Layer adaptive node selection in Bayesian neural networks: Statistical guarantees and implementation details
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Jantre, Sanket (ORCID:0000000336110255)↗
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Phase II Trial of Neoadjuvant Chemotherapy, Chemoradiotherapy, and Laparoscopic Surgery with Selective Lateral Node Dissection for Poor-Risk Low Rectal Cancer
Purpose: The aim of this study is to evaluate the safety and efficacy of induction modified 5-fluorouracil, leucovorin, and oxaliplatin (mFOLFOX6) plus bevacizumab followed by S− 1-based chemoradiotherapy in magnetic resonance imaging (MRI)-defined poor-risk locally advanced low rectal cancer. Patients and Methods: This was a prospective phase II trial at a single comprehensive cancer center. The primary endpoint was the pathological complete response (pCR) rate. Eligible patients had clinical stage II–III low rectal adenocarcinoma with any of the following MRI-defined poor-risk features: circumferential resection margin (CRM) ≤ 1 mm, cT4, positive lateral nodes, mesorectal N2 disease, and/or requiring abdominoperineal resection. Patients received six cycles of mFOLFOX6 with 5 mg/kg bevacizumab followed by oral S-1 (80 mg/m{sup 2}/day on days 1–14 and 22–35) plus radiotherapy (50.4 Gy). Surgery was conducted through a laparoscopic approach. Lateral node dissection was selectively added when the patient had enlarged lateral nodes. Results: A total of 43 patients were enrolled. Grade 3–4 adverse events occurred in nine patients during induction chemotherapy and in five patients during chemoradiotherapy. One patient declined surgery with a clinical complete response. Forty-two patients underwent surgery, and 16 had pCR [37.2%, 95% confidence interval (CI) 24.4–52.1%]. All underwent R0 resection without conversion, including combined resection of adjacent structures (n = 14) and lateral node dissection (n = 30). Clavien–Dindo grade 3–4 complications occurred in six patients (14.3%). With median follow-up of 52 months, six developed recurrences (lung n = 5, local n = 1; 3-year relapse-free survival 86.0%). Conclusions: This study achieved a high pCR rate with favorable toxicity and postoperative complications in poor-risk locally advanced low rectal cancer. Multicenter study is warranted to evaluate this regimen.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Concurrent Selective Lymph Node Radiotherapy and S-1 Plus Cisplatin for Esophageal Squamous Cell Carcinoma: A Phase II Study
Background: The efficacy, toxicity, and patterns of failure of esophageal squamous cell carcinoma (ESCC) treated with selective lymph node (SLN) conventional fraction radiotherapy (CFRT) and S-1 plus cisplatin (CDDP) were evaluated. Patients and Methods: 67 Patients with clinical stage II–IVa ESCC were enrolled. The total dose of SLN CFRT was 60 Gy in 30 fractions over 6 weeks. The first course of radiation covered the primary and metastatic regional tumors and high-risk lymph nodal regions, given at 2 Gy/fraction for a dose of 40 Gy. In the second course, CFRT was delivered to the boost volume for an additional 20 Gy in 10 days, using 2 Gy/fraction. Two cycles of chemotherapy were given at the beginning of radiotherapy. CDDP at 25 mg/m{sup 2}/day was given on days 1–3 and days 22–24, and S-1 at 80 mg/m{sup 2}/day on days 1–14 and days 22–35. Patients achieving objective response after concurrent chemoradiotherapy underwent two additional cycles of chemotherapy. Results: The objective response rate (ORR) was 82.5%. Grade 3 or 4 toxicities included leukopenia (23.8%), neutropenia (14.3%), thrombocytopenia (14.3%), hemoglobin (4.8%), gastrointestinal (12.7%), skin (1.6%), and esophagus fistula (1.6%). One patient died of severe pneumonia, and two died of late toxicity because of esophagus fistula. With median follow-up of 32 months, the overall survival (OS) and progression-free survival (PFS) at 1 year and 2 years were 81.0% and 73.0%, and 63.5% and 49.2%, respectively. Conclusions: SLN RT concurrent with S-1 plus CDDP may represent a better strategy for treatment of ESCC patients.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Autoencoder node saliency: Selecting relevant latent representations
The autoencoder is an artificial neural network that performs nonlinear dimension reduction and learns hidden representations of unlabeled data. With a linear transfer function it is similar to the principal component analysis (PCA). While both methods use weight vectors for linear transformations, the autoencoder does not come with any indication similar to the eigenvalues in PCA that are paired with eigenvectors. In this work, we propose a novel autoencoder node saliency method that examines whether the features constructed by autoencoders exhibit properties related to known class labels. The supervised node saliency ranks the nodes based on their capability of performing a learning task. It is coupled with the normalized entropy difference (NED). We establish a property for NED values to verify classifying behaviors among the top ranked nodes. Lastly, by applying our methods to real datasets, we demonstrate their ability to provide indications on the performing nodes and explain the learned tasks in autoencoders.
97 MATHEMATICS AND COMPUTING↗
Performing optimized collective operations in an irregular subcommunicator of compute nodes in a parallel computer
In a parallel computer, performing optimized collective operations in an irregular subcommunicator of compute nodes may be carried out by: identifying, within the irregular subcommunicator, regular neighborhoods of compute nodes; selecting, for each neighborhood from the compute nodes of the neighborhood, a local root node; assigning each local root node to a node of a neighborhood-wide tree topology; mapping, for each neighborhood, the compute nodes of the neighborhood to a local tree topology having, at its root, the local root node of the neighborhood; and performing a one way, rooted collective operation within the subcommunicator including: performing, in one phase, the collective operation within each neighborhood; and performing, in another phase, the collective operation amongst the local root nodes.
Davis, Kristan D.↗
Performing optimized collective operations in a irregular subcommunicator of compute nodes in a parallel computer
In a parallel computer, performing optimized collective operations in an irregular subcommunicator of compute nodes may be carried out by: identifying, within the irregular subcommunicator, regular neighborhoods of compute nodes; selecting, for each neighborhood from the compute nodes of the neighborhood, a local root node; assigning each local root node to a node of a neighborhood-wide tree topology; mapping, for each neighborhood, the compute nodes of the neighborhood to a local tree topology having, at its root, the local root node of the neighborhood; and performing a one way, rooted collective operation within the subcommunicator including: performing, in one phase, the collective operation within each neighborhood; and performing, in another phase, the collective operation amongst the local root nodes.
Davis, Kristan Suzanne D.↗
Tuning the Properties of Zr 6 O 8 Nodes in the Metal Organic Framework UiO-66 by Selection of Node-Bound Ligands and Linkers
The metal organic framework (MOF) UiO-66, which incorporates Zr 6 O 8 nodes, exhibits high stability under a wide range of conditions that commends it for potential applications. The MOF properties can be tuned by the choice of the groups bonded to the node defect sites and to the linkers. In this paper, we report data for UiO-66 incorporating formate, acetate, benzoate, and trifluoroacetate on the nodes and -NH 2 , -OH, -NO 2 , and phenyl substituents on the benzene rings of the benzene-1,4-dicarboxylic acid-derived linkers. The MOFs were characterized by IR and 1 H NMR spectroscopies, thermal gravimetric analysis, N 2 adsorption, X-ray diffraction crystallography, scanning and transmission electron microscopy, and electronic structure calculations. The ligands on the nodes were identified and quantified by 1 H NMR spectra of the MOFs digested in NaOH/D 2 O solutions. Lastly, the effects of the node and linker groups on the electronic properties of the nodes have been quantified with IR spectra of the node μ 3 -OH groups and by density functional theory calculations, which are in good agreement with one another.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Distributed gradient descent for solving optimal power flow in radial networks
Node controllers and power distribution networks in accordance with embodiments of the invention enable distributed power control. One embodiment includes a node controller comprising a memory containing: a plurality of node operating parameters; and a plurality of node operating parameters describing operating parameters for a set of at least one node selected from the group consisting of at least one downstream node and at least one upstream node; wherein the processor is configured by the node controller application to: receive and store in memory a plurality of coordinator parameters describing operating parameters of a node coordinator by the network interface; and calculate updated node operating parameters using an iterative gradient projection process to determine updated node parameters using node operating parameters that describe operating parameters of node and operating parameters of the set of at least one node, where each iteration is determined by the coordinator parameters.
Gan, Lingwen↗
Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems
Detecting and localizing anomalies in cyber-physical systems (CPS) has become increasingly challenging as systems grow in complexity, particularly due to varying sensor reliability and node failures in distributed environments. While federated learning (FL) offers a foundation for distributed model training, existing approaches lack mechanisms to handle these CPS-specific challenges. This paper presents an enhanced FL framework that introduces three key innovations: adaptive model aggregation based on sensor reliability, dynamic node selection for resource optimization, and Weibull-based checkpointing for fault tolerance. Our framework enables reliable condition monitoring while addressing the computational and reliability challenges of industrial CPS deployments. Experiments on NASA Bearing and Hydraulic System Datasets demonstrate superior performance over state-of-the-art FL methods, achieving 99.5% AUC-ROC in anomaly detection and maintaining accuracy under node failures. Statistical validation using Mann-Whitney (U) test confirms significant improvements (p < 0.05) in both detection accuracy and computational efficiency across diverse operational scenarios.1
Marfo, William [University of Texas at El Paso,Dep↗
Development of a Hardware-in-The-Loop Testbed for a Decentralized, Data-Driven Electric Vehicle Charging Control Algorithm
This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.
33 ADVANCED PROPULSION SYSTEMS↗
pnnl/hypernetx-widget
Hnx-widget is a Jupyter notebook widget for visualizing hyper graphs that is intended to work in conjunction with the HyperNetX open source package developed by PNNL. It renders hyper graphs using Euler diagrams (a Venn diagram is a special type of Euler diagram). The tool contains simple interactions that allow the user to select nodes and change their appearance. It scales to small to medium sized (100 node) hyper graphs
Arendt, Dustin↗
Archival, anonymization and presentation of HTCondor logs with GlideinMonitor
GlideinWMS is a pilot framework to provide uniform and reliable HTCondor clusters using heterogeneous and unreliable resources. The Glideins are pilot jobs that are sent to the selected nodes, test them, set them up as desired by the user jobs, and ultimately start an HTCondor schedd to join an elastic pool. These Glideins collect information that is very useful to evaluate the health and efficiency of the worker nodes and invaluable to troubleshoot when something goes wrong. This data, including local stats, the results of all the tests, and the HTCondor log files, is packed and sent to the GlideinWMS Factory. To access this information, developers and troubleshooters must exchange emails with Factory operators and dig manually into files. Furthermore, these files contain also information like email and IP addresses, and user IDs, that we want to protect and limit access to. GlideinMonitor is a Web application to make these logs more accessible and useful: it organizes the logs in an efficient compressed archive; it allows to search, unpack, and inspect them, all in a convenient and secure Web interface; via plugins like the log anonymizer, it can redact protected information preserving the parts useful for troubleshooting.
Mambelli, Marco↗
Selective catalytic chemistry at Rhodium(II) nodes in bimetallic metal-organic frameworks
Explore the source record for details and available documents.
99 GENERAL AND MISCELLANEOUS↗
Selective Catalytic Chemistry at Rhodium(II) Nodes in Bimetallic Metal-Organic Frameworks
Explore the source record for details and available documents.
99 GENERAL AND MISCELLANEOUS↗
Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles with High Penetration of Behind-The-Meter Solar
The ever-growing integration of distributed energy resources (DERs), especially behind-the-meter (BTM) solar generations, poses imperative operational challenges to system operators such as regional transmission organizations (RTOs). It is important for RTOs to effectively and accurately extract actual load profiles at the transmission level for a single node with significant BTM solar injection. This paper first illustrates the necessity of disaggregating the daily actual load profile of a single node. Furthermore, by segmenting nodes with selected timeseries features, nodes with significant BTM solar generation are identified. Lastly, a bi-level framework is proposed, comprising reference node disaggregation and DeepFM nodal disaggregation, aimed at disaggregating the nodal load profiles from which system operators require more information. By adopting a hybrid Deep Factorization Machine (DeepFM) model, the model achieve accurate results by extracting both linear and nonlinear relations between nodes in the same region and the zonal load and nodal load profile. To overcome the lack of ground truth, this paper segments the load profile into daytime, nighttime, and zero-crossing points and utilizes the latter two for evaluation purposes. The proposed disaggregation procedure is validated using real world, minute-level, normalized, and anonymized nodal data in the PJM service territory.
42 ENGINEERING↗
Dynamic frequency control in power networks
Node controllers in power distribution networks in accordance with embodiments of the invention enable dynamic frequency control. One embodiment includes a node controller comprising a network interface a processor; and a memory containing a frequency control application; and a plurality of node operating parameters describing the operating parameters of a node, where the node is selected from a group consisting of at least one generator node in a power distribution network wherein the processor is configured by the frequency control application to calculate a plurality of updated node operating parameters using a distributed process to determine the updated node operating parameter using the node operating parameters, where the distributed process controls network frequency in the power distribution network; and adjust the node operating parameters.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Site-Selective Probes of Mixed-Node Metal Organic Frameworks for Photocatalytic Hydrogen Generation
Metal organic frameworks (MOFs) have emerged as a novel platform offering new promise for photocatalytic applications. In this work, we report a mixed-node MOF (MOF-74) based on earth-abundant metals (i.e., Co and Fe) as highly active and robust photocatalysts for visible light-driven H 2 generation. Using in situ X-ray absorption spectroscopy which allows us to selectively probe the individual metal node in MOFs, we unambiguously identified the catalytic active sites and captured the key intermediate species under the standard catalytic conditions. We show that both Co and Fe centers in MOF-74 are the reversible active sites, and the accumulation of their reduced states is responsible for the observed induction period for H 2 generation. Moreover, we captured the key intermediate species formed when the reaction reaches equilibrium, which is featured by an elongated Co–O bond at the Co center and a more distorted octahedral geometry at the Fe center with respect to their original geometry. These results not only demonstrate the potential of MOF-74 as a single site catalyst for H 2 generation but also provide new insight into the catalytic mechanism for further development.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Node-Solution Microenvironment Governs the Selectivity of Thioanisole Oxidation within Catalytic Zr-Based Metal–Organic Framework
Lewis acidic metal oxides, including zirconia (ZrO 2 ), are catalytically active toward oxidative reactions in the presence of sacrificial oxidants like t-butyl hydroperoxide (TBHP). The structural ambiguity and heterogeneity of the ZrO 2 surface impose challenges to chemists in understanding the reaction mechanism down to atomic-level precision. The inorganic, Zr-oxo nodes of many crystalline metal–organic frameworks (MOFs) structurally mimic ZrO 2 . Herein, we report three novel findings: (A) Zr-based MOF, Zr-MOF-808 is catalytically competent in activating TBHP to induce oxygen atom transfer (OAT) reactions to a model substrate, thioanisole, at room temperature, (B) its reaction mechanism can be derived with greater structural precision owing to the crystallinity of the MOF, and (C) the node-binding agent and other reaction conditions significantly impact the selectivity between the singly oxidized methyl phenyl sulfoxide vs the doubly oxidized sulfone. These findings suggest that both the activity and selectivity of OAT reactions within Zr-MOF-808 are governed by the chemistry occurring at the interface of the node and the surrounding reaction medium. Implications of these findings in OAT reactions and other MOF/metal oxide-catalyzed relevant catalysis are discussed.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗