificial intelligence models in constrained compute environments that enable remote rural areas and small farmers to join the data revolution and start contribution to the digital economy and empowers the world through the data to create a sustainable food for our collective future.
Keywords: edge | IoT device | artificial intelligence | Kalman filter | dairy cloud | small scale farmers | hardware constrained model | tiny ML| Hanumayamma | cow necklace
Women entrepreneurs as agents of change : A comparative analysis of social entrepreneurship processes in emerging markets
زنان کارآفرین به عنوان عوامل تغییر: تحلیل مقایسه ای فرایندهای کارآفرینی اجتماعی در بازارهای نوظهور-2020
In recent years, social and women entrepreneurship have become two growing fields of entrepreneurship research. In the context of social entrepreneurship, earlier research indicates that women are a better fit for leading social enterprises. However, the relevance of gender in the field of social entrepreneurship is underexplored and calls for further research, framing the mainstay of this study. Through a multiple case study approach employing four firms from two emerging markets – India and Colombia – we analyze how women entrepreneurs engage in social entrepreneurship processes in uncertain Base of the Pyramid environments. We use the effectuation lens to investigate the entrepreneurial journey and decision-making logics employed at various stages of the venture development. Findings show that women social entrepreneurs are highly motivated concerning social issues. Also, women entrepreneurs show a subtle transition between the two approaches of causation and effectuation during the venture creation processes. This study highlights the specific challenges that women entrepreneurs face in the emerging market context and the inclusive strategies they employ to enhance socio-economic development.
Keywords: Social entrepreneurship | Women entrepreneurship | Inclusive business | BoP | Emerging economies
Influencing factors on energy management in industries
تأثیر عوامل مؤثر بر مدیریت انرژی در صنایع-2020
Energy management has been considered in the global agenda as a way to improve energy performance and greenhouse gas reduction in organizations. Industries account for a significant part of energy use worldwide and present opportunities for energy efficiency improvements. Within the industry, energy management is a complex task, regarding scenarios with variables related to the following perspectives: economics, contingency, technological change and behavioural. This paper aims at analyzing the influencing factors on energy management in industries from these perspectives. A survey with 40 variables was carried out with middle managers from different industrial sectors in Brazil. The variables were divided into three groups: drivers for investments in energy efficiency; organizational processes and actions in energy management; involvement of middle managers. Initially, an exploratory factor analysis technique was employed aiming at specifying the main factors influencing energy management. In the sequence, a confirmatory factor analysis was used to associate the variables to the main factors as well as to know how the factors relate to each other. The study showed a positive correlation among all the factors identified. Statistical tests suggested that the factors could not be explained separately. Hypotheses tests were applied to verify the influence of the factors among the groups surveyed. The final model comprised eight factors into the three groups: organizational (strategic, operational), involvement (motivation, support), drivers (production, economics, competitiveness, environment). The results and the main implications of the study are discussed in the paper.
Keywords: Energy management | ISO 50001 | Energy efficiency | Industries | Factor analysis