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A direct active matching method with parametric amplifiers to electrically small antennas waspreviously proposed, which showed its benefits over conventional approaches for its stability and low-noise characteristics. In addition, theoretical derivations were conducted based on this proposed circuit model. In this thesis, an MMIC design of the parametric receiver using the GCS D10 technology is created and simulated to validate the theories. Also, various challenges regarding noise performance come along with the realization of the design. Multiple approaches are presented to counteract environmental noise coming from both signal and pump ports of the parametric amplifier to preserve its intrinsic noise performance.

Code smell is a widely recognized term in the software community, used to describe low-quality software designs. It adversely affects the comprehensibility and maintainability of software systems, ultimately reducing their overall quality. Despite the development of various static analysis tools for identifying code smells, many complex code smells are not properly detected because they cannot be fully captured by specific rules and patterns. The emergence of Large Language Models (LLMs) provides a new opportunity to identify code smells through natural language processing, which may be advantageous for detecting complex code smells. In this paper, we present experiments exploring the efficacy of GPT-3.5 in code smell detection, conducted on 14 Open-Source Software (OSS) projects, totaling 9,740 Python files. We select three code smells in Python to assess: too many parameters (TMP), too many nested blocks (TMNB), and unused variable (UV). We use the static analysis tool Pylint to automate the detection of code smells and create a dataset. Subsequently, we fine-tune the GPT-3.5 model specifically for code smell detection. To evaluate the performance of the fine-tuned model, we apply it to four additional OSS projects. Our results demonstrate that GPT-3.5 has the potential to replace traditional static analysis tools in code smell detection. However, it still requires more data and refinement to improve accuracy and reliability for certain smells. Additionally, we reveal that the size of the dataset significantly influences the performance of GPT-3.5. We also present conjectures for future research in code smell detection and LLM fine-tuning.

Archived patient-derived tissue specimens play a central role in understanding disease and developing therapies. To address specificity and sensitivity shortcomings of existing single-cell resolution proteoform analysis tools, we introduce a hybrid microfluidic platform (DropBlot) designed for proteoform analyses in chemically fixed single cells. DropBlot serially integrates droplet-based encapsulation and lysis of single fixed cells, with on-chip microwell-based antigen retrieval, with single-cell western blotting of target antigens. A water-in-oil droplet formulation withstands the harsh chemical (SDS, 6 M urea) and thermal conditions (98 °C, 1-2 hr) required for effective antigen retrieval, and supports analysis of retrieved protein targets by single-cell electrophoresis. We demonstrate protein-target retrieval from unfixed, paraformaldehyde-fixed (PFA), and methanol-fixed cells. Key protein targets (HER2, GAPDH, EpCAM, Vimentin) retrieved from PFA-fixed cells were resolved and immunoreactive. Relevant to biorepositories, DropBlot profiled targets retrieved from human-derived breast tumor specimens archived for six years, offering a workflow for single-cell protein-biomarker analysis of sparing biospecimens.

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Rotationsgeschwindigkeitsmessung ist von großer Bedeutung in Gebiete wie der Automobilindustrie oder erneuerbare Energie. Self-mixing Interferometry ist ein preiswerte und vielversprechende Messmethode, die präzise Messungen ermöglichen kann. Doch viele Herausforderungen wie Spektralverbreiterung erschweren genaue Messungen. In dieser Arbeit sind mithilfe von Korrelationsfunktionen neue Signalverarbeitungsmethoden entwickelt worden, mit denen diese Herausforderungen überwunden werden sollen., Rotational speed measurement is of great importance in many industries like automotive or renewable energy. Self-mixing interferometry is a very inexpensive promising non-contact measurement technology that can provide precise rotational speed measurement. But many challenges like spectral broadening complicate precise measurements significantly. In this work, new signal processing methods based on correlation functions have been developed to overcome these challenges.

By setting the inverse temperature $\beta$ loose to occupy the complex plane, Michael E. Fisher showed that the zeros of the complex partition function $Z$, if approaching the real $\beta$ axis, reveal a thermodynamic phase transition. More recently, Fisher zeros have been used to mark the dynamical phase transition in quench dynamics. The success of Fisher zeros however seems limited, and it is unclear how they can be employed to shed light on quantum phase transitions or the non-unitary dynamics of open quantum systems. Here we answer this question by a comprehensive analysis of the (analytically continued) one-dimensional transverse field Ising model. We exhaust all the Fisher zeros to show that in the thermodynamic limit they congregate into a remarkably simple pattern in the form of continuous open or closed lines. These Fisher lines evolve smoothly as the coupling constant is tuned, and a qualitative change identifies the quantum critical point. By exploiting the connection between $Z$ and the thermofield double states, we obtain analytical expressions for the short- and long-time dynamics of the survival amplitude and the scaling of recurrence time at the quantum critical point. We further point out $Z$ can be realized and probed in monitored quantum circuits. The analytical results are corroborated by numerical tensor renormalization group which elevates the approach outlined here to a powerful tool for interacting quantum systems., Comment: 11 pages; 3+4 figures

The demand for segment concrete increases rapidly with the expansion of urban rail transit and underground space, which may lead to carbon emissions (CE). In the production of segment concrete, using supplement cementitious materials (SCM) can be used to reduce CE instead of cement. However, SCM contents are usually determined by many orthogonal experiments, which are often time-consuming and only consider the limited experimental variables. Therefore, a novel hybrid intelligent algorithm combining Bayesian optimization (BO), natural gradient boosting (NGBoost) and non-dominated sorting genetic algorithm (NSGA)-III is proposed, which overcomes these disadvantages. Firstly, NGBoost model after hyperparameter optimization using BO is used to establish a nonlinear mapping relationship between segment concrete mix proportion and performance, which was used as the fitness function of the non-dominated sorting genetic algorithm (NSGA-III). Secondly, the optimal Pareto solutions were obtained through NSGA-III, and the reasonable range of segment concrete mixing proportion was obtained. Finally, a segment concrete production case was used to verify the effectiveness of the proposed algorithm. The results showed that the proposed hybrid algorithm combining proposed scheme decision strategy can obtain an optimal design scheme for concrete mix proportion. In addition, the comprehensive optimization rate of the obtained optimal scheme compared to the experimental optimal group was 11.5%. Moreover, the amount of phosphorous slag (PS) and granulated blast furnace slag (GBFS) in the optimal scheme was 26%, with a mass ratio of 1:2. Compared to the experimental optimal group, the optimal scheme can reduce the cost and CE per cubic meter by 31.64 yuan and 31.04 kg, respectively. Furthermore, the hybrid algorithm proposed can accurately obtains an optimal amount of SCM, which could be used as a supporting tool to reduce the production cost and CE of segment concrete. Overall, this re

The demand for segment concrete increases rapidly with the expansion of urban rail transit and underground space, which may lead to carbon emissions (CE). In the production of segment concrete, using supplement cementitious materials (SCM) can be used to reduce CE instead of cement. However, SCM contents are usually determined by many orthogonal experiments, which are often time-consuming and only consider the limited experimental variables. Therefore, a novel hybrid intelligent algorithm combining Bayesian optimization (BO), natural gradient boosting (NGBoost) and non-dominated sorting genetic algorithm (NSGA)-III is proposed, which overcomes these disadvantages. Firstly, NGBoost model after hyperparameter optimization using BO is used to establish a nonlinear mapping relationship between segment concrete mix proportion and performance, which was used as the fitness function of the non-dominated sorting genetic algorithm (NSGA-III). Secondly, the optimal Pareto solutions were obtained through NSGA-III, and the reasonable range of segment concrete mixing proportion was obtained. Finally, a segment concrete production case was used to verify the effectiveness of the proposed algorithm. The results showed that the proposed hybrid algorithm combining proposed scheme decision strategy can obtain an optimal design scheme for concrete mix proportion. In addition, the comprehensive optimization rate of the obtained optimal scheme compared to the experimental optimal group was 11.5%. Moreover, the amount of phosphorous slag (PS) and granulated blast furnace slag (GBFS) in the optimal scheme was 26%, with a mass ratio of 1:2. Compared to the experimental optimal group, the optimal scheme can reduce the cost and CE per cubic meter by 31.64 yuan and 31.04 kg, respectively. Furthermore, the hybrid algorithm proposed can accurately obtains an optimal amount of SCM, which could be used as a supporting tool to reduce the production cost and CE of segment concrete. Overall, this re

Economic productivity depends on reliable access to electricity, but the extreme shortage events of variable wind-solar systems may be strongly affected by climate change. Here, hourly reanalysis climatological data are leveraged to examine historical trends in defined extreme shortage events worldwide. We find uptrends in extreme shortage events regardless of their frequency, duration, and intensity since 1980. For instance, duration of extreme low-reliability events worldwide has increased by 4.1 hours (0.392 hours per year on average) between 1980-2000 and 2001-2022. However, such ascending trends are unevenly distributed worldwide, with a greater variability in low- and middle-latitude developing countries. This uptrend in extreme shortage events is driven by extremely low wind speed and solar radiation, particularly compound wind and solar drought, which however are strongly disproportionated. Only average 12.5% change in compound extremely low wind speed and solar radiation events may give rise to over 30% variability in extreme shortage events, despite a mere average 1.0% change in average wind speed and solar radiation. Our findings underline that wind-solar systems will probably suffer from weakened power security if such uptrends persist in a warmer future.

Debate remains around the anatomical origins of specific brain cell subtypes and lineage relationships within the human forebrain1-7. Thus, direct observation in the mature human brain is critical for a complete understanding of its structural organization and cellular origins. Here we utilize brain mosaic variation within specific cell types as distinct indicators for clonal dynamics, denoted as cell-type-specific mosaic variant barcode analysis. From four hemispheres and two different human neurotypical donors, we identified 287 and 780 mosaic variants, respectively, that were used to deconvolve clonal dynamics. Clonal spread and allele fractions within the brain reveal that local hippocampal excitatory neurons are more lineage-restricted than resident neocortical excitatory neurons or resident basal ganglia GABAergic inhibitory neurons. Furthermore, simultaneous genome transcriptome analysis at both a cell-type-specific and a single-cell level suggests a dorsal neocortical origin for a subgroup of DLX1+ inhibitory neurons that disperse radially from an origin shared with excitatory neurons. Finally, the distribution of mosaic variants across 17 locations within one parietal lobe reveals that restriction of clonal spread in the anterior-posterior axis precedes restriction in the dorsal-ventral axis for both excitatory and inhibitory neurons. Thus, cell-type-resolved somatic mosaicism can uncover lineage relationships governing the development of the human forebrain.

As global temperatures continue to rise, a key uncertainty of terrestrial carbon (C)–climate feedback is the rate of C loss upon abrupt permafrost thaw. This type of thawing—termed thermokarst—may in turn accelerate or dampen the response of microbial degradation of soil organic matter and carbon dioxide (CO2) release to climate warming. However, such impacts have not yet been explored in experimental studies. Here, by experimentally warming three thermo-erosion gullies in an upland thermokarst site combined with incubating soils from five additional thermokarst-impacted sites on the Tibetan Plateau, we investigate how warming responses of soil CO2 release would change upon upland thermokarst formation. Our results show that warming-induced increase in soil CO2 release is ~5.5 times higher in thermokarst features than the adjacent non-thermokarst landforms. This larger warming response is associated with the lower substrate quality and higher abundance of microbial functional genes for recalcitrant C degradation in thermokarst-affected soils. Taken together, our study provides experimental evidence that warming-associated soil CO2 loss becomes stronger upon abrupt permafrost thaw, which could exacerbate the positive soil C–climate feedback in permafrost-affected regions.

This article presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed new hybrid models (named hybridM) merge the Monte Carlo Tree Search structure (MCTS) for categorical variables with Gaussian Processes (GP) for continuous ones. hybridM leverages the upper confidence bound tree search (UCTS) for MCTS strategy, showcasing the tree architecture’s integration into Bayesian optimization. Our innovations, including dynamic online kernel selection in the surrogate modeling phase and a unique UCTS search strategy, position our hybrid models as an advancement in mixed-variable surrogate models. Numerical experiments underscore the superiority of hybrid models, highlighting their potential in Bayesian optimization. Supplementary materials for this article are available online.

Hongqing Zhao,1,2,* Yang Liu,1,2,* Na Cai,3 Xiaolin Liao,1,2 Lin Tang,2,4 Yuhong Wang1,2 1Science & Technology Innovation Center, Hunan University of Chinese Medicine, Changsha, Hunan, People’s Republic of China; 2Hunan Key Laboratory of Traditional Chinese Medicine Prevention & Treatment of Depressive Diseases, Changsha, Hunan, People’s Republic of China; 3Outpatient Department, the First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, People’s Republic of China; 4Department of Pharmacy, the First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lin Tang; Yuhong Wang, Email tanglin0018@163.com; wangyh107@126.comAbstract: Over the past decade, the idea of targeting the endocannabinoid system to treat anxiety disorders has received increasing attention. Previous studies focused more on developing cannabinoid receptor agonists or supplementing exogenous cannabinoids, which are prone to various adverse effects due to their strong pharmacological activity and poor receptor selectivity, limiting their application in clinical research. Endocannabinoid hydrolase inhibitors are considered to be the most promising development strategies for the treatment of anxiety disorders. More recent efforts have emphasized that inhibition of two major endogenous cannabinoid hydrolases, monoacylglycerol lipase (MAGL) and fatty acid amide hydrolase (FAAH), indirectly activates cannabinoid receptors by increasing endogenous cannabinoid levels in the synaptic gap, circumventing receptor desensitization resulting from direct enhancement of endogenous cannabinoid signaling. In this review, we comprehensively summarize the anxiolytic effects of MAGL and FAAH inhibitors and their potential pharmacological mechanisms, highlight reported novel inhibitors or natural products, and provide an outlook on future dir

In this study, we investigated the effects of predation by the whelk Neptunea arthritica cumingii on the culture and survival of scallops (Mizuhopecten yessoensis) during bottom culture. The behavioral characteristics of three sizes (small, medium, and large) of scallops were assessed in response to exposure to N. cumingii. We also measured the activities of superoxide dismutase, catalase, arginine kinase, and octopine dehydrogenase in the gill, adductor muscle, and mantle tissues of scallops before and after exposure to predation. We found that scallops escaped from N. cumingii predation by continuous shell closure or movement. The shell closure force increased with the scallop size, and scallops of the same size that were stimulated by the presence of N. cumingii closed their shells more frequently than control scallops. The shell closure ability of scallops of all sizes decreased as the duration of the experiment continued. The enzyme activities in the three scallop tissues changed significantly after continuous stimulation, and the trend was particularly obvious for the enzyme activities in the adductor muscles of medium-sized scallops. Transcriptome analysis of the adductor muscles from medium-sized scallops detected 405 differentially expressed genes (172 upregulated and 233 downregulated), where the expression levels of RYK, APC, BAMBI, WNT16, CACYBP, and RUVBL1 changed after stimulation by N. cumingii. The sequencing results were verified by quantitative real-time PCR. We hypothesize these genes may be related to muscle movement and regulation in M. yessoensis after stimulation.

Background: Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case–control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD. Methods: Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. Findings: Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability. Interpretation: These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD. Funding: National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

In economic studies and clinical trials, it is prevalent to observe heterogeneous treatment effects that vary depending on the relative locations of units in the distribution of responses. In this study, we propose using quantile regression to estimate and conduct inference for conditional quantile treatment effects (cQTEs) in covariate-adaptive randomized experiments. First, we present sufficient conditions for consistently estimating the cQTEs, concerning the bias due to omitting important covariates in the inference stage. Second, we derive the weak convergence of the quantile regression process and develop a covariate-adaptive randomized bootstrap (CAR-BS) for standard error estimation. Our theoretical results indicate that the Wald test adjusted by CAR-BS is valid in terms of the Type I error, for a large class of covariate-adaptive randomization procedures at different quantiles, regardless of the choice of covariates used in inference. We perform extensive numerical and empirical studies to demonstrate advantages of the new method in various settings.

Anomaly detection (AD) plays a pivotal role in numerous web-based applications, including malware detection, anti-money laundering, device failure detection, and network fault analysis. Most methods, which rely on unsupervised learning, are hard to reach satisfactory detection accuracy due to the lack of labels. Weakly Supervised Anomaly Detection (WSAD) has been introduced with a limited number of labeled anomaly samples to enhance model performance. Nevertheless, it is still challenging for models, trained on an inadequate amount of labeled data, to generalize to unseen anomalies. In this paper, we introduce a novel framework, Knowledge-Data Alignment (KDAlign), to integrate rule knowledge, typically summarized by human experts, to supplement the limited labeled data. Specifically, we transpose these rules into the knowledge space and subsequently recast the incorporation of knowledge as the alignment of knowledge and data. To facilitate this alignment, we employ the Optimal Transport (OT) technique. We then incorporate the OT distance as an additional loss term to the original objective function of WSAD methodologies. Comprehensive experimental results on five real-world datasets demonstrate that our proposed KDAlign framework markedly surpasses its state-of-the-art counterparts, achieving superior performance across various anomaly types. Our codes are released at https://github.com/cshhzhao/KDAlign. © 2024 ACM.

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters. Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy. We provide an exhaustive categorization for VFL settings and privacy-preserving protocols and comprehensively analyze the privacy attacks and defense strategies for each protocol. In the end, we propose a unified framework, termed VFLow, which considers the VFL problem under communication, computation, privacy, as well as effectiveness and fairness constraints. Finally, we review the most recent advances in industrial applications, highlighting open challenges and future directions for VFL. © 1989-2012 IEEE.

In a multi-party machine learning system, different parties cooperate on optimizing towards better models by sharing data in a privacy-preserving way. A major challenge in learning is the incentive issue. For example, if there is competition among the parties, one may strategically hide their data to prevent other parties from getting better models. In this paper, we study the problem through the lens of mechanism design and incorporate the features of multi-party learning in our setting. First, each agent's valuation has externalities that depend on others' types and actions. Second, each agent can only misreport a type lower than his true type, but not the other way round. We provide the optimal truthful mechanism in the separable utility setting, as well as necessary and sufficient conditions for truthful mechanisms in general cases. Finally, we propose an algorithm to find the desirable mechanism that is truthful, individually rational, efficient and weakly budget-balanced, and analyze the computational complexity of the algorithm. © 2024 Elsevier B.V.

Acute kidney injury (AKI) stands as a prevalent and economically burdensome condition worldwide, yet its complex molecular mechanisms remain incompletely understood. To address this gap, our study employs a multifaceted approach, combining mass spectrometry and RNA sequencing technologies, to elucidate the intricate molecular landscape underlying nephrotoxin-induced AKI in mice by cisplatin- and LPS-induced. By examining the protein and RNA expression profiles, we aimed to uncover novel insights into the pathogenesis of AKI and identify potential diagnostic and therapeutic targets. Our results demonstrate significant down-regulation of Slc34a1 and Slc34a3, shedding light on their crucial roles in AKI pathology and highlighting their promise as actionable targets for diagnosis and treatment. This comprehensive analysis not only enhances our understanding of AKI pathophysiology but also offers valuable avenues for the development of targeted interventions to mitigate its clinical impact. Significance: Nephrotoxicity acute kidney injury (AKI) is a common clinical condition whose pathogenesis is the process by which some drugs, chemicals or other factors cause damage to the kidneys, resulting in impaired kidney function. Although it has been proved that different nephrotoxic substances can affect the kidney through different pathways, whether they have a commonality has not been registered. Here, we combined transcriptomics and proteomics to study the molecular mechanism of LPS and cisplatin-induced nephrotoxic acute kidney injury finding that the down-regulation of Slc34a1 and Slc34a3 may be a critical link in nephrotoxic acute kidney injury, which can be used as a marker for its early diagnosis. © 2024 Elsevier B.V.

Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale modeling to analyze. Existing deep learning methods on this best ft to univariate time series only, and have not sufciently considered sub-series modeling and decomposition completeness. To address these challenges, we propose MSD-Mixer, a Multi-Scale Decomposition MLP-Mixer, which learns to explicitly decompose and represent the input time series in its diferent layers. To handle the multi-scale temporal patterns and multivariate dependencies, we propose a novel temporal patching approach to model the time series as multi-scale patches, and employ MLPs to capture intra- and inter-patch variations and channel-wise correlations. In addition, we propose a novel loss function to constrain both the mean and the autocorrelation of the decomposition residual for better decomposition completeness. Through extensive experiments on various real-world datasets for fve common time series analysis tasks, we demonstrate that MSD-Mixer consistently and signifcantly outperforms other state-of-the-art algorithms with better efciency. © 2024, VLDB Endowment. All rights reserved.

SARS-CoV-2 entry into host cells is facilitated by the interaction between the receptor-binding domain of its spike protein (CoV2-RBD) and host cell receptor, ACE2, promoting viral membrane fusion. The virus also uses endocytic pathways for entry, but the mediating host factors remain largely unknown. It is also unknown whether mutations in the RBD of SARS-CoV-2 variants promote interactions with additional host factors to promote viral entry. Here, we used the GST pull-down approach to identify novel surface-located host factors that bind to CoV2-RBD. One of these factors, SH3BP4, regulates internalization of CoV2-RBD in an ACE2-independent but integrin- and clathrin-dependent manner and mediates SARS-CoV-2 pseudovirus entry, suggesting that SH3BP4 promotes viral entry via the endocytic route. Many of the identified factors, including SH3BP4, ADAM9, and TMEM2, show stronger affinity to CoV2-RBD than to RBD of the less infective SARS-CoV, suggesting SARS-CoV-2–specific utilization. We also found factors preferentially binding to the RBD of the SARS-CoV-2 Delta variant, potentially enhancing its entry. These data identify the repertoire of host cell surface factors that function in the events leading to the entry of SARS-CoV-2.

Tropical cyclones (TCs) are expected to produce more intense precipitation under global warming. However, substantial uncertainties exist in the projection of coarse-resolution global climate models. Here, we use deep learning to aid targeted cloud-resolving simulations of extreme TCs. Contrary to the Clausius-Clapeyron (CC) scaling, which indicates a 7% moisture increase per K warming, our simulations reveal more complex responses of TC rainfall. TCs will not intensify via strengthened updrafts but through the expansion of deep convective cores with suppression of shallow cumulus and congestus. Consequently, while localized hourly rainfall may adhere to the CC scaling, precipitation accumulation over city-sized areas could surge by 18%K-1. This super-CC intensification due to changing TC structure has profound implications for floods and landslides. Estimations using Hong Kong’s slope data confirm this concern and suggest an up to 215% increase in landslide risks with 4-K warming, highlighting amplified threats from compound disasters under climate change. © The Author(s) 2024.

In our previous paper [1, 2], we proposed a probabilistic argument to explain the reason why the cosmological constant is very small in 4D. It is natural to ask if the result can be generalized to D-dimension. Moreover, in higher dimensional theory motivated by string theory the Gauss-Bonnet term plays an important role. Therefore, in this paper, we generalize our result to arbitrary D dimensions including the Gauss-Bonnet term. As a result, we have two main results. We find that the Euclidean action of the bounce, B, describing the decay of a de Sitter vacuum, is proportional to $\Lambda^{-\frac{1}{2}(D-2)}$, which has a pole as $\Lambda \rightarrow 0$ where $\Lambda$ is the cosmological constant of the parent vacuum. This result is similar to the result in 4D. The other result is that we find a new decay channel, describing up-tunnelling from anti-de Sitter into de Sitter. The meaning of this new decay channel in the string landscape should be explored in the future., Comment: arXiv admin note: text overlap with arXiv:2303.17723

We study Sp(2$N_c$) gauge dynamics with two Dirac fermion flavours in the fundamental representation. These strongly coupled systems underlie some composite Higgs models with a global symmetry breaking pattern SU(4)$\rightarrow$Sp(4), leading to a light quartet of pseudo-Goldstone bosons that can play the role of the Higgs. Including four-fermion interactions can rotate the vacuum to a technicolour breaking pattern. Using gauge/gravity duality, we study the underlying gauge dynamics. Our model incorporates the $N_c$-specific running of the fermion anomalous dimension and can thus distinguish between specific gauge groups. We determine the bound-state spectrum of the UV SU(4)$\rightarrow$Sp(4) symmetry breaking model, also including fermion masses and mass splitting, and display the $N_c$ dependence. The inclusion of a four-fermion interaction shows the emergence of three Goldstone bosons on the path to technicolour dynamics., Comment: 43 pages, 42 figures, 3 tables. v2 added new citations, 44 pages. v3 minor changes. v4 minor changes

Double Field Theory suggests that people can view the whole massless NS-NS sector as the gravitational unity. The $O(D,D)$ covariance and the doubled diffeomorphisms determine precisely how the Standard Model as well as a relativistic point particle should couple to the NS-NS sector. The theory also refines the notion of singularity. In [1], the authors derive analytically the most general, spherically symmetric, asymptotically flat, static vacuum solution to $D = 4$ Double Field Theory. The solution contains three free parameters and consequently generalizes the Schwarzschild geometry. In this paper, we generalize the metric in [1] to obtain the 'Kerr-like' metric in $D = 4$ double field theory (DFT) in the Einstein frame and string frame. Then we apply 'covariant phase space' approach to study the thermodynamic properties of the metric we have obtained. We explore the first law of black hole thermodynamics and Hawking radiation for this metric carefully. As a special case, the 'Schwarzschild-like' metric in $4D$ double field theory in [1] can be recovered., Comment: 27 pages

We perform a thorough analysis of de Sitter vacua in O(d,d) invariant cosmologies. Starting with a homogeneous and isotropic framework we examine conditions for the existence of such vacua, non-perturbative in \alpha' in both the string frame and the Einstein frame. We elucidate the nature of the instability in the string frame vacuum. For the Einstein frame, we demonstrate that the de Sitter vacuum cannot be eternal. We then extend our analysis to include Bianchi I universes where the O(d,d) symmetry includes scale factor exchange as well as scale factor duality. We show how the theory can be extended to the anisotropic case so that it admits de Sitter vacua, noting the crucial role played by the O(d,d) symmetry in satisfying any additional constraints., Comment: Paper updated with references added, most notably that of Rost and Nunez which includes details of additional dS solutions

Background Small songbirds respond and adapt to various geographical barriers during their annual migration. Global flyways reveal the diverse migration strategies in response to different geographical barriers, among which are high-elevation plateaus. However, few studies have been focused on the largest and highest plateau in the world, the Qinghai-Tibet Plateau (QTP) which poses a significant barrier to migratory passerines. The present study explored the annual migration routes and strategies of a population of Siberian Rubythroats (Calliope calliope) that breed on the north-eastern edge of the QTP. Methods Over the period from 2021 to 2023, we applied light-level geolocators (13 deployed, seven recollected), archival GPS tags (45 deployed, 17 recollected), and CAnMove multi-sensor loggers (with barometer, accelerometer, thermometer, and light sensor, 20 deployed, six recollected) to adult males from the breeding population of Siberian Rubythroat on the QTP. Here we describe the migratory routes and phenology extracted or inferred from the GPS and multi-sensor logger data, and used a combination of accelerometric and barometric data to describe the elevational migration pattern, flight altitude, and flight duration. All light-level geolocators failed to collect suitable data. Results Both GPS locations and positions derived from pressure-based inference revealed that during autumn, the migration route detoured from the bee-line between breeding and wintering grounds, leading to a gradual elevational decrease. The spring route was more direct, with more flights over mountainous areas in western China. This different migration route during spring probably reflects a strategy for faster migration, which corresponds with more frequent long nocturnal migration flights and shorter stopovers during spring migration than in autumn. The average flight altitude (1856 +/- 781 m above sea level) was correlated with ground elevation but did not differ between the seasons. Co

Background Small songbirds respond and adapt to various geographical barriers during their annual migration. Global flyways reveal the diverse migration strategies in response to different geographical barriers, among which are high-elevation plateaus. However, few studies have been focused on the largest and highest plateau in the world, the Qinghai–Tibet Plateau (QTP) which poses a significant barrier to migratory passerines. The present study explored the annual migration routes and strategies of a population of Siberian Rubythroats (Calliope calliope) that breed on the north-eastern edge of the QTP. Methods Over the period from 2021 to 2023, we applied light-level geolocators (13 deployed, seven recollected), archival GPS tags (45 deployed, 17 recollected), and CAnMove multi-sensor loggers (with barometer, accelerometer, thermometer, and light sensor, 20 deployed, six recollected) to adult males from the breeding population of Siberian Rubythroat on the QTP. Here we describe the migratory routes and phenology extracted or inferred from the GPS and multi-sensor logger data, and used a combination of accelerometric and barometric data to describe the elevational migration pattern, flight altitude, and flight duration. All light-level geolocators failed to collect suitable data. Results Both GPS locations and positions derived from pressure-based inference revealed that during autumn, the migration route detoured from the bee-line between breeding and wintering grounds, leading to a gradual elevational decrease. The spring route was more direct, with more flights over mountainous areas in western China. This different migration route during spring probably reflects a strategy for faster migration, which corresponds with more frequent long nocturnal migration flights and shorter stopovers during spring migration than in autumn. The average flight altitude (1856 ± 781 m above sea level) was correlated with ground elevation but did not differ between the seasons. Conc

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system., CC BY 4.0 DEED© 2024 The AuthorsCorrespondence Address: Y. Liu; Department of Management and Engineering, Linköping University, Linköping, SE-581 83, Sweden; email: yang.liu@liu.se; CODEN: JCROE

Background Small songbirds respond and adapt to various geographical barriers during their annual migration. Global flyways reveal the diverse migration strategies in response to different geographical barriers, among which are high-elevation plateaus. However, few studies have been focused on the largest and highest plateau in the world, the Qinghai–Tibet Plateau (QTP) which poses a significant barrier to migratory passerines. The present study explored the annual migration routes and strategies of a population of Siberian Rubythroats (Calliope calliope) that breed on the north-eastern edge of the QTP. Methods Over the period from 2021 to 2023, we applied light-level geolocators (13 deployed, seven recollected), archival GPS tags (45 deployed, 17 recollected), and CAnMove multi-sensor loggers (with barometer, accelerometer, thermometer, and light sensor, 20 deployed, six recollected) to adult males from the breeding population of Siberian Rubythroat on the QTP. Here we describe the migratory routes and phenology extracted or inferred from the GPS and multi-sensor logger data, and used a combination of accelerometric and barometric data to describe the elevational migration pattern, flight altitude, and flight duration. All light-level geolocators failed to collect suitable data. Results Both GPS locations and positions derived from pressure-based inference revealed that during autumn, the migration route detoured from the bee-line between breeding and wintering grounds, leading to a gradual elevational decrease. The spring route was more direct, with more flights over mountainous areas in western China. This different migration route during spring probably reflects a strategy for faster migration, which corresponds with more frequent long nocturnal migration flights and shorter stopovers during spring migration than in autumn. The average flight altitude (1856 ± 781 m above sea level) was correlated with ground elevation but did not differ between the seasons. Conc

Background Small songbirds respond and adapt to various geographical barriers during their annual migration. Global flyways reveal the diverse migration strategies in response to different geographical barriers, among which are high-elevation plateaus. However, few studies have been focused on the largest and highest plateau in the world, the Qinghai–Tibet Plateau (QTP) which poses a significant barrier to migratory passerines. The present study explored the annual migration routes and strategies of a population of Siberian Rubythroats (Calliope calliope) that breed on the north-eastern edge of the QTP. Methods Over the period from 2021 to 2023, we applied light-level geolocators (13 deployed, seven recollected), archival GPS tags (45 deployed, 17 recollected), and CAnMove multi-sensor loggers (with barometer, accelerometer, thermometer, and light sensor, 20 deployed, six recollected) to adult males from the breeding population of Siberian Rubythroat on the QTP. Here we describe the migratory routes and phenology extracted or inferred from the GPS and multi-sensor logger data, and used a combination of accelerometric and barometric data to describe the elevational migration pattern, flight altitude, and flight duration. All light-level geolocators failed to collect suitable data. Results Both GPS locations and positions derived from pressure-based inference revealed that during autumn, the migration route detoured from the bee-line between breeding and wintering grounds, leading to a gradual elevational decrease. The spring route was more direct, with more flights over mountainous areas in western China. This different migration route during spring probably reflects a strategy for faster migration, which corresponds with more frequent long nocturnal migration flights and shorter stopovers during spring migration than in autumn. The average flight altitude (1856 ± 781 m above sea level) was correlated with ground elevation but did not differ between the seasons. Conc

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system., CC BY 4.0 DEED© 2024 The AuthorsCorrespondence Address: Y. Liu; Department of Management and Engineering, Linköping University, Linköping, SE-581 83, Sweden; email: yang.liu@liu.se; CODEN: JCROE

With the expanding application of Large Language Models (LLMs) in various domains, it becomes imperative to comprehensively investigate their unforeseen behaviors and consequent outcomes. In this study, we introduce and systematically explore the phenomenon of “glitch tokens”, which are anomalous tokens produced by established tokenizers and could potentially compromise the models’ quality of response. Specifically, we experiment on seven top popular LLMs utilizing three distinct tokenizers and involving a totally of 182,517 tokens. We present categorizations of the identified glitch tokens and symptoms exhibited by LLMs when interacting with glitch tokens. Based on our observation that glitch tokens tend to cluster in the embedding space, we propose GlitchHunter, a novel iterative clustering-based technique, for efficient glitch token detection. The evaluation shows that our approach notably outperforms three baseline methods on eight open-source LLMs. To the best of our knowledge, we present the first comprehensive study on glitch tokens. Our new detection further provides valuable insights into mitigating tokenization-related errors in LLMs.

Background Small songbirds respond and adapt to various geographical barriers during their annual migration. Global flyways reveal the diverse migration strategies in response to different geographical barriers, among which are high-elevation plateaus. However, few studies have been focused on the largest and highest plateau in the world, the Qinghai–Tibet Plateau (QTP) which poses a significant barrier to migratory passerines. The present study explored the annual migration routes and strategies of a population of Siberian Rubythroats (Calliope calliope) that breed on the north-eastern edge of the QTP. Methods Over the period from 2021 to 2023, we applied light-level geolocators (13 deployed, seven recollected), archival GPS tags (45 deployed, 17 recollected), and CAnMove multi-sensor loggers (with barometer, accelerometer, thermometer, and light sensor, 20 deployed, six recollected) to adult males from the breeding population of Siberian Rubythroat on the QTP. Here we describe the migratory routes and phenology extracted or inferred from the GPS and multi-sensor logger data, and used a combination of accelerometric and barometric data to describe the elevational migration pattern, flight altitude, and flight duration. All light-level geolocators failed to collect suitable data. Results Both GPS locations and positions derived from pressure-based inference revealed that during autumn, the migration route detoured from the bee-line between breeding and wintering grounds, leading to a gradual elevational decrease. The spring route was more direct, with more flights over mountainous areas in western China. This different migration route during spring probably reflects a strategy for faster migration, which corresponds with more frequent long nocturnal migration flights and shorter stopovers during spring migration than in autumn. The average flight altitude (1856 ± 781 m above sea level) was correlated with ground elevation but did not differ between the seasons. Conc

The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy., Correction in: Trends in Microbiology, vol. 32, issue 7, p. 722DOI: 10.1016/j.tim.2024.01.010

Lactate is used in the food and pharmaceutical industries and is a crucial intermediate for synthesis. Plasma-activated lactate (PAL) in Ringer's solution was recently shown to have effective antitumor action. Small molecule aldehydes, ketones, and organic acids were produced from lactate during plasma exposure, and five-membered conjugated lactone isomers of furanone (C5H6O2) were detected formed by interactions of lactate or its fragments with •OH, organic radicals, and H2O2. 2,3-Dimethyl-tartaric acid may be the effective component in PAL for the selective killing of cancer but not normal cells and possible pathways for its synthesis are provided. Aqueous reaction mechanisms are explained, including dehydration, esterification, hydrolysis, and dimerization. This study will help develop novel cancer therapies and further plasma organic chemistry.

Lactate is used in the food and pharmaceutical industries and is a crucial intermediate for synthesis. Plasma-activated lactate (PAL) in Ringer's solution was recently shown to have effective antitumor action. Small molecule aldehydes, ketones, and organic acids were produced from lactate during plasma exposure, and five-membered conjugated lactone isomers of furanone (C5H6O2) were detected formed by interactions of lactate or its fragments with •OH, organic radicals, and H2O2. 2,3-Dimethyl-tartaric acid may be the effective component in PAL for the selective killing of cancer but not normal cells and possible pathways for its synthesis are provided. Aqueous reaction mechanisms are explained, including dehydration, esterification, hydrolysis, and dimerization. This study will help develop novel cancer therapies and further plasma organic chemistry.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of

In this paper we describe the development of a streamlined framework for large-scale ATLAS pMSSM reinterpretations of LHC Run-2 analyses using containerised computational workflows. The project is looking to assess the global coverage of BSM physics and requires running O(5k) computational workflows representing pMSSM model points. Following ATLAS Analysis Preservation policies, many analyses have been preserved as containerised Yadage workflows, and after validation were added to a curated selection for the pMSSM study. To run the workflows at scale, we utilised the REANA reusable analysis platform. We describe how the REANA platform was enhanced to ensure the best concurrent throughput by internal service scheduling changes. We discuss the scalability of the approach on Kubernetes clusters from 500 to 5000 cores. Finally, we demonstrate a possibility of using additional ad-hoc public cloud infrastructure resources by running the same workflows on the Google Cloud Platform., Comment: 8 pages, 9 figures. Contribution to the Proceedings of the 26th International Conference on Computing In High Energy and Nuclear Physics (CHEP 2023)

Linyuan Gui,1,* Qingyuan Ye,2,* Lu Yu,3 Geng Dou,1 Yang Zhou,1 Yang Liu,4 Yanqi Zhang,5 Xiaoshan Yang,1,6 Fang Jin,5 Shiyu Liu,1 Yan Jin,1 Lili Ren1 1State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi International Joint Research Center for Oral Diseases, Center for Tissue Engineering, School of Stomatology, The Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 2State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Key Laboratory of Stomatology, Digital Dentistry Center, School of Stomatology, The Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 3Department of Periodontology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, Shandong, 250012, People’s Republic of China; 4Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 5State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Orthodontics, School of Stomatology, The Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 6Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, Guangdong, 510280, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lili Ren; Yan Jin, State Key Laboratory of Oral & Maxillofaci

Laminaria japonica (LJ) is a common seaweed, which has been widely used as functional food and medicine, but its pharmaceutical activities in angiogenic regulation are unclear. Here, different lines of studies provide evidence to the suppressive effects of LJ extract in VEGF-medicated angiogenic function. In cellular level, LJ extract significantly inhibited VEGF-mediated endothelial cell proliferation, migration, invasion and tube formation. For in vivo verification, LJ extract suppressed the VEGF-induced formation of sub-intestinal vessels in zebrafish embryo model. In signalling cascades, LJ extract decreased the amount of phosphorylated VEGF receptor-2, as triggered by VEGF, and subsequently some key related downstream elements were obviously inactivated. In parallel, NO production and ROS formation, as induced by VEGF, were significantly attenuated. Thus, these preclinical findings promoted the possible development of LJ extract as a potential therapeutic agent for prevention and clinical treatment of angiogenesis-related diseases. © 2024 The Authors

Yuling Gao,1 Yanrui Zhao,1 Hanzhou Wang,1 Xiaopei Xu,1 Zhexian Cheng,2 Tianchao Lu,1 Junlin Zhou,1 Yang Liu1 1Orthopedics Department, Affiliated Beijing Chaoyang Hospital of Capital Medical University, Bejing, People’s Republic of China; 2Preventive Dentistry Department, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, People’s Republic of ChinaCorrespondence: Junlin Zhou; Yang Liu, Beijing Chaoyang Hospital, Capital Medical University, Gongtinan Road 8#, Beijing, 100020, People’s Republic of China, Email junlinzhou_article@outlook.com; y0ring1996@163.comBackground: This study aimed to create three-dimensional heat map and study the characteristic of fracture lines and represented fragments of OTA/AO type 43C pilon fractures.Methods: CT scan was performed in105 fractures diagnosed with OTA/AO type 43C pilon fractures between January 2017 and December 2022. Three-dimensional pilon fracture maps were created and converted into fracture heat maps. CT scan graphic parameters including the fracture line height, α angle, β angle, the ratio of the area and size of bone fragment represented by the fracture line to the total articular surface were measured.Results: The study included 105 patients with 91 males and 14 females. The fractures included C1 (n=16), C2 (n=23), and C3 (n=66). There was no statistically different among the most parameters except in the fracture-line height of the anterior fracture line (p=0.03) and the sagittal fracture line (p=0.02) between C2 and C3 pilon fractures. The average size of the anterolateral fragment, occupied approximately 13.5% of the articular surface area, was (11.5± 2.8) mm × (20.5± 6.3) mm with the average height of 29.8 mm. The average size of the posterolateral fragment, occupied approximately 13.0% of the articular surface area, was (15.7± 4.6) mm × (19.3± 4.0)

Yang Liu,* Xiaoli Wu,* Siting Chen, Wenxia Meng Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, 510280, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wenxia Meng, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, 510280, People’s Republic of China, Email mengwx2008@foxmail.comAbstract: Acanthosis nigricans (AN) is a dermatological condition characterised by the symmetrical development of velvety, hyperpigmented plaques predominantly in intertriginous areas such as the axillae, neck, inframammary regions, and groin. The malignant variant of AN is frequently associated with internal malignancies, particularly gastric adenocarcinoma, accounting for 55– 61% of cases. Patients exhibiting characteristic skin lesions are commonly initially evaluated in dermatology departments. This case report details a rare instance of a patient diagnosed with malignant acanthosis nigricans, presenting with only a mild form of florid oral papillomatosis concomitant with ovarian carcinoma. The early identification and management of these subtle clinical manifestations enabled timely intervention for the tumor, resulting in patient survival. There are few reported cases of malignant acanthosis nigricans associated with ovarian cancer. Oral medicine specialists should be cognisant of conditions manifesting as extensive oral papillary hyperplasia, and the possibility of an underlying malignant disease should be considered, particularly in cases of elderly-onset AN presenting exclusively with oral lesions.Keywords: malignant acanthosis nigricans, ovarian cancer, papillary hyperplasia

This study proposed a semi-analytical solution for 2D electro-osmotic consolidation of double-layered soil. The method of Laplace transform is employed to derive the solution in the frequency domain. Then the numerical approach of inverse Laplace transform is used to obtain the explicit solution in the time domain. A finite element simulation is conducted to validate the proposed solution's accuracy. The verification example shows excellent agreement between the two methods. Based on the parametric study, it is found that the value of soil parameters in the second layer has significant influences on the behaviour of electro-osmotic consolidation. The larger the electro-osmosis coefficient in the second layer, the smaller the excess pore pressure at a given time. Additionally, a higher coefficient of volume compressibility in the second layer results in higher excess pore pressure. Compared with the 1D case, the distribution of excess pore pressure at the steady state is non-linear with depth in the 2D electro-osmotic consolidation, rather than a linear distribution. Furthermore, the dissipation rate and steady-state excess pore pressure could be underestimated if the electro-osmotic consolidation is modelled as a 1D case. © 2023 Elsevier Ltd

The COVID-19 pandemic presents challenges to psychological well-being, but how can we predict when people suffer or cope during sustained stress? Here, we test the prediction that specific types of momentary emotional experiences are differently linked to psychological well-being during the pandemic. Study 1 used survey data collected from 24,221 participants in 51 countries during the COVID-19 outbreak. We show that, across countries, well-being is linked to individuals' recent emotional experiences, including calm, hope, anxiety, loneliness, and sadness. Consistent results are found in two age, sex, and ethnicity-representative samples in the United Kingdom (n = 971) and the United States (n = 961) with preregistered analyses (Study 2). A prospective 30-day daily diary study conducted in the United Kingdom (n = 110) confirms the key role of these five emotions and demonstrates that emotional experiences precede changes in well-being (Study 3). Our findings highlight differential relationships between specific types of momentary emotional experiences and well-being and point to the cultivation of calm and hope as candidate routes for well-being interventions during periods of sustained stress. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Federated learning (FL) is a joint training pattern that fully utilizes data information whereas protecting data privacy. A key challenge in FL is statistical heterogeneity, which arises on account of the heterogeneity of local data distributions among clients, leading to inconsistency in local optimization goals and ultimately reducing the performance of globally aggregated models. We propose the Federated Ensemble Learning (FedEL), which makes full use of the heterogeneity of data distribution among clients to train a group of weak learners with diversity to construct a global model, which is a novel solution to the non-independent identical distribution (non-IID) problem. Experiments demonstrate that the proposed FedEL can improve performance in non-IID data scenarios. Even under extreme statistical heterogeneity, the average accuracy of FedEL is 3.54% higher than the state-of-the-art FL method. Moreover, the proposed FedEL reduces model storage and reasoning costs compared with traditional ensemble learning. The proposed FedEL demonstrates good generalization ability in experiments across different datasets, including natural scene image datasets and medical image datasets. © 2023 Elsevier Ltd

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system.

As the world's largest coal consumer, China is facing the dual challenge of implementing strict coal reduction policies while heavily relying on coal. It is crucial to comprehend the pressures exerted on carbon dioxide emissions from coal consumption as China strives to transition towards a carbon-neutral era. This study defines and classifies the pressure of carbon dioxide emissions resulting from coal consumption, referred to as "carboncoal pressure", in 30 provinces (including municipalities and autonomous regions) from 1997 to 2019. This classification enriches the study of the pressure on carbon dioxide emissions by specific energy types. By calculating the centre of gravity of the carbon-coal pressure and its evolution trends, the spatial pattern of the carbon-coal pressure and the evolution characteristics of the centre of gravity of the pressure are revealed. The results demonstrate that, despite the continuous growth in total coal consumption and carbon dioxide emissions in China, the carbon-coal pressure index exhibits a decreasing trend in certain regions, with significant interregional differences. Most provinces fall into the high-pressure and higher-pressure categories. The number of high-pressure provinces has decreased by 33% from 18 to 8, while the number of low-pressure provinces has risen from 0 to 1, and both higher and medium pressure types have increased. The overall stress index decreases from 0.79 to 0.7. The pressure centre displays a similar spatial trend to the overall changes in coal consumption and carbon dioxide emissions centre, albeit with a smaller magnitude of change. Beijing stands out as the only province with low pressure., Funding Agencies|China Scholarship Council Ph.D. program [202206430037]; Key Program of National Social Science Fund of China [23AGL033]