Selective Scheme Research Articles

Risk factors of high somatic cell count and differential somatic cells in early lactation associated with selective dry cow therapy.

The routine use of intramammary antimicrobial products in all dairy cows at the beginning of the dry period is no longer allowed in European Union (EU) countries due to the new Regulation (EU) 2019/6 to reduce antimicrobial resistance. This study investigated the application of a selective dry cow therapy scheme and the risk factors of high individual milk somatic cell count (SCC) and individual neutrophil count in early lactation, as a response to the application of a selective dry cow therapy (SDCT) protocol. The study was carried out on three commercial farms, and a total of 243 lactating cows were monitored at the end of lactation and at the beginning of the next one, 91 of which were dried off without the use of antimicrobials (NoT) based on milk SCC, differential somatic cell count (DSCC), and the response of Vetscan DC-Q milk analyser, using a secret algorithm. The remaining 152 cows received antimicrobials (T). After calving, similar means were observed between the two treatment groups for SCC (4.8 vs 4.9 log10 cells/ml for T and NoT, respectively, P=0.5) and total milk leucocyte count (TLC) (5 vs 5.1 log10 cells/ml for T and NoT, respectively, P=0.7) in milk. However, the use of antimicrobials led to a lower DSCC (58 vs 64% for T and NoT, respectively, P=0.01) and lower percentage of neutrophils (59 vs 64% for T and NoT, respectively, P=0.05), although the levels of DSCC and percentage of neutrophils in cows dried off without antimicrobials remained lower than the risk threshold suggested by the international literature. A logistic regression was computed after the application of selective dry cow therapy to identify risk factors of high milk SCC (≥100000 cells/mL) at the beginning of lactation. Increased milk SCC after calving was related to high SCC at the end of lactation and abandonment of antimicrobial therapy at dry-off. Moreover, the length of the dry period, milk protein content, and flank cleanliness in the last test day before dry-off were other factors in the logistic regression. Neutrophil counts at the beginning of the next lactation were affected by the same factors that influenced SCC, together with milk production, TLC, and macrophages on the last test day. The results obtained in these studied farms showed that selective dry cow therapy may be applied without adversely affecting the next lactation.

Area-Selective Deposition by Cyclic Adsorption and Removal of 1-Nitropropane.

The ever-greater complexity of modern electronic devices requires a larger chemical toolbox to support their fabrication. Here, we explore the use of 1-nitropropane as a small molecule inhibitor (SMI) for selective atomic layer deposition (ALD) on a combination of SiO2, Cu, CuOx, and Ru substrates. Results using water contact angle goniometry, Auger electron spectroscopy, and infrared spectroscopy show that 1-nitropropane selectively chemisorbs to form a high-quality inhibition layer on Cu and CuOx at an optimized temperature of 100 °C, but not on SiO2 and Ru. When tested against Al2O3 ALD, however, a single pulse of 1-nitropropane is insufficient to block deposition on the Cu surface. Thus, a new multistep process is developed for low-temperature Al2O3 ALD that cycles through exposures of 1-nitropropane, an aluminum metalorganic precursor, and coreactants H2O and O3, allowing the SMI to be sequentially reapplied and etched. Four different Al ALD precursors were investigated: trimethylaluminum (TMA), triethylaluminum (TEA), tris(dimethylamido)aluminum (TDMAA), and dimethylaluminum isopropoxide (DMAI). The resulting area-selective ALD process enables up to 50 cycles of Al2O3 ALD on Ru but not Cu, with 98.7% selectivity using TEA, and up to 70 cycles at 97.4% selectivity using DMAI. This work introduces a new class of SMI for selective ALD at lower temperatures, which could expand selective growth schemes to biological or organic substrates where temperature instability may be a concern.

S3R: Shape and Semantics-Based Selective Regularization for Explainable Continual Segmentation Across Multiple Sites.

In clinical practice, it is desirable for medical image segmentation models to be able to continually learn on a sequential data stream from multiple sites, rather than a consolidated dataset, due to storage cost and privacy restrictions. However, when learning on a new site, existing methods struggle with a weak memorizability for previous sites with complex shape and semantic information, and a poor explainability for the memory consolidation process. In this work, we propose a novel Shape and Semantics-based Selective Regularization ( [Formula: see text]) method for explainable cross-site continual segmentation to maintain both shape and semantic knowledge of previously learned sites. Specifically, [Formula: see text] method adopts a selective regularization scheme to penalize changes of parameters with high Joint Shape and Semantics-based Importance (JSSI) weights, which are estimated based on the parameter sensitivity to shape properties and reliable semantics of the segmentation object. This helps to prevent the related shape and semantic knowledge from being forgotten. Moreover, we propose an Importance Activation Mapping (IAM) method for memory interpretation, which indicates the spatial support for important parameters to visualize the memorized content. We have extensively evaluated our method on prostate segmentation and optic cup and disc segmentation tasks. Our method outperforms other comparison methods in reducing model forgetting and increasing explainability. Our code is available at https://github.com/jingyzhang/S3R.

Threshold Attribute-Based Credentials With Redactable Signature

Threshold attribute-based credentials are suitable for decentralized systems such as blockchains as such systems generally assume that authenticity, confidentiality, and availability can still be guaranteed in the presence of a threshold number of dishonest or faulty nodes. Coconut (NDSS'19) was the first selective disclosure attribute-based credentials scheme supporting threshold issuance. However, it does not support threshold tracing of user identities and threshold revocation of user credentials, which is desired for internal governance such as identity management, data auditing, and accountability. The communication and computation complexities of Coconut for verifying credentials are linear in the number of each user's attributes and thus costly. Addressing these issues, we propose a novel efficient threshold attribute-based anonymous credential scheme. While retaining all the features of Coconut, our scheme supports threshold tracing of user identities and threshold revocation of user credentials, and it significantly reduces the computational and communication complexities of credential verification. In addition, we prove that our scheme enjoys strong security features, including anonymity, blindness, traceability, and non-frameability.

A Current Selective Tripping Protection Scheme for the Distribution Network with PV

At present, the global energy demand keeps rising due to population growth. Therefore, large numbers of photovoltaics (PV) are being integrated with power systems. Solar PV’s installed power capacity is poised to surpass that of coal by 2027, becoming the largest in the world. The integration of PV has changed the direction of the power flow. Under these circumstances, the changed magnitudes and directions of fault current may result in maloperations and non-operations of conventional relays. In this work, a simple and reliable current selective tripping protection scheme is proposed, which is based on the direct communication between overcurrent protective devices on both sides of the line. Through logical programming of the operation information of each protection, the fault location is detected, and the instantaneous trip is realized. The simulation analysis of PSCAD/EMTDC shows that the protection scheme can reliably detect and isolate faults happening at the feeder and bus under different fault conditions; besides, it has good performance in detecting certain resistance grounding faults. The proposed protection scheme can effectively solve the problems caused by PV systems penetration and improve system safety.

Smart Droplet Microfluidic System for Single-Cell Selective Lysis and Real-Time Sorting Based on Microinjection and Image Recognition.

Single-cell analysis has important implications for understanding the specificity of cells. To analyze the specificity of rare cells in complex blood and biopsy samples, selective lysis of target single cells is pivotal but difficult. Microfluidics, particularly droplet microfluidics, has emerged as a promising tool for single-cell analysis. In this paper, we present a smart droplet microfluidic system that allows for single-cell selective lysis and real-time sorting, aided by the techniques of microinjection and image recognition. A custom program evolved from Python is proposed for recognizing target droplets and single cells, which also coordinates the operation of various parts in a whole microfluidic system. We have systematically investigated the effects of voltage and injection pressure applied to the oil-water interface on droplet microinjection. An efficient and selective droplet injection scheme with image feedback has been demonstrated, with an efficiency increased dramatically from 2.5% to about 100%. Furthermore, we have proven that the cell lysis solution can be selectively injected into target single-cell droplets. Then these droplets are shifted into the sorting area, with an efficiency for single K562 cells reaching up to 73%. The system function is finally explored by introducing complex cell samples, namely, K562 cells and HUVECs, with a success rate of 75.2% in treating K562 cells as targets. This system enables automated single-cell selective lysis without the need for manual handling and sheds new light on the cooperation with other detection techniques for a broad range of single-cell analysis.

Energy Outage Analysis of Cloud-Aerial RAN

This paper proposes a cloud-aerial radio access network (CA-RAN) architecture utilizing unmanned aerial vehicles (UAVs) to rapidly deploy mobile networks for disaster recovery and emergency situations. The architecture aims to provide reliable and energy-efficient data coverage through the use of millimeter-wave bands for increased data rates and both RF and solar energy harvesting techniques for sustainable energy provision. To improve the system’s reliability, an opportunistic selective scheme is implemented. We evaluate the performance of the CA-RAN based system using the energy-outage probability metric, which, to the best of our knowledge, has not been studied in UAV-based networks. Finally, numerical results are presented to illustrate the impact of different parameters on the system’s performance.

Image segmentation by selecting eigenvectors based on extended information entropy

AbstractFor spectral clustering algorithm, the quality of eigenvectors of graph affinity matrix is very important for the clustering result. So, how to obtain high‐quality eigenvectors is crucial. In this paper, the authors aim to propose some new measurement methods to evaluate each eigenvector of affinity matrix for spectral selection. Based on extended information entropy, three criteria, i.e. Spectral Distinguishability (SD), Spectral Distinguishability Validity (SDV) and pectral Distinguishability ‐Degree (SDD), are defined respectively. The compactness of clusters for each eigenvector is measured by SD; SDV is used to remove the inefficient eigenvectors for clustering; SDD is used to evaluate the contribution of eigenvectors to clustering and is exploited to build a selective spectral ensemble scheme. To indicate the merits of the authors’ algorithm, the authors consider varied artificial data and natural images, including Berkeley image segmentation data set as benchmark data set. The authors’ simulation results confirm the superior performance of the proposed method in developing spectral clustering compared to conventional clustering methods and recent eigenvectors‐selection‐based algorithms.

Targeted treatment strategies for the control of gastrointestinal nematodes in a goat flock with anthelmintic resistant worm populations and poor nutrition

Combined targeted selective treatment schemes using FAMACHA©, body condition score (BCS), and faecal egg counts (FEC) can reduce the number of anthelmintic (AH) treatments per year in goat farms, which can slow down the selection of AH-resistant worm populations. Supplementary feeding can further reduce the FEC of goats at the farm level. A 19-month survey was implemented to (i) identify the number of times that goats crossed the thresholds of FAMACHA©, BCS, and FEC, (ii) identify the monthly FEC reductions of goats treated with AH in the targeted scheme, and (iii) identify the effect of a supplementation scheme on the goats’ FEC. The survey was performed at a goat farm in tropical México. Goats grazed natural vegetation four h/day and received concentrate feed daily (14% crude protein, 150 g each). Each month, goats (n = 88) were examined, and those with FAMACHA© ≥ 4, BCS ≤ 2, and FEC ≥ 750 eggs per gram of faeces (EPG) were identified (Day 0). From September 2018 to September 2019, goats crossing the FEC threshold (events) were allocated to two different groups distributed according to FEC values: deworming group (DG; n = 71 events) and supplemented group (SG; n = 72 events). From October 2019, all the goats were assigned only to the DG (n = 31 events) due to management problems in the study farm. The DG goats were individually weighed and treated with levamisole (LEV, 12 mg/kg body weight (BW), subcutaneous (sc)) and their normal diet. From November 2019 to March 2020, the DG goats were treated with LEV and benzimidazole (BZ, 7 mg/kg BW, sc). The SG goats were individually supplemented with an additional 300 g of concentrate feed for ten days. From the 88 goats surveyed, 70.5% crossed the FAMACHA©/BCS thresholds, with goats sampled 1–7 times. From the 369 sampled events, only 56% had ≥ 750 EPG. The FEC reduction of DG goats treated with LEV ranged from 46.7% to 96.1%, and with LEV+BZ combination was 100%. The FEC reduction in the SG goats ranged from 27.7% to 95.5%. Supplementation of SG goats had a positive impact on most goats and helped to limit the number of dewormed goats. To be effective, the supplement must be an addition to the normal diet.

Fast selective encryption algorithms based on moments and chaos theory

In this work, we introduce a novel selective encryption scheme based on chaos theory and momentstransforms, two moments families were considered namely Tchebichef and Hahn. The goal is to propose afast and secure encryption scheme that can be deployed in real world scenarios. The proposed algorithmsoperate in the transform domains of Tchebichef and Hahn moments. We encrypt only the most significantcoefficients of the moments transforms. First, we down sample the computed moments’ matrices coefficients,then we use two logistic maps for confusion and diffusion of the down-sampled Tchebichef’s and Hahn’scoefficients, the resulting matrix is the encrypted image. This approach improves drastically the timeperformance of the encryption algorithm while keeping a “good” security level. In order to prove theperformance of our algorithms, we run different experiments and test the algorithms on different criteria:MSE, correlation coefficient, differential analysis, entropy and time performance. The presented results provethat the encryption algorithms proposed are secure and outperform state-of-the-art algorithms.

Genomic analysis and prediction of genomic values for distichiasis in Staffordshire bull terriers.

Distichiasis is a condition characterized by aberrant hairs along the eyelid margins. The symptoms are usually mild but can lead to ulcerations and lesions of the cornea in severe cases. It is the most frequently noted ocular disorder in Norwegian Staffordshire bull terriers (SBT), with a prevalence above 18% in the adult population. A complex inheritance is assumed, but there is sparse knowledge about the genetic background of distichiasis in dogs. We have performed a genome-wide association study of distichiasis in SBT and used genomic data in an attempt to predict genomic values for the disorder. We identified four genetic regions on CFA1, CFA18, CFA32 and CFA34 using a mixed linear model association analysis and a Bayesian mixed model analysis. Genomic values were predicted using GBLUP and a Bayesian approach, BayesR. The genomic prediction showed that the 1/4 of dogs with predicted values most likely to acquire distichiasis had a 3.9 -4.0 times higher risk of developing distichiasis compared to the quarter (1/4) of dogs least likely to acquire the disease. There was no significant difference between the two methods used. Four genomic regions associated with distichiasis were discovered in the association analysis, suggesting that distichiasis in SBT is a complex trait involving numerous loci. The four associated regions need to be confirmed in an independent sample. We also used all 95K SNPs for genomic prediction and showed that genomic prediction can be a helpful tool in selective breeding schemes at breed level aiming at reducing the prevalence of distichiasis in SBTs in the future, even if the predictive value of single dogs may be low.

Valorización hidrometalúrgica de pilas Ni-Cd gastadas, utilizando ácidos orgánicos como agentes lixiviantes selectivos

In this paper, we propose a hydrometallur-gical procedure to treat spent Ni-Cd batte-ries using organic acids as leaching agents. The electrode materials of the batteries were extracted and leached using a combi-nation of acetic, citric, and sulfuric acids and their mixtures in a two-stage selective scheme. This procedure aimed to recover metal-bearing solid products. The leachates were treated by evaporation/crystallization or chemical precipitation to obtain the so-lid products. These products were charac-terized using scanning electron microsco-py, chemical analysis by atomic absorption spectroscopy, mineralogical phases by x-ray diffraction, and infrared spectroscopy. In the first leaching step, 15 % m/m citric acid was used to recover 85±5 wt.% of the cad-mium present as a carboxylate salt. In the second leaching stage (5 % m/m acetic acid mixed with 3 % m/m sulfuric acid), 50±3 wt.% of nickel was recovered, in the form of a product consisting of a mixture of hy-droxides and salts. The remaining solid from the second leaching stage was a valua-ble metal product containing nickel (96±4 wt.%). The analysis of the results allowed us to depict a conceptual process flow diagram for the selective leaching of the spent Ni-Cd batteries, and the possible industrial use of the solid products obtained.

SELID: Selective Event Labeling for Intrusion Detection Datasets.

A large volume of security events, generally collected by distributed monitoring sensors, overwhelms human analysts at security operations centers and raises an alert fatigue problem. Machine learning is expected to mitigate this problem by automatically distinguishing between true alerts, or attacks, and falsely reported ones. Machine learning models should first be trained on datasets having correct labels, but the labeling process itself requires considerable human resources. In this paper, we present a new selective sampling scheme for efficient data labeling via unsupervised clustering. The new scheme transforms the byte sequence of an event into a fixed-size vector through content-defined chunking and feature hashing. Then, a clustering algorithm is applied to the vectors, and only a few samples from each cluster are selected for manual labeling. The experimental results demonstrate that the new scheme can select only 2% of the data for labeling without degrading the F1-score of the machine learning model. Two datasets, a private dataset from a real security operations center and a public dataset from the Internet for experimental reproducibility, are used.

Joint PMD Tracking and Nonlinearity Compensation With Deep Neural Networks

Overcoming fiber nonlinearity is one of the core challenges limiting the capacity of optical fiber communication systems. Machine learning based solutions such as learned digital backpropagation (LDBP) and the recently proposed deep convolutional recurrent neural network (DCRNN) have been shown to be effective for fiber nonlinearity compensation (NLC). Incorporating distributed compensation of polarization mode dispersion (PMD) within the learned models can improve their performance even further but at the same time, it also couples the compensation of nonlinearity and PMD. Consequently, it is important to consider the time variation of PMD for such a joint compensation scheme. In this paper, we investigate the impact of PMD drift on the DCRNN model with distributed compensation of PMD. We propose a transfer learning based selective training scheme to adapt the learned neural network model to changes in PMD. We demonstrate that fine-tuning only a small subset of weights as per the proposed method is sufficient for adapting the model to PMD drift. Using decision directed feedback for online learning, we track continuous PMD drift resulting from a time-varying rotation of the state of polarization (SOP). We show that transferring knowledge from a pre-trained base model using the proposed scheme significantly reduces the re-training efforts for different PMD realizations. Applying the hinge model for SOP rotation, our simulation results show that the learned models maintain their performance gains while tracking the PMD.

A new method for constructing discrete hyperchaotic systems with a controllable range of Lyapunov exponents and its application in information security

Since people use chaos extensively for a wide range of applications in data encryption and secure communications, a new method for designing practical high-dimensional discrete hyperchaotic systems is proposed for the first time in this paper. This method controls the range of the Lyapunov exponents in reverse by adding control variables so that the range of the values of the Lyapunov exponents is controlled within a specified interval, which is more suitable for engineering applications. Then, it is mathematically proved that the method ensures that the orbits of chaotic systems are globally finite and their Lyapunov exponents are bounded. In addition, as a practical demonstration of the selective image encryption scheme based on target template matching introduced in this paper, a 6D discrete hyperchaotic system was created, and the analysis of the simulation results verifies the applicability of the 6D hyperchaotic system designed by the method presented in this paper in the field of image encryption.

Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites

A new hybrid genetic-simulated-annealing (GSA) optimization algorithm is introduced to solve the multivariable minimization problem for surface wave inversion. The algorithm is effective for both global and local searches due to its combination of the reproduction and selective generation schemes from genetic algorithms (GA) with the nonlinear scaling fitness function and perturbation scheme from simulated annealing (SA). The hybrid GSA algorithm can reduce the risk of a solution becoming trapped in a local minimum and improve global searching efficiency. A mathematical test function as well as surface wave examples are used to examine the advantages and performance of the GSA algorithm. Comparisons of GA, SA, and GSA inversion results demonstrates that GSA can yield the smallest uncertainty and greatest efficiency, and improve the statistical confidence of using surface wave testing for shear-wave velocity profiling.

III–V selective regrowth on SOI for telecom lasers in silicon photonics

To realize fully integrated silicon photonics (Si photonics), reliable III–V light sources that can be efficiently coupled with Si/SiN waveguides are essential. Here, based on a monolithic InP/silicon-on-insulator (SOI) platform, we developed a selective regrowth scheme and constructed a regrowth platform for on-chip lasers that can be efficiently coupled with Si/SiN waveguides. InP and InGaAs/InP multi-quantum wells (MQWs) were regrown on the regrowth template on SOI as well as patterned commercial InP wafers in the same growth run for comparison. A flat (001) top surface after regrowth with a low roughness of 0.38 nm was obtained on SOI. Benefitting from the high quality of MQWs regrowth, strong photoluminescence emission at telecom band can be obtained on both growth templates. Also, multi-wavelength emission on the same chip can be potentially achieved by designing various regrowth openings. Furthermore, the large material volume with vertical stacking structure and intimate placement of MQWs and the Si layer of SOI allow for the potential demonstration of electrically pumped lasers and efficient light coupling between them and Si/SiN waveguides. Therefore, the demonstrated regrowth method provides a promising solution for the monolithic integration of III–V on-chip lasers on Si.

Selective element domain interpolation technique for assumed rotations and shear strains in polygonal finite element thick/thin plate analysis

This paper proposes a novel selective element domain interpolation technique, named SI-ARS-Poly, to develop a high-performance spatially isotropic polygonal element for thick/thin plate analysis. In the SI-ARS-Poly approach, nodal assumed rotations on the element boundaries are directly computed at the mid-nodes of element edges by linearly approximating based on the first-order one-dimensional shape functions. Then, a selective interpolation scheme is applied to approximate the nodal assumed rotations and shear strains on element boundaries into the element domain using piecewise-linear-based first- and second-order shape functions. The proposed method passes spatially isotropic, zero-energy mode, and bending path tests, and exhibits a high performance with uniform and excellent convergent rates compared to existing methods.

Selective Auto-Reclosing of Mixed Circuits Based on Multi-Zone Differential Protection Principle and Distributed Sensing

Environmental concerns and economic constraints have led to increasing installations of mixed conductor circuits comprising underground cables (UGCs) and overhead transmission lines (OHLs). Faults on the OHL sections of such circuits are usually temporary, while there is a higher probability that faults on UGC sections are permanent. To maintain power system reliability and security, auto-reclose (AR) schemes are typically implemented to minimize outage duration after temporary OHL faults while blocking AR for UGC faults to prevent equipment damage. AR of a hybrid UCG–OHL transmission line, therefore, requires effective identification of the faulty section. However, the different electrical characteristics of UGC and OHL sections present significant challenges to existing protection and fault location methods. This paper presents a selective AR scheme for mixed conductor circuits based on the evaluation of differential currents in multiple defined protection zones, using distributed current transformer (CT) measurements provided by passive optical sensing. Case studies are conducted with a number of different UGC–OHL configurations, and the results demonstrate that the proposed scheme can accurately identify the faulty section, enabling effective selective AR of a comprehensive range of mixed conductor circuit topologies. The proposed scheme is also more cost effective, with reduced hardware requirements compared to conventional solutions. This paper thereby validates the optimal solution for mixed circuit protection as described in CIGRE Working Group B5.23 report 587.

Ambient noise-based weakly supervised manhole localization methods over deployed fiber networks.

We present a manhole localization method based on distributed fiber optic sensing and weakly supervised machine learning techniques. For the first time to our knowledge, ambient environment data is used for underground cable mapping with the promise of enhancing operational efficiency and reducing field work. To effectively accommodate the weak informativeness of ambient data, a selective data sampling scheme and an attention-based deep multiple instance classification model are adopted, which only requires weakly annotated data. The proposed approach is validated on field data collected by a fiber sensing system over multiple existing fiber networks.