Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity of a World Ocean GCM to Changes in Subsurface Mixing Parameterization

Abstract The sensitivity of a coarse-resolution model of the World Ocean to parameterization of subgrid-scale mixing is examined. The model is based on the GFDL code. Results are presented from a series of model runs where the subsurface mixing parameterization is sequentially upgraded toward a more physical representation. The surface forcing is the same for all principal model runs and features a strong relaxation of surface temperature and salinity toward perpetual wintertime observed values. One model version is rerun with a full annual cycle of surface forcing and verifies that use of the perpetual winter surface relaxation introduces only minor biases in the essential characteristics of the solution. Runs 1 and 2 feature the diffusivity tensor in the traditional horizontal/vertical orientation, and examines the effect of different vertical diffusivity profiles on the solution. Results are compared with those of previous studies. In both cases, the water mass properties (espcially the salinity Acids).
A novel immune-nutritional score predicts response to neoadjuvant immunochemotherapy after minimally invasive esophagectomy for esophageal squamous cell carcinoma

Background The role of neoadjuvant immunochemotherapy (NICT) has gradually attracted attention in recent years. To date, sensitive and reliable blood indicators to forecast the therapeutic response are still lacking. This study aimed to conduct a novel predictive score based on a variety of peripheral hematological immune-nutritional indicators to predict the therapeutic response in esophageal squamous cell carcinoma (ESCC) receiving NICT. Methods There were 206 ESCC patients receiving NICT retrospectively recruited. With pathological complete response (pCR) as the dependent variable, independent risk variables of various peripheral blood immune-nutritional indexes were screened by logistic regression analyses to establish an integrative score. Results By logical regression analyses, lymphocyte to monocyte ratio (LMR) and body mass index (BMI) were independent risk factors among all immune-nutritional indices. Then, an integrative score named BMI-LMR score (BLS) was established. Compared with BMI or LMR, BLS was related to complications, especially for respiratory complication (P=0.012) and vocal cord paralysis (P=0.021). Among all patients, 61 patients (29.6%) achieved pCR after NICT. BLS was significantly related to pCR [odds ratio (OR)=0.269, P<0.001)]. Patients in high BLS cohort demonstrated higher 3-year overall survival (OS) (89.9% vs. 67.9%, P=0.001) and disease-free survival (DFS) (81.2% vs. 62.1%, P=0.001). BLS served as an independent factor of DFS [hazard ratio (HR) =2.044, P =0.020) and OS (HR =2.960, P =0.019). Conclusion The BLS, based on immune-nutritional indicators of BMI and LMR, employed as a straightforward, accurate, and useful indicator of pCR and prognostic prediction in ESCC patients undergoing NICT.
75 Years of Research on the Property Tax

This paper examines the evolution of academic research on the effects of the property tax over the past 75 years, with a special emphasis on articles that have appeared in the National Tax Journal over that time period. The primary topics covered are property tax relief in the form of property tax limitations, property tax administration with a focus on assessment practices and assessment inequities, and — the topic that receives the most attention — the critical and long-standing debate on whether the incidence of the property tax is best described by the “capital tax view” or the “benefit tax view. ”
An 8-bit Single Perceptron Processing Unit for Tiny Machine Learning Applications

We present a tiny MultiLayer Perceptron (MLP) accelerator named Single Perceptron Linear Vector Processor (SPLVP) that aims at extending the capabilities of limited resources MCUs, enabling inference time speedup and main CPU off-load. It is based on a single perceptron hardware unit, enhanced with an additional accumulation input and scaling features, that is sequentially scheduled to cover all the nodes of the network. The accelerator supports both linear and Rectified Linear Unit (ReLU) activation and its firmware can be generated from 8-bit tflite quantized models. We also present a complete design toolchain that encompasses supervised learning, compilation, assembly, simulation, and device programming. The hardware support for extra accumulation input and scaling, together with the processor memory partitioning, are the key features that enable significant speedups. By solving a toy recognition problem based on image data captured from an infra-red camera, measurements show that the execution speed of SPLVP at 80MHz outperforms an ARM Cortex-M4 STM32L476 microcontroller by a factor of 9.2 when the same ANN is translated to MCU code using the STM CubeMX-Ai converter at the same clock frequency. SPLVP is synthesized on a low-cost and gate-count Cyclone 10 LP FPGA resulting in an 18% logic and 77% memory occupation. The SPLVP assembly code can be directly converted into a VHDL description that directly hardcodes the ANN. The execution speed of an ANN model for Iris classification, fully synthesized, improves by a factor of 209 compared to firmware execution on the MCU. To verify the operation of SPLVP and its design framework, we have designed various tiny Machine Learning (ML) classifiers, for which we briefly discuss the obtained performance and the preprocessing techniques used. Across all these classifiers, the obtained speedup compared to the STM32 is 8.3–14.9 $\times $ .
Synthetic Receptors for Early Detection and Treatment of Cancer

Over recent decades, synthetic macrocyclic compounds have attracted interest from the scientific community due to their ability to selectively and reversibly form complexes with a huge variety of guest moieties. These molecules have been studied within a wide range of sensing and other fields. Within this review, we will give an overview of the most common synthetic macrocyclic compounds including cyclodextrins, calixarenes, calixresorcinarenes, pillarenes and cucurbiturils. These species all display the ability to form a wide range of complexes. This makes these compounds suitable in the field of cancer detection since they can bind to either cancer cell surfaces or indeed to marker compounds for a wide variety of cancers. The formation of such complexes allows sensitive and selective detection and quantification of such guests. Many of these compounds also show potential for the detection and encapsulation of environmental carcinogens. Furthermore, many anti-cancer drugs, although effective in in vitro tests, are not suitable for use directly for cancer treatment due to low solubility, inherent instability in in vivo environments or an inability to be adsorbed by or transported to the required sites for treatment. The reversible encapsulation of these species in a macrocyclic compound can greatly improve their solubility, stability and transport to required sites where they can be released for maximum therapeutic effect. Within this review, we intend to present the use of these species both in cancer sensing and treatment. The various macrocyclic compound families will be described, along with brief descriptions of their synthesis and properties, with an outline of their use in cancer detection and usage as therapeutic agents. Their use in the sensing of environmental carcinogens as well as their potential utilisation in the clean-up of some of these species will also be discussed.
A rapid semi-quantitative screening method to assess chemicals present in heated e-liquids and e-cigarette aerosols

Abstract. ​​​​​​​Electronic cigarettes (e-cigarettes) lack regulatory status as therapeutic products in all jurisdictions worldwide. They are potentially unsafe consumer products, with significant evidence they pose a risk to human health. Therefore, developing rapid, economical test methods to assess the chemical composition of e-liquids in heated and unheated forms and the aerosols produced by e-cigarettes is crucial. Four different e-liquids were heated using two different methods: (1) “typical” vaping using an e-cigarette device, by cycling “on” for 3 s every minute for 2 h (e-liquid obtained from remainder in the tank and aerosol collected in an impinger), and (2) “accelerated” heating, using an e-cigarette coil submerged in e-liquid and heating in short 20 s bursts on then 20 s off for 2 min only (liquid traps aerosol produced). All e-liquids were then analysed to test for the presence and quantity of 13 chemicals by gas chromatography–mass spectrometry and compared to an unheated sample. E-liquids heated with the accelerated method showed a comparable trend to the typical heating method, i.e. increase or decrease in chemical compound quantity, for more than two-thirds of the detected compounds analysed over all e-liquids. Six chemicals were detected as aerosol from the impinger fluid with the typical heating method at negligible levels. We propose that this accelerated version of the typical vaping method could form the basis of a standardized screening tool to test heated e-liquids (and e-cigarette aerosols) for harmful or banned substances. This will ensure that only approved products reach the consumer and reduce potential e-cigarette harm.
A Novel Angle Segmentation Method for Magnetic Encoders Based on Filtering Window Adaptive Adjustment Using Improved Particle Swarm Optimization

In this paper we outline newly-developed control algorithms, designed to achieve high-precision feedback for a motor control system using a magnetic encoder. The magnetic encoder, combing single-pole and multi-pole magnetic steels, was adopted to extend the resolution of the magnetic encoder. First, with a view to settling the issue of the jump points of the multi-pole angle value at the convergence of two adjacent magnetic poles, the angle segmentation method, which uses the window filter discrimination method, is employed to determine the actual angle value. The appropriate filter window width is selected via the improved particle swarm optimization (IPSO) algorithm, and an expanded resolution is achieved. Second, a compensation table is completed via a linear compensation algorithm based on virtual cutting to enhance the accuracy of the combined magnetic encoder. On this basis, a linear difference algorithm is used to achieve deviation correction of the angle. Finally, the jump points can be restrained effectively via the angle segmentation method. The resolution reaches 0.05°, and the accuracy is 0.045°.
Estimation of Trajectory of High speed Artillery Shell

This paper presents a novel technique that uses the stereoscopic arrangement of multiple cameras to determine the trajectory of a high-speed projectile. It can be used to detect and track artillery shells moving at high speed in the air toward friendly territory. A system with the proposed concept can enhance retaliation success in battlefield countermeasures. There are many state-of-the-art Radar-based systems to detect moving artillery shells and mortars, but the cost and size of those products make them not so easily deployable in all kinds of terrains. A system with multiple cameras is discussed in this paper as an alternative solution. The experimental results, after algorithms were applied to simulated videos of expected scenes showed that the proposed technique is feasible. The proposed technique is fast and accurate and can be converted into deployable hardware. It can lead to realizing a system that has utility in saving precious lives in critical circumstances.
A data-driven approach to understand factors contributing to exoskeleton use-intention in construction

Work-related musculoskeletal disorders (WMSDs) remain an important heath concern for construction workers. Occupational exoskeletons (EXOs) are a new ergonomic intervention to control WMSD risk, yet their adoption has been low in construction. We explored contributing factors to EXO use-intention, by building a decision tree to predict the intention to try an exoskeleton using responses to an online survey. Variable selection and hyperparameter tuning were used respectively to reduce the number of potential predictors, and for a better prediction performance. Performance was assessed using four common metrics. The importance of variables in the final tree was calculated to understand which variable had a greater influence. The final tree had moderate prediction performance. Important variables identified were associated with opinions on EXO use, demographics, job demands, and perceived potential risks. The key influential variables were EXOs becoming standard equipment and fatigue reduction with EXO use. Practical implications of the findings are discussed.
Optimizing Sensor Array DOA Estimation With the Manifold Reconstruction Unitary ESPRIT Algorithm

This letter introduces a new algorithm called manifold reconstruction unitary ESPRIT (MR-UESPRIT) for accurately estimating the direction of arrival (DOA). The algorithm combines manifold reconstruction and unitary ESPRIT to overcome the limitations of traditional DOA estimation methods. In this approach, two smaller overlapping subarrays are used to derive a rotational invariance equation, which helps recover the array manifold matrix. As a result, the DOA estimation problem is divided into $K$-individual single-source DOA estimation problems. Each DOA is estimated by minimizing a nonlinear least squares fitting criterion using a computationally efficient Newton's method. Computer simulations are presented to demonstrate the effectiveness of the MR-UESPRIT method compared with standard DOA estimation methods.
Edinburgh Research Explorer On line bisection

This study assessed pseudoneglect using line bisection and perceptual landmark tasks in two matched online sessions. Line bisection bias was characterized by the traditional measure of Directional Bisection Error (DBE), and by Endpoint Weightings Bias (EWB), derived from an “ endpoint weightings ” analysis, made possible by the independent manipulation of left and right endpoints. EWB is proposed to index the relative attentional allocation to the two ends of the line. The expected leftward bias (pseudoneglect) was found, with larger e ﬀ ect sizes for EWB ( d = − 0.34 in both sessions) than for DBE ( − 0.22 in Session 1 and − 0.14 in Session 2). Although EWB was slightly less reliable than DBE, it was more sensitive to pseudoneglect, and the endpoint weightings method has further advantages, including the option of an additional measure of non-lateralized attention. A substantial proportion of participants had di ﬃ culty following the instructions for the landmark task, which highlights the need for clear instructions and performance checks for this task. This study shows that line bisection can be used to measure pseudoneglect online, and provides grounds to suggest that the task should routinely include the independent manipulation of left and right endpoints, so that an endpoint weightings analysis can be performed.
A Comparative Study on various Flight Termination System Technologies

In a test range, a Flight Termination System (FTS) plays a prominent role in protecting range and flight personnel along with surrounding area population in case the flight vehicle is out of its trajectory range and possesses a threat to surrounding. An efficient FTS should posses high Signal to Noise Ratio, link robustness to ensure reliable communication between ground transmitting and onboard receiving systems and should be immune to interference. This article presents a detailed report on the requirement and features of FTS & technologies used in it. Techniques such as tone based, code based and Code Division Multiple Access (CDMA) are considered. Analysis of RF spectrum, gain and noise figure measurement and channel power are presented. Advanced methods like Enhanced Flight Termination System (EFTS) and Autonomous Flight Termination System (AFTS) are considered which show supportable and upgradable technologies, better range understanding, unique vehicle id & higher data rate. AFTS being self-reliant provides superior performance in terms of GPS, launch responsiveness, multiple flight vehicle control and auto pilot.
Exploratory Analysis of Automated Vehicle Crashes Using an NLP Pipeline

This study utilized a recently released crash dataset of Level 3 automated vehicles (AVs) made publicly available by the National Highway Traffic Safety Administration (NHTSA). The primary objective was to investigate various crash types and identify factors that influence crash severity. To achieve this, we employed a lightweight Natural Language Processing (NLP) pipeline to automatically extract relevant information from crash narratives and categorized the crashes into 15 distinct types. By analyzing the dependency triples derived from the crash narrative using the Stanford CoreNLP library, we determined the similarity between each narrative and the predefined categories. Our findings highlight safety-critical crash scenarios based on real-world data encompassing diverse operational design domains (ODDs), revealing a statistically significant impact of lighting conditions on crash severity. These results contribute to a better understanding of AV crashes and provide valuable insights to enhance the safe testing, integration, and development of AVs in real-world environments.
Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review

The rapid growth of the energy and transport sectors has led to an increase in fuel consumption, resulting in a significant rise in greenhouse gas emissions. Switching to renewable energy sources and replacing internal combustion engines with electric vehicles (EVs) can significantly reduce greenhouse gas emissions. In recent years, the electrification of the transportation sector has become a primary focus of research and development efforts. However, if EVs are charged using conventional energy sources, we are unable to fully capitalize on their potential to reduce emissions. Charging EVs using renewable energy sources is the optimal solution. Otherwise, the increased number of EVs on the roads can significantly impact the stability of existing electric grids. As a result, smart homes with EV charging stations are becoming increasingly popular worldwide. This review focuses on the concept of grid-connected rooftop solar photovoltaic (PV) smart homes integrated with EVs and energy management systems in Australia. Australia can reduce emissions in the building and transport sectors by electrifying a range of vehicles and ultimately powering them with 100% renewable energy sources. The benefits of EV integration alongside rooftop solar systems for smart homes with house-to-vehicle or vehicle-to-house, as well as vehicle-to-grid or grid-to-vehicle (bidirectional EV charging) capabilities are also explored in this article. By adopting these systems, these smart homes can provide energy schemes for commercial use, ultimately contributing to the owner’s economic benefit.
Examination of Human Spatial Reasoning Capability and Simulated Autonomous Rendezvous and Docking Monitoring Performance

Autonomous rendezvous and docking (ARD) maneuvers are challenging tasks that require collaboration between a human and a spacecraft to be successful. As automation becomes more integrated into ARD systems, it is important to consider when and why a human may take control. Intrinsic human characteristics can influence these decisions. We consider how human spatial orientation capacity affects participants when monitoring a simulated ARD maneuver and initiating takeover when the system is perceived to be failing. Participants’ spatial reasoning capability was assessed and compared to performance in the monitoring task and perceived mental workload. While participants showed high rates of success in the task, they showed a wide range in spatial reasoning capacity and perceived mental demand. Spatial reasoning capacity did not indicate participants’ mental workload, which has implications for the human as the supervisor. These results inform future work on augmentative displays that may incorporate exocentric and egocentric views.
Elucidating the Pharmacological Properties of Zingiber officinale Roscoe (Ginger) on Muscle Ageing by Untargeted Metabolomic Profiling of Human Myoblasts

(1) Background: Muscle loss is associated with frailty and a reduction in physical strength and performance, which is caused by increased oxidative stress. Ginger (Zingiber officinale Roscoe) is a potential herb that can be used to reduce the level of oxidative stress. This study aimed to determine the effect of ginger on the expression of metabolites and their metabolic pathways in the myoblast cells to elucidate the mechanism involved and its pharmacological properties in promoting myoblast differentiation. (2) Methods: The myoblast cells were cultured into three stages (young, pre-senescent and senescent). At each stage, the myoblasts were treated with different concentrations of ginger extract. Then, metabolomic analysis was performed using liquid chromatography-tandem mass spectrometry (LCMS/MS). (3) Results: Nine metabolites were decreased in both the pre-senescent and senescent control groups as compared to the young control group. For the young ginger-treated group, 8-shogaol and valine were upregulated, whereas adipic acid and bis (4-ethyl benzylidene) sorbitol were decreased. In the pre-senescent ginger-treated group, the niacinamide was upregulated, while carnitine and creatine were downregulated. Ginger treatment in the senescent group caused a significant upregulation in 8-shogaol, octadecanamide and uracil. (4) Conclusions: Ginger extract has the potential as a pharmacological agent to reduce muscle loss in skeletal muscle by triggering changes in some metabolites and their pathways that could promote muscle regeneration in ageing.
STMotif Explorer: A Tool for Spatiotemporal Motif Analysis

Pattern discovery is an important task in time series mining. A pattern that occurs a significant number of times in a time series is called a motif. Several approaches have been developed to discover motifs in time series. However, we can observe a clear gap in exploring the spatial-time series data. It is challenging to understand and characterize the meaning of the motif obtained concerning the data domain, comparing different approaches and analyzing the quality of the results obtained. We propose STMotif Explorer, a spatial-time motif analysis system that aims to interactively discover and visualize spatial-time motifs in different domains, offering insight to users. STMotif Explorer enables users to use and implement novel spatiotemporal motif detection techniques and then run this across various domains. Besides, STMotif Explorer offers the users a set of interactive resources where it is possible to visualize and analyze the discovered motifs and compare the results from different techniques. We show the features of our system with different approaches using real data.