This review provides an overview of recent advances and developments in electrospinning technology and the recent progress and applications of electrospun nanofiber membranes to expel various pollutants from water.

The membrane distillation (MD)-based desalination process is thought to be a promising strategy to address global challenges, such as safe water-energy crisis and environmental pollution. Here, we demonstrate a novel self-heated vacuum (MD) system employing a graphene–PVDF flat sheet membrane Joule heater that enables the combination of the interfacial heating of a feed stream and conventional VMD for energy efficient sea water desalination. In this system, the direct delivery of low voltage DC power to a superhydrophobic graphene–PVDF flat sheet membrane Joule heater was placed in a filtration module, and effectively heats the feed stream directly at the feed/membrane interface. Thus, it eliminates the preheating of feed solution and temperature polarization (TP), along with enhanced permeate flux, which in turn significantly reduces the energy requirement for the desalination process. The graphene–PVDF flat sheet membrane Joule heater was obtained via simple phase inversion technique, and exhibited excellent Joule heating performance, superhydrophobicity, and graphene laminar stacking-induced high porosity. While treating a saline feed without preheating, and with optimum membrane and experimental variables, our self-heated MD system achieved a permeate flux of 23.44 L m⁻² h⁻¹ and salt rejection of 99.41%, with the lowest specific heating energy (Q_sh) consumption of 0.109 kW h L⁻¹ and the highest gain output ratio (GOR) of 5.72, respectively. This is the greatest performance achieved so far, compared to the performance of solar heating (photothermal) and induction heating-based self-heated MD systems reported previously. The optimum membrane surface temperature (T* = 38 °C) and effective membrane area (71.1%) were assessed using FVM simulation, and were validated by the corresponding experimental results. This energy efficient self-heated vacuum MD system is suitable for high purity water production and effective sea water desalination. Furthermore, by harvesting the electrical energy required for membrane Joule heating from renewable energy (solar energy) and using feed streams with low grade waste heat, we could further increase the economic viability and application potential of the system. The self-heated vacuum MD system demonstrated here constitutes an effective approach for a sustainable, decentralized water desalination technology.

Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI–distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.

Reliability-based design optimization (RBDO) problems have been intensively studied for many decades. Since Hasofer and Lind [1974, “Exact and Invariant Second-Moment Code Format,” J. Engrg. Mech. Div., 100 (EM1), pp. 111–121] defined a measure of the second-moment reliability index, many RBDO methods utilizing the concept of reliability index have been introduced as the reliability index approach (RIA). In the RIA, reliability analysis problems are formulated to find the reliability indices for each performance constraint and the solutions are used to evaluate the failure probability. However, the traditional RIA suffers from inefficiency and convergence problems. In this paper, we revisited the definition of the reliability index and revealed the convergence problem in the traditional RIA. Furthermore, a new definition of the reliability index is proposed to correct this problem and a modified reliability index approach is developed based on this definition. The strategies to solve RBDO problems with non-normally distributed design variables by the modified RIA are also investigated. Numerical examples using both the traditional and modified RIAs are compared and discussed.

This article proposes a method for analyzing the gravity balancing reliability of spring-articulated serial robots with uncertainties. Gravity balancing reliability is defined as the probability that the torque reduction ratio (the ratio of the balanced torque to the unbalanced torque) is less than a specified threshold. In this paper, the reliability analysis is performed by exploiting a Monte Carlo simulation (MCS) with consideration of the uncertainties in the link dimensions, masses, and compliance parameters. A reliability-based design optimization (RBDO) method is also developed to seek reliable spring setting parameters for maximized balancing performance under a prescribed uncertainty level. The RBDO is formulated with consideration of a probabilistic reliability constraint and solved by using a particle swarm optimization (PSO) algorithm. A numerical example is provided to illustrate the gravity balancing performance and reliability of a robot with uncertainties. A sensitivity analysis of the balancing design is also performed. Lastly, the effectiveness of the RBDO method is demonstrated through a case study in which the balancing performance and reliability of a robot with uncertainties are improved with the proposed method.