Green Building Insurance

This paper addresses fundamental issues in green building insurance. Thus it discusses the modernity of the insurance for such buildings, the scope of insurance liability in green insurance, the role of the judge in developing the provisions of this contract through his modernized judgment, parties to the green insurance contract and the nature of the green insurance contracts for them, along with the applicable law, and it highlights the cases that may arise between the parties. The paper concludes a number of results, most important, that the nature and novelty of green buildings may be accompanied by unusual risks in traditional buildings which requires special insurance contract to cover such risks and to create specialized experts in green buildings to be relied upon by the court in issuing decisions as the judiciary may play a major role in developing the legal rules for green building, and finally it stresses the necessity to make the green building insurance contract obligatory. Key words: green building, LEED, traditional buildings, green insurance, risks, green material

Contractual Suggestions for the Contractor in Green Buildings

Many contractors are seeking to practice green buildings and to enter green market without realizing risks that might rise thereof. This paper will suggest the important issues relating to green construction contracts, to be considered by the contractor whether to include or exclude from the contract with an owner, to avoid liability. Thus, it discusses the material used in green building, guarantees for obtaining green certification, the delays in works implementation, determination of obligation and its related risk and the contractor understanding of his obligation. It concludes that the contractor should take care upon conclusion of the contract to include all the provisions that ensure the integrity of performance and to avoid liability and the Jordanian legislator shall provide legal ambit for green building construction.   Key word: green building, construction, liability, contractor

Contractual Suggestions for Engineer in Green Buildings

This paper discusses green buildings construction contracts with very important suggestions related to an engineer who is either a consultant, a designer, a site engineer or a supervisor over such constructions. This research discusses the commitment of an engineer to the green buildings’ code and the importance of previous consultancy on such constructions in addition to the commitment of an engineer to green engineer standard and adoption of typical contracts. The engineer should consider the consequences of using green materials and the additional works, services and guarantees that should be provided after the implementation. In addition, an engineer should avoid the disadvantages of offering his previous experience and promises extensively. This paper concludes with many results and recommendations, most important of which is that the commitment to green codes is absolutely different from traditional standards required by an engineer to avoid liability. Keywords: Green engineer, green engineer standard, green buildings, green code, construction materials, additional works, experience, LEED, and traditional buildings

Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease

Huntington’s disease (HD) is characterised by a triad of cognitive, behavioural, and motor symptoms which lead to functional decline and loss of independence. With potential disease-modifying therapies in development, there is interest in accurately measuring HD progression and characterising prognostic variables to improve efficiency of clinical trials. Using the large, prospective Enroll-HD cohort, we investigated the relative contribution and ranking of potential prognostic variables in patients with manifest HD. A random forest regression model was trained to predict change of clinical outcomes based on the variables, which were ranked based on their contribution to the prediction. The highest-ranked variables included novel predictors of progression—being accompanied at clinical visit, cognitive impairment, age at diagnosis and tetrabenazine or antipsychotics use—in addition to established predictors, cytosine adenine guanine (CAG) repeat length and CAG-age product. The novel prognostic variables improved the ability of the model to predict clinical outcomes and may be candidates for statistical control in HD clinical studies

Old-School Chemotherapy in Immunotherapeutic Combination in Cancer, A Low-cost Drug Repurposed

©2016 American Association for Cancer Research.Peer reviewedPostprin

Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks

Machine-learning architectures, such as Convolutional Neural Networks (CNNs) are vulnerable to adversarial attacks: inputs crafted carefully to force the system output to a wrong label. Since machine-learning is being deployed in safety-critical and security-sensitive domains, such attacks may have catastrophic security and safety consequences. In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine-learning classifiers. We show that successful adversarial attacks against the exact classifier have poor transferability to the approximate implementation. Surprisingly, the robustness advantages also apply to white-box attacks where the attacker has unrestricted access to the approximate classifier implementation: in this case, we show that substantially higher levels of adversarial noise are needed to produce adversarial examples. Furthermore, our approximate computing model maintains the same level in terms of classification accuracy, does not require retraining, and reduces resource utilization and energy consumption of the CNN. We conducted extensive experiments on a set of strong adversarial attacks; We empirically show that the proposed implementation increases the robustness of a LeNet-5, Alexnet and VGG-11 CNNs considerably with up to 50% by-product saving in energy consumption due to the simpler nature of the approximate logic.Comment: arXiv admin note: substantial text overlap with arXiv:2006.0770

Robust thalamic nuclei segmentation method based on local diffusion magnetic resonance properties.

The thalamus is an essential relay station in the cortical-subcortical connections. It is characterized by a complex anatomical architecture composed of numerous small nuclei, which mediate the involvement of the thalamus in a wide range of neurological functions. We present a novel framework for segmenting the thalamic nuclei, which explores the orientation distribution functions (ODFs) from diffusion magnetic resonance images at 3 T. The differentiation of the complex intra-thalamic microstructure is improved by using the spherical harmonic (SH) representation of the ODFs, which provides full angular characterization of the diffusion process in each voxel. The clustering was performed using the k-means algorithm initialized in a data-driven manner. The method was tested on 35 healthy volunteers and our results show a robust, reproducible and accurate segmentation of the thalamus in seven nuclei groups. Six of them closely matched the anatomy and were labeled as anterior, ventral anterior, medio-dorsal, ventral latero-ventral, ventral latero-dorsal and pulvinar, while the seventh cluster included the centro-lateral and the latero-posterior nuclei. Results were evaluated both qualitatively, by comparing the segmented nuclei to the histological atlas of Morel, and quantitatively, by measuring the clusters' extent and the clusters' spatial distribution across subjects and hemispheres. We also showed the robustness of our approach across different sequences and scanners, as well as intra-subject reproducibility of the segmented clusters using additional two scan-rescan datasets. We also observed an overlap between the path of the main long-connection tracts passing through the thalamus and the spatial distribution of the nuclei identified with our clustering algorithm. Our approach, based on SH representations of the ODFs, outperforms the one based on angular differences between the principle diffusion directions, which is considered so far as state-of-the-art method. Our findings show an anatomically reliable segmentation of the main groups of thalamic nuclei that could be of potential use in many clinical applications