Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes of seasonal storm properties in California and Nevada from an ensemble of climate projections

Precipitation characteristics, such as intensity, frequency, duration, and event pattern, are changing due to the increases in greenhouse gases, transition of ocean oscillation phases, etc. In this paper, we evaluate the ability of 11 realizations from multiple regional climate model (RCM)/global climate model pairs in the North American Regional Climate Change Assessment Program (NARCCAP) to simulate the seasonal variability and magnitude of storm properties, including storm duration, interstorm period, and storm intensity. The results indicate that NARCCAP RCMs simulate the seasonal variability better in the Greater Sacramento and San Joaquin than in Las Vegas, which may be due to the RCMs' inability to simulate local convective precipitation associated with the North American Monsoon. We also investigate the impacts of climate change on these storm characteristics by comparing the percentage change and absolute change of storm properties determined from NARCCAP historical runs and future runs. We find that individual RCMs exhibit great uncertainty in the percentage changes in storm duration, interstorm period, and average storm intensity. The ensemble means of storm properties across 11 future NARCCAP RCM projections show different responses to climate change in different locations. Our analyses provide guidelines for selecting the appropriate RCMs for hydrologic studies related to storm properties and provide forecasters and water managers with detailed information of future changes in storm properties so that they can sustainably manage water resources. Our results may also contribute to the nonstationary precipitation scenario development by incorporating the percentage changes of storm properties caused by human‐induced warming into the stochastic precipitation model.
Design Space Enumerations for Pneumatically Actuated Soft Continuum Manipulators

Pneumatically actuated soft continuum manipulators (SCMs) are constructed by combining several extending or contracting fiber reinforced elastomeric enclosure (FREE) actuators in series, parallel and a combination thereof. While it is well known that architectures with serial combinations of FREEs yield large workspace and dexterity, they suffer from design and control complexity, increased number of valves and inertia. Recent advances in exploring the FREE design space has demonstrated using parallel combinations of dissimilar FREEs (bending and rotating) to improve workspace and dexterity. This paper presents a comprehensive investigation of SCM design architectures by enumerating possibilities of serial and parallel combinations of similar and dissimilar FREEs. A novel dexterity metric is proposed to enable objective comparison of different SCM designs based on shape similarity and end-effector tangent. Given a fixed resource of control inputs (actuator and valve inputs), the paper systematically selects the best architecture of the SCM (serial, parallel, similar or dissimilar FREE) that maximizes dexterity and workspace. It is seen that optimal designs are heavily dependent on the context of the application, which may change how these manipulators are deployed. The paper presents two practical design applications that demonstrate the usefulness of the enumeration framework. While in general, serial design combinations using symmetric bending actuators result in larger workspace and dexterity, some architectures with asymmetric combinations of FREEs may see similar levels of dexterity and workspace.
Approximations of New MV-Valued Types of Fuzzy Sets

: Many of the new types of fuzzy sets, such as intuitionistic, neutrosophic, multi-level or fuzzy soft sets and their combinations, can be transformed into one common type of fuzzy sets, called ( R , R ∗ ) -fuzzy sets, with values in a set R that is a common underlying set of complete commutative idempotent semirings R and R ∗ . For ( R , R ∗ ) -fuzzy sets, the theory of lower and upper approximations by ( R , R ∗ ) -relations is deﬁned and the basic properties of these approximations are presented. Using examples of the transformation of some new types of MV -valued fuzzy sets and corresponding fuzzy relations into R-fuzzy sets and R-fuzzy relations, examples of approximation of these new types of fuzzy sets through their fuzzy relations are presented, without having to deﬁne these operators separately for each new type of fuzzy set.
Research on the Hydrodynamic Noise Characteristics of a Mixed-Flow Pump

This study presents a comprehensive investigation of the internal noise characteristics of a mixed-flow pump by combining computational fluid dynamics (CFD) and computational acoustics. The turbulent flow field of the pump is simulated using the unsteady SST k-ω turbulence model in CFD. The contributions of the volute, guide vanes, and impeller to the internal noise are analyzed and compared using the Lighthill theory, FW-H formula, and LMS Virtual Lab software for acoustic simulation. The research findings indicate that the energy of pressure fluctuations in the mixed-flow pump is predominantly concentrated at the blade passing frequency and its low-frequency harmonics. This suggests that the internal noise is mainly in the low-frequency range, with higher energy at the blade passing frequency and its harmonics. Under the 0.6Qdes flow condition, the flow inside the pump becomes more complex, resulting in higher sound pressure levels and sound power levels compared to higher flow conditions. However, for flow conditions ranging from 0.8Qdes to 1.2Qdes, the sound pressure levels gradually increase with increasing flow rate, with the sound pressure level at 1.0Qdes being nearly identical to that at 1.2Qdes. The analysis of sound power level spectra at different flow rates reveals that the distribution characteristics of internal vortex structures directly impact the hydrodynamic noise inside the mixed-flow pump. These research findings provide a significant theoretical basis for noise control in mixed-flow pumps.
Quantifying Sharpness and Nonlinearity in Neonatal Seizure Dynamics

The integration of multiple electrophysiological biomarkers is crucial for monitoring neonatal seizure dynamics. The present study aimed to characterize the temporal dynamics of neonatal seizures by analyzing intrinsic waveforms of epileptic electroencephalogram (EEG) signals. We proposed a complementary set of methods considering envelope power, focal sharpness changes, and nonlinear patterns of EEG signals of 79 neonates with seizures. Features derived from EEG signals were used as input to the machine learning classifier. All three characteristics were significantly elevated during seizure events, as agreed upon by all viewers (P < 0.0001). Envelope power was elevated in the entire seizure period, and the degree of nonlinearity rose at the termination of a seizure event. Epileptic sharpness effectively characterizes an entire seizure event, complementing the role of envelope power in identifying its onset. However, the degree of nonlinearity showed superior discriminability for the termination of a seizure event. The proposed computational methods for intrinsic sharp or nonlinear EEG patterns evolving during neonatal seizure could share some features with envelope power. Current findings may be helpful in developing strategies to improve neonatal seizure monitoring.
Martian Atmospheric Aerosols Composition and Distribution Retrievals During the First Martian Year of NOMAD/TGO Solar Occultation Measurements: 1. Methodology and Application to the MY 34 Global Dust Storm

Since the beginning of the Trace Gas Orbiter (TGO) science operations in April 2018, its instrument “Nadir and Occultation for MArs Discovery” (NOMAD) supplies detailed observations of the IR spectrums of the Martian atmosphere. We developed a procedure that allows us to evaluate the composition and distribution's parameters of the atmospheric Martian aerosols. We use a retrieval program (RCP) in conjunction with a radiative forward model (KOPRA) to evaluate the vertical profile of aerosol extinction from NOMAD measurements. We then apply a model/data fitting strategy of the aerosol extinction. In this first article, we describe the method used to evaluate the parameters representing the Martian aerosol composition and size distribution. MY 34 GDS showed a peak intensity from LS 190° to 210°. During this period, the aerosol content rises multiple scale height, reaching altitudes up to 100 km. The lowermost altitude of aerosol's detection during NOMAD observation rises up to 30 km. Dust aerosols reff were observed to be close to 1 μm and its νeff lower than 0.2. Water ice aerosols reff were observed to be submicron with a νeff lower than 0.2. The vertical aerosol structure can be divided in two parts. The lower layers are represented by higher reff than the upper layers. The change between the lower and upper layers is very steep, taking only few kilometers. The decaying phase of the GDS, LS 210°–260°, shows a decrease in altitude of the aerosol content but no meaningful difference in the observed aerosol's size distribution parameters.
Bio‐based laminated truss structures with bolted steel connections: Experiment, modeling, and model‐updating

Bio‐based laminated structures offer a timely solution to meet the pressing demand for resource‐efficient and environmentally responsible construction practices in civil engineering. Bolted connections are commonly used in truss joints and significantly impact their global mechanical behaviors. However, existing studies on the complex behavior of joints for integrated bio‐based structures are lacking. Based on previous work by the authors about joint behavior, this study seeks to experimentally investigate the axial behavior of bolted steel to laminated timber and bamboo (glubam) connections with slotted‐in steel plates in planar truss structures. First, cyclic tests were conducted on Bio‐based laminated planar truss structures to evaluate their global and local hysteretic behavior. The test matrix encompasses eight distinct connection configurations, carefully selected to assess the connection behavior of Laminated Veneer Lumber (LVL) and Glued Laminated Bamboo (glubam). Subsequently, through the incorporation of the hysteretic model for truss joints, an advanced hybrid truss model was developed within the OpenSees framework and successfully validated using experimental data. Finally, this study proposes and demonstrates the effectiveness of an efficient model‐updating framework, which combines the power of the genetic algorithm (GA) and neural network, resulting in a significantly enhanced accuracy of the model.
Salt Effects on Caffeine across Concentration Regimes

Salts affect the solvation thermodynamics of molecules of all sizes; the Hofmeister series is a prime example in which different ions lead to salting-in or salting-out of aqueous proteins. Early work of Tanford led to the discovery that the solvation of molecular surface motifs is proportional to the solvent accessible surface area (SASA), and later studies have shown that the proportionality constant varies with the salt concentration and type. Using multiscale computer simulations combined with vapor-pressure osmometry on caffeine-salt solutions, we reveal that this SASA description captures a rich set of molecular driving forces in tertiary solutions at changing solute and osmolyte concentrations. Central to the theoretical work is a new potential energy function that depends on the instantaneous surface area, salt type, and concentration. Used in, e.g., Monte Carlo simulations, this allows for a highly efficient exploration of many-body interactions and the resulting thermodynamics at elevated solute and salt concentrations.
Impact of Thresholds of Univariate Filters for Predicting Species Distribution

: Researchers rely on species distribution models (SDMs) to establish a correlation between species occurrence records and environmental data. These models offer insights into the ecological and evolutionary aspects of the subject. Feature selection (FS) aims to choose useful interlinked features or remove those that are unnecessary and redundant, reduce model costs, storage needs, and make the induced model easier to understand. Therefore, to predict the distribution of three bird species, this study compares five filter-based univariate feature selection methods to select relevant features for classification tasks using five thresholds, as well as four classifiers; Support Vector Machine (SVM), Light gradient-boosting machine (LGBM), Decision Tree (DT), and Random Forest (RF). The empirical evaluations involve several techniques, such as the 5-fold cross-validation method, the Scott Knott (SK) test, and Borda Count. In addition, we used three performance criteria (accuracy, kappa and F1-score). Experiments showed that 40% and 50% thresholds were the best choice for classifiers, with RF outperforming LGBM, DT and SVM. Finally, the best combination for each classifier is as follows: RF and LGBM classifiers using Mutual information with 40% threshold, DT using ReliefF with 50% thresholds, and SVM using Anova F-value with 40% thresholds.
A Study on Evaluation of Ecological Community Based on Principal Component Analysis Method——A case study within outer ring road of Shanghai City

Based on the ecological system and sustainable development theory.he indicator system and model for evaluation of ecological community were designed by using principal component analysis method and Remote Sensing/Geographical Information System technology.The evaluating computation was applied in the case study area----within outer ring road of Shanghai City.The results show that:(1) there are disparities of communities within outer ring road of Shanghai City.The score of components spread unevenly within one community or between different communities. (2) In the central area of Puxi,the commonality serving establishments of most communities are perfect,but the housing condition and ecological environment of some communities need further improvements. (3) Although it shows high ecological index and ecological environment quality at some communities in Pudong area,due to the imperfect commonality serving establishments,public traffic system still need to be ameliorated.
An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems

: Anomaly detection is critical for the secure and reliable operation of industrial control systems. As our reliance on such complex cyber-physical systems grows, it becomes paramount to have automated methods for detecting anomalies, preventing attacks, and responding intelligently. This paper presents a novel deep generative model to meet this need. The proposed model follows a variational autoencoder architecture with a convolutional encoder and decoder to extract features from both spatial and temporal dimensions. Additionally, we incorporate an attention mechanism that directs focus towards specific regions, enhancing the representation of relevant features and improving anomaly detection accuracy. We also employ a dynamic threshold approach leveraging the reconstruction probability and make our source code publicly available to promote reproducibility and facilitate further research. Comprehensive experimental analysis is conducted on data from all six stages of the Secure Water Treatment (SWaT) testbed, and the experimental results demonstrate the superior performance of our approach compared to several state-of-the-art baseline techniques.
A New Approach to the Analysis of the Compensation Networks of WPT Systems

The wireless power transfer systems represent a viable solution to make more user friendly the procedure of charging the battery of electric vehicles. The performances of these systems are greatly affected by their compensation networks and are usually studied by considering the first harmonic equivalent circuit of the system. This paper presents a new approach to writing the equations that describe these circuits and expresses them in a form that highlight the contribution of the coupled coils and of their compensation networks. The procedure to obtain the equations is described in details and the equations themselves are analyzed to derive from them the qualitative characteristics of the wireless power transfer system. In the last part of the paper a numerical analysis of a study case is reported, supporting the results by means of simulations.
Advancing Flotation Process Optimization Through Real-Time Machine Vision Monitoring: A Convolutional Neural Network Approach

: The mining industry’s continuous pursuit of sustainable practices and enhanced operational efficiency has led to an increasing interest in leveraging innovative technologies for process monitoring and optimization. This study focuses on the implementation of Convolutional Neural Networks (CNN) for real-time monitoring of differential flotation circuits in the mining sector. Froth flotation, a widely used technique for mineral separation, necessitates precise control and monitoring to achieve maximum recovery of valuable minerals and separate them from gangue. The research delves into the significance of froth surface visual properties and their correlation with flotation froth quality. By capitalizing on CNN’s ability to identify valid, hidden, novel, potentially useful and meaningful information from image data, this study showcases how it surpasses traditional techniques for the flotation monitoring. The paper provides an in-depth exploration of the dataset collected from various stages of the Zinc flotation banks, labeled with elemental grade values of Zinc (Zn), Iron (Fe), Copper (Cu), and Lead (Pb). CNNs’ implementation in a regression problematic allows for real-time monitoring of mineral concentrate grades, enabling precise assessments of flotation performance. The successful application of CNNs in the Zinc flotation circuit opens up new possibilities for improved process control and optimization in mineral processing. By continuously monitoring froth characteristics, engineers and operators can make informed decisions, leading to enhanced mineral recovery and reduced waste.
POPULATION AND EMPLOYMENT CHANGES IN REGIONAL AUSTRALIA

Regional Australia has experienced significant changes in population and employment since the early 1990s. Evidence regarding these changes has often been anecdotal, with references in political and media spheres to a ‘Sea Change’ or ‘Tree Change’. There has also been considerable public discussion about the effect that the structural changes and misfortunes within the agricultural sector have had on localities in rural regions. The purpose of this paper is to provide and analyse data on regional population and employment changes since the early 1990s. It will also examine the role that the agricultural sector may have had in these changes. This will provide a basis for informed debate and analysis of population changes in regional Australia and the causes and implications of these changes.
Developing The Strategy for Steel Sales by Scenario Planning Approach

The steel industry, often regarded as the mother of industry, holds a pivotal role in fostering national economic independence and supporting vital infrastructure projects. In Indonesia, the development of the steel industry stands as a key indicator of industrial advancement. However, the landscape has evolved dramatically, with escalating competition not just from PT Krakatau Steel but also from numerous emerging competitors producing similar products. This dynamic situation demands an aggressive sales strategy to secure market dominance, especially given the challenges posed by both local and international competitors. Addressing this, PT Krakatau Steel faces the essential question of how to optimize steel sales, approaching or even matching the plant's installed capacity, amidst this competitive environment. The solution lies in proactive strategic planning, which involves a meticulous analysis of both external and internal factors. Recognizing the need for foresight, the company has employed scenario planning, envisioning potential futures for the steel industry. By considering variables like raw material prices and government policies, four scenarios emerged: Armageddon, The King, The King's Speech, and Cast Away. Each scenario prompts a specific set of strategies and actions. Moreover, the company has established early warning systems to discern which scenario might unfold, enabling timely and precise responses. This forward-thinking approach culminated in a comprehensive strategic plan designed to guide PT Krakatau Steel through the complexities and uncertainties of the business landscape over the next seven years.
A study on persistent organochlorine pesticide residues in fish tissues and water from an agricultural fish pond.

Organochlorine pesticides (OCPs), (α BHC, β BHC, γ BHC, Heptachlor, Aldrin, Chlordane, p, p' DDE, o, p' DDD, p, p' DDD, Dieldrin, Endrin, Endosulfan and p, p' DDT) were determined in water and fish samples from the fish pond of Oke-Osun Farm Settlement, Osogbo, Nigeria that receive runoffs from agricultural lands. The aim of the study was to investigate the pollution status of the pond water and fish samples. Qualitative and quantitative analysis of the OCPs after clean-up on silica gel adsorbent were carried out using Gas Chromatography equipped with Electron Capture Detector (GC-ECD). The results showed higher levels of OCPs in the pond water during the rainy season than during the dry season. Higher levels of OCPs were found in the fish samples in the dry season than during the rainy season. Significant positive correlations at p ≤ 0.05 level were observed for β BHC and aldrin, chlordane, p, p' DDE, dieldrin and endrin; γ BHC and heptachlor, p, p' DDE, dieldrin and endrin; heptachlor and p, p' DDD, dieldrin and endrin; heptachlor and p, p' DDD, dieldrin and endrin; p, p' DDE and dieldrin and endrin; and dieldrin and endrin. The mean seasonal levels of OCPs in the fish were higher, in most cases, than the recommended WHO Maximum Residue Levels (MRLs) in food items and should give cause for concern. Due to possible toxicity and bioaccumulation tendency of the OCPs by the predators higher in the food chain, the levels of OCPs detected in the fish samples could be a source of future health problems. The OCPs detected are consistent with the agricultural activities of the study area due to pesticide usage.
Design of Highly Birefringence and Nonlinear Modified Honeycomb Lattice Photonic Crystal Fiber (MHL-PCF) for Broadband Dispersion Compensation in E+S+C+L Communication Bands

: This paper investigates the design and tuning of a Broadband Dispersion Compensating Modiﬁed Honeycomb Lattice Photonic Crystal Fiber (MHL-PCF) with outstanding features such as strong birefringence and nonlinearity. The proposed PCF for y polarization exhibits a negative dispersion coefﬁcient of − 263.9 ps/(nm · km) at 1.55 µ m operating frequency and a high negative dispersion of − 652.9 ps/(nm · km) when air-ﬁlled fraction (d c / Λ ) grows from 0.35 to 0.65. Because it is a polarization-maintaining ﬁber, it also exhibits birefringence. At 1.55 µ m operating frequency, the suggested ﬁber exhibits 1.482 × 10 − 2 birefringence. The suggested MHL-PCF has a high nonlinear coefﬁcient of 34.68 W − 1 km − 1 at the same operating frequency. Numerical aperture is also investigated for MHL-PCF as it inﬂuences their light-guiding capabilities, light-coupling efﬁciency, mode control, dispersion qualities, and sensitivity in sensing applications. The numerical aperture of the proposed MHL-PCF at 1550 nm is 0.4175, demonstrating excellent light-coupling properties. The purpose of this research is to satisfy the growing need for improved optical communication systems capable of managing high data rates across long transmission distances. The suggested MHL-PCF structure has distinct features that make it an attractive choice for dispersion correction and nonlinear optical applications