NIRS potential use for the determination of natural resources quality from dehesa (acorn and grass) in Montanera system for Iberian pigs.

NIRS technology has been used as an alternative to conventional methods to determinate the content of nutrients of acorns and grass from dehesa ecosystem. Dry matter (DM), crude fat (CF), crude protein (CP), starch, total phenolic compounds (TP), α-tocopherol, γ-tocopherol, fatty acids, neutral detergent fiber (NDF), total antioxidant activity (TAA) and total energy (TE) were determined by conventional methods for later development of NIRS predictive equations. The NIR spectrum of each sample was collected and for all studied parameters, a predictive model was obtained and external validated. Good prediction equations were obtained for moisture, crude fat, crude protein, total energy and γ-tocopherol in acorns samples, with high coefficients of correlation (1-VR) and low standard error of prediction (SEP) (1-VR=0.81, SEP=2.62; 1-VR=0.92, SEP=0.54; 1-VR=0.86, SEP=0.47; 1-VR=0.84, SEP=0.2; 1-VR=0.88, SEP=5.4, respectively) and crude protein, NDF, α-tocopherol and linolenic acid content in grass samples (1-VR=0.9, SEP=1.99; 1-VR=0.87, SEP=4.13; 1-VR=0.76, SEP=10.9; 1-VR=0.82, SEP=0.6, respectively). Therefore, these prediction models could be used to determinate the nutritional composition of Montanera natural resources

Integration of virtual reality within the built environment curriculum

Virtual Reality (VR) technology is still perceived by many as being inaccessible and cost prohibitive with VR applications considered expensive to develop as well as challenging to operate. This paper reflects on current developments in VR technologies and describes an approach adopted for its phased integration into the academic curriculum of built environment students. The process and end results of implementing the integration are discussed and the paper illustrates the challenges of introducing VR, including the acceptance of the technology by academic staff and students, interest from industry, and issues pertaining to model development. It sets out to show that fairly sophisticated VR models can now be created by non-VR specialists using commercially available software and advocates that the implementation of VR will increase alongside industryis adoption of these tools and the emergence of a new generation of students with VR skills. The study shows that current VR technologies, if integrated appropriately within built environment academic programmes, demonstrate clear promise to provide a foundation for more widespread collaborative working environments

Mesenteric-Portal Vein Resection during Pancreatectomy for Pancreatic Cancer

The aim of the present study was to determine the outcome of patients undergoing pancreatic resection with (VR+) or without (VR 12) mesenteric-portal vein resection for pancreatic carcinoma. Between January 1998 and December 2012, 241 patients with pancreatic cancer underwent pancreatic resection: in 64 patients, surgery included venous resection for macroscopic invasion of mesenteric-portal vein axis. Morbidity and mortality did not differ between the two groups (VR+: 29% and 3%; VR 12: 30% and 4.0%, resp.). Radical resection was achieved in 55/64 (78%) in the VR+ group and in 126/177 (71%) in the VR 12 group. Vascular invasion was histologically proven in 44 (69%) of the VR+ group. Survival curves were not statistically different between the two groups. Mean and median survival time were 26 and 15 months, respectively, in VR 12 versus 20 and 14 months, respectively, in VR+ group . In the VR+ group, only histologically proven vascular invasion significantly impacted survival , while, in the VR 12 group, R0 resection and tumor\u2019s grading significantly influenced long-term survival. Vascular resection during pancreatectomy can be performed safely, with acceptable morbidity and mortality. Long-term survival was the same, with or without venous resection. Survival was worse for patients with histologically confirmed vascular infiltration

Echo State Learning for Wireless Virtual Reality Resource Allocation in UAV-enabled LTE-U Networks

In this paper, the problem of resource management is studied for a network of wireless virtual reality (VR) users communicating using an unmanned aerial vehicle (UAV)-enabled LTE-U network. In the studied model, the UAVs act as VR control centers that collect tracking information from the VR users over the wireless uplink and, then, send the constructed VR images to the VR users over an LTE-U downlink. Therefore, resource allocation in such a UAV-enabled LTE-U network must jointly consider the uplink and downlink links over both licensed and unlicensed bands. In such a VR setting, the UAVs can dynamically adjust the image quality and format of each VR image to change the data size of each VR image, then meet the delay requirement. Therefore, resource allocation must also take into account the image quality and format. This VR-centric resource allocation problem is formulated as a noncooperative game that enables a joint allocation of licensed and unlicensed spectrum bands, as well as a dynamic adaptation of VR image quality and format. To solve this game, a learning algorithm based on the machine learning tools of echo state networks (ESNs) with leaky integrator neurons is proposed. Unlike conventional ESN based learning algorithms that are suitable for discrete-time systems, the proposed algorithm can dynamically adjust the update speed of the ESN's state and, hence, it can enable the UAVs to learn the continuous dynamics of their associated VR users. Simulation results show that the proposed algorithm achieves up to 14% and 27.1% gains in terms of total VR QoE for all users compared to Q-learning using LTE-U and Q-learning using LTE

Design of an ontology for decision support in VR exposure therapy

Virtual Reality (VR) is finding its way into many domains, including healthcare. Therapists greatly benefit from having any scenario in VR at their disposal for exposure therapy. However, adapting the VR environment to the needs of the patient is time-consuming. Therefore, an intelligent decision support system that takes context information into account would be a big improvement for personalised VR therapy. In this paper, a semantic ontology is presented for modelling relevant concepts and relations in the context of anxiety therapy in VR. The necessary knowledge was collected through workshops with therapists, this resulted in a layered ontology. Furthermore, semantic reasoning through logical rules enables deduction of interesting high-level knowledge from low-level data. The presented ontology is a starting point for further research on intelligent adaptation algorithms for personalised VR exposure therapy