Energy harvesting from electric and magnetic fields in substations for powering autonomous sensors

This poster presentation looks at energy harvesting from electric and magnetic fields in substations for powering autonomous sensor

The impact of land use/cover change on extreme temperatures on the Yangtze River Delta, China

The contribution from land use/cover change (LUCC) toward temperature in recent decades is of great concern across the globe. Although there have been many studies, most of them focus on the discussion of average temperature and lack a discussion of extreme temperatures. In this study, we first investigated the spatio-temporal changes in extreme temperatures in the Yangtze River Delta during 1980–2020 using the ensemble empirical mode decomposition (EEMD) method. Then, we explored the impact of LUCC on extreme temperatures using the observation minus reanalysis (OMR) method. Finally, the relationship between the normalized difference vegetation index (NDVI) and extreme temperatures was analyzed using the correlation analysis method. We found that: (1) extreme temperatures have a nonlinear variation characteristics on different time scales. Extremely high temperatures (EHT) clearly exhibited a monthly time scale (quasi-3-month), an interannual time scale (quasi-1-year, quasi-2-year, quasi-3-year and quasi-5-year), and an interdecadal time scale (quasi-10-year and quasi-35-year). Extremely low temperatures (ELT) also clearly exhibited a monthly time scale (quasi-3-month), an interannual scale (quasi-1-year, quasi-2-year, quasi-3-year and quasi-6-year), and an interdecadal scale (quasi-10-year and quasi-20-year). (2) EHT showed an east–middle–west staggered phase and ELT showed a southeast–northwest anti-phase characteristic in spatial distribution. (3) The contribution rates of LUCC on EHT and ELT are 53.6% and 92.4%, respectively, which are higher than for the average temperature (40%). (4) The monthly time scale response of the NDVI to extreme temperatures is more regionally concentrated and significant than that on the interannual time scale in spatial distribution. This paper makes up for the insufficiency of the impact of land use/cover changes on extreme temperature changes at multiple time scales and enriches our understanding of climate change

CHEMICAL AND PHASE TRANSFORMATION FROM VANADIUM SULFIDE TO OXIDE VIA A NEW CHEMICAL ROUTE FOR THE SYNTHESIS OF Βʹ-LIXV2O5 AS A HIGH PERFORMANCE CATHODE

The used of rechargeable lithium ion batteries are so widely nowadays on consumer electronics especially portable devices such as cellphones, laptops and etc. The advancement of technology has created batteries with providing high energy density without memory effect and minimum the self-discharge on standby mode. Even with these features, researchers are still trying to improve the batteries with more energy density, low cost, better safety and high durability. The energy density improves with high operation voltage and high capacity. All these features came from one source, material. The resources for current commercial cathode material are decreasing and so new alternative cathode with high performance is needed to replace the commercial cathode in the future. The high temperature vanadium pentoxide phase, βʹ-LixV2O5, was synthesized via a new chemical synthesis involving the evolution of vanadium oxides from the 600°C heat treatment of the pure LiVS2 in air. By employing this method of synthesis, well-crystalized, rod-shaped βʹ-LixV2O5 particles 20 – 30 μm in length and 3 – 6 μm in width were obtained. Moreover, the surface of βʹ-LixV2O5 particles was found to be coated by an amorphous vanadium oxysulfide film (~20 nm in thickness). In contrast to a low temperature vanadium pentoxide phase (LixV2O5), the electrochemical intercalation of lithium into the βʹ-LixV2O5 was fully reversible where 0.0 < x < 2.0, and it delivered a capacity of 310 mAh/g at a current rate of 0.07 C between 1.5 V and 4 V. Good capacity retention of more than 88% was also observed after 50 cycles even at a higher current rate of 2 C

Sideband pump-probe technique resolves nonlinear modulation response of PbS/CdS quantum dots on a silicon nitride waveguide

For possible applications of colloidal nanocrystals in optoelectronics and nanophotonics, it is of high interest to study their response at low excitation intensity with high repetition rates, as switching energies in the pJ/bit to sub-pJ/bit range are targeted. We develop a sensitive pump-probe method to study the carrier dynamics in colloidal PbS/CdS quantum dots deposited on a silicon nitride waveguide after excitation by laser pulses with an average energy of few pJ/pulse. We combine an amplitude modulation of the pump pulse with phase-sensitive heterodyne detection. This approach permits to use co-linearly propagating co-polarized pulses. The method allows resolving transmission changes of the order of 10(-5) and phase changes of arcseconds. We find a modulation on a sub-nanosecond time scale caused by Auger processes and biexciton decay in the quantum dots. With ground state lifetimes exceeding 1 mu s, these processes become important for possible realizations of opto-electronic switching and modulation based on colloidal quantum dots emitting in the telecommunication wavelength regime

Using system dynamics modelling to assess the economic efficiency of innovations in the public sector - a systematic review

© 2022 Jadeja et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.Background: Decision-makers for public policy are increasingly utilising systems approaches such as system dynamics (SD) modelling, which test alternative interventions or policies for their potential impact while accounting for complexity. These approaches, however, have not consistently included an economic efficiency analysis dimension. This systematic review aims to examine how, and in what ways, system dynamics modelling approaches incorporate economic efficiency analyses to inform decision-making on innovations (improvements in products, services, or processes) in the public sector, with a particular interest in health. Methods and findings: Relevant studies (n = 29) were identified through a systematic search and screening of four electronic databases and backward citation search, and analysed for key characteristics and themes related to the analytical methods applied. Economic efficiency analysis approaches within SD broadly fell into two categories: as embedded sub-models or as cost calculations based on the outputs of the SD model. Embdedded sub-models within a dynamic SD framework can reveal a clear allocation of costs and benefits to periods of time, whereas cost calculations based on the SD model outputs can be useful for high-level resource allocation decisions. Conclusions: This systematic review reveals that SD modelling is not currently used to its full potential to evaluate the technical or allocative efficiency of public sector innovations, particularly in health. The limited reporting on the experience or methodological challenges of applying allocated efficiency analyses with SD, particularly with dynamic embedded models, hampers common learning lessons to draw from and build on. Further application and comprehensive reporting of this approach would be welcome to develop the methodology further.Peer reviewedFinal Published versio

Integration of antenatal care services with health programmes in low- and middle-income countries: systematic review.

BACKGROUND: Antenatal care (ANC) presents a potentially valuable platform for integrated delivery of additional health services for pregnant women-services that are vital to reduce the persistently high rates of maternal and neonatal mortality in low- and middle-income countries (LMICs). However, there is limited evidence on the impact of integrating health services with ANC to guide policy. This review assesses the impact of integration of postnatal and other health services with ANC on health services uptake and utilisation, health outcomes and user experience of care in LMICs. METHODS: Cochrane Library, MEDLINE, Embase, CINAHL Plus, POPLINE and Global Health were searched for studies that compared integrated models for delivery of postnatal and other health services with ANC to non-integrated models. Risk of bias of included studies was assessed using the Cochrane Effective Practice and Organisation of Care (EPOC) criteria and the Newcastle-Ottawa Scale, depending on the study design. Due to high heterogeneity no meta-analysis could be conducted. Results are presented narratively. FINDINGS: 12 studies were included in the review. Limited evidence, with moderate- to high-risk of bias, suggests that integrated service delivery results in improved uptake of essential health services for women, earlier initiation of treatment, and better health outcomes. Women also reported improved satisfaction with integrated services. CONCLUSIONS: The reported evidence is largely based on non-randomised studies with poor generalizability, and therefore offers very limited policy guidance. More rigorously conducted and geographically diverse studies are needed to better ascertain and quantify the health and economic benefits of integrating health services with ANC

Analyzing drop coalescence in microfluidic devices with a deep learning generative model

Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering

Analyzing drop coalescence in microfluidic devices with a deep learning generative model

Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering

Room-temperature multiferroic hexagonal LuFeO3_3 films

The crystal and magnetic structures of single-crystalline hexagonal LuFeO$_3$ films have been studied using x-ray, electron and neutron diffraction methods. The polar structure of these films are found to persist up to 1050 K; and the switchability of the polar behavior is observed at room temperature, indicating ferroelectricity. An antiferromagnetic order was shown to occur below 440 K, followed by a spin reorientation resulting in a weak ferromagnetic order below 130 K. This observation of coexisting multiple ferroic orders demonstrates that hexagonal LuFeO$_3$ films are room-temperature multiferroics