Description: A RF powering circuit used in radio-frequency identification (RFID) tags and other batteryless embedded devices is presented in this paper. The RF powering circuit harvests energy from electromagnetic waves and converts the RF energy to a stable voltage source. Analysis of a NMOS gate-cross connected bridge rectifier is conducted to demonstrate relationship between device sizes and power conversion efficiency (PCE) of the rectifier. A rectifier with 38.54% PCE under normal working conditions is designed. Moreover, a stable voltage regulator with a temperature and voltage optimizing strategy including adoption of a combination resistor is developed, which is able to accommodate a large input range of 4 V to 12 V and be immune to temperature variations. Latch-up prevention and noise isolation methods in layout design are also presented. Designed with the HJTC 0.25 μm process, this regulator achieves 0.04 mV/°C temperature rejection ratio (TRR) and 2.5 mV/V voltage rejection ratio (VRR). The RF powering circuit is also fabricated in the HJTC 0.25 μm process. The area of the RF powering circuit is 0.23 × 0.24 mm2. The RF powering circuit is successfully integrated with ISO/IEC 15693-compatible and ISO/IEC 14443-compatible RFID tag chips.

Description: Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based on a fuzzy rough set and evidential reasoning (FRSER), for land cover classification in an urban fringe area. The approach is implemented on Landsat Operation Land Imager data covering the urban fringe area of Wuhan city, China. A fuzzy rough set is first used to define a decision table from multispectral imagery and ground reference data. Then the fuzzy rough information system is interpreted using the Dempster–Shafer theory, based on an evidential reasoning system. A final land cover classification with uncertainty is achieved by evidential reasoning. The results are compared with the traditional maximum likelihood classifier (MLC) and some rough set-based classifiers including classical rough set classifier (RS), fuzzy rough set classifier (FRS), and variable precision fuzzy rough set classifier (VPFRS). The better overall accuracy, user’s and producer’s accuracies, and the kappa coefficient, in comparison with the other classifiers, suggest that the proposed approach can effectively discriminate land cover types in urban fringe areas with high inter-class similarities and intra-class heterogeneity. It is also capable of handling the uncertainty in data processing, and the final land cover map comes with a degree of uncertainty. The proposed approach that can efficiently integrate the merits of both the fuzzy rough set and DS theory provides an efficient method for urban fringe land cover classification.

Description: Colorimetric sensing, which transduces environmental changes into visible color changes, provides a simple yet powerful detection mechanism that is well-suited to the development of low-cost and low-power sensors. A new approach in colorimetric sensing exploits the structural color of photonic crystals (PCs) to create environmentally-influenced color-changeable materials. PCs are composed of periodic dielectrics or metallo-dielectric nanostructures that affect the propagation of electromagnetic waves (EM) by defining the allowed and forbidden photonic bands. Simultaneously, an amazing variety of naturally occurring biological systems exhibit iridescent color due to the presence of PC structures throughout multi-dimensional space. In particular, some kinds of the structural colors in living organisms can be reversibly changed in reaction to external stimuli. Based on the lessons learned from natural photonic structures, some specific examples of PCs-based colorimetric sensors are presented in detail to demonstrate their unprecedented potential in practical applications, such as the detections of temperature, pH, ionic species, solvents, vapor, humidity, pressure and biomolecules. The combination of the nanofabrication technique, useful design methodologies inspired by biological systems and colorimetric sensing will lead to substantial developments in low-cost, miniaturized and widely deployable optical sensors.

Description: Sensors, Vol. 18, Pages 1293: Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks Sensors doi: 10.3390/s18041293 Authors: Weigang Bai Haiyan Wang Ke He Ruiqin Zhao The packets carried along a pre-defined route in underwater sensor networks are very vulnerble. Node mobility or intermittent channel availability easily leads to unreachable routing. Opportunistic routing has been proven to be a promising paradigm to design routing protocols for underwater sensor networks. It takes advantage of the broadcast nature of the wireless medium to combat packet losses and selects potential paths on the fly. Finding an appropriate forwarding candidate set is a key issue in opportunistic routing. Many existing solutions ignore the impact of candidates location distribution on packet forwarding. In this paper, a path diversity improved candidate selection strategy is applied in opportunistic routing to improve packet forwarding efficiency. It not only maximizes the packet forwarding advancements but also takes the candidate’s location distribution into account. Based on this strategy, we propose two effective routing protocols: position improved candidates selection (PICS) and position random candidates selection (PRCS). PICS employs two-hop neighbor information to make routing decisions. PRCS only uses one-hop neighbor information. Simulation results show that both PICS and PRCS can significantly improve network performance when compared with the previous solutions, in terms of packet delivery ratio, average energy consumption and end-to-end delay.

Description: Sustainability, Vol. 10, Pages 1813: The Impact of the Allocation of Facilities on Reducing Carbon Emissions from a Green Container Terminal Perspective Sustainability doi: 10.3390/su10061813 Authors: Yun Peng Wenyuan Wang Ke Liu Xiangda Li Qi Tian The main contribution of this paper is to quantify the impact of the allocation of facilities, including the number of facilities and the fuels adopted by facilities, on carbon emissions. In order to deal with the complex queuing network of container terminals, a simulation model is established with the changing of the number of and the fuel adopted by facilities as inputs. Firstly, the operation process and complex queuing network of container terminals are described to explain why simulation technology needs to be used. Then, various simulation experiments based on a container terminal in Algeria are designed and carried out. Finally, the carbon emissions from facilities and ships at berth and inside container terminals, and the total carbon emissions inside container terminals, are obtained and analyzed. Results show that the emissions from facilities are only a small fraction of the total emissions of container terminals. Improving the number of trucks and yard cranes can help reduce carbon emissions, but when the number continues to rise, the emissions are decreased very slightly. The results obtained and proposed method can be applied to build a green container terminal, which can also be used for similar problems.

Description: IJERPH, Vol. 14, Pages 1385: Prevalence of Mental Health Problems and Associated Risk Factors among Rural-to-Urban Migrant Children in Guangzhou, China International Journal of Environmental Research and Public Health doi: 10.3390/ijerph14111385 Authors: Jun Wang Ke Liu Jing Zheng Jiali Liu Liming You Rural-to-urban migration, which has achieved a huge scale during China’s economic reform, is a potential risk factor for the mental health of migrant children. To test this hypothesis, this study assessed the mental health status of rural-to-urban migrant children. Guided by Andersen’s behavioral model, the study explored the risk factors associated with mental health. The study recruited 1182 fifth/sixth-grade children from four private and four public primary schools in Guangzhou in 2014 in a descriptive cross-sectional design. Mental health status was measured by the strengths and difficulties questionnaire. Predisposing characteristics including demographics (e.g., age, gender), social structure (e.g., education, occupation) and health beliefs (health attitude) were recorded. Enabling characteristics including family and community resources and the need for health services were analyzed to explore the risk factors. The results indicate that more rural-to-urban migrant children were classified in the abnormal (21.0%) or borderline (18.8%) categories based on the total difficulties scores, the proportions of which were much higher than those of local children (9.8% abnormal, 13.8% borderline). Factors associated with a greater likelihood of mental health problems included single-parent families, seeking health information actively, family income cannot meet basic needs and poor perceived health status. Compared with the local children, the rural-to-urban migrant children had relatively poor mental health, hence monitoring and supporting mental health for rural-urban migrant children is critical.

Description: Measuring the spatial distribution of heavy metal contaminants is the basis of pollution evaluation and risk control. Considering the cost of soil sampling and analysis, spatial interpolation methods have been widely applied to estimate the heavy metal concentrations at unsampled locations. However, traditional spatial interpolation methods assume the sample sites can be located stochastically on a plane and the spatial association between sample locations is analyzed using Euclidean distances, which may lead to biased conclusions in some circumstances. This study aims to analyze the spatial distribution characteristics of copper and lead contamination in river sediments of Daye using network spatial analysis methods. The results demonstrate that network inverse distance weighted interpolation methods are more accurate than planar interpolation methods. Furthermore, the method named local indicators of network-constrained clusters based on local Moran’ I statistic (ILINCS) is applied to explore the local spatial patterns of copper and lead pollution in river sediments, which is helpful for identifying the contaminated areas and assessing heavy metal pollution of Daye.

Description: The aim of investigating the coordination of the complex system constituted by resources, environment, ecology, economy and society subsystems (CSR3ES), is to achieve sustainable development by: (1) describing the complicated relationships of the inner-subsystems and inter-systems; (2) designing the calculation method of the coordination degree for CSR3ES; (3) analyzing its developing trends from a macro point of view through comprehensive coordination degree; and (4) to determine the direction of system restoration from a micro point of view based on the inner-subsystem and inter-subsystem coordination degree, and to develop specific improvement strategies. By setting a province-level administrative region as a decision-making unit (DMU), the nationwide coordination of CSR3ES was studied. The main conclusions are: (1) The coordination degree between the subsystems is directly influenced by interactions between the elements. (2) Within the provinces and autonomous regions (PARs) in China, the levels of coordinated development of inter-subsystems are high, while the coordinated development between the elements within a single subsystem level are low. Furthermore, a positive coherent effect is exerted on the comprehensive coordination degree of CSR3ES by the inter-system synergistic effects. (3) In terms of spatial correlation, five comprehensive coordinated development modes of CSR3ES are formed: the northeast regional coordination mode group; the northwest regional uncoordinated mode group; the southeast regional coordination mode group; the central regional random mode group; and the Beijing-Tianjin-Hebei polarization mode.

Description: The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method.