Standard clinical care in neonatal and pediatric intensive-care units (NICUs and PICUs, respectively) involves continuous monitoring of vital signs with hard-wired devices that adhere to the skin ...and, in certain instances, can involve catheter-based pressure sensors inserted into the arteries. These systems entail risks of causing iatrogenic skin injuries, complicating clinical care and impeding skin-to-skin contact between parent and child. Here we present a wireless, non-invasive technology that not only offers measurement equivalency to existing clinical standards for heart rate, respiration rate, temperature and blood oxygenation, but also provides a range of important additional features, as supported by data from pilot clinical studies in both the NICU and PICU. These new modalities include tracking movements and body orientation, quantifying the physiological benefits of skin-to-skin care, capturing acoustic signatures of cardiac activity, recording vocal biomarkers associated with tonality and temporal characteristics of crying and monitoring a reliable surrogate for systolic blood pressure. These platforms have the potential to substantially enhance the quality of neonatal and pediatric critical care.
Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for ...complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model.
All‐solid‐state lithium batteries (ASSLBs) are considered promising alternatives to current lithium‐ion batteries that employ liquid electrolytes due to their high energy density and enhanced safety. ...Among various types of solid electrolytes, sulfide‐based electrolytes are being actively studied, because they exhibit high ionic conductivity and high ductility, which enable good interfacial contacts in solid electrolytes without sintering at high temperatures. To improve the energy density of the sulfide‐based ASSLBs, it is essential to increase the loading of active material in the composite cathode. In this study, the Ni‐rich LiNixCoyMn1‐x‐yO2 (NCM) materials are explored with different Ni content, particle size, and crystalline form to probe suitable cathode active materials for high‐performance ASSLBs with high energy density. The results reveal that single‐crystalline LiNi0.82Co0.10Mn0.08O2 material with a small particle size exhibits the best cycling performance in the ASSLB assembled with a high mass loaded cathode (active mass loading: 26 mg cm−2, areal capacity: 5.0 mAh cm−2) in terms of discharge capacity, capacity retention, and rate capability.
Single‐crystalline LiNi0.82Co0.10Mn0.08O2 material with a small particle size exhibits the best cycling performance in the all‐solid‐state lithium batteries (ASSLBs) assembled with a high mass loaded cathode (active mass loading: 26 mg cm−2, areal capacity: 5.0 mAh cm−2) in terms of discharge capacity, capacity retention, and rate capability. The results reveal conclusive guidance for selecting suitable cathode active material for ASSLBs.
This paper studies the spectral/energy efficiency (SE/EE) of a heterogeneous network with the backhaul enabled by low-resolution analog-to-digital converters (ADCs) quantized full-duplex massive ...multiple-input multiple-output (MIMO) over Rician channels. Backhaul communication is completed over two phases. During the first phase, the macro-cell (MC) base station (BS) deploys massive receive antennas and a few transmit antennas; the small-cell (SC) BSs employ large-scale receive antennas and a single transmit antenna. For the second phase, the roles of the transmit and receive antennas are switched. Due to the low-resolution ADCs, we account for quantization noise (QN). We characterize the joint impact of the number of antennas, self-interference, SC-to-SC interference, QN, and Rician <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-factor. For the first phase, the SE is enhanced with the massive receive antennas and the loss due to QN is limited. For the second phase, the desired signal and QN have the same order. Therefore, the SE saturates with the massive transmit antennas. As the Rician <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-factor increases, the SE converges. Power scaling laws are derived to demonstrate that the transmit power can be scaled down proportionally to the massive antennas. We investigate the EE/SE trade-offs. The envelope of the EE/SE region grows with increase in the Rician <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-factor.
In the present study, a 100 kW organic Rankine cycle is suggested to recover heat energy from commercial ships. A radial-type turbine is employed with R1233zd(E) and back-to-back layout. To improve ...the performance of an organic Rankine power system, the efficiency of the turbine is significant. With the conventional approach, the optimization of a turbine requires a considerable amount of time and involves substantial costs. By combining design of experiments, an artificial neural network, and Latin hypercube sampling, it becomes possible to reduce costs and achieve rapid optimization. A radial basis neural network with machine learning technique, known for its advantages of being fast and easily applicable, has been implemented. Using such an approach, an increase in efficiency greater than 1% was achieved with minimal design changes at the first and second turbines.
The integration of unmanned aerial vehicles (UAVs) into spectrum sensing cognitive communication networks can offer many benefits for massive connectivity services in 5G communications and beyond; ...hence, this work analyses the performance of non-orthogonal multiple access-based cognitive UAV-assisted ultra-reliable and low-latency communications (URLLCs) and massive machine-type communication (mMTC) services. An mMTC service requires better energy efficiency and connection probability, whereas a URLLC service requires minimising the latency. In particular, a cognitive UAV operates as an aerial secondary transmitter to a ground base station by sharing the unlicensed wireless spectrum. To address these issues, we derive the analytical expressions of throughput, energy efficiency, and latency for mMTC/URLLC-UAV device. We also formulate an optimisation problem of energy efficiency maximisation to satisfy the needs of URLLC latency and mMTC throughput and solve it using the Lagrangian method and the Karush-Kuhn-Tucker conditions. The algorithm is presented by jointly optimising the transmission powers of the mMTC and URLLC users. The derived expressions and algorithm are then used to evaluate the performance of the proposed system model. The numerical results show that the proposed algorithm improves the energy efficiency and satisfies the latency requirement of the mMTC/URLLC-UAV device.
The outstanding performance of nanomaterials in chemical, magnetic, electrical, catalytic, and mechanical properties has paved the way for the huge market in material fabrication, energy storage, ...electronics, and many other industries. Traditional synthesis methods are relatively stunted in industrial applicability and scalability due to complex manufacturing processes and long preparation times, hampering the development and commercialisation of nanomaterials. On the other hand, the flame synthesis method emerges as an inexpensive, efficient, and easily scalable method for the commercial production of nanoparticles. The present review aims to highlight the research status and applications of the nanomaterials synthesised by the flame aerosol method. The advancement of flame aerosol synthesis technologies is reviewed, with emphasis on the state-of-the-art flame reactor configuration and design. Critical flame parameters that govern the formation of nanoparticles in the flame are reviewed to provide an understanding of the formation criteria and growth of nanomaterials in the flame environment. The properties and characteristics of carbon-based, platinum group metal, metal oxide, bimetallic nanoparticles, perovskite and high entropy oxide nanomaterials produced by flame synthesis are extensively reviewed. In addition, commercial manufacturing of flame-synthesised materials along with applications of the nanomaterials in the field of thermal or photocatalytic energy storage, fuel cells, and gas sensing are presented.
Synthesis of nanomaterials using flame aerosol technologies and applications of the nanomaterials in the field of thermal or photocatalyst, energy storage, fuel cell, thermochemical catalyst and gas sensing. Display omitted
•Flame aerosol technology has the advantage of rapid production and scalability.•Formation of complex nanoparticles with multi-component is feasible with flame aerosol method.•Bimetallic materials production can be achieved by optimizing the processing parameters in flame.•The residence time of precursor in flame may affect the product crystallisation.•The applications of different flame-synthesised nanomaterials in the energy applications are reviewed.
This letter studies physical layer security (PLS) in non-orthogonal multiple access (NOMA) power line communication (PLC) networks. A source node transmits information to two nodes of near and far ...users with different power allocations. We consider two different cases of eavesdropping: internal and external. The PLC channel and noise are modeled by correlated log-normal fading and Bernoulli-Gaussian random process, respectively. We derive closed-form expressions for the secrecy outage probability (SOP) of both the internal and external cases by applying Gauss-Chebyshev quadrature, which are verified by Monte Carlo simulations. We observe that higher impulsive noise degrades the SOP while higher channel correlation can improve the SOP. In addition, the analytic results reveal the impact of power allocation on the SOP performance.
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•Pure Jatropha oil with high acid value is not suitable for direct transesterification.•Esterified Jatropha oil and waste cooking oil were blended to produce ...biodiesel.•Transesterification of WO/EJO produced 90.9 % biodiesel with suitable properties.•The biodiesel-diesel blend improved the calorific value and oxidation stability.•Antioxidants increased the acid value and kinematic viscosity of biodiesel.
Fatty acids from non-edible bioresources are highly sought after as biofuel feedstock and the use of multi-stream feedstock for biodiesel production is of interest. This study explores the potential of using blended feedstock consisting of inedible jatropha oil (JO) and waste cooking oil (WO) for biodiesel production. Prior to blending, the unfavourable high acid value of jatropha oil was esterified under the most optimal conditions of 60 °C, 1% H2SO4 catalyst and alcohol to oil molar ratio of 11:1 to maximise the esterified yield (81.1 %). Based on the acid value measurement, the optimum volumetric blend of WO/EJO was determined to be 90/10 with the lowest acid value of 1.9 mg KOH g−1, which was then utilised as feedstock for base-catalysed transesterification. The KOH catalysed transesterification was optimised at 60 °C, 1 wt% KOH catalyst and alcohol to oil molar ratio of 6:1 to produce biodiesel with low acid value (0.2 mg KOH g−1), high calorific value (38.4 MJ kg−1), high oxidation stability (∼11 h) and favourable viscosity (4.7 mm2 s−1). The results show that the produced biodiesel has acceptable physicochemical properties but its properties can further be improved by blending with petroleum diesel and antioxidant. Among those produced blend derivatives, petroleum diesel and biodiesel blend (80:20) or B20 showed the best improvement with high calorific value (46.6 MJ/kg), high oxidation stability (∼37 h) and low acid value (0.3 mg KOH g−1). Based on the study, in situ feedstock blending of WO/EJO can improve the physicochemical properties of the produced biodiesel and reduce the dependency on single feedstock. Biodiesel blending with commercial diesel can enhance the biodiesel fuel properties and such derivatives can be directly applied in an existing engine.
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•A flexible solid electrolyte sheet was obtained as a free-standing thin film (75 μm).•The electrolyte exhibited a high ionic conductance and good mechanical properties.•The solid ...lithium-ion cell with a solid electrolyte showed good cycling performance.
All-solid-state lithium batteries (ASSLBs) with solid electrolytes are promising battery systems capable of improving the safety and energy density of current lithium-ion batteries. Reducing the thickness of the solid electrolyte while preventing the short circuit between the anode and cathode is imperative to increase the energy density of ASSLBs. Sulfide-based solid electrolytes have high ionic conductivities; however, they are brittle, difficult to be processed into a thin film, and challenging to form stable interfaces with electrodes of large volume change. In this study, flexible thin-solid electrolyte sheets with Li+-ion conductive polymer network were prepared and characterized for ASSLB applications. They exhibited higher ionic conductance and superior mechanical properties than those of pristine Li6PS5X (argyrodite) pellets. The all-solid-state lithium-ion cell (graphite/LiNi0.7Co0.15Mn0.15O2) with a solid electrolyte sheet delivered a high discharge capacity of 182.5 mAh g−1 and showed good cycling stability at 0.33 C and 25 ℃, demonstrating that flexible and thin sheets are promising solid electrolytes for ASSLBs operating at room temperature.
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