The operation and planning of distribution grids require the joint processing of measurements from different grid locations. Since measurement devices in low- and medium-voltage grids lack precise clock synchronization, it is important for data management platforms of distribution system operators to be able to account for the impact of nonideal clocks on measurement data. This paper formally introduces a metric termed Additive Alignment Error to capture the impact of misaligned averaging intervals of electrical measurements. A trace-driven approach for retrieval of this metric would be computationally costly for measurement devices, and therefore, it requires an online estimation procedure in the data collection platform. To overcome the need of transmission of high-resolution measurement data, this paper proposes and assesses an extension of a Markov-modulated process to model electrical traces, from which a closed-form matrix analytic formula for the Additive Alignment Error is derived. A trace-driven assessment confirms the accuracy of the model-based approach. In addition, the paper describes practical settings where the model can be utilized in data management platforms with significant reductions in computational demands on measurement devices.This paper addresses ground target tracking (GTT) for airborne radar. Digital terrain elevation data (DTED) are widely used for GTT as prior information under the premise that ground targets are constrained on terrain. Existing works fuse DTED to a tracking filter in a way that adopts only the assumption that the position of the target is constrained on the terrain. However, by kinematics, it is natural that the velocity of the moving ground target is constrained as well. Furthermore, DTED provides neither continuous nor accurate measurement of terrain elevation. To overcome such limitations, we propose a novel soft terrain constraint and a constraint-aided particle filter. To resolve the difficulties in applying the DTED to the GTT, first, we reconstruct the ground-truth terrain elevation using a Gaussian process and treat DTED as a noisy observation of it. Then, terrain constraint is formulated as joint soft constraints of position and velocity. Finally, we derive a Soft Terrain Constrained Particle Filter (STC-PF) that propagates particles while approximately satisfying the terrain constraint in the prediction step. In the numerical simulations, STC-PF outperforms the Smoothly Constrained Kalman Filter (SCKF) in terms of tracking performance because SCKF can only incorporate hard constraints.Small and medium-sized enterprises represent the majority of enterprises globally and yet have some difficulties in understanding the impact that cybersecurity threats could have on their businesses and the damage they could do to their assets. This study aims to measure the effectiveness of security practices at small-sized enterprises in Saudi Arabia in the event of a cybersecurity attack. Our paper is among the first research papers to measure the effectiveness of cybersecurity practices and the threat posed by cybersecurity breaches among small enterprises in the event of cybersecurity attacks. A total of 282 respondents participated, all of them representing small-sized enterprises in Saudi Arabia. The study applies multiple regression tests to analyze the effectiveness of 12 cybersecurity practices in three aspects financial damage, loss of sensitive data, and restoration time, at small enterprises. The findings indicate that having an inspection team and a recovery plan may limit the financial damage caused by cybersecurity attacks on small enterprises. The results also show that cybersecurity awareness, knowledge of cybersecurity damage, and professionals' salaries were related to the loss of sensitive data. Furthermore, the results indicate that contact with cybersecurity authorities and having an inspection team have statistically significant effects on restoration time.Millions of individuals suffer from upper extremity paralysis caused by neurological disorders including stroke, traumatic brain injury, or spinal cord injury. Robotic hand exoskeletons can substitute the missing motor control and help restore the functions in daily operations. However, most of the hand exoskeletons are bulky, stationary, and cumbersome to use. We have modified a recent existing design (Tenoexo) to prototype a motorized, lightweight, fully wearable rehabilitative hand exoskeleton by combining rigid parts with a soft mechanism capable of producing various grasps needed for the execution of daily tasks. Mechanical evaluation of our exoskeleton showed that it can produce fingertip force up to 8 N and can cover 91.5° of range of motion in just 3 s. We further tested the performance of the developed robotic exoskeleton in two quadriplegics with chronic hand paralysis and observed immediate success on independent grasping of different daily objects. The results suggested that our exoskeleton is a viable option for hand function assistance, allowing patients to regain lost finger control for everyday activities.One of the major concerns in 5G IoT networks is that most of the sensor nodes are powered through limited lifetime, which seriously affects the performance of the networks. In this article, Compressive sensing (CS) technique is used to decrease transmission cost in 5G IoT networks. Sparse basis is one of the important steps in the CS. However, most of the existing sparse basis-based method such as DCT (Discrete cosine transform) and DFT (Discrete Fourier Transform) basis do not capture data structure characteristics in the networks. They also do not take into consideration multi-resolution representations. In addition, some of sparse basis-driven methods exploit either spatial or temporal features, resulting in performance degradation of CS-based strategies. To address these challenging problems, we propose a novel spatial-temporal correlation basis algorithm (SCBA). Subsequently, an optimal basis algorithm (OBA) is provided considering greedy scoring criteria. To evaluate the efficiency of OBA, orthogonal wavelet basis algorithm (OWBA) by employing NS (Numerical Sparsity) and GI (Gini Index) sparse metrics is also presented. In addition, we discuss the complexity of the above three algorithms, and prove that OBA has low numerical rank. After experimental evaluation, we found that OBA is capable of the sparsest representing original signal compared to spatial, DCT, haar-1, haar-2, and rbio5.5. Furthermore, OBA has the low recovery error and the highest efficiency.The number of technical solutions to remotely monitoring elderly citizens and detecting hazard situations has been increasing in the last few years. These solutions have dual purposes to provide a feeling of safety to the elderly and to inform their relatives about potential risky situations, such as falls, forgotten medication, and other unexpected deviations from daily routine. Most of these solutions are based on IoT (Internet of Things) and dedicated sensors that need to be installed at the elderly's houses, hampering mass adoption. This justifies the search for non-invasive technical alternatives with smooth integration that relying only on existent devices, without the need for any additional installations. Therefore, this paper presents the SecurHome TV ecosystem, a technical solution based on the elderly's interactions with their TV sets-one of the most used devices in their daily lives-acting as a non-invasive sensor enabling one to detect potential hazardous situations through an elaborated warning algorithm. Thus, this paper describes in detail the SecurHome TV ecosystem, with special emphasis on the warning algorithm, and reports on its validation process. We conclude that notwithstanding some constraints while setting the user's pattern, either upon the cold start of the application or after an innocuous change in the user's TV routine, the algorithm detects most hazardous situations contributing to monitor elderly well-being at home.Rapid urbanization has made urban space thermal environment (USTE) problems increasingly prominent. USTE research is important for improving urban ecological environment and building energy consumption. Most studies on USTE research progress have focused on meteorological observations and remote sensing methods, and few studies on USTE are based on computational fluid dynamics (CFD). During the past two decades, with the increasing applications of CFD in USTE research, comprehensively summarizing the phased results have become necessary tasks. This paper analyzes the current research status of CFD-based USTE simulation from six perspectives. First, we summarize the current research status of USTE simulation with CFD models that integrate ground observations and remote sensing technology. Second, we define and classify the spatial scope of CFD-based USTE simulations at different scales. Third, we systematically analyze the quantitative relationships among urban land type, the underlying surface structure, water bodies, green space and the corresponding changes in CFD-based USTE simulations. Fourth, we quantitatively analyze the impact of anthropogenic heat in CFD-based USTE simulations. Fifth, we summarize the corresponding USTE mitigation measures and methods based on the CFD simulation results. Finally, the outlooks and the existing problems in current research on CFD simulations of the USTE are analyzed.In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. https://www.selleckchem.com/ Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies.Motor bearings are one of the most critical components in rotating machinery. Envelope demodulation analysis has been widely used to demodulate bearing vibration signals to extract bearing defect frequency components but one of the main challenges is to accurately locate the major fault-induced frequency band with a high signal-to-noise ratio (SNR) for demodulation. Hence, an enhanced fault detection method combining the maximal overlap discrete wavelet packet transform (MODWPT) and the Teager energy adaptive spectral kurtosis (TEASK) denoising algorithms is proposed for identifying the weak periodic impulses. The Teager energy power spectrum (TEPS) defines the sparse representation of the filtered signals of the MODWPT in the frequency domain via the Teager energy operator (TEO); the TEASK helps determine the most informative frequency band for demodulation. The methodology is compared in terms of performance with the fast Kurtogram and the Autogram methods. The simulation and practical application examples have shown that the proposed MODWPT-TEASK method outperforms the above two methods in diagnosing defects of motor bearings.