Distribution Of Activity Research Articles

Proton spot dose estimation based on positron activity distributions with neural network.

Positron emission tomography (PET) has been investigated for its ability to reconstruct proton-induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning-based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy. This study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U-Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice. Proton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one-dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics. The U-Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases. Both the U-Net and the Transformer have certain advantages in the proton dose estimation task. The U-Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose-guided proton therapy.

CFDI: Coordinated false data injection attack in active distribution network

AbstractThe active distribution network (ADN) can obtain measurement data, estimate system states, and control distributed energy resources (DERs) and flexible loads to ensure voltage stability. However, the ADN is more vulnerable to cyber attacks due to the recent wave of digitization and automation efforts. In this article, false data injection (FDI) attacks are focused on and they are classified into two types, that is, type I attacks on measurement data and type II attacks on control commands. After studying the impact of these two FDI attacks on the ADN, a new threat is revealed called coordinated FDI attack, which can maximize the voltage deviation by coordinating type I and type II FDI attacks. From the attacker's perspective, the scheme of CFDI is proposed and an algorithm is developed to find the optimal attack strategy. The feasibility of CFDI attacks has been validated on a smart distribution testbed. Moreover, simulation results on an ADN benchmark have demonstrated that CFDI attacks could cause remarkable voltage deviation that may deteriorate the stability of the distribution network. Moreover, the impact of CFDI attacks is higher than pure type I or type II attacks. To mitigate the threat, some countermeasures against CFDI attacks are also proposed.

Stochastic Optimal Operation of SOP-Assisted Active Distribution Networks with High Penetration of Renewable Energy Sources

This paper introduces a mixed-integer convex model for optimizing the scheduling of soft open points (SOPs) integrated with energy storage (ES) in active distribution networks (ADNs) with high proportions of photovoltaic sources, designed to ensure zero risk of constraint violations. A stochastic optimization model for ADNs is proposed to maximize the benefits of SOPs while simultaneously minimizing system power losses, SOP power losses, voltage deviations, PV power curtailment, battery energy storage system (BESS) operation cost, and utility power purchase. Uncertainties in PV generation and load demand are considered by Monte Carlo simulation and k-means technologies. Finally, simulation cases from a 21-bus distribution network show that the curtailment of PV sources is minimized and the power fluctuations of the BESS are reduced in comparison to the case without SOP. Constraints in the nodal voltages, power outputs, energy balance, and power flow are all satisfied.

3D-QSAR, Scaffold Hopping, Virtual Screening, and Molecular Dynamics Simulations of Pyridin-2-one as mIDH1 Inhibitors

Isocitrate dehydrogenase 1 (IDH1) is a necessary enzyme for cellular respiration in the tricarboxylic acid cycle. Mutant isocitrate dehydrogenase 1 (mIDH1) has been detected overexpressed in a variety of cancers. mIDH1 inhibitor ivosidenib (AG-120) was only approved by the Food and Drug Administration (FDA) for marketing, nevertheless, a range of resistance has been frequently reported. In this study, several mIDH1 inhibitors with the common backbone pyridin-2-one were explored using the three-dimensional structure–activity relationship (3D-QSAR), scaffold hopping, absorption, distribution, metabolism, excretion (ADME) prediction, and molecular dynamics (MD) simulations. Comparative molecular field analysis (CoMFA, R2 = 0.980, Q2 = 0.765) and comparative molecular similarity index analysis (CoMSIA, R2 = 0.997, Q2 = 0.770) were used to build 3D-QSAR models, which yielded notably decent predictive ability. A series of novel structures was designed through scaffold hopping. The predicted pIC50 values of C3, C6, and C9 were higher in the model of 3D-QSAR. Additionally, MD simulations culminated in the identification of potent mIDH1 inhibitors, exhibiting strong binding interactions, while the analyzed parameters were free energy landscape (FEL), radius of gyration (Rg), solvent accessible surface area (SASA), and polar surface area (PSA). Binding free energy demonstrated that C2 exhibited the highest binding free energy with IDH1, which was −93.25 ± 5.20 kcal/mol. This research offers theoretical guidance for the rational design of novel mIDH1 inhibitors.

Uplift of the Tibetan Plateau driven by mantle delamination from the overriding plate

AbstractThe geodynamic evolution of the Tibetan Plateau remains highly debated. Any model of its evolution must explain the plateau’s growth as constrained by palaeo-altitude studies, the spatio-temporal distribution of magmatic activity, and the lithospheric mantle removal inferred from seismic velocity anomalies in the underlying mantle. Several conflicting models have been proposed, but none of these explains the first-order topographic, magmatic and seismic features self-consistently. Here we propose and test numerically an evolutionary model of the plateau that involves gradual peeling of the lithospheric mantle from the overriding plate and consequent mantle and crustal melting and uplift. We show that this model successfully reproduces the successive surface uplift of the plateau to more than 4 km above sea level and is consistent with the observed migration of magmatism and geometry of the lithosphere–asthenosphere boundary resulting from subduction of the Indian plate and delamination of the mantle lithosphere of the Eurasian plate. These comparisons indicate that mantle delamination from the overriding plate is the driving force behind the uplift of the Tibetan Plateau and, potentially, orogenic plateaus more generally.

μPMU Placement Considering the Distribution of Observation Redundancy and Topology Changes in Active Distribution Networks

μPMU Placement Considering the Distribution of Observation Redundancy and Topology Changes in Active Distribution Networks

The primate cortical LFP exhibits multiple spectral and temporal gradients and widespread task dependence during visual short-term memory.

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3 and 80 Hz that differ between the two monkeys. The LFP power in each band, as well as the sample entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in cortical pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task-dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread, and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.NEW & NOTEWORTHY We recorded extracellular electrophysiological signals from roughly the breadth and depth of a cortical hemisphere in nonhuman primates (NHPs) performing a visual memory task. Analyses of the band-limited local field potential (LFP) power displayed widespread, frequency-dependent cortical gradients in spectral power. Using a machine learning classifier, these features allowed robust cortical area decoding. Further task dependence in LFP power were found to be widespread, indicating large-scale gradients of LFP activity, and task-related activity.

CNN data-driven active distribution network: Integration research of topology reconstruction and optimal scheduling in multi-source uncertain environment

The access of high proportion of uncertain factors makes the operating conditions of active distribution network more complicated. In addition to the scheduling optimization of active management device, changing the network topology is also an effective way to improve the reliability of power supply and reduce cost. However, the integration of topology reconfiguration and scheduling makes the problem more complicated, and the solving speed is challenged. Therefore, an integrated decision-making method based on CNN data-driven is proposed. Firstly, by combining the conditions of network operation connectivity with the necessary conditions of topological radiation structure, the full constraint conditions of distribution network connectivity and radiation are obtained. Secondly, Monte Carlo stochastic method is used to simulate a variety of uncertain factors. By combining with the active management device scheduling model and taking the minimum total cost as the objective function, GUROBI solver is used to find optimal output of the active management device and optimal branch switch combination in various random scenarios, and a large amount of effective historical optimization data is obtained. The topology information is also marked as a topology label. Finally, the CNN is trained with node loads and uncertainty factors as input data and topology label and optimal scheduling as output data. The improved IEEE-33 node system is taken as a simulation example, and the results show that the topology reconfiguration can further reduce the power cost and improve the reliability of power supply on the basis of optimal scheduling; The integrated decision-making method based on CNN data-drive can quickly obtain the optimal output of active management device and the optimal topology without power flow calculation under various uncertain scenarios. The classification and regression prediction speeds of CNN are 886 times and 736 times faster than the GUROBI solver respectively.

An Identification Method for Anomaly Types of Active Distribution Network Based on Data Mining

With the increasing penetration of distributed generators (DGs) and the growing demand for reliable power sources, it has become imperative to promptly identify anomalies in active distribution networks (ADNs). Additionally, anomaly identification is also crucial to assisting in targeting maintenance to handle failures after the action of relay protections. This study presents a highly accurate and rapidly responsive approach to identifying anomalies in ADNs. The proposed method uses three layers of intricately interconnected modules. In the first module, an iterative clustering layer is devised to process the original data. In the second module, an association rule layer is designed to identify hidden patterns based on the types of anomalies and the post-fault voltage. In the third module, a regression training layer is employed to train accurate identification models. The case study utilizes data samples collected by an IEEE 33-bus system with distributed generations as well as post-fault data from a practical distribution network. The results of comparative tests demonstrate that the proposed data-mining structure achieves reliable performance with high identification accuracy and short reaction times, indicating its ability to be applied to the state awareness system of ADNs.

New perspective on sustainable practices in the events industry

PurposeThe aim of this study is to systematically review scientific research on sustainability in the event industry to understand the current state of affairs and provide a roadmap for future studies. By combining the topics of the event industry and sustainability, it aims to thematically analyze the scientific literature in this area.Design/methodology/approachThis study analyzed 1,710 studies in the Web of Science (WoS) database as of June 1, 2024. The keywords “event industry” and “sustainability” were used to search for academic articles, review articles, and conference proceedings. The data were analyzed geographically, thematically, and temporally. Cross-national research contributions, document types, researcher profiles, and interdisciplinary relationships were examined in detail.FindingsThe analysis shows that sustainability is an increasingly important issue in the events industry, with countries such as the USA, China and Australia leading the way. Most research is in the form of articles, with conference proceedings and reviews playing an important role. There is a particular concentration in categories such as “management,” “hospitality, leisure, sport and tourism” and “sustainability science.” Even during the pandemic, there has been a significant increase in research activity, underscoring the importance of this topic on a global scale. Researchers have made significant scientific contributions in this area. These interdisciplinary studies have revealed important intersections between environmental sustainability and event management.Originality/valueThis study provides a comprehensive overview of the current state of research on the event industry and sustainability, illustrating the interdisciplinary nature of the field and the geographical distribution of research activity. A systematic review of the literature consolidates the body of knowledge in this field and provides directions for future research. This study highlights the need for more in-depth and comprehensive research on sustainability, and provides an important framework for strengthening the links between event management and sustainability.

Изучение радиоактивности вод горных рек Кабардино-Балкарской Республики с использованием детектора NaI в низкофоновой защите

The results of studies of the radioactivity of the waters of the mountain rivers of the Kabardino-Balkarian Republic using the NaI detector in low-background protection are presented. The paper presents a method of low-background gamma-ray spectrometry for detecting radioactivity in drinking water, which is an effective method, has high sensitivity to radioactive radiation and allows you to accurately determine the level of radioactivity. The MULTIRAD MKS-01A spectrometric installation was used to measure radiation in samples under low background conditions. The identification and calculation of the volumetric activity of radionuclides was carried out using special software "Progress". Concentration levels and specific activity distribution of natural radionuclides 137Cs, 232Th, 40K, 226Ra and 222Rn were studied. Water samples were taken from the rivers of the Baksan, Cherek gorges and the Malka River of the Kabardino-Balkarian Republic. Studies have shown that radiation levels were within the norm set for drinking water.

Analyzing impact of network constraints on feasible operation region of the radial distribution networks

Recently, there has been lot of research on finding the feasible operating region (FOR) of the active distribution network (ADN) to provide ancillary services to the transmission grid. The current focus of research is on finding methods that can accurately determine the shape of the FOR and have fast computation time. However, there is little focus on analyzing how network constraints impact the shape of the FOR. This paper attempts to address this issue and provides a mathematical derivation of how network constraints, such as cable loading and voltage limits, would influence FOR. The approach is based on a linear distflow mathematical analysis. The relations derived in the analysis are verified with numerical simulation on radial low-voltage (LV) and medium-voltage (MV) networks. The results show that cable loading constraints cut the FOR in a circular way and voltage constraints cut the FOR with a straight line of slope −ϵ, which is the ratio Rn/Xn of the path from root node to the node under analysis of the radial distribution network.

Single-cell analysis reveals an active and heterotrophic microbiome in the Guaymas Basin deep subsurface with significant inorganic carbon fixation by heterotrophs.

The global subsurface is the largest reservoir of microbial life on the planet yet remains poorly characterized. The activity of life in this realm has implications for long-term elemental cycling, particularly of carbon, as well as how life survives in extreme environments. Here, we recovered cells from the deep subsurface of the Guaymas Basin and investigated the level and distribution of microbial activity, the physicochemical drivers of activity, and the relative significance of organic versus inorganic carbon to subsurface biomass. Using a sensitive single-cell assay, we found that the majority of cells are active, that activity is likely driven by the availability of energy, and that although heterotrophy is the dominant metabolism, both organic and inorganic carbon are used to generate biomass. Using a new approach, we quantified inorganic carbon assimilation by heterotrophs and highlighted the importance of this often-overlooked mode of carbon assimilation in the subsurface and beyond.

Smart Operation Control of Power and Heat Demands in Active Distribution Grids Leveraging Energy Flexibility

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

Patient-specific radiation dose for Chinese pediatric patients undergoing whole-body PET/CT examinations

Objective. To assess potential variations in the absorbed dose between Chinese and Caucasian children exposed to 18F-FDG PET scan and to investigate the factors contributing to dose differences, this work employed patient-specific phantoms and our compartment model for calculating the patient-specific absorbed dose in Chinese children. Approach. Data of 29 Chinese pediatric patients undergoing whole-body 18F-FDG PET/CT studies were retrospectively collected, including PET images for activity distributions and corresponding CT images for organ segmentation and phantom construction. A biokinetic compartment model was implemented to obtain cumulated activities. Absorbed radiation dose for both CT and PET component were calculated using Monte Carlo simulations. Regression models were fitted to time integrated activity coefficient (TIAC) and organ absorbed dose for each patient. Main results. TIACs of all the organs in our compartment model and the organ dose for 12 organs were correlated with patients’ weight. Young children have significantly large uptake in brain compared to adults. The distinctions of anatomical and biological characteristics between Chinese and Caucasian children contribute to variations in the absorbed dose of 18F-FDG PET scans. PET contributed more in organ dose than CT did in most organs, especially in brain and bladder. The average effective dose (± SD) was 4.5 mSv (± 1.12 mSv), 7.8 mSv (± 3.2 mSv) and 12.3 mSv (± 3.5 mSv) from CT, PET and their sum respectively. PET contributed 1.7 times higher than CT. Significance. To the best of our knowledge, this work represents the first attempt to estimate patient-specific radiation doses from PET/CT for Chinese pediatric patients. TIACs derived from our methodology in both age groups exhibited significant differences from the that reported in ICRP 128. Substantial differences in absorbed and effective doses were observed between Chinese and Caucasian children across all age groups. These disparities are attributed to markedly distinct anatomical and pharmacokinetic characteristics among adults and pediatric patients, and different racial groups. The application of data derived from adults to pediatric patients introduces considerable uncertainty. Our methodology offers a valuable approach not only for estimating pharmacokinetic characteristics and patient-specific radiation doses in pediatric patients undergoing 18F-FDG studies but also for other cohorts with similar characteristics.

Research on a Three-Stage Dynamic Reactive Power Optimization Decoupling Strategy for Active Distribution Networks with Carbon Emissions

The reactive power optimization of an active distribution network can effectively deal with the problem of voltage overflows at some nodes caused by the integration of a high proportion of distributed sources into the distribution network. Aiming to address the limitations in previous studies of dynamic reactive power optimization using the cluster partitioning method, a three-stage dynamic reactive power optimization decoupling strategy for active distribution networks considering carbon emissions is proposed in this paper. First, a carbon emission index is proposed based on the carbon emission intensity, and a dynamic reactive power optimization mathematical model of an active distribution network is established with the minimum active power network loss, voltage deviation, and carbon emissions as the satisfaction objective functions. Second, in order to satisfy the requirement for the all-day motion times of discrete devices, a three-stage dynamic reactive power optimization decoupling strategy based on the partitioning around a medoids clustering algorithm is proposed. Finally, taking the improved IEEE33 and PG&E69-node distribution network systems as examples, the proposed linear decreasing mutation particle swarm optimization algorithm was used to solve the mathematical model. The results show that all the indicators of the proposed strategy and algorithm throughout the day are lower than those of other methods, which verifies the effectiveness of the proposed strategy and algorithm.

Real-time outage management in active distribution networks using reinforcement learning over graphs

Self-healing smart grids are characterized by fast-acting, intelligent control mechanisms that minimize power disruptions during outages. The corrective actions adopted during outages in power distribution networks include reconfiguration through switching control and emergency load shedding. The conventional decision-making models for outage mitigation are, however, not suitable for smart grids due to their slow response and computational inefficiency. Here, we present a graph reinforcement learning model for outage management in the distribution network to enhance its resilience. The distinctive characteristic of our approach is that it explicitly accounts for the underlying network topology and its variations with switching control, while also capturing the complex interdependencies between state variables (along nodes and edges) by modeling the task as a graph learning problem. Our model learns the optimal control policy for power restoration using a Capsule-based graph neural network. We validate our model on three test networks, namely the 13, 34, and 123-bus modified IEEE networks where it is shown to achieve near-optimal, real-time performance. The resilience improvement of our model in terms of loss of energy is 607.45 kWs and 596.52 kWs for 13 and 34 buses, respectively. Our model also demonstrates generalizability across a broad range of outage scenarios.

Current spectral norm and phase variation based fault region identification for active distribution network

The paper presents a fault region identification method for the active distribution network (ADN) with limited PMU. First, PMU configuration and region division strategies are proposed based on the network topology. Next, the difference in three-phase current variations between the normal and fault regions of the ADN is analyzed. A multi-dimensional state monitoring matrix is built using the measured current data. The spectral norm ratio coefficient is constructed based on the 2-norm to lower the complexity of the multi-dimensional state monitoring matrix and quantify the difference in state changes before and after the fault occurs in each region. Then, the fault region is identified by combining each region’s spectral norm ratio coefficient and the change of the current phase. Finally, an IEEE 33-node simulation model is created to simulate faults under different working conditions. According to the simulation results, the suggested approach is less impacted by fault type, neutral point grounding mode, and transition resistance. Furthermore, even if the communication does not match the rigorous synchronization requirements, the proposed method can still complete the fault identification of the distribution network correctly and has high robustness.

Towards personalized patient 3D Monte Carlo dosimetry for Lu-177-psma prostate treatments

Prostate-Specific Membrane Antigen (PSMA)-labeled Lu-177 therapy is a novel nuclear medicine therapy for metastatic prostate cancer, increasingly adopted worldwide due to the benefits observed in different patient studies. Administered in several cycles, each with a single injection and with a standard activity, the therapy requires customized dosimetry to evaluate efficacy and toxicity. The uptake of Lu-177-PSMA-617 in different organs strongly depends on the patient's anatomy, metastasis distribution, and the activity injected, underscoring the need for personalized dosimetry. This study aims to conduct dosimetry research on patients after several cycles of Lu-177-PSMA administration by Monte Carlo simulation using MCNP6.2. In order to make this study as realistic as possible, a high-resolution anthropomorphic computational phantom is used, the “Mesh-type Reference Computational Phantom” (MRCP) described in ICRP publication 145. This methodology is applied to different distributions of metastatic tissue, based on SPECT images where activity distribution within the patient's body is localized. Since Lu-177-PSMA-617 is deposited in those regions where prostate cancer metastasis has occurred, in Monte Carlo simulations these organs are considered as a source of irradiation with different emission activities probabilities depending on the patient. Once the organ activity distribution is determined, the simulation is performed in MCNP6.2 and the 3D dose distribution in the phantom is evaluated. Based on Monte Carlo results, doses at organs at risk are evaluated, estimating the total absorbed doses until the complete disintegration of Lu-177.Simulation results enable personalized adjustment of injected Lu-177 according to the needs of the clinical case. This approach has proven to be a valuable tool for assessing individual patient doses, treatment effectiveness, and healthy organ irradiation levels.

Topology of surface electromyogram signals: hand gesture decoding on Riemannian manifolds

Objective. Decoding gestures from the upper limb using noninvasive surface electromyogram (sEMG) signals is of keen interest for the rehabilitation of amputees, artificial supernumerary limb augmentation, gestural control of computers, and virtual/augmented realities. We show that sEMG signals recorded across an array of sensor electrodes in multiple spatial locations around the forearm evince a rich geometric pattern of global motor unit (MU) activity that can be leveraged to distinguish different hand gestures. Approach. We demonstrate a simple technique to analyze spatial patterns of muscle MU activity within a temporal window and show that distinct gestures can be classified in both supervised and unsupervised manners. Specifically, we construct symmetric positive definite covariance matrices to represent the spatial distribution of MU activity in a time window of interest, calculated as pairwise covariance of electrical signals measured across different electrodes. Main results. This allows us to understand and manipulate multivariate sEMG timeseries on a more natural subspace—the Riemannian manifold. Furthermore, it directly addresses signal variability across individuals and sessions, which remains a major challenge in the field. sEMG signals measured at a single electrode lack contextual information such as how various anatomical and physiological factors influence the signals and how their combined effect alters the evident interaction among neighboring muscles. Significance. As we show here, analyzing spatial patterns using covariance matrices on Riemannian manifolds allows us to robustly model complex interactions across spatially distributed MUs and provides a flexible and transparent framework to quantify differences in sEMG signals across individuals. The proposed method is novel in the study of sEMG signals and its performance exceeds the current benchmarks while being computationally efficient.