Sleep deprivation, a consequence of multiple health problems or a cause of many major health risks, is a significant public health concern in this era. In the recent years, numerous reports have been added to the literature to provide explanation and to answer previously unanswered questions on this important topic but comprehensive updates and reviews in this aspect remain scarce. The present study identified 135 papers that investigated the association between sleep deprivation and health risks, including cardiovascular, respiratory, neurological, gastrointestinal, immunology, dermatology, endocrine, and reproductive health. In this review, we aimed to provide insight into the association between sleep deprivation and the development of diseases. We reviewed the latest updates available in the literature and particular attention was paid to reports that detailed all possible causal relationships involving both extrinsic and intrinsic factors that may be relevant to this topic. Various mechanisms by which sleep deprivation may affect health were presented and discussed, and this review hopes to serve as a platform for ideas generation for future research.
Research on joint torque reduction in robot manipulators has received considerable attention in recent years. Minimizing the computational complexity of torque optimization and the ability to calculate the magnitude of the joint torque accurately will result in a safe operation without overloading the joint actuators. This paper presents a mechanical design for a three dimensional planar redundant manipulator with the advantage of the reduction in the number of motors needed to control the joint angle, leading to a decrease in the weight of the manipulator. Many efforts have been focused on decreasing the weight of manipulators, such as using lightweight joints design or setting the actuators at the base of the manipulator and using tendons for the transmission of power to these joints. By using the design of this paper, only three motors are needed to control any n degrees of freedom in a three dimensional planar redundant manipulator instead of n motors. Therefore this design is very effective to decrease the weight of the manipulator as well as the number of motors needed to control the manipulator. In this paper, the torque of all the joints are calculated for the proposed manipulator (with three motors) and the conventional three dimensional planar manipulator (with one motor for each degree of freedom) to show the effectiveness of the proposed manipulator for decreasing the weight of the manipulator and minimizing driving joint torques.
Recently one dimensional (1-D) nanostructured metal-oxides have attracted much attention because of their potential applications in gas sensors. 1-D nanostructured metal-oxides provide high surface to volume ratio, while maintaining good chemical and thermal stabilities with minimal power consumption and low weight. In recent years, various processing routes have been developed for the synthesis of 1-D nanostructured metal-oxides such as hydrothermal, ultrasonic irradiation, electrospinning, anodization, sol-gel, molten-salt, carbothermal reduction, solid-state chemical reaction, thermal evaporation, vapor-phase transport, aerosol, RF sputtering, molecular beam epitaxy, chemical vapor deposition, gas-phase assisted nanocarving, UV lithography and dry plasma etching. A variety of sensor fabrication processing routes have also been developed. Depending on the materials, morphology and fabrication process the performance of the sensor towards a specific gas shows a varying degree of success. This article reviews and evaluates the performance of 1-D nanostructured metal-oxide gas sensors based on ZnO, SnO(2), TiO(2), In(2)O(3), WO(x), AgVO(3), CdO, MoO(3), CuO, TeO(2) and Fe(2)O(3). Advantages and disadvantages of each sensor are summarized, along with the associated sensing mechanism. Finally, the article concludes with some future directions of research.
Attention has shifted to the development of villages in Europe and other parts of the world with the goal of combating rural-urban migration, and moving toward self-sufficiency in rural areas. This situation has birthed the smart village idea. Smart village initiatives such as those of the European Union is motivating global efforts aimed at improving the live and livelihood of rural dwellers. These initiatives are focused on improving agricultural productivity, among other things, since most of the food we eat are grown in rural areas around the world. Nevertheless, a major challenge faced by proponents of the smart village concept is how to provide a framework for the development of the term, so that this development is tailored towards sustainability. The current work examines the level of progress of climate smart agriculture, and tries to borrow from its ideals, to develop a framework for smart village development. Given the advances in technology, agricultural development that encompasses reduction of farming losses, optimization of agricultural processes for increased yield, as well as prevention, monitoring, and early detection of plant and animal diseases, has now embraced varieties of smart sensor technologies. The implication is that the studies and results generated around the concept of climate smart agriculture can be adopted in planning of villages, and transforming them into smart villages. Hence, we argue that for effective development of the smart village framework, smart agricultural techniques must be prioritized, viz-a-viz other developmental practicalities.
The domain of underwater wireless sensor networks (UWSNs) had received a lot of attention recently due to its significant advanced capabilities in the ocean surveillance, marine monitoring and application deployment for detecting underwater targets. However, the literature have not compiled the state-of-the-art along its direction to discover the recent advancements which were fuelled by the underwater sensor technologies. Hence, this paper offers the newest analysis on the available evidences by reviewing studies in the past five years on various aspects that support network activities and applications in UWSN environments. This work was motivated by the need for robust and flexible solutions that can satisfy the requirements for the rapid development of the underwater wireless sensor networks. This paper identifies the key requirements for achieving essential services as well as common platforms for UWSN. It also contributes a taxonomy of the critical elements in UWSNs by devising a classification on architectural elements, communications, routing protocol and standards, security, and applications of UWSNs. Finally, the major challenges that remain open are presented as a guide for future research directions.
This study aims to investigate the effects of workplace noise on neural activity and alpha asymmetries of the prefrontal cortex (PFC) during mental stress conditions. Workplace noise exposure is a pervasive environmental pollutant and is negatively linked to cognitive effects and selective attention. Generally, the stress theory is assumed to underlie the impact of noise on health. Evidence for the impacts of workplace noise on mental stress is lacking. Fifteen healthy volunteer subjects performed the Montreal imaging stress task in quiet and noisy workplaces while their brain activity was recorded using electroencephalography. The salivary alpha-amylase (sAA) was measured before and immediately after each tested workplace to evaluate the stress level. The results showed a decrease in alpha rhythms, or an increase in cortical activity, of the PFC for all participants at the noisy workplace. Further analysis of alpha asymmetry revealed a greater significant relative right frontal activation of the noisy workplace group at electrode pairs F4-F3 but not F8-F7. Furthermore, a significant increase in sAA activity was observed in all participants at the noisy workplace, demonstrating the presence of stress. The findings provide critical information on the effects of workplace noise-related stress that might be neglected during mental stress evaluations.
In high-voltage (HV) insulation, electrical trees are an important degradation phenomenon strongly linked to partial discharge (PD) activity. Their initiation and development have attracted the attention of the research community and better understanding and characterization of the phenomenon are needed. They are very damaging and develop through the insulation material forming a discharge conduction path. Therefore, it is important to adequately measure and characterize tree growth before it can lead to complete failure of the system. In this paper, the Gaussian mixture model (GMM) has been applied to cluster and classify the different growth stages of electrical trees in epoxy resin insulation. First, tree growth experiments were conducted, and PD data captured from the initial to breakdown stage of the tree growth in epoxy resin insulation. Second, the GMM was applied to categorize the different electrical tree stages into clusters. The results show that PD dynamics vary with different stress voltages and tree growth stages. The electrical tree patterns with shorter breakdown times had identical clusters throughout the degradation stages. The breakdown time can be a key factor in determining the degradation levels of PD patterns emanating from trees in epoxy resin. This is important in order to determine the severity of electrical treeing degradation, and, therefore, to perform efficient asset management. The novelty of the work presented in this paper is that for the first time the GMM has been applied for electrical tree growth classification and the optimal values for the hyperparameters, i.e., the number of clusters and the appropriate covariance structure, have been determined for the different electrical tree clusters.
Although wireless sensor networks (WSNs) have been the object of research focus for the past two decades, fault diagnosis in these networks has received little attention. This is an essential requirement for wireless networks, especially in WSNs, because of their ad-hoc nature, deployment requirements and resource limitations. Therefore, in this paper we survey fault diagnosis from the perspective of network operations. To the best of our knowledge, this is the first survey from such a perspective. We survey the proactive, active and passive fault diagnosis schemes that have appeared in the literature to date, accenting their advantages and limitations of each scheme. In addition to illuminating the details of past efforts, this survey also reveals new research challenges and strengthens our understanding of the field of fault diagnosis.
Recent research in underwater wireless sensor networks (UWSNs) has gained the attention of researchers in academia and industry for a number of applications. They include disaster and earthquake prediction, water quality and environment monitoring, leakage and mine detection, military surveillance and underwater navigation. However, the aquatic medium is associated with a number of limitations and challenges: long multipath delay, high interference and noise, harsh environment, low bandwidth and limited battery life of the sensor nodes. These challenges demand research techniques and strategies to be overcome in an efficient and effective fashion. The design of routing protocols for UWSNs is one of the promising solutions to cope with these challenges. This paper presents a survey of the routing protocols for UWSNs. For the ease of description, the addressed routing protocols are classified into two groups: localization-based and localization-free protocols. These groups are further subdivided according to the problems they address or the major parameters they consider during routing. Unlike the existing surveys, this survey considers only the latest and state-of-the-art routing protocols. In addition, every protocol is described in terms of its routing strategy and the problem it addresses and solves. The merit(s) of each protocol is (are) highlighted along with the cost. A description of the protocols in this fashion has a number of advantages for researchers, as compared to the existing surveys. Firstly, the description of the routing strategy of each protocol makes its routing operation easily understandable. Secondly, the demerit(s) of a protocol provides (provide) insight into overcoming its flaw(s) in future investigation. This, in turn, leads to the foundation of new protocols that are more intelligent, robust and efficient with respect to the desired parameters. Thirdly, a protocol can be selected for the appropriate application based on its described merit(s). Finally, open challenges and research directions are presented for future investigation.
Semantic segmentation of street view images is an important step in scene understanding for autonomous vehicle systems. Recent works have made significant progress in pixel-level labeling using Fully Convolutional Network (FCN) framework and local multi-scale context information. Rich global context information is also essential in the segmentation process. However, a systematic way to utilize both global and local contextual information in a single network has not been fully investigated. In this paper, we propose a global-and-local network architecture (GLNet) which incorporates global spatial information and dense local multi-scale context information to model the relationship between objects in a scene, thus reducing segmentation errors. A channel attention module is designed to further refine the segmentation results using low-level features from the feature map. Experimental results demonstrate that our proposed GLNet achieves 80.8% test accuracy on the Cityscapes test dataset, comparing favorably with existing state-of-the-art methods.
Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers' future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2-13.6% and 10.2-12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9-12.7% and 6.9-8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account-namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.
The security warning is a representation of communication that is used to warn and to inform a person whether security menaces have been discovered in order to prevent any consequences of damage from taking place. The purpose of a security warning is to provide a legitimate alert (to notify and to warn) to the users so that a secure manner of action is safely conducted. It is worth noting that the majority of computer users prefer to dismiss security warnings due to lack of attention, the use of technical words, and the deficiency of information provided. This paper determines to achieve two outcomes: firstly, a thorough review of problems, challenges, and approaches to improving security warnings. Our work complements the previous classifications in the identification of problems and challenges in security warnings by value-adding a new classification, namely immersion in the primary task. Then, we add other related works within the known classifications accordingly. In addition, our work also presents the classifications of approaches to improving security warnings. Secondly, we propose two timelines by addressing the problems, challenges, and approaches to improving security warnings. It is expected that the outcomes of this research will be useful to researchers within the niche area for analysing trends and providing the groundwork in security warning studies, respectively.
Exploring and monitoring the underwater world using underwater sensors is drawing a lot of attention these days. In this field cooperation between acoustic sensor nodes has been a critical problem due to the challenging features such as acoustic channel failure (sound signal), long propagation delay of acoustic signal, limited bandwidth and loss of connectivity. There are several proposed methods to improve cooperation between the nodes by incorporating information/game theory in the node's cooperation. However, there is a need to classify the existing works and demonstrate their performance in addressing the cooperation issue. In this paper, we have conducted a review to investigate various factors affecting cooperation in underwater acoustic sensor networks. We study various cooperation techniques used for underwater acoustic sensor networks from different perspectives, with a concentration on communication reliability, energy consumption, and security and present a taxonomy for underwater cooperation. Moreover, we further review how the game theory can be applied to make the nodes cooperate with each other. We further analyze different cooperative game methods, where their performance on different metrics is compared. Finally, open issues and future research direction in underwater acoustic sensor networks are highlighted.
Gel polymer electrolytes using imidazolium based ionic liquids have attracted much attention in dye-sensitized solar cell applications. Hydroxypropyl cellulose (HPC), sodium iodide (NaI), 1-methyl-3-propylimidazolium iodide (MPII) as ionic liquid (IL), ethylene carbonate (EC) and propylene carbonate (PC) are used for preparation of non-volatile gel polymer electrolyte (GPE) system (HPC:EC:PC:NaI:MPII) for dye-sensitized solar cell (DSSC) applications. The highest ionic conductivity of 7.37 × 10(-3) S cm(-1) is achieved after introducing 100% of MPII with respect to the weight of HPC. Temperature-dependent ionic conductivity of gel polymer electrolytes is studied in this work. XRD patterns of gel polymer electrolytes are studied to confirm complexation between HPC polymer, NaI and MPII. Thermal behavior of the GPEs is studied using simultaneous thermal analyzer (STA) and differential scanning calorimetry (DSC). DSSCs are fabricated using gel polymer electrolytes and J-V centeracteristics of fabricated dye sensitized solar cells were analyzed. The gel polymer electrolyte with 100 wt.% of MPII ionic liquid shows the best performance and energy conversion efficiency of 5.79%, with short-circuit current density, open-circuit voltage and fill factor of 13.73 mA cm(-2), 610 mV and 69.1%, respectively.
Observations and modeling studies have shown that during CO2 injection into underground carbonate reservoirs, the dissolution of CO2 into formation water forms acidic brine, leading to fluid-rock interactions that can significantly impact the hydraulic properties of the host formation. However, the impacts of these interactions on the pore structure and macroscopic flow properties of host rock are poorly characterized both for the near-wellbore region and deeper into the reservoir. Little attention has been given to the influence of pressure drop from the near-wellbore region to reservoir body on disturbing the ionic equilibrium in the CO2-saturated brine and consequent mineral precipitation. In this paper, we present the results of a novel experimental procedure designed to address these issues in carbonate reservoirs. We injected CO2-saturated brine into a composite core made of two matching grainstone carbonate core plugs with a tight disk placed between them to create a pressure profile of around 250 psi resembling that prevailing in reservoirs during CO2 injection. We investigated the impacts of fluid-rock interactions at pore and continuum scale using medical X-ray CT, nuclear magnetic resonance, and scanning electron microscopy. We found that strong calcite dissolution occurs near to the injection point, which leads to an increase in primary intergranular porosity and permeability of the near injection region, and ultimately to wormhole formation. The strong heterogeneous dissolution of calcite grains leads to the formation of intra-granular micro-pores. At later stages of the dissolution, the internal regions of ooids become accessible to the carbonated brine, leading to the formation of moldic porosity. At distances far from the injection point, we observed minimal or no change in pore structure, pore roughness, pore populations, and rock hydraulic properties. The pressure drop of 250 psi slightly disturbed the chemical equilibrium of the system, which led to minor precipitation of sub-micron sized calcite crystals but due to the large pore throats of the rock, these deposits had no measurable impact on rock permeability. The trial illustrates that the new procedure is valuable for investigating fluid-rock interactions by reproducing the geochemical consequences of relatively steep pore pressure gradients during CO2 injection.
Carbonyl iron particles (CIPs) is one of the key components in magnetic rubber, known as magnetorheological elastomer (MRE). Apart from the influence of their sizes and concentrations, the role of the particle' shape is pronounced worthy of the attention for the MRE performance. However, the usage of CIPs in MRE during long-term applications may lead to corrosion effects on the embedded CIPs, which significantly affects the performance of devices or systems utilizing MRE. Hence, the distinctions between the two types of MRE embedded in different shapes of spherical and plate-like CIPs, at both conditions of non-corroded and corroded CIPs were investigated in terms of the field-dependent rheological properties of MRE. The plate-like shape was produced from spherical CIPs through a milling process using a rotary ball mill. Then, both shapes of CIPs individually subjected to an accelerated corrosion test in diluted hydrochloric (HCl) at different concentrations, particularly at 0.5, 1.0, and 1.5 vol.% for 30 min of immersion time. Eight samples of CIPs, including non-corroded for both CIPs shapes, were characterized in terms of a morphological study by field emission scanning electron microscope (FESEM) and magnetic properties via vibrating sample magnetometer (VSM). The field-dependent rheological properties of MREs were analyzed the change in the dynamic modulus behavior of MREs via rheometer. From the application perspective, this finding may be useful for the system to be considered that provide an idea to prolong the performance MRE by utilizing the different shapes of CIPs even when the material is fading.
The widespread use of magnetorheological elastomer (MRE) materials in various applications has yet to be limited due to the fact that there are substantial deficiencies in current experimental and theoretical research on its microstructural durability behavior. In this study, MRE composed of silicon rubber (SR) and 70 wt% of micron-sized carbonyl iron particles (CIP) was prepared and subjected to stress relaxation evaluation by torsional shear load. The microstructure and particle distribution of the obtained MRE was evaluated by a field emission scanning electron microscopy (FESEM). The influence of constant low strain at 0.01% is the continuing concern within the linear viscoelastic (LVE) region of MRE. Stress relaxation plays a significant role in the life cycle of MRE and revealed that storage modulus was reduced by 8.7%, normal force has weakened by 27%, and stress performance was reduced by 6.88% along approximately 84,000 s test duration time. This time scale was the longest ever reported being undertaken in the MRE stress relaxation study. Novel micro-mechanisms that responsible for the depleted performance of MRE was obtained by microstructurally observation using FESEM and in-phase mode of atomic force microscope (AFM). Attempts have been made to correlate strain localization produced by stress relaxation, with molecular deformation in MRE amorphous matrix. Exceptional attention was focused on the development of molecular slippage, disentanglement, microplasticity, microphase separation, and shear bands. The relation between these microstructural phenomena and the viscoelastic properties of MRE was diffusely defined and discussed. The presented MRE is homogeneous with uniform distribution of CIP. The most significant recent developments of systematic correlation between the effects of microstructural deformation and durability performance of MRE under stress relaxation has been observed and evaluated.
Multifocal pupillographic objective perimetry (mfPOP) has recently been shown to be able to measure cortical function. Here we assessed 44 regions of the central 60 degrees of the visual fields of each eye concurrently in 7 minutes/test. We examined how foveally- and peripherally-directed attention changed response sensitivity and delay across the 44 visual field locations/eye. Four experiments were completed comparing white, yellow and blue stimulus arrays. Experiments 1 to 4 tested 16, 23, 9 and 6 subjects, 49/54 being unique. Experiment 1, Experiments 2 and 3, and Experiment 4 used three variants of the mfPOP method that provided increasingly improved signal quality. Experiments 1 to 3 examined centrally directed attention, and Experiment 4 compared effects of attention directed to different peripheral targets. Attention reduced the sensitivity of the peripheral locations in Experiment 1, but only for the white stimuli not yellow. Experiment 2 confirmed that result. Experiment 3 showed that blue stimuli behaved like white. Peripheral attention showed increased sensitivity around the attentional targets. The results are discussed in terms of the cortical inputs to the pupillary system. The results agree with those from multifocal and other fMRI and VEP studies. mfPOP may be a useful adjunct to those methods.
The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the genetic regulatory processes would bring significant implications in the biomedical fields and many other pharmaceutical industries. As a result, various mathematical and computational methods have been used to model gene regulatory network from microarray data. Amongst those methods, the Bayesian network model attracts the most attention and has become the prominent technique since it can capture nonlinear and stochastic relationships between variables. However, structure learning of this model is NP-hard and computationally complex as the number of potential edges increase drastically with the number of genes. In addition, most of the studies only focused on the predicted results while neglecting the fact that microarray data is a fragmented information on the whole biological process. Hence this study proposed a network-based inference model that combined biological knowledge in order to verify the constructed gene regulatory relationships. The gene regulatory network is constructed using Bayesian network based on low-order conditional independence approach. This technique aims to identify from the data the dependencies to construct the network structure, while addressing the structure learning problem. In addition, three main toolkits such as Ensembl, TFSearch and TRANSFAC have been used to determine the false positive edges and verify reliability of regulatory relationships. The experimental results show that by integrating biological knowledge it could enhance the precision results and reduce the number of false positive edges in the trained gene regulatory network.
The recent interest to nanotechnology aims not only at device miniaturisation, but also at understanding the effects of quantised structure in materials of reduced dimensions, which exhibit different properties from their bulk counterparts. In particular, quantised metal nanowires made of silver, gold or copper have attracted much attention owing to their unique intrinsic and extrinsic length-dependent mechanical properties. Here we review the current state of art and developments in these nanowires from synthesis to mechanical properties, which make them leading contenders for next-generation nanoelectromechanical systems. We also present theories of interatomic interaction in metallic nanowires, as well as challenges in their synthesis and simulation.