Defining the Pakistani Nation in History Education: An Examination of Pakistan Studies Textbooks

Master'sMASTER OF ART

Folded Polynomial Codes for Coded Distributed AA⊤AA^\top-Type Matrix Multiplication

In this paper, due to the important value in practical applications, we consider the coded distributed matrix multiplication problem of computing $AA^\top$ in a distributed computing system with $N$ worker nodes and a master node, where the input matrices $A$ and $A^\top$ are partitioned into $p$-by-$m$ and $m$-by-$p$ blocks of equal-size sub-matrices respectively. For effective straggler mitigation, we propose a novel computation strategy, named \emph{folded polynomial code}, which is obtained by modifying the entangled polynomial codes. Moreover, we characterize a lower bound on the optimal recovery threshold among all linear computation strategies when the underlying field is real number field, and our folded polynomial codes can achieve this bound in the case of $m=1$. Compared with all known computation strategies for coded distributed matrix multiplication, our folded polynomial codes outperform them in terms of recovery threshold, download cost and decoding complexity.Comment: 14 pages, 2 tabl

Reliability-oriented adaptive switching frequency scheme for modular multilevel converters

Modular multilevel converters (MMCs) are widely utilized in medium voltage grid-connected applications, typically employing carrier phase shift modulation. However, the high switching frequency associated with this modulation scheme often increases power losses and thermal stress on semiconductor devices, negatively impacting their efficiency and reliability. In this paper, we propose an adaptive switching frequency scheme that divides the carrier frequency into several discrete zones based on load conditions. Through analytical evaluation of the carrier frequency, our proposed method optimizes it to meet power quality and capacitor voltage ripple requirements, effectively reducing power losses and thermal stress. A simulation case study based on a 15-MVA MMC demonstrates a remarkable 21% reduction in annual power losses and a 12% reduction in annual damage, thereby improving efficiency and reliability. Additionally, experimental measurements conducted on a 15-kW downscale platform validate around 10% reduction in power losses while fulfilling power quality and capacitor voltage ripple requirements

Research on the Compliant Control of Electro-Hydraulic Servo Drive Force/Position Switching for a Lower Limb Exoskeleton Robot

In order to improve the flexibility of the foot landing of a lower limb exoskeleton robot based on an electro-hydraulic servo drive and to reduce its impact with the ground, an active compliance control method for force/position switching based on fuzzy control is proposed. According to the mathematical model of each component of the electro-hydraulic servo system of the core drive unit of the lower limb exoskeleton robot, the transfer functions of the position control system and the force control system are obtained respectively, and then its specific working characteristics are studied. Before the feet hit the ground, the position servo control system under the action of a fuzzy controller is used to achieve the movement of legs in free and unconstrained space, and the moment the foot touches the ground, the system is switched to a force servo control system to precisely control the output force, thereby reducing the rigid impact between the feet. In the meantime, the validity of the designed switching method and controller is verified by the joint simulation of MATLAB and AMESIM. The simulation results show that the electro-hydraulic servo force/position switching method based on a fuzzy algorithm is able not only to guarantee the movement accuracy of the foot end of the lower limb exoskeleton robot, but can also effectively reduce the impact force between the foot end and the ground

Domain Adaptive Semantic Segmentation by Optimal Transport

Scene segmentation is widely used in the field of autonomous driving for environment perception, and semantic scene segmentation (3S) has received a great deal of attention due to the richness of the semantic information it contains. It aims to assign labels to pixels in an image, thus enabling automatic image labeling. Current approaches are mainly based on convolutional neural networks (CNN), but they rely on a large number of labels. Therefore, how to use a small size of labeled data to achieve semantic segmentation becomes more and more important. In this paper, we propose a domain adaptation (DA) framework based on optimal transport (OT) and attention mechanism to address this issue. Concretely, first we generate the output space via CNN due to its superiority of feature representation. Second, we utilize OT to achieve a more robust alignment of source and target domains in output space, where the OT plan defines a well attention mechanism to improve the adaptation of the model. In particular, with OT, the number of network parameters has been reduced and the network has been better interpretable. Third, to better describe the multi-scale property of features, we construct a multi-scale segmentation network to perform domain adaptation. Finally, in order to verify the performance of our proposed method, we conduct experimental comparison with three benchmark and four SOTA methods on three scene datasets, and the mean intersection-over-union (mIOU) has been significant improved, and visualization results under multiple domain adaptation scenarios also show that our proposed method has better performance than compared semantic segmentation methods

The Mechanisms of Nerve Injury Caused by Viral Infection in the Occurrence of Gastrointestinal Motility Disorder-Related Diseases

Gastrointestinal motility refers to the peristalsis and contractility of gastrointestinal muscles, including the force and frequency of gastrointestinal muscle contraction. Gastrointestinal motility maintains the normal digestive function of the human body and is a critical component of the physiological function of the digestive tract. At present, gastrointestinal motility disorder-related diseases are gradually affecting human production and life. In recent years, it has been consistently reported that the enteric nervous system has a coordinating and controlling role in gastrointestinal motility. Motility disorders are closely related to functional or anatomical changes in the gastrointestinal nervous system. At the same time, some viral infections, such as herpes simplex virus and varicella-zoster virus infections, can cause damage to the gastrointestinal nervous system. Therefore, this paper describes the mechanisms of viral infection in the gastrointestinal nervous system and the associated clinical manifestations. Studies have indicated that the means by which viruses can cause the infection of the enteric nervous system are various, including retrograde transport, hematogenous transmission and centrifugal transmission from the central nervous system. When viruses infect the enteric nervous system, they can cause clinical symptoms, such as abdominal pain, abdominal distension, early satiation, belching, diarrhea, and constipation, by recruiting macrophages, lymphocytes and neutrophils and regulating intestinal microbes. The findings of several case‒control studies suggest that viruses are the cause of some gastrointestinal motility disorders. It is concluded that one of the causes of gastrointestinal motility disorders is viral infection of the enteric nervous system. In such disorders, the relationships between viruses and nerves remain to be studied more deeply. Further studies are necessary to evaluate whether prophylactic antiviral therapy is feasible in gastrointestinal motility disorders

Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks

To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end devices and edge servers to process data closer to where it is generated. A key technology for edge intelligence is the privacy-protecting machine learning paradigm known as Federated Learning (FL), which enables data owners to train models without having to transfer raw data to third-party servers. However, FL networks are expected to involve thousands of heterogeneous distributed devices. As a result, communication efficiency remains a key bottleneck. To reduce node failures and device exits, a Hierarchical Federated Learning (HFL) framework is proposed, where a designated cluster leader supports the data owner through intermediate model aggregation. Therefore, based on the improvement of edge server resource utilization, this paper can effectively make up for the limitation of cache capacity. In order to mitigate the impact of soft clicks on the quality of user experience (QoE), the authors model the user QoE as a comprehensive system cost. To solve the formulaic problem, the authors propose a decentralized caching algorithm with federated deep reinforcement learning (DRL) and federated learning (FL), where multiple agents learn and make decisions independentl

The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides.

In the synthesis of inorganic materials, reactions often yield non-equilibrium kinetic byproducts instead of the thermodynamic equilibrium phase. Understanding the competition between thermodynamics and kinetics is a fundamental step towards the rational synthesis of target materials. Here, we use in situ synchrotron X-ray diffraction to investigate the multistage crystallization pathways of the important two-layer (P2) sodium oxides Na0.67MO2 (M = Co, Mn). We observe a series of fast non-equilibrium phase transformations through metastable three-layer O3, O3' and P3 phases before formation of the equilibrium two-layer P2 polymorph. We present a theoretical framework to rationalize the observed phase progression, demonstrating that even though P2 is the equilibrium phase, compositionally unconstrained reactions between powder precursors favour the formation of non-equilibrium three-layered intermediates. These insights can guide the choice of precursors and parameters employed in the solid-state synthesis of ceramic materials, and constitutes a step forward in unravelling the complex interplay between thermodynamics and kinetics during materials synthesis

Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health