Preparation of cellulose nanofibers with hydrophobic surface characteristics.

The aim of this study was to develop cellulose nanofibers with hydrophobic surface characteristics using chemical modification. Kenaf fibers were modified using acetic anhydride and cellulose nanofibers were isolated from the acetylated kenaf using mechanical isolation methods. Fourier transform infrared spectroscopy (FTIR) indicated acetylation of the hydroxyl groups of cellulose. The study of the dispersion demonstrated that acetylated cellulose nanofibers formed stable, well-dispersed suspensions in both acetone and ethanol. The contact angle measurements showed that the surface characteristics of nanofibers were changed from hydrophilic to more hydrophobic when acetylated. The microscopy study showed that the acetylation caused a swelling of the kenaf fiber cell wall and that the diameters of isolated nanofibers were between 5 and 50 nm. X-ray analysis showed that the acetylation process reduced the crystallinity of the fibers, whereas mechanical isolation increased it. The method used provides a novel processing route for producing cellulose nanofibers with hydrophobic surfaces

Seizure episodes detection via smart medical sensing system

Cyber-physical systems (CPS) consist of seamless network of sensors and actuators integrated with physical processes related to human activities. The CPS exploits sensors and actuators to monitor and control different physical process that can affect the computations of the devices. This paper presents the monitoring of physical activities exploiting wireless devices as sensors used in medical cyber-physical systems. Patients undergoing epileptic seizures experience involuntary body movements such as jerking, muscle twitching, falling, and convulsions. The proposed method exploits S-Band sensing used in medical CPS that leverage wireless devices such as omni-directional antenna at the transmitter side, four-beam patch antenna at the receiver side, RF signal generator and vector signal analyzer that perform signal conditioning by providing amplitude and raw phase data. The method uses wireless monitoring and recording system for measurement and classification of a clinical condition (epileptic seizures) versus normal daily routine activities. The data acquired that are perturbations of the radio signal is analyzed as amplitude, phase information, and statistical models. Extracting the statistical features, we leverage various machine learning algorithms such as support vector machine, random forest, and K-nearest neighbor that classify the data to differentiate patient’s various activities such as press-ups, walking, sitting, squatting, and seizure episodes. The performance parameters used in three machine learning algorithms are accuracy, precision, recall, Cohen’s Kappa coefficient, and F-measure. The values obtained using five performance parameters provide the accuracy of more than 90%

Italian guidelines for primary headaches: 2012 revised version

The first edition of the Italian diagnostic and therapeutic guidelines for primary headaches in adults was published in J Headache Pain 2(Suppl. 1):105–190 (2001). Ten years later, the guideline committee of the Italian Society for the Study of Headaches (SISC) decided it was time to update therapeutic guidelines. A literature search was carried out on Medline database, and all articles on primary headache treatments in English, German, French and Italian published from February 2001 to December 2011 were taken into account. Only randomized controlled trials (RCT) and meta-analyses were analysed for each drug. If RCT were lacking, open studies and case series were also examined. According to the previous edition, four levels of recommendation were defined on the basis of levels of evidence, scientific strength of evidence and clinical effectiveness. Recommendations for symptomatic and prophylactic treatment of migraine and cluster headache were therefore revised with respect to previous 2001 guidelines and a section was dedicated to non-pharmacological treatment. This article reports a summary of the revised version published in extenso in an Italian version

A multivariant secure framework for smart mobile health application

This is an accepted manuscript of an article published by Wiley in Transactions on Emerging Telecommunications Technologies, available online: https://doi.org/10.1002/ett.3684 The accepted version of the publication may differ from the final published version.Wireless sensor network enables remote connectivity of technological devices such as smart mobile with the internet. Due to its low cost as well as easy availability of data sharing and accessing devices, the Internet of Things (IoT) has grown exponentially during the past few years. The availability of these devices plays a remarkable role in the new era of mHealth. In mHealth, the sensors generate enormous amounts of data and the context-aware computing has proven to collect and manage the data. The context aware computing is a new domain to be aware of context of involved devices. The context-aware computing is playing a very significant part in the development of smart mobile health applications to monitor the health of patients more efficiently. Security is one of the key challenges in IoT-based mHealth application development. The wireless nature of IoT devices motivates attackers to attack on application; these vulnerable attacks can be denial of service attack, sinkhole attack, and select forwarding attack. These attacks lead intruders to disrupt the application's functionality, data packet drops to malicious end and changes the route of data and forwards the data packet to other location. There is a need to timely detect and prevent these threats in mobile health applications. Existing work includes many security frameworks to secure the mobile health applications but all have some drawbacks. This paper presents existing frameworks, the impact of threats on applications, on information, and different security levels. From this line of research, we propose a security framework with two algorithms, ie, (i) patient priority autonomous call and (ii) location distance based switch, for mobile health applications and make a comparative analysis of the proposed framework with the existing ones.Published onlin

Caracterização morfológica de nanocristais de celulose por microscopia de força atômica

RESUMO O isolamento de nanocristais de celulose (CNCs) de fibras vegetais é uma alternativa promissora para sua aplicação como reforço em matrizes poliméricas. A caracterização dos CNCs é fundamental para a confiabilidade da técnica, além de determinar as aplicações possíveis a partir de cada tipo de fibra. A partir da técnica de microscopia de força atômica, um estudo da morfologia e distribuição dos CNCs de semente de manga, vagem de algaroba, pseudocaule da bananeira e fibra do mesocarpo de dendê foi realizado neste trabalho. Os CNCs foram obtidos via reação hidrolítica com ácido sulfúrico em concentrações que variaram de acordo com a fonte da fibra. Os resultados obtidos revelaram dimensões variando de 300 a 500 nm em comprimento e 4 a 16 nm em diâmetro. A apresentação morfológica em forma de agulha demonstrou que o isolamento das fibras de celulose em CNCs foi efetiva. A razão de aspecto associada à formação cilíndrica em agulha dos CNCs isolados evidenciou o alto potencial das fontes de dendê e de vagem de algaroba para o reforço de bionanocompósitos