A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems

Cultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The purpose of this review is determining the various relationships in fuzzy inference systems currently used for the modelling, prediction, and control of humidity in greenhouses and how they have changed over time to be able to develop more robust and easier to understand models. The methodology follows the PRISMA work guide. A total of 93 investigations in 4 academic databases were reviewed; their bibliometric aspects, which contribute to the objective of the investigation, were extracted and analysed. It was finally concluded that the development of models based in Mamdani fuzzy inference systems, integrated with optimization and fuzzy clustering techniques, and following strategies such as model-based predictive control guarantee high levels of precision and interpretability.

Analysis of Security Based Accessibility Control Models for Cloud Computing

Access control has become the most necessary requirement to limit unauthorized and privileged access to information systems in cloud computing. Access control models counter the additional security challenges like rules, domain names, job allocation, multi hosting and separation of tasks. This paper classifies the conventional and modern access control models which has been utilized to restrain these access flaws by employing a variety of practices and methodologies. It examine the frequent security threats to information confidentiality, integrity, data accessibility and their approach used for cloud solutions. This paper proposed a priority based task scheduling access control (PbTAC) model to secure and scheduled access of resources & services rendered to cloud user. PbTAC model will ensure the job allocation, tasks scheduling and security of information through its rule policies during transmission between parties. It also help in reducing system overhead by minimize the computation and less storage cost.

Service-Based Hybrid Access Control Technology with Priority Level for the Internet of Vehicles under the Cloud Architecture

The communication process of devices in IoV under cloud architecture needs to be protected by access control models. However, existing access control models have difficulty establishing the appropriate granularity of permissions in the face of large amounts of data in IoV. Moreover, the access control model may need to temporarily change user privileges to accommodate the dynamic nature of IoV scenarios, a requirement that is difficult to implement for traditional access control models. The unstable connection status of devices in IoV also creates problems for access control. The service (composed of role and attribute) based access control model (in IoV) S-RABAC (V), under the Cloud computing architecture, introduces a formal theoretical model. The model uses attribute grouping and prioritization mechanisms to form a hierarchical structure. The permission combination pattern in the hierarchical structure can avoid duplicate permissions and reduce the number of permissions while ensuring fine-grained permissions. Different layers in the model have different priorities, and when a user’s permission requires temporary changes, it can be adjusted to the corresponding layers according to the user’s priority. In addition, users are allowed to keep their assigned privileges for a period to avoid frequent access control because of unstable connections. We have implemented the proposed access control model in Alibaba Cloud Computing and given six example demonstrations. The experiment shows that this is an access control model that can protect IoV security more effectively. Various unique mechanisms in the model enable S-RABAC(V) to improve the overall access control efficiency. The model adds some extra features compared to ABAC and RBAC and can generate more access control decisions using the priority mechanism.

Access Control Models

Access control is a part of the security of information technologies. Access control regulates the access requests to system resources. The access control logic is formalized in models. Many access control models exist. They vary in their design, components, policies and areas of application. With the developing of information technologies, more complex access control models have been created. This paper is concerned with overview and analysis for a number of access control models. First, an overview of access control models is presented. Second, they are analyzed and compared by a number of parameters: storing the identity of the user, delegation of trust, fine-grained policies, flexibility, object-versioning, scalability, using time in policies, structure, trustworthiness, workflow control, areas of application etc. Some of these parameters describe the access control models, while other parameters are important characteristics and components of these models. The results of the comparative analysis are presented in tables. Prospects of development of new models are specified.

Explaining and avoiding failures modes in goal-directed generation

Despite growing interest and success in automated in-silico molecular design, doubts remain regarding the ability of goal-directed generation algorithms to perform unbiased exploration of novel chemical spaces. A specific phenomenon has recently been highlighted: goal-directed generation guided with machine learning models produce molecules with high scores according to the optimization model, but low scores according to control models, even when trained on the same data distribution and the same target. In this work, we show that this worrisome behavior is actually due to issues with the predictive models and not the goal-directed generation algorithms. We show that with appropriate predictive models, this issue can be resolved, and molecules generated have high scores according to both the optimization and the control models.

Explaining and avoiding failures modes in goal-directed generation

Despite growing interest and success in automated in-silico molecular design, doubts remain regarding the ability of goal-directed generation algorithms to perform unbiased exploration of novel chemical spaces. A specific phenomenon has recently been highlighted: goal-directed generation guided with machine learning models produce molecules with high scores according to the optimization model, but low scores according to control models, even when trained on the same data distribution and the same target. In this work, we show that this worrisome behavior is actually due to issues with the predictive models and not the goal-directed generation algorithms. We show that with appropriate predictive models, this issue can be resolved, and molecules generated have high scores according to both the optimization and the control models.

A Convenient Approach towards the Synthesis of ADMDP Type Iminosugars and Nojirimycin Derivatives from Sugar-Derived Lactams

An efficient method for the synthesis of nojirimycin- and pyrrolidine-based iminosugar derivatives has been developed. The strategy is based on the partial reduction in sugar-derived lactams by Schwartz’s reagent and tandem stereoselective nucleophilic addition of cyanide or a silyl enol ether dictated by Woerpel’s or diffusion control models, which affords amino-modified iminosugars, such as ADMDP or higher nojirimycin derivatives.

Deep Q-network-based traffic signal control models

Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method.