Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical image steganographic algorithm via modified LSB method and chaotic map

Many methods of hiding information in an image are existing now. The least signiﬁcant bit is the famous method used in steganographic algorithms. Medical image steganography is a technique used to make the transmission of these images secure so that the decision of the Specialist physician based on these images is not affected. In this paper, medical image steganographic algorithm using modiﬁed least signiﬁcant bit and chaotic map is proposed. The main problem is that the selection of embedding pixels within the host image is not protected enough in most existing methods. So, the author used two-dimensional piecewise smooth chaotic map to select the positions of these pixels randomly. On the other hand, all bits in the secret medical image are transmitted without losing any bit. To do that, the secret medical image is encrypted using one-dimensional piecewise chaotic map (Tent map). Then, the steganographic algorithm is used to hide the bits of the encrypted secret medical image. The bits of each embedded pixel are shufﬂed before the embedding pro-cess randomly. After that, the stego image is created. The host image and stego image are analysed with the peak signal-to-noise ratio, the mean square error, histogram test, image quality measure and relative entropy test. The stego image displays acceptable result when comparing with the host image. Also, the chi-square attack test is performed and the stego image can resist it. The proposed algorithm can assist the sending of medical images via communication media.
“An Entrainment Paradox: How Hysteretic Saltation and Secondary Transport Augment Atmospheric Uptake of Aeolian Source Materials”

Aerodynamic surface stress imposed by the atmospheric surface layer (ASL) drives aeolian sediment transport processes. When the imposed stress exceeds fluid threshold, splashing sand grains release fine (aerosol) particles. This process stops when the imposed stress falls below an impact threshold. Turbulence in the ASL is composed of elongated streaks of relatively high and low streamwise momentum (high‐momentum and low‐momentum regions, HMR and LMR, respectively), and these streaks are aligned with the prevailing winds. Streamwise‐wall‐normal turbulent stress is the concurrent product of fluctuations in streamwise and vertical velocity. These production mechanisms are categorized into quadrants: sweeps, inner interactions, ejections, and outer interactions, where the first two and last two occur within HMRs and LMRs, respectively. Under typical ASL conditions, the time‐averaged shear velocity is bound between 0.3 and 0.5 m/s, demonstrating the importance of fluctuations in mobilizing sand grains via saltation: time‐averaged shear velocity exceeding threshold does not correspond with typical ASL conditions. Given the aforementioned contributions to streamwise‐wall‐normal turbulent stresses from sweeps and ejections, it is self‐evident that under typical conditions only sweeps possess the momentum required to exceed threshold. But, any dust released is trapped by downwelling from aloft; ejections rarely exceed threshold, but they contain the positive vertical velocity needed to entrain. These attributes represent an entrainment paradox. Complementary field data are used to demonstrate the existence of an entrainment paradox. Large eddy simulation has been used to capture space‐time evolution of an idealized ASL and identify mechanistic flow physics central to entrainment.
A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques

With the rapid development of information and communication techniques, dam structural health assessment based on data collected from structural health monitoring systems has become a trend. This allows for applying data-driven methods for dam safety analysis. However, data-driven models in most related literature are statistical and shallow machine learning models, which cannot capture the time series patterns or learn from long-term dependencies of dam structural response time series. Furthermore, the effectiveness and applicability of these models are only validated in a small data set and part of monitoring points in a dam structural health monitoring system. To address the problems, this article proposes a new modeling paradigm based on various deep learning and transfer learning techniques. The paradigm utilizes one-dimensional convolutional neural networks to extract the inherent features from dam structural response–related environmental quantity monitoring data. Then bidirectional gated recurrent unit with a self-attention mechanism is used to learn from long-term dependencies, and transfer learning is utilized to transfer knowledge learned from the typical monitoring point to the others. The proposed paradigm integrates the powerful modeling capability of deep learning networks and the flexible transferability of transfer learning. Rather than traditional models that rely on experience for feature selection, the proposed deep learning–based paradigm directly utilizes environmental monitoring time series as inputs to accurately estimate dam structural response changes. A high arch dam in long-term service is selected as the case study, and three monitoring items, including dam displacement, crack opening displacement, and seepage are used as the research objects. The experimental results show that the proposed paradigm outperforms conventional and shallow machine learning–based methods in all 41 tested monitoring points, which indicates that the proposed paradigm is capable of dealing with dam structural response estimation with high accuracy and robustness.
Green project management from employees' perspective in Hong Kong's engineering and construction sectors

PurposeThe study investigates employees’ perceptions of green project management in Hong Kong's engineering and construction sectors.Design/methodology/approachGreen project management attributes were identified and categorized in terms of organization and process aspects based on a literature review. A questionnaire was developed to collect responses from employees working in Hong Kong's engineering and construction sectors.FindingsRespondents perceived “using Design for Environment approach,” “training employees about green project management” and “recycling the used or excessive materials/components” as the most important attributes. Generally, females gave higher importance ratings than males to most attributes. Respondents who had higher education qualifications or held senior positions perceived green project management attributes as more important than their counterparts with lower education qualifications or in lower positions. Green project management was found to have four distinct factors: “Management Commitment,” “Green Technologies and Processes,” “Green Partnerships” and “External Communication. ”Originality/valueThe study is one of the first empirical works on green project management in Hong Kong's engineering and construction sectors. It demonstrates that green project management should be characterized as a multidimensional concept.
Martensitic microstructure evolution in austenitic steel: A thermomechanical polycrystalline phase field study

We have performed coupled thermomechanical phase field simulations of martensitic transformation in single-crystalline and polycrystalline 301-stainless steel by coupling Ginzburg–Landau equation to the mechanical equilibrium and non-stationary heat conduction equations. Simulations accounted for elastic anisotropy, under different thermal and mechanical BCs, i.e., clamped, embedded, and near-open, as well as adiabatic and isothermal with and without latent heat effects. To the best of authors’ knowledge, no such simulations are reported in literature on polycrystalline materials which provides the effects of various thermomechanical BCs incorporating latent heat. We have utilized a multivariant single embryo as an initial condition in both systems to mitigate the lack of nucleation criteria at the mesoscale; without any initial constraint, the model can select the martensitic transformation path and final microstructure. Simulations showed excellent capabilities in predicting influence of such thermomechanical BCs on martensitic microstructural formation including autocatalytic transformation process in polycrystalline steel and its evolution. Martensitic microstructure in a single-crystalline, and b polycrystalline steel, in deformed configuration (deformation scale factor is 1×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1\times$$\end{document}) for near-open, embedded, and clamped boundary conditions (from left to right). Dataset edges in blue show initial undeformed configuration
Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects

A key aspect of the development of Smart Cities involves the efficient and effective management of resources to improve liveability. Achieving this requires large volumes of sensors strategically deployed across urban areas. In many cases, however, it is not feasible to install devices in remote and inaccessible areas, resulting in incomplete data coverage. In such situations, citizens can often play a crucial role in filling this data collection gap. A popular complimentary science to traditional sensor-based data collection is to design Citizen Science (CS) activities in collaboration with citizens and local communities. Such activities are also designed with a feedback loop where the Citizens benefit from their participation by gaining a greater sense of awareness of their local issues while also influencing how the activities can align best with their local contexts. The participation and engagement of citizens are vital and yet often a real challenge in ensuring the long-term continuity of CS projects. In this paper, we explore engagement factors, factors that help keeping engagement high, in technology-centric CS projects where technology is a key enabler to support CS activities. We outline a literature review of exploring and understanding various motivational and engagement factors that influence the participation of citizens in technology-driven CS activities. Based on this literature, we present a mobile-based flood monitoring citizen science application aimed at supporting data collection activities in a real-world CS project as part of an EU project. We discuss the results of a user evaluation of this app, and finally discuss our findings within the context of citizens’ engagement.
A Subthreshold Voltage Reference with Coarse- Fine Voltage Trimming

This paper presents a self-biased subthreshold voltage reference with several types of trimming options. A current trim is used to achieve similar currents across process corners. A coarse/fine voltage trim was implemented, which utilized different Vth's of the process as well as the reverse short-channel effect. The range of voltages obtained vary from 86mV to 536mV with steps of 16mV. The circuit was designed in a standard 0.18 μm CMOS process. Simulation results at Vcc=3D1V shows a temperature coefficient (TC) between 40 to 400 ppm/oC at different trims over a 120oC range. The nominal power consumption was 1.35nW with a highly compact area of 0.002 mm2.
A fast and efficient feature extraction methodology for structural damage localization based on raw acceleration measurements

Many damage detection strategies have been developed within the field of structural health monitoring, showing promising results in real‐world applications. Most of them rely on the use of modal parameters to locate damage in structures. However, they are highly dependent on the process of extracting such characteristics, which contains uncertainties and needs to be adapted specifically for each structure. Recently, a special focus is being given to techniques based only on the use of raw structural acceleration measurements due to their relatively lower computational complexity manipulation. Thus, this work proposes an efficient feature extraction methodology for structural damage localization based on raw vibration signals. Furthermore, its ability to indicate damage quantification is also investigated. The proposed approach consists in extracting sensitive features from time, frequency, and quefrency domains. Then, percentile intervals are defined for each feature regarding the structures' healthy state. Finally, a damage index is estimated based on the number of outlier features of the structures' damaged state. In this sense, the sensor with the highest number of outlier features indicates the damage location. To assess this methodology, four different applications are studied: a numerical two‐dimensional frame, a simply supported beam tested in laboratory, a three‐dimensional frame also tested in laboratory, and the Tianjin Yonghe Bridge in China. Results show that the proposed approach is not only able to correctly indicate damage locations but also to give insights about their magnitudes.
Truck fleet size selection in open-pit mines based on the match factor using a MINLP model

The present study aims to propose new strategies based on mixed-integer non-linear programming (MINLP) models for the equipment sizing (ES) problem to verify the overall efficiency of the fleet. The developed models estimate the optimal size of trucks concerning the match factor value with two different strategies. The first strategy deals with each loader type, and the second one is applied simultaneously with all types of loaders. The proposed approaches are compared to a simulation strategy to assess the models. Implementing models with a copper mine case study provides a more efficient haul fleet size than the decisions offered by the simulation method. Moreover, the presented strategies provide an effective way to improve equipment performance where the current mine strategy does not adapt well. A key contribution of this research is the development, implementation, and verification of new optimization and simulation methods to address the ES problem in open-pit mines.
A critical review of metamaterial in wireless power transfer system

Wireless power transfer (WPT) is a promising charging method without physical contact for the electronic products energy supply. However, low transfer efﬁciency, short transmission distance, as well as the electromagnetic (EM) leakage are commonly main bottlenecks of the WPT technology in the practical applications. The electromagnetic metamaterials (EMMMs) used in WPT system have opened up a brand-new approach for better transfer properties and EM shielding. This paper is intended to provide an overview of the current studies and breakthroughs of the WPT system based on EMMMs. First, we demonstrate the fundamentals of the WPT based on the magnetic resonant coupling. Then, various key considerations of the EMMMs, such as working mechanisms, fabrications have been taken into accounted in detail. Moreover, the applications of EMMM in WPT systems are elaborated. Finally, the technical challenges in connection with working frequency and design miniaturization are investigated in this paper. This novel interdisciplinary technology shows important meanings on the extensive application of energy transmission in the future.
EyeHealer: A large-scale anterior eye segment dataset with eye structure and lesion annotations

ABSTRACT Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide, including diseases associated with corneal pathologies, anterior chamber abnormalities (e.g. blood or inflammation), and lens diseases. The construction of an automatic tool for segmentation of anterior segment eye lesions would greatly improve the efficiency of clinical care. With research on artificial intelligence progressing in recent years, deep learning models have shown their superiority in image classification and segmentation. The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise; however, such data are relatively scarce in the domain of medicine. Herein, the authors developed a new medical image annotation system, called EyeHealer. It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level. Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation. The results showed that semantic segmentation models outperformed medical segmentation models. This paper describes the establishment of the system for automated classification and segmentation tasks. The dataset will be made publicly available to encourage future research in this area.
The Recreational value of Atlantic salmon angling under different fishing regulations

Atlantic salmon, Salmo salar L. , stocks worldwide have dwindled, and stricter recreational fishing regulations have been implemented. This study estimates the recreational value of salmon fishing under different regulatory schemes and identifies factors explaining changes in net economic value of sport fishing. A Contingent Valuation mail survey in the Verdal River, Norway, asked anglers their willingness-to- pay for increased fishing expenditures or willingness- to- accept compensation in reduced fishing expenditures, to estimate recreational value. The average annual recreational value per angler decreased under stricter regulations from about €420 (in 2007– 2009) to €280 (2010– 2012) to €180 for the future scenario (2013– 2015). Although an overall net decline in recreational value was found with stricter regulations, anglers have heterogeneous preferences. Anglers with non- catch motives or high place attachment have higher recreational value with stricter regulations than others. If stricter regulations remain over time, younger, less consumptive- oriented anglers would probably dominate the fishery.
Quality Evaluation of Indoor-Grown Microgreens Cultivated on Three Different Substrates

The microgreens are innovative products in the horticultural sector. They are appreciated by consumers thanks to their novelty and health-related benefits, having a high antioxidant concentration. This produce can be adopted for indoor production using hydroponic systems. The aim of the present work was to investigate the influence of three growing media (vermiculite, coconut fiber, and jute fabric) on yield and quality parameters of two basil varieties (Green basil—Ocimum basilicum L., Red basil—Ocimum basilicum var. Purpurecsens) and rocket (Eruca sativa Mill.) as microgreens. Microgreens were grown in floating, in a Micro Experimental Growing (MEG®) system equipped with LED lamps, with modulation of both energy and spectra of the light supplied to plants. Results showed high yield, comprised from 2 to 3 kg m−2. Nutritional quality varied among species and higher antioxidant compounds were found in red basil on vermiculite and jute. Coconut fiber allowed the differentiation of crop performance in terms of sucrose and above all nitrate. In particular, our results point out that the choice of the substrate significantly affected the yield, the dry matter percentage and the nitrate concentration of microgreens, while the other qualitative parameters were most influenced by the species.
Kinetic Control Concept for the Diffusion Processes of Paracetamol Active Molecules Across Affinity Polymer Membranes

Background: Paracetamol compound remains the most used pharmaceutical as an analgesic and antipyretic for pain and fever. It has been detected in aquatic environments. The recovery of this compound from wastewater is one of the important operations carried out by modern industries. Its recovery is especially important for environmental protection. Currently, research is focused on membrane technology that has gained considerable interest over the last decades due to the various advantages that it presents. Result: Our work reports the selective extraction of paracetamol from liquid solution using two types of affinity polymer membranes: (i) polymer inclusion membrane (PIM) and (ii) grafted polymer membrane (GPM). The same extractive agent, gluconic acid (GA), is used for both. After total characterization, the developed membranes were adopted. Kinetic and thermodynamic models have been used to determine the values of various macroscopic parameters, permeability (P), and initial flux (J0), to understand the membrane performance. The same techniques have been used to determine the values of different microscopic parameters, association constant (Kass), and apparent diffusion coefficient (D*) that determine the interaction between the paracetamol substrates and the extractive agent, necessary for the diffusion of paracetamol molecules through the membrane. Similarly, the effects of initial concentration (C0), acidity (pH), and temperature were examined. Conclusion: The experimental results allow the determination of values of activation and thermodynamic parameters (Ea, ΔH # , ΔS #, ΔH # dis, and ΔH # th). The results explain the membrane performances and confirm that the energetic or kinetic aspects control the mechanisms related to the oriented processes. The results also indicate that it is possible to recycle wastewater and eliminate contaminants such as paracetamol.
Swelling kinetics and cross-linking stability of gelatins with different gel strength

The purpose of this study was to investigate swelling kinetics and stability of gelatins with different gel strengths,as well as the component which might affect gel strength and cross-linking reactivity.SDS-PAGE assay was undertaken to determine the content of gelatin components-α,β and γ component which were isolated through CM52 cation exchange column,and the cross-linking activity of the components was determined by the formaldehyde cross-linking simulation study.At the same time,the equilibrium swelling ratio(Seq) and e-amino acid residue content indicative of the swelling kinetics and the stability of cross-linking were assessed according to the methods reported in the literatures.It was shown that the increase in α-component led to the corresponding increase in the gel strength of gelatin but the decrease in the equilibrium swelling ratio.The e-amino acid residue contents of α,β and γ components decreased in the order of α β γ. It was found that the gel strength increased as the e-amino acid content did.Accelerating study showed that instability-indicative cross-linking could occour in the gelatin with higher gel strength during storage.
Extraction of Cannabinoids from Cannabis sativa L. (Hemp)—Review

Cannabis plant has long been execrated by law in different nations due to the psychoactive properties of only a few cannabinoids. Recent scientific advances coupled with growing public awareness of cannabinoids as a medical commodity drove legislation change and brought about a historic transition where the demand rose over ten-fold in less than five years. On the other hand, the technology required for cannabis processing and the extraction of the most valuable chemical compounds from the cannabis flower remains the bottleneck of processing technology. This paper sheds light on the downstream processing steps and principles involved in producing cannabinoids from Cannabis sativa L. (Hemp) biomass. By categorizing the extraction technology into seed and trichome, we examined and critiqued different pretreatment methods and technological options available for large-scale extraction in both categories. Solvent extraction methods being the main focus, the critical decision-making parameters in each stage, and the applicable current technologies in the field, were discussed. We further examined the factors affecting the cannabinoid transformation that changes the medical functionality of the final cannabinoid products. Based on the current trends, the extraction technologies are continuously being revised and enhanced, yet they still fail to keep up with market demands.