Non-orthogonal multiple access (NOMA) based wireless caching network (WCN) is considered as a promising technology for next-generation wireless communications since it can significantly improve the spectral efficiency. In this letter, we propose a quality of service (QoS)-oriented dynamic power allocation strategy for NOMA-WCN. In content placement phase, base station (BS) sends multiple files to helpers by allocating different powers according to the different QoS targets of files, for ensuring that all helpers can successfully decode the two most popular files. In content delivery phase, helpers serve two users at the same time by allocating the minimum power to far user according to the QoS requirement, and then all the remaining power is allocated to near user. Hence, our proposed method is able to increase the hit probability and drop the outage probability compared with conventional methods. Simulation results confirm that the proposed power allocation method can significantly improve the caching hit probability and reduce the user outage probability.

In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this article, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this article is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.

Background: The use of trolleys for transporting the patients and lifting and lowering them in the trolley is a repeated activity in the daily work of a nurse, and a very common cause of the load of the lumbosacral part of the spine and the consequent pathological deformity, and the onset of clinical symptomatology of painful lumbo-sacral syndrome. The high level of excessive biomechanical stress is associated with the established practice of using standard medical wheelchairs to move patients inside the hospital. The process itself depends on the characteristics of the patient, his or her weight, as well as his/her cooperativeness, but primarily depends on the nurse's mobility. Although nurses strive to be in a position that reduces the load on the lumbosacral part of the spine during practice, this is often impracticable due to the patient's inconsistency. Objective: To present the ergonomic analysis of the medical nurse's workplace while lifting the patient into the wheelchair and to display solution for improving working conditions and prevention of musculoskeletal disorders. Results:  By ergonomic module of this software, we got results that present load on lumbosacral region of spine of   medical nurses during their daily activities, especially in the position of lifting and lowering patients. It was concluded that maximal spinal loading decreases significantly and becomes less than critical (3,100 N) in the case of a wheelchair that has ability to automatically lift and lower patient. Conclusions:  The use of hospital wheelchairs with an mechanism for the automatic lifting and lowering of patients and with a sliding seat will reduce the load on the lumbosacral region of the spine, prevent the onset of lumbosacral pain syndrome, facilitates work for the medical nurse and allows nurse to handle the patient on her own. The prevention of lumbosacral pain syndrome improves the quality of work of the nurse and extends the working life. Use of this type of wheelchair is justified in terms of cost-benefit analysis.

This article reports on the development of tools to measure rights-based social work practices in Bosnia and Herzegovina. Previously validated scales - Human Rights Exposure in Social Work (1 scale), Human Rights Lens in Social Work (2 scales), and Human Rights Methods in Social Work (8 scales) - were translated and adapted for the Bosnian context. To complete the validation, social work students (N=296) were surveyed, and the resulting data were analyzed using exploratory and confirmatory factor analysis. The results yield 11 valid scales for measuring human rights constructs and promoting human rights goals in social work education, research, and practice in Bosnia and Herzegovina.

Every organization needs to invest in order for it to grow, and investments are made through projects. Thus, investment management is performed by applying project management techniques. Different project management software programs are used to manage multiple projects. There is a lot of project management software on the market, and four pieces of the software were selected and analyzed. In this paper, the best management software rated by the beneficiaries of these projects in the United Arab Emirates are explored. The research required for this study was conducted in the United Arab Emirates. The MARCOS method was used to evaluate the program. The results showed that Smartsheet had been rated the best by users. This paper provides an overview of how multicriteria analysis methods can be used when ranking project management programs.

Early characterization of security requirements supports system designers to integrate security aspects into early architectural design. However, distinguishing security related requirements from other functional and non-functional requirements can be tedious and error prone. To address this issue, machine learning techniques have proven to be successful in the identification of security requirements. In this paper, we have conducted an empirical study to evaluate the performance of 22 supervised machine learning classification algorithms and two deep learning approaches, in classifying security requirements, using the publicly availble SecReq dataset. More specifically, we focused on the robustness of these techniques with respect to the overhead of the pre-processing step. Results show that Long short-term memory (LSTM) network achieved the best accuracy (84%) among non-supervised algorithms, while Boosted Ensemble achieved the highest accuracy (80%), among supervised algorithms.

The ever‐increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low‐power, high‐speed, and noise‐tolerant computing capabilities of the brain, may provide such a shift. Many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop neuromorphic computing platforms. These platforms are being designed using various hardware technologies, including the well‐established complementary metal‐oxide semiconductor (CMOS), and emerging memristive technologies such as SiOx‐based memristors. Herein, recent progress in CMOS, SiOx‐based memristive, and mixed CMOS‐memristive hardware for neuromorphic systems is highlighted. New and published results from various devices are provided that are developed to replicate selected functions of neurons, synapses, and simple spiking networks. It is shown that the CMOS and memristive devices are assembled in different neuromorphic learning platforms to perform simple cognitive tasks such as classification of spike rate‐based patterns or handwritten digits. Herein, it is envisioned that what is demonstrated is useful to the unconventional computing research community by providing insights into advances in neuromorphic hardware technologies.