Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Based Control of the Scanning Tunneling Microscope: Enabling New Modes of Imaging, Spectroscopy, and Lithography

The principle of scanning tunneling microscopy is based on the quantum mechanical phenomenon of “tunneling,” whereby the wavelike properties of electrons allow them to “tunnel” beyond the surface of a solid into regions of space that are forbidden under the rules of classical physics [1]. This phenomenon is used in the scanning tunneling microscope (STM), where electrons tunnel from the apex of a sharp tip to a conducting surface held at a different potential (see “Summary”). The probability of electrons tunneling decreases exponentially as the distance between the two surfaces increases [2]. Hence, electrons tunnel mostly from the very last atom on the tip apex to the surface. The STM makes use of this extreme sensitivity to distance. A positioner brings the sharp tip of a tungsten probe to a distance of a few angstroms from the sample surface. A bias voltage is applied between the probe tip and the surface, causing electrons to tunnel across the gap.
Drug prescription pattern in exotic pet and wildlife animal practice: a retrospective study in a Spanish veterinary teaching hospital from 2018 to 2022

Exotic companion animals have had an important role in our society since ancient times. Preserving animal health is necessary to do a responsible use of veterinary medicines. This study aimed to describe the prescription patterns of drugs in exotic pets and wildlife animals attending the Veterinary Teaching Hospital of the University of León (HVULE). A retrospective study was carried out between 2018 and 2022. Birds were the largest group of exotic animals attending the HVULE. Visits were related to emergency reasons and for musculoskeletal disorders. One-third of the animals were eventually euthanised. Regarding pharmacological treatments, the most frequently active ingredients used were pentobarbital, isoflurane, meloxicam, and within antibiotics, marbofloxacin (category B in the classification of European Medicines Agency).
Financial Reduction of Aircraft Maintenance Plan Under Safety and Maintenance Constraints

This paper presents a potentially scalable method able to reduce maintenance costs associated with Candidate Certification Maintenance Requirement (CCMR) plans without compromising safety. The failure condition is the floating free caused by the horizontal stabilizer trim actuator (HSTA), which is representative and based on a real case study. An Excel file was created to let the user input the fault tree which was reduced using the cutset theory. In Matlab was developed both the solution algorithm using Matlab heuristic, which takes the Excel file as input, and an interface for the user to input the cost of each maintenance task, latent failure rates, evident failure probabilities, lower and upper bounds, and whether it is flight control. Finally, a cost-benefit analysis was conducted to reduce system latency. The results demonstrated significant cost savings over the life cycle compared to pre-optimization expenses.
Breaking the linear mould: exploring the non-linear relationship between board independence and investment efficiency

PurposeThis study examines the non-linear effect of board independence on the investment efficiency of listed firms worldwide. This study further tests whether the COVID-19 pandemic, industry competition and economic development influence the relationship between board independence and investment efficiency.Design/methodology/approachThe data are retrieved from the Thomson Reuters (Refinitiv) database and include international data from 33 countries, comprising 21,363 firm-year observations. The authors' regression analyses include firm-specific variables as controls that may impact investment efficiency. The authors also perform various robustness tests including, alternative measures of investment efficiency, weighted least squares regression, quantile regression and endogeneity issues.FindingsThe results reveal a non-linear relationship between board independence and investment efficiency. Specifically, the relationship follows a U-shaped pattern, indicating that the negative impact of board independence on investment efficiency becomes positive after it reaches its optimal point, thus supporting optimal board structure theory. Interestingly, the authors find no significant evidence of board independence’s effect on investment efficiency during the pandemic. In contrast, the relationship between board independence and investment efficiency is significant only during the non-pandemic period. Furthermore, the authors discover evidence of a U-shaped relationship in both emerging and developed markets, as well as in industries with high and low competition.Research limitations/implicationsThe authors' study discovers new evidence on the non-linear impact of board independence on investment efficiency, which has not been explored previously in existing research.Practical implicationsThis study has practical implications for investors by emphasising the importance of corporate governance and the appointment of independent directors. Investors should consider the findings of this study when making decisions related to corporate governance, as they can impact a firm's investment efficiency.Originality/valueDespite a considerable body of literature exploring the link between corporate governance and investment effectiveness, there is a dearth of research on the non-linear effects of board independence. Furthermore, the effects of the COVID-19 pandemic, industry competition and economic development remain unexplored.
Omics-driven exploration and mining of key functional genes for the improvement of food and fiber crops

The deployment of omics technologies has obtained an incredible boost over the past few decades with the advances in next-generation sequencing (NGS) technologies, innovative bioinformatics tools, and the deluge of available biological information. The major omics technologies in the limelight are genomics, transcriptomics, proteomics, metabolomics, and phenomics. These biotechnological advances have modernized crop breeding and opened new horizons for developing crop varieties with improved traits. The genomes of several crop species are sequenced, and a huge number of genes associated with crucial economic traits have been identified. These identified genes not only provide insights into the understanding of regulatory mechanisms of crop traits but also decipher practical grounds to assist in the molecular breeding of crops. This review discusses the potential of omics technologies for the acquisition of biological information and mining of the genes associated with important agronomic traits in important food and fiber crops, such as wheat, rice, maize, potato, tomato, cassava, and cotton. Different functional genomics approaches for the validation of these important genes are also highlighted. Furthermore, a list of genes discovered by employing omics approaches is being represented as potential targets for genetic modifications by the latest genome engineering methods for the development of climate-resilient crops that would in turn provide great impetus to secure global food security.
The influence of “industry policy” and “financial institution” configuration effect on innovation performance of China’s biomedical industry-based on necessary condition analysis and qualitative comparative analysis

Biomedical industry is a strategic emerging industry in China, especially the outbreak of the Covid pandemic. The biomedical industry is characterized by high risk, high investment, high technology and long cycle, and each stage contains risks and challenges. How to optimize the policy environment and financial environment, explore the unique “policy” and “finance” model for the development of the biomedical industry, and improve the innovation performance has become an important issue. This paper analyzes the relationship among industry policy, financial institution and innovation performance in the biomedical industry from the configuration perspective, combining necessary condition analysis (NCA) and qualitative comparative analysis (QCA) research methods, using the A-share listed enterprises in Shanghai and Shenzhen in the biomedical industry from 2012 to 2020 as the research objects. It is found that (1) individual policy preference or financial institution dimension cannot constitute a necessary condition for generating high innovation performance of biomedical company, but increasing tax incentive and raising the proportion of equity-based financing method play a significant role in generating high innovation performance; (2) four “political” and “financial “synergistic grouping paths can generate high innovation performance, including tax incentives, financial institutions’ professional level and institutional background synergistic drive type; government subsidy, financing method and financial institutions’ professional level synergistic drive type; tax incentive and financing method synergistic drive type; tax incentive and institutional background drive type. Different synergistic grouping paths represent various ways to achieve high innovation performance of biomedical enterprise. In addition, the results show that the two “political” and “financial” groupings lead to low-to-medium innovation performance, which indicates that industry policy plays a very important role in the innovation performance and that the government’s support for emerging industries through policy is a significant force for the innovation development. This paper introduces the “political” and “financial” aspects to investigate the configuration effect of industry policy and financial institutions on the innovation performance of biomedical enterprise. The findings have important theoretical and practical implications for revealing the synergistic path of high innovation performance in the Chinese biomedical industry.
Multiframe Maximum Likelihood Distortionless Response Filter for Single-Channel Speech Enhancement

Speech enhancement is an essential and challenging task in the signal-processing field. In this article, a multiframe maximum likelihood distortionless response (MFMLDR) filter is proposed for single-channel speech enhancement. Specifically, in accordance with the multiframe signal model, the MFMLDR filter is obtained by constructing the log-likelihood objective function with a nondistorted speech correlation component constraint. Then, to optimize the performance of the MFMLDR filter, the interframe correlation matrix is estimated more accurately by leveraging the sparsity of speech in the time-frequency domain. Finally, an online MFMLDR (OL-MFMLDR) filter is presented by the recursive least squares (RLS) algorithm for online applications. The proposed method can make a good tradeoff between noise suppression and speech distortion reduction. Both objective and subjective evaluation results indicate that the proposed method outperforms the compared single-channel approaches in overall speech enhancement performance, particularly at low signal-to-noise ratios (SNRs).
Cross-Load Generalization of Bearing Fault Recognition with Decision Trees

The literature on condition monitoring is nowadays characterized by a wide variety of machine learning approaches. We argue that, in most of the works, the experimental evaluation is conducted in an oversimplified scenario, where training and test data contain samples obtained under the same radial and torsional load conditions. In this paper, we propose to apply an interpretable machine learning model, namely decision trees, to perform fault detection and recognition across different load configurations, a challenging benchmark that requires general-ization capabilities. The rules extracted from the trees provide explanations of the classification process.
The RCS Scaled-Measurement and Inversion Method for Metal Targets With Rough Surfaces Based on the Distribution of Surface Elements in the THz Band

In the terahertz (THz) band, due to the non-ignorable surface roughness, which is comparable to the wavelength, the scaled-radar cross section (RCS) measurement faces new challenges, which is referred to as the inconsistency with the electrodynamics similitude. As a solution, an RCS scaled-measurement and inversion method for rough metal objects is proposed based on the distribution of surface elements. With the consideration of the random fluctuation, the distribution of the facets was first studied, and a criterion was provided for quantitatively evaluating the difference in their distribution using Kullback–Leibler divergence (KL divergence). The core idea of our method is to construct an RCS estimation system of linear equations consisting of weight factors solved by relevant parameters of prior models. Both simulations and measurements on different scaled rough plates were conducted, demonstrating the effectiveness of the proposed method. Besides providing an effective solution to the scaled-RCS measurement for rough objects in the THz band, this work also introduces a new aspect in studying and estimating the scattering characteristics of rough surfaces.
The role of platelets in the blood-brain barrier during brain pathology

Platelets play critical roles in maintaining hemostasis. The blood brain barrier (BBB), a significant physical and metabolic barrier, helps maintain physiological stability by limiting transportations between the blood and neural tissues. When the brain undergoes inflammation, tumor, trauma, or bleeding, the platelet responses to help with maintaining BBB homeostasis. In the traditional point of view, activated platelets aggregate to form thrombi which cover the gaps of the blood vessels to protect BBB. However, increasing evidences indicate that platelets may harm BBB by enhancing vascular permeability. Hereby, we reviewed recently published articles with a special focus on the platelet-mediated damage of BBB. Factors released by platelets can induce BBB permeability, which involve platelet-activating factors (PAF), P-selectin, ADP, platelet-derived growth factors (PDGF) superfamily proteins, especially PDGF-AA and PDGF-CC, etc. Platelets can also secrete Amyloid-β (Aβ), which triggers neuroinflammation and downregulates the expression of tight junction molecules such as claudin-5 to damage BBB. Additionally, platelets can form aggregates with neutrophils to release reactive oxygen species (ROS), which can destroy the DNA, proteins, and lipids of endothelial cells (ECs). Moreover, platelets participate in neuroinflammation to affect BBB. Conversely, some of the platelet released factors such as PDGF-BB, protects BBB. In summary, platelets play dual roles in BBB integrity and the related mechanisms are reviewed.
Optimizing V2X Communication: Spectrum Resource Allocation and Power Control Strategies for Next-Generation Wireless Technologies

The upcoming wireless technology developments in the next generations are expected to substantially transform the vehicle-to-everything (V2X) communication network. The challenge of limited spectrum resources in V2X communication, caused by the need for high data rates, necessitates a thorough analysis of spectrum resource allocation and power control. This complex problem falls under the domain of mixed-integer nonlinear programming; a strategic approach is implemented to overcome these issues, which divides the main challenge into two sub-problems. The issue of resource allocation is addressed by implementing a multiaccess spectrum allocation method, which is deliberately designed to optimize the utilization of the spectrum resources that are currently accessible. Concurrently, the power control issue is resolved by employing a continuous convex approximation technique, which effectively converts non-convex power-allocation issues into convex equivalents. This approach helps to alleviate interference between users. Finally, the simulation results prove that the proposed approaches can improve vehicle performance. The algorithms proposed in this article significantly improve the system throughput and access rate of vehicular user equipment (VUEs) while ensuring the data rate of cellular user equipment (CUEs).
The Impact of Fiber Source on Digestive Function, Fecal Microbiota, and Immune Response in Adult Dogs

Simple Summary Dietary fiber has been widely recognized to have a profound impact on gut microbiota and health. However, the ideal fiber intake in dogs still needs to be determined. Fiber is not a single kind of molecule, but a plethora of compounds that are diverse in chemical composition and properties, including solubility, viscosity, and fermentability. Moreover, fiber can be provided in the diet by various fibrous sources that can also offer other bioactive molecules with potential health benefits. In this study, we tested two different diets in dogs, differing in the type of fiber source: derived from cereals or fruits. The results revealed the impacts of both diets on digestive function, lipid metabolism, intestinal fermentation, and fecal microbiota taxonomy, but with a differential footprint. While diets based on cereal fibers appeared to have a greater impact on the microbial ecosystem, diets based on fruit fibers could have additional modulatory properties. This study demonstrated that not only the level or solubility of dietary fiber was important but also the selection of fibrous ingredients in the design of health-promoting diets for dogs. Abstract This study evaluated the impact of different fiber sources on intestinal function, fecal microbiota, and overall health in dogs. Twelve dogs were used in a crossover design, involving three periods of 6 weeks and three diets: a low-fiber diet (CTR), a cereal-fiber and beet-pulp-supplemented diet (BRA), and a fruit-fiber-supplemented diet (FRU). Each period included a digestibility trial and fecal and blood sampling in the last week. Short-chain fatty acids (SCFAs) and microbiota taxonomy (16S rRNA Illumina-MiSeq) and functionality (Shotgun-NovaSeq 6000) were determined in the feces. General biochemistry, complete blood cells, and lymphocyte subsets were also analyzed. The fiber-supplemented diets showed lower digestibility without significant changes in the fecal consistency. The BRA diet showed higher total SCFA concentrations (p = 0.056), with increases in alpha diversity and particular beneficial genera, such as Lachnospira, Bifidobacterium, and Faecalibacterium. The BRA microbiota was also associated with an overabundance of genes related to carbohydrate and amino acid metabolism. The FRU diet had a distinct impact on the microbiota composition and functionality, leading to higher levels of CD8 lymphocytes. These findings emphasize the importance of selecting the right fiber source when formulating dog diets, as it can have a differential impact on gut microbiota and animal health.
Biscale Convolutional Self-Attention Network for Hyperspectral Coastal Wetlands Classification

Coastal wetlands classification is a hot but challenging issue. Hyperspectral image (HSI) can provide abundant spectral information for coastal wetlands, and deep learning excels at extracting abstract features. However, effectively leveraging global and local features to enhance the accuracy of coastal wetlands classification remains a significant challenge. In this letter, we propose a biscale convolutional self-attention network (termed as HyperBCS) for hyperspectral coastal wetlands classification. HyperBCS consists of biscale adding module (BSAM) and convolutional self-attention module (CSM). On the one hand, BSAM uses two branches to extract features of different scales. On the other hand, CSM is a paralleled structure of convolution and self-attention, which can effectively extract both local and global features. Experiments on two hyperspectral datasets captured by Zhuhai-1 satellite indicate that HyperBCS can improve the accuracy of hyperspectral coastal wetlands classification, showcasing the highest accuracy (OA = 98.29% and 96.82%, Kappa = 0.976 and 0.958 in two datasets) compared with other methods. Our code and datasets are available at https://github.com/JeasunLok/HyperBCS.
Robust Model-Free Fault-Tolerant Predictive Control for PMSM Drive System

The parameter mismatch caused by the parameter uncertainties and unknown disturbances degrades the performance of finite-control-set model predictive control (FCS-MPC). This paper presents a model-free fault-tolerant predictive control (MFFTPC) method based on an extended sliding mode observer (ESMO) for the surface-mounted permanent magnet synchronous motor (SPMSM) drive system. First, considering parameter uncertainties and unknown disturbances, a novel ultra-local model (ULM) is established for the PMSM drive system. Next, a finite-control-set model-free fault-tolerant predictive current controller (FCS-MFFTPCC) is designed in the current loop, and the model-free deadbeat fault-tolerant predictive speed controller (MFDFTPSC) is designed in the speed loop. Then, unknown parts of the novel ULM are estimated by the designed ESMO and compensated for the errors caused by the parameter mismatches. Thus, the presented method reduces the dependence on the precise model and eliminates the effect caused by parameter mismatches on the MPC control performance of the SPMSM drive system.
Assessing the Risk of Heavy Metal Mixture Exposure and its Association with Cancer Mortality

This study examined the combined effects of lead, cadmium, and mercury exposure on malignant neoplasms-related mortality among 6,014 adults who participated in NHANES between 2003 and 2014. Using unsupervised clustering, individuals were stratified into different co-exposure patterns, with the high co-exposure group having a significantly higher risk of malignant neoplasms-related deaths compared to the low co-exposure group. The impact of co-exposure varied in subpopulations with different social/economic statuses. Older participants, low-income, alcohol consumption, men, non-Hispanic Black, and obese participants seemed to be more vulnerable to high co-exposure to the metals. Therefore, comprehensive strategies to prevent deaths from cancer should include efforts to reduce exposure to these toxic metals, especially in vulnerable subpopulations.
A Systematic Model to Improve Productivity in a Transformer Manufacturing Company: A Simulation Case Study

The global economy’s slow recovery has led to an increased need for transformers in organizations in recent years. An optimal strategy for production line optimization is to enhance the allocation of staff at each workstation and increase the amount of operational equipment. The focus of this study is the investigation of the transformer production line. This study carried out a comprehensive examination of manufacturing area one, manufacturing area two, and manufacturing area three, respectively. The findings revealed that the case factory requires enhancements in the allocation of its workers. The simulation approach allows for the implementation of multi-scenario evaluation and adjustment, ensuring optimal utilization of resources in the enhanced production line, hence enhancing production efficiency and total productivity. Implementing both rotational shifts and night shifts in manufacturing area one enhances the overall production efficiency of the manufacturing area. By redistributing the workforce in area two, it proved feasible to manage the production capacity of a manufacturing area and maintain the operation of the gas-phase drying furnace. With regard to the final aspect, it is imperative to enhance the processing time of preprocessing goods in order to guarantee a consistent supply of the appropriate quantity of products. This will effectively minimize production line delays and enhance overall production efficiency. These enhancement strategies aid the manufacturing company in optimizing resource allocation to enhance production efficiency and productivity.