Selection Of Pairs Research Articles

Variation of the quantity of lymph in the subcutaneous sinuses in the Common frog (<i>Rana temporaria</i>)

Seasonal variation in the amount of fluid in the subcutaneous lymphatic sinuses of the common frog (Rana temporaria) and differences in the dynamics of its mass in adult males and females from autumn to spring are shown. The hypothesis that enlarged lymphatic sinuses (one of the elements of the “nuptial attire” of R. temporaria) can be used by small frogs to gain competitive advantages in assortative pair selection during reproduction is tested.

Automatic Orbital Pair Selection for Multilevel Local Coupled-Cluster Based on Orbital Maps.

We present an automatic, orbital-map based orbital-pair selection scheme for multilevel local coupled-cluster approaches that exploits the locality of chemical reactions by focusing on the part of the molecule directly involved in the reaction. The previously introduced pair-selected multilevel extension to domain-based local pair natural orbital coupled-cluster with singles, doubles, and semicanonical perturbative triples [DLPNO-CCSD(T0)] partitions the orbital pairs according to relative changes in pair correlation energies [Bensberg, M.; Neugebauer, J. Orbital pair selection for relative energies in the domain-based local pair natural orbital coupled-cluster method. J. Chem. Phys. 2022, 157, 064102. 10.1063/5.0100010]. To this end, maps between localized orbitals are required which in turn require maps between the atoms of structures along reaction paths. So far, these atom maps have been manually determined, which can be a (human) time-consuming procedure. Here, we present an automatic atom mapping algorithm based on the principle of minimum chemical distance that incorporates orientation dependence through dihedral angles. A similar strategy is then introduced to obtain orbital maps, which proves advantageous over the previously used direct orbital selection. Along with a modified orbital pair prescreening, this results in an improved variant of the pair-selected multilevel DLPNO-CCSD(T0) method. The performance of this approach is demonstrated for various reaction types showing a significant efficiency gain and accurate results due to beneficial, systematic error cancellation. The presented method operates in a black-box manner due to its fully automatized algorithms with only the need to specify a single target-accuracy parameter. Additionally, we demonstrate that basis set extrapolation techniques can be applied. In this context, the approach shows deficiencies for the use of large basis sets, especially with diffuse basis functions, which can be traced back to the semicanonical triples correction.

Novel FRET-Based Biosensors for Real-Time Monitoring of Estrogen Receptor Dimerization and Translocation Dynamics in Living Cells.

Estrogen receptors (ERs), comprising ER α and ER β, are crucial for regulating cell growth and differentiation via homo- and hetero-dimer formation. However, accurately detecting ER dimerization with precise spatiotemporal resolution remains a significant challenge. In this study, fluorescence resonance energy transfer-based biosensors to monitor ER dynamics in real-time, are developed and optimized.This approach involves comprehensive structural analysis, linker comparison, and the selection of optimal fluorescent protein pairs, resulting in three distinct biosensors capable of detecting all ER homo- and hetero-dimerizations within the nucleus. These biosensors are utilized to reveal interactions between ER α/β and calmodulin during dimer formation.Furthermore, by leveraging the ligand-binding domain (LBD) of ER β, ER ββ LBD biosensor is designed for real-time analysis of ER ββ homodimerization in the cytoplasm, enhancing the ability to screen ER dimerization-related drugs.Additionally, we developed a novel ER ββ translocation biosensor, which enables real-time observation of ER ββ translocation to the nucleus-a capability previously unavailable, is developed. This spatiotemporal analysis demonstrates the relevance of ER translocation in response to drug binding efficacy and extracellular matrix changes. Our biosensors offertransformative tools for studying ER dynamics, providing valuable insights for drug screening and the investigation of ER-related cellular processes.

Ni-Catalyzed Asymmetric Reductive Arylation of α-Substituted Imides.

α-Aryl imides are common structural motifs in bioactive molecules and proteolysis-targeting chimeras designed for targeted protein degradation. An asymmetric Ni-catalyzed reductive cross-coupling of imide electrophiles and (hetero)aryl halides has been developed to synthesize enantioenriched α-arylglutarimides from simple starting materials. Judicious selection of electrophile pairs allows for coupling of both electron-rich and electron-deficient (hetero)aryl halides in good yields and enantioselectivities.

Selection of parental pairs when obtaining F1 hybrids of white cabbage with resistance to the tip burn of the inner leaves

Relevance. During the formation of cabbage heads, under certain conditions, white cabbage is damaged by a burn of the top of the inner leaves of the head, which abroad received the stable name “tip burn”. This disorder is interpreted as a physiological or non-infectious disease, the cause of which is a violation of calcium intake. The disease manifests itself inside the cabbage head, resulting from the interaction of environmental stress factors and the predisposed genotype. The most effective way to combat this disorder is to develop highly tolerant hybrids. This is especially true for the south of Russia, where the stress load on plants is maximum.Purpose of the study – to develop a principle for selecting parental pairs for developing hybrids that are resistant to the tip burn of the inner leaves of a cabbage head, and to identify promising hybrids. Materials and methods. In 2021-2022 in FSBSI "Federal Scientific Rice Centre", in the department of vegetable growing, 45 hybrid combinations obtained on the basis of 10 inbred lines were assessed under field conditions for the trait "burn damage to the inner leaves of the cabbage head." An analysis of the combining ability of lines according to Griffin, and an analysis of the inheritance of the trait according to Heiman were carried out. In 2023, a competitive test of promising hybrids was carried out.Results. In both years of research, 5 lines had negatively stable TCA for burn damage: Ten4270, Yas25p, Bs1f, Agr 82, 270Hn111. SCA of lines is more subject to changes from year to year. The effects of SCA in hybrids due to dominance, epistasis and overdominance in individual combinations significantly influence the manifestation of the trait in the direction of decrease or increase. The principle of selecting parental pairs to develop stable hybrids is based on a combination of lines with a negative SCA value; in some cases, it is permissible to include one line with an average SCA and low SCA. Analysis of genetic parameters according to Heiman confirms the prevalence of non-additive inheritance of the lesion; in unstable forms, the disorder is controlled by dominant polygenes and is highly heritable.

Photo‐Mediated RAFT Step‐Growth Polymerization With Diacrylate Monomers: Investigating Versatility and Oxygen Tolerance

AbstractPhotomediated reversible addition fragmentation chain transfer (RAFT) step‐growth polymerization is performed using a trithiocarbonate‐based chain transfer agent (CTA) and acrylate‐based monomers both with and without a photocatalyst. The versatility of photo‐mediated RAFT step‐growth is demonstrated by one‐pot synthesis of a graft copolymer via sequential monomer addition. Furthermore, oxygen‐tolerant photo‐mediated RAFT step‐growth is demonstrated, facilitated by the appropriate selection of photocatalyst and solvent pair (zinc tetraphenyl porphyrin [ZnTPP] and dimethyl sulfoxide [DMSO]), enabling ultralow volume polymerization under open‐air conditions.

Detection of PCBs and OCPs in the Irtysh River Water (GC-MS/MS) and ecological risk assessment

This study optimized a gas chromatography-tandem triple quadrupole mass spectrometry (GC-MS/MS) method for the determination of 21 persistent organic pollutants (POPs) in Irtysh River water, including 14 organochlorines (OCPs) and 7 polychlorinated biphenyls (PCBs). Factors such as column temperature ramping, selection of qualitative and quantitative ion pairs and collision energy were considered to achieve perfect separation and accurate quantification of all 21 target compounds. The limits of detection (LOD) for PCBs and OCPs ranged from 0.21 to 1.18 ng/L. Applying this method to detect POPs in the Irtysh River revealed concentrations of OCPs ranging from ND to 20.2 ng/L and PCBs from ND to 0.411 ng/L. Source analysis indicated that POPs in the Irtysh River mainly originate from historical industrial and agricultural activities, particularly the deliberate use of pesticides. To ensure ecological safety and human health, expanding the range of target analytes and monitoring periods is necessary. This study provides:•Qualitative and quantitative analysis methods for 7 PCBs and 14 OCPs.•Recoveries achieved ranged between 74.6 to 109 % with RSD less than 15 %.•Analysis of sources, transport pathways, accumulation status, and ecological risks of PCBs and OCPs in the Irtysh River.

Machine-learning-aided method for optimizing beam selection and update period in 5G networks and beyond

Finding the optimal beam pair and update time in 5G systems operating at mmWave frequencies is time-intensive and resource-demanding. This intricate procedure calls for the proposal of more intelligent approaches. Therefore, this work proposes a machine learning-based method for optimizing beam pair selection and its update time. The method is structured around three main modules: spatial characterization of beam pair service areas, training of a machine learning model using collected beam pair data, and an algorithm that uses the decision function of the trained model to compute the optimal update time for beam pairs based on the spatial position and velocity of user equipment. When the machine learning model is deployed in a network with a single gNB equipped with a 8×8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\ imes 8$$\\end{document} UPA and one UE equipped with a 1×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\ imes 2$$\\end{document} UPA in an mmWave scenario simulated in NS3, improvements in SINR and throughput up to 407%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$407\\%$$\\end{document}, were observed. Improvements are gathered because of a reduction of 85.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$85.7\\%$$\\end{document} in beam pair selections because of an increase of approximately 1543%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1543\\%$$\\end{document} in the effective time between successive beam pair searches. This method could offer real-time optimization of the beam pair procedures in 5G networks and beyond.

Carbon‐Based Textile‐Structured Triboelectric Nanogenerators for Smart Wearables

Recent advances in wearable electronics have been propelled by the rapid growth of microelectronics and Internet of Things. The proliferation of electronic devices and sensors relies heavily on power sources, predominantly batteries, with significant implications for the environment. To address this concern and to reduce carbon emissions, there is a growing emphasis on renewable energy harvesting technologies, among which textile‐based triboelectric nanogenerators (T‐TENGs) stand out as an innovative and sustainable solution due to having the interesting characteristics like large contact area, lightweight design, flexibility, comfort, scalability, and breathability. T‐TENGs can harness mechanical energy from human movement and convert it into electric energy. However, one of the challenges is low electric power output, which can be addressed by meticulous selection of material pairs with significant differences in work function and optimizing contact areas. The incorporation of carbon‐based nanomaterials, such as carbon nanotubes and graphene, emerges as a key strategy to enhance output. This review delineates recent progress in T‐TENGs incorporating carbonaceous nanofillers, comprehensively addressing fundamental classification, operational mode, structural design, working performance, and potential challenges that are hindering commercialization. By doing this, this review aims to stimulate future investigations into sustainable, high‐performance smart wearables integrated with T‐TENGs.

Towards a rigorous analysis of mutual information in contrastive learning

Contrastive learning has emerged as a cornerstone in unsupervised representation learning. Its primary paradigm involves an instance discrimination task utilizing InfoNCE loss where the loss has been proven to be a form of mutual information. Consequently, it has become a common practice to analyze contrastive learning using mutual information as a measure. Yet, this analysis approach presents difficulties due to the necessity of estimating mutual information for real-world applications. This creates a gap between the elegance of its mathematical foundation and the complexity of its estimation, thereby hampering the ability to derive solid and meaningful insights from mutual information analysis. In this study, we introduce three novel methods and a few related theorems, aimed at enhancing the rigor of mutual information analysis. Despite their simplicity, these methods can carry substantial utility. Leveraging these approaches, we reassess three instances of contrastive learning analysis, illustrating the capacity of the proposed methods to facilitate deeper comprehension or to rectify pre-existing misconceptions. The main results can be summarized as follows: (1) While small batch sizes influence the range of training loss, they do not inherently limit learned representation’s information content or affect downstream performance adversely; (2) Mutual information, with careful selection of positive pairings and post-training estimation, proves to be a superior measure for evaluating practical networks; and (3) Distinguishing between task-relevant and irrelevant information presents challenges, yet irrelevant information sources do not necessarily compromise the generalization of downstream tasks.

On the selection of aircraft-engine pairs for medium-haul Low-Cost Carriers operations

The selection of aircraft and engine models poses a significant challenge for Low-Cost Carriers (LCCs) striving for profitability. This decision is compounded by numerous subjective and objective variables and the vast array of available aircraft and engine configurations. Current methodologies, utilizing Multi-Criteria Decision Making (MCDM) and fuzzy logic, often simplify the problem, neglecting the engine consideration and limiting variables and alternatives. In this paper, we propose a novel methodology that circumvents these simplifications, considering engine configurations within the aircraft selection and addressing this problem for 27 parameters and real 51 selection alternatives. Through the consultation of different experts in the aeronautic field, we establish a comprehensive approach to assess the relative and overall performance of multiple variables, as well as their weights and hierarchy in the selection scheme. We apply this methodology to the aircraft selection problem in the medium-haul market, providing a framework and a solution for this currently unexplored literature domain. The results provide valuable insights for validating the devised methodology and providing relevant insights on the characteristics of the medium-haul market for LCCs.

Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction.

Synthetic lethality (SL) has shown great promise for the discovery of novel targets in cancer. CRISPR double-knockout (CDKO) technologies can only screen several hundred genes and their combinations, but not genome-wide. Therefore, good SL prediction models are highly needed for genes and gene pairs selection in CDKO experiments. However, lack of scalable SL properties prevents generalizability of SL interactions to out-of-sample data, thereby hindering modeling efforts. In this paper, we recognize that SL connectivity is a scalable and generalizable SL property. We develop a novel two-step multilayer encoder for individual sample-specific SL prediction model (MLEC-iSL), which predicts SL connectivity first and SL interactions subsequently. MLEC-iSL has three encoders, namely, gene, graph, and transformer encoders. MLEC-iSL achieves high SL prediction performance in K562 (AUPR, 0.73; AUC, 0.72) and Jurkat (AUPR, 0.73; AUC, 0.71) cells, while no existing methods exceed 0.62 AUPR and AUC. The prediction performance of MLEC-iSL is validated in a CDKO experiment in 22Rv1 cells, yielding a 46.8% SL rate among 987 selected gene pairs. The screen also reveals SL dependency between apoptosis and mitosis cell death pathways.

Error Correction of the RapidEye Sub-Pixel Correlation: A Case Study of the 2019 Ridgecrest Earthquake Sequence.

The optical image sub-pixel correlation (SPC) technique is an important method for monitoring large-scale surface deformation. RapidEye images, distinguished by their short revisit period and high spatial resolution, are crucial data sources for monitoring surface deformation. However, few studies have comprehensively analyzed the error sources and correction methods of the deformation field obtained from RapidEye images. We used RapidEye images without surface deformation to analyze potential errors in the offset fields. We found that the errors in RapidEye offset fields primarily consist of decorrelation noise, orbit error, and attitude jitter distortions. To mitigate decorrelation noise, the careful selection of offset pairs coupled with spatial filtering is essential. Orbit error can be effectively mitigated by the polynomial fitting method. To address attitude jitter distortions, we introduced a linear fitting approach that incorporated the coherence of attitude jitter. To demonstrate the performance of the proposed methods, we utilized RapidEye images to extract the coseismic displacement field of the 2019 Ridgecrest earthquake sequence. The two-dimensional (2D) offset field contained deformation signals extracted from two earthquakes, with a maximum offset of 2.8 m in the E-W direction and 2.4 m in the N-S direction. A comparison with GNSS observations indicates that, after error correction, the mean relative precision of the offset field improved by 92% in the E-W direction and by 89% in the N-S direction. This robust enhancement underscores the effectiveness of the proposed error correction methods for RapidEye data. This study sheds light on large-scale surface deformation monitoring using RapidEye images.

A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma

BackgroundCancer-associated fibroblast (CAF)-cancer cell crosstalk (CCCT) plays an important role in tumor microenvironment shaping and immunotherapy response. Current prognostic indexes are insufficient to accurately assess immunotherapy response in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a CCCT-related gene prognostic index (CCRGPI) for assessing the prognosis and response to immune checkpoint inhibitor (ICI) therapy of HNSCC patients.MethodsTwo cellular models, the fibroblast-cancer cell indirect coculture (FCICC) model, and the fibroblast-cancer cell organoid (FC-organoid) model, were constructed to visualize the crosstalk between fibroblasts and cancer cells. Based on a HNSCC scRNA-seq dataset, the R package CellChat was used to perform cell communication analysis to identify gene pairs involved in CCCT. Least absolute shrinkage and selection operator (LASSO) regression was then applied to further refine the selection of these gene pairs. The selected gene pairs were subsequently subjected to stepwise regression to develop CCRGPI. We further performed a comprehensive analysis to determine the molecular and immune characteristics, and prognosis associated with ICI therapy in different CCRGPI subgroups. Finally, the connectivity map (CMap) analysis and molecular docking were used to screen potential therapeutic drugs.ResultsFCICC and FC-organoid models showed that cancer cells promoted the activation of fibroblasts into CAFs, that CAFs enhanced the invasion of cancer cells, and that CCCT was somewhat heterogeneous. The CCRGPI was developed based on 4 gene pairs: IGF1-IGF1R, LGALS9-CD44, SEMA5A-PLXNA1, and TNXB-SDC1. Furthermore, a high CCRGPI score was identified as an adverse prognostic factor for overall survival (OS). Additionally, a high CCRGPI was positively correlated with the activation of the P53 pathway, a high TP53 mutation rate, and decreased benefit from ICI therapy but was inversely associated with the abundance of various immune cells, such as CD4+ T cells, CD8+ T cells, and B cells. Moreover, Ganetespib was identified as a potential drug for HNSCC combination therapy.ConclusionsThe CCRGPI is reliable for predicting the prognosis and immunotherapy response of HSNCC patients and may be useful for guiding the individualized treatment of HNSCC patients.

Powering the Future: Advanced Battery Management Systems (BMS) for Electric Vehicles

This paper introduces a novel approach for rapidly balancing lithium-ion batteries using a single DC–DC converter, enabling direct energy transfer between high- and low-voltage cells. Utilizing relays for cell pair selection ensures cost-effectiveness in the switch network. The control system integrates a battery-monitoring IC and an MCU to oversee cell voltage and ensure battery protection. A prototype circuit with twelve lithium-ion batteries demonstrates the method’s efficacy, achieving a remarkable balancing time of 48 min during charging with a maximum efficiency of 89.85%. Comparative analysis with other methods underscores the superior performance of the proposed balancing circuit in terms of balancing time and implementation cost. Furthermore, this paper delves into hardware aspects of battery management systems (BMSs) for electric vehicles and stationary applications. It offers an overview of prevailing concepts in state-of-the-art systems, aiding readers in assessing considerations essential for BMS design in various applications. The discussion includes examples of battery packs sourced from commercially available electric vehicles. Subsequently, the manuscript addresses implementation aspects concerning the measurement of critical physical variables such as voltage, current, and temperature, alongside balancing strategies.

Guided contrastive boundary learning for semantic segmentation

Semantic segmentation, a fundamental task in environmental understanding, aims to assign each image pixel to a specific class. Despite recent progress, segmentation accuracy in boundary regions remains suboptimal. This paper introduces Guided Contrastive Boundary Learning (GCBL), a novel framework designed to enhance feature representation learning, thereby improving boundary segmentation performance. Unlike conventional contrastive learning, GCBL guides inter-class representation learning by weighting pixel contributions based on their estimated probabilities. For intra-class learning, it leverages the neural collapse phenomenon, encouraging representations to align with last-layer classifier weights. Additionally, an asymmetric distance boundary pixel search strategy ensures a more reasonable selection of contrastive pairs. To prevent weight collapse in learning, a regularization term is applied to the last-layer classifier’s weights. The GCBL method is readily integrable into existing and future segmentation frameworks. Extensive experiments on the Cityscapes, ADE20K, and S3DIS datasets demonstrate the effectiveness and generalizability of our approach. Code is available at https://github.com/skyshoumeng/GCBL.

Repellent potential of agroecological extracts: Exploring their efficacy against the leaf‐cutting ant Acromyrmex subterraneus in the laboratory

AbstractThe increasing restrictions on commercial toxic baits for controlling leaf‐cutting ants have started a “gold rush” for alternative compounds, with agroecological extracts emerging as a promising option. Here, we tested for the first time the repellent effects of concentrated and diluted ant extracts and cow urine solution on laboratory colonies of Acromyrmex subterraneus (Forel) (Hymenoptera: Formicidae). The agroecological extracts preparation and application followed the information provided by peasants from the Movimento dos Trabalhadores Rurais Sem Terra in Brazil who carry out agroecology. First, we applied the agroecological extracts in the middle segment of the upper path of a foraging trail to determine whether they act as a chemical barrier. All agroecological extracts induced the deviation of the foraging flow from the upper path to the lateral path. However, only the concentrated ant extract reduced the leaf delivery rate. Also, we applied the agroecological extracts directly on the leaf disks and conducted a paired selection test. The proportion of transported leaf disks with concentrated ant extract and cow urine solution was lower than that of control disks. We suggest that the observed repellent efficacy of the concentrated ant extract may stem from its content of chemical compounds belonging to the same chemical group as the alarm pheromones found by other Acromyrmex species. As for the cow urine solution, we attribute its repellent effect to the presence of urea. Our findings highlight a significant repellent potential of the concentrated ant extract and cow urine solution for leaf‐cutting ants, which may be applied both on foraging trails and on leaves of cultivated plants.

Authentication of seafood species on the ASFIS list (FAO) by in-silico evaluation of primers for metabarcoding

Given the remarkable biodiversity among the seafood species entering the food supply chain, authentication is a crucial component in ensuring transparent trade. DNA metabarcoding stands out as the leading methodology for determining species composition within complex food matrices. Nevertheless, the selection of primer pairs is critical, as it may introduce biases into species identification. In this investigation, we evaluated previously described primers for their in-silico efficacy in identifying both edible marine species on the ASFIS (FAO) list and the main terrestrial animal species frequently incorporated as ingredients in processed seafood products. Selected primers covered the Cytochrome c Oxidase subunit I (COI), the Cytochrome b (cytb), the 16S rRNA (16S) and the 12S rRNA (12S) mitochondrial regions. Results showed that, out of 53 pairs analysed, one primer pair targeting the COI region obtained the best performance in terms of taxonomic coverage for both ASFIS and terrestrial species. Notably, each primer pair showed a unique taxonomic profile, suggesting their potential for addressing various seafood authentication issues as the detection of novel food ingredients (e.g., jellyfish, red algae, insects) or endangered species. Overall, these outcomes deliver valuable insights for selecting metabarcoding primers to improve the traceability of seafood products and contribute to the development of an innovative Food Safety Management System.

ECLB: Efficient contrastive learning on bi-level for noisy labels

For contrastive learning has achieved remarkable success in self-supervised and supervised informative representation, learning with noisy labels based on contrastive learning is becoming the research consensus. However, under noisy labels, how to efficiently leverage informative representation of various levels and how to effectively screen reliable positive pairs for the optimization of the contrastive learning model are still challenges. To address these issues, we innovatively propose a method named efficient contrastive learning on bi-level for noisy labels (ECLB), which is jointly implemented by both self-supervised and supervised contrastive learning. For the accessible informative representation, we propose to perform contrastive learning at two different levels: (1) feature level, where feature representation is jointly optimized by supervised and self-supervised feature contrastive loss; (2) label level, where feature and label representation are optimized by label distribution supervised contrastive loss. Furthermore, to alleviate the impact of noisy labels on the selection of reliable positive pairs in supervised contrastive learning and to reduce labor cost and computational complexity, we propose an efficient adaptive mask, which is dynamically generated by label self-equality mask, prediction self-equality mask, label-prediction equality mask, and feature similarity mask. Extensive experiments show that our proposed method outperforms other state-of-the-art methods in terms of robustness and generalization. Our code is publicly available at: https://github.com/whyandbecause/ECLB

Working fluid pair selection of thermally integrated pumped thermal electricity storage system for waste heat recovery and energy storage

Global issues such as the energy crisis and carbon emissions impulse the development of waste heat recovery and energy storage technologies. In most practical industrial scenarios, the electricity supply and consumption cannot be perfectly matched and effective utilization of waste heat is in urgent need. In the present study, we develop a mathematical model to evaluate the thermally integrated pumped thermal electricity storage (TI-PTES) system to achieve off-peak electricity storage along with low-grade waste heat recovery. A double-layer optimization for screening working fluid pairs with high round-trip efficiency is carried out from 24 fluids of the heat pump and 21 fluids of the Organic Rankine cycle (ORC). In the first-layer multi-objective optimization, 3 types of working fluid pair combination strategies are compared and the great improvement of round-trip efficiency by using zeotropic fluids is proved. Among 7 energy storage temperatures covering from 393.15 K to 423.15 K with an increment interval of 5 K, the highest round-trip efficiency of 101.29% is achieved by adopting the zeotropic fluid pair [90Diethyl ether_10Pentane - 80Butane_20Pentane] at 398.15 K. Furthermore, in the second-layer single-objective optimization, the thermo-economic performance indicators of TI-PTES is evaluated and compared under different designing weighting factor groups, which effectively contributes to the screening of working fluids according to designer's trade-off. Finally, through varying energy storage temperatures and designing weighting factors, optimal working fluid pair recommendations including pure fluids and zeotropic ones were proposed to the fluid selection of TI-PTES.