Granular crystals in palm oil based shortening/margarine : a review

Palm oil based shortenings and margarines are important products within the lipid industry. However, a widespread quality deterioration issue is often reported regarding their long-term storage: the appearance of granular crystals or grains that are regarded as unwanted because of the deflecting visual appearance and the negative mouthfeel during consumption. In this review, the role of fat blends composition, crystallization process and storage conditions in the formation and growth of these unwanted granular crystals will be discussed and summarized. In addition, some potential approaches in preventing the formation of granular crystals in palm oil based shortening and margarine are also introduced

Key Management Techniques for Wireless Mesh Network

Key management is one of the most important tasks in wireless mesh network. This service is responsible for key generation, distribution, and key exchange in a cryptography-based system. Due to the shared nature of WMNs and absence of globally trusted central authority, key management becomes more challenging. This chapter introduces several key management methods that can address these challenges. The fundamental approach is the secret sharing scheme created by A. Shamir, which effectively distributes keys to all participants’ network. Based on Shamir’s scheme, many authors proposed other algorithms to secure the communication channel in such a way that adversary cannot steal any information about the secret. In addition, in this chapter, a new secret sharing method using real-time synchronization among transceiver devices is presented. In this method, each node generates its key depending on its physical information and the real-time clock. Therefore, public and private keys can be managed efficiently for data encryption and prevent several external attacks to WMNs. A specific protocol is proposed to secure keys while transferring between devices to prevent internal attacks

Tuning the photoreactivity of photocycloaddition by halochromism

Harnessing the power of light for chemical transformation is a long-standing goal in organic synthesis, materials fabrication and engineering. Amongst all photochemical reactions, [2 + 2] photocycloadditions are inarguably the most important and most frequently used. These photoreactions have green characteristics by enabling new bond formation in a single step procedure under light irradiation, without the need for heat or chemical catalysis. More recently, substantial progress has been made in red-shifting the activation wavelength of photocycloadditions in response to research trends moving towards green and sustainable processes, and advanced applications in biological environments. In the past 5 years, our team has further expanded the toolbox of photocycloaddition reactions that can be triggered by visible light. In our exploration of photochemical reactivity, we found that reactivity is often red-shifted compared to the substrate’s absorption spectrum. Our efforts have resulted in red-shifted photochemical reactions, providing some of the lowest energy – and catalyst-free – photo-activated [2 + 2] cycloadditions (up to 550 nm). More recently, we introduced an additional level of control over such finely wavelength gated reactions by altering the pH of the reaction environment, thus exploiting halochromic effects to enhance or impede the photoreactivity of red-shifted [2 + 2] photocycloaddition reactions. In this account, we discuss the current state of halochromically regulated photochemical reactions and their potential in soft matter materials on selected examples

Rice seed image classification based on HOG descriptor with missing values imputation

Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach

Maximum convergence algorithm for WiFi based indoor positioning system

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points