Set Of Networks Research Articles

An automated construction method of 3D knowledge graph based on multi-agent systems in virtual geographic scene

ABSTRACT Virtual geographic scenes can significantly facilitate the visualization and comprehension of the real world. However, the multitude of object types and complex relationships they contain make it challenging to display embedded geographic knowledge. Knowledge graphs help organize object entities and their relationships within scenes, which makes it easier to store and express geographic knowledge. Current methods for constructing knowledge graphs for virtual geographic scenes suffer from issues such as weak automation, low dimensionality, and poor cognitive efficiency. Thus, this paper presents a method for automatically constructing knowledge graphs by integrating intelligent systems. The method involves designing a multiagent system to extract entities and relationships hidden in virtual geographic scenes. In addition, a three-dimensional visualization approach is proposed for knowledge graphs constrained by spatiotemporal relationships. A virtual scene containing 2,183 objects was selected as the experimental area for constructing and displaying the knowledge graph. The results demonstrate that the method constructs a knowledge graph with 12,958 relationships within 300 s in common network and hardware settings. In addition, a knowledge graph offers better completeness at small scales compared to other geographic knowledge graphs, and it can also enhance the cognitive efficiency of understanding scene relationships.

High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings

In this work, we present novel protocols over rings for semi-honest secure three-party computation (3PC) and malicious four-party computation (4PC) with one corruption. While most existing works focus on improving total communication complexity, challenges such as network heterogeneity and computational complexity, which impact MPC performance in practice, remain underexplored. Our protocols address these issues by tolerating multiple arbitrarily weak network links between parties without any substantial decrease in performance. Additionally, they significantly reduce computational complexity by requiring up to half the number of basic instructions per gate compared to related work. These improvements lead to up to twice the throughput of state-of-the-art protocols in homogeneous network settings and up to eight times higher throughput in real-world heterogeneous settings. These advantages come at no additional cost: Our protocols maintain the best-known total communication complexity per multiplication, requiring 3 elements for 3PC and 5 elements for 4PC.We implemented our protocols alongside several state-of-the-art protocols (Replicated 3PC, ASTRA, Fantastic Four, Tetrad) in a novel open-source C++ framework optimized for high throughput. Five out of six implemented 3PC and 4PC protocols achieve more than one billion 32-bit multiplications or over 32 billion AND gates per second using our implementation in a 25 Gbit/s LAN environment. This represents the highest throughput achieved in 3PC and 4PC so far, outperforming existing frameworks like MP-SPDZ, ABY3, MPyC, and MOTION by two to three orders of magnitude.

Two Approaches to Constructing Certified Dominating Sets in Social Networks

Two Approaches to Constructing Certified Dominating Sets in Social Networks

Regression study on fruit-setting days of purple eggplant fruit based on in situ VIS-NIRS and attention cycle neural network.

In the intelligent harvesting of eggplant, the lack of in situ identification technology makes it challenging to determine the maturity of purple eggplant fruit. The length of the fruit-setting date can determine when the eggplant is ready to be harvested. This study uses deep learning techniques to predict the date of fruit maturity. First, we proposed a fruit-setting days prediction method based on fruit spectroscopy and neural networks. Second, we collected the field in situ spectral data of purple eggplant fruit during 15-33 days of fruit setting using a portable spectrometer, covering 500-1000nm. A fruit-setting time regression network combining multi-scale convolution, multi-head attention mechanism, and long short-term memory recurrent neural network was constructed using the collected in situ spectral data. The model demonstrated better fitting performance than traditional machine learning models such as backpropagation neural network, random forest, support vector machine, and partial least squares regression in the regression task of fruit-setting days. After testing various spectral preprocessing methods, the best fitting effect was found on the standard normal variate-processed dataset, with R2 of 0.876 and RMSE(root mean square error) of 2.148 days. Furthermore, the feasibility of each model module was analyzed in depth through ablation experiments, confirming each component's role in improving the model's performance. The network attention weight was also analyzed, and the model has strong detail mining ability in a specific spectral interval. In summary, the combination of visible and near-infrared spectroscopy and attention cycle neural network is an effective method to predict the fruit-setting days of purple eggplant fruit. PRACTICAL APPLICATION: A prediction method of fruit-setting days based on fruit spectral characteristics and recurrent neural network regression was proposed. A novel approach to detecting and disclosing in situ surface VIS-NIRS reflectance data of eggplant fruit during ripening is presented for the first time. A set of long-term and short-term memory networks based on multi-scale convolution and multi-head attention mechanisms was constructed for spectral data fitting. Through the ablation test method and attention weight analysis, the function of each module in the network and the interpretability of feature extraction are explored.

A New Dual‐Band Filtering Power Divider Based on a Single Multimode Modified Triangle Patch Resonator

ABSTRACTThis letter proposes a new design of a dual‐band (DB) filtering power divider (FPD) on a single multimode‐modified triangle patch resonator. The proposed design leverages the resonant characteristics of an equilateral triangle patch resonator to exploit the TM10, TM11, TM21, and TM30 resonant modes within a single multimode‐modified triangle patch resonator, enabling the realization of an FPD with DB operation. To achieve two assumed second‐order filtering passbands, a metal via and multiple perturbed slots are introduced, providing flexible control over the resonant mode frequencies. The first passband is formed by the TM10 and TM11 modes, while the second passband is formed by the TM21 and TM30 modes. To ensure in‐band isolation for both passbands, two sets of isolation networks are developed. A prototype is designed, fabricated, and measured to validate the proposed method. The measured results demonstrate excellent agreement with the simulated ones.

MSR1 in lung squamous cell carcinoma: Prognostic and immunological values in pan-cancer and single-cell analyses and a cohort study.

Lung squamous cell carcinoma (LUSC) constitutes approximately 40% of lung cancer cases and lacks effective treatments, needing new diagnostic and prognostic tools. Macrophage scavenger receptor 1 (MSR1), as a key receptor in macrophages, is essential in tumor immunity. However, its mechanisms in regulating tumor progression and immunity and its prognostic value in LUSC remain unclear. MSR1 expression in pan-cancer, particularly LUSC across distinct clinical subgroups, was identified utilizing TIMER, GEPIA, and UALCAN databases. Prognosis analysis of MSR1 in pan-cancer was conducted using SangerBox, GEPIA, PrognoScan and Kaplan-Meier plotter. Using SangerBox and TIMER, association between MSR1 expression and infiltrating immune cells was investigated. MSR1 gene co-expression network and Gene Set Enrichment Analysis (GSEA) in LUSC were constructed using LinkedOmics database. The analysis of single-cell RNA-sequencing (scRNA-seq) was conducted using the GEO database. Association between plasma MSR1 levels and LUSC risk was evaluated in a cohort study with 49,566 UK Biobank participants. MSR1 was dysregulated in various cancers and lowly expressed in LUSC tissues than in the normal. Higher MSR1 expression was substantially correlated with poor LUSC overall survival. MSR1 positively associated with tumor-associated macrophage (TAM) infiltrations and its markers (CCL2, CD68, IL10). MRS1 closely related to the immune-suppression of macrophages in LUSC. Higher plasma MSR1 levels were positively correlated with increased LUSC risk (HR = 1.33, 95% CI: 1.07-1.64; P = 0.01). MSR1 has significant prognostic and immunological values in pan-cancer and represents a possible biomarker for prognosis and diagnosis in LUSC patients.

Practical Two-party Computational Differential Privacy with Active Security

In this work we revisit the problem of using general-purpose MPC schemes to emulate the trusted dataholder in differential privacy (DP), to achieve the same accuracy but without the need to trust one single dataholder. In particular, we consider the two-party model where two computational parties (or dataholders), each with their own dataset, wish to compute a canonical DP mechanism on their combined data and to do so with active security. We start by remarking that available definitions of computational DP (CDP) for protocols are somewhat ill-suited for such a use-case, due to them either poorly capturing some strong security guarantees commonly given by general-purpose MPC protocols, or having too strict requirements in the sense that they need significant adjustment in order to be satisfiable by using common DP and MPC techniques. With this in mind, we propose a new version of simulation-based CDP, called SIM*-CDP, and prove it to be stronger than the IND-CDP and SIM-CDP and incomparable to SIM+-CDP. We demonstrate the usability of the SIM*-CDP definition by showing how to satisfy it by the use of an available distributed protocol for sampling truncated geometric noise. Further, we use the protocol to compute two-party inner-products with CDP and active security, and with accuracy equal to that of the central model, being the first to do so. Finally, we provide an open-sourced implementation and benchmark its practical performance. Our implementation generates a truncated geometric sample in between about 0.035 and 3.5 seconds (amortized), depending on network and parameter settings, comparing favourably to existing implementations.

Connectome-Based Predictive Modeling of Trait Mindfulness.

Trait mindfulness refers to one's disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome-based predictive modeling analysis in 367 meditation-naïve adults across three samples collected at different sites. In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

Discovery of phloridzin as a new antagonist for Di(2-ethylhexyl) phthalate-induced male reproductive toxicity based on the adverse outcome pathway network and drug-target gene set enrichment analysis.

Di(2-ethylhexyl) phthalate (DEHP) is a widespread ubiquitous phthalate environmental contaminant. The male reproductive toxicity (MRT) from exposure to DEHP and its main metabolite, mono(2-ethylhexyl) phthalate (MEHP), has been well documented. Fully elucidating its toxic mechanism and discovering effective antagonists are desirable means to reduce the health risks of DEHP. In this study, 552 genes related to MRT induced by DEHP/MEHP were screened out from the Comparative Toxicogenomics Database (CTD) and DisGeNET database. Next, we developed a global adverse outcome pathway (AOP) network based on the existed AOP-wiki. After functional enrichment analyses and mapping to the global AOP network, we found that the increased ROS level, cell cycle arrest, and increased apoptosis are key events (KEs) involved in DEHP-mediated MRT, which was validated in TM3 Leydig cell model. Among them, cellular apoptosis is the core KE in DEHP-induced MRT via network topological analysis. Eventually, we developed a novel in silico antagonist screening platform (http://43.136.69.224:3838/wlab/) based on drug-target gene set enrichment analysis (dtGSEA version 2.0). Several potential candidates that mitigate DEHP-mediated cellular apoptosis have been screened out, including quercetin, taurine, methionine, and phloridzin. Further experimental results demonstrated that phloridzin provided the most effective protection against MEHP-induced apoptosis in TM3 cells probably through the p53 and MAPK signaling pathways. Molecular docking and molecular dynamics simulations suggest that STAT3 and RUNX1 may be important targets for phloridzin to antagonize MEHP-induced MRT. Our study provides a new approach to discover the antagonists for the toxicity of environmental contaminants based on AOP network and dtGSEA methods.

A Latency Composition Analysis for Telerobotic Performance Insights Across Various Network Scenarios

Telerobotics involves the operation of robots from a distance, often using advanced communication technologies combining wireless and wired technologies and a variety of protocols. This application domain is crucial because it allows humans to interact with and control robotic systems safely and from a distance, often performing activities in hazardous or inaccessible environments. Thus, by enabling remote operations, telerobotics not only enhances safety but also expands the possibilities for medical and industrial applications. In some use cases, telerobotics bridges the gap between human skill and robotic precision, making the completion of complex tasks requiring high accuracy possible without being physically present. With the growing availability of high-speed networks around the world, especially with the advent of 5G cellular technologies, applications of telerobotics can now span a gamut of scenarios ranging from remote control in the same room to robotic control across the globe. However, there are a variety of factors that can impact the control precision of the robotic platform and user experience of the teleoperator. One such critical factor is latency, especially across large geographical areas or complex network topologies. Consequently, military telerobotics and remote operations, for example, rely on dedicated communications infrastructure for such tasks. However, this creates a barrier to entry for many other applications and domains, as the cost of dedicated infrastructure would be prohibitive. In this paper, we examine the network latency of robotic control over shared network resources in a variety of network settings, such as a local network, access-controlled networks through Wi-Fi and cellular, and a remote transatlantic connection between Finland and the United States. The aim of this study is to quantify and evaluate the constituent latency components that comprise the control feedback loop of this telerobotics experience—of a camera feed for an operator to observe the telerobotic platform’s environment in one direction and the control communications from the operator to the robot in the reverse direction. The results show stable average round-trip latency of 6.6 ms for local network connection, 58.4 ms when connecting over Wi-Fi, 115.4 ms when connecting through cellular, and 240.7 ms when connecting from Finland to the United States over a VPN access-controlled network. 255,235,61. These findings provide a better understanding of the capabilities and performance limitations of conducting telerobotics activities over commodity networks, and lay the foundation of our future work to use these insights for optimizing the overall user experience and the responsiveness of this control loop.

Optimizing Physics-Informed Neural Networks with hybrid activation functions: A comparative study on improving residual loss and accuracy using partial differential equations

Physics-informed neural networks have bridged the gap between traditional numerical and deep learning based approaches in scientific computing. However, they still face limitations regarding improving convergence, accuracy, and minimizing residual loss, where the activation function plays a crucial role. Traditional activation functions often undergo vanishing gradient problems during backpropagation, highlighting the need for better alternatives for efficient training of Physics Informed Neural Networks. In this paper, new hybrid activation functions were proposed which combine the salient characteristics of traditional activation functions. These activation functions were tested with different network hyperparameters on the Swift–Hohenberg equation, a leading tool for modeling pattern development and evolution in fields like thermal convection, fluid, and temperature dynamics, as well as the Burgers equation. Manual tuning of hyperparameters is employed to critically assess the behavior of new activation functions in different experimental settings. Results show that hybrid activation functions have better learning capabilities compared to traditional activation functions. The GaussSwish hybrid activation function, in particular, proved to be highly effective across different network settings, showing better learning ability in training models for complex problems. This research also reveals that not only activation function but residual points sampled through different Monte Carlo sequences also influence the performance of Physics Informed Neural Networks.

Coupled Alternating Neural Networks for Solving Multi-Population High-Dimensional Mean-Field Games

Multi-population mean-field game is a critical subclass of mean-field games (MFGs). It is a theoretically feasible multi-agent model for simulating and analyzing the game between multiple heterogeneous populations of interacting massive agents. Due to the factors of game complexity, dimensionality disaster and disturbances should be taken into account simultaneously to solve the multi-population high-dimensional stochastic MFG problem, which is a great challenge. We present CA-Net, a coupled alternating neural network approach for tractably solving multi-population high-dimensional MFGs. First, we provide a universal modeling framework for large-scale heterogeneous multi-agent game systems, which is strictly expressed as a multi-population MFG problem. Next, we generalize the potential variational primal–dual structure that MFGs exhibit, then phrase the multi-population MFG problem as a convex–concave saddle-point problem. Last but not least, we design a generative adversarial network (GAN) with multiple generators and multiple discriminators—the solving network—which parameterizes the value functions and the density functions of multiple populations by two sets of neural networks, respectively. In multi-group quadcopter trajectory-planning numerical experiments, the convergence results of HJB residuals, control, and average speed show the effectiveness of the CA-Net algorithm, and the comparison with baseline methods—cluster game, HJB-NN, Lax–Friedrichs, ML, and APAC-Net—shows the progressiveness of our solution method.

Delay-dependent observer based control for uncertain fuzzy-chaotic systems with deception attacks and faulty actuators

In this study, the robust fault-tolerant control problem for T-S fuzzy chaotic systems is studied with the use of a proportional integral observer. Particularly, to accurately consider the real-world scenario, the chaotic system model incorporates the parameter uncertainties, input delay and external disruptions. Furthermore, a fuzzy-based observer is put forward based on analyzed system output with the intent of estimating the states of undertaken chaotic systems. Herein, the system output is susceptible to randomly occurring deception attacks and adheres to the Bernoulli distribution. Precisely, the consideration of deception attacks in the output channel allows for more secure state estimation in a networked setting. Subsequently, we develop a proportional integral observer-based fault-tolerant control that allows for the attainment of goals despite faults and delays in the input channel. Moreover, by setting up the Lyapunov-Krasovskii functional and blending it with Wirtinger's integral inequalities, we put together adequate conditions that ensure the asymptotic stability of the closed-loop systems by establishing conditions in the form of linear matrix inequalities. In the follow-up, based on the established matrix inequalities, the exact procedure for computing the gain matrices is outlined. As a last step, we put forward numerical simulation outcomes that exhibit the viability of the theoretical findings.

Coupled lattice discrete particle model for the simulation of water and chloride transport in cracked concrete members

AbstractA novel coupled mechanical and mass transport lattice discrete particle model is developed to quantitatively assess the impact of cracks on the mass transport properties in concrete members subjected to short‐ and long‐term loading conditions. In the developed approach, two sets of dual lattice networks are generated: one to resolve the mechanical response and another for mass transport analysis. The cracks simulated by the mechanical lattice are mapped onto the transport elements to investigate the effect of cracks on the global transport properties in concrete members. A new quantitative relationship is proposed for the estimation of the diffusion coefficient based on local crack information, and the developed model is capable of describing both convection and diffusion mechanisms. Moreover, creep behavior is incorporated to account for the influence of cracks induced by long‐term loading conditions. Numerical results, in the form of dynamic changes in cumulative water and chloride contents in concrete members under tension, compression, and bending with various stress levels show remarkable accuracy when compared to available experimental observations. The developed model provides an effective means for incorporating mesoscale information in simulations of water and chloride transport in concrete members under varying short‐ and long‐term loading conditions.

Optimizing and Analyzing Traffic Data Using Data Mining Techniques

This is the project titled Optimizing and Analyzing of Traffic Data using Data Mining techniques and under this project goals will be defined which may include enhancing network perfomance ,reducing latency and strenghtening security measures. Next, critical network componemts are identified ,such as routers, switches and firewalls which require analysis and optimization. Network traffic data is collected using monitoring tools or packet sniffers, encompassing both inbound and outbound traffic. This data is then analyzed to identify patterns trends and potential security threats. Network bottlenecks and performance issues are also identified. Based on the analysis, optimizaion techniques are implemented. This may involve reconfigurring network settings, adjusting routing protocols or upgrading hard watre components, Simultaneously, security measures are implemented to address identified vulnerabilities .Perfomance improvements are continously monitored and measured using metrics such as network latency, throughput and packet loss. The optimizations are finetuned periodically based on ongoing and new traffic patterns

Reliable and cost-efficient session provisioning in CRNs using spectrum sensing as a service

With the advancement of wireless communication technologies, and the growing number of wireless and IoT applications that demand various types and volumes of data, Sensing as a Service (SaaS) has emerged as a necessary enabling business model for many of those applications. Spectrum Sensing as a Service (SSaaS) has also emerged as a form of SaaS that is concerned with the monitoring of wireless spectrum to facilitate its safe reuse by cognitive radio-enabled wireless users. SSaaS was primarily motivated by the need for a low-cost, accurate, and reliable spectrum sensing service to support a plethora of heterogeneous wireless devices and applications. Under the SSaaS model, clients need to pay the service provider for the sensing service they receive. In this paper, we address the problem of allocating spectrum channels to links of a given communication session in a cognitive radio network (CRN) that utilizes SSaaS. The objective is to allocate channels such that the worst link availability among the session is maximized and the spectrum access cost is minimized. A number of multi-objective evolutionary optimization algorithms (MOEAs) were used to solve this multi-objective optimization problem. Extensive experimentation was conducted to compare between these algorithms and identify the best ones to use. We also propose a post-processing greedy algorithm to further enhance the solution obtained by a MOEA algorithm. Results show that an improvement of up to 20% can be achieved using the proposed greedy algorithm under some network settings.

Intrusion detection in software defined network using deep learning approaches

Ensuring robust network security is crucial in the context of Software-Defined Networking(SDN). Which, becomes a multi-billion dollar industry, and it’s deployed in many data centers nowadays. The new technology provides network programmability, network centralized control, and a global view of the network. But, unfortunately, it comes with new vulnerabilities, and new attack vectors compared to the traditional network. SDN network cybersecurity became a trending research topic due to the hype of Machine Learning (ML) when a group of Machine Learning(ML) techniques called Deep Learning(DL) started to take shape in the setting of SDN networks. This paper focuses on developing advanced Deep Learning(DL) models to address the inherent new attack vectors. In this paper, we have built and compared two models that can be used for building a complete Intrusion Detection System(IDS) solution, one using a hybrid CNN-LSTM architecture and the other using Transformer encoder-only architecture. We specifically target the SDN controller where it represents a crucial point. We utilized the InSDN dataset for training and testing our models, this dataset captures real-world traffic within the SDN environment. For evaluation, we have used accuracy, precision, recall, and F1 Score. Our experiment results show that the Transformer model with 48 features achieves the highest accuracy at 99.02%, while the CNN-LSTM model achieves 99.01%. We have reduced the features to 6 and 4, which gave us varying impacts on the models’ performance. We have merged 4 poorly represented attacks in one class, which enhanced the accuracy by a significant score. Additionally, we investigate binary classification by merging all attack types into a single class, as a result, the accuracy increased for both models. The CNN-LSTM model achieves the best results with an accuracy of 99.19% for 6 feature sets, this enhances the state-of-the-art results.

The diagnostic value investigation of programmed cell death genes in heart failure

BackgroundWe aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF).MethodsThree HF gene expression data were extracted from the GEO database, including GSE57345 (training data), GSE141910 and GSE76701 (validation data), followed by differentially PCD related genes (DPCDs) was shown between HF and control samples. Enrichment and protein-protein interaction (PPI) network analyses were performed based on the DPCDs. Subsequently, a diagnostic model was constructed and validated after exploring the diagnostic markers using machine learning. A nomogram was used to determine the clinical diagnostic value. Diagnostic marker-based immune, transcription network, and gene set enrichment (GSE) analyses were performed. Finally, the drug-target network was investigated.ResultsTwenty DPCDs were revealed between the two groups. These genes, such as Serpin Family E Member 1 (SERPINE1), are mainly enriched in pathways such as the regulation of the inflammatory response. A PPI network was constructed using 14 DPCDs. Eight diagnostic markers, such as SERPINE1, CD38 molecule (CD38), and S100 calcium-binding protein A9 (S100A9), were explored using machine learning algorithms, followed by diagnostic model construction. A nomogram and immune-associated analysis was used to validate the diagnostic value of these genes and the model. Moreover, the transcription regulation network and drug-target interactions were further investigated. Finally, qRT-PCR confirmed that the expression levels of eight signature genes (CD14, CD38, CTSK, LAPTM5, S100A9, SERPINE1, SLC11A1, and STAT3) were significantly elevated in the observation group, consistent with the results of bioinformatics analysis.ConclusionsThis study constructed a valuable diagnostic model for HF using the eight identified DPCDs as diagnostic markers.

Application of a modified set of GoogLeNet and ResNet-18 convolutional neural networks towards the identification of environmentally derived-MPLs in the Yadkin-pee dee river basin

Microplastic (MPL) abundance is a well-elucidated problem in the marine environment but not so much in the terrestrial environment. In order to contribute to this research gap, a field study was performed in the Yadkin-Pee Dee River Basin. Due to their heterogenous nature and difficulty in characterization, a diverse set of pictorial training data from µ-Raman was used to perform transfer learning on 2 CNNs of interest: GoogLeNet (GN) and ResNet-18 (RN). In the first trial, using 10% of the initial training dataset, the CNNs exhibited high levels of accuracy rates, generally above 90%. Irrespective of spectroscopic mode, marginal improvements in accuracy rates were seen, with the best improvements occurring in the Raman-based models (U[GN(FTIR), RN(Raman), GN(FTIR), and RN(Raman)]: 39, 42, 38.5, and 34.5; p-value: 1, .6753, .9719, and .4978). However, for the external trial, pictorial data from Primpke (FTIR) and DongMiller (Raman) was predicted less accurately, with the largest loss occurring across the following sets: U[GN(Raman) and RN(FTIR)], 45.5 and 35; p-value:, .3268 and .5476. However, set RN fared marginally better, and due to the usage of µ-Raman, and its performance in the 10% trial, RN18_ADAM_.0011 was selected as the champion model for the field study data. In the unknown microparticle (MP) trial, generally, the most ID’d polymer type was CA, PET, and PE representing a relative concentration range for a given water source and area (MPL/MP) of 4.17–37.5%, 4.17–8.33%, and 4.17–8.33% for CNN and OpenSpecy (OS). A FEDS algorithm, equipped with natural and synthetic polymer standards and biological material, used to compare the strength of each model determined similar frequency in ascertaining positive MPL results across both models with corroboration between the CNN and OS around 1/3 of the time. Results indicate the models detect MPLs with similar frequency elucidating comparable strength of the CNN as well as a focus on particle type distribution rather than individual identification. Moreover, the largest influential factor in this study appears to be either laundry wastewater effluent or atmospheric deposition, which is stressed as a primary focus of remediating MPLs in similar freshwater environments. Lastly, it appears that these MPL are of primary origin as opposed to secondary in the oceanic and coastal environments.

Personalized Pricing and Assortment Optimization Under Consumer Choice Models with Local Network Effects

We introduce a consumer choice model where each consumer’s utility is affected by their neighbors’ purchase probabilities in a network. We first characterize the choice probabilities and then consider the associated personalized assortment optimization problem. Although this problem is NP-hard, we show that for star networks, the optimal assortment to the central consumer cannot be strictly larger than that without network effects; and the optimal assortment to each peripheral consumer must be a revenue-ordered assortment. Then, we propose a novel idea in which the sellers are allowed to offer “randomized assortments” to each node in the network by viewing each node as a consumer group. We show that randomized assortments may further increase the revenue, and for general network setting, under certain conditions, the optimal assortment must be a combination of two adjacent revenue-ordered assortments. Finally, we study the associated optimal pricing problem and develop structural properties and efficient algorithms.