Miracle-dz/UniFAR-FTUs huggingface dataset:
Response between aerenchyma and radial oxygen loss of Acorus calamus Linn. under high nitrogen and phosphorus stress

Acorus calamus Linn. has been selected as the experimental plant,and the variation of its aerenchyma and the quantity of its radial oxygen loss ( ROL) under the stress of three different concentrations of nitrogen and phosphorus effluent ( Group 1: N 40 mg /L,P 4 mg /L; Group 2: N 80 mg /L,P 8 mg /L; Group 3: N 120 mg /L,P 12 mg /L) in the water culture has been investigated. The results show that the stress of high concentrations of nitrogen and phosphorus significantly inhibited the growth of root length and plant height of Acorus calamus Linn. and reduced the number of plant roots; it could also increase the quantity of ROL and promote the formation of aerenchyma. The gross quantity of ROL in the stress group was less than that in the control group due to the decrease of root length and quantity. The ROL trend has not changed by the stress of high concentrations of nitrogen and phosphorus,and the further distance away from the roots,the smaller quantity of the ROL. This research also found that the amount of the root tip's ROL showed positive correlation with aerenchyma while the amount of ROL of basal zone and mature zone showed insignificant correlation with the aerenchyma.
Improving Digital Forensic Security: A Secure Storage Model With Authentication and Optimal Key Generation Based Encryption

Secure storage model for digital forensics represents essential progress in the domain, addressing the major problems associated with protecting and maintaining digital evidence. This method employs recent encryption systems and optimal key generation methods to ensure the confidentiality and integrity of data throughout the investigative process. Cloud forensics is an intelligent development of digital forensics to be preserved against online hacking. But, centralized evidence gathered and preservation reduces the reliability of digital evidence. The architecture for digital forensics in an Infrastructure as a Service (IaaS) cloud platform is a crucial structure intended to simplify the collection and protection of evidence while preserving the integrity and origin of digital objects within cloud-based methods. This architecture integrates numerous modules and methods to address the exclusive tasks modeled by cloud computing (CC) environments in the framework of forensic investigations. This paper develops a new digital forensic architecture utilizing the Authentication with Optimal Key Generation Encryption (DFA-AOKGE) technique. The main intention of the DFA-AOKGE method is to use a BC-distributed design to allocate data between numerous peers for data collection and safe storage. Additionally, the DFA-AOKGE model uses the Secure Block Verification Mechanism (SBVM) for the authentication procedure. Also, the secret keys can be produced by the usage of the Enhanced Equilibrium Optimizer (EEO) model. Furthermore, the encryption of the data takes place using a multikey homomorphic encryption (MHE) approach and is then saved in the cloud server. The simulation value of the DFA-AOKGE methodology takes place in terms of different aspects. The simulation results exhibited that the DFA-AOKGE system shows prominent performance over other recent approaches in terms of different measures.
Teaching computer architecture by designing and simulating processors from their bits and bytes

Teaching computer architecture (Comp-Arch) courses in undergraduate curricula is becoming more of a challenge as most students prefer software-oriented courses. In some computer science/engineering departments, Comp-Arch courses are offered without the lab component due to resource constraints and differing pedagogical priorities. This article demonstrates how students working in teams are motivated to study the Comp-Arch course and how instructors can increase student motivation and knowledge by taking advantage of hands-on practices. The teams are asked to design and implement a 16-bit MIPS-like processor with constraints as a specific instruction set, and limited data and instruction memory. Student projects include following three phases, namely, design, desktop simulator implementation, and verification using hardware description language (HDL). In the design phase, teams develop their Comp-Arch to implement specified instructions. A range of designs resulted, e.g., (a) a processor with extensive user-defined instructions resulting in longer cycle times (b) a processor with a minimal instruction set but with a faster clock cycle time. Next, teams developed a desktop simulator in any programming language to execute instructions on the architecture. Finally, students engage in Verilog Hardware Description Language (HDL) projects to simulate and verify the data-path designed during the initial phase. Student feedback and their current understanding of the project were collected through a questionnaire featuring varying Likert scale questions, some with a ten-point scale, and others with a five-point scale. Results of the survey show that the hands-on approach increases students’ motivation and knowledge in the Comp-Arch course, which is centered around computer system design principles. This approach can also be effectively extended to related courses, such as Microprocessor Design, which delves into the intricacies of creating and implementing microprocessors or central processing units (CPUs) at the hardware level. Furthermore, the present study demonstrates that interactions, specifically through peer reviews and public presentations, between students in each phase increases their knowledge and perspective on designing custom processors.
Nurses' Performance Regarding Intravenous Access For Neonates in Intensive Care Unit: An Assessment Study

Background: Newborn babies who need intensive medical care are often admitted to neonatal intensive care unit. Neonates with health conditions such as respiratory disorders, heart problems, infections. Aim of the study, this study aimed to assess the nurses' performance regarding intravenous access for neonates in neonatal intensive care unit. Research design: descriptive design was used . Sample: A convenient sample included all available nurses at the NICU. Setting: This study was conducted at neonatal intensive care unit at Menyat El Naser hospital affiliated to Dakahliya Directorate of health Tools: Three tools were used for data collection (1) Predesigned questionnaire format, to assess characteristics of studied nurses and studied neonates also to assess nurse's knowledge regarding intravenous access in neonatal intensive care unit . (2) Observational check list to assess nurses practice regarding intravenous access in neonatal intensive care unit . (3) Attitude assessment scale to assess nurses attitude regarding intravenous access in the NICU . Result Displays that there is a relation between nurses’ total knowledge regarding intravenous access and their qualification and years of experience with highly statistically significant difference (P<0.01). Demonstrates that there is a positive correlation between total nurses’ knowledge, their total practices and total attitude regarding intravenous access for neonates. Conclusions: This study reveals that there is a positive correlation between total nurses’ knowledge, practices and attitude regarding intravenous access for neonates Recommendations : Design training programs at NICU to improve and refresh nurses’ practice regarding the invasive procedures such as intravenous access for neonates and update their performance regarding umbilical venous catheter based on scientific evidence base.
Monte Carlo estimates of uncertainties in outputs of regional ozone models due to uncertainties in inputs to the BEIS3 biogenics emissions model

A Monte Carlo (MC) probabilistic approach is used to estimate uncertainties of the emissions outputs of the Biogenics Emissions Inventory System Version 3 (BEIS3) model (Pierce, 2001) and subsequent predictions by three chemical transport models (CTMs) due to uncertainties in BEIS3 model parameters and input variables. BEIS3 has been developed by the U.S. EPA to estimate emissions of isoprene, monoterpenes, and oxygenated volatile organic compounds (OVOCs) due to biological activity in or on plant tissues, and to estimate emissions of biogenic nitric oxide (BNO) due to biological activity in soils. Outputs of CTMs, based on the BEIS3 emissions estimates, are then used to set policies concerning emission reductions needed from industrial plants and other man-made sources. Biogenic VOC emissions are estimated to be of the same order of magnitude as man-made VOC emissions in many parts of the U.S. The results briefly described in this paper are discussed in detail in two reports (Hanna et al., 2002 and 2003).
Drug-eluting stents for coronary artery disease in the perspective of bibliometric analysis

Drug-eluting stents (DES) play a crucial role in treating coronary artery disease (CAD) by preventing restenosis. These stents are coated with drug carriers that release antiproliferative drugs within the vessel. Over the past two decades, DES have been employed in clinical practice using various materials, polymers, and drug types. Despite optimizations in their design and materials to enhance biocompatibility and antithrombotic properties, evaluating their long-term efficacy and safety necessitates improved clinical follow-up and monitoring. To delineate future research directions, this study employs a bibliometric analysis approach. We comprehensively surveyed two decades' worth of literature on DES for CAD using the Web of Science Core Collection (WOSCC). Out of 5,778 articles, we meticulously screened them based on predefined inclusion and exclusion criteria. Subsequently, we conducted an in-depth analysis encompassing annual publication trends, authorship affiliations, journal affiliations, keywords, and more. Employing tools such as Excel 2021, CiteSpace 6.2R3, VOSviewer 1.6. 19, and Pajek 5.17, we harnessed bibliometric methods to derive insights from this corpus. Analysis of annual publication data indicates a recent stabilisation or even a downward trend in research output in this area. The United States emerged as the leading contributor, with Columbia University and CRF at the forefront in both publication output and citation impact. The most cited document pertained to standardized definitions for clinical endpoints in coronary stent trials. Our author analysis identifies Patrick W. Serruys as the most prolific contributor, underscoring a dynamic exchange of knowledge within the field.Moreover, the dual chart overlay illustrates a close interrelation between journals in the “Medicine,” “Medical,” and “Clinical” domains and those in “Health,” “Nursing,” and “Medicine. ” Frequently recurring keywords in this research landscape include DES coronary artery disease, percutaneous coronary intervention, implantation, and restenosis. This study presents a comprehensive panorama encompassing countries, research institutions, journals, keyword distributions, and contributions within the realm of DES therapy for CAD. By highlighting keywords exhibiting recent surges in frequency, we elucidate current research hotspots and frontiers, thereby furnishing novel insights to guide future researchers in this evolving field.
Identification and collation of Australia’s shelf mapping datasets and development of a national geomorphological classification scheme for reef systems - Phase 1 Workshop Report. Report to the National Environmental Science Programme

Rocky reefs form an important habitat on the continental shelf and are subject to disproportionate fishing pressure given the high productivity of this habitat relative to adjacent sandy seabed. Despite this, little is known of the extent and nature of these systems beyond their value to the fishing industry. This project collates all known mapping data from government and industry (including data acquired during CERF and NERP Hubs) to provide an updated map of this key habitat around Australia, and will identify critical gaps in this knowledge to be filled by targeted surveys. This will significantly improve the knowledge of these environmental assets within state waters and the Commonwealth Marine Area, improve our understanding of assets in marine protected areas and inform environmental assessment of proposed activities and developments required by environmental legislation. Collated information also contributes to development of a blueprint for monitoring key ecological features of the Commonwealth Marine Area. A geomorphological classification system is also being developed for these reefs, and associated cross-shelf habitats with the aim of it being accepted and adopted nationally, and it is being tested and refined for biological applicability. This milestone report documents the major outcomes of a national workshop intended to identify existing shelf-reef datasets, key stakeholders and develop a pathway to sharing our current data holdings nationally, and identifying priority knowledge gaps to prioritise future research projects in this space. It also documents workshop outcomes focussed on developing a nationally accepted classification for cross-shelf reef systems and associated habitats, and progress made subsequently in refining a scheme suitable for Australian conditions and agencies.
Effect of Creatine Supplementation on Body Composition and Malnutrition-Inflammation Score in Hemodialysis Patients: An Exploratory 1-Year, Balanced, Double-Blind Design

Hemodialysis has a detrimental effect on fat-free mass (FFM) and muscle strength over time. Thus, we aimed to evaluate the effect of creatine supplementation on the body composition and Malnutrition-Inflammation Score (MIS) in patients with chronic kidney disease (CKD) undergoing hemodialysis. An exploratory 1-year balanced, placebo-controlled, and double-blind design was conducted with hemodialysis patients (≥18 years). The creatine group (CG) received 5 g of creatine monohydrate and 5 g of maltodextrin per day and the placebo group (PG) received 10 g of maltodextrin per day. MIS and body composition were analyzed at three time points: pre, intermediate (after 6 months), and post (after 12 months). After 6 months, 60% of patients on creatine experienced an increase in FFM compared to a 36.8% increase for those on placebo. Moreover, 65% of patients on creatine increased their skeletal muscle mass index (SMMI) compared to only 15.8% for those on placebo. Creatine increased intracellular water (ICW) in 60% of patients. MIS did not change after the intervention. In the CG, there was an increase in body weight (p = 0.018), FFM (p = 0.010), SMMI (p = 0.022). CG also increased total body water (pre 35.4 L, post 36.1 L; p = 0.008), mainly due to ICW (pre 20.2 L, intermediate 20.7 L, post 21.0 L; p = 0.016). Long-term creatine supplementation in hemodialysis patients did not attenuate the MIS, but enhanced FFM and SMMI, which was likely triggered by an increase in ICW.
A nudging data assimilation algorithm for the identification of groundwater pumping

This study develops a nudging data assimilation algorithm for estimating unknown pumping from private wells in an aquifer system using measured data of hydraulic head. The proposed algorithm treats the unknown pumping as an additional sink term in the governing equation of groundwater flow and provides a consistent physical interpretation for pumping rate identification. The algorithm identifies the unknown pumping and, at the same time, reduces the forecast error in hydraulic heads. We apply the proposed algorithm to the Las Posas Groundwater Basin in southern California. We consider the following three pumping scenarios: constant pumping rates, spatially varying pumping rates, and temporally varying pumping rates. We also study the impact of head measurement errors on the proposed algorithm. In the case study we seek to estimate the six unknown pumping rates from private wells using head measurements from four observation wells. The results show an excellent rate of convergence for pumping estimation. The case study demonstrates the applicability, accuracy, and efficiency of the proposed data assimilation algorithm for the identification of unknown pumping in an aquifer system.
Learning-Based Multimodal Control for a Supernumerary Robotic System in Human-Robot Collaborative Sorting

In this letter, a multi-modal learning and control framework is proposed for the control of a supernumerary robotic limb (SRL). The SRL is a wearable robotic arm designed to enhance the manipulation capabilities of its human user and extend the workspace by reaching greater heights. The multi-modal learning interface incorporates a neural network to estimate the user's hand position in space and to replicate the human grasp force during object handovers. This is essential to ensure that the grasp forces applied by the user are accurately mimicked, thus achieving a reliable grip, which may vary depending on the object's characteristics, such as its weight. The interface also includes a Gaussian Mixture Model (GMM) based intention estimation algorithm to identify the target reaching locations by the SRL. Electromyography and IMU information collected from a single armband are the only input information provided to the multi-modal learning interface. The execution of the target locations and grasp forces is achieved through the trajectory planner and the controller of the SRL. To evaluate the system and its control framework, we initially compute the coefficient of determination for grasping force regression, yielding a high value of 0.993. In addition, the hand position prediction error is determined to be $1.99\;\text{cm} \pm \text{1.39}\;{\text{cm}}$. Next, the overall SRL is tested in a human-robot collaborative sorting task through a user study with 7 participants. The results show the effectiveness of the proposed SRL control framework.
Water and growth: An econometric analysis of climate and policy impacts

Water‐related hazards such as floods, droughts, and disease cause damage to an economy through the destruction of physical capital including property and infrastructure, the loss of human capital, and the interruption of economic activities, like trade and education. The question for policy makers is whether the impacts of water‐related risk accrue to manifest as a drag on economic growth at a scale suggesting policy intervention. In this study, the average drag on economic growth from water‐related hazards faced by society at a global level is estimated. We use panel regressions with various specifications to investigate the relationship between economic growth and hydroclimatic variables at the country‐river basin level. In doing so, we make use of surface water runoff variables never used before. The analysis of the climate variables shows that water availability and water hazards have significant effects on economic growth, providing further evidence beyond earlier studies finding that precipitation extremes were at least as important or likely more important than temperature effects. We then incorporate a broad set of variables representing the areas of infrastructure, institutions, and information to identify the characteristics of a region that determine its vulnerability to water‐related risks. The results identify water scarcity, governance, and agricultural intensity as the most relevant measures affecting vulnerabilities to climate variability effects.
Facial Expression Realization of Humanoid Robot Head and Strain-Based Anthropomorphic Evaluation of Robot Facial Expressions

The facial expressions of humanoid robots play a crucial role in human–computer information interactions. However, there is a lack of quantitative evaluation methods for the anthropomorphism of robot facial expressions. In this study, we designed and manufactured a humanoid robot head that was capable of successfully realizing six basic facial expressions. The driving force behind the mechanism was efficiently transmitted to the silicone skin through a rigid linkage drive and snap button connection, which improves both the driving efficiency and the lifespan of the silicone skin. We used human facial expressions as a basis for simulating and acquiring the movement parameters. Subsequently, we designed a control system for the humanoid robot head in order to achieve these facial expressions. Moreover, we used a flexible vertical graphene sensor to measure strain on both the human face and the silicone skin of the humanoid robot head. We then proposed a method to evaluate the anthropomorphic degree of the robot’s facial expressions by using the difference rate of strain. The feasibility of this method was confirmed through experiments in facial expression recognition. The evaluation results indicated a high degree of anthropomorphism for the six basic facial expressions which were achieved by the humanoid robot head. Moreover, this study also investigates factors affecting the reproduction of expressions. Finally, the impulse was calculated based on the strain curves of the energy consumption of the humanoid robot head to complete different facial expressions. This offers a reference for fellow researchers when designing humanoid robot heads, based on energy consumption ratios. To conclude, this paper offers data references for optimizing the mechanisms and selecting the drive components of the humanoid robot head. This was realized by considering the anthropomorphic degree and energy consumption of each part. Additionally, a new method for evaluating robot facial expressions is proposed.
Meeting climate targets by direct CO2 injections:
What price would the ocean have to pay?

Abstract. We investigate the climate mitigation potential and collateral effects of direct injections of captured CO2 into the deep ocean as a possible means to close the gap between an intermediate CO2 emissions scenario and a specific temperature target, such as the 1.5 °C target aimed for by the Paris Agreement. For that purpose, a suite of approaches for controlling the amount of direct CO2 injections at 3000 m water depth are implemented in an Earth System Model of intermediate complexity. Following the representative concentration pathway RCP4.5, which is a medium mitigation CO2 emissions scenario, cumula-tive CO2 injections required to meet the 1.5 °C climate goal are found to be 390 Gt C by the year 2100 and 1562 Gt C at the end of simulations, by the year 3020. The latter includes a cumulative leakage of 602 Gt C that needs to be re-injected in order to sustain the targeted global mean temperature. CaCO3 sediment and weathering feedbacks reduce the required CO2 injections that comply with the 1.5 °C target by about 13 % in 2100 and by about 11 % at the end of the simulation. With respect to the injection-related impacts we find that average pH values in the surface ocean are increased by about 0.13 to 0.18 units, when compared to the control run. In the model, this results in significant increases in potential coral reef habi-tats, i.e., the volume of the global upper ocean (0 to 130 m depth) with omega aragonite > 3.4 and ocean temperatures be-tween 21 °C and 28 °C, compared to the control run. The potential benefits in the upper ocean come at the expense of strongly acidified water masses at depth, with maximum pH reductions of about −2.37 units, relative to preindustrial, in the vicinity of the injection sites. Overall, this study demonstrates that massive amounts of CO2 would need to be injected into the deep ocean in order to reach and maintain the 1.5 °C climate target in a medium mitigation scenario on a millennium timescale, and that there is a trade-off between injection-related reductions in atmospheric CO2 levels accompanied by reduced upper-ocean acidification and adverse effects on deep ocean chemistry, particularly near the injection sites.