Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Cost-Effective Evaluation Metrics

Deep learning has been a driving force behind recent breakthroughs in artificial intelligence. In particular, neural networks have shown great promise in handling a wide range of tasks. With the improvement of performance, networks need computing power and space are also increasing day by day. However, in practical applications, platforms with low computing power, such as edge devices, have limited computing, it is necessary to select a high cost-effective model. Traditional methods require a lot of experience to select cost-effective models. Therefore, there is an urgent need for a deep learning cost-effective evaluation metrics to measure this performance. To address this, we propose a cost-effective evaluation metric called TALES, which takes a common approach to evaluating deep learning models in practical applications. Our metric objectively evaluates networks based on FLOPs, Params, and predicted latency, which are key factors in determining model performance. After conducting a statistical survey of nearly 300 networks, we conclude that the growth in accuracy and the growth in Euclidean distance computed by FLOPs, Params, and Latency have a very similar mathematical pattern to the Sigmoid function. We then fit the relationship curve using mathematical modeling. Our final results show that our designed cost-effective evaluation metric can effectively and objectively evaluate the relationship between accuracy and network complexity of each network. The results are consistent with objective cognition in the field of deep learning. Through the metric, we can effectively evaluate the performance of deep learning models in practical applications and reducing costs and improving efficiency.
Enhancing Indoor Millimeter Radio Communication: A Probabilistic Approach to RSS Map Estimation

In Cyber-Physical Systems (CPS), the reliability of wireless communication is paramount to ensuring safety. Received Signal Strength (RSS) map is particularly beneficial for safe robot operation, for example. However, accurately estimating an RSS map poses significant challenges, particularly when higher frequency bands are employed for broadband communications. Therefore, it is also important to not only further enhance the accuracy of the estimation, which is the target of many existing methods, but also design system that can tolerate errors in the estimation. In this paper, we propose a method for constructing a digital twin that represents the quality of the wireless network using probability distributions. Representing data probabilistically proves effective for risk-sensitive robot control or robust planning of base station positioning. We propose using a graphical model known as Markov Random Field (MRF), to depict the spatial structure of the RSS map. The probability distribution of the RSS value at each point within the space is provided as the marginal probabilities of the MRF. We then evaluated the proposed method in our own 28 GHz Private 5G environment and extensively studied the characteristics of indoor millimeter radio communication. We confirmed that each estimated point can be well estimated by probability distribution using the proposed method. In addition, the design of error toler-ance based probability distribution is discussed. By determining a margin on the estimated expected value based on the standard deviation of the estimated distribution, the margin can be set more efficiently than by determining a uniform margin for each points.
Effects of Hypoxia and Metabolic Inhibitors on the Intrinsic Heart Rate and Myocardial Contractility in Dogs

Previous studies have shown that after autonomic blockade by propranolol and atropine, the intrinsic rate and contractile function of the heart were related in patients with myocardial failure. In this study the intrinsic rate and ventricular contractile force were measured in dogs during acute heart failure produced by agents which impair myocardial energy syntinesis, to see whether these conditions reproduced the relationship found in naturally occurring myocardial disease in man. Anesthetized dogs, after propranolol and atropine, were either ventilated with 6% oxygen or given sodium cyanide, parachloromercuribenzoate, or dinitrophenol by intravenous infusion. Hypoxia and cyanide initially increased and then depressed both rate and contraction. Parachloromercuribenzoate depressed both. Dinitrophenol depressed only contraction. During hypoxia, cyanide, and parachloromercuribenzoate the percent changes in contractile force and intrinsic rate were linearly related (r=0.87) with slopes of 3.2, 2.7, and 3.3, respectively. These results together with previous data during pentobarbital and aminophylline were consistent with a single linear relationship between rate and contractility (r=0.89, slope 3.2). This resembled closely the relationship found previously in man. It is suggested that in both situations, this relationship may be determined by a dependence of rate and contractility on similar limited sources of energy.
Fast Global Maximum Power Point Tracking for Photovoltaic Generation Systems Under Partial Shading Conditions

Conventional Maximum Power Point Tracking (MPPT) methods for Photovoltaic Generation Systems (PVGSs) fail to prevent the system from operating at Local Maximum Power Points (LMPP) under Partial Shading Conditions (PSCs), resulting in decreased overall efficiency. To address this issue, a Global MPPT (GMPPT) featuring a segmental Prediction and Jump (P&J) method is proposed in this paper. The technique aims to determine the segment of the GMPP by utilizing two measured voltages and currents from one voltage segment, along with the characteristic output function of the PVGS. Using the Newton-Raphson method, the proposed segmental P&J method directly calculates the solar irradiance (S) and the number of solar cells connected in series (N). With $S$ and N, the segmental P&J method can predict the possible voltage segment and jump to it. The voltages and currents in the jumping voltage segment are then measured, and $S$ and N are estimated again until the segment of GMPP is determined. A GMPPT is then used to track the actual GMPP in the determined GMPP segment. Experimental results demonstrate that the proposed method efficiently achieves a high tracking accuracy.
Interlaboratory Evaluation of Rodent Pulmonary Responses to Engineered Nanomaterials: The NIEHS Nano GO Consortium

Background: Engineered nanomaterials (ENMs) have potential benefits, but they also present safety concerns for human health. Interlaboratory studies in rodents using standardized protocols are needed to assess ENM toxicity. Methods: Four laboratories evaluated lung responses in C57BL/6 mice to ENMs delivered by oropharyngeal aspiration (OPA), and three labs evaluated Sprague-Dawley (SD) or Fisher 344 (F344) rats following intratracheal instillation (IT). ENMs tested included three forms of titanium dioxide (TiO2) [anatase/rutile spheres (TiO2-P25), anatase spheres (TiO2-A), and anatase nanobelts (TiO2-NBs)] and three forms of multiwalled carbon nanotubes (MWCNTs) [original (O), purified (P), and carboxylic acid “functionalized” (F)]. One day after treatment, bronchoalveolar lavage fluid was collected to determine differential cell counts, lactate dehydrogenase (LDH), and protein. Lungs were fixed for histopathology. Responses were also examined at 7 days (TiO2 forms) and 21 days (MWCNTs) after treatment. Results: TiO2-A, TiO2-P25, and TiO2-NB caused significant neutrophilia in mice at 1 day in three of four labs. TiO2-NB caused neutrophilia in rats at 1 day in two of three labs, and TiO2-P25 and TiO2-A had no significant effect in any of the labs. Inflammation induced by TiO2 in mice and rats resolved by day 7. All MWCNT types caused neutrophilia at 1 day in three of four mouse labs and in all rat labs. Three of four labs observed similar histopathology to O-MWCNTs and TiO2-NBs in mice. Conclusions: ENMs produced similar patterns of neutrophilia and pathology in rats and mice. Although interlaboratory variability was found in the degree of neutrophilia caused by the three types of TiO2 nanoparticles, similar findings of relative potency for the three types of MWCNTs were found across all laboratories, thus providing greater confidence in these interlaboratory comparisons.
Automatic Extraction of Drug Indications from FDA Drug Labels

Extracting computable indications, i.e. drug-disease treatment relationships, from narrative drug resources is the key for building a gold standard drug indication repository. The two steps to the extraction problem are disease named-entity recognition (NER) to identify disease mentions from a free-text description and disease classification to distinguish indications from other disease mentions in the description. While there exist many tools for disease NER, disease classification is mostly achieved through human annotations. For example, we recently resorted to human annotations to prepare a corpus, LabeledIn, capturing structured indications from the drug labels submitted to FDA by pharmaceutical companies. In this study, we present an automatic end-to-end framework to extract structured and normalized indications from FDA drug labels. In addition to automatic disease NER, a key component of our framework is a machine learning method that is trained on the LabeledIn corpus to classify the NER-computed disease mentions as "indication vs. non-indication. " Through experiments with 500 drug labels, our end-to-end system delivered 86.3% F1-measure in drug indication extraction, with 17% improvement over baseline. Further analysis shows that the indication classifier delivers a performance comparable to human experts and that the remaining errors are mostly due to disease NER (more than 50%). Given its performance, we conclude that our end-to-end approach has the potential to significantly reduce human annotation costs.
Late fracture of vascularized fibular grafts

Subsequent fractures following the bone union were analyzed in 62 cases of vascularized fibular graft reconstruction. Fifteen cases (24%) of fractures, including four cases of two fractures, were observed. All fractures but one occurred within a year after obtaining bone union. Eighteen fractures occurred in the lower extremity. In the patients with tibial reconstructions, a significantly higher incidence of fractures was demonstrated in cases where there was a large bone defect and an inaccurate alignment of the graft. Proper graft alignment is an important factor in preventing stress fractures. Vascularized fibular grafts should be protected against excessive mechanical stresses during the first year following bony graft union. © 1998 Wiley‐Liss, Inc. MICROSURGERY 18:337–343, 1998
Fractional Order PID Controller for Quadrotor

In recent years, research on Unmanned Aerial Vehicles (UAVs) has intensified due to their versatile applications. While there are various forms of UAVs, each with its distinct properties and uses, controlling them remains a challenge. This paper focuses on the use of a quadcopter to control altitude via a fractional order PID (FOPID). Additionally, a genetic algorithm (GA) is employed to tune the parameters of the FOPID controller. The research incorporates a simulation, executed in Simulink/MATLAB, which illustrates the efficacy of the FOPID. A notable advantage of the fractional order PID is its enhanced response and superior adaptability, which culminates in improved, consistent performance.
Csf1r-mApple Transgene Expression and Ligand Binding In Vivo Reveal Dynamics of CSF1R Expression within the Mononuclear Phagocyte System

CSF1 is the primary growth factor controlling macrophage numbers, but whether expression of the CSF1 receptor differs between discrete populations of mononuclear phagocytes remains unclear. We have generated a Csf1r-mApple transgenic fluorescent reporter mouse that, in combination with lineage tracing, Alexa Fluor 647–labeled CSF1-Fc and CSF1, and a modified ΔCsf1–enhanced cyan fluorescent protein (ECFP) transgene that lacks a 150 bp segment of the distal promoter, we have used to dissect the differentiation and CSF1 responsiveness of mononuclear phagocyte populations in situ. Consistent with previous Csf1r-driven reporter lines, Csf1r-mApple was expressed in blood monocytes and at higher levels in tissue macrophages, and was readily detectable in whole mounts or with multiphoton microscopy. In the liver and peritoneal cavity, uptake of labeled CSF1 largely reflected transgene expression, with greater receptor activity in mature macrophages than monocytes and tissue-specific expression in conventional dendritic cells. However, CSF1 uptake also differed between subsets of monocytes and discrete populations of tissue macrophages, which in macrophages correlated with their level of dependence on CSF1 receptor signaling for survival rather than degree of transgene expression. A double ΔCsf1r-ECFP-Csf1r-mApple transgenic mouse distinguished subpopulations of microglia in the brain, and permitted imaging of interstitial macrophages distinct from alveolar macrophages, and pulmonary monocytes and conventional dendritic cells. The Csf1r-mApple mice and fluorescently labeled CSF1 will be valuable resources for the study of macrophage and CSF1 biology, which are compatible with existing EGFP-based reporter lines.
A New Approach to XR Education: Student-Centred Learning and Gamification in Electric Power Training

Extended reality is changing the educational experience by transforming the way we teach and learn. Despite the education community promoting XR in education actively, their implementation is consistently disappointing the users, due to lack of interest and often failure to upgrade in time. Our study combines student-centred learning and gamification into the XR educational systems, considering what kind of systems are more engaging, more efficient, and have higher levels of user satisfaction. We first reviewed relevant literature to understand the theoretical foundations of student-centred learning and gamification, as well as their functions and significance in the XR educational system. Then, we constructed a theoretical model based on them, combining the core elements of student-centred learning and gamification strategies in the XR educational system, trying to provide theoretical guidance for future practice. We chose electric power training, which has the characteristics of complexity, high risk, and high investment, as the research object and tried to design an interesting and efficient virtual simulation electric power training system. By comparing and analyzing the theoretical model, we found that student-centred learning and gamification can significantly improve the effectiveness and user satisfaction for the training system.
Memristor-Based Analog Multiplier

Memristors, as a novel non-volatile circuit component, hold significant promise in electronic circuit applications. This article presents a novel memristor-based multiplier design, consisting of three key stages: an absolute value circuit, a multiplication circuit, and a sign circuit. The multiplier is capable of performing multiplication operations within the range of 1V to 10V, and it can achieve multiplication even when both input values are set to zero through deliberate circuit design, thereby eliminating the need for analog-to-digital signal conversion. This innovation reduces circuit complexity and energy consumption, enhancing overall circuit efficiency.
Optimization of Metal-Matrix Composites by W-EDM with CNT

The EDM procedure is typically used to machining the Al6061 metal matrix composites with silicon carbide (SiC) with carbon nanotube reinforcement (CNT). The Metal Removal Rate (MRR), Surface Roughness (SR) as well as Tool Wear Rate (TWR) of the work piece material could potentially be measured using this method. Fundamental machining characteristics impacting the process's performance includingpulse ON time, discharge current as well as duty cycle were input attributes using Taguchi's parameter formulation. For each possible arrangement, a performance criterion has indeed been computed. The MRR, SR and TWR for every combination were computed in this investigation. The acquired outcomes have been tabulated. The research indicates that by employing grey relational analysis (GRA) to optimise the EDM parameters, improved MRR, TWR, and SR could be attained. MRR, TWR, and SR were 0.301 g/$\mathrm{m}{{\mathrm{m}}^3}$, 0.003 g/$\mathrm{m}{{\mathrm{m}}^3}$, and 4.03 m, respectively, according to the optimum outcomes of this research.
Transpiration‐ and precipitation‐induced subsurface water flow observed using the self‐potential method

Movement of soil moisture associated with tree root‐water uptake is ecologically important but technically challenging to measure. Here, the self‐potential (SP) method, a passive electrical geophysical method, is used to characterize water flow in situ. Unlike tensiometers, which use a measurement of state (i.e., matric pressure) at two locations to infer fluid flow, the SP method directly measures signals generated by water movement. We collected SP measurements in a two‐dimensional array at the base of a Douglas‐fir tree (Pseudotsuga menziesii) in the H.J. Andrews Experimental Forest in western Oregon over 5 months to provide insight on the propagation of transpiration signals into the subsurface under variable soil moisture. During dry conditions, SP data appear to show downward unsaturated flow, whereas nearby tensiometer data appear to suggest upward flow during this period. After the trees enter dormancy in the fall, precipitation‐induced vertical flow dominates in the SP and tensiometer data. Diel variations in SP data correspond to periods of tree transpiration. Changes in volumetric water content occurring from soil moisture movement during transpiration are not large enough to appear in volumetric water content data. Fluid flow and electrokinetic coupling (i.e., electrical potential distribution) were simulated using COMSOL Multiphysics to explore the system controls on field data. The coupled model, which included a root‐water uptake term, reproduced components of both the long‐term and diel variations in SP measurements, thus indicating that SP has potential to provide spatially and temporally dense measurements of transpiration‐induced changes in water flow. This manuscript presents the first SP measurements focusing on the movement of soil moisture in response to tree transpiration.
GPT-DETOX: An In-Context Learning-Based Paraphraser for Text Detoxification

Harmful and offensive communication or content is detrimental to social bonding and the mental state of users on social media platforms. Text detoxification is a crucial task in natural language processing (NLP), where the goal is removing profanity and toxicity from text while preserving its content. Supervised and unsupervised learning are common approaches for designing text detoxification solutions. However, these methods necessitate fine-tuning, leading to computational overhead. In this paper, we propose GPT-DETOX as a framework for prompt-based in-context learning for text detoxification using GPT-3.5 Turbo. We utilize zero-shot and few-shot prompting techniques for detoxifying input sentences. To generate few-shot prompts, we propose two methods: word-matching example selection (WMES) and context-matching example selection (CMES). We additionally take into account ensemble in-context learning (EICL) where the ensemble is shaped by base prompts from zero-shot and all few-shot settings. We use ParaDetox and APPDIA as benchmark detoxification datasets. Our experimental results show that the zero-shot solution achieves promising performance, while our best few-shot setting outperforms the state-of-the-art models on ParaDetox and shows comparable results on APPDIA. Our EICL solutions obtain the greatest performance, adding at least 10% improvement. against both datasets.
Development and validation of a consensus methodology for the classification of the ANCA-associated vasculitides and polyarteritis nodosa for epidemiological studies

Background: The classification of antineutrophil cytoplasmic antibody-associated vasculitis (AAV) and polyarteritis nodosa (PAN) for epidemiology studies is confusing. The existing schemes such as American College of Rheumatology (ACR) criteria, Chapel Hill Consensus Conference (CHCC) definitions and Lanham criteria produce overlapping and conflicting classifications, making it difficult to compare incidence figures. Aim: To develop a consensus method of using these criteria and definitions for epidemiological studies to permit comparison without confounding by classification. Methods: A stepwise algorithm was developed by consensus between a group of doctors interested in the epidemiology of vasculitis. The aim was to categorise patients with Wegener’s granulomatosis, microscopic polyangiitis (MPA), Churg–Strauss syndrome (CSS) and PAN into single clinically relevant categories. The ACR and Lanham criteria for CSS, and ACR criteria for Wegener’s granulomatosis were applied first, as these were considered to be the most specific. Surrogate markers for Wegener’s granulomatosis were included to distinguish Wegener’s granulomatosis from MPA. MPA was classified using the CHCC definition and surrogate markers for renal vasculitis. Finally, PAN was classified using the CHCC definition. The algorithm was validated by application to 20 cases from each centre and 99 from a single centre, followed by a paper case exercise. Results: A four-step algorithm was devised. It successfully categorises patients into a single classification. There was good correlation between observers in the paper case exercise (91.5%; unweighted κ = 0.886). Conclusion: The algorithm achieves its aim of reliably classifying patients into a single category. The use of the algorithm in epidemiology studies should permit comparison between geographical areas.
A Review of Sentimental Analysis for Vaccine Dataset Using BI-LSTM Method

In the age of digital communication, the realm of public opinion, particularly on pressing issues like vaccination, has found its voice through myriad online platforms. Given the surge of discussions around vaccines, especially in the wake of the COVID-19 pandemic, there is an imperative need to decipher the underlying sentiments from these vast datasets. Sentiment analysis, a prominent branch of Natural Language Processing (NLP), provides the tools to extract, process, and categorize such sentiments. While traditional machine learning models have held their ground in sentiment analysis tasks, the intricate nature of human emotions and language patterns demands more refined techniques. The Bidirectional Long Short-Term Memory (BI-LSTM) model, an enhanced variant of recurrent neural networks, emerges as a promising candidate with its capability to capture contextual information from both past and future data points in sequences. This review paper delves into the application and efficacy of the BI-LSTM method for sentiment analysis on vaccine-related datasets. Through a comprehensive analysis, we evaluate its performance metrics, benefits, limitations, and its positioning against other prevalent models. The findings suggest that BI-LSTM holds significant potential in providing nuanced insights into public sentiment regarding vaccines, which is instrumental for stakeholders to craft informed strategies and communications.