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Passive location systems receive electromagnetic waves at one or multiple base stations to locate the transmitters, which are widely used in security fields. However, the geometric configurations of stations can greatly affect the positioning precision. In the literature, the geometry of the passive location system is mainly designed based on empirical models. These empirical models, being hard to track the sophisticated electromagnetic environment in the real world, result in suboptimal geometric configurations and low positioning precision. In order to master the characteristics of complicated electromagnetic environments to improve positioning performance, this paper proposes a novel geometry optimization method based on multiagent reinforcement learning. In the proposed method, agents learn to optimize the geometry cooperatively by factorizing team value function into agentwise value functions. To facilitate cooperation and deal with data transmission challenges, a constraint is imposed on the data sent from the central station to vice stations to ensure conciseness and effectiveness of communications. According to the empirical results under direct position determination systems, agents can find better geometric configurations than the existing methods in complicated electromagnetic environments.

More and more security situation awareness strategies in power network use deep learning as the data processing method. However, the endogenous security problem of deep learning poses a challenge to the robustness required for power network security situation awareness, and gives attackers an opportunity to add small perturbation to the network and fail the security situation awareness strategy. In this paper, we build the deep neural networks for the power network dataset. We verify a variety of adversarial example attack methods against deep neural networks, and tested the success rate of attacks on different types of networks to compare the difference of attack methods and target model. We can see from the experiment results that adversarial example attacks can have a great impact on the power network security situation awareness and pose a threat to the power network.

Background: Metformin is the first-line drug for type II diabetes, and recent studies indicate that metformin plays an inhibitory role in multiple cancers. Metformin can also enhance the effect of chemotherapy. Although head and neck squamous cell carcinoma cells are sensitive to metformin, the mechanisms related to the metformin response and the chemosensitization effect have not been fully studied. Results: In this study, we aimed to elucidate the molecular mechanisms of metformin in HNSCC by transcriptome analysis and to reveal the underlying mechanisms of the sensitizing effects of metformin by combined online dataset analysis. mRNA sequencing and functional analysis of HNSCC samples after metformin treatment and functional analysis of mRNAs with opposite metformin-induced effects in chemosensitive versus chemoresistant cells revealed the molecular pathways, mainly the base excision repair pathway, by which this small molecule drug sensitizes HNSCC cells to treatment. Conclusions: These findings indicate that metformin exerts a hypersensitization effect by regulating the BER pathway in tumour cells, reducing their self-repair capacity after chemotherapy-induced DNA damage. In addition, the genes identified by transcriptome analysis are candidates for further investigation into the effector targets of metformin in the inhibition of HNSCC and could be applied to improve the treatment in HNSCC patients who develop resistance after advanced chemotherapy.

Medium-voltage DC (MVDC) and low-voltage dc (LVDC) grids are regarded as a promising solution for electric shipboard power system (SPS), battery energy storage system (BESS), electric vehicle (EV) charging system, etc. In MVDC system, dc distribution converter (DDC) or dc transformer (DCX) is a crucial component dedicated to interfacing different dc buses. DDC is required to regulate the bus voltage to maintain system reliability and operability. Since the bus voltage variation is usually small, e.g. 10% of the rated bus voltage, input-parallel output-series (IPOS) partial power (PP) converter is suitable for DDC application. This paper proposes an overall design procedure for IPOS PP DDC. Take a shipboard MVDC system as an example, two turns ratio designs are examined. And then, the scalability of PP architectures is analyzed. Finally, the hardware design is conducted with efficiency optimization. The proposed design procedure is verified by an 1S kW500kHz PP DDC prototype, achieving 9S.S% peak efficiency and 142 W/in3 power density.

With the increasing demand for energy storage system in transportation and stationary applications, a high-density high-efficient bi-directional dc-dc converter with galvanic isolation and scalable architecture for different battery systems is desired. This paper adopts a 500 kHz high-efficiency isolated bi-directional soft-switching resonant converter with a partial power processing (PPP) architecture for energy storage systems. Compared with existing PPP or two-stage architectures, the proposed resonant converter utilizes a low voltage (LV) buck regulator for battery interface. Due to the LV regulator, dc transformer (DCX) operates at a fixed switching frequency without any additional ac inductor and zero current detection (ZCD) components. To minimize ac loop inductance in the proposed DCX, a two-dimensional (2-D) flux cancelation layout is proposed, which achieves loop inductance of 4.24 nH. The paper presents the analysis of the converter operation, detailed 18 kW converter design of using 1.2 kV silicon carbide (SiC) MOSFETs and 650/150 V gallium nitride (GaN) FETs, achieving efficiency 98.8% and 142 W/in3 power density.

Membrane distillation (MD) has great potential as a desalination technology because of its good compatibility with other technologies. However, its low efficiency and large energy consumption impede applications due to the temperature polarization effect. Herein, we describe solar membrane distillation (SMD) using a photothermal membrane with thermal concentration that could overcome this problem. A superhydrophobic polydimethylsiloxane/multiwalled carbon nanotube/poly (vinylidene fluoride) (PDMS/MWCNT/PVDF) composite membrane with excellent light capture and photothermal conversion abilities was prepared for the SMD process. The photothermal conversion ability and solar membrane performance were experimentally evaluated, while the enhancement process was investigated by simulations. It was found that this enhancement is due to the higher temperature near the membrane resulting from thermal concentration and localized heating effects. Notably, using a two-level thermal concentration method in a larger SMD module would realize additional desalination performance, and fresh water productivity could reach about 1.1 kg m−2 h−1. Using solar energy and benefitting from the thermal concentration effect, our designed SMD system could produce fresh water at a rate of 0.65 kg m−2 h−1 from 3.5 wt% salt water, with an energy consumption of pure solar energy of about 1 kW m−2. This represents a significant improvement over the conventional SMD process. Combining the SMD technology with a renewable energy source would further reduce the cost and energy consumption, and promote its industrial application.

Membrane distillation as a means of seawater desalination using solar energy has the potential to help address the challenges associated with the water-energy nexus and reduce energy consumption. However, the development of an efficient, scalable, stable, and sustainable novel solar membrane distillation (SMD) process remains challenging due to the limited number of membrane preparation procedures and system designs. This work, inspired by solar-driven interfacial evaporation, describes an SMD process employing a hydrophobic trilayer membrane and a two-level SMD structure for effective seawater desalination. A polydimethylsiloxane/carbon nanotube/poly(vinylidene fluoride) (PDMS/CNT/PVDF) membrane, prepared by electrospinning and spraying, exhibited broad light absorption, good photothermal conversion, and localized heating of seawater. The novel two-level SMD structure effectively reused the condensed heat and ensured a greater temperature difference for interfacial evaporation and steam transport, thereby improving fresh water productivity. The greatest productivity (1.43 kg m−2 h−1) and salt rejection (99.9%) were achieved under 1 kW m−2 of solar illumination. This low-energy SMD system using solar energy is suitable for emergency water production and offshore seawater desalination.

Photo-thermal conversion and energy storage using phase change materials are now being applied in industrial processes and technologies, particularly for electronics and thermal systems. This method relies on adding high thermal conductivity fillers, such as nanoparticles, to enhance the phase change process. In the long term, dynamic tuning heat transfer provides a superior means of tuning control the heat transfer efficiency and speed. In this study, a nanofluid phase change material was prepared by adding magnetic nanoparticles, effectively combining the properties of high thermal conductivity and magnetism, in an attempt to develop a magnetically enhanced approach for dynamic tuning of photo-thermal conversion charging. Our results showed that the phase change efficiency of magnetic phase change material can be improved by magnetic field application. With the increasing magnetic strength, the photo-thermal storage efficiency was enhanced, and storage capacity was improved by more than 48%. Meanwhile the steady temperature of the magnetic phase change material increased, enhancing the open-circuit voltage in photo-thermoelectricity experiments. Thus, the proposed method demonstrated in this study achieved superior phase change heat transfer characteristics within a photo-thermal conversion process by dynamically controlling the magnetic field distribution, which should be especially useful for direct solar energy utilisation.

Quinoxaline 1,4-dioxides (QdNOs) are widely used as a kind of antibacterial growth promoter in animal husbandry. The adrenal cortex was found to be one of the main toxic targets of QdNOs, accompanied by a decreased aldosterone level. However, the way in which QdNOs decrease production of the hormone aldosterone is far from clear. To illustrate the mechanism by which QdNOs damage the adrenal cortex and decrease aldosterone hormone levels, the QdNOs were screened to choose the drug with most toxic effects on aldosterone production, and then to reveal the mechanism between the gene and protein profiles in human adrenocortical cells (NCI-H295R cells). The results found that quinocetone (QCT) showed the highest adrenal toxic effect among QdNOs. After exposing H295R cells to 10 and 20μM QCT for 24h, compared with blank cells, the gene and protein expression profiles obtained were analyzed by microarray and MALDI TOF/TOF mass spectrometry, respectively. The results of microarray analysis suggested that ABCG1 and SREBF1, which were involved in the cholesterol biosynthetic and metabolic processes, and CYP17A1, NR4A2 and G6PD, which were related to aldosterone biosynthesis, were important molecular targets. It has been speculated that PKC and ERK pathways might be involved in the reduction of aldosterone production caused by QCT, through enhanced mRNA expression of CYP17A1. Additionally, JNK and p38MAPK signal transduction pathways might participate in apoptosis induced by QCT. Twenty-nine and 32 protein spots were successfully identified when cells were treated with 10 and 20μM QCT, respectively. These identified proteins mainly included material synthesis and energy metabolism-related proteins, transcription/translation processing-related proteins, signal transduction proteins, cytoskeletal proteins, molecular chaperones, proteins related to response to stress, and transport proteins. Further investigations suggested that oxidative stress caused by QCT was exacerbated through disruption of the Keap1/Nrf2/ARE anti-oxidative stress pathway. Taken together, the data demonstrated for the first time that the Keap1/Nrf2/ARE pathway plays a crucial role in adrenal toxicity, and that CYP17A1 was the key switch to reduce the aldosterone production induced by QCT. Furthermore, large numbers of genes and proteins and entry points for research in the inhibition of aldosterone synthesis induced by QCT were offered, which will provide new insight into the adrenal toxicity of QdNOs and help to provide a theoretical foundation for the formulation of safety controls for products obtained from animals and to design new QdNOs with less harmful effects.