Independent Sources Research Articles

Lipophilicity of fentalogs: Comparison of experimental and computationally derived data.

Although fentanyl and a small number of derivatives used for medical or veterinary procedures are well characterized, physiochemical properties have not been determined for many of the newer fentanyl analogs. Partition coefficients (Log P) were determined for 19 fentalogs using the shake-flask method and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Experimentally determined partition coefficients were compared with computationally derived data using six independent software sources (ACD/LogP, LogKOWWIN v 1.69, miLogP 2.2, OsirisP, XLOGP 3.0, ALogPS 2.1). Fentalogs with a wide variety of structural modifications were intentionally selected, yielding Log P values ranging from 1.21 to 4.90. Comparison of experimental and computationally derived Log P values were highly correlated (R2 0.854-0.967). Overall, substructure-based modeling using fragmental methods or property-based topological approaches aligned more closely with experimentally determined Log P values. LC-MS/MS was also used to estimate pKa values for fentalogs with no previously reported data. Lipophilicity and pKa are important considerations for analytical detection and toxicological interpretation. In silico methods allow the determination of physicochemical information prior to certified reference materials being readily available for in vitro or in vivo studies. Computationally derived data can provide insight regarding physiochemical characteristics of future fentalogs and other classes of synthetic analogs that have yet to emerge.

Design of a 21‐level multilevel inverter with minimum number of devices count

SummaryMultilevel inverters (MLIs) have attracted the attention of researchers for their needs in industrial applications, renewable energy systems, and electric vehicles. MLIs require a large number of power electronic components to synthesize higher levels at the output voltage. However, overuse of power electronic devices increases the complexity, losses, and cost of MLIs. In this study, a new MLI has been proposed with a reduced number of power switches. The basic unit of the proposed MLI comprises only three independent DC sources and 10 switches (eight unidirectional and two bidirectional) to produce 21 levels at the output voltage waveform. The nearest level control (NLC) modulation method has been used to produce gate pulses. Furthermore, three extension topologies have been proposed to generate a higher number of levels, and the extension parameters have been compared with recently introduced and conventional topologies. The comparative study shows that the proposed MLI topology requires fewer components in terms of power electronics parameters than the others. On the other hand, the presented first extension study that can be used for all non‐extendable basic units is one of the prominent values of the study. Simulation studies showing modulation methods, switching patterns, and signal outputs were performed with Matlab/Simulink. A prototype of the proposed main module has been realized and tested in the laboratory with an FPGA processing board. Experimental results have been verified with simulation results, and the performance of the proposed topology has been proven.

Intensity interferometry for holography with quantum and classical light.

As first demonstrated by Hanbury Brown and Twiss, it is possible to observe interference between independent light sources by measuring correlations in their intensities rather than their amplitudes. In this work, we apply this concept of intensity interferometry to holography. We combine a signal beam with a reference and measure their intensity cross-correlations using a time-tagging single-photon camera. These correlations reveal an interference pattern from which we reconstruct the signal wavefront in both intensity and phase. We demonstrate the principle with classical and quantum light, including a single photon. Since the signal and reference do not need to be phase-stable nor from the same light source, this technique can be used to generate holograms of self-luminous or remote objects using a local reference, thus opening the door to new holography applications.

Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma Part II: New Multi-Sensor Soil Moisture (MSSM) Product

This study develops a triple-collocation (TC) based, multi-source shallow-soil moisture product for Oklahoma. The method uses a least squared weights (LSW) optimization to find the set of parameters that result in the lowest root mean squared error (RMSE) with respect to the “unknown truth”. Soil moisture information from multiple sources and resolutions, including the Soil Moisture Active Passive SMAP L3_SM_P_E (9 km, daily), the physically-based, land surface model (LSM) estimates from NLDAS_NOAH0125_H (1/8°, hourly), and the Oklahoma Mesonet ground sensor network (9 km interpolated from point, 30 min) is merged into a 9 km spatial and daily temporal resolution product across the state of Oklahoma from April 2015 to July 2019. This multi-sensor surface soil moisture (MSSM) product is assessed in terms of a state-wide benchmark and previously tested, in situ-based soil moisture product and SMAP L4. Results show that: (1) independent source products have differential values according to the regional conditions they represent, including land cover type, soils, irrigation, or climate regime; (2) beyond serving as validation sets, in situ measurements are of significant value for improving the accuracy of multi-sensor soil moisture datasets through TC; and (3) state-wide RMSE values obtained with MSSM are similar to the typical measurement error found on in situ ground measurements which provides some degree of confidence on the new product. MSSM is an improvement over currently available products in Oklahoma due to its minimized uncertainty, easiness of production, and continuous temporal and geographic coverage. Nevertheless, to exploit its utility, further tests of this methodology are needed in different climates, land cover types, geographic regions, and for other independent products and spatiotemporal resolutions.

The effect of environmental competitiveness, customer and competitor orientation on export performance

PurposeCustomer orientation (CO) means meeting customers’ needs better than competitors. Competitor orientation means using and acting upon the knowledge of competitors. Thus, the main aim of this study is to analyze the effects of environmental competitiveness on export performance by examining the moderating roles of these variables.Design/methodology/approachBased on data from 5,000 firms from a survey run in 2021 in Turkey facilitated by the Chamber of Commerce, the authors tested their hypotheses using partial least squares structural equation modeling and correlation analysis.FindingsThe results show that competitor orientation positively affects the relationship between a competitive environment and export performance. However, the authors do not find evidence of a significant effect on CO.Practical implicationsService sector managers should sacrifice customers to gain export market share in favor of superiority with competitors.Originality/valuePrior research has yet to emphasize the importance of competition intensity in export performance for service-logistics firms. However, the environmental competitiveness–performance relationship is better explained with the help of these test results and the two additional moderators suggested in this work. Moreover, the export performance indicators were gathered from an independent source.

Observers for Robust Rotor State Estimation

The paper discusses the synthesis of linear and nonlinear observers to estimate rotor states from fuselage state measurements alone. First, the paper reviews two forms of the classical Luenberger linear observer applied to the rotor state estimation problem and identifies some limitations thereof. Thereafter, the paper proposes a new robust nonlinear discontinuous observer based on the sliding mode theory to simultaneously estimate rotor flapping and lead-lagging states from fuselage state measurements. For this new nonlinear observer, the paper presents stability analyses to determine conditions that guarantee rotor state estimation accuracy despite unknown but bounded turbulence input. The nonlinear observer also lends itself to the online and real-time estimation of the unknown turbulence input. Simulation results in calm and turbulent air conditions highlight the efficacy and performance of the nonlinear discontinuous observer. Such rotor state observers could provide an independent source of online and real-time rotor states estimates to complement or supplement in situ rotor state measurement apparatus for various flight control and health-monitoring functions.

False Memory Facilitation Through Semantic Overlap.

This study investigated the effects of semantic overlap from multiple sources on false memories. Participants were presented with paired study lists comprising items highly associated with one nonstudied critical item. There were three types of list pairs: (1) the sharing condition, in which the words in both lists were classified into different semantic groups that converged on the same critical word (semantic overlap), (2) the repetition condition, in which the two lists comprised identical words, and (3) the single condition, in which the paired lists were attributed to different semantic groups that did not share a critical item. In Experiment 1, participants were presented with the paired study lists and responded to free recall tests and a recognition test including remember-know judgments. In Experiment 2, the participants responded to a recognition test, and the participants in Experiment 3 recalled the studied items. The results indicated that the false recall and false recognition rates in the sharing condition were higher than those in the repetition and single conditions. These results suggest that activation from multiple independent sources may have an accumulative additive effect. The findings are discussed in relation to the Activation-Monitoring theory.

Регульований смуговий фільтр на основі кільцевих резонаторів для мікрохвильових застосунків

The paper presents the principle of a microwave adaptive band-pass filter based on the cascade of ring resonators. The filter is performed as a planar microstrip technology with thickness of around 1 mm to ensure compactness of the device. The ring resonators, being a part of metamaterials, can be considered as an equivalence of an oscillator and is conventionally used in the microwaves. While a quite number of the filters with predefined parameters are well-known, the adaptive filters the throughput and output characteristics of which can be adjusted depending on the input ones is the topical problematic nowadays and, at least, for the closest future. We have investigated in the paper the possibilities of adjustment the considered filter’s transmission characteristics through the ring design, distance between them, and discussed other features, which have impact. For example, it allows expanding of the filter bandwidth from 250 to 60 MHz for simultaneous change of a distance between the adjacent rings. We have suggested the approach how to do the filter time-dependent. A varactor diode inserted into the gap of the middle ring is controlled with an independent external source and can adjust the filter bandwidth from 80 up to 140 MHz for the varactor capacitance variation from 16 to 6 pF (bias voltage variation from 1 to 7 V) that covers the existing communication networks, such as mobile generations, Bluetooth, Wi-Fi, etc. and can be applied for modern smart technologies of the Internet-of-Things for a remote control. It becomes possible because different sensing elements, such as photodiodes, Hall effect sensors, photoresistors, etc., can be exploited as the aforementioned external source.

Developing a convolutional neural network for classifying tumor images using Inception v3

Deep learning algorithms rely on digital pathology to classify tissue tumors, where the whole tissue slides are digitized and imaged. The produced multi-resolution whole slide images (MWSIs) are with high resolution that may range from about 100,000 to 200,000 pixels. MWSIs are often stored in a multi-resolution configuration to simplify the processing of images, navigation, and efficient exposition. This work develops a network for classifying MWSIs that require high memory employing a deep neural Inception-v3 architecture. This work employs the MWSIs from Camelyon16, which is around 451 GB in size of Challenge dataset from two independent sources including 400 MWSIs as a total of lymph nodes. The training dataset contains 111 MWSIs of tumor tissue and lymph nodes and 159 WSIs of normal lymph nodes. The developed model uses sample-based processing to train extensive MWSIs employing the MATLAB platform. The model introduces transfer learning techniques with an Inception-v3-based architecture to categorize separate samples as a tumor or normal. Therefore, the main aim here is to achieve two-classes binary segmentation containing normal and tumor. This includes creating a new fully connected layer for the Inception-v3 architecture with two classes and compensating new layers instead of the original final fully-connected layers. The results obtained demonstrated that the heatmap visualization can recognize the boundary coordinates of ground truth as sketchy Region Of Interest (ROI), where the green boundary represents the normal regions and the tumor area with red boundaries. The proposed Inception v3 Convolutional Neural Network (CNN) architecture can achieve more than 92.8 % accuracy for such MWSIs dataset to categorize brain tumors into normal and tumor tissue

Legislation of the judiciary in the administration of justice, as a source of suffrage

The article is devoted to the coverage of certain theoretical issues of electoral law of Ukraine. Attention is focused on revealing the terminological features of the category “sources of electoral law”. The opinions of a number of constitutionalist scientists regarding the analyzed concept are given.The author focuses his attention on the peculiarities of judicial law-making, as a special source of electoral law. It is indicated that individual decisions of courts of general jurisdiction, in modern conditions, should be recognized as sources of electoral law, provided that:
 a) they contain norms-precepts that directly affect the regulation of social relations that are the subject of electoral law; b) the effect of these decisions extends to a significant range of subjects of electoral relations;
 c) cause-and-effect (deterministic) relationships between the will of the court and changes in the behavior or activity of thesubjects for which the court’s decision has a relevant significance are clearly tracked.In addition, it is determined, based on logical reasoning, about the need to take into account the nature and place of the acts of the Constitutional Court of Ukraine in the process of analyzing the nature of legal positions, it is possible to assumean organic combination of the issue of attributing the acts of the Court and the legal positions of the Court to the sources of law. Therefore, the legal positions of the Constitutional Court of Ukraine have properties and features that can be characterizedas independent sources of law and the actual impossibility of unequivocally assigning them to any of the well-known sources of law. So, in particular, the legal positions of the body of constitutional justice express the will of the state (after all, they arise as an act of a state body); they are official and generally binding; they play the role of a normative basis in law-making and serve as a guideline in lawmaking and law enforcement for most subjects of legal relations; they have a universal character, that is, they are not the result of a decision and the subject of application in a specific case, but can also be used in similar or similar situations for the purpose of substantiating positions; legal positions are a reflection of legal certainty, and should beformulated in such a way as to prevent their unequal, distorted understanding or improper application; legal positions have a permanent nature, that is, when deciding the following cases, the Constitutional Court of Ukraine is guided by previously expressed legal positions.

Boosted federated learning based on improved Particle Swarm Optimization for healthcare IoT devices

As healthcare data becomes increasingly available from various sources, including clinical institutions, patients, insurance companies, and pharmaceutical industries, machine learning (ML) services are becoming more significant in healthcare-facing domains. Therefore, it is imperative to ensure the integrity and reliability of ML models to maintain the quality of healthcare services. Particularly due to the growing need for privacy and security, healthcare data has resulted in each Internet of Things (IoT) device being treated as an independent source of data, isolated from other devices. Moreover, the limited computational and communication capabilities of wearable healthcare devices hinder the applicability of traditional ML. Federated Learning (FL) is a paradigm that maintains data privacy by storing only learned models on a server and advances with data from scattered clients, making it ideal for healthcare applications where patient data must be safeguarded. The potential of FL to transform healthcare is significant, as it can enable the development of new ML-powered applications that can enhance the quality of care, lower costs, and improve patient outcomes. However, the accuracy of current Federated Learning aggregation methods suffers greatly in unstable network situations due to the high volume of weights transmitted and received. To address this issue, we propose an alternative approach to Federated Average (FedAvg) that updates the global model by gathering score values from learned models primarily utilized in Federated Learning, using an improved version of Particle Swarm Optimization (PSO) called FedImpPSO. This approach boosts the robustness of the algorithm in erratic network conditions. To further enhance the speed and efficiency of data exchange within a network, we modify the format of the data clients send to servers using the FedImpPSO method. The proposed approach is evaluated using the CIFAR-10 and CIFAR-100 datasets and a Convolutional Neural Network (CNN). We found that it yielded an average accuracy improvement of 8.14% over FedAvg and 2.5% over Federated PSO (FedPSO). This study evaluates the use of FedImpPSO in healthcare by training a deep-learning model over two case studies to evaluate the effectiveness of our approach in healthcare. The first case study involves the classification of COVID-19 using public datasets (Ultrasound and X-ray) and achieved an F1-measure of 77.90% and 92.16%, respectively. The second case study was conducted over the cardiovascular dataset, where our proposed FedImpPSO achieves 91.18% and 92% accuracy in predicting the existence of heart diseases. As a result, our approach demonstrates the effectiveness of using FedImpPSO to improve the accuracy and robustness of Federated Learning in unstable network conditions and has potential applications in healthcare and other domains where data privacy is critical.

Cloud type frequency over Poland (2003–2021) revealed by independent satellite‐based (MODIS) and surface‐based (SYNOP) observations

AbstractRecent studies have identified several statistically significant trends in cloud genera frequency over Poland. Notably, there has been an increase in high and convective clouds, along with a decrease in Stratus, Altostratus and Nimbostratus. These earlier studies were, however, only based on traditional, surface data. As the ability to observe mid and high‐level clouds from the ground is limited, due to overlapping, we address the question of whether these trends are confirmed by other sources of cloud data, such as satellite records. In this paper, we analyse cloud data from two fully independent sources to characterize the cloud type climatology over Poland for the period 2003–2021: cloud optical thickness (COT) and cloud top pressure (CTP) parameters retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS), and surface (SYNOP) observations of cloud genera from the country's 27 ground‐based stations. The International Satellite Cloud Climatology Project (ISCCP) COT–CTP classification was used to define MODIS cloud types. We explore the hypothesis that MODIS data, classified using the ISCCP philosophy, confirms the changes in cloud type frequencies found in SYNOP observations. Our analysis is based on the application of the Spearman rank correlation test to mean monthly SYNOP and MODIS cloud type frequencies, and we compare per decade trends for two time series. Our hypothesis was confirmed, but only for Cirrus, Altostratus + Nimbostratus and Cumulus. We therefore conclude that the two analysed data sources are not fully consistent, and cannot substitute for each other in cloud type climatologies. Hence, at least for Poland, they should be treated as independent rather than complementary. We also demonstrate that the increase in high‐level cloud frequency over Poland, which has been observed by other authors who based their research on synoptic data, is not due to a decrease in the frequency of low‐ and mid‐level clouds. Our findings are confirmed by satellite records.

ISEE: A Wearable Image-Sound Translation System for Partially Sighted People

As a growing number of blinds, many blind aid systems which use the way of converting image to sound or vibration have been designed. However, phonetic prompt and vibration are very limited in information expression. Therefore, a system called Image-Sound Eye-substitution Equipment (ISEE) is designed in this paper. This system consists of binocular camera, Raspberry Pi, and stereo headsets. It can convert image to sound by mapping each pixel into an independent sound source based on sensory substitution technology and enable blinds to browse pictures, appreciate scenery, and avoid obstacles for its high-density information expression. Its core of data processing design is the Sensory Substitution Technology-based Image-to-Sound Transform (SST-IST) algorithm. The SST-IST mainly consists of 3 steps, i.e., the image reshape, image-to-sound conversion matrix generation, and image-to-sound transmission. First, a single-frequency pitch of pixel is generated, followed by wave interception and localization to obtain independent sound clips to express pixel information. Second, the sound chips based on Head Related Transfer Functions (HRTF) are stereo processed. Third, a series of single-frequency sound chips are accumulated with weights to generate a complete sound. Extensive experiments show that our SST-IST can restore image information well and the sound restore rate can achieve 73.0%. Also, it is universally adaptive and robust to the changes in ambient light and noises within a certain range. The auditory discrimination experiments show that, with 4-5 hours training, the users can recognize basic graphics by using ISEE system.

Towards a minimal example of quantum nonlocality without inputs

The network scenario offers interesting new perspectives on the phenomenon of quantum nonlocality. Notably, when considering networks with independent sources, it is possible to demonstrate quantum nonlocality without the need for measurement inputs, i.e., with all parties performing a fixed quantum measurement. Here we aim to find minimal examples of this effect. Focusing on the minimal case of the triangle network, we present examples involving output cardinalities of $3\text{\ensuremath{-}}3\text{\ensuremath{-}}3$ and $3\text{\ensuremath{-}}3\text{\ensuremath{-}}2$. A key element is a rigidity result for the parity token counting distribution, which represents a minimal example of rigidity for a classical distribution. Finally, we discuss the prospects of finding an example of quantum nonlocality in the triangle network with binary outputs and point out a connection to the Lovasz local lemma.

Novel comprehensive feedback theory—Part I: Generalization of the cut‐insertion theorem and demonstration of feedback universality

SummaryThe previous cut‐insertion theorem, defined for linear circuits and for particular three‐terminal circuits (TTCs) to be inserted into the cut, is here extended to any TTC and, in a simplified form, to non‐linear networks. The TTC may include dependent and independent sources and any other passive and/or independent elements, and, together with the remaining part of the circuit, determines a feedback loop for which the “transmission factors” and can be defined and computed. The theorem allows to obtain all the circuit properties: the overall gain, the driving‐point immittances, a new properly defined “cut immittance,” the sensitivity, and so on. For becomes the “classic” loop gain. As will be shown in Part II of the paper, the new approach, depending on the TTC implementation, includes and unifies all the previous feedback models and enables the creation of new ones. Moreover, starting from the paradigmatic definition of a system with a feedback as a system in which the output quantity is in part an effect of itself, the universal presence of feedback in any system is shown. Finally, the results are highlighted by the application to unilateral non‐linear systems, such as digital latches, and to bilateral linear circuits, such as bridged‐ networks.

Comparison of thermomechanical responses of single-arc and dual-arc parallel additive manufacturing

Printed metallic parts often suffer from thermomechanical defects such as delamination, buckling and warping, and this effect is exacerbated in multi-arc additive manufacturing (AM) due to the extensive heat input and large molten pool. These defects originate primarily because of high residual stresses accumulated during layer-by-layer deposition. Here we develop, validate and employ a three-dimensional finite element model with two independent heat sources to analyse the thermomechanical responses in dual-arc parallel AM of Ti6Al4V thin-walled parts. The results are compared with those of the conventional single-arc AM. Although the deformation in dual-arc AM is slightly larger than that in single-arc AM, the stresses at the substrate-deposit interface for dual-arc AM are reduced by 53% due to the lower cooling rate.

Automated Model Selection of the Two-Layer Mixtures of Gaussian Process Functional Regressions for Curve Clustering and Prediction

As a reasonable statistical learning model for curve clustering analysis, the two-layer mixtures of Gaussian process functional regressions (TMGPFR) model has been developed to fit the data of sample curves from a number of independent information sources or stochastic processes. Since the sample curves from a certain stochastic process naturally form a curve cluster, the model selection of TMGPFRs, i.e., the selection of the number of mixtures of Gaussian process functional regressions (MGPFRs) in the upper layer, corresponds to the discovery of the cluster number and structure of the curve data. In fact, this is rather challenging because the conventional model selection criteria, such as BIC and cross-validation, cannot lead to a stable result in practice even with a heavy burden of repetitive computation. In this paper, we improve the original TMGPFR model and propose a Bayesian Ying-Yang (BYY) annealing learning algorithm for the parameter learning of the improved model with automated model selection. The experimental results of both synthetic and realistic datasets demonstrate that our proposed algorithm can make correct model selection automatically during parameter learning of the model.

Optimal Quantum Violations of n‐Locality Inequalities with Conditional Dependence on Inputs

AbstractBell experiment in a network gives rise to a form of quantum nonlocality which is conceptually different from traditional multipartite Bell nonlocality. In this work, the star‐network configuration involving arbitrary n independent sources and parties, including n edge parties and one central party is considered. Each of the n edge parties shares a physical system with the central party. Each edge party receives number of inputs, and the central party receives an arbitrary m number of inputs. The conditional dependence on the inputs of each edge party is imposed so that the local probabilities satisfy a set of constraints. A family of generalized n‐locality inequalities is proposed in the arbitrary input scenario by imposing the set of constraints on inputs. The optimal quantum violation of the inequalities is derived by using an elegant sum‐of‐squares approach without specifying the dimension of the quantum system. Notably, the optimal quantum value is achieved only when the set of linear constraints on inputs is satisfied, which, in turn, self‐tests the observables required for each edge party. It shows that while conditional dependence on inputs significantly reduces the n‐local bound of the inequalities, the optimal quantum violation remains invariant. It argues that this implies a more robust test of network non‐locality, which can be revealed for smaller visibility parameters of the corresponding state. Further, the network nonlocality is characterized and examine its correspondence with suitably derived standard Bell nonlocality.

Effects of individual strategies for resource access on collaboratively maintained irrigation infrastructure

AbstractBuilt infrastructure for water and energy supply, transportation, and other such services underpins human well‐being and socioeconomic development. A fundamental understanding of how infrastructure design and user strategies interact can guide important design decisions as well as policy formulation for ensuring long‐term infrastructure viability in conjunction with improved individual user benefits. In this work, an agent based model (ABM) is developed to study this issue for the specific case of irrigation canals. Cooperatively maintained irrigation canals serve essential roles in sustaining agriculture‐based economies in many regions. Canal system design can strongly affect benefits derived by distributed users, regional agricultural output, and the long‐term viability of the shared infrastructure itself. Here, an ABM is used to investigate how an option to use an independent water source interacts with canal design to affect canal maintenance cooperation and farmer income. The independent water source is stylized as a well that provides access to groundwater and represents a strategically robust design option; a design option that reduces the implementer's utility vulnerability to unfavorable actions by other actors. Research in other systems has demonstrated that strategically robust designs can improve both implementer utility and the probability of collaboration. The results of this research, in contrast, demonstrate that the option of individual resource access, the strategically robust design option, as represented by a well, reduces cooperative maintenance in most cases. However, wells also improve farmer income, especially for downstream farmers that are most affected by water theft.

CT radiomic signature to predict overall survival and chemotherapy benefit in stage I and II HPV-associated oropharyngeal carcinoma.

6073 Background: Chemoradiation is the standard of care for human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC). However, not all patients would benefit from chemotherapy, especially patients with low-risk characteristics. We aim to develop and validate a prognostic and predictive radiomic image signature (pRiS) to inform survival and chemotherapy benefit in stage I and stage II HPV-associated OPSCC. Methods: Radiographic scans for 491 patients with stage I and stage II HPV-associated OPSCC were acquired from 4 independent sources and divided into three cohorts D1-D3. D1 comprised computed tomography (CT) scans from 60 radiotherapy treated patients and was used to identify prognostic features via a LASSO Cox model to predict overall and disease-free survival. The prognostic performance of pRiS was evaluated on two test sets (D2, n = 162; D3, n = 269) using concordance index (C-index). An integrated nomogram was developed to demonstrate the incremental value of the pRiS to the existing clinical factors for individualized survival estimation. Patients from D2 and D3 who received either radiotherapy alone or chemoradiation were used to validate pRiS as predictive of added benefit of chemotherapy. Results: Seven radiomic features were selected to construct the image biomarker pRiS, which was found to be prognostic of overall survival (OS) on univariate analysis in D2 (hazard ratio [HR] = 2.14, 95% confidence interval [CI], 1.1–4.16, p = 0.02) and D3 (HR = 2.74, 95% CI, 1.34–5.62, p = 0.006). Chemotherapy was associated with improved OS for high-pRiS patients in both D2 (radiation vs chemoradiation, HR = 4.47, 95% CI, 1.73–11.6, p = 0.002) and D3 (radiation vs chemoradiation, HR = 2.99, 95% CI, 1.04–8.63, p = 0.04). In contrast, chemotherapy did not improve OS for low-pRiS patients, which indicates these patients did not derive additional benefit from chemotherapy and could be considered for treatment de-escalation. Conclusions: The proposed radiomic signature was prognostic of patient survival and the binary pRiS group informed chemotherapy benefit for stage I and II HPV-associated OPSCC patients. [Table: see text]