Discrimination Scheme Research Articles

Sulfated fucan could serve as a species marker of sea cucumber with endo-1,3-fucanase as the essential tool

In the past few decades, sulfated fucan from sea cucumber had attracted considerable interest owing to its abundant physiological activities. Nevertheless, its potential for species discrimination had not been investigated. Herein, particular attention was given to sea cucumber Apostichopus japonicus, Acaudina molpadioides, Holothuria hilla, Holothuria tubulosa, Isostichopus badionotus and Thelenota ananas to examine the feasibility of sulfated fucan as a species marker of sea cucumber. The enzymatic fingerprint suggested that sulfated fucan exhibited significant interspecific discrepancy and intraspecific stability, which revealed that sulfated fucan could serve as the species marker of sea cucumber, by utilizing the overexpressed endo-1,3-fucanase Fun168A and the ultra-performance liquid chromatography-high resolution mass spectrum. Moreover, oligosaccharide profile of sulfated fucan was determined. The oligosaccharide profile combined with hierarchical clustering analysis and principal components analysis further confirmed that sulfated fucan could serve as a marker with a satisfying performance. Besides, load factor analysis showed that the minor structure of sulfated fucan also contributed to the sea cucumber discrimination, besides the major structure. The overexpressed fucanase played an indispensable role in the discrimination, due to its specificity and high activity. The study would lead to a new strategy for species discrimination of sea cucumber based on sulfated fucan.

Hybrid near-optimum binary receiver with realistic photon-number-resolving detectors

We propose a near-optimum receiver for the discrimination of binary phase-shift-keyed coherent states employing photon-number-resolving detectors. The receiver exploits a discrimination strategy based on both so-called homodyne-like and direct detection, thus resulting in a hybrid scheme. We analyze the performance and robustness of the proposed scheme under realistic conditions, namely, in the presence of inefficient detection and dark counts. We show that the present hybrid setup is near- optimum and beats both the standard quantum limit and the performance of the Kennedy receiver.

Intertemporal Price Discrimination via Randomized Promotions

Problem definition: The undesirable but inevitable consequence of running promotions is that consumers can be trained to time their purchases strategically. In this paper, we study randomized promotions, where the firm randomly offers discounts over time, as an alternative strategy of intertemporal price discrimination. Methodology/results: We consider a base model where a monopolist sells a single product to a market with a constant stream of two market segments. The segments are heterogeneous in both their product valuations and patience levels. The firm precommits to a price distribution, and in each period, a price is randomly drawn from the committed distribution. We characterize the optimal price distribution as a randomized promotion policy and show that it serves as an intertemporal price discrimination mechanism such that high-valuation customers would make a purchase immediately at a regular price upon arrival, and low-valuation customers would wait for a random promotion. Compared against the optimal cyclic pricing policy, which is optimal within the strategy space of all deterministic pricing policies, the optimal randomized pricing policy beats it if low-valuation customers are sufficiently patient and the absolute discrepancy between high and low customer valuations is large enough. We extend the model in three directions. First, we consider the case where a portion of customers are myopic and would never wait. We show that the existence of myopic customers is detrimental to the firm’s profitability, and the expected profit from an optimal randomized pricing policy decreases as the proportion of myopic customers in the population increases. Second, we consider Markovian pricing policies where prices are allowed to be intertemporally correlated in a Markovian fashion. This additional maneuver allows the firm to reap an even higher profit when low-valuation customers are sufficiently patient by avoiding consecutive promotions but, on average, running the promotion more frequently with a smaller discount size. Lastly, we consider a model with multiple customer segments and show that a two-point price distribution remains optimal, and our conclusion from the two-segment base model still holds under certain conditions that are adopted in the literature. Managerial implications: Our results imply that the firm may want to deliberately randomize promotions in the presence of forward-looking customers. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201124], the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295], and the Youth Project of the Humanities and Social Science Foundation of the Ministry of Education of China [Grant 22YJC630006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1194 .

Asylum-seeker emergency and third spaces in the Inspector Montalbano TV series

ABSTRACT By analysing the TV episode ‘The Other End of the line’ (2019) through the lens of Bhabha’s concepts of ‘third space’ and ‘hybridity’ (1994), and Soja’s formulation of ‘thirdspace,’ this article argues that, far from being escapist viewing, the Inspector Montalbano TV series (Italy, 1999–2021) is a ‘geopolitical’ crime series (Saunders 2020, 1) that challenges the ‘nation-centred view of sovereign citizenship’ (Bhabha 38) embodied by Matteo Salvini, the leader of the xenophobic Italian political party, Lega. This episode provides criticism of Salvini’s policy of closed ports, which aimed to stop migration fluxes from Northern Africa, when the Lega was in power in Italy (2018–2019). By visualizing several ‘thirdspaces’, such as a rescue vessel, a port, and the village of Vigàta, where the story takes place, and populating the plot with characters who defy a Manichean view of ‘us-versus-them’, ‘The Other End of the line’ endorses the idea of an inclusive society characterized by ‘a cultural hybridity that entertains difference without an assumed or imposed hierarchy’ (Bhabha 5). This article also argues that this episode invites viewers to act ‘as active citizens’ who ‘must vigilantly guard against the state’s strategies of exclusion and discrimination in the midst of its promises of formal equality and procedural democracy’ (Bhabha xxi).

Colorimetric detection of SARS‐CoV‐2 variants with paper‐based analytical devices

Colorimetric detection of SARS‐CoV‐2 variants with paper‐based analytical devices

A fast and highly efficient strategy for discrimination and detection of three nitrophenol isomers

Novel catalysts of strongly coupled nickel-cobalt nitrides/carbon nanocage (NiCoN/C nanocages) with good water solubility and low cost are utilized to simultaneously catalytically reduce three nitrophenol isomers (2-NP, 3-NP and 4-NP) into corresponding aminophenol (2-AP, 3-AP and 4-AP) within nearly 5 min at room temperature. It is the first report that only one reagent (dopamine) can achieve fast discrimination of three nitrophenol isomers one by one. The reduced product of 3-NP (3-AP) can selectively form fluorescent substance with dopamine with maximum emission wavelength at 465 nm. The reduced product of 4-NP (4-AP) can selectively react with dopamine to form purple solution with absorption peak at 550 nm. It achieves fluorescent detection of 3-NP from 50 nM to 1.2 μM with the limit detections (LOD) of 17 nM for 3-NP, and colorimetric detection of 4-NP from 5 μM to 200 μM with LOD of 1.7 μM. It also realizes nitrophenol detection in tap water. When 0.30, 0.60, 1.00 μM 3-NP and 10, 50, 100 μM 4-NP are added to water samples, recoveries are 98.8–104.6% for 3-NP and 98.8–101.8% for 4-NP.

Strategies for single-shot discrimination of process matrices

The topic of causality has recently gained traction quantum information research. This work examines the problem of single-shot discrimination between process matrices which are an universal method defining a causal structure. We provide an exact expression for the optimal probability of correct distinction. In addition, we present an alternative way to achieve this expression by using the convex cone structure theory. We also express the discrimination task as semidefinite programming. Due to that, we have created the SDP calculating the distance between process matrices and we quantify it in terms of the trace norm. As a valuable by-product, the program finds an optimal realization of the discrimination task. We also find two classes of process matrices which can be distinguished perfectly. Our main result, however, is a consideration of the discrimination task for process matrices corresponding to quantum combs. We study which strategy, adaptive or non-signalling, should be used during the discrimination task. We proved that no matter which strategy you choose, the probability of distinguishing two process matrices being a quantum comb is the same.

Open set domain adaptation with latent structure discovery and kernelized classifier learning

Numerous visual domain adaptation methods have been proposed for transferring knowledge from a well-labeled source domain to an unlabeled but related target domain. Most of existing works are only geared to closed set domain adaptation, where an identical label space is shared between two domains. In this paper, we focus on a more realistic but challenging scenario, open set domain adaptation, where the target domain contains unknown classes that do not appear in the label space of source domain. The main task of open set domain adaptation is to simultaneously recognize the target images of known classes and those of unknown classes correctly. To achieve this goal, in this paper, we propose a novel open set domain adaptation method, which consists of two parts: latent structure discovery and kernelized classifier learning. In the first part, we employ an adaptive discriminative graph learning strategy to capture the intrinsic manifold structure of the source and target domain data in the latent feature space, such that the boundaries among all classes will be delineated more clearly. In the second part, the samples from the latent feature space are mapped into a high-dimensional kernel space to make them linearly separable, and a linear classifier is learned by jointly operating unknown target samples separating, known samples matching and local structure preserving. As the optimization problem is not convex with all variables, we devise an efficient iterative algorithm to solve it. The extensive experimental results on five image datasets confirm the superiority of the proposed method compared with the state-of-the-art traditional and deep competitors.

Dual‐Modal Iridium‐Based Self‐Immolative Chemosensors for Differential Responses against Reactive Oxygen Species and their Applications to Detect Diabetes

AbstractIn diabetes, platelets are activated by several stimuli, and the activated platelets generate reactive oxygen species (ROS) to induce the aggregation of platelets followed by thrombus formation resulting in various cardiovascular diseases. Therefore, detecting ROS perturbations in platelets can provide a clue to diagnosing diabetes. In this paper, iridium‐based self‐immolative probes (1a‐1c) are reported to monitor perturbations of ROS in the blood through photoluminescence (PL) and electrochemiluminescence (ECL). The probes are designed based on an iridium complex conjugated with phenylboronic acid pinacol ester through carbamate moiety. Three probes contain distinct electron‐withdrawing groups at the ortho‐position in the benzyl linker; thus, subtle reactivity differences are expected against ROS. As expected, all three probes exhibit the most apparent PL changes against hydrogen peroxide (H2O2), but their response patterns against ROS are interestingly distinctive. Utilizing such differential ROS responsive pattern, a discrimination strategy is established using a combination of PL and ECL responses, and discrimination of platelets from diabetic and control rats is successfully demonstrated.

An integrated strategy for thegeographical origin traceability of Goji berries by antioxidants characteristic fingerprint based online ultra-performance liquid chromatography-2,2-diphenyl-1-picrylhydrazyl- photodiode array detector-mass spectrometry combined with multivariate statistics analysis.

Goji berries are now becoming increasingly popular in the human diet due to their potential health benefits. Unscrupulous traders deliberately mislabel with certain origins to gain illegal profits, which seriously affected the consumers' benefits. In this study, an online ultra-performance liquid chromatography-2,2-diphenyl-1 -picrylhydrazyl-photodiode array detector-electrospray ionization-quadrupole time of flight mass was developed for rapid screening and identification of the antioxidants from Goji berry; then, the antioxidants characteristic fingerprint was established and explored in the origins discrimination of Goji berries from China combined with multivariate statistics analysis. As a result, twenty-eight compounds were screened from Goji berry extract, 19 of which were identified by accurate molecular and ultraviolet information according to references. Principal components analysis and partial least squares discrimination analysis achieved the accurate classification from the four regions, eight compounds were selected as origin-related antioxidant markers with variable importance in projection >1 and one-way analysis of variance (P<0.05), including rutin, rutin di-hexose, P-coumaric acid tri-hexose, dicaffeoylquinic acid isomer, Quercetin-rhamno-di-hexoside, peak14, peak16, and peak27. This study provides a feasible strategy for the geographical origins discrimination of Goji berries based on antioxidant ingredients difference and will be helpful for improving the quality control level of Goji berries.

Mathematical modeling and biochemical analysis support partially ordered calmodulin-myosin light chain kinase binding

Activation of myosin light chain kinase (MLCK) by calcium ions (Ca2+) and calmodulin (CaM) plays an important role in numerous cellular functions including vascular smooth muscle contraction and cellular motility. Despite extensive biochemical analysis, aspects of the mechanism of activation remain controversial, and competing theoretical models have been proposed for the binding of Ca2+ and CaM to MLCK. The models are analytically solvable for an equilibrium steady state and give rise to distinct predictions that hold regardless of the numerical values assigned to parameters. These predictions form the basis of a recently proposed, multi-part experimental strategy for model discrimination. Here we implement this strategy by measuring CaM-MLCK binding using an invitro FRET system. Interpretation of binding data in light of the mathematical models suggests a partially ordered mechanism for binding CaM to MLCK. Complementary data collected using orthogonal approaches that assess CaM-MLCK binding further support this conclusion.

Strategy for neonatal discrimination between Filaggrin-wildtype neonates and Fillagrin loss of function mutation carriers for the purpose of preventative AD-treatment

Loss-of-function filaggrin (LoF-FLG)-mutation carriers are at higher risk of early, more severe Atopic Dermatitis (AD) than wild type (WT-FLG) infants . We present a strategy for early discrimination between WT-FLG and LoF-FLG-mutation carriers, based on Raman spectroscopic analysis of Natural Moisturizer Factor (NMF) content in the stratum corneum of the thenar eminence.

Fault location discrimination and protection strategy for DC distribution network based on PET

After the bipolar short circuit fault occurs on the low-voltage DC side of the low-voltage DC distribution network with a power electronic transformer, the fault current amplitude is high and the rise speed is fast, which leads to the rapid locking of power electronic devices, and the loss of fault information of line protection. Based on the multi-branch DC distribution network with CHB-type PET, this paper proposes a fault current limiting strategy. PET starts current limiting measures after detecting the DC side fault, which can provide stable and continuous fault transient information for line protection while providing a small amount of short circuit current. On this basis, using the fault current at both ends of the line, longitudinal polarity protection without strict communication synchronization is proposed. The simulation analysis based on PSCAD/EMTDC software shows that the scheme can realize fast and reliable fault line selection and has strong resistance to transition resistance.

A Nonparametric Cumulative Sum-Based Fault Detection Method for Rolling Bearings Using High-Level Extended Isolated Forest

As an advanced anomaly detection method, extended isolated forest (EIF) has great potential for use in bearings. A low-dimensional data transformation method based on EIF with high extension level and an early fault detection strategy for rolling bearings based on feature combination are proposed due to the limitation of EIF extension level on low-dimensional data. First, a preliminary sequence base is generated through sliding windows, and the sequences related to the number of transformed dimensions are selected based on the dynamic time warped (DTW) distance of the original sequence to perform weighted DTW barycenter averaging (WDBA), which enables preprocessing of the maximum extension level of EIF. Then, to realize early fault detection of rolling bearings, the nonparametric cumulative sum (NCUSUM) algorithm is used to design a joint threshold discrimination scheme for root mean square (rms) and kurtosis of the new and original sequences. We tested the algorithm on fault simulation using FEMTO-ST and XJTU-SY bearing datasets. The results show that WDBA-EIF algorithm can better remove uncorrelated noise from time series at high extension level. Compared with several anomaly detection methods, under 60% datasets, it has the best detection result of an early weak anomaly in the running process of rolling bearings and has a low false alarm rate.

Sound-Based Intelligent Detection of FOD in the Final Assembly of Rocket Tanks

The traditional method of relying on human hearing to detect foreign object debris (FOD) events during rocket tank assembly processes has the limitation of strong reliance on humans and difficulty in establishing objective detection records. This can lead to undetected FOD entering the engine with the fuel and causing major launch accidents. In this study, we developed an automatic, intelligent FOD detection system for rocket tanks based on sound signals to overcome the drawbacks of manual detection, enabling us to take action to prevent accidents in advance. First, we used log-Mel transformation to reduce the high sampling rate of the sound signal. Furthermore, we proposed a multiscale convolution and temporal convolutional network (MS-CTCN) to overcome the challenges of multi-scale temporal feature extraction to detect suspicious FOD events. Finally, we used the proposed post-processing strategies of label smoothing and threshold discrimination to refine the results of FOD event detection and ultimately determine the presence of FOD. The proposed method was validated through FOD experiments. The results showed that the method had an accuracy rate of 99.16% in detecting FOD and had a better potential to prevent accidents compared to the baseline method.

An attention-guided and wavelet-constrained generative adversarial network for infrared and visible image fusion

The GAN-based infrared and visible image fusion methods have gained ever-increasing attention due to its effectiveness and superiority. However, the existing methods adopt the global pixel distribution of source images as the basis for discrimination, which fails to focus on the key modality information. Moreover, the dual-discriminator based methods suffer from the confrontation between the discriminators. To this end, we propose an attention-guided and wavelet-constrained GAN for infrared and visible image fusion (AWFGAN). In this method, two unique discrimination strategies are designed to improve the fusion performance. Specifically, we introduce the spatial attention modules (SAM) into the generator to obtain the spatial attention maps, and then the attention maps are utilized to force the discrimination of infrared images to focus on the target regions. In addition, we extend the discrimination range of visible information to the wavelet subspace, which can force the generator to restore the high-frequency details of visible images. Ablation experiments demonstrate the effectiveness of our method in eliminating the confrontation between discriminators. And the comparison experiments on public datasets demonstrate the effectiveness and superiority of the proposed method.

Asta-Ja Framework: A Peaceful Approach to Food, Water, Climate, and Environmental Security Coupled with Sustainable Economic Development and Social Inclusion in Nepal

Sustainable conservation, development, and utilization of natural and humanresources is necessary for accelerated economic growth and fast-pacedsocio-economic transformation of Nepal. Asta-Ja Framework, which is atheoretically grounded grassroots based peaceful and self-reliant planningand development approach, offers practical strategies for sustainable con-servation and development of natural and human resources enhancing food,water, climate, and environmental security, accelerated economic growth, andsocio-economic transformation of Nepal. Asta-Ja includes interconnectedeight resources in Nepali letter, Ja, – Jal (water), Jamin (land), Jungle(forest), Jadibuti (medicinal and aromatic plants), Janashakti (manpower),Janawar (animal), Jarajuri (crop plants) and Jalabayu (climate). Asta-JaFramework is a unifying framework for planning and resources develop-ment and has a strong footing on science, business, and eastern philosophy.While providing practical guidelines for achieving food, water, climate and environmental security, this article presents Nepal Vision 2040, which isdeveloped considering challenges that Nepal is currently facing and itsavailable Asta-Ja resources, envisioning that Nepal’s economic developmentreaching at the par of developed nations by 2040. Key strategic sectorsidentified in Nepal Vision 2040 include smallholder mixed-farming system,agro-jadibuti industrialization, protection of drinking water sources, climatechange adaptation, environmental pollution control, conservation of naturalresources, infrastructure, tourism, renewable energy, alleviation of inequali-ties, and good governance. This article demonstrates strategies for addressingsocial discrimination and inequalities through the process of Asta-Ja com-munity capacity-building and self-reliant development. Ecological balanceof Asta-Ja resources is necessary for sustainable natural resources, economicdevelopment, and community resiliency. The Government of Nepal is sug-gested to adopt Asta-Ja Framework as its national planning and developmentframework for sustainable economic growth and fast-paced socio-economictransformation of the country.

Do telecommunications prices depend on consumer engagement?

This paper analyzes how customers’ heterogeneous search and switching habits affect the prices paid for telecommunication services in a context in which operators use price discrimination strategies to retain their customers and attract those of their rivals. Drawing on a representative sample of Spanish households (N=3,113), we show that engaged consumers pay 9.4% less than completely unengaged consumers for their telecommunication services, after controlling for the characteristics of the bundle of services contracted. We also find that highly engaged consumers (i.e. those that have called their operator to obtain a better deal and who have switched to a different operator at least once) pay 13.6% less for their services than unengaged consumers, that consumers who have switched operator at least once pay 8.4% less, and that consumers who call their operator to request better conditions for their contracts pay 5.8% less. Finally, we show that the excess price paid by unengaged consumers increases as they contract more sophisticated services, such as premium television content and additional mobile lines.

Consensus based target tracking against deception jamming in distributed radar networks

AbstractIn this study, an anti‐deception jamming target tracking algorithm based on the data‐level fusion is proposed in a distributed radar network. First, the consensus covariance intersection (CCI) algorithm is proposed to improve tracking performance by introducing the consensus theory to the field of covariance intersection fusion. Second, unscented Kalman filter is utilised to implement the preprocessing of distributed filtering. Then, two distributed target discrimination schemes are developed for non‐collaborative and collaborative false targets respectively. For non‐collaborative false targets, according to the characteristics that estimated spatial locations of the false target from different radars are relatively scattered, a false target elimination method based on nearest neighbour algorithm is proposed. For collaborative false targets, after equipped with passive radars that are less vulnerable to interference, the consensus algorithm is introduced to convey the real target information to the unconnected active radars, thus achieving an effect of global anti‐deception jamming. Finally, the CCI algorithm is deployed for distributed fusion of physical target tracking, so as to improve the overall estimation performance.

Time-resolved enantiomer-exchange probed by using the orbital angular momentum of X-ray light.

Molecular chirality, a geometric property of utmost importance in biochemistry, is now being investigated in the time-domain. Ultrafast chiral techniques can probe the formation or disappearance of stereogenic centers in molecules. The element-sensitivity of X-rays adds the capability to probe chiral nuclear dynamics locally within the molecular system. However, the implementation of ultrafast techniques for measuring transient chirality remains a challenge because of the intrinsic weakness of chiral-sensitive signals based on circularly polarized light. We propose a novel approach for probing the enantiomeric dynamics by using the orbital angular momentum (OAM) of X-ray light, which can directly monitor the real-time chirality of molecules. Our simulations probe the oscillations in excited chiral formamide on different potential energy surfaces and demonstrate that using the X-ray OAM can increase the measured asymmetry ratio. Moreover, combining the OAM and SAM (spin angular momentum) provides stronger dichroic signals than linearly polarized light, and offers a powerful scheme for chiral discrimination.