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Abstract Crystalline solids play a fundamental role in a host of materials and technologies, ranging from pharmaceuticals to renewable energy. The thermodynamic properties of these solids are crucial determinants of their stability and therefore their behavior. The advent of large density functional theory databases with properties of solids has stimulated research on predictive methods for their thermodynamic properties, especially for the enthalpy of formation Δf H. Increasingly sophisticated artificial intelligence and machine learning (ML) models have primarily driven development in this field in recent years. However, these models can suffer from lack of generalizability and poor interpretability. In this work, we explore a different route and develop and evaluate a framework for the application of reaction network (RN) theory to the prediction of Δf H of crystalline solids. For an experimental dataset of 1550 compounds we are able to obtain a mean absolute error w.r.t Δf H of 29.6 meV atom−1 using the RN approach. This performance is better than existing ML-based predictive methods and close to the experimental uncertainty. Moreover, we show that the RN framework allows for straightforward estimation of the uncertainty of the predictions.

BackgroundInflammatory myofibroblastic tumors (IMTs) are rare soft-tissue neoplasms. Accordingly, there is no standardized therapy for unresectable or advanced IMT. Chemotherapy, radiotherapy, and targeted molecular therapy play an important role in unresectable or advanced IMT.Case presentationWe present a 54-year-old man with a cough and chest distress case report. The thoracic surgeon performed the right upper pulmonary occupying lesion wedge resection and enlarged lymph node excision biopsy. Pathologic diagnosis revealed that the morphology of “right upper lung mass” was considered as Inflammatory Myofibroblastic Tumor (IMT). Radiotherapy was indicated at a high dose: 5400cGy in 27 fractions of 2Gy over 5 weeks were delivered combined with cisplatin. The patient was given a CT/MRI and hematological index every 3 months and experienced no more adverse events. The patient survives with no tumor recurrence as of the last follow-up. Progression-free survival (PFS) exceeded 5 years.ConclusionsWe have reviewed the literature and summarized and discussed the radiotherapy treatment options and challenges for IMT. We first reported high-dose radiotherapy combined with chemotherapy treatment for unresectable IMT. Concurrent radiochemotherapy may be considered an intensive treatment for local progress, local recurrence, and nonresectable IMT patients.

ABSTRACT: Avian paramyxoviruses (APMV) belong to the subfamily Avulavirinae of the family Paramyxoviridae and include 22 distinct subtypes or serotypes (1–22). Avian paramyxovirus serotype 12 (APMV-12) is found sporadically in wild birds worldwide, and reports from only Italy and Taiwan have been published to date; information on its genetic variation and biological characteristics is still limited. In this study, 3 APMV-12 strains, designated WB19, LY9, and LY11, were isolated from 8643 wild bird faecal samples during the annual influenza virus surveillance of wild birds in Guangdong, China between 2018 and 2024, which is first reported in mainland China. The complete genomes of the 3 viruses with 6 gene segments, 3′-N-P-M-F-HN-L-5′, were 15,231 nt in length. Phylogenetic analysis based on the whole genome showed that the 3 APMV-12 strains had the highest homology with an APMV-12 strain isolated from Taiwan in 2015, followed by the prototype APMV-12 strains isolated from mallard ducks in Italy in 2005. Genetic analysis of the whole gene of each of them indicated that they were derived from a Eurasian lineage. This study provides additional evidence that wild birds transmit viruses between countries, and this should be monitored to understand APMV transmission, evolution and epidemiology.

Malassezia is a group of resident fungi on the skin, which can cause diseases under certain conditions. Among the multiple complicated factors involved in the pathogenesis, secreted proteases of Malassezia are the key factors. Secreted proteases are enzymes capable of hydrolyzing protein peptide bonds, which has special significance in the skin environment enriched in proteins and lipids. So far, 21 species of Malassezia have been identified. Most studies are related to identification and naming, protease activity and pathogenicity of secreted proteases secreted by Malassezia furfur, Malassezia globosa, Malassezia pachydermatis and Malassezia restricta. This paper aims to review the research progress on the secreted proteases of Malassezia furfur, Malassezia globose, Malassezia pachydermatis and Malassezia restricta.

Abstract Annexin A2 (ANXA2) is a widely reported oncogene. However, the mechanism of ANXA2 in esophageal cancer is not fully understood. In this study, we provided evidence that ANXA2 promotes the progression of esophageal squamous cell carcinoma (ESCC) through the downstream target threonine tyrosine kinase (TTK). These results are consistent with the up-regulation of ANXA2 and TTK in ESCC. In vitro experiments by knockdown and overexpression of ANXA2 revealed that ANXA2 promotes the progression of ESCC by enhancing cancer cell proliferation, migration, and invasion. Subsequently, animal models also confirmed the role of ANXA2 in promoting the proliferation and metastasis of ESCC. Mechanistically, the ANXA2/TTK complex activates the Akt/mTOR signaling pathway and accelerates epithelial-mesenchymal transition (EMT), thereby promoting the invasion and metastasis of ESCC. Furthermore, we identified that TTK overexpression can reverse the inhibition of ESCC invasion after ANXA2 knockdown. Overall, these data indicate that the combination of ANXA2 and TTK regulates the activation of the Akt/mTOR pathway and accelerates the progression of ESCC. Therefore, the ANXA2/TTK/Akt/mTOR axis is a potential therapeutic target for ESCC.

In this paper, a robust, multi-modal deep-learning-based fault identification method is proposed for solar photovoltaic (PV) systems, capable of detecting a wide range of faults at PV arrays, inverters, sensors, and grid connections. The proposed method combines residual convolutional neural networks (CNNs) and gated recurrent units (GRUs) to effectively extract both spatial and temporal features from raw PV data. To enhance the proposed model’s robustness and accuracy, a probabilistic loss function based on the entropy theory is formulated. The proposed method is validated using both experimental data obtained from a PV emulator-based test system and simulation data, achieving over 98% accuracy in fault identification under various noise conditions. The results indicate that the proposed model outperforms conventional CNN- and MSVM-based methods, demonstrating its potential in providing precise fault diagnostics in PV systems.

A multi-microgrid (MMG) consists of several individual microgrids (MGs) within a distribution system to improve the system’s stability and reliability. A MMG can operate in grid-connected or island mode and requires advanced control techniques and effective energy management. This paper proposes a novel energy management approach for a MMG at the tertiary level control (TLC) using an adaptive optimal control model. Operational costs of the MMG are minimized for short-term planning while satisfying operational constraints of the network; the influential indices, the energy not supplied (ENS) and fatigue life (FL), remain balanced. The information gap decision theory (IGDT) is used to consider uncertainties in power generation and consumptions. MATLAB and DigSilent are used simultaneously to model optimally connected individual MGs within a MMG. The Tunicate Swarm Algorithm (TSA) is used for TLC for cost calculation and forming optimal connection models of individual MGs. The proposed method is validated through several case studies, showing superior performance.

The dissolved gas analysis (DGA) data play a crucial role in evaluating the transformer health index (HI). In recent years, data-driven approaches have attracted significant research interest for the HI prediction with various health condition data. However, the DGA data collection is prone to missing or erroneous data due to sensors or data transfer issues. Consequently, handling missing data requires careful attention for accurate HI computation. In this paper, a novel data-driven hybrid approach is proposed that leverages the Light Gradient Boosting Machine (LightGBM) as a regression method and the Robust Expectation-Maximization (robust-EM) as a missing data imputation technique to predict the HI of transformers using DGA data. The proposed method is evaluated through five case studies with the percentage of missing data at 0%, 5%, 10%, 15%, and 20%. The proposed method has been compared with seven benchmark methods through six evaluation metrics, showing superior performance. The proposed method is also analyzed with and without robust-EM, and 22% – 71% performance improvements across various case studies and performance metrics have been achieved with robust-EM.

The Volt/Var optimization (VVO) enables advanced control strategy development for voltage regulation. With the recent advancement of data-driven approaches and communication infrastructure, realtime decision-making through VVO can effectively address distributed energy resources (DERs) uncertainties without relying on models and topologies of distribution networks. In this paper, a comprehensive review on data-driven VVO in distribution networks is presented, focusing on statistics and machine learning (supervised/unsupervised, ensemble, and reinforcement learning (RL)). State-of-the-art monitoring devices essential in data-driven VVO frameworks are firstly discussed. How data-driven structures serve as primary or supplementary tools in VVO frameworks is then detailed. Since RL is increasingly used, RL-based algorithms (value-based, policy-based, actor-critic-based, and graph-based algorithms) are reviewed. Decision-making processes for RL-based VVO frameworks, such as the Markov decision process (MDP), Markov game, constrained Markov decision process, constrained Marko game, and adversarial Markov decision process, are also surveyed. Future research directions in this area are recommended in the paper.

Abstract The relationship between total lymphocyte counts (TLCs) and survival is not well documented in rectal cancer. This study aimed to investigate the association between TLCs and disease-free survival (DFS) and identify factors associated with lymphopenia in locally advanced rectal cancer patients receiving chemoradiotherapy. Thirty-six patients with locally advanced rectal cancer were retrospectively analyzed. TLCs were evaluated before surgery (pre-S), before radiotherapy (pre-RT), and during concurrent chemoradiotherapy (CCRT). The relationship between TLCs and DFS was analyzed by univariate and multivariate analysis. Potential clinical factors associated with lymphopenia were also evaluated. Median TLC declined significantly during radiotherapy. Severe lymphopenia during CCRT was significantly associated with poorer DFS on Kaplan–Meier analysis (p = 0.01), univariate regression analysis (p = 0.036), and multivariate regression analysis (p = 0.038). Pre-S TLCs (p = 0.009) and pre-RT TLCs (p = 0.042) were significantly associated with severe lymphopenia on univariate regression analysis; however, only pre-S TLCs (p = 0.026) were significantly associated with severe lymphopenia on multivariate regression analysis. Severe lymphopenia was a predictor of poorer DFS in patients with locally advanced rectal cancer receiving adjuvant chemoradiotherapy. Pre-S TLCs were predictors of severe lymphopenia. Further study is warranted to reduce the rate of severe lymphopenia.

Abstract Background The epidermis of cotton ovule produces fibers, the most important natural cellulose source for the global textile industry. However, the molecular mechanism of fiber cell growth is still poorly understood. Results Here, we develop an optimized protoplasting method, and integrate single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) to systematically characterize the cells of the outer integument of ovules from wild type and fuzzless/lintless (fl) cotton (Gossypium hirsutum). By jointly analyzing the scRNA-seq data from wildtype and fl, we identify five cell populations including the fiber cell type and construct the development trajectory for fiber lineage cells. Interestingly, by time-course diurnal transcriptomic analysis, we demonstrate that the primary growth of fiber cells is a highly regulated circadian rhythmic process. Moreover, we identify a small peptide GhRALF1 that circadian rhythmically controls fiber growth possibly through oscillating auxin signaling and proton pump activity in the plasma membrane. Combining with scATAC-seq, we further identify two cardinal cis-regulatory elements (CREs, TCP motif, and TCP-like motif) which are bound by the trans factors GhTCP14s to modulate the circadian rhythmic metabolism of mitochondria and protein translation through regulating approximately one third of genes that are highly expressed in fiber cells. Conclusions We uncover a fiber-specific circadian clock-controlled gene expression program in regulating fiber growth. This study unprecedentedly reveals a new route to improve fiber traits by engineering the circadian clock of fiber cells.

With increasing penetration of wind power, accurate prediction of wind speed is essential for planning and operation of power grids. In this paper, a novel two-dimensional (2D) convolutional neural network (CNN)-based wind speed forecasting technique is proposed for an hour-ahead wind speed prediction. The wind speed at a specific time can be predicted in less than a few milliseconds using the proposed approach and meteorological data from a few hours earlier. The input feature selection, data preprocessing, and model evaluation of the proposed approach are presented; the efficiency of 2D CNN is compared to that of one-dimensional (1D) CNN, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP). A three-year historical wind speed dataset from 2020 to 2022 collected at Saskatoon International Airport in Saskatoon, Saskatchewan, Canada, is used in this study. It is found that 2D CNN shows superior performance in addressing regression and prediction challenges. Experimental results verify that the proposed 2D CNN-based forecasting techniques can provide accurate wind speed prediction. Using deep learning for wind speed prediction can reduce costs while boost energy output and contribute to sustainable and green energy development in Saskatchewan and beyond.

In this paper, a single-inductor multi-input single-output (SI-MISO) DC-DC boost converter is used for solar photovoltaic (PV) systems in DC or AC grids. The converter with N number of inputs combines energy from PV panels along with fuel cells (FCs) to maintain stability. By implementing a new switching algorithm in the converter, the output voltage of the converter and the power absorbed from each source can be controlled independently, resulting in a simple and unique controller. In addition, the multivariable control system can be controlled as N number of single-input single-output (SISO) systems without any delay between switches due to the small-signal model of the converter and the proposed proportional-integral-derivative (PID) cascade control scheme. For each PV source, an independent maximum power point tracking (MPPT) algorithm can be employed, and the current sharing among different sources can be achieved independently. The converter’s operating modes and the designed controller are present in the paper. Experimental results using a 100-W prototype converter validate the accuracy and efficiency of the proposed control scheme.

Overloading and load imbalance have a significant impact on the health of distribution transformers. The load of a distribution transformer can be considered in a hierarchical way: individual single-phase customers connected directly to the transformer (the bottom level), the load at each phase (the middle level), and the total load among three phases (the top level). Load at each hierarchical level can be predicted individually, known as “base forecast”, through a state-of-the-art forecasting method, but this practice often leads to incoherency and bias, i.e., forecasts at a lower hierarchical level are not aggregated correctly to the forecast at a higher-hierarchical-level. In this paper, a novel load aggregation technique based on minimum trace (MinT)-based optimal reconciliation is proposed to improve the accuracy of prediction models. Base forecasts at each hierarchical level are firstly determined using independent autoregressive integrated moving average (ARIMA) models; MinT is then used to optimally reconcile base forecasts to ensure higher accuracy. The proposed method is validated by case studies. Advanced metering infrastructure (AMI) data recorded by Saskatoon Light and Power in Saskatoon; Canada is used as historical data in this study.

Day-ahead forecasts are required by electricity market investors to make informed decisions on the trading floor. Whereas it is relatively easier to predict the performance of a few large-scale photovoltaic (PV) systems, a large number of small-scale PV systems with a wide geographical spread poses more challenges because they are often not metered for real-time monitoring. This paper proposes an artificial neural network (ANN)-based model to achieve regional-scale day-ahead PV power forecasts based on weather variables from numerical weather predictions (excluding solar irradiance) as inputs. The model was first implemented by dividing a region into clusters and selecting a representative site for each cluster using data dimension reduction algorithms. Solar irradiance forecasts were then generated for each representative PV system and the corresponding PV power was simulated. The cluster power output was obtained using a linear upscaling model and summed to produce regional-scale power forecasts. The model’s accuracy is validated using power generation data of several distributed systems in California. The results show at least a 29-percent root mean square error reduction over the benchmarking models.

Lithium-ion batteries are currently the most popular and widely used energy storage devices, almost omnipresent within modern society in portable devices, electrical vehicles, energy storage stations, and so on. The demand for more efficient, more durable, and more sustainable batteries is rapidly growing. The electrolyte is a key element to improve the performance of lithium-ion batteries. In this work, we focus on quaternary ionic liquid electrolyte (ILE), which uses a four-component ionic liquid as the solvent. Quaternary ILE has found wide applications in energy storage systems, but the ion transport in the electrolyte has not been fully characterized to provide the best strategy for performance optimisation. In this work, we systematically analyse the ion transport in the quaternary ILE and uncover how the correlations between various ions affect the conductivity of the electrolyte. We have found that lithium ions are transported in charge clusters, leading to a negative effective transference number of lithium ions. Furthermore, we identify the stable cluster conformations in ILE by cluster analysis and quantum chemical computing. This work highlights the necessity of considering ion correlations in multi-component electrolyte systems.

Abstract Histones are DNA‐binding basic proteins found in chromosomes. After the histone translation, its amino tail undergoes various modifications, such as methylation, acetylation, phosphorylation, ubiquitination, malonylation, propionylation, butyrylation, crotonylation, and lactylation, which together constitute the “histone code.” The relationship between their combination and biological function can be used as an important epigenetic marker. Methylation and demethylation of the same histone residue, acetylation and deacetylation, phosphorylation and dephosphorylation, and even methylation and acetylation between different histone residues cooperate or antagonize with each other, forming a complex network. Histone‐modifying enzymes, which cause numerous histone codes, have become a hot topic in the research on cancer therapeutic targets. Therefore, a thorough understanding of the role of histone post‐translational modifications (PTMs) in cell life activities is very important for preventing and treating human diseases. In this review, several most thoroughly studied and newly discovered histone PTMs are introduced. Furthermore, we focus on the histone‐modifying enzymes with carcinogenic potential, their abnormal modification sites in various tumors, and multiple essential molecular regulation mechanism. Finally, we summarize the missing areas of the current research and point out the direction of future research. We hope to provide a comprehensive understanding and promote further research in this field.

High-resolution flame temperature images are essential indicators for evaluating combustion conditions. Tunable diode laser absorption spectroscopy (TDLAS) is an effective combustion diagnostic method. In actual engineering, due to the limitation of line-of-sight (LOS) measurement, TDLAS technology has the problems of small data volume and low dimensionality in measuring combustion fields, which seriously limits the development of TDLAS in combustion diagnosis. This article demonstrates a TDLAS imaging method based on a 64-pixel area array sensor to reconstruct the two-dimensional temperature field of the flame. This paper verifies the robustness of the Algebraic Reconstruction Technique (ART) algorithm through numerical simulation and studies the effects of temperature, concentration, and pressure on the second harmonic intensity based on the HITRAN database. The two-dimensional temperature field of the flame was reconstructed, and reconstruction accuracy was verified using thermocouples. The maximum relative error was 3.71%. The TDLAS detection system based on a 64-pixel area array sensor provides a way to develop high-precision, high-complexity flame temperature measurement technology.

The growing interconnection between information networks and power grids enables a more efficient and economical operation, but also introduces significant challenges in modern cyber-physical power systems, such as malicious cyber-attacks. Several cyber-attacks have been reported in the recent decade, affecting hundreds of thousands of people. These attacks can lead to large power system blackouts, and optimization models are essential tools for optimal decision-making in reliable and secure operation of cyber-physical power systems. In this paper, optimization models in cyber-physical power systems are extensively reviewed and classified based on their applications, including cyber-security and optimal operation. One major application of optimization is in cyber-security evaluation of smart grids. This paper investigates the models to implement cyber-attacks against state estimation, coordinated cyber-physical attacks aiming to cover each other, and financially-motivated attacks in electricity markets; optimal defense strategies and interactions between the attacker and the defender are also introduced. Furthermore, optimization models used in the operation and dispatching of cyber-physical power and energy systems, optimal routing of information networks, and privacy-preserving models are presented. Finally, as solving optimization models is a crucial step in optimal decision-making, solvers in the literature are also introduced.

Load forecasting is a critical aspect for power systems planning, operation and control. In this paper, as part of research efforts of an ambitious project at Memorial University of Newfoundland in St. John’s, Canada, to achieve more energy efficient and environmental friendly “Sustainable Campus”, we present a day-ahead load forecasting approach for the energy management system of the project. The hourly load consumption dataset from January 1, 2016 to March 31, 2020 is used in the paper, which was collected from two power meters on campus. Using the load consumption dataset along with the collected meteorological dataset, a total of 19 regression model-based day-ahead load forecasting algorithms for Memorial University of Newfoundland’s campus load are developed and evaluated in this paper. These 19 models belong to five families of regression models in MATLAB Regression Toolbox: Linear Regression, Regression Trees, Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Ensemble of Trees. It is found that the family of GPR models shows the best load forecasting performance because they are nonparametric kernel-based probabilistic models. Two GPR models, Rational Quadratic GPR and Exponential GPR, are recommended as the best models for load forecasting through this study.

Microgrid formation is a promising solution to enhance resiliency of distribution networks. The self-adequacy feature of a microgrid enables continuity of power supply through distributed generation (DG) units during severe faults and natural disasters. In this paper, different methods commonly used to partition a distribution network into multiple microgrids are presented, including the graph theory, heuristic rule-based algorithm, cluster-based technique, and mixed integer programming. Advantages and disadvantages of these techniques and future research directions are presented. This review provides an excellent summary on service restoration through micrgrid formation, and offers a valuable reference for researchers working on grid modernization of distribution networks.

A novel strategy was developed to fabricate nano-porous silver (NPS) with a three-dimensional bi-continuous open porous structure. A high temperature oxidation (HTO) craft and a strong oxidant-assisted low-temperature oxidation method were used to tune the dealloying of AgXCu(1-X) ribbons. The NPS fabricated from the Ag15Cu85 ribbon by the HTO pretreatment exhibited a ligament size of ∼841 nm after 1 h of dealloying. However, the NPS fabricated from the Ag15Cu85 ribbon by the H2O2 craft exhibited a ligament size of ∼61 nm after 1 h of dealloying. In addition, the NPS fabricated by the strong oxidant-assisted dealloying craft exhibited an ultrahomogeneous ligament pore structure and a higher Tafel slope in regard to its hydrogen evolution reaction.

Salvianolic acid B is extracted from the roots and rhizomes of Danshen (Salvia miltiorrhiza Bge., family Labiatae). It is a water-soluble, weakly acidic drug that has demonstrated antitumor and anti-inflammatory effects on various organs and tissues such as the lung, heart, kidney, intestine, bone, liver, and skin and protective effects in diseases such as depression and spinal cord injury. The mechanisms underlying the protective effects of salvianolic acid B are mainly related to its anti-inflammatory, antioxidant, anti- or pro-apoptotic, anti- or pro-autophagy, anti-fibrotic, and metabolism-regulating functions. Salvianolic acid B can regulate various signaling pathways, cells, and molecules to achieve maximum therapeutic effects. This review summarizes the safety profile, combination therapy potential, and new dosage forms and delivery routes of salvianolic acid B. Although significant research progress has been made, more in-depth pharmacological studies are warranted to identify the mechanism of action, related signaling pathways, more suitable combination drugs, more effective dosage forms, and novel routes of administration of salvianolic acid B.

Long non-coding RNA LINC00152 (cytoskeleton regulator, or LINC00152) is an 828-bp lncRNA located on chromosome 2p11.2. LINC00152 was originally discovered during research on hepatocarcinogenesis and has since been regarded as a crucial oncogene that regulates gene expression in many cancer types. LINC00152 is aberrantly expressed in various cancers, including gastric, breast, ovarian, colorectal, hepatocellular, and lung cancer, and glioma. Several studies have indicated that LINC00152 is correlated with cell proliferation, apoptosis, migration, invasion, cell cycle, epithelial-mesenchymal transition (EMT), chemotherapy and radiotherapy resistance, and tumor growth and metastasis. High LINC00152 expression in most tumors is significantly associated with poor patient prognosis. Mechanistic analysis has demonstrated that LINC00152 can serve as a competing endogenous RNA (ceRNA) by sponging miRNA, regulating the abundance of the protein encoded by a particular gene, or modulating gene expression at the epigenetic level. LINC00152 can serve as a diagnostic or prognostic biomarker, as well as a therapeutic target for most cancer types. In the present review, we discuss the roles and mechanisms of LINC00152 in human cancer, focusing on its functions in chemotherapy and radiotherapy resistance.

The long non-coding RNA (lncRNA) PVT1 was first found to activate variant translocations in the plasmacytoma of mice. Human lncPVT1 is located on chromosome 8q24.21, at the same locus as the well-known MYC oncogene. LncPVT1 has been found to promote the progression of various malignancies. Chemoresistance and radioresistance seriously affect tumor treatment efficacy and are associated with the dysregulation of physiological processes in cancer cells, including apoptosis, autophagy, stemness (for cancer stem cells, CSC), hypoxia, epithelial–mesenchymal transition (EMT), and DNA damage repair. Previous studies have also implicated lncPVT1 in the regulation of these physiological mechanisms. In recent years, lncPVT1 was found to modulate chemoresistance and radioresistance in some cancers. In this review, we discuss the mechanisms of lncPVT1-mediated regulation of cellular chemoresistance and radioresistance. Due to its high expression in malignant tumors and sensitization effect in chemotherapy and radiotherapy, lncPVT1 is expected to become an effective antitumor target and chemotherapy and radiotherapy sensitizer, which requires further study.

Machine learning has paved its way into induction motors fault diagnosis area, where supervised learning and deep learning have been employed. However, both learning methods require a large amount of labeled data to train the model, which pose significant challenges in real life applications. To overcome this issue, in this paper, the graph-based semi-supervised learning (GSSL) is adopted to develop a fault diagnosis method for direct online induction motors due to GSSL's superior feature that only a small amount of labeled data is needed in training datasets. To evaluate its suitability, the greedy-gradient max cut (GGMC) algorithm in the GSSL family is chosen in this study, and an effective fault diagnosis approach is developed using experimental stator currents recorded in the lab for two induction motors. The developed approach can conduct binary and multiclass classifications for faults on direct online induction motors. As a critical step, curve fitting equations are developed to calculate features for untested motor loadings by using experimental data for tested motor loadings, which enables the proposed approach to remain effective under all potential motor loading conditions.

In this paper, a novel fault diagnosis method for variable frequency drive (VFD)-fed induction motors is proposed using Wavelet Packet Decomposition (WPD) and greedy-gradient max-cut (GGMC) learning algorithm. The proposed method is developed using experimental stator current data in the lab for two 0.25 HP induction motors fed by a VFD, subjected to healthy and faulty cases under various operating frequencies and motor loadings. The features are extracted from stator current signals using WPD by evaluating energy eigenvalues and feature coefficients at decomposition levels. The proposed method is validated by comparing with other graph-based semi-supervised learning (GSSL) algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF). To enable fault diagnosis for untested motor operating conditions, mathematical equations to calculate features for untested cases are developed through surface fitting using features extracted from tested cases.

As a dominant form of renewable energy sources with significant technical progress over the past decades, wind power is increasingly integrated into power grids. Wind is chaotic, random and irregular. For proper planning and operation of power systems with high wind power penetration, accurate wind speed forecasting is essential. In this paper, a novel hybrid Neural Network (NN)-based day-ahead (24 hour horizon) wind speed forecasting is proposed, where five hybrid neural network algorithms are evaluated. The five algorithms include Wavelet Neural Network (WNN) trained by Improved Clonal Selection Algorithm (ICSA), WNN trained by Particle Swarm Optimization (PSO), Extreme Learning Machine (ELM)-based neural network, Radial Basis Function (RBF) neural network, and Multi-Layer Perceptron (MLP) Neural Network. Single- and multi-features and their effect on the accuracy of wind speed prediction are also analyzed. The wind speed dataset used in this paper is Saskatchewan’s recorded historical wind speed data. Despite the excellent wind power potential, only 6.5% of the total electricity demand is currently supplied by wind power in Saskatchewan, Canada. This study paves the way for economical operation, planning, and optimization of Saskatchewan’s future wind power generation.

A railway running near power lines is subjected to electromagnetic interference from power lines, which can adversely affect normal operation of the railway’s communication and signaling system, resulting in safety hazard on the railway equipment and personnel. Therefore, it is very important to assess such electromagnetic interference issues to ensure the railway system’s reliability and the public safety. In this paper, a literature review is conducted on electromagnetic interference between railways and nearby power lines, which has not been widely researched in the past. Although IEEE Standard 2746–2020 has raised the importance of such electromagnetic interference, but it only offers very basic information without an in-depth coverage. This paper provides a review, where critical aspects of electromagnetic interference between railways and power lines are summarized and the future research direction in this area is recommended.

In this paper, an expandable non-isolated multi-input single-output DC-DC high voltage gain converter with the capability of cancelling input current ripples is proposed, which can be applied in photovoltaic (PV) systems. The proposed converter has several advantages including: 1) it provides a single voltage gain for the whole range of duty cycles; 2) the duty cycle’s operating range is not decreased when increasing the number of input stages; 3) it has a simple switching pattern, and all switches go on and off, simultaneously; 4) the current ripple of each input stage is eliminated for the whole range of duty cycles; 5) the output load has a common ground with all input voltage sources; and 6) the voltage stress on switches is low. In this paper, the proposed converter in a dual-input single-output (DISO) form is introduced at first, and its general form with multi-input single-output (MISO) is then analyzed. Following that, the voltage gain, voltage and current stresses on switches, diodes and capacitors, and input current ripple cancelling condition are calculated. Finally, experimental results are used to validate the calculated theoretical results.

Water is a fascinating substance with lots of properties not encountered in other compounds. It has been suggested that making use of water's “anomalous” properties can lead to exciting applications in various disciplines e.g. engineering, medicine and physiology. The origin of these anomalous properties is far from clear, with the strong hydrogen bonds of water being only part of the answer. Moreover, water's structure and dynamics are not entirely understood either and new theories have appeared and debated during the 21st century. This review, aiming at a broader/general audience, attempts to review briefly some research trends related to water's structure, properties and applications. New experimental results for the debated phenomena of water bridge and exclusion zone are also presented and discussed. Various explanatory mechanisms for the exclusion zone are reviewed and a new proposal is put forward. A –hopefully- unbiased discussion is presented for both “mainstream” and unconventional theories and trends and future directions are also outlined.

BackgroundEpidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are effective treatments for non-small cell lung cancer (NSCLC) patients with activating EGFR mutations. There are many uncommon and rare mutations in the EGFR gene. The efficacy of the EGFR-TKIs is largely unknown for cancers harboring uncommon or rare EGFR mutations.Case PresentationA 69-year-old woman was diagnosed with adenocarcinoma cT4N2M1c, stage IVB. Next-generation sequencing (NGS) confirmed a rare EGFR V786M mutation. During chemotherapy, immune checkpoint inhibitor (ICI), and anti-angiogenic treatment, no radiological response was observed. Subsequent third-generation EGFR TKI showed a remarkable therapeutic effect. Structural prediction revealed that the V786M mutation induces conformational change at the dimer interface, without altering the ATP binding to the EGFR tyrosine kinase domain (TKD). Consistently, docking simulations indicated that the affinity of ATP to the V786M mutant was not disturbed, which explained the TKI sensitivity.ConclusionsOur data confirmed the activating role on EGFR V786M mutation. Together with structural predictions and clinical evidence for activity of TKIs against EGFR V786M mutations, these findings warrant further investigation.

Background: The patterns and risk factors of intentional injuries compared to unintentional injuries among Chinese children and adolescents have not been examined in depth. This work comprehensively describes patterns of intentional injuries in China, for which little information has been previously published. Methods: All cases involving individuals 0–17 years old registered at emergency rooms and outpatient clinics were examined using data submitted to the National Injury Surveillance System from 2006 through 2017. A logistic regression model was performed to explore the risk factors related to intentional injuries compared to unintentional injuries. Results: A total of 81,459 (95.1%) unintentional injuries, 4,218 (4.9%) intentional injuries (4,013 violent attacks and 205 self-mutilation/suicide) cases were identified. Blunt injuries accounted for 59.4% of violent attacks, while cuts and poisoning accounted for 37.1% and 23.4% of injuries involving self-mutilation/suicide, respectively. For unintentional injuries, falls (50.4%) ranked first. Additional risk factors for intentional injuries included being male (odds ratio [OR] 1.6), coming from rural areas (OR 1.9), being staff or workers (OR 2.2), and being a student (OR 1.8). As the age of the patients increased, so did the risk of intentional injuries (OR 5.0 in the 15–17 age group). Intentional injuries were more likely to occur at 00:00–03:00 am (OR 2.0). Conclusions: Intentional injuries affected more males, rural and older children, school students, and staff or workers. The mechanisms and occurrence times differed according to age group. Preventive measures should be taken to reduce the dropout of rural students, strengthen the school’s violence prevention plan, and reduce self-harm.

This paper proposes a LoRa-based wireless communication system for data transfer in microgrids. The proposed system allows connection of multiple sensors to the LoRa transceivers, and enables data collection from various units within a microgrid. The proposed system focuses on communications at the secondary communication level of the microgrid between local controllers of each distributed generation (DG) unit and the microgrid central controller due to the possibility of applying low-bandwidth communication systems at this level. In a proof of concept test bed setup, the data collected by the sensors are sent to the LoRa gateway, which serves as the central monitoring system from which control messages are sent to various microgrid components through their local controllers such as DG units, storage systems and load. In this work, to improve communication security, a private server has been developed using Node-Red instead of cloud servers that are currently used in most Internet-of-Things (IoT) monitoring systems. A range test of the proposed system is carried out to observe the rate of data delivery. It demonstrated over 90% data delivery at 4 km. Finally, a test bed experiment is conducted to validate key features of the proposed system by achieving one-directional data transfer in a grid monitoring system.

Electric submersible pumps (ESPs) are widely used for high volumetric oil and gas recovery from downhole wells. ESPs in oil fields can be equipped with multiple ac motors in tandem. To assist a motor start-up, a solid-state soft-starter can be deployed in such ESP wells. ESP shafts have a long axial length, and thus experience significant dynamic torsion when started from reduced voltage soft-starters. Premature shaft breakdowns had occurred in the field during the motor start-up for soft-starter fed ESPs. This paper investigates the shaft breakdown phenomenon in a 750-HP ESP system using a real-world case scenario. A lumped parameter electro-mechanical model for the ESP system is developed to observe its dynamics. The effects of static as well as fluctuating torsional stress on the interconnected pump shafts are analyzed to determine the root cause of the shaft breakdown. Criteria for successful start-up for soft-starter driven ESP systems are also presented to increase the equipment's operational life.

In this paper, a graph-based semi-supervised learning (GSSL) algorithm, greedy-gradient max cut (GGMC), based fault diagnosis method for direct online induction motors is proposed. Two identical 0.25 HP three-phase squirrel-cage induction motors under healthy, single- and multi-fault conditions were tested in the lab. Three-phase stator currents and three-dimensional vibration signals of the two motors were recorded simultaneously in each test, and used as datasets in this study. Features for machine learning are extracted from experimental stator currents and vibration data by the discrete wavelet transform (DWT). To validate the effectiveness of the proposed GGMC-based fault diagnosis method, its classification accuracy using binary classification and multiclass classification for faults of the two motors are compared with other two GSSL algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF). In this study, the performance of stator currents and vibration as a monitoring signal is evaluated, it is found that stator currents perform much better than vibration signals for multiclass classification, while they both perform well for binary classification.

Purpose: To investigate the survival outcomes, prognostic factors and treatment modalities of stage I-III cervical esophageal carcinoma (CEC) patients using data from the Surveillance, Epidemiology, and End Results (SEER) database from the period 2004–2016.Methods: Patients with a histopathologic diagnosis of CEC were included. The primary endpoint was overall survival (OS). Univariate and multivariate analyses of OS were performed using Cox proportional hazards models, and OS was compared using the Kaplan-Meier method and log-rank test.Results: A total of 347 patients in the SEER database were enrolled. The median OS was 14.0 months, with a 5-year OS rate of 20.9%. The parameters that were found to significantly correlate with OS in the multivariate analysis were age at diagnosis [P < 0.001, hazard ratio (HR) = 1.832], sex [P < 0.001, HR= 1.867], histology [P = 0.001, HR = 0.366], surgery at the primary site [P = 0.021, HR = 0.553], radiotherapy (RT, P = 0.017, HR = 0.637) and chemotherapy (CT, P < 0.001, HR = 0.444). Comparison among the three treatment modalities demonstrated that a triple therapy regimen consisting of surgery, RT and CT was associated with a longer survival time than the other two treatment modalities before and after propensity score matching (PSM). However, triple therapy showed no significant survival benefit over double therapy (P = 0.496 before PSM and P = 0.184 after PSM).Conclusions: The survival of patients with CEC remains poor. Surgery, RT and CT were all strongly correlated with OS. We recommend a triple therapy regimen for select CEC patients based on the findings of the current study, although this recommendation should be further confirmed by prospective studies with large sample sizes.

Abstract Background Colorectal cancer is the fourth most common cancer globally and neoadjuvant concurrent chemoradiotherapy (nCRT) and surgery are the standard treatments for locally advanced colorectal carcinoma. This study investigated the association between dynamic changes in absolute lymphocyte counts (ALCs) and disease-free survival (DFS) in rectal cancer patients receiving nCRT and identified factors associated with these changes. Methods We retrospectively examined 34 patients with locally advanced rectal cancer who received nCRT followed by surgery and adjuvant chemotherapy. The association between ALCs and DFS and that between ALCs and downstaging were analyzed and potential clinical- and treatment-related factors related to dynamic changes in ALCs were subsequently evaluated. The patient eligibility criteria were as follows: pathologically confirmed rectal adenocarcinoma, clinical stages II–III, ≥ 18 years of age, and so on. Pre-RTL was defined as ALCs obtained before the initiation of nCRT and pre-SL was defined as ALCs obtained before surgery. We measured pre-SL to pre-RTL ratio (pre-SLR), DFS, and ALCs. Results The median ALC declined significantly during nCRT. A lower pre-SLR was associated with poorer DFS with statistical significance in Kaplan–Meier (p = 0.007), univariate regression (hazard ratio [HR] = 6.287, 95% confidence interval [CI] 1.374–28.781, p = 0.018), and multivariable regression (HR = 7.347, 95% CI 1.595–33.850, p = 0.011) analyses. Neither patient characteristics nor treatment-related factors were related to downstaging. The pelvic bone marrow (PBM) volume receiving at least 30 Gy (V30) was significantly associated with pre-SLR in the univariate (HR = 5.760, 95% CI 1.317–25.187, p = 0.020) and multivariable (HR = 5.760, 95% CI 1.317–25.187, p = 0.020) regression analyses. Limitations Our study had several limitations. The sample size was small and the study was performed in a selected population, which may limit the generalization of the findings. Conclusions Radiotherapy had a profound impact on the change in ALCs. A lower pre-SLR was significantly associated with poorer DFS in rectal cancer patients receiving nCRT. The V30 of PBM was a predictor of pre-SLR.

Based on thermal–mechanical coupling simulation analysis and physical engineering tracking observation, the mechanical behavior and response of a continuously reinforced concrete and asphalt concrete (CRC + AC) composite pavement layer were analyzed, and the causes of cracking on the surface and bottom of the asphalt layer were revealed. Studies have shown that under normal driving conditions, the AC layer, which is usually in the position of the wheel load gap and wheel load side, more easily generates a longitudinal “corresponding crack”. Compared to normal driving, longitudinal cracks are generated more easily inside of the curve, and transverse cracks occur more easily on poor stadia curves. When the AC layer thickness is less than 8 cm, the AC layer is more prone to bottom-up cracking, and it is more prone to top-down cracking when it is more than 8 cm thick. Comprehensively considering the tensile stress, shear stress, and the thickness of the AC layer, it is recommended that the suitable thickness range of the AC layer is 8 cm~14 cm. The calculated results show good agreement with the physical engineering investigation. The research results can provide a theoretical and scientific basis for cracking control and the rational design of a CRC + AC composite pavement layer.

The long noncoding RNA (lncRNA) LINC00152, also known as CYTOR, displays aberrant expression in various cancers. However, its clinical value and functional mechanisms in breast cancer remain insufficiently understood. Our study found that LINC00152 is significantly upregulated in breast cancer, and that it acts as an indicator of poor survival prognosis. Further studies revealed that LINC00152 knockdown suppresses cell proliferation and tumorigenicity in vitro and in vivo. Mechanistic analyses demonstrated that LINC00152 directly binds to KLF5 protein and increases KLF5 stability. Moreover, LINC00152 is also a KLF5-responsive lncRNA, and KLF5 activates LINC00152 transcription by directly binding to its promoter. Our study suggests that LINC00152 promotes tumor progression by interacting with KLF5. LINC00152 may be a valuable prognostic predictor for breast cancer, and the positive feedback loop of LINC00152-KLF5 could be a therapeutic target in pharmacological strategies.

BackgroundThe long noncoding RNA actin filament associated protein 1 antisense RNA1 (AFAP1-AS1) is a critical player in various cancers. However, the clinical value and functional mechanisms of AFAP1-AS1 during the tumorigenicity of nasopharyngeal carcinoma (NPC) remain unclear. Here, we investigated the clinical application and potential molecular mechanisms of AFAP1-AS1 in NPC tumorigenesis and progression.MethodsThe expression level of AFAP1-AS1 was determined by qRT-PCR in 10 paired fresh human NPC tissues and adjacent normal tissues. RNAscope was performed on 100 paired paraffin-embedded NPC and adjacent nontumor specimens. The biological functions of AFAP1-AS1 were assessed by in vitro and in vivo functional experiments. RNA-protein pull-down assays were performed to detect and identify the AFAP1-AS1-interacting protein KAT2B. Protein-RNA immunoprecipitation (RIP) assays were conducted to examine the interaction of AFAP1-AS1 and KAT2B. Chromatin immunoprecipitation (ChIP) and luciferase analyses were utilized to identify the binding site of transcription intermediary factor 1 alpha (TIF1α) and H3K14ac on the RBM3 promoter.ResultsAFAP1-AS1 is upregulated in NPC and is a poor prognostic indicator for survival in NPC patients. AFAP1-AS1 was required for NPC proliferation in vitro and tumorigenicity in vivo. Mechanistic investigations suggested that AFAP1-AS1 binds to KAT2B and promotes acetyltransferase activation at two residues (E570/D610). KAT2B further promotes H3K14 acetylation and protein binding to the bromo domain of TIF1α. Consequently, TIF1α acts as a nuclear transcriptional coactivator of RBM3 transcription, leading to YAP mRNA stabilization and enhanced NPC tumorigenicity.ConclusionsOur findings suggest that AFAP1-AS1 functions as an oncogenic biomarker and promotes NPC tumorigenicity through enhanced KAT2B acetyltransferase activation and YAP mRNA stabilization.

Summary: Attenuated DNA repair leads to genomic instability and tumorigenesis. BRCA1/BARD1 are the best-known tumor suppressors that promote homology recombination (HR) and arrest cell cycle. However, it remains ambiguous whether and how their E3 ligase activity regulates HR. Here, we demonstrate that upon genotoxic stress, BRCA1 together with BARD1 catalyzes the K48 polyubiquitination on LARP7, a 7SK RNA binding protein known to control RNAPII pausing, and thereby degrades it through the 26S ubiquitin-proteasome pathway. Depleting LARP7 suppresses the expression of CDK1 complex, arrests the cell at the G2/M DNA damage checkpoint, and reduces BRCA2 phosphorylation, which thereby facilitates RAD51 recruitment to damaged DNA to enhance HR. Importantly, LARP7 depletion observed in breast cancer patients leads to chemoradiotherapy resistance both in vitro and in vivo. Altogether, this study unveils a mechanism by which BRCA1/BARD1 control HR and cell cycle, and highlights LARP7 as a potential target for cancer prevention and therapy.

Ionic liquids (ILs) are used as electrolytes in high-performance lithium-ion batteries, which can effectively improve battery safety and energy storage capacity. All atom molecular dynamics simulation and experiment were combined to investigate the effect of the concentration of lithium salt on the performance of electrolytes of four IL solvents ([Cnmim][TFSI] and [Cnmim][FSI], n = 2, 4). The IL electrolytes exhibit higher density and viscosity; meanwhile, larger lithium ion transfer numbers as the concentration of LiTFSI increases. Furthermore, in order to explore the effect of the concentration of lithium salt on the ionic associations of Li+ and anion of IL, the microstructures of the lithium salt in various IL electrolytes at different concentrations were investigated. The structural analysis indicated that strong bidentate and monodentate coordination was found between Li+ and anion of all IL electrolytes. Both cis and trans isomerism of [FSI]− were observed in [FSI]−-type IL electrolyte systems. Furthermore, the existence of the ion cluster [Li[anion]x](x−1)− in IL electrolytes and the cluster became more closed and compact as the concentration of LiTFSI increases.

Traffic information, including vehicle weight and axle spacing, is vital for bridge safety. The bridge weigh-in-motion (BWIM) system remotely estimates the axle weights of moving vehicles using the response measured from instrumented bridges. It has been proved more accurate and durable than the traditional pavement-based method. However, the main drawback of conventional BWIM algorithms is that they can only identify the axle weight and the information of axle configuration (the number of axles and axle spacing) is required to be determined using an extra device in advance of the weight identification procedure. Namely, dedicated sensors (pressure-sensitive sensors placed on the deck surface or under the soffit of a bridge) in addition to weighing sensors must be adopted for identifying the axle configuration, which significantly decreases the utility, feasibility, and economic efficiency of BWIM technology. In this study, a new iterative procedure simultaneously identifying axle spacing as well as axle weights and gross weights of vehicles is proposed. The novel method is based on k-means clustering and the gradient descent method. In this method, both the axle weight and the axle location are obtained by using the same global response of bridges; thus the axle detectors are no longer required, which makes it economical and easier to be implemented. Furthermore, the proposed optimization method has good computational efficiency and thus is practical for real-time application. Comprehensive numerical simulations and laboratory experiments based on scaled vehicle and bridge models were conducted to verify the proposed method. The identification results show that the proposed method has good accuracy and high computational efficiency in axle spacing and axle weight identification.

Promoter proximal RNAPII pausing is a rate-limiting transcriptional mechanism. Chen et al. show that this process is essential in angiogenesis by demonstrating that the endothelial master transcription factor ETS1 promotes global RNAPII pause release, and that this process is governed by VEGF.

Umuokpo Amumara is a village with an estimated population of 9,000 people and about 800 households located in the eastern region of Nigeria in West Africa. This village has no access to power grids for over a decade of existence. Umuokpo, by virtue of its location 5°27′35.9″N 7°19′60.0″E, on the average receives about 6 hours of sunlight with a daily average irradiance of 6.12 kWh/m2. The solar energy can be tapped and harnessed to generate quality electricity for this small village. Since the wind speed is low (ranging between 3.0 m/s and 3.5 m/s), the wind resource cannot be incorporated into the design. The average load demand of the village is 9.422 MWh/day with a peak load of 1.3 MW. This paper is aimed at designing a small hybrid power system that can generate sustainable electricity for the village from renewable energy sources. The design also considers a backup diesel generator and an energy storage system. The designed system consists of a 2,750 kW solar photovoltaic (PV), a 21,600 kWh battery storage, a 1,500 kW power electronic converter, and a 1,000 kW diesel generator. The simulation suggests that the proposed system can adequately meet the electricity needs of the village. A sensitivity analysis is also carried out on the system to observe its behavior with varying levels of irradiation and load.

In order to further increase the volume, reduce the weight and manufacturing cost, the key structural parameters of thin-walled metal packing container are optimized. The instability conditions under circumferential external pressure and axial load are analyzed, a mathematical model with the constraint of critical instability strength, the maximum volume and minimum mass as the objective is constructed. Multi-objective optimization method with nonlinear constraints is used to solve the key structural parameters, such as wall thickness, diameter and height, and the optimization result is calculated by fgoalattain() function in the Matlab optimization toolbox. The instability pressure test system is constructed, the instability pressure of the optimized thin-wall metal packing container is tested. The results show that the unstable pressure is higher than 120kPa, which are better than the design index.