Partial Mapping Research Articles

PAPR reduction of OTFS using an automatic amplitude reduction neural network with vendermonde matrix-based PTS and SLM algorithms

Orthogonal time frequency selectivity is considered one of the most promising advanced waveforms for high-mobility conditions in beyond-fifth-generation networks. A high peak-to-average power ratio in orthogonal time frequency introduces nonlinearity and degrades the power amplifier performance. In this work, we propose a novel automatic amplitude reduction neural network algorithm combined with partial transmission sequence and selective mapping methods using the Vendermonde matrix for generating phase sequences. In a conventional selective mapping methods or partial transmission sequence, selecting an optimal phase increases the complexity, which is overcome via Vendermonde matrix in a proposed framework. The minimization of the peak-to-average power ratio is obtained at the double-stage process: in the first stage, the threshold amplitude is set, and the amplitude exceeding the threshold is reduced by using an automatic amplitude reduction neural network and then the partial transmission scheme or selective mapping methods. The simulation results reveal that, compared with the conventional algorithms, the proposed algorithms yield a significant peak average-to-power ratio, bit error rate, and power spectrum density performance.

Earth Observation Data Utilisation for Rock Phosphate Exploration in Jhabua, Madhya Pradesh, India

ABSTRACT In this study, we processed Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) data using partial subpixel mapping methods for delineating surface occurrences of fertilizer mineral i.e., phosphorite in dolomite and carbonate-bearing chert in the Aravalli Supergroup. We applied feature-oriented principal component analysis to derive an image composite for delineating the spatial extent of carbonate rich zones within the Lunaveda Group of rocks. Subsequently, we used image spectra of the surface exposure of rock phosphate of the existing mine at Khatama area as the reference for implementing adaptive coherence estimator (ACE) algorithm in the VNIR -SWIR bands of ASTER data to derive the abundance map of phosphorite. We confirmed the presence of phosphate in the identified phosphate anomaly zones during field surveys by implementing rapid colorimetric test that is proven for identifying the presence of phosphate in the samples. We also used the available surface geochemical data of P2O5 to establish the synergy between geochemical data and identified geochemical anomaly.

Dynamic selection for reconstructing instance-dependent noisy labels

As an inevitable issue in annotating large-scale datasets, instance-dependent label noise (IDN) can cause serious overfitting in neural networks. To combat IDN, label reconstruction methods have been developed with noise transition matrices or DNNs to simulate the transition from clean labels to noisy labels. Nevertheless, the absence of correct supervisions will lead to learning wrong noise transitions. This motivates us to select samples with clean labels to fetch the correct supervisions. However, the difficulty in obtaining prior knowledge of the noise rate prohibits the use of existing sample selection methods. To this end, we propose a dynamic sample selection method, namely Identity Mapping (IdMap), to overcome this limitation. Inspired by the feature-dependent characteristic of IDN, we first introduce the extracted instance features and pseudo-ground-truth labels to reconstruct noisy labels. A partial identity mapping between two labels is then established and samples with consistent identity mapping output are selected as clean data to update the classifier. Extensive experiments on both artificial and real-world noisy datasets demonstrate the superiority of IdMap compared with other state-of-the-art methods.

Alkaloid profiling of the new species Corydalis huangshanensis and other 13 medicinal plants in genus Corydalis.

Corydalis DC., the largest genus of Papaveraceae, comprises numerous species known for their abundant alkaloid content and historical use in clinical medicine. Recently, a new species of genus Corydalis named Corydalis huangshanensis Lu Q. Huang & H. S. Peng was discovered in the Huangshan Mountains of Anhui Province, China. To compare the chemical characteristics of C. huangshanensis and other 13 Corydalis species, aiming to elucidate the potential medicinal value of this new species. The chemical constituents of C. huangshanensis and other 13 medicinal plants of genus Corydalis were analyzed using ultra-high-performance liquid chromatography Q-Exactive Plus hybrid quadrupole-Orbitrap mass spectrometer (Q-Orbitrap) mass technology. The differences in the alkaloids in the 14 species were distinguished by chemometrics. The mass spectrometry fragmentation information and relative content of 72 alkaloids were obtained. Orthogonal partial least squares discriminant analysis (OPLS-DA) and cluster heat mapping analysis showed that these 14 species were divided into two groups. The clustering relationship between C. huangshanensis and C. decumbens (Thunb.) Pers. was similar, exhibiting similar chemical compositions and characteristics. These results indicate the potential pharmacological effects of C. huangshanensis. This study enhances our understanding of the chemical classification of Corydalis and provides a basis for speculations on the medicinal value of C. huangshanensis.

Asteroid follow-up and precovery problem: Partial banana mapping solution

Asteroid follow-up and precovery problem: Partial banana mapping solution

Partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation

Accurate segmentation of retinal vessels in fundus images is of great importance for the diagnosis of numerous ocular diseases. However, due to the complex characteristics of fundus images, such as various lesions, image noise and complex background, the pixel features of some vessels have significant differences, which makes it easy for the segmentation networks to misjudge these vessels as noise, thus affecting the accuracy of the overall segmentation. Therefore, accurately segment retinal vessels in complex situations is still a great challenge. To address the problem, a partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation is proposed. The core idea of the proposed network is first to use the partial class activation mapping guided graph convolutional network to eliminate the differences of local vessels and generate feature maps with global consistency, and subsequently these feature maps are further refined by segmentation network U-Net to achieve better segmentation results. Specifically, a new neural network block, called EdgeConv, is stacked multiple layers to form a graph convolutional network to realize the transfer an update of information from local to global, so as gradually enhance the feature consistency of graph nodes. Simultaneously, in an effort to suppress the noise information that may be transferred in graph convolution and thus reduce adverse effects of noise on segmentation results, the partial class activation mapping is introduced. The partial class activation mapping can guide the information transmission between graph nodes and effectively activate vessel feature through classification labels, thereby improving the accuracy of segmentation. The performance of the proposed method is validated on four different fundus image datasets. Compared with existing state-of-the-art methods, the proposed method can improve the integrity of vessel to a certain extent when the pixel features of local vessels are significantly different, caused by objective factors such as inappropriate illumination and exudates. Moreover, the proposed method shows robustness when segmenting complex retinal vessels.

Research on the Propagation Model of Unsafe Behaviors among Construction Workers Based on a Two-Layer NAN-SIRS Network

Unsafe behaviors among construction workers are a leading cause of safety accidents in the construction industry, and studying the mechanism of unsafe behavior propagation among construction workers is essential for reducing the occurrence of safety accidents. Safety attitude plays a pivotal role in predicting workers’ behavioral intentions. We propose a propagation model of unsafe behaviors based on a two-layer complex network, in which the upper layer depicts the change in construction workers’ safety attitudes, and the lower layer represents the propagation of unsafe behaviors. In this model, we consider the impact of individual heterogeneity and herd mentality on the transmission rate, establishing a partial mapping relationship based on behavioral feedback. After that, by building a probability transition tree, we establish the risk state transition equation in detail using the microscopic Markov chain approach (MMCA) and analyze the established equations to deduce the propagation threshold of unsafe behaviors analytically. The results show that enhancing the influence of individual heterogeneity and behavioral feedback increases the threshold for the spread of unsafe behaviors, thereby reducing its scale, while herd mentality amplifies the spread. Furthermore, the coexistence of safety education and behavioral feedback may lead to one of the mechanisms fails. This research enhances understanding of the propagation mechanism of unsafe behaviors and provides a foundation for managers to implement effective measures to suppress the propagation of unsafe behaviors among construction workers.

Partial Cross Mapping Based on Sparse Variable Selection for Direct Fault Root Cause Diagnosis for Industrial Processes.

Root cause diagnosis of process industry is of significance to ensure safe production and improve production efficiency. Conventional contribution plot methods have challenges in root cause diagnosis due to the smearing effect. Other traditional root cause diagnosis methods, such as Granger causality (GC) and transfer entropy, have unsatisfactory performance in root cause diagnosis for complex industrial processes due to the existence of indirect causality. In this work, a regularization and partial cross mapping (PCM)-based root cause diagnosis framework is proposed for efficient direct causality inference and fault propagation path tracing. First, generalized Lasso-based variable selection is performed. The Hotelling T2 statistic is formulated and the Lasso-based fault reconstruction is applied to select candidate root cause variables. Second, the root cause is diagnosed through the PCM and the propagation path is drawn out according to the diagnosis result. The proposed framework is studied in four cases to verify its rationality and effectiveness, including a numerical example, the Tennessee Eastman benchmark process, the wastewater treatment process (WWTP), and the decarburization process of high-speed wire rod spring steel.

The non-homogeneous Cauchy problem of semigroup of linear operators

This paper presents the results of ω-order reversing partial contraction mapping generated by the non-homogeneous Cauchy problem. We investigated several concepts of solution for non-homogeneous Cauchy problem where an analysis was arrived out on the relationship between C' strong and respectively C0-solution. We showed that Z is the infinitesimal generator of a C0-semigroup of contractions. Finally, we obtained that the problem has a unique classical solution and f, a member of L1(a,b; X) is continuous on (a,b).

Acquisition of modal readings of the Imperfect tense in L2 Spanish

ABSTRACT The Imperfect/Preterite aspectual contrast is one of the most studied topics in Spanish as a second language research. However, there are few works focused on describing the acquisition of modal uses of the Imperfect by L2 speakers. This paper investigates the L1 Russian L2 Spanish speakers’ mastery of politeness, evidential and nonfactual modal values of the Imperfect. Based on a Timed Acceptability Judgments Task (TAJT), this study examines the degree of acceptability of Imperfect forms in modal contexts in three groups of advanced level L2 Spanish learners. The results show that only the Imperfect of politeness achieves acceptance rates similar to those of native speakers at the most advanced levels of proficiency. The high degree of lexicalization of some imperfect forms, which function as pragmatic routines in certain communicative situations, is alleged to facilitate this tendency. In contrast, the evidential and nonfactual Imperfect forms present greater challenges for the L2 Spanish speakers. The results of our study indicate that (i) the imperfect forms are not recognized as legitimate alternatives for marking evidentiality and nonfactuality and (ii) reflect a partial form-function mapping of learners’ knowledge of the Imperfect and its associated values.

Monogenic free inverse semigroups and partial automorphisms of regular rooted trees

For a one-to-one partial mapping on an infinite set, we present a criterion in terms of its cycle-chain decomposition that the inverse subsemigroup generated by this mapping is monogenic free inverse.
 We also give a sufficient condition for a regular rooted tree partial automorphism to extend to a partial automorphism of another regular rooted tree so that the inverse semigroup gene\-ra\-ted by this extended partial automorphism is monogenic free inverse. The extension procedure we develop is then applied to $n$-ary adding machines.

Results of semigroup of linear operators in extrapolation spaces

Results of an omega-order preserving partial contraction mapping (omega-OCPn) in generalized spaces are presented in this study. Assumed to be a closed linear operator on a Banach space X with a non-empty resolvent set rho(A) is A in omega-OCPn. If A is densely defined, the extrapolation spaces X-1 and X-1 will be associated with A in agreement. However, X-1 is a proper closed subspace of X-1 if A is not densely defined. Then, we demonstrated that the reason these spaces exist is because (X*)-1 and D(A0) are naturally isomorphic to (X*)-1 and (X*)-1, respectively.

A two-layer model with partial mapping: Unveiling the interplay between information dissemination and disease diffusion

This study delves into the pivotal role of information dissemination in public health, particularly how it influences the spread of diseases. By implementing a sophisticated two-layer partial mapping network model (UAU-SIRS), we investigate the dynamic relationship between information flow and disease transmission. Our approach utilizes extensive multiplexed network data, processed through a micro Markov chain (MMC) model, to simulate the interplay between information spread and disease dynamics. The findings reveal a noteworthy positive correlation between the rates of information dissemination, recovery in the network, and the epidemic threshold. Conversely, the conversion rate is inversely related to this threshold. A critical observation is that Scale-free (SF) networks, characterized by their uneven node distribution, are more susceptible to the impacts of information spread on their outbreak thresholds compared to Erdős-Rényi (ER) networks. This research offers crucial insights for epidemic prevention strategies and provides valuable guidance for managing the dissemination of disease-related information within complex network structures.

On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-Agent Deep Reinforcement Learning Approach

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This paper studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where the data from an incoming task is partitioned into two parts, with one part executed locally and the other offloaded to the edge infrastructure for execution. These two parts are independent of each other, but both need to be processed by the same SFC. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where two agents interact with each other. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms with respect to cumulative reward and it overweighs a number of baseline algorithms in terms of execution delay, energy consumption, and usage charge.

Expansion of the Analytical Modeling of Capacitance for 1-N-1 Multilayered CID Structures with Monotonically Increasing/Decreasing Permittivity.

Capacitive sensors that utilize the Coplanar Interdigitated (CID) electrode structure are widely employed in various technical and analytical domains, such as healthcare, infectious disease management, pharmaceuticals, metrology, and environmental monitoring. The present exigency for lab-on-a-chip contrivances and the requisite for the miniaturization of sensors have led to the widespread adoption of CID sensors featuring multiple dielectric layers (DLs), either in the form of substrates or superstrates. Previously, we derived an analytical model for the capacitance of CID capacitive sensors with four distinct 1-N-1 patterns (namely, 1-1-1, 1-3-1, 1-5-1, and 1-11-1) using partial capacitance (PC) and conformal mapping (CM) techniques. The aforementioned model has been employed in various applications wherein the permittivity of successive layers exhibits a monotonic decrease as one moves away from the electrode plane, resulting in highly satisfactory outcomes. Nevertheless, the PC technique is inadequate for structures with multiple layers where the permittivity exhibits a monotonic increase as the distance from the electrodes increases. Given these circumstances, it is necessary to adapt the initial PC method to incorporate these novel configurations. In this work, we have discussed a new approach, splitting the concept of PC into partial parallel capacitance (PPC) and partial serial capacitance (PSC), where new CM transformations are proposed for the latter case. Thus, the present study proposes a novel methodology to expand upon our prior analytical framework, which aims to incorporate scenarios where the permittivity experiences a reduction across successive layers. The outcomes are juxtaposed with the finite element simulation and analytical findings.

Impact of simplicial complexes on epidemic spreading in partially mapping activity-driven multiplex networks.

Over the past decade, the coupled spread of information and epidemic on multiplex networks has become an active and interesting topic. Recently, it has been shown that stationary and pairwise interactions have limitations in describing inter-individual interactions , and thus, the introduction of higher-order representation is significant. To this end, we present a new two-layer activity-driven network epidemic model, which considers the partial mapping relationship among nodes across two layers and simultaneously introduces simplicial complexes into one layer, to investigate the effect of 2-simplex and inter-layer mapping rate on epidemic transmission. In this model, the top network, called the virtual information layer, characterizes information dissemination in online social networks, where information can be diffused through simplicial complexes and/or pairwise interactions. The bottom network, named as the physical contact layer, denotes the spread of infectious diseases in real-world social networks. It is noteworthy that the correspondence among nodes between two networks is not one-to-one but partial mapping. Then, a theoretical analysis using the microscopic Markov chain (MMC) method is performed to obtain the outbreak threshold of epidemics, and extensive Monte Carlo (MC) simulations are also carried out to validate the theoretical predictions. It is obviously shown that MMC method can be used to estimate the epidemic threshold; meanwhile, the inclusion of simplicial complexes in the virtual layer or introductory partial mapping relationship between layers can inhibit the spread of epidemics. Current results are conducive to understanding the coupling behaviors between epidemics and disease-related information.

Supply Chain Risk Diffusion in Partially Mapping Double-Layer Hypernetworks.

The impact of COVID-19 is global, and uncertain information will affect product quality and worker efficiency in the complex supply chain network, thus bringing risks. Aiming at individual heterogeneity, a partial mapping double-layer hypernetwork model is constructed to study the supply chain risk diffusion under uncertain information. Here, we explore the risk diffusion dynamics, drawing on epidemiology, and establish an SPIR (Susceptible-Potential-Infected-Recovered) model to simulate the risk diffusion process. The node represents the enterprise, and hyperedge represents the cooperation among enterprises. The microscopic Markov chain approach (MMCA) is used to prove the theory. Network dynamic evolution includes two removal strategies: (i) removing aging nodes; (ii) removing key nodes. Using Matlab to simulate the model, we found that it is more conducive to market stability to eliminate outdated enterprises than to control key enterprises during risk diffusion. The risk diffusion scale is related to interlayer mapping. Increasing the upper layer mapping rate to strengthen the efforts of official media to issue authoritative information will reduce the infected enterprise number. Reducing the lower layer mapping rate will reduce the misled enterprise number, thereby weakening the efficiency of risk infection. The model is helpful for understanding the risk diffusion characteristics and the importance of online information, and it has guiding significance for supply chain management.

Exploring the Sensory Properties and Preferences of Fruit Wines Based on an Online Survey and Partial Projective Mapping.

Non-grapefruits with unique sensory properties and potential health benefits provide added value to fruit wine production. This study aimed to explore consumers' fruit wine preferences and descriptors for the varied fruit wines. First, 234 consumers participated in an online survey concerning their preferences for different wines (grape, blueberry, hawthorn, goji, Rosa roxburghii, and apricot). In addition, their attitudes towards general health interests, food neophobia, alcoholic drinks, and sweetness were collected. Grape wine and blueberry wine were the most favored wines, and goji wine was the least liked fruit wine sample. Moreover, 89 consumers were invited to evaluate 10 commercial fruit wines by using partial projective mapping based on appearance, aroma, and flavor (including taste and mouthfeel) to obtain a comprehensive sensory characterization. Multifactor analysis results showed that consumers could differentiate the fruit wines. Participants preferred fruit wines with "sweet", "sour", and "balanced fragrance", whereas "bitter", "astringent", "deep appearance", and "medicinal fragrance" were not preferred. Attitudes toward health, food neophobia, alcohol, and sweetness had less influence than taste and aroma (sensory attributes) on the preferences for fruit wine products. More frequent self-reported wine usage resulted in higher consumption frequency and liking ratings compared to non-users. Overall, the main factors influencing consumer preference for fruit wines were the sensory characteristics of the products, especially the taste.

Beyond rights-based social protection for refugees

The use of social protection measures has garnered increasing attention in recent years from academics and policymakers aspiring to unite the humanitarian origins and development ambitions of displacement governance regimes. Much of this attention has been focused on establishing and strengthening national systems of social protection provision. Analysis of policy approaches to social protection has become increasingly detailed, but typically does not extend beyond formal rights-based provision. This article seeks to address the paucity of literature on how refugees strategise around access to social assistance beyond Northern-mandated approaches. We review existing research on Syrian displacement in Lebanon to interrogate assumptions that refugees automatically seek institutionalised assistance. Drawing on postcolonial literature, we explore why modalities of social and humanitarian assistance offered through a rights-based approach represent only a partial mapping of the social protection that refugees avail themselves of. In doing so, we signal a move beyond the narrow and restrictive binary of formal/informal and attempt to consider the range of social protection opportunities from the perspective of refugees. Though unequal, we argue that both national systems of social protection provision and alternative approaches identified by displaced people are currently necessary, although a language of rights is only applicable to the former. Ultimately, greater coordination between the two is required. In conclusion, this article describes directions for future research aimed at a holistic understanding of how social protection is accessed in displacement and a more explicit interrogation of the impact of social protection measures in displacement settings.

Results of Semigroup of Linear Operators Generating a General Class of Semilinear Initial Value Problems

This paper present results of ω-order preserving partial contraction mapping generating a general class of semilinear initial value problems. We consider the use of fractional powers of unbounded linear operators for its application by starting with some results concerning such fractional powers. We assume A to be the infinitesimal generator of an analytic semigroup in a Banach space X, 0 ∈ ρ(A) and defined the fractional powers of A for 0 < α ≤ 1. We also show that Aα is a closed linear operator whose domain D(Aα) ⊃ D(A) is dense in X. Finally we established that the operator is bounded, continuous and Holder continuous.