Two-tier relaxed heaps

We introduce a data structure which provides efficient heap operations with respect to the number of element comparisons performed. Let n denote the size of the heap being manipulated. Our data structure guarantees the worst-case cost of O(1) for finding the minimum, inserting an element, extracting an (unspecified) element, and replacing an element with a smaller element; and the worst-case cost of O(lg n) with at most lg n + 3 lg lg n + O(1) element comparisons for deleting an element. We thereby improve the comparison complexity of heap operations known for run-relaxed heaps and other worst-case efficient heaps. Furthermore, our data structure supports melding of two heaps of size m and n at the worst-case cost of O(min {lg m, lg n}). A preliminary version of this paper [8] was presented at the 17th International Symposium on Algorithms and Computation held in Kolkata in December 2006. Partially supported by the Danish Natural Science Research Council under contracts 21-02-0501 (project Practical data structures and algorithms) and 272-05-0272 (project Generic programming—algorithms and tools).     

Application of headspace-solid-phase microextraction and HPLC for the analysis of the aroma volatile components of treacle and determination of its content of 5-hydroxymethylfurfural (HMF)

In this investigation, the headspace volatiles of treacle samples from three different producers were subjected to GC-MS and HPLC analysis in order to assess the variation in their aroma volatile composition and their content of 5-hydroxymethylfurfural (HMF). The volatile constituents were rich in aliphatic short chain acids, specially acetic acid, alcohols, aldehydes, ketones, and furan derivatives. The HMF content varied according to the producer from 66 mg/kg to 179 mg/kg. The present investigation is considered to be the first, which determine the volatile aroma profile and HMF content for treacle.     

钢筋混凝土超高层建筑地震脆性分析的框架模型

提出了钢筋混凝土超高层建筑的地震脆性评估分析的框架模型和应用案例。对于这类结构，由于没有任何随机的脆性关系存在，本文工作填补了区域地震影响评估的空白。所提出框架模型的关键是发展了典型的复杂超高层建筑系统的简单集中参数模型方法。在ZEUS-NL环境中所构建的这个模型能够有效进行高层结构的动力响应序列分析，     

On the coexistence of a primary user with an energy harvesting secondary user: a case of cognitive cooperation

In this paper, we consider a cognitive scenario where an energy harvesting secondary user shares the spectrum with a primary user. The secondary source helps the primary source in delivering its undelivered packets during periods of silence of the primary source. The primary source has a queue for storing its data packets, whereas the secondary source has two data queues: a queue for storing its own packets and the other for storing the fraction of the undelivered primary packets accepted for relaying. The secondary source is assumed to be a battery‐based node, which harvests energy packets from the environment. In addition to its data queues, the secondary user has an energy queue to store the harvested energy packets. The secondary energy packets are used for primary packets decoding and data packets transmission. More specifically, if the secondary energy queue is empty, the secondary source can neither help the primary source nor transmit a packet from the data queues. The energy queue is modeled as a discrete‐time queue with Markov arrival and service processes. Because of the interaction of the queues, we provide inner and outer bounds on the stability region of the proposed system. We investigate the impact of the energy arrival rate on the stability region. Numerical results show the significant gain of cooperation.Copyright © 2014 John Wiley & Sons, Ltd.     

Comments on the security of Chen’s authenticated encryption scheme

Chen (Computers and Electrical Engineering, vol. 30, 2004) illustrated that Tseng et al.’s authenticated encryption schemes, with message linkages for message flows, do not achieve their claimed integrity and authenticity properties. Furthermore, Chen presented some modified schemes to repair these flaws. In this paper, we show that the modified schemes proposed by Chen are not secure. In particular, we present an attack that allows a dishonest referee, in case of a dispute, to decrypt all the future and past authenticated ciphertext between the contended parties. We also present a simple fix to prevent this attack.     

Elastic stability analysis of loosely fitted thin liners – A proposed simplified procedure and evaluation of existing solutions

The paper reports the results of an extensive numerical simulation to estimate the elastic buckling pressure and the corresponding thrust and bending moment induced in loosely fitted thin liners. The study is conducted numerically using a two-dimensional (2D) non-linear finite element model that accounts for the effects of large deformations on the stability of loosely fitted liners. The finite element results together with a non-linear multi-variant regression analysis are used to develop simplified non-dimensional formulae that provide the critical pressure, thrust and bending moments, at buckling, based on the liner’s geometry and material properties.In addition, several other analytical and numerical solutions for the same problem are revisited and critically reviewed. For comparison purposes, two of such methods are extended to allow for the calculation of the thrust and bending moment developed at the most heavily stressed point in the loosely fitted liners at the onset of buckling. In general, the comparison reveals the appropriateness of the proposed regression models in predicting the critical pressure and the associated thrust and bending moment induced in imperfect loosely fitted liners. Developed formulae provide designers with a simple and reliable means for ensuring stability and safety of such special type of structures.     

Reconciliation of Owner and Contractor Views in Heavy Construction Projects

Delay and loss of productivity are the two main types of damage experienced by the contractor when the owner issues a change order. Courts have recognized critical path method schedule analysis as the preferred method of identifying and quantifying critical delays. As for the inefficiency damages, there is no direct way of measuring inefficiency due to its qualitative nature and the difficulty of linking the cause of the productivity loss to the damage. Most of the scholarly work published in this area was based on data supplied by the contractors; and that explains why there are discrepancies between what the contractor asks for and what the owner believes the contractor is entitled to. This study addresses the need for a statistical model to quantify the productivity loss from verifiable site data such as owner’s daily reports, change orders, drawings, and specifications, rather than rely solely on contractor surveys. A model is developed and validated to quantify the productivity loss in pipe work in roadway projects due to the change orders. The productivity loss study analyzed two sets of data that include: (1) variables that predict which of the two parties, the owner and the contractor, contributed to the productivity loss; and (2) variables that predict, from the legal viewpoint, productivity losses which only the owner is responsible for. The study showed the difference between what the contractor asked for and what he/she is actually entitled to. This model can be used by both the owner and the contractor to quantify the productivity loss due to change orders, and to offer an objective approach to reconcile their differences. This study concludes with an example to demonstrate the use of the model.     

Artificial neural network-driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

In recent years, smart healthcare, artificial intelligence (AI)-aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL-based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre-established datasets that fail to capture real-life applicability. Therefore, this study proposes an AI-enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare-based wearable devices (WD) for real-time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL-based architecture aggregates the optimizer weights of many clients using a fifth-generation (5G) communication channel. Then, the performance of the proposed FL-based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.     

Time constraints set by prime contractors for their sub-contractors

The concept of pacing is described in an articulate manner which will enable readers to discuss the views put forwards. The nine major activities are divided into two types and are discussed in detail together with the methods used by the prime contractors to allocate time windows and the underlying logic employed.