Discrimination Scheme Research Articles

Loss of Coherence and Change in Emission Physics for Radio Emission from Very Inclined Cosmic-Ray Air Showers.

Next-generation radio experiments such as the radio detector of the upgraded Pierre Auger Observatory and the planned GRAND and BEACON arrays target the detection of ultra-high-energy particle air showers arriving at low elevation angles. These inclined cosmic-ray air showers develop higher in the atmosphere than vertical ones, enhancing magnetic deflections of electrons and positrons inside the cascade. We evidence two novel features in their radio emission: a new polarization pattern, consistent with a geosynchrotron emission model and a coherence loss of the radio emission, both for showers with zenith angle θ≳65° and strong enough magnetic field amplitude (typical strength of B∼50 μT). Our model is compared with both ZHAireS and CoREAS MonteCarlo simulations. Our results break the canonical description of a radio signal made of Askaryan and transverse current emission only, and provide guidelines for the detection and reconstruction strategies of next-generation experiments, including cosmic-ray or neutrino discrimination.

현상학적 방법을 통해서 바라본 한국인의 하느님관(觀)

Western civilization developed in a scheme of confrontation and discrimination. The ontological constellation that has become a global destiny with the spread of Western power is fundamentally rooted in the masculine and patriarchal view of God in the West. Therefore, the salvation on which our future depends will be accomplished with the change of the view of God. And the new view of God that is to be sought should be one that harmonizes all kinds of conflicts such as God and nature, nature and man, man and woman. This article aims to show that the new view of God that we are waiting for to arrive clearly appears in Korean ancient history and Donghak. The harmonization of impersonal universal divinity and personal supreme deity, as well as the ontological role of human beings as the center of this harmonization, are all emphasized in the doctrin of God of ancient times and Donghak. This article understands that the ancient viewpoint of God refers to that of the Donghak beforehand and the latter proves the former. We take the phenomenology of 'fact itself', especially the phenomenology used by Heidegger in his thoughts on Being as a method. The intention of this article is to reveal that the coming view of God that will be the foundation for opening a new world of coexistence and prosperity is at the same time the oldest one. Our attempt is made through phenomenological methods.

Maximum information measurement for qubit states

We determine the optimal measurement that maximizes the average information gain about the state of a qubit system. The qubit is prepared in one of two known states with known prior probabilities. To treat the problem analytically we employ the formalism developed for the maximum confidence quantum state discrimination strategy and obtain the POVM which optimizes the information gain for the entire parameter space of the system. We show that the optimal measurement coincides exactly with the minimum-error quantum measurement only for two pure states, or when the two states have the same Bloch radius or they are on the same diagonal of the Bloch disk.

Discrimination on Sharing Economy Platforms: A Systematic Review of Cases and Coping Strategies

This paper presents a systematic review of empirical research on the phenomenon of discrimination on sharing economy platforms. Sharing platforms such as Airbnb, Uber, and BlaBlaCar have recently become notorious as hotbeds of digital discrimination. Difficult to detect and deter, digital discrimination describes the state when an online transaction is influenced by the race, gender, age, or other non-business characteristics of providers or consumers. Four key themes emerged from our analysis: forms of discrimination, domains (industries), outcomes and coping strategies. There is strong evidence of bias on both the demand side (consumers, guests) and the supply side (providers, hosts) and across domains such as accommodation sharing, ridesharing, skills sharing, and peer-to-peer lending. We propose a framework to organize the existing studies and guide future research and practical solutions to reduce inequality on online platforms.

A General Strategy for Food Traceability and Authentication Based on Assembly-Tunable Fluorescence Sensor Arrays.

Food traceability and authentication systems play an important role in ensuring food quality and safety. Current techniques mainly rely on direct measurement by instrumental analysis, which is usually designed for one or a group of specific foods, not available for various food categories. To develop a general strategy for food identification and discrimination, a novel method based on fluorescence sensor arrays is proposed, composed of supramolecular assemblies regulated by non-covalent interactions as an information conversion system. The stimuli-responsiveness and tunability of supramolecular assemblies provided an excellent platform for interacting with various molecules in different foods. In this work, five sensor arrays constructed by supramolecular assemblies composed of pyrene derivatives and perylene derivatives are designed and prepared. Assembly behavior and sensing mechanisms are investigated systematically by spectroscopy techniques. The traceability and authentication effects on several kinds of food from different origins or grades are evaluated and verified by linear discriminant analysis (LDA). It is confirmed that the cross-reactive signals from different sensor units encompassing all molecular interactions can generate a unique fingerprint pattern for each food and can be used for traceability and authentication toward universal food categories with 100% accuracy.

Resisting and transgressing cisheteronormativity at home: LGBT+ youths’ active strategies

Many of the struggles and advances in LGBT+ rights are fought in domestic spaces. However, the political and emotional complexity of the home in relation to gender and sexual dissidence has received limited attention. This research aims to explore the discrimination and coping strategies of young LGBT+ youth in their family and personal home. Through a qualitative methodology, I examine how 27 LGBT+ youth from Bages (Catalonia) experience different home spaces according to their age, gender, and sexual orientation. The results show adultism and pressure to conform to cisheteronormative expectations at the family home. However, these norms dissolve in the personal home, allowing the free development of their LGBT+ identities. The study reveals the resistance and transformation strategies they use to tackle cisheteronormativity, highlighting home as a place of politics, negotiation, power, and complex emotions.

Discrimination of Brazilian green canephora coffee beans by ultraviolet–visible spectroscopy as a non-target analysis: A tool for recognizing geographical indications

Specialty green coffee beans have a higher commercial value and some of them have recently been classified in Brazil based on the indication of provenance and denomination of origin. In this context, the classification of the type of coffee bean is still a challenge, using traditional analytical techniques. Thus, alternative analytical techniques, such as ultraviolet–visible spectroscopy (UV–Vis), as non-target analysis can be applied as a quick and reliable method of coffee classification using data science, such as chemometric tools. In the present study, UV–Vis were evaluated as a new strategy for discrimination of green beans of Brazilian specialty canephora coffees with recognized geographical indications (Robusta Amazônico and Conilon from state of Espírito Santo), for the first time. Spectra obtained from the aqueous extract of 222 samples. The Principal Component Analysis (PCA) was performed and subsequently Partial Least Squares with Discriminant Analysis (PLS-DA) model developed. The PCA indicated tendency to group the samples in their respective classes, pointing to the similarities in the spectra of samples of the same origin. The PLS-DA model obtained showed figures of merit values starting at 89.3% in the test set. The VIP scores showed that the variables associated with chlorogenic acids, caffeine and chlorophyll are the most important for differentiating the studied coffees. The results obtained showed that UV–Vis fingerprint − non-targeted analysis associated with PLS-DA is appropriate for the discrimination of green beans of Brazilian specialty coffee from different origins, in a simple way, using common equipment in several laboratories.

Serving the unserved: a market segmentation, price discrimination based customer-centeric approach to home loans in india

The housing finance sector in India has witnessed remarkable growth in recent years, yet a significant portion of the population remains excluded from accessing formal home loan facilities. This master thesis report aims to address this gap by proposing a customer-centric approach to home loans, utilizing market segmentation and price discrimination strategies to serve the unserved segment of the market effectively. The study begins by identifying the need to bridge the gap in home loan accessibility and explores the reasons behind the exclusion of certain demographic groups. Through a comprehensive literature review, the report examines existing research on home loans, market segmentation, price discrimination, and customer-centric approaches in the financial services industry. The research methodology employed in this study utilizes a mixed-methods approach, combining qualitative and quantitative techniques to gather insights into the preferences, challenges, and perceptions of potential home loan borrowers. A carefully designed sampling plan ensures the representativeness and reliability of the data collected. Data analysis reveals patterns and trends within the home loan market, shedding light on the effectiveness of different segmentation and pricing strategies in reaching the unserved population. The questionnaire used for data collection is provided in the report, along with a discussion of the limitations, challenges, and barriers encountered during the research process. In conclusion, the report summarizes key findings and recommends strategies for policymakers, financial institutions, and other stakeholders to enhance inclusivity and efficiency in the home loan sector. By adopting a customer-centric approach and leveraging market segmentation and price discrimination techniques, the goal of serving the unserved can be achieved, contributing to greater financial inclusion and societal welfare.

Analysis of quantum key distribution based on unified model of sequential state discrimination strategy

The quantum key distribution for multiparty is one of the essential subjects of study. Especially, without using entangled states, performing the quantum key distribution for multiparty is a critical area of research. For this purpose, sequential state discrimination, which provides multiparty quantum communication and quantum key distribution for multiple receivers, has recently been introduced. Moreover, the sequential state discrimination is applicable for the security analysis against an eavesdropper’s attack. In this work, we provide the security analysis of quantum key distribution by proposing a unified model of sequential state discrimination including an eavesdropper. In this model, the success probability of eavesdropping is used as a figure of merit for the security analysis. Moreover, we obtain a non-zero secret key rate between the sender and receiver, which implies that the sender and receiver can share a secret key despite the eavesdropper’s scheme that optimizing the success probability of eavesdropping. Further, we propose an experimental methodology for the proposed model, which is implementable with linear optics. We observe that the secret key between the sender and receiver can be non-zero, even with imperfections.

Provenance of low-grossular–high-pyrope detrital garnets from beach sands of East Coast of India between Gosthani and Vamsadhara rivers

Major element compositional spectrum, formation in diverse magmatic and metamorphic rocks, and relative stability during sediment transport, burial, and diagenesis make garnet an important indicator of sedimentary provenances. However, a fundamental question yet to be answered is whether the low-grossular–high-pyrope detrital garnets reflect source characteristics or record sedimentary processes. To address this problem, we have analysed garnets from the beach sands of the East Coast of India and in the source lithologies of the catchment area within the Eastern Ghats Mobile Belt. Investigated garnets show a broad compositional spectrum; most garnets are dominated by almandine49–86% with variable contents of pyrope9–47%, minor grossular0.8–15%, and low spessartine0.5–4%. Using multiple proxies, including a state-of-the-art machine-learning-based garnet discrimination scheme, we found that 96 % of analysed beach sand garnets are of metamorphic origin and 4 % igneous. Amongst the metamorphic garnets, 85 % were derived from granulite facies, 5 % from eclogite/ultrahigh-pressure facies, 5 % from amphibolite facies, and 1 % from blueschist/greenschist facies rocks. Concerning host-rock bulk composition, 93 % of garnets belong to intermediate–felsic/meta-sedimentary class and 7 % belong to the mafic category. The chemistries of the beach sand detrital garnets of the East Coast of India are readily coupled with the compositions of the garnets from source rock lithologies of the Eastern Ghats Mobile Belt. Very low-grossular garnets are derived from meta-pelitic rocks (khondalites) and mafic granulites, whereas slightly grossular-rich garnets are derived from charnockites. The present study indicates that the low-grossular–high-pyrope detrital garnets are vital signposts of sedimentary provenances. We advocate that the high P-T granulite facies mobile belts are distinctive sources for the low-grossular–high-pyrope detrital garnets.

Modeling rapidly discriminative strategies of Cr contaminated soils through machine learning

Soil washing is employed to prevent the issue of Cr re-oxidation following the remediation of Cr-contaminated soil by transferring contaminants from the soil to the wash solution through the dissolving action. Nevertheless, rapidly screening effective washing agents remains challenging. This study conducted batch experiments to gather dataset on key factors (soil properties, Cr sequential extraction and type of washing agent) and Cr content after washing with 250 experimental data points. We developed 12 machine learning models, among which the light gradient boosting machine model excelled in predicting Cr content after washing, determining soil suitability for washing. Furthermore, the adaptive boosting and random forest models preferably predicted Cr content after various washing agents treatments, facilitating optimal detergent identification. Based on the feature analysis, soil pH, exchangeable potassium, reactive iron oxides, and Cr sequential extraction can account for most of the differences in washing treatment. To further validate our model, we used an additional 60 experimental data points, including soil properties with pH values beyond the range of the initial 250 data points. The R2 value of the predicted fit to the actual results was 0.801. This study provides the feasibility of improving the efficiency of technical remediation of Cr-contaminated soils.

A Hierarchical approach for isolating sensor faults from un-stealthy attacks in large-scale systems

This paper proposes a novel hierarchical-adaptive threshold abnormal behavior discrimination scheme (HATAD) for Lipschitz nonlinear large-scale systems with disturbances and uncertain dynamics. Two layers of groups of interlinked observers, namely the supervisor and local layers, are designed. The local layer using open loop observers, switching technique, and local controllers is first developed to protect the physical system from the attack effects, where the proposed switching technique can connect the plant to the local controller under the attack conditions. At the supervisor layer, the main controllers and three types of observers are constructed to detect and discriminate the abnormal behaviors and determine the faulty unit. This architecture leads to less restrictive mathematical conditions and analyses, which ensure the stability and the L2−L∞ performance of the estimation error dynamics. To demonstrate the effectiveness of the proposed approach, a simulation study has been conducted on a large-scale system of two units of benchmark continuous stirred tank reactor (CSTR).

Dual-Dependency Attention Transformer for Fine-Grained Visual Classification.

Visual transformers (ViTs) are widely used in various visual tasks, such as fine-grained visual classification (FGVC). However, the self-attention mechanism, which is the core module of visual transformers, leads to quadratic computational and memory complexity. The sparse-attention and local-attention approaches currently used by most researchers are not suitable for FGVC tasks. These tasks require dense feature extraction and global dependency modeling. To address this challenge, we propose a dual-dependency attention transformer model. It decouples global token interactions into two paths. The first is a position-dependency attention pathway based on the intersection of two types of grouped attention. The second is a semantic dependency attention pathway based on dynamic central aggregation. This approach enhances the high-quality semantic modeling of discriminative cues while reducing the computational cost to linear computational complexity. In addition, we develop discriminative enhancement strategies. These strategies increase the sensitivity of high-confidence discriminative cue tracking with a knowledge-based representation approach. Experiments on three datasets, NABIRDS, CUB, and DOGS, show that the method is suitable for fine-grained image classification. It finds a balance between computational cost and performance.

Solvent-tuned perovskite heterostructures enable visual linoleic acid assay and edible oil species discrimination via wavelength shift

Linoleic acid (LA) detection and edible oils discrimination are essential for food safety. Recently, CsPbBr3@SiO2 heterostructures have been widely applied in edible oil assays, while deep insights into solvent effects on their structure and performance are often overlooked. Based on the suitable polarity and viscosity of cyclohexane, we prepared CsPbBr3@SiO2 Janus nanoparticles (JNPs) with high stability in edible oil and fast halogen-exchange (FHE) efficiency with oleylammonium iodide (OLAI). LA is selectively oxidized by lipoxidase to yield hydroxylated derivative (oxLA) capable of reacting with OLAI, thereby bridging LA content to naked-eye fluorescence color changes through the anti-FHE reaction. The established method for LA in edible oils exhibited consistent results with GC–MS analysis (p > 0.05). Since the LA content difference between edible oils, we further utilized chemometrics to accurately distinguish (100%) the species of edible oils. Overall, such elaborated CsPbBr3@SiO2 JNPs enable a refreshing strategy for edible oil discrimination.

IAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks

Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emergence of COVID-19 has intensely increased different nosocomial fungal infections. The existing wet-laboratory-based medications are expensive, time-consuming, and may have adverse side effects on normal cells. In the last decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction scheme called iAFPs-Mv-BiTCN to predict antifungal peptides correctly. The training peptides are encoded using word embedding methods such as skip-gram and attention mechanism-based bidirectional encoder representation using transformer. Additionally, transform-based evolutionary features are generated using the Pseduo position-specific scoring matrix using discrete wavelet transform (PsePSSM-DWT). The fused vector of word embedding and evolutionary descriptors is formed to compensate for the limitations of single encoding methods. A Shapley Additive exPlanations (SHAP) based global interpolation approach is applied to reduce training costs by choosing the optimal feature set. The selected feature set is trained using a bi-directional temporal convolutional network (BiTCN). The proposed iAFPs-Mv-BiTCN model achieved a predictive accuracy of 98.15 % and an AUC of 0.99 using training samples. In the case of the independent samples, our model obtained an accuracy of 94.11 % and an AUC of 0.98. Our iAFPs-Mv-BiTCN model outperformed existing models with a ~4 % and ~5 % higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed iAFPs-Mv-BiTCN model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.

Consistency label-activated region generating network for weakly supervised medical image segmentation

The current methods of auto-segmenting medical images are limited due to insufficient and ambiguous pathonmorphological labeling. In clinical practice, rough classification labels (such as disease or normal) are more commonly used than precise segmentation masks. However, there is still much to be explored regarding utilizing these weak clinical labels to accurately determine the lesion mask and guide medical image segmentation. In this paper, we proposed a weakly supervised medical image segmentation model to directly generate the lesion mask through a class activation map (CAM) guided cycle-consistency label-activated region transferring network. Cycle-consistency enforces that the mappings between the two domains should be reversible, which ensures that the original image can be reconstructed from the translated image. We developed a complementary branches fusion module to address the issue of blurry boundaries in CAM-guided segmentation. The complementary branch preserves the original semantic information of the non-lesion region and perfectly fuses the transferred feature of the lesion region with a complementary mask-constrained fake image generation process to clear the boundary of the lesion and non-lesion regions. This module allows the class transformation to focus solely on the label-activated region, resulting in more explicit segmentation. This model can accurately identify different region of medical images at the pixel-level while preserving the overall semantic structure semantion. It organizes disease labels and corresponding regions during image synthesis. Our method utilizes a joint discrimination strategy that significantly enhances the precision of the produced lesion mask. Extensive experiments of the proposed method on BraTs, ISIC and COVID-19 datasets demonstrate superior performance over existing state-of-the-art methods. The code and datasets are available at: https://github.com/mlcb-jlu/MedImgSeg.

Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses.

Whole blood models are rapid and versatile for determining immune responses to inflammatory and infectious stimuli, but they have not been used for bacterial discrimination. Staphylococcus aureus, S. epidermidis and Escherichia coli are the most common causes of invasive disease, and rapid testing strategies utilising host responses remain elusive. Currently, immune responses can only discriminate between bacterial 'domains' (fungi, bacteria and viruses), and very few studies can use immune responses to discriminate bacteria at the species and strain level. Here, whole blood was used to investigate the relationship between host responses and bacterial strains. Results confirmed unique temporal profiles for the 10 parameters studied: IL-6, MIP-1α, MIP-3α, IL-10, resistin, phagocytosis, S100A8, S100A8/A9, C5a and TF3. Pairwise analysis confirmed that IL-6, resistin, phagocytosis, C5a and S100A8/A9 could be used in a discrimination scheme to identify to the strain level. Linear discriminant analysis (LDA) confirmed that (i) IL-6, MIP-3α and TF3 could predict genera with 95% accuracy; (ii) IL-6, phagocytosis, resistin and TF3 could predict species at 90% accuracy and (iii) phagocytosis, S100A8 and IL-10 predicted strain at 40% accuracy. These data are important because they confirm the proof of concept that host biomarker panels could be used to identify bacterial pathogens.

Chemical characterization of different parts of Forsythia suspensa and α-glucosidase and pancreatic lipase inhibitors screening based on UPLC-QTOF-MS/MS and plant metabolomics analysis

Forsythia suspensa (Thunb.) Vahl (FS) is an important plant with high edible and medicinal values. The edible fruit of FS is a common traditional medicine in China, Japan and Korea. Compared to the research on the phytochemistry and pharmacology of the fruits, study on the other parts of FS, such as leaves and flowers is still limited. In this study, an integrated strategy based on ultra-performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF-MS/MS), plant metabolomics and correlation analysis was established for comprehensively chemical characterization of fruits, leaves and flowers of FS, and discovery of α-glucosidase and pancreatic lipase inhibitory metabolites. The plant metabolic profiling of fruits, leaves and flowers of FS was performed by UPLC-QTOF-MS/MS, and a total of 74 secondary metabolites, including 15 phenylethanoid glycosides, 20 lignans, 10 cyclohexanol derivatives, 11 organic acids, 9 flavonoids, 3 triterpenes, and 10 other compounds were identified. Then, 29 differential secondary metabolites that responsible to distinguish the fruits, leaves and flowers of FS were further screened out by multivariate statistical analysis. Meanwhile, the α-glucosidase and pancreatic lipase inhibition of different parts of FS were evaluated and compared by in vitro experiments. The results demonstrated that the leaves of FS showed the highest inhibition on α-glucosidase and pancreatic lipase with IC50 of 0.17 ± 0.04 mg/mL and 0.56 ± 0.33 mg/mL, respectively. Then, the correlation between differential metabolites and enzyme inhibitory activities were investigated by Pearson correlation analysis, and 12 potential α-glucosidase and pancreatic lipase inhibitors were screen out. Additionally, the α-glucosidase and pancreatic lipase inhibitory activities of five potential enzyme inhibitory components, including quercitrin, rutin, kaempferol-3-O-rutinoside, pinoresinol-4-O-glucoside and phillyrin were further validated by in vitro assays and molecular docking analysis. The results showed that all the five potential inhibitors showed good inhibitory effects on α-glucosidase and pancreatic lipase, and the binding of the five inhibitors to enzymes mainly through hydrogen bonding, hydrophobic force, and ionic bonding. This study provided a feasible strategy for comparison and discrimination of different parts of medicinal plant and discovery of bioactive components, and also provided useful information for future utilization of different parts of FS.

Vibration-based structural damage localization through a discriminant analysis strategy with cepstral coefficients

Over the past decades, Vibration-Based Methods (VBMs) have consistently exhibited exceptional effectiveness in the field of Structural Health Monitoring when it comes to assessing structural damage in both civil and mechanical structures. Recently, the progress made in data-driven strategies for localizing structural damage through the VBMs has resulted in substantial benefits. These advanced strategies not only enable an efficient decision-making process but also provide precise identification of repair locations for monitored structures. Importantly, they achieve this without the inherent complexity and computational burden typically associated with traditional model-based methods. In this study, an innovative data-driven method for localizing and quantifying structural damage is proposed. The method is developed on the basis of the principles of Linear Discriminant Analysis (LDA) and a newly devised modeling strategy that utilizes the power cepstral coefficients extracted from the structural acceleration response. The developed LDA model is able to highlight the local structural characteristics (i.e., mode shape-related information) embedded in the cepstral coefficients in the LDA latent space. Based on the highlighted local characteristics of the cepstral coefficients, a statistical pattern recognition strategy is proposed to effectively conduct the quantification and localization of structural damage. The proposed damage localization method is designed to function in a completely unsupervised-learning manner, which eliminates the requirement for the model to have access to any prior knowledge or information regarding structural damage during the training phase. The proposed method has been validated by both numerical simulations and experimental data.

Convolutional neural network intelligent fault diagnosis method for rotating machinery based on discriminant correlation analysis multi-domain feature fusion strategy

Aiming at solving the problems of limited training data, single input information, and limited diagnostic accuracy under the influence of strong background noise in fault diagnosis of rotating machinery, this paper proposes a fault diagnosis method based on the combination of discriminant correlation analysis (DCA) and convolutional neural network (CNN). Firstly, the original vibration signal is divided into several segments in the time domain, and the training data is directly processed by one CNN branch to extract multi-scale time domain features. Simultaneously, the divided data is subjected to discrete wavelet transform (DWT), and processed by another branch of CNN to extract multi-scale time-frequency features. Then, the DCA feature fusion mechanism is adopted to fuse the two-domain features extracted in the parallel branches to improve the model’ detection ability. Finally, the fused features are input into the deep CNN for training and learning to extract new features and output the classification results. Through the experimental analysis of two different types of data, the results show that the proposed method can be used for fault diagnosis of rotating machinery effectively. Compared with the single CNN network, the proposed method combines the multi-domain multi-scale feature extraction module with the DCA feature fusion module to enrich the feature information extraction ability. At the same time, the network performance is improved to get higher fault classification accuracy higher.