Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-carbon Economic Operation of the CHP System with P2G Heat Recovery and Heat Storage Tanks for Wind Power Consumption

In response of the current situation of the low efficiency of power to gas utilization and the limitations of only configuring heat storage tanks to improve the ability of absorbing wind power, this paper constructs a combined heat and power system with the coordination operation of power to gas heat recovery and heat storage tanks. The principle of the system for wind power consumption is firstly deeply described, and the carbon trading mechanism is introduced into the model considering the carbon emission. Furthermore, a low-carbon economic optimization model is set up with the objective to minimize the total cost of system operation. Finally, the simulation analysis in different operation scenes for a regional combined heat and power system is discussed in detail to verify the validity and availability of the proposed method in the paper.
Modified Dual-Buck Inverter Based on Resonant Link

The efficiency and reliability of the dual-buck inverter (DBI) were greatly improved by eliminating the shoot-through problem and optimally designing the freewheeling diode. The traditional DBI suffers from large harmonic components with low output voltage and large capacity output filter inductor. To overcome the aforementioned disadvantages, a modified DBI (MBDI) was proposed by adopting a quasi-resonant link and pulse density modulation (PDM). This paper first introduces the working principle of the MBDI and PDM, and then the selection principle of system parameters is presented. Finally, a mathematical model of the MBDIis built, and an experiment prototype is set up. Simulation and experimental results verify the correctness of the theoretical analysis and the feasibility of the scheme.
Review and Comparative Study of Energy Routing Algorithms for Energy Internet

The Energy Internet (EI) is recently proposed as a new energy system framework for future smart grid where all distributed electrical devices are interconnected via energy routers (ERs). With the novel EI, the traditional unidirectional power supply is transformed into a multi-source networked supply, which generates the problem of energy routing. Although various methods for energy routing have been proposed, a comparison of the existing energy routing algorithms is not existent and is necessary to facilitate future research. In this paper, energy routing algorithms in EI are discussed and classified based on the application scenario and the way of power transmission. Finally, several classical energy routing algorithms are selected for detailed comparison by extensive simulations.
The structure of insulin granule core determines secretory capacity being reduced in type-2 diabetes

Exocytosis in excitable cells is essential for their physiological functions. Although the exocytotic machinery controlling cellular secretion has been well investigated, the function of the vesicular cargo, i.e. secretory granular content remains obscure. Here we combine dSTORM imaging and single-domain insulin antibody, to dissect the in situ structure of insulin granule cores (IGCs) at nano level. We demonstrate that the size and shape of the IGCs can be regulated by the juxta-granular molecules Nucleobindin-2 and Enolase-1, that further contribute to the stimulated insulin secretion. IGCs located at the plasma membrane are larger than those in the cytosol. The IGCs size is decreased by ∼20% after glucose stimulation due to the release of the peripheral part of IGCs through incomplete granule fusion. Importantly, the reduction of the IGCs size is also observed in non-stimulatory pancreatic β-cells from diabetic db/db mice, Akita (Ins2+/-) mice and human Type-2 diabetic donors, in accordance with impaired secretion. These findings overall highlight the structure of exocytotic insulin cores as a novel modality amenable to targeting in the stimulated exocytosis in β-cells with impaired insulin secretion.
BET-independent MLV integration is retargeted in vivo and selects distinct genomic elements for lymphomagenesis

Moloney murine leukemia virus (MLV) infects BALB/c mice and induces T-cell lymphoma in mice. Retroviral integration is mediated by the interaction of the MLV integrase (IN) with members of the bromodomain and extra-terminal motif (BET) protein family (BRD2, BRD3 and BRD4). Introduction of the W390A mutation in MLV IN abolishes BET interaction. Here we compared the replication of W390A MLV and WT MLV in adult BALB/c mice to study the role of BET proteins in replication, integration and tumorigenesis in vivo. Comparing WT and W390A MLV infection revealed similar viral loads in blood, thymus and spleen cells. Interestingly, W390A MLV integration was retargeted away from GC-enriched genomic regions. However, both WT MLV and W390A MLV developed T cell lymphoma after a similar latency represented by an enlarged thymus and spleen and multi-organ tumor infiltration. Integration site sequencing from splenic tumor cells revealed clonal expansion in all WT MLV- and W390A MLV-infected mice. However, the integration profile of W390A MLV and WT MLV differed significantly. Integrations were enriched in enhancers and promoters but compared to WT, W390A MLV integrated less frequently into enhancers and more into oncogene bodies, such as Notch1 and Ppp1r16b. We conclude that host factors direct MLV in vivo integration site selection. Although, BET proteins target WT MLV integration preferentially towards enhancers and promoters, insertional lymphomagenesis can occur independently from BET, likely due to the intrinsically strong enhancer/promoter of the MLV LTR.
Temporal continuity shapes visual responses of macaque face patch neurons

Macaque inferior temporal cortex neurons respond selectively to complex visual images, with recent work showing that they are also entrained reliably by the evolving content of natural movies. To what extent does visual continuity itself shape the responses of high-level visual neurons? We addressed this question by measuring how cells in face-selective regions of the macaque temporal cortex were affected by the manipulation of a movie’s temporal structure. Sampling the movie at 1s intervals, we measured neural responses to randomized, brief stimuli of different lengths, ranging from 800 ms dynamic movie snippets to 100 ms static frames. We found that the disruption of temporal continuity strongly altered neural response profiles, particularly in the early onset response period of the randomized stimulus. The results suggest that models of visual system function based on discrete and randomized visual presentations may not translate well to the brain’s natural modes of operation.
Regional and cell type-specific afferent and efferent projections of the mouse claustrum

The claustrum (CLA) is a conspicuous subcortical structure interconnected with cortical and subcortical regions. However, its regional anatomy and cell-type-specific connections in the mouse remain largely undetermined. Here, we accurately delineated the boundary of the mouse CLA and quantitatively investigated its inputs and outputs brain-wide using anterograde and retrograde viral tracing and fully reconstructed single claustral principal neurons. At a population level, the CLA reciprocally connects with all isocortical modules. It also receives inputs from at least 35 subcortical structures but sends projections back to only a few of them. We found that cell types projecting to the CLA are differentiated by cortical areas and layers. We classified single CLA principal neurons into at least 9 cell types that innervate the diverse sets of functionally linked cortical targets. Axons of interneurons within the CLA arborize along almost its entire anteroposterior extent. Together, this detailed wiring diagram of the cell-type-specific connections of the mouse CLA lays a foundation for studying its functions.
Multiscale Modeling of Hepatitis B Virus Capsid Assembly and its Dimorphism

Hepatitis B Virus (HBV) is an endemic, chronic virus that leads to 800,000 deaths per year. Central to the HBV lifecycle, the viral core has a protein capsid assembled from many copies of a single protein. The capsid protein adopts different (quasi-equivalent) conformations to form icosahedral capsids containing 180 or 240 proteins, T=3 or T=4 respectively in Caspar-Klug nomenclature. HBV capsid assembly has become an important target for new antivirals; nonetheless the assembly pathways and mechanisms that control HBV dimorphism remain unclear. We describe computer simulations of HBV assembly, using a coarse-grained model that has parameters learned from all-atom molecular dynamics simulations of a complete HBV capsid, and yet is computationally tractable. Dynamical simulations with the resulting model reproduce experimental observations of HBV assembly pathways and products. By constructing Markov state models and employing transition path theory, we identify pathways leading to T=3, T=4, and other experimentally observed capsid morphologies. The analysis identifies factors that control this polymorphism, in particular, the conformational free energy landscape of the capsid proteins and their interactions.
A novel analgesic pathway from parvocellular oxytocin neurons to the periaqueductal gray

The hypothalamic neuropeptide, oxytocin (OT), exerts prominent analgesic effects via central and peripheral action. Here we discovered a novel subset of OT neurons whose projections preferentially terminate on OT receptor (OTR)-expressing neurons in the ventrolateral periaqueductal gray (vlPAG). Using a newly generated line of transgenic rats (OTR-IRES-Cre), we determined that most of the vlPAG OTR expressing cells being targeted by OT projections are GABAergic in nature. Both optogenetically-evoked axonal OT release in the vlPAG as well as chemogenetic activation of OTR vlPAG neurons results in a long-lasting overall increase of vlPAG neuronal activity. This then leads to an indirect suppression of sensory neuron activity in the spinal cord and strong analgesia. Finally, we describe a novel OT→vlPAG→spinal cord circuit that seems critical for analgesia in the context of both inflammatory and neuropathic pain. Highlights - We generated a new transgenic knock-in rat line (OTR-IRES-Cre) - A distinct parvOT neuronal population projects to vlPAG but not the SON or spinal cord - OT excites vlPAG OTR neurons which indirectly inhibit SC WDR neurons - This novel parvOT→vlPAG→SC pathway alleviates nociception but not the affective component of pain
Functional Definition of Thyroid Hormone Response Elements Based on a Synthetic STARR-seq Screen

When bound to thyroid hormone, the nuclear receptor TRα1 activates the transcription of a number of genes in many cell types. It mainly acts by binding DNA as an heterodimer with RXRs at specific response elements related to the DR4 consensus sequence. However, the number of DR4-like elements in the genome exceed by far the number of occupied sites, indicating that minor variations in nucleotides composition deeply influence the DNA-binding capacity and transactivation activity of TRα1. An improved protocol of synthetic STARR-seq was used to quantitatively assess the transcriptional activity of thousands of synthetic sites in parallel. This functional screen highlights a strong correlation between the affinity of the heterodimers for DNA and their capacity to mediate the thyroid hormone response.
Neural kernels for recursive support vector regression as a model for episodic memory

Retrieval of episodic memories requires intrinsic reactivation of neuronal activity patterns. The content of the memories is thereby assumed to be stored in synaptic connections. This paper proposes a theory in which these are the synaptic connections that specifically convey the temporal order information contained in the sequences of a neuronal reservoir to the sensory-motor cortical areas that give rise to the subjective impression of retrieval of sensory motor events. The theory is based on a novel recursive version of support vector regression that allows for efficient continuous learning that is only limited by the representational capacity of the reservoir. The paper argues that hippocampal theta sequences are a potential neural substrate underlying this reservoir. The theory is consistent with confabulations and post hoc alterations of existing memories.
The Cif proteins from Wolbachia prophage WO modify sperm genome integrity to establish cytoplasmic incompatibility

Inherited microorganisms can selfishly manipulate host reproduction to drive through populations. In Drosophila melanogaster, germline expression of the native Wolbachia prophage WO proteins CifA and CifB cause cytoplasmic incompatibility (CI) in which embryos from infected males and uninfected females suffer catastrophic mitotic defects and lethality; however, in infected females, CifA expression rescues the embryonic lethality and thus imparts a fitness advantage to the maternally-transmitted Wolbachia. Despite widespread relevance to sex determination, evolution, and vector control, the mechanisms underlying when and how CI impairs male reproduction remain unknown and a topic of debate. Here we use cytochemical, microscopic, and transgenic assays in D. melanogaster to demonstrate that CifA and CifB proteins of wMel localize to nuclear DNA throughout the process of spermatogenesis. Cif proteins cause abnormal histone retention in elongating spermatids and protamine deficiency in mature sperms that travel to the female reproductive tract with Cif proteins. Notably, protamine gene knockouts enhance wild type CI. In ovaries, CifA localizes to germ cell nuclei and cytoplasm of early-stage egg chambers, however Cifs are absent in late-stage oocytes and subsequently in fertilized embryos. Finally, CI and rescue are contingent upon a newly annotated CifA bipartite nuclear localization sequence. Together, our results strongly support the Host Modification model of CI in which Cifs initially modify the paternal and maternal gametes to bestow CI-defining embryonic lethality and rescue.
Explainable Artificial Intelligence for Magnetic Resonance Imaging Aging Brainprints: Grounds and challenges

Marked changes occur in the brain during people’s lives, and individual rates of aging have revealed pronounced differences, giving rise to subject-specific brainprints that are the signature of the brain. These are shaped by a great variety of factors, both endogenous and exogenous. Accurate predictions of brain age (BA) can be derived from neuroimaging endophenotypes by using machine and deep learning (DL) techniques. Predictive models leading to accurate estimates while revealing which features contribute the most to final predictions are key to unveiling the mechanisms underlying the evolution of brain aging patterns. Explainable artificial intelligence (XAI) methods are emerging as enabling technology in different fields, and biomedicine is no exception. Within this framework, this article examines BA and presents a comprehensive review of recent advances in the exploitation of explainable machine learning (ML)/DL methods, highlighting the main open issues and providing hints for future directions.
Fault State Operation Analysis for Offshore Wind Farm with Ring-Topology Collector System: An OPF and Topology Optimization Method

With offshore wind power developing rapidly, ring-topology collector system is proposed because it has a significant advantage of reliability. But its fault state operation is hard to analyze due to the flexibility of power flow and breaker configuration. This paper proposes a novel method to analyze the fault state operation of ring topology. First, a simplified linear power flow model is derived to describe power flow distribution. Then, the coupling constraints between the branches are constructed to describe the branch breaking caused by the fault. Finally, an operation optimization model is built to reach the best topology and minimum power shedding. The proposed method is tested with cases of complex ring topology, and the results show the effectiveness of the approach and the accuracy of the power flow model.
A Two-Step Quadratic-Convex Based Tightening Method for Electricity-Gas System Optimization Considering Current Margins

With the development of energy interconnection, electricity and gas systems transfer energy with each other frequently. Thus, the demand for improving the connected system's efficiency and reliability is required. This paper proposes a two-step approach for solving the optimization problem in the electricity-gas system by quadratic-convex based tightening method. The first step is to convexify the non-linear and non-convex optimization problem by quadratic-convex relaxations, which enlarge the solution region. The second step involves multiple tightening iterations to narrow the relaxed solution region. Besides, the security indices of the electricity system are considered in the co-optimization model. The implement of the current margins prevents the overload current of transmission lines. The applicability and effectiveness of the proposed method are tested through case studies.
An Efficient Machine Learning Approach for Fingerprint Authentication Using Artificial Neural Networks

Fingerprint identification is a biometric technique that identifies persons by using categorization models and computations on images at the most basic level. For feature extraction, a fingerprint detection framework that employs a number of image pre-processing methods as well as a variety of image locality bifurcations is described and assessed. For classification, an Artificial Neural Network (ANN) is suggested. This paper introduces the Gabor filterfor feature extraction and the ANN machine learning method for classification. The feature vector is created by combining Gabor filtering features with machine learning techniques such as Artificial Neural Networks. Using the extracted features, a multiclass classifier was developed. The experiments in this study were conducted using the NIST Special Database 4 (NISTSD4). In addition, the confusion matrix supported these findings and the proposed approach in terms of accuracy (97.95 %) outperformed more recent Machine learning classification techniques such as Random Forest, KNN, Support Vector Machine and Decision Tree.
Power Management Strategy Considering the Mountainous Terrain Driving Pattern for Three Wheeler Electric Vehicle

This paper presents the power management strategy utilized for the three-wheeler electric vehicle (EV) between the North and South Campus of IIT Mandi, India situated in the lap of the Himalayas. The hill-climbing power requirement from the energy sources is peaky with a high magnitude as compared to other traction components acting on EV. Also, the opposing wind flow in these mountainous terrains has high speed demanding a significant amount of power from the source. The power-split technique is used such that the slow varying power requirements are supplied by the battery. The transient or fast varying power components are supplied by the Ultra-Capacitor (UC) for the required traction. The nature of the power requirement for hill climbing impacts the battery cycle life reducing its capacity. The power management strategy reduces the battery stress by adding an auxiliary energy storage system in the form of UC packs. The technique considered has been implemented in MATLAB/Simulink. It also helps in storing the regenerated power efficiently using UC which in turn increases the range of EV and reliability of complete energy storage systems.
Coal-rock Interface Recognition Method Based on GAF-deep Learning

Aiming at the problem of whether the shearer can recognize the coal-rock interface during the mining process, a coal-rock interface recognition method based on Gramian Angular Field (GAF)-deep learning is proposed. First, the vibration signal generated by the shearer in cutting different proportions of coal and rock is the sample information database of different cutting signals. Secondly, using the Gramian Angular Difference Field (GADF) and the Gramian Angular Sum Field (GASF) in the Gram angle field, the vibration signal generated during the cutting process is converted into a 2D image. And the image input is introduced into the ResNet-34 network model of migration learning, and finally the ideal recognition accuracy is obtained, and the classification and recognition of the coal-to-rock ratio of the coal-rock interface is realized. Identifying the coal-rock interface is beneficial to reduce the wear of the shearer picks, reduce the workload of subsequent coal washing and coal preparation, and contribute to the reduction of carbon emissions and the realization of smart mine construction.
Assessing the Operational Potential of Pumped-Storage Hydro Generators for Supporting the Grid Integration of Wind Farms

This study evaluates the operational potential of pumped-storage hydro generators (PSHGs) as a regulating power source for the stable use of wind power generation. In recent years, the concept of a balancing group (BG), consisting of multiple generators, has been considered to alleviate the imbalance, defined as a discrepancy between planned and actual power supply values. Combining unstable power sources, such as wind power with controllable sources such as PSHG, is expected to reduce the imbalance in BG. For the effective utilization of PSHG, power system operators need to determine a schedule of operation considering the uncertainty in the wind farm (WF) output on the preceding day. In this study, the operation scheduling of PSHG, based on the interval prediction results of wind power generation, is discussed and the potential contribution of PSHG operation is assessed from the perspective of the imbalance reduction within this framework. The operational potential is evaluated from the viewpoint of the types of PSHG, that is, fixed-speed and variable-speed PSHGs, by the annual operational simulation using real-world WF output data. The results show that the appropriate operational scheduling of a single variable-speed PSHG can contribute to a reduction in imbalance comparable to the reduction achieved by two fixed-speed PSHGs.
Transient Stability Self-Adaptive Assessment Based on Sample Enhancement of DCGAN

Since there are very few transient instabilities in the actual power system, the stable and unstable samples are extremely unbalanced, which brings great difficulties to the data-driven transient stability prediction. And due to the time-varying nature of the power system, the rapidity of the generation of new training samples in the online application of temporary stability prediction faces challenges. Aiming at the problem of decreased prediction accuracy when the AI network is applied to the transient stability prediction of the power system when the operation mode or topology of the power grid changes greatly, a method of transient stability self-adaptive assessment based on sample enhancement of improved DCGAN is proposed in this paper. While offline, perform cluster analysis on the historical topology of the power grid, generate a historical sample database, and train the temporary stability prediction model. When the topology of power grid changes, the new scene is matched with the historical scene and the samples are transfered to the new scene through a certain transfered principle. Then, the instability sample set is screened out, and the instability sample is enhanced by the improved Deep Convolutional Generative Adversarial Network(DCGAN). Finally, the offline model is fine-tuned with the new training samples after transfer and enhancement to realize online transient stability self-adaptive assessment. The experimental results of the Central China Power Grid show that the method can generate effective training samples for changing systems, balance power system instability data, and greatly reduce the sample generation time when the model is updated online, and improve the accuracy and Gmean value of transient stability prediction in new scenarios, which considering both the speed and accuracy of self-adaptive assessment.