Search Results (25)

Ostrich meat is an interesting alternative to poultry or beef due to its nutritional value. The addition of three plant species (hot peppers, acerola, Schisandra chinesis) was suggested as a method to improve the quality, safety, and consumer acceptance of sausages prepared from ostrich meat. A series of microbiological and chemical analyses (including, inter alia, content of biogenic amines, heavy metals, and bioactive compounds) of the products as well as their sensory evaluation was performed to verify this claim. The microflora of all sausages was dominated by lactic acid bacteria. The biggest threat to consumers’ health could be connected to the presence of biogenic amines formed through the enzymatic activity of lactic acid bacteria. The sausages with plant additives had better antioxidative and anti-inflammatory properties and lower fat oxidation—these features were correlated with the presence of vitamin C. Sausages with plant additives had a higher acceptability in terms of taste and smell. Full article

This study constructs an agent-based model suitable for analyzing the propagation of economic shocks based on a macroeconomic agent-based model structure that covers major economic entities. Instead of setting an upstream and downstream structure of firms in the inter-firm networks, our model includes a mechanism that connects each firm through supplier–customer relationships and incorporates interactions between firms mutually buying and selling intermediate input materials. It is confirmed through the proposed model’s simulation analysis that, although a firm’s sales volume temporarily falls due to an economic shock of the type that causes a sharp decline in households’ final demand, the increase in assets held by households as they refrain from spending rather expands their capacity for consumption. As a result, after the economic shock ceases to exist, the firm’s sales volume tends to be even greater than that of the preceding periods of the shock. Furthermore, we found that when the sales volume of products in a final consumer goods sector falls during the shock, the falls in sales in the non-final consumer goods sectors are suppressed due to replacement demand, and the increase in sales volume for the non-final consumer goods sectors is moderated after the shock ceases to exist. Full article

While megaprojects can be defined as highly complex, time-consuming, and cost-intensive endeavors, for rail infrastructures they are even more problematic. As a starting point, for rail megaprojects, their scope may, at times, alter due to some risks and uncertainties. As many such projects exceed many years in development, their scope and formation will not be a linear trajectory. It is, therefore, the aim of this paper to evaluate the difficulties in determining the scope of rail megaprojects. This paper first introduces the theoretical framework via adaptive decision-making and policy setting when dealing with mega rail projects. Through sustainable development, carefully applied research is undertaken to highlight some of the key shortfalls of current practices when dealing with mega rail projects. This includes categorizing sustainability into four dimensions: social, economic, environmental, and engineering for rail infrastructure. To address the existing gap in the literature, including the appropriate alignment of policy planning and design, this paper will carefully review the complex science of rail megaprojects. This can be seen as a conceptual framework, which combines complex theory and practice to develop a theoretical perspective to initiate, plan, execute, and commission mega rail projects. Particularly with an international focus, this paper will review global development, targeting rail infrastructures. For rail megaprojects, strategically integrated objectives are traditionally key functions within the regional land transport network along with the national network and are necessary to (i) improve connectivity, both nationally and inter-regionally for people, communities, regions, and industry via effectively linking the existing broad-based transport network; (ii) enhance logistical systems and trade; (iii) provide a consistent framework for continuous sustainable development; and (iv) provide a consistent framework for long-term economic and social benefits. Full article

The internet’s future architecture, known as Named Data Networking (NDN), is a creative way to offer content-based services. NDN is more appropriate for content distribution because of its special characteristics, such as naming conventions for packets and methods for in-network caching. Mobility is one of the main study areas for this innovative internet architecture. The software-defined networking (SDN) method, which is employed to provide mobility management in NDN, is one of the feasible strategies. Decoupling the network control plane from the data plane creates an improved programmable platform and makes it possible for outside applications to specify how a network behaves. The SDN is a straightforward and scalable network due to its key characteristics, including programmability, flexibility, and decentralized control. To address the problem of consumer mobility, we proposed an efficient SDPCACM (software-defined proactive caching architecture for consumer mobility) in NDN that extends the SDN model to allow mobility control for the NDN architecture (NDNA), through which the MC (mobile consumer) receives the data proactively after handover while the MC is moving. When an MC is watching a real-time video in a state of mobility and changing their position from one attachment point to another, the controllers in the SDN preserve the network layout and topology as well as link metrics to transfer updated routes with the occurrence of the handoff or handover scenario, and through the proactive caching mechanism, the previous access router proactively sends the desired packets to the new connected routers. Furthermore, the intra-domain and inter-domain handover processing situations in the SDPCACM for NDNA are described here in detail. Moreover, we conduct a simulation of the proposed SDPCACM for NDN that offers an illustrative methodology and parameter configuration for virtual machines (VMs), OpenFlow switches, and an ODL controller. The simulation result demonstrates that the proposed scheme has significant improvements in terms of CPU usage, reduced delay time, jitter, throughput, and packet loss ratio. Full article

Leishmaniasis is a zoonotic disease transmitted in humans by the bite of Leishmania-infected phlebotomine sandflies. Each year approximately 58,500 cases of leishmaniasis are diagnosed across the globe, with a mortality rate of nearly seven percent. There are over 20 parasitic strains of Leishmania which are known to cause distinct types of leishmaniasis and pose an endemic threat to humans worldwide. Therefore, it is crucial to develop potential medications and vaccines to combat leishmaniasis. However, the task of developing therapeutic solutions is challenging due to Leishmania’s digenetic lifecycle. The challenge is further intensified by cases of resistance against the available drugs. Owing to these challenges, the conventional drug development regimen is further limited by target discovery and ligand suitability for the targets. On the other hand, as an added advantage, the emergence of omics-based tools, such as high-end proteomics, transcriptomics and genomics, has hastened the pace of target discovery and target-based drug development. It is now becoming apparent that multi-omics convergence and an inter-connected systems approach is less time-consuming and more cost-effective for any drug-development process. This comprehensive review is an attempt to summarize the current knowledge on the muti-omics approach in drug development against leishmaniasis. In particular, it elaborates the potential target identification from secreted proteins in various stages of Leishmania infection and also illustrates the convergence of transcriptomic and genomic data towards the collective goal of drug discovery. This review also provides an understanding of the potential parasite’s drug targets and drug resistance characteristics of the parasite, which can be used in designing effective and specific therapeutics. Full article

The goal of 5G wireless networks is to address the growing need for network services among users. User equipment has progressed to the point where users now expect diverse services from the network. The latency, reliability, and bandwidth requirements of users can all be classified. To fulfil the different needs of users in an economical manner, while guaranteeing network resources are resourcefully assigned to consumers, 5G systems plan to leverage technologies like Software Defined Networks, Network Function Virtualization, and Network Slicing. For the purpose of ensuring continuous handover among network slices, while catering to the advent of varied 5G application scenarios, new mobility management techniques must be adopted in Sliced 5G networks. Users want to travel from one region of coverage to another region without any fading in their network connection. Different network slices can coexist in 5G networks, with every slice offering services customized to various QoS demands. As a result, when customers travel from one region of coverage to another, the call can be transferred to a slice that caters to similar or slightly different requirements. The goal of this study was to develop an intra- and inter-slice algorithm for determining handover decisions in sliced 5G networks and to assess performance by comparing intra- and inter-slice handovers. The proposed work shows that an inter-slice handover algorithm offers superior quality of service when compared to an intra-slice algorithm. Full article

Kidney cancer has several types, with renal cell carcinoma (RCC) being the most prevalent and severe type, accounting for more than 85% of adult patients. The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis. However, the manual identification of RCC is time-consuming and prone to inter-subject variability. In this paper, we aim to distinguish between benign tissue and malignant RCC tumors and identify the tumor subtypes to support medical therapy management. We propose a novel multiscale weakly-supervised deep learning approach for RCC subtyping. Our system starts by applying the RGB-histogram specification stain normalization on the whole slide images to eliminate the effect of the color variations on the system performance. Then, we follow the multiple instance learning approach by dividing the input data into multiple overlapping patches to maintain the tissue connectivity. Finally, we train three multiscale convolutional neural networks (CNNs) and apply decision fusion to their predicted results to obtain the final classification decision. Our dataset comprises four classes of renal tissues: non-RCC renal parenchyma, non-RCC fat tissues, clear cell RCC (ccRCC), and clear cell papillary RCC (ccpRCC). The developed system demonstrates a high classification accuracy and sensitivity on the RCC biopsy samples at the slide level. Following a leave-one-subject-out cross-validation approach, the developed RCC subtype classification system achieves an overall classification accuracy of 93.0% ± 4.9%, a sensitivity of 91.3% ± 10.7%, and a high classification specificity of 95.6% ± 5.2%, in distinguishing ccRCC from ccpRCC or non-RCC tissues. Furthermore, our method outperformed the state-of-the-art Resnet-50 model. Full article

The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency. Full article

The reservoir characterization aims to provide the analysis and quantification of the injection-production relationship, which is the fundamental work for production management. The connectivity between injectors and producers is dominated by geological properties, especially permeability. However, the permeability parameters are very heterogenous in oil reservoirs, and expensive to collect by well logging. The commercial simulators enable to get accurate simulation but require sufficient geological properties and consume excessive computation resources. In contrast, the data-driven models (physical models and machine learning models) are developed on the observed dynamic data, such as the rate and pressure data of the injectors and producers, constructing the connectivity relationship and forecasting the productivity by a series of nonlinear mappings or the control of specific physical principles. While, due to the “black box” feature of machine learning approaches, and the constraints and assumptions of physical models, the data-driven methods often face the challenges of poor interpretability and generalizability and the limited application scopes. To solve these issues, integrating the physical principle of the waterflooding process (material balance equation) with an artificial neural network (ANN), a knowledge interaction neural network (KINN) is proposed. KINN consists of three transparent modules with explicit physical significance, and different modules are joined together via the material balance equation and work cooperatively to approximate the waterflooding process. In addition, a gate function is proposed to distinguish the dominant flowing channels from weak connecting ones by their sparsity, and thus the inter-well connectivity can be indicated directly by the model parameters. Combining the strong nonlinear mapping ability with the guidance of physical knowledge, the interpretability of KINN is fully enhanced, and the prediction accuracy on the well productivity is improved. The effectiveness of KINN is proved by comparing its performance with the canonical ANN, on the inter-well connectivity analysis and productivity forecast tasks of three synthetic reservoir experiments. Meanwhile, the robustness of KINN is revealed by the sensitivity analysis on measurement noises and wells shut-in cases. Full article

Automotive cyber-physical systems are in transition from the closed-systems to open-networking systems. As a result, in-vehicle networks such as the controller area network (CAN) have become essential to connect to inter-vehicle networks through the various rich interfaces. Newly exposed security concerns derived from this requirement may cause in-vehicle networks to pose threats to automotive security and driver’s safety. In this paper, to ensure a high level of security of the in-vehicle network for automotive CPS, we propose a novel lightweight and practical cyber defense platform, referred to as CANon (CAN with origin authentication and non-repudiation), to be enabled to detect cyber-attacks in real-time. CANon is designed based on the hierarchical approach of centralized-session management and distributed-origin authentication. In the former, a gateway node manages each initialization vector and session of origin-centric groups consisting of two more sending and receiving nodes. In the latter, the receiving nodes belonging to the given origin-centric group individually perform the symmetric key-based detection against cyber-attacks by verifying each message received from the sending node, namely origin authentication, in real-time. To improve the control security, CANon employs a one-time local key selected from a sequential hash chain (SHC) for authentication of an origin node in a distributed mode and exploits the iterative hash operations with randomness. Since the SHC can constantly generate and consume hash values regardless of their memory capacities, it is very effective for resource-limited nodes for in-vehicle networks. In addition, through implicit key synchronization within a given group, CANon addresses the challenges of a key exposure problem and a complex key distribution mechanism when performing symmetric key-based authentication. To achieve lightweight cyber-attack detection without imposing an additive load on CAN, CANon uses a keyed-message authentication code (KMAC) activated within a given group. The detection performance of CANon is evaluated under an actual node of Freescale S12XF and virtual nodes operating on the well-known CANoe tool. It is seen that the detection rate of CANon against brute-force and replay attacks reaches 100% when the length of KMAC is over 16 bits. It demonstrates that CANon ensures high security and is sufficient to operate in real-time even on low-performance ECUs. Moreover, CANon based on several software modules operates without an additive hardware security module at an upper layer of the CAN protocol and can be directly ported to CAN-FD (CAN with Flexible Data rate) so that it achieves the practical cyber defense platform. Full article

The Internet of Things (IoT) is changing the way consumers, businesses, and governments interact with the physical and cyber worlds. More often than not, IoT devices are designed for specific functional requirements or use cases without paying too much attention to security. Consequently, attackers usually compromise IoT devices with lax security to retrieve sensitive information such as encryption keys, user passwords, and sensitive URLs. Moreover, expanding IoT use cases and the exponential growth in connected smart devices significantly widen the attack surface. Despite efforts to deal with security problems, the security of IoT devices and the privacy of the data they collect and process are still areas of concern in research. Whenever vulnerabilities are discovered, device manufacturers are expected to release patches or new firmware to fix the vulnerabilities. There is a need to prioritize firmware attacks, because they enable the most high-impact threats that go beyond what is possible with traditional attacks. In IoT, delivering and deploying new firmware securely to affected devices remains a challenge. This study aims to develop a security model that employs Blockchain and the InterPlanentary File System (IPFS) to secure firmware transmission over a low data rate, constrained Long-Range Wide Area Network (LoRaWAN). The proposed security model ensures integrity, confidentiality, availability, and authentication and focuses on resource-constrained low-powered devices. To demonstrate the utility and applicability of the proposed model, a proof of concept was implemented and evaluated using low-powered devices. The experimental results show that the proposed model is feasible for constrained and low-powered LoRaWAN devices. Full article

Recently, smart home products have shown signs of rapid development and increasing awareness of smart home platforms. In order to make smart home enterprises enter the era of Smart Home 2.0, it is necessary to consider the elements related to smart home platforms. This study examines the relationship between consumers’ value perception and the platform ecosystem theory and how this relationship contributes to their perception of smart home products’ value. This study aims to reveal the influence of smart home platform elements on the value perception of consumers regarding consumers’ perception of the smart home products’ value. To achieve this goal, an online survey (n = 595) was implemented to collect data from Japanese respondents. The analytical results presented in this study indicated that consumers, who sense the value of modularization of smart home products and inter-consumer connectivity, can sense the value of smart home products. In addition, consumers who can perceive the value of a platform service can indirectly feel the value of smart home products through modularity and inter-consumer connectivity. The results presented in this study provide new insights into product development in Smart Home 2.0. Full article

Terrestrial features extraction, such as roads and buildings from aerial images using an automatic system, has many usages in an extensive range of fields, including disaster management, change detection, land cover assessment, and urban planning. This task is commonly tough because of complex scenes, such as urban scenes, where buildings and road objects are surrounded by shadows, vehicles, trees, etc., which appear in heterogeneous forms with lower inter-class and higher intra-class contrasts. Moreover, such extraction is time-consuming and expensive to perform by human specialists manually. Deep convolutional models have displayed considerable performance for feature segmentation from remote sensing data in the recent years. However, for the large and continuous area of obstructions, most of these techniques still cannot detect road and building well. Hence, this work’s principal goal is to introduce two novel deep convolutional models based on UNet family for multi-object segmentation, such as roads and buildings from aerial imagery. We focused on buildings and road networks because these objects constitute a huge part of the urban areas. The presented models are called multi-level context gating UNet (MCG-UNet) and bi-directional ConvLSTM UNet model (BCL-UNet). The proposed methods have the same advantages as the UNet model, the mechanism of densely connected convolutions, bi-directional ConvLSTM, and squeeze and excitation module to produce the segmentation maps with a high resolution and maintain the boundary information even under complicated backgrounds. Additionally, we implemented a basic efficient loss function called boundary-aware loss (BAL) that allowed a network to concentrate on hard semantic segmentation regions, such as overlapping areas, small objects, sophisticated objects, and boundaries of objects, and produce high-quality segmentation maps. The presented networks were tested on the Massachusetts building and road datasets. The MCG-UNet improved the average F1 accuracy by 1.85%, and 1.19% and 6.67% and 5.11% compared with UNet and BCL-UNet for road and building extraction, respectively. Additionally, the presented MCG-UNet and BCL-UNet networks were compared with other state-of-the-art deep learning-based networks, and the results proved the superiority of the networks in multi-object segmentation tasks. Full article

The Internet of Things is emerging as a vast, inter-connected space of devices and things surrounding people, many of which are increasingly capable of autonomous action, from automatically sending data to cloud servers for analysis, changing the behaviour of smart objects, to changing the physical environment. A wide range of ethical concerns has arisen in their usage and development in recent years. Such concerns are exacerbated by the increasing autonomy given to connected things. This paper reviews, via examples, the landscape of ethical issues, and some recent approaches to address these issues concerning connected things behaving autonomously as part of the Internet of Things. We consider ethical issues in relation to device operations and accompanying algorithms. Examples of concerns include unsecured consumer devices, data collection with health-related Internet of Things, hackable vehicles, behaviour of autonomous vehicles in dilemma situations, accountability with Internet of Things systems, algorithmic bias, uncontrolled cooperation among things, and automation affecting user choice and control. Current ideas towards addressing a range of ethical concerns are reviewed and compared, including programming ethical behaviour, white-box algorithms, black-box validation, algorithmic social contracts, enveloping IoT systems, and guidelines and code of ethics for IoT developers; a suggestion from the analysis is that a multi-pronged approach could be useful based on the context of operation and deployment. Full article

In railway traction, the definition of “electromagnetic field” is functionally connected to the concept of the reactive power consumed by the electric rolling stock, and characterized by the running and standing electromagnetic waves in the space of the inter-substation zones from the site of the AC traction system. Such a definition is established and theoretically justified by the theory of electromagnetic fields. This article uses the methodology of this theory, in particular, a method for power balance estimation in electromagnetic fields based on Maxwell’s equations, as well as methods for the analysis of running and standing electromagnetic waves based on the theory of reflection, propagation and transmission of plane harmonic waves. The research considers the regularities of standing electromagnetic waves in the space of inter-substation zones of electric traction systems, which occur due to the incomplete reflection of incident waves from the contact wire and metal parts of the roof surface and the frontal part of the body of the electric rolling stock. The flow of electricity to the roof surface and the frontal part of the body of an electric locomotive is considered. The possibility of using existing methods to reduce wave reflections and thereby to effectively compensate for reactive power in the space of inter-substation zones is discussed. Full article