High Penetration Research Articles

3D indoor area recognition for personnel security using integrated UWB and barometer approach

Real-time location tracking is essential for personal security in industrial scenes, e.g. monitoring worker safety in power substations. Ultra wideband (UWB) technology is suitable for indoor positioning thanks to its high penetration capability, high ranging accuracy and low power consumption. However, UWB based trilateration positioning requires high workload for field deployment of base stations. Indoor complex topology results in multipath and non-line of sight (NLOS) conditions of UWB signals, and degrades the positioning performance in terms of accuracy and reliability. This paper proposes a three-dimensional (3D) area recognition solution by integrating UWB time of flight (TOF) ranging and barometer measurements. The proposed solution utilizes a multi-tier distributed joint probabilistic inference model, which accomplishes the indoor 3D area recognition exploiting multiple clustering and prediction algorithms of machine learning. The field experiments showed that the proposed method can achieve an accuracy of 3D area recognition of more than 99.2%. The proposed method improves the computing efficiency by 93%. The errors of improved differential barometric height estimation method are less than 1 m, which means a success rate of 100% for floor identification, given a floor separation of 3–4 m. The proposed solution is suitable for personnel security applications of industrial scenes, which requires reliable real-time area information rather than just coordinates.

Ultrasound-triggered piezoelectric polyetheretherketone with boosted osteogenesis via regulating Akt/GSK3β/β-catenin pathway

Maxillofacial bone defects can severely impact quality of life by impairing physiological functions such as chewing, breathing, swallowing, and pronunciation. Polyether ether ketone (PEEK) is commonly used for the repair of maxillofacial defects due to its mechanical adaptability, while its osteogenic properties still need refinement. Herein, we have utilized the piezoelectric effect exhibited by barium titanate (BTO) under low-intensity pulsed ultrasound (LIPUS) to develop an ultrasound responsive PEEK (PDA@BTO-SPEEK, PBSP) through the mediating effect of polydopamine (PDA), for repairing maxillofacial bone defects. After modification by PDA@BTO, PBSP possesses better hydrophilicity, which is conducive to cell growth and adhesion. Simultaneously, by virtue of the piezoelectric characteristics of BTO, PBSP obtains a piezoelectric coefficient that matches the bone cortex. Notably, when PBSP is stimulated by LIPUS, it can generate stable electricity and effectively accelerate the osteogenic differentiation of osteoblasts through the regulation of the Piezo1-induced calcium (Ca2+) influx and Akt/GSK3β/β-catenin pathway. In addition, PBSP presents satisfactory therapeutic effects in rat skull defect models, and its osteogenic efficiency can be further improved under LIPUS stimulation with high tissue penetration. Collectively, PBSP + LIPUS exhibits great potential as a promising alternative strategy for the repair of maxillofacial bone defects.

Calibration-free temperature monitoring of pulverized coal powder based on the dual-mode acoustic tomography

Accurate temperature measurement of the pulverized coal powder is crucial in detecting and preventing coal spontaneous combustion. Compared to other temperature measurement techniques, acoustic tomography using low frequency sound waves has the advantage of high penetration ability for non-intrusive temperature measurement inside the pulverized coal powder. Additionally, by employing an array of acoustic transducers mounted around the coal powder, the entire sensing area can be covered, enabling a comprehensive temperature distribution analysis through tomographic reconstructions. However, the main drawback of the acoustic temperature measurement is the model complexity in describing the dependence of acoustic sound speed on temperature in the porous pulverized coal powder. Accurate calibration is required to determine the model parameters for temperature retrieval, however, which is difficult for in situ industrial application. To solve this problem, we proposed a calibration-free temperature monitoring method based on the dual-mode acoustic tomography for pulverized coal. The three-parameter JCAL model is used to characterize the acoustic propagation in coal powder. The temperature and micro-structure parameters can be jointly retrieved from the reconstructed sound slowness and attenuation distributions at different frequencies. The feasibility and effectiveness of the proposed method are validated in the simulation study. Results show that the dual-mode acoustic tomography can provide calibration-free simplicity for pulverized coal temperature measurement, which has a great potential for industrial applications.

Identifying interaction boundary of inverter-based generation in assessing system strength of power grids using relative electrical distance concept

The increasing use of inverter-based generation (IBG) in power grids raises concern about system strength. This is partly due to the inherent interactions among multiple IBGs in close proximity to one another. This paper proposes an approach to identifying the existential boundary of interaction in a network using the relative electrical distance (RED) concept. The mathematical formulation of the RED concept to address the interaction problem among the IBGs involved utilising the power system network’s admittance matrix to capture its structural characteristics. An interaction matrix derived from the RED values of all IBG pairs was then developed to identify the interacting IBG groups. The proposed approach was demonstrated using the IEEE 39-bus system and a practical 72-bus Nigerian power grid. Results showed that RED values effectively group interacting IBGs, with values closer to 0 signifying higher interaction levels, values closer to 1 indicating lower interaction, and a value of 1 denoting no interaction. Time-domain simulations confirmed the accuracy of the approach, demonstrating that the effect of control interaction propagates proportionally to neighbouring IBGs based on RED values. However, fault currents can influence the impact of control interactions. This approach, which requires less computational effort, provides a quick identification tool for potential areas of concern based on the degree of interaction, enhancing the reliability of power grids with high IBG penetration.

A single-chain fab derived drug conjugate for HER2 specific delivery

Despite the development of antibody-drug conjugates, the fragment Fab-based drug conjugates offer some unique capabilities in terms of safety, clearance, penetration and others. Current methods for preparing Fab drug conjugates are limited by the availability and stability of Fab proteins, leaving reports on this rare. Here, we found that a single-chain scaffold of Fab enables stabilization of the paired structure and supports high-yield expression in bacteria cytoplasm. Furthermore, we conjugated anti-neoplastic agent SN38 to the C-terminus by sortase A ligation and generated a homogenous Fab conjugate with the drug-to-Fab ratio of 1. The resulting anti-HER2 Fab-SN38 conjugate demonstrated potent and antigen-dependent cell-killing ability with the aid of its special cathepsin-triggered cyclization-promoted release mechanism. In vivo, Fab-SN38 can prevent growths of HER2-positive tumors in athymic mice and be well tolerated to the treatment at 7 mg/kg per dose. Anti-tumor activity, high dose tolerance and penetration advantage observed in this study would merit Fab conjugate investigation in target chemotherapy.

An Enhanced Continuation Power Flow Method Using Hybrid Parameterization

The rapid integration of renewable energy sources and the increasing complexity of modern power systems urge the development of advanced methods for ensuring power system stability. This paper presents a novel continuation power flow (CPF) method that combines two well-known parameterization techniques: natural parameterization and arc-length parameterization. The proposed hybrid approach significantly improves computational efficiency, reducing processing time by 32.76% compared to conventional methods while maintaining high accuracy. The method enables faster and more reliable stability assessments by efficiently managing the complexities and uncertainties, particularly in grids with high penetration of renewable energy.

Achieving Ultrasound-Excited Emission with Organic Mechanoluminescent Materials.

Unlike traditional photoluminescence (PL), mechanoluminescence (ML) achieved under mechanical excitation demonstrates unique characteristics such as high penetrability, spatial resolution, and signal-to-background ratio (SBR) for bioimaging applications. However, bioimaging with organic mechanoluminescent materials remains challenging because of the shallow penetration depth of ML with short emission wavelengths and the absence of a suitable mechanical force to generate ML in vivo. To resolve these issues, the present paper reports the achievement of ultrasound (US)-excited fluorescence and phosphorescence from purely organic luminogens for the first time with emission wavelengths extending to the red/NIR region, with the penetrability of the US-excited emission being considerably higher than that of PL. Consequently, US-excited subcutaneous phosphorescence imaging can be achieved using a mechanoluminescent-luminogen-based capsule device with a quantified intensity of 9.15 ± 1.32 × 104 p s-1 cm-2 sr-1 and an SBR of 24. Moreover, the US-excited emission can be adequately tuned using the packing modes of the conjugated skeletons, dipole orientation of mechanoluminescent luminogens, and strength and direction of intermolecular interactions. Overall, this study innovatively expands the kind of excitation sources and the emission wavelengths of organic mechanoluminescent materials, paving the way for practical biological applications based on US-excited emission.

Evaluating the feasibility and economics of hydrogen storage in large-scale renewable deployment for decarbonization

Renewable energy (RE) is pivotal for achieving a net-zero future, with energy storage systems essential for maximizing its utility. This study introduces a modeling framework that simulates large-scale RE deployment in Taiwan, emphasizing hydrogen as a primary storage medium. Utilizing hourly time steps, the model assesses various energy generation and storage configurations to demonstrate the technical feasibility of meeting Taiwan's 2050 net-zero target, which calls for a 60 % RE share primarily through solar and wind resources. However, achieving this target and the ambitious goal of 100 % RE penetration requires substantial enhancements in both generation capacity and storage solutions. The research evaluates the economic impacts by analyzing the levelized cost of energy (LCOE), revealing that optimal configurations, such as a wind capacity of 30 GW with a 5 % minimum capacity factor constraint on electrolyzers, significantly reduce LCOE to $0.176/kWh, underscoring the cost-effectiveness of hydrogen storage compared to battery alternatives in large-scale settings. The study underscores the necessity for ongoing investigations into replacing thermal power plants and maintaining grid stability amidst high RE penetration. To fully harness Taiwan's RE potential and contribute to global sustainability, effective deployment of hydrogen storage will require robust stakeholder support, continual technological advancements, and enabling policies. This framework, while tailored to Taiwan's power system, offers insights applicable to various global contexts.

Optimal Sizing and Siting of Fresh and Second-Life Battery Energy Storage Systems Based on Linearized Optimal Power Flow for High Photovoltaic Penetration: A Comparative Study

Optimal Sizing and Siting of Fresh and Second-Life Battery Energy Storage Systems Based on Linearized Optimal Power Flow for High Photovoltaic Penetration: A Comparative Study

Time Series Forecasting for Sparse Ring-shaped Array Photoacoustic Imaging Reconstruction

Abstract Photoacoustic computed tomography (PACT), which provides high optical absorption contrast and deep acoustic penetration, plays an important role in non-invasive biomedical imaging area. As the decrease of array elements, the reconstructed image suffers from severe artifacts. Recent studies utilize deep learning methods to improve the imaging quality of PACT based on image network design, but few were reported with raw data. To address this issue, this paper proposes a Wave to Wave Convolution Gate Recurrent-Net (WWCG-Net) to reconstruct photoacoustic image based on time series acoustic signal prediction. Simulation and experiment results show the superiority of our method compared with linear interpolation (LI) and eXtreme Gradient Boosting (XGBoost) in term of suppress artifact and improve resolution.

Optimization of Local Voltage Control With Coordinating Droop Functions Under High PV Penetration

Optimization of Local Voltage Control With Coordinating Droop Functions Under High PV Penetration

Ultra-short-term global horizontal irradiance forecasting based on a novel and hybrid GRU-TCN model

The need for energy is increasing globally due to a several factors, including population growth and economic development. Achieving this energy demand in the face of global warming and the depletion of fossil fuels requires the use of renewable energy. Photovoltaic energy is one of the renewable energy sources that is widely used in many nations across the world. Photovoltaic (PV) energy integration into the grid has significant benefits for the environment and economy, but at high penetration levels, its intermittent nature makes system stability difficult to maintain. Accurate ultra-short-term global horizontal irradiance forecasting is necessary in order to guarantee the most optimal use of photovoltaic power production sources. For GHI forecasting, a novel GRU-TCN-based model is proposed in this paper. It is composed of two neural networks: a temporal convolutional network and a gated recurrent unit. After extracting the temporal features from time-series solar irradiance data using GRU, the spatial features are obtained from the correlation matrix of different meteorological variables of the target and its neighbor position using TCN. Univariate and multivariate GRU-TCN models have been used for GHI ultra short-term forecasting. This paper compares the univariate and multivariate GRU-TCN models with TCN, LSTM, and GRU models based on three evaluation metrics in order to investigate how different combinations of variables affect the accuracy of the GRU-TCN models for one-step forecasting. The findings indicate the adoption of a univariate model using historical data of GHI is suitable to obtain reliable forecasting with 23.02 (W/m2) in MAE as opposed to the best multivariate model that achieved 25.67 (W/m2) in MAE. According to the results, the proposed model outperforms the other models assessed and offers a practical alternative for ultra-short-term GHI forecasting.

Virtual impedance optimization control strategy for wind turbine grid-forming converters in weak grid

Abstract Grid forming(GFM) technology is key to the effective consumption and grid-friendly integration of large-scale wind power. However, as the new type of power system sees a high penetration of new energy sources and a decrease in circuit capacity, leading to systems exhibiting weak grid characteristics, this paper studies the adaptive characteristics of grid-forming wind turbine converters under weak grid conditions. It investigates the dynamic relationship between power coupling characteristics and the system impedance ratio based on the virtual impedance method and proposes an impedance optimization method to improve the power decoupling accuracy and dynamic response capability of grid-forming converters. The research results show that the grid-forming controller can operate stably under weak grid conditions, an increase in grid impedance ratio leads to stronger power coupling intensity, and the voltage support capability of grid-forming converters weakens. The simulation verifies that the virtual impedance optimization strategy proposed in this paper can effectively improve the power coupling characteristics of the grid-forming converter and enhance the converter’s dynamic response performance under weak grid conditions.

Circular phase shift migration based photoacoustic computational tomography

Abstract In the recent several decades, photoacoustic technique has proved to be capable of noninvasive biomedical imaging with scalable spatial resolution and high penetration depth. To achieve faithful image reconstruction, various method has been proposed, such as back projection, phase shift migration, model-based method, etc. These methods have simple implementations in homogeneous media, whereas in the cases of heterogeneous media (e.g., layered media), complicated modifications with respect to wave propagation at layer interface are always necessary. Here, we propose a frequency domain algorithm extension of phase shift migration in polar coordinates, which is obtained by making second order approximation to the optoacoustic point spread function in cylindrical coordinates and thereafter step by step wavenumber-frequency domain wave field extrapolation along the axial direction. Theoretical simulation and phantom experimental results demonstrate that the proposed circular phase shift migration method can well solve the layered heterogeneous reconstructive problem without modifying the wave propagation model. The acoustic diffraction limited acoustic resolution (up to 225 μm) and high imaging SNR (at least 16 dB) are obtained.

Low‐frequency oscillations in MMC‐MVDC systems with high PV penetration: Modelling, mechanism, and assessment

AbstractThis study investigates the low‐frequency oscillations (LFOs) in MMC‐MVDC systems with high photovoltaic (PV) penetration, considering the whole operating conditions of the grid‐connected MMC, including both the inverter and rectifier modes. First, the multi‐port impedance models of converters and networks are developed, upon which a stability criterion is defined. The mechanism of LFOs is then identified using bus participation analysis and parameter sensitivity analysis. In addition, the stable zones of the dominant parameters are identified, taking into account the whole operating conditions of the grid‐connected MMC. Interestingly, a phase‐locked loop may induce LFOs in two low‐frequency ranges. Finally, the experimental results confirm the correctness of the theoretical analysis.

Multi-objective coordinated optimization of low-carbon building energy systems based on high renewable energy penetration

In the context of China's targets to achieve a "carbon peak" by 2030 and "carbon neutrality" by 2060, the operation and maintenance of buildings with low-carbon and zero-energy consumption emerge as a critical technology for accomplishing the "dual carbon" goals in the building sector. As a novel energy supply system, low-carbon building energy systems can provide low-carbon, efficient, and economical energy supplies. Accordingly, this paper initially constructs a low-carbon building energy system based on a high penetration of renewable energy sources. Subsequently, considering the benefits of economic efficiency, carbon emissions, and renewable energy utilization, a multi-objective cooperative optimization model for low-carbon building energy systems with high renewable energy penetration is established. Finally, a multi-objective particle swarm optimization algorithm is applied to solve the model, and the system is studied and analyzed using a large domestic park as a case study. The results indicate that the low-carbon building energy system with high renewable energy integration, aiming for economic efficiency, carbon reduction, and increased renewable energy consumption, achieves supply energy costs of $ 19.2/m2 and carbon emissions of 49.1 kg/m2. These values represent reductions of $ 7.4/m2 and 60.9 kg/m2 compared to separate supply systems. Lastly, the study of the low-carbon building energy system provides theoretical references for renewable energy integration and energy transition in the building sector.

Distributed control of virtual energy storage systems for voltage regulation in low voltage distribution networks subjects to varying time delays

Distributed communication-based strategies are popular for regulating nodal voltages in distribution networks with high penetration of Photovoltaic (PV) sources. Time delays inevitably pose challenges to efficient voltage regulation and power sharing. In response, this paper presents a distributed, event-triggered voltage regulation approach that enables power sharing across virtual energy storage systems (VESS) with different parameters while accommodating diverse time delays. The process begins by determining the delay margin for the primary Volt/Watt controller in a low-voltage distribution network (LVDN), laying the foundation for stable feedback control gain design. To accommodate any arbitrary large and time-varying delays, a distributed resilient event-triggered secondary controller for VESS is designed to share voltage regulation tasks according to their capacities and state of energy (SoE) values. A mathematical analysis, grounded in the Lyapunov–Krasovskii approach, evaluates the stability and convergence of the proposed controller amidst time delays thoroughly. To validate the proposed method, simulation studies are conducted, which demonstrate the effectiveness in mitigating voltage limit violations, even with variable communication delays, and highlight balanced power sharing and SoE among VESS.

An NSCT-Based Multifrequency GPR Data-Fusion Method for Concealed Damage Detection

Ground-penetrating radar (GPR) is widely employed as a non-destructive tool for subsurface detection of transport infrastructures. Typically, data collected by high-frequency antennas offer high resolution but limited penetration depth, whereas data from low-frequency antennas provide deeper penetration but lower resolution. To simultaneously achieve high resolution and deep penetration via a composite radargram, a Non-Subsampled Contourlet Transform (NSCT) algorithm-based multifrequency GPR data-fusion method is proposed by integrating NSCT with appropriate fusion rules, respectively, for high-frequency and low-frequency coefficients of decomposed radargrams and by incorporating quantitative assessment metrics. Despite the advantages of NSCT in image processing, its applications to GPR data fusion for concealed damage identification of transport infrastructures are rarely reported. Numerical simulation, tunnel model test, and on-site road test are conducted for performance validation. The comparison between the evaluation metrics before and after fusion demonstrates the effectiveness of the proposed fusion method. Both shallow and deep hollow targets hidden in the simulated concrete structure, real tunnel model, and road are identified through one radargram obtained by fusing different radargrams. The significance of this study is producing a high-quality composite radargram to enable multi-depth concealed damage detection and exempting human interference in the interpretation of multiple radargrams.

Microbial Squalene: A Sustainable Alternative for the Cosmetics and Pharmaceutical Industry - A Review.

Squalene is a natural triterpenoid and a biosynthetic precursor of steroids and hopanoids in microorganisms, plants, humans, and other animals. Squalene has exceptional properties, such as its antioxidant activity, a high penetrability of the skin, and the ability to trigger the immune system, promoting its application in the cosmetic, sustenance, and pharmaceutical industries. Because sharks are the primary source of squalene, there is a need to identify low-cost, environment friendly, and sustainable alternatives for producing squalene commercially. This shift has prompted scientists to apply biotechnological advances to research microorganisms for synthesizing squalene. This review summarizes recent metabolic and bioprocess engineering strategies in various microorganisms for the biotechnological production of this valuable molecule.

Microfluidic-enabled aptamer-modified liposomal probes for targeted transient triplet differential photoacoustic imaging

Photoacoustic (PA) imaging, merging high spatial resolution and deep tissue penetration, holds promise for biomedical applications, particularly cancer diagnosis. The effectiveness of PA imaging heavily relies on the use of contrast agents that enhance the PA signal. However, the efficient preparation of targeted PA contrast agents remains a challenge. Here, we report the development of microfluidic-enabled aptamer-modified liposomal probes for targeted transient triplet differential (TTD) PA imaging. Utilizing projection micro stereolithography (PµSL) 3D printing, we fabricated cost-effective microfluidic chips for the rapid synthesis of methylene blue (MB)-loaded anti-programmed cell death ligand-1 (anti-PDL1) aptamer-modified liposomes (Apt-MB-Lip). We conducted the TTD-PA imaging of Apt-MB-Lip to improve imaging sensitivity by reducing background noise. In vivo TTD-PA experiments demonstrated the superior targeting ability and sustained presence of the Apt-MB-Lip in tumors. Our findings underscore the potential of these targeted probes for advanced diagnostic and therapeutic applications.