A new nanostructured material based on fluorophosphate incorporated into a zinc–aluminium layered double hydroxide by ion exchange

Layered double hydroxides (LDHs), also called anionic clays, consist of cationic brucite-like layers and exchangeable interlayer anions. These hydrotalcite-like compounds, with Zn and Al in the layers and chloride in the interlayer space, were prepared following the coprecipitation method at constant pH. The effect of pH, aging time and anion concentration on the intercalation of fluorophosphate \((\hbox {PO}_{3}\hbox {F}^{2-}\), FP) in the [Zn–Al] LDH was investigated. The best crystalline material, with high exchange extent, was obtained by carrying out the exchange at 25\({^{\circ }}\hbox {C}\) in a 0.03 M FP solution at pH 7 with at least 42 h of aging time. A mechanism for the FP intercalation was confirmed by X-ray diffraction, infrared spectroscopy and thermogravimetry (TG) analyses (TG and DTG curves).     

Study and characterization of PD in impregnated polypropylenecapacitors

Measurements of partial discharge (PD) in models of impregnated polypropylene capacitors with and without specific defects have been conducted. Two different regimes of discharges (in the liquid and in gas bubbles) are illustrated. The effect of temperature on the decrease of the insulation property is underlined. Four specific defects have been artificially introduced in the test cell (poor impregnation, bad contact, fold in the polypropylene (PP) film, cavity). The relevance of parameters such as skewness and kurtosis to characterize PD distributions is discussed. New parameters based upon the mean position of the PD in the voltage phase are proposed for a better recognition of the defects inducing PD     

Pain monitoring: A dynamic and context-sensitive system

The current paper presents an automatic and context sensitive system for the dynamic recognition of pain expression among the six basic facial expressions and neutral on acted and spontaneous sequences. A machine learning approach based on the Transferable Belief Model, successfully used previously to categorize the six basic facial expressions in static images [2], [61], is extended in the current paper for the automatic and dynamic recognition of pain expression from video sequences in a hospital context application. The originality of the proposed method is the use of the dynamic information for the recognition of pain expression and the combination of different sensors, permanent facial features behavior, transient features behavior, and the context of the study, using the same fusion model. Experimental results, on 2-alternative forced choices and, for the first time, on 8-alternative forced choices (i.e. pain expression is classified among seven other facial expressions), show good classification rates even in the case of spontaneous pain sequences. The mean classification rates on acted and spontaneous data reach 81.2% and 84.5% for the 2-alternative and 8-alternative forced choices, respectively. Moreover, the system performances compare favorably to the human observer rates (76%), and lead to the same doubt states in the case of blend expressions.     

Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses

The relationship between nonverbal behavior and severity of depression was investigated by following depressed participants over the course of treatment and video recording a series of clinical interviews. Facial expressions and head pose were analyzed from video using manual and automatic systems. Both systems were highly consistent for FACS action units (AUs) and showed similar effects for change over time in depression severity. When symptom severity was high, participants made fewer affiliative facial expressions (AUs 12 and 15) and more non-affiliative facial expressions (AU 14). Participants also exhibited diminished head motion (i.e., amplitude and velocity) when symptom severity was high. These results are consistent with the Social Withdrawal hypothesis: that depressed individuals use nonverbal behavior to maintain or increase interpersonal distance. As individuals recover, they send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and revealed the same pattern of findings suggests that automatic facial expression analysis may be ready to relieve the burden of manual coding in behavioral and clinical science.     

The Geodesic Distance on the Generalized Gamma Manifold for Texture Image Retrieval

Journal of Mathematical Imaging and Vision - In this paper, the similarity measurement issue, in the context of texture images comparison, is tackled from a geometrical point of view by computing...     

Are waterpipe users interested in quitting?

Waterpipe smoking has increased dramatically worldwide in recent years, with an estimated 100 million daily users. Research on this traditional Middle Eastern tobacco use method is in its infancy, and little is known about users' cessation-related attitudes and experiences. A random sample of 268 narghile (waterpipe) smokers (40% female; mean age = 30 years; range = 18-68) was obtained from cafes and restaurants in Aleppo, Syria. The majority of users (86.5%) believed they could quit using waterpipes at any time, but that belief was inversely related to perceived dependence, with only 48.7% of those who thought they were "very hooked" believing they could quit. Interest in quitting was expressed by 28.4% of subjects, with the majority (89.2%) reporting health concerns as a primary reason, and 59.2% having made an unsuccessful quit attempt in the past year. In a logistic regression model, independent predictors of interest in quitting included being married, having smoked for fewer years, not increasing the frequency of smoking over time, and having family members who do not smoke a waterpipe and disapprove of its use. Results indicate that a sizable percentage of waterpipe users express interest in quitting and have tried unsuccessfully in the past to quit. Waterpipe use needs to be considered in developing effective tobacco use cessation programs in the Middle East.     

Nanosheets of Nonlayered Aluminum Metal–Organic Frameworks through a Surfactant‐Assisted Method

During the last decade, the synthesis and application of metal–organic framework (MOF) nanosheets has received growing interest, showing unique performances for different technological applications. Despite the potential of this type of nanolamellar materials, the synthetic routes developed so far are restricted to MOFs possessing layered structures, limiting further development in this field. Here, a bottom‐up surfactant‐assisted synthetic approach is presented for the fabrication of nanosheets of various nonlayered MOFs, broadening the scope of MOF nanosheets application. Surfactant‐assisted preorganization of the metallic precursor prior to MOF synthesis enables the manufacture of nonlayered Al‐containing MOF lamellae. These MOF nanosheets are shown to exhibit a superior performance over other crystal morphologies for both chemical sensing and gas separation. As revealed by electron microscopy and diffraction, this superior performance arises from the shorter diffusion pathway in the MOF nanosheets, whose 1D channels are oriented along the shortest particle dimension.     

Dynamic proof of retrievability based on public auditing for coded secure cloud storage

Multimedia Tools and Applications - Cloud storage represents a cloud delivered-service model which draws the attention of organizations and individuals due to its uncounted attractive advantages....     

A Lightweight Approach (BL-DAC) to Secure Storage Sharing in Cloud-IoT Environments

The growing advent of the Internet of Things (IoT) users is driving the adoption of cloud computing technologies. The integration of IoT in the cloud enables storage and computational capabilities for IoT users. However, security has been one of the main concerns of cloud-integrated IoT. Existing work attempts to address the security concerns of cloud-integrated IoT through authentication, access control, and blockchain-based methods. However, existing frameworks are somewhat limited by scalability, privacy, and centralized structures. To mitigate the existing problems, we propose a blockchain-based distributed access control method for secure storage in the IoT cloud (BL-DAC). Initially, the BL-DAC performs decentralized authentication using the Quantum Neural Network Cryptography (QNNC) algorithm. IoT users and edge nodes are authenticated in the blockchain deployed by distributed Trusted Authorities (TAs) using multiple credentials. The user data is classified into sensitive and non-sensitive categories using the Enhanced Seagull Optimization (ESO) algorithm. Also, the authentication to access this data is performed by a decentralized access control method using smart contract policy. Sensitive user data is encrypted using the QNNC algorithm and stored in the private cloud. In contrast, non-sensitive data is stored in the public cloud, and IPFS is used to store data in a decentralized manner with high reliability. In addition, data security is improved by using a hierarchical blockchain which improves scalability by managing the multiple blockchains hierarchically and is lightweight using Proof of Authentication Consensus (PoAH). The BL-DAC is simulated and validated using the Network Simulator-3.26 simulation tool and validated. This work shows better results than the compared ones in terms of validation metrics such as throughput (26%), encryption time (19%), decryption time (16%), response time (15%), block validation time (31%), attack detection rate (16%), access control precision (13%), and scalability (28%).