Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Collaboration Preserving Projection for Monitoring of Complex Industrial Process

To fuse and mine multisource and multimodal data in monitoring complex industrial processes effectively, a new dimensionality reduction method called “Multiple Collaborative Preserving Projection” (MCPP) has been proposed. MCPP is innovative in the sense that it is capable of extracting and preserving the correspondences of heterogeneous data in temporal series and spatial structure. In temporal series, both local and global correlations of raw datasets are preserved; in spatial structure, the local structure of data is extracted based on the captured global structure, and a data projection method is integrated to reduce the adverse impact of industrial noise in raw data; moreover, the collaboration among multiple views is achieved by specifying the direction in projecting to subspaces; this maintains the consistency of common features in multiple views for a lower dimensional subspace. The effectiveness of MCPP has been verified in the case studies of the Tennessee Eastman process (TEP) and an industrial process to monitor the conditions of tuyere raceway in an actual blast furnace (BF).
Load-Impedance-Insensitive Design of High-Efficiency Class EF Inverters

This letter develops a high-efficiency Class EF inverter under ultrawide load impedance variation. The inequality constraints are introduced to fully release the design potential when the partial zero voltage switching operation serves as the primary goal. The load-reactance insensitivity is discussed based on the inverter's robustness under circuit parameter variation. The switch voltage stress is also considered for the final tradeoff design. In the experiment, when the load resistance varies in [15, 40] <inline-formula><tex-math notation="LaTeX">$\Omega$</tex-math></inline-formula> and load reactance varies [--17, 17] <inline-formula><tex-math notation="LaTeX">$\Omega$</tex-math></inline-formula>, a 1-MHz inverter is built and shown to operate normally with efficiency above 92%.
Let's Explore! The Factor, Reliability, and Validity Analyses of Readiness for a Knowledge-Based Economy Among Undergraduate Students

Knowledge-based economy is an economic model students need to be prepared for a future economic model that uses knowledge as its main resource. Therefore, this study developed and validated instruments for constructing knowledge-based economy readiness among undergraduate students. This study used an online questionnaire with 120 respondents of economic education students in educational universities in East Java, Indonesia, for exploratory factor analysis and 417 respondents for confirmatory factor analysis. Then, statistical analysis was conducted using exploratory factor analysis in SPSS and confirmatory factor analysis in AMOS. This study first developed five factors of knowledge of economics, readiness for economic challenges, readiness for education, readiness for infrastructure, and readiness for innovation, consisting of 27 items. However, one item was removed because the loading factor was below . 50. Consequently, 26 items were retained because the loading factor was significantly greater than . 50. The Cronbach's alpha value for each item of the knowledge-based economy readiness construct was >. 60 and met all goodness of fit index criteria, which means that it meets the requirements and can measure the construct of knowledge-based economy readiness. Since this study meets the validity and reliability requirements of the constructs leading to knowledge-based economy readiness, these results will help students prepare for the current and future knowledge-based economy. They can be used in developing economic education curricula in higher education.
Attention-Based API Locating for Malware Techniques

This paper presents APILI, an innovative approach to behavior-based malware analysis that utilizes deep learning to locate the API calls corresponding to discovered malware techniques in dynamic execution traces. APILI defines multiple attentions between API calls, resources, and techniques, incorporating MITRE ATT&CK framework, adversary tactics, techniques and procedures, through a neural network. We employ fine-tuned BERT for arguments/resources embedding, SVD for technique representation, and several design enhancements, including layer structure and noise addition, to improve the locating performance. To the best of our knowledge, this is the first attempt to locate low-level API calls that correspond to high-level malicious behaviors (that is, techniques). Our evaluation demonstrates that APILI outperforms other traditional and machine learning techniques in both technique discovery and API locating. These results indicate the promising performance of APILI, thus allowing it to reduce the analysis workload.
Streamflow prediction using data-driven models: Case study of Wadi Hounet, northwestern Algeria

Streamflow modelling is a very important process in the management and planning of water resources. However, complex processes associated with the hydro-meteorological variables, such as non-stationarity, non-linearity, and randomness, make the streamflow prediction chaotic. The study developed multi linear regression (MLR) and back propagation neural network (BPNN) models to predict the streamflow of Wadi Hounet sub-basin in north-western Algeria using monthly hy-drometric data recorded between July 1983 and May 2016. The climatological inputs data are rainfall ( P ) and reference evapotranspiration ( ETo ) on a monthly scale. The outcomes for both BPNN and MLR models were evaluated using three statistical measurements: Nash–Sutcliffe efficiency coefficient ( NSE ), the coefficient of correlation ( R ) and root mean square error ( RMSE ). Predictive results revealed that the BPNN model exhibited good performance and accuracy in the prediction of streamflow over the MLR model during both training and validation phases. The outcomes demonstrated that BPNN-4 is the best performing model with the values of 0.885, 0.941 and 0.05 for NSE , R and RMSE , respectively. The highest NSE and R values and the lowest RMSE for both training and validation are an indication of the best network. Therefore, the BPNN model provides better prediction of the Hounet streamflow due to its capability to deal with complex nonlinearity procedures.
Solid lipid nanoparticle formulation and antihyperglycemic activity test of sea cucumber (Stichopus hermanii)

This study aims to develop a formulation with the active ingredient Stichopus hermanii for antidiabetic treatment or function to lower blood glucose levels. The formulation approach taken is to design a formula in the form of solid lipid nanoparticles (SLN), where this system is expected to optimize absorption and improve the performance of the active substance. The SLN characterization includes particle size measurement, zeta potential, polydispersity index analysis, and morphology analysis using TEM. The antihyperglycemic activity test will be tested in vivo using male white rats using the glucose tolerance test method and induced by streptozotocin. The results of the TEM analysis showed that the sea cucumber spherical in shape. The particle size of the formula was found to be 0.25 nm. The polydispersion index (PDI) of the formulation was 67.5%, 2%, and 0.5%, respectively. In the same test, male rats were given treatment according to the group division, namely 50% glucose at a dose of 3 g/kg bw 30 minutes later and the blood sugar levels were measured at 30, 60, 90, and 120 minutes using a glucometer. In a follow-up test, the administration of test samples (days -3, 6, 9, 12, and 15) of CMC Na, metformin, and 25 mg/kg body weight of the test group showed that blood sugar level decreased with the highest decrease in positive control group. In conclusion, the results showed that sea cucumbers could be developed as a functional food product to help battle the onset of diabetes and diabetic complications.
Mortar-FP8: Morphing the Existing FP32 Infrastructure for High-Performance Deep Learning Acceleration

Vanilla deep neural networks (DNNs) after training are represented with native floating-point 32 (fp32) weights. We observe that the bit-level sparsity of these weights is very abundant in the mantissa and the distribution of exponent is aggregated, which can all be directly exploited to speed up model inference. In this article, we propose Mortar and Mortar-FP8, the offline/online software and hardware collaborative approaches for fp32 DNN acceleration. The proposed methods include the software algorithms to morph the mantissa and convert fp32 weights to fp8 format, as well as associated hardware accelerator architecture to accelerate general-purpose deep learning through optimized algorithm and specialized hardware. We highlight the following results by evaluating various deep learning tasks, including image classification, object detection, video understanding, video, and image super-resolution: 1) Mortar increase mantissa sparsity up to <inline-formula> <tex-math notation="LaTeX">$1.58\times -. 09\times $ </tex-math></inline-formula> with only a negligible ~0.2% accuracy loss; 2) Mortar-FP8 morph the fp32 weights to fp8 format with a minimal accuracy loss of ~0.3%; and 3) the corresponding hardware accelerator significantly outperforms baselines, achieving up to <inline-formula> <tex-math notation="LaTeX">$6.032\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$6.99\times $ </tex-math></inline-formula> performance improvements. The area and power of Mortar are 0.031 mm2 and 68.58 mW. Those metrics are 0.0505 mm2 and 25.16 mW for Mortar-FP8.
The Active Role of the Natural Rate of Unemployment During Cyclical Recoveries

We propose that the natural rate of unemployment has an active role in the business cycle, in contrast to the prevailing view that the rate is essentially constant. We demonstrate that this tendency to treat the natural rate as near-constant would explain the surprisingly low slope of the Phillips curve. We show that the natural rate closely tracked the actual rate during the long recovery that began in 2009 and ended in 2020. We explain how the common finding of research in the Phillips-curve framework of low-often extremely low-response of inflation to unemployment could be the result of fairly close tracking of the natural rate and the actual rate in recoveries. Our interpretation of the data contrasts to that of most Phillips-curve studies, that conclude that inflation has little relation to unemployment. We suggest that the at Phillips curve is an illusion caused by assuming that the natural rate of unemployment has little or no movement during recoveries.
A Research Protocol to Study the Priming Effects of Breathing Low Oxygen on Enhancing Training-Related Gains in Walking Function for Persons With Spinal Cord Injury: The BO2ST Trial

Brief episodes of low oxygen breathing (therapeutic acute intermittent hypoxia; tAIH) may serve as an effective plasticity-promoting primer to enhance the effects of transcutaneous spinal stimulation-enhanced walking therapy (WALKtSTIM) in persons with chronic (>1 year) spinal cord injury (SCI). Pre-clinical studies in rodents with SCI show that tAIH and WALKtSTIM therapies harness complementary mechanisms of plasticity to maximize walking recovery. Here, we present a multi-site clinical trial protocol designed to examine the influence of tAIH + WALKtSTIM on walking recovery in persons with chronic SCI. We hypothesize that daily (eight sessions, 2 weeks) tAIH + WALKtSTIM will elicit faster, more persistent improvements in walking recovery than either treatment alone. To test our hypothesis, we are conducting a placebo-controlled clinical trial on 60 SCI participants who randomly receive one of three interventions: tAIH + WALKtSTIM; Placebo + WALKtSTIM; and tAIH + WALKtSHAM. Participants receive daily tAIH (fifteen 90-sec episodes at 10% O2 with 60-sec intervals at 21% O2) or daily placebo (fifteen 90-sec episodes at 21% O2 with 60-sec intervals at 21% O2) before a 45-min session of WALKtSTIM or WALKtSHAM. Our primary outcome measures assess walking speed (10-Meter Walk Test), endurance (6-Minute Walk Test), and balance (Timed Up and Go Test). For safety, we also measure pain levels, spasticity, sleep behavior, cognition, and rates of systemic hypertension and autonomic dysreflexia. Assessments occur before, during, and after sessions, as well as at 1, 4, and 8 weeks post-intervention. Results from this study extend our understanding of the functional benefits of tAIH priming by investigating its capacity to boost the neuromodulatory effects of transcutaneous spinal stimulation on restoring walking after SCI. Given that there is no known cure for SCI and no single treatment is sufficient to overcome walking deficits, there is a critical need for combinatorial treatments that accelerate and anchor walking gains in persons with lifelong SCI. Trial Registration: ClinicalTrials.gov, NCT05563103.
The Relationship Between Cardiac Troponin in People Hospitalised for Exacerbation of COPD and Major Adverse Cardiac Events (MACE) and COPD Readmissions

Background No single biomarker currently risk stratifies chronic obstructive pulmonary disease (COPD) patients at the time of an exacerbation, though previous studies have suggested that patients with elevated troponin at exacerbation have worse outcomes. This study evaluated the relationship between peak cardiac troponin and subsequent major adverse cardiac events (MACE) including all-cause mortality and COPD hospital readmission, among patients admitted with COPD exacerbation. Methods Data from five cross-regional hospitals in England were analysed using the National Institute of Health Research Health Informatics Collaborative (NIHR-HIC) acute coronary syndrome database (2008–2017). People hospitalised with a COPD exacerbation were included, and peak troponin levels were standardised relative to the 99th percentile (upper limit of normal). We used Cox Proportional Hazard models adjusting for age, sex, laboratory results and clinical risk factors, and implemented logarithmic transformation (base-10 logarithm). The primary outcome was risk of MACE within 90 days from peak troponin measurement. Secondary outcome was risk of COPD readmission within 90 days from peak troponin measurement. Results There were 2487 patients included. Of these, 377 (15.2%) patients had a MACE event and 203 (8.2%) were readmitted within 90 days from peak troponin measurement. A total of 1107 (44.5%) patients had an elevated troponin level. Of 1107 patients with elevated troponin at exacerbation, 256 (22.8%) had a MACE event and 101 (9.0%) a COPD readmission within 90 days from peak troponin measurement. Patients with troponin above the upper limit of normal had a higher risk of MACE (adjusted HR 2.20, 95% CI 1.75–2.77) and COPD hospital readmission (adjusted HR 1.37, 95% CI 1.02–1.83) when compared with patients without elevated troponin. Conclusion An elevated troponin level at the time of COPD exacerbation may be a useful tool for predicting MACE in COPD patients. The relationship between degree of troponin elevation and risk of future events is complex and requires further investigation.
A Comparison of Pan and Zoom and Rubber Sheet Navigation

As information visualization tools are used to visualize datasets of increasing size, there is a growing need for techniques that facilitate efficient navigation. Pan and zoom navigation enables users to display areas of interest at different resolutions. Focus+context techniques aim to overcome the drawbacks of pan and zoom by dynamically integrating areas of interest and context regions. To date, empirical comparisons of these two navigation paradigms have been limited in scope and inconclusive. In two controlled studies, we evaluated navigation techniques representative of the pan and zoom and focus+context approaches. The particular focus+context technique examined was rubber sheet navigation, implemented in a way that afforded a set of navigation actions similar to pan and zoom navigation. The two techniques were used by 40 subjects in each study to perform a navigation-intensive task in a large tree dataset. Study 1 investigated the effect of the amount of screen real estate devoted to context regions for each navigation technique. Performance with both techniques was not significantly affected by this factor, but was influenced by technique-specific strategies developed by subjects. Study 2 compared the performance of the two techniques. Pan and zoom navigation was found to be faster than rubber sheet navigation and was rated by subjects as easier and less mentally demanding. We discuss the implications of these results, including the relationship between navigation technique, task, and user strategy, and propose directions for future work.
Contemporary Approaches for Amide Bond Formation

Amide bond construction has garnered significant interest in recent decades due to amides being one of the most prevalent functional groups among bioactive molecules. Out of the thirty‐seven new drugs approved by the FDA in 2022, eleven are small molecules containing at least one amide bond. Additionally, there are nineteen large molecules approved as new drugs, some of which have peptide structures, and therefore, also bear amide bonds. In recent years, multiple teams have embraced the challenge of developing more efficient methods for amide bond formation. This dedication has led to numerous publications appearing monthly in prestigious journals, showcasing significant advancements in this field. The primary goal of this review is to present the most viable strategies for constructing the amide bond. It is crucial to differentiate between amide bond formation and amide synthesis; hence, the focus is on describing specific methods for forming new C(O)‐N bonds. In particular, this review concentrates on the methods developed within the last six years. There is a particular emphasis on new approaches that consider the thought process when selecting the starting materials and functional groups. This approach ensures coverage of all the most common chemical transformations that yield new amide bonds.
An Empirical Study of DDPG and PPO-Based Reinforcement Learning Algorithms for Autonomous Driving

Autonomous vehicles mitigate road accidents and provide safe transportation with a smooth traffic flow. They are expected to greatly improve the quality of the elderly or people with impairments by improving their mobility due to the ease of access to transportation. Autonomous vehicles sense the driving environment and navigate through it without human intervention. And, Deep Reinforcement Learning (DRL) is witnessed as a powerful machine learning solution to address a sequential decision problem in autonomous vehicles. The detailed state-of-the-art work in autonomous vehicles using DRL algorithms along with future research directions is discussed. Due to the high dimensional action space, two continuous action space DRL algorithms: Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) are chosen to address the complex autonomous driving problem. The proposed DDPG and PPO based decision-making models are trained and tested using the TORC simulator. Both the algorithms are trained for the same number of episodes for lane keeping as well as multi-agent collision avoidance scenarios. To the best of our knowledge, this is the first paper to present the comparative performance analysis of these two algorithms, and DDPG is found to perform better in terms of higher reward and faster convergence than PPO. Hence, DDPG is a suitable option in the design of a decision model for autonomous driving.
The Boundary Waters Canoe Area Wilderness: Examining Changes in Use, Users, and Management Challenges

The purpose of this study was to determine trends in use and user characteristics at the Boundary Waters Canoe Area Wilderness. Based on data from 1969, 1991, and 2007, the average age of visitors has increased significantly, education levels have increased, and visitors remain predominantly male. Visitors in 2007 report seeing twice as many groups since 1961 and 1991, but the number of encounters are not exceeding expectations. Findings emerged related to gender ratios and evaluating resource conditions. These findings may need further investigation and future management action to provide opportunities for meaningful wilderness experiences while protecting wilderness character.
Assessing the effects of Pennisetum glaucum on the soil properties, growth, nutritive, and physiological aspects of the edible pulse crop (Vigna radiata L. Wilczek)

Abstract Mung bean cropping plays a vital role in providing essential nutrition and serving as a significant summer crop to feed billions of people worldwide. However, allelopathy poses a threat to the sustainability and productivity of this system. The residues of pearl millet plants contain allelochemicals that can negatively impact weed growth in various crops. This study aimed to evaluate the potential allelopathic effects of pearl millet powder on the germination, growth, pigments, protein, and carbohydrate levels of mung beans. Different quantities of whole plant powder, namely 0, 5, 10, and 15 g of pearl millet, were blended into medium-sized pots, each containing 500 g of soil. Mung bean seeds were grown in these pots for 3–4 days in a greenhouse before transplanting them into an open field. The findings revealed that as the quantity of pearl millet powder increased, the germination percentage, germination speed, plant height, fresh weight, dry weight, chlorophyll content, carotenoid, protein, and carbohydrate amount of mung beans decreased. The physio-chemical properties of soils treated with pearl millet exhibited higher pH, conductivity, total solids, and heavy metal content than control soils. The decrease in the aforementioned attributes in soils treated with pearl millet powder could be due to the release of allelochemicals and/or the accumulation of heavy metals in the soil.These results demonstrate that using pearl millet as an herbicide to control weeds is effective, but caution must be exercised if heavy metals accumulate in the pearl millet tissue.
Metadata Representations for Queryable Repositories of Machine Learning Models

Machine learning (ML) practitioners and organizations are building model repositories of pre-trained models, referred to as model zoos. These model zoos contain metadata describing the properties of the ML models and datasets. The metadata serves crucial roles for reporting, auditing, ensuring reproducibility, and enhancing interpretability. Despite the growing adoption of descriptive formats like datasheets and model cards, the metadata available in existing model zoos remains notably limited. Moreover, existing formats have limited expressiveness, thus constraining the potential use of model repositories, extending their purpose beyond mere storage for pre-trained models. This paper proposes a unified metadata representation format for model zoos. We illustrate that comprehensive metadata enables a diverse range of applications, encompassing model search, reuse, comparison, and composition of ML models. We also detail the design and highlight the implementation of an advanced model zoo system built on top of our proposed metadata representation.