Differentiation between Normal and White Striped Turkey Breasts by Visible/Near Infrared Spectroscopy and Multivariate Data Analysis

The appearance of white striations over breast meat is an emerging and growing problem. The main purpose of this study was to employ the reflectance of visible-near infrared (VIS/NIR) spectroscopy to differentiate between normal and white striped turkey breasts. Accordingly, 34 turkey breast fillets were selected representing a different level of white striping (WS) defects (normal, moderate and severe). The findings of VIS/NIR were analyzed by principal component (PC1) analysis (PCA). It was found that the first PC1 for VIS, NIR and VIS/NIR region explained 98%, 97%, and 96% of the total variation, respectively. PCA showed high performance to differentiate normal meat from abnormal meat (moderate and severe WS). In conclusion, the results of this research showed that VIS/NIR spectroscopy was satisfactory to differentiate normal from severe WS turkey fillets by using several quality traits

Fog-Driven Context-Aware Architecture for Node Discovery and Energy Saving Strategy for Internet of Things Environments

The consolidation of the Fog Computing paradigm and the ever-increasing diffusion of Internet of Things (IoT) and smart objects are paving the way toward new integrated solutions to efficiently provide services via short-mid range wireless connectivity. Being the most of the nodes mobile, the node discovery process assumes a crucial role for service seekers and providers, especially in IoT-fog environments where most of the devices run on battery. This paper proposes an original model and a fog-driven architecture for efficient node discovery in IoT environments. Our novel architecture exploits the location awareness provided by the fog paradigm to significantly reduce the power drain of the default baseline IoT discovery process. To this purpose, we propose a deterministic and competitive adaptive strategy to dynamically adjust our energy-saving techniques by deciding when to switch BLE interfaces ON/OFF based on the expected frequency of node approaching. Finally, the paper presents a thorough performance assessment that confirms the applicability of the proposed solution in several different applications scenarios. This evaluation aims also to highlight the impact of the nodes' dynamic arrival on discovery process performance

Extended Bit-Plane Compression for Convolutional Neural Network Accelerators

After the tremendous success of convolutional neural networks in image classification, object detection, speech recognition, etc., there is now rising demand for deployment of these compute-intensive ML models on tightly power constrained embedded and mobile systems at low cost as well as for pushing the throughput in data centers. This has triggered a wave of research towards specialized hardware accelerators. Their performance is often constrained by I/O bandwidth and the energy consumption is dominated by I/O transfers to off-chip memory. We introduce and evaluate a novel, hardware-friendly compression scheme for the feature maps present within convolutional neural networks. We show that an average compression ratio of 4.4 7 relative to uncompressed data and a gain of 60% over existing method can be achieved for ResNet-34 with a compression block requiring <300 bit of sequential cells and minimal combinational logic

The Effect of Timing and Frequency of Push Notifications on Usage of a Smartphone-Based Stress Management Intervention: An Exploratory Trial

Push notifications offer a promising strategy for enhancing engagement with smartphone-based health interventions. Intelligent sensor-driven machine learning models may improve the timeliness of notifications by adapting delivery to a user\u2019s current context (e.g. location). This exploratory mixed-methods study examined the potential impact of timing and frequency on notification response and usage of Healthy Mind, a smartphone-based stress management intervention. 77 participants were randomised to use one of three versions of Healthy Mind that provided: intelligent notifications; daily notifications within pre-defined time frames; or occasional notifications within pre-defined time frames. Notification response and Healthy Mind usage were automatically recorded. Telephone interviews explored participants\u2019 experiences of using Healthy Mind. Participants in the intelligent and daily conditions viewed (d = .47, .44 respectively) and actioned (d = .50, .43 respectively) more notifications compared to the occasional group. Notification group had no meaningful effects on percentage of notifications viewed or usage of Healthy Mind. No meaningful differences were indicated between the intelligent and non-intelligent groups. Our findings suggest that frequent notifications may encourage greater exposure to intervention content without deterring engagement, but adaptive tailoring of notification timing does not always enhance their use. Hypotheses generated from this study require testing in future work

Spatio-temporal techniques for user identification by means of GPS mobility data

One of the greatest concerns related to the popularity of GPS-enabled devices and applications is the increasing availability of the personal location information generated by them and shared with application and service providers. Moreover, people tend to have regular routines and be characterized by a set of \u201csignificant places\u201d, thus making it possible to identify a user from his/her mobility data. In this paper we present a series of techniques for identifying individuals from their GPS movements. More specifically, we study the uniqueness of GPS information for three popular datasets, and we provide a detailed analysis of the discriminatory power of speed, direction and distance of travel. Most importantly, we present a simple yet effective technique for the identification of users from location information that are not included in the original dataset used for training, thus raising important privacy concerns for the management of location datasets

BRAF exon 15 mutations in papillary carcinoma and adjacent thyroid parenchyma: A search for the early molecular events associated with tumor development

BRAF exon 15 mutations are the most common molecular alterations found in papillary thyroid carcinoma (PTC). To date, there is no information regarding BRAF alterations in the thyroid parenchyma surrounding the tumor. To explore the early events associated with the development of PTC, we used massively parallel sequencing to investigate BRAF exon 15 in 30 PTCs and in 100 samples from the thyroid parenchyma surrounding the tumor. BRAF p.V600E was identified in 19/30 PTCs (63.3%). BRAF p.V600E mutations were identified in the tissue adjacent the PTC only in samples containing psammoma bodies. The other samples were either BRAF wild type (WT) or carried BRAF non p.V600E mutations. Specifically, BRAF p.G593D,-p.A598T,-p.V600M,-p.R603Q,-p.S607F, and-p.S607P were identified in 4 of 36 (11.1%) samples with follicular cell atypia, in 2 of 16 (12.5%) with follicular cell hyperplasia, and in 1 of 33 (3.0%) histologically normal samples\u2014only in tissue surrounding BRAF p.V600E mutated PTCs. These mutations are predicted to affect protein function in silico but, in vitro, have kinase activity and BRAF phosphorylation levels similar to BRAF WT. No BRAF exon 15 mutations were identified in samples adjacent to PTCs that were BRAF WT. A mutagenic process affecting BRAF exon 15 occurs in a subset of thyroid glands that develop BRAF p.V600E mutated PTCs

Search for anomalous electroweak production of vector boson pairs in association with two jets in proton-proton collisions at 13 TeV

A search for anomalous electroweak production of WW, WZ, and ZZ boson pairs in association with two jets in proton-proton collisions at 1as = 13 TeV at the LHC is reported. The data sample corresponds to an integrated luminosity of 35.9 fb 121 collected with the CMS detector. Events are selected by requiring two jets with large rapidity separation and invariant mass, one or two leptons (electrons or muons), and a W or Z boson decaying hadronically. No excess of events with respect to the standard model background predictions is observed and constraints on the structure of quartic vector boson interactions in the framework of dimension-8 effective field theory operators are reported. Stringent limits on parameters of the effective field theory operators are obtained. The observed 95% confidence level limits for the S0, M0, and T0 operators are 122.7 < fS0/4 < 2.7, 121.0 < fM0/4 < 1.0, and 120.17 < fT0/4 < 0.16, in units of TeV 124. Constraints are also reported on the product of the cross section and branching fraction for vector boson fusion production of charged Higgs bosons as a function of mass from 600 to 2000 GeV. The results are interpreted in the context of the Georgi\u2013Machacek model

Mobile health applications to promote active and healthy ageing

The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet the societal challenge of changing demography, mobile health solutions are warranted that support older adults to stay healthy and active and that can prevent or delay functional decline. This paper reviews the literature on mobile technology, in particular wearable technology, such as smartphones, smartwatches, and wristbands, presenting new ideas on how this technology can be used to encourage an active lifestyle, and discusses the way forward in order further to advance development and practice in the field of mobile technology for active, healthy ageing

Bioactivity of olive oil phenols in neuroprotection

Neurological disorders such as stroke, Alzheimer\u2019s and Parkinson\u2019s diseases are associated with high morbidity and mortality, and few or no effective options are available for their treatment. These disorders share common pathological characteristics like the induction of oxidative stress, abnormal protein aggregation, perturbed Ca2+ homeostasis, excitotoxicity, inflammation and apoptosis. A large body of evidence supports the beneficial effects of the Mediterranean diet in preventing neurodegeneration. As the Mediterranean diet is characterized by a high consumption of extra-virgin olive oil it has been hypothesized that olive oil, and in particular its phenols, could be responsible for the beneficial effect of the Mediterranean diet. This review provides an updated vision of the beneficial properties of olive oil and olive oil phenols in preventing/counteracting both acute and chronic neurodegenerative diseases

Alexithymia is related to the need for more emotional intensity to identify static fearful facial expressions

Individuals with high levels of alexithymia, a personality trait marked by difficulties in identifying and describing feelings and an externally oriented style of thinking, appear to require more time to accurately recognize intense emotional facial expressions (EFEs). However, in everyday life, EFEs are displayed at different levels of intensity and individuals with high alexithymia may also need more emotional intensity to identify EFEs. Nevertheless, the impact of alexithymia on the identification of EFEs, which vary in emotional intensity, has largely been neglected. To address this, two experiments were conducted in which participants with low (LA) and high (HA) levels of alexithymia were assessed in their ability to identify static (Experiment 1) and dynamic (Experiment 2) morphed faces ranging from neutral to intense EFEs. Results showed that HA needed more emotional intensity than LA to identify static fearful - but not happy or disgusted - faces. On the contrary, no evidence was found that alexithymia affected the identification of dynamic EFEs. These results extend current literature suggesting that alexithymia is related to the need for more perceptual information to identify static fearful EFEs