Input Values Research Articles

Peer mentor contributions to an early intervention vocational rehabilitation specialist service following trauma: A qualitative study

BackgroundPeer mentors have a role in facilitating the participation, health and well-being of people who have had a traumatic injury. Few studies have explored the involvement of peer mentors in an early intervention vocational rehabilitation (EIVR) service following trauma. ObjectiveThis study aimed to explore the experience of implementing peer support within the context of an EIVR service from the perspectives of the peer mentors themselves, the vocational therapists supervising them, and the patients that received peer mentoring. MethodsSemi-structured interviews were conducted with twenty participants from three groups: peer mentors (n = 4); vocational therapists (n = 3); and patients who received the EIVR intervention (n = 24). Data were thematically analysed. ResultsThree themes were identified: The value of peer input in an EIVR service, The facilitators impacting the value of peer involvement as part of the EIVR service, The challenges impacting peer input as part of an EIVR service. ConclusionsThe inclusion of peer mentors early after major traumatic injury was a unique and valuable addition to the EIVR service. Offering peer support early on in rehabilitation enabled patients to gain a sense of hope for their future, and the expectation that returning to work was a realistic option. The careful selection of peer mentors, and ensuring they receive adequate preparation and ongoing supervision are vital to support their well-being during the intervention. Aiming to match peer mentors with similar injuries and work backgrounds to patients is an important contributor to the likely ongoing engagement of the mentee with the mentor.

Application of min-max normalization in backpropagation to detect early malnutrition

Malnutrition is the nutritional condition of toddlers which is characterized by a very thin condition, along with or without edema on the backs of both legs, weight according to body length or weight compared to height less than -3 standard deviations and/or upper arm circumference less than 11.5 cm in children aged 0-59 months. The contribution of this study is to apply min-max normalization to detect malnutrition using the Backpropagation method. This study uses the Backpropagation method with CRISP-DM, the input value of the artificial neural network is in an infinite range to a finite output value, which is in a range of 0 to 1. In order for this value to be met, a min-max normalization is carried out on the input value. The test results on this prototype achieved a maximum accuracy value of k= 1 with an accuracy value of 69%, Precision of 94% and Sensitivity of 69%. The results of the recommendation of the condition of children who are detected with malnutrition are very useful as an initial preventive measure in order to improve the nutritional condition of children with the right special treatment early and comprehensively.

Extremal values-based aggregation functions

We introduce and study aggregation functions based on extremal values, namely extended (l,u)-aggregation functions whose outputs only depend on a fixed number l of extremal lower input values and a fixed number u of extremal upper input values, independently of the arity of the input n-tuples (n≥l+u). We discuss several general properties of (l,u)-aggregation functions and we study special (l,u)-aggregation functions with neutral element, including t-conorms, t-norms and uninorms. We also study (l,u)-aggregation functions defined by means of integrals with respect to discrete fuzzy measures, as well as (l,u)-ordered weighted quasi-arithmetic means based on appropriate weighting vectors. We also stress some generalizations based on recently introduced new types of monotonicity. Some possible applications are sketched, too.

High‐throughput in‐memory bitwise computing based on a coupled dual‐SRAM array with independent operands

AbstractThe successful implementation of artificial intelligence algorithms depends on the capacity to execute numerous repeated operations, which, in turn, requires systems with high data throughput. Although emerging computing‐in‐memory (CIM) eliminates the need for frequent data transfer between the memory and processing blocks and enables parallel activation of multiple rows, the traditional structure, where each row has only one identical input value, significantly limits its further application. To solve this problem, this study proposes a dual‐SRAM CIM architecture in which two SRAM arrays are coupled such that all operands are different, thus rendering the use of CIM considerably more flexible. The proposed dual‐SRAM array was implemented through a 55‐nm process, essentially delivering a frequency of 361 MHz for a 1.2‐V supply and energy efficiency of 161 TOPS/W at 0.9 V supply.

Solar Energy Received on Flat-Plate Collectors Fixed on 2-Axis Trackers: Effect of Ground Albedo and Clouds

This study investigates the performance of isotropic and anisotropic diffuse models to estimate the total solar energy received on flat-plate collectors fixed on dual-axis trackers. These estimations are applied at twelve sites selected in both hemispheres with different terrain and environmental conditions. The diffuse (or transposition) models used in this study are the isotropic Liu-Jordan (L&J), Koronakis (KOR), Badescu (BAD), and Tian (TIA), and the anisotropic Hay (HAY), Reindl (REI), Klucher (KLU), Skartveit and Olseth (S&O), and Steven and Unsworth (S&U). These models were chosen because of their simplicity in the calculations and minimum number of input values. The results show that a single transposition model is not efficient for all sites; therefore, the most appropriate models are selected for each site under all, clear, intermediate, and overcast conditions in skies. On the other hand, an increase in the ground albedo in the vicinity of the solar installation can increase the annual inclined solar availability on a two-axis tracker by at least 9% on average. Further, a linear dependence of the annual inclined solar energy on the variation of the ground albedo was found. Also, a linear relationship exists between the annual diffuse-fraction and cloud-modification factor values at the 12 sites.

Cost-effectiveness analysis of budesonide/formoterol SMART therapy versus salmeterol/fluticasone plus as-needed SABA among patients ≥12 years with moderate asthma from the Chinese societal perspective

Objectives To evaluate the cost-effectiveness of budesonide/formoterol reliever and maintenance therapy compared with salmeterol/fluticasone plus salbutamol as reliever therapy for asthma patients ≥12 years from the societal perspective in China. Methods A Markov model was developed with three health states (non-exacerbation, exacerbation, and death) with a lifetime horizon. The exacerbation rates were obtained from a prospective cohort study conducted in Chinese asthma patients. Healthcare resources utilization data were estimated based on current clinical asthma management guidelines. Asthma-related mortality, cost input and utility values were derived from public database and literature. Model robustness was assessed with one-way sensitivity and probabilistic sensitivity analyses. Results Compared with salmeterol/fluticasone plus salbutamol, budesonide/formoterol reliever and maintenance therapy led to fewer exacerbation events (13.6 vs. 15.9) and 0.0077 quality-adjusted life years (QALY) gain at an additional cost of ¥196.38 over lifetime. The base case incremental cost-effectiveness ratio (ICER) was ¥25,409.98 per QALY gained. The variables that had most impact on the model output included drug costs and medication adherence. At a willingness-to-pay threshold of ¥257,094/QALY (3 times of gross domestic product per capita in China in 2022), the probability of budesonide/formoterol maintenance and reliever therapy being cost-effective versus salmeterol/fluticasone plus as-needed salbutamol was 83.00%. Conclusion From the societal perspective, budesonide/formoterol reliever and maintenance therapy is likely to be a cost-effective option compared with salmeterol/fluticasone plus as-needed salbutamol for Chinese asthma patients ≥12 years.

Enhancing LES efficacy in wind load evaluation of low-rise buildings using synthesized inflow turbulence

In Large-Eddy Simulation (LES), the turbulence inflow can be generated synthetically, where the input value of the maximum turbulence frequency can impact the accuracy. LES efficacy can be influenced by the designated computational domain (CD) size and grid specifications. This study proposes multiple parametric studies that aim to correlate the effective parameters controlling the accuracy of numerical wind load evaluation on a low-rise building without compromising the computational cost. The study includes the efficient selection of turbulence maximum frequency (fmax) that can accurately capture the pressure fluctuation induced on the building facade. Both CD size, which involves the distances from the building to the boundary condition, and CD discretization in terms of grid size scheme and shape, are examined. The error assessment is conducted by comparing wind flow aerodynamics and evaluating the RMSE for the pressure parameters with respect to the wind tunnel test. The findings of this study suggest using fmax which can be resolved by the minimum grid size as an input in the inflow turbulence generated to accurately transport the majority of eddies when a satisfactory wavelength of 4 Δx is utilized. This study shows that computational domain size and discretization can be significant sources of error that can reach 21 %, compromising the reliability of computational wind load predictions. The current code in selecting computational minimum width and length is found to be insufficient. In addition, it is found that there are currently no recommendations for selecting the width computational dimension specifications when examining low-rise buildings oriented in an oblique wind direction. The domain grid specification induced a considerable error of 14 % in the mean pressure across the roof when tetrahedral grid shapes were used. Overall, this study's proposed recommendations can significantly impact the efficacy of computational wind load evaluation for low-rise building geometry.

Incidental learning of collocations through different multimodal input: The role of learners’ initial L2 proficiency

This study investigated the impact of one unimodal (i.e., reading-only) and two different multimodal input modes (i.e., reading-while-listening and viewing with captions) on incidental collocation learning, alongside examining the relationship between learning gains and overall L2 proficiency. A total of 180 university students at pre-intermediate and upper-intermediate levels from China were randomly assigned to one of the input modes. Target collocation knowledge tests, including form recognition and meaning recall, were administered immediately after exposure to the input material and after a 2-week interval. Results indicated that while learners across different proficiency levels acquired collocation knowledge through all input modes to varying degrees, those in the multimodal conditions outperformed their counterparts in terms of both recognition and recall dimensions. Additionally, after 2 weeks, despite decreased learning gains for all learner groups, multimodal input was found to benefit upper-intermediate learners' meaning recall of the target collocations. Learners’ language proficiency significantly predicted their immediate learning outcomes in both recognition and recall. These findings offer further support for multimedia learning theory and dual code theory, contributing to a deeper understanding of the value of multimodal meaning-focused language input.

Имитационная модель дисковой лесопосадочной машины

To carry out artificial reforestation, it is necessary to use mechanized means – tree-planting machines. The lack of domestic tree-planting machines is one of the problems of artificial reforestation in Russia. The purpose of the study is to substantiate the design parameters of a tree-planting machine with a disk operating device. To achieve this goal, the use of modern software environments is required with the possibility of full consideration of all factors affecting the machine. In the course of this research, a solid model of a tree-planting machine has been developed by means of a computer-aided design system. The “Universal Mechanism 9.1” software package, designed for modeling the dynamics and kinematics of mechanical systems, has been chosen as a computational environment studying the parameters of the model. Only the input values for the simulation model have been introduced into the software package, since the mathematical apparatus is embedded in the core of the program. Using the software package, a simulation model of a tree-planting machine has been created, taking into account both the interaction of the machine links and the discrete contact interaction of the seedling with the machine and the soil. A computational experiment has been carries out and the kinematic and dynamic parameters of the tree-planting machine have been established, in which the feeder would not interfere with the free fallout of the briquette with the seedling. The geometric parameters of the mechanism have been determined to match the cam profile with its movement along with the disk and the operation of the roller tappet so that the grippers install the briquette with the seedling into the soil in a timely manner. The trajectory of movement of the briquette with the seedling has been found. With the help of FDM 3D-printing, a full-size model of a disk planting apparatus has been made, and a laboratory experiment has been conducted to establish the required torque on the hub of the planting apparatus disk and the shaft of the drum. The results of the experiment have confirmed the adequacy of the developed simulation model.

Maintain Power Transmission and Efficiency Tracking Using Variable Capacitors for Dynamic WPT Systems

This study introduces a new method for real-time efficiency tracking and stable output power of Dynamic Wireless Power Transfer (DWPT) systems using variable capacitors. A preliminary detailed discussion and an analysis of the DWPT system are carried out to show how the system can optimize power transmission and efficiency when the relative positions of transmitter and receiver change using a dynamic real-time control of the variable capacitors belonging to the compensation networks. This paper shows a detailed model of the DWPT system, including magnetic coupling analysis, circuit dynamics analysis, and efficiency characteristics analysis, in order to modify the control input values as needed. By utilizing a group optimization strategy, the transmission efficiency can be quickly maximized without using a position detection module. Simulation results demonstrate the effectiveness of the proposed method under various dynamic conditions, achieving significant improvements in energy efficiency and transmission reliability of the DWPT system. This research provides a powerful method to increase the overall performances of DWPT systems, which will help the development of future wireless charging technology.

My child's medication journey: A parent's view.

This article discusses the personal experiences of the parents regarding their child's medication journey. Parents can play a vital role in their child's overall mental well-being, ensuring they receive the care and support they need. To navigate these difficult circumstances, it is crucial for parents to maintain open lines of communication with their child as well as with the health care professionals involved in their care. Professionals should be aware that parents' perspectives toward mental health medication can evolve over time, influenced by the experiences and challenges faced by both child and family. Excluding parents from their child's treatment limits the potential for a comprehensive and holistic treatment approach. By recognizing the value of parental input, health care professionals can create a collaborative environment that maximizes the chances of finding the most suitable treatment strategy. It is important for health care professionals to engage families in a respectful and empathetic manner, recognizing the challenges they face. Providing them with the necessary support and guidance can help reduce feelings of helplessness and ensure their active involvement in their child's mental health journey (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An Adjustment Strategy for Tilted Moiré Fringes via Deep Q-Network

Overlay accuracy, one of the three fundamental indicators of lithography, is directly influenced by alignment precision. During the alignment process based on the Moiré fringe method, a slight angular misalignment between the mask and wafer will cause the Moiré fringes to tilt, thereby affecting the alignment accuracy. This paper proposes a leveling strategy based on the DQN (Deep Q-Network) algorithm. This strategy involves using four consecutive frames of wafer tilt images as the input values for a convolutional neural network (CNN), which serves as the environment model. The environment model is divided into two groups: the horizontal plane tilt environment model and the vertical plane tilt environment model. After convolution through the CNN and training with the pooling operation, the Q-value consisting of n discrete actions is output. In the DQN algorithm, the main contributions of this paper lie in three points: the adaptive application of environmental model input, parameter optimization of the loss function, and the possibility of application in the actual environment to provide some ideas. The environment model input interface can be applied to different tilt models and more complex scenes. The optimization of the loss function can match the leveling of different tilt models. Considering the application of this strategy in actual scenarios, motion calibration and detection between the mask and the wafer provide some ideas. To verify the reliability of the algorithm, simulations were conducted to generate tilted Moiré fringes resulting from tilt angles of the wafer plate, and the phase of the tilted Moiré fringes was subsequently calculated. The angle of the wafer was automatically adjusted using the DQN algorithm, and then various angles were measured. Repeated measurements were also conducted at the same angle. The angle deviation accuracy of the horizontal plane tilt environment model reached 0.0011 degrees, and the accuracy of repeated measurements reached 0.00025 degrees. The angle deviation accuracy of the vertical plane tilt environment model reached 0.0043 degrees, and repeated measurements achieved a precision of 0.00027 degrees. Moreover, in practical applications, it also provides corresponding ideas to ensure the determination of the relative position between the mask and wafer and the detection of movement, offering the potential for its application in the industry.

Experimental and FEA Simulations Using ANSYS on the Mechanical Properties of Laminated Object Manufacturing (LOM) 3D-Printed Woven Jute Fiber-Reinforced PLA Laminates

The mechanical properties of woven jute fiber-reinforced PLA polymer laminates additively manufactured through Laminated Object Manufacturing (LOM) technology are simulated using the finite element method in this work. Woven jute fiber reinforcements are used to strengthen bio-thermoplastic PLA polymers in creating highly biodegradable composite structures that can serve as one of the environmentally friendly alternatives for synthetic composites. A LOM 3D printer prototype was designed and built by the authors. All woven jute/PLA biocomposite laminated specimens made using the built prototype in this study had their tensile and flexural properties measured using ASTM test standards. These laminated structures were modeled using the ANSYS Mechanical Composite PrepPost (ACP) module, and then both testing processes were simulated using the experimentally measured input values. The FEA simulation results indicated a close match with experimental results, with a maximum difference of 9.18%. This study served as an exemplary case study using the FEA method to predict the mechanical behaviors of biocomposite laminate materials made through a novel manufacturing process.

AI in Creative Arts: Advancements and Innovations in Artificial Intelligence

The advent of Artificial Intelligence (AI) has revolutionized the creative landscape, blurring the lines between human and machine innovation. This paper delves into the fascinating realm of AI in Creative Arts, exploring the capabilities and implications of AI-generated art, music, and literature. Through a comprehensive review of existing literature and case studies, we examine the current state of AI-powered creative tools and software, highlighting their potential to augment and transform human creativity. We also investigate the challenges and controversies surrounding AI-generated art, including issues of authorship, ownership, and the role of human imagination. Our analysis reveals that AI-generated art, music, and literature not only demonstrate technical proficiency but also exhibit creative potential, often surpassing human expectations. However, the reliance on algorithms and data raises important questions about the nature of creativity and the value of human input. This paper contributes to the ongoing discourse on AI in Creative Arts, providing insights into the possibilities and limitations of AI-generated content. Our findings have significant implications for the future of creative industries, highlighting the need for collaboration between humans and machines to foster innovative and meaningful artistic expression. Ultimately, this research demonstrates that AI in Creative Arts is not a replacement for human creativity but a transformative force that can enhance and expand our understanding of art, music, and literature. By embracing this synergy, we can unlock new possibilities for artistic expression and push the boundaries of human creativity.

Using the illness-death model to estimate age- and sex-standardized incidence rates of diabetes in Mexico from 2003 to 2015

BackgroundWe aimed to estimate the age-specific and age-standardized incidence rate of diabetes for men and women in Mexico between 2003 and 2015, and to assess the relative change in incidence of diabetes between 2003 and 2015.MethodsWe use a partial differential equation describing the illness-death model to estimate the incidence rate (IR) of diabetes for the years 2003, 2009 and 2015 based on prevalence data from National Health Surveys conducted in Mexico, the mortality rate of the Mexican general population and plausible input values for age-specific mortality rate ratios associated with diabetes.ResultsThe age-standardized IR of diabetes per 1000 person years (pryr) was similar among men (IRm) and women (IRw) in the year 2003 (IRm 6.1 vs. IRw 6.5 1000/pryr), 2009 (IRm: 7.0 vs. IRw: 8.4 1000/pryr), and in 2015 (IRm 8.0 vs. IRw 10.6 1000/pryr). The highest incident rates were observed among men and women in the 60–69 age group.ConclusionsOverall, the incidence rate of diabetes in Mexico between the years 2003 and 2015 remained stable. However, rates were markedly higher among women in the age group 40–49 and 50–59 in the year 2015 compared with rates in 2003.

ИЗУЧЕНИЕ МНОГОЛЕТНЕЙ ДИНАМИКИ ВНЕСЕНИЯ МИНЕРАЛЬНЫХ И ОРГАНИЧЕСКИХ УДОБРЕНИЙ В РЕСПУБЛИКЕ ХАКАСИЯ ЗА 1966–2022 гг.

The purpose of the study is to analyze the application of mineral and organic fertilizers for arable land in the conditions of the Republic of Khakassia for the period 1966–2022. The object of the study is the long-term dynamics of mineral and organic fertilizers. Calculations were performed in accordance with the methodology set out in the Order of the Ministry of Natural Resources and Ecology of the Russian Federation “On approval of methods for quantitative determination of greenhouse gas emissions and greenhouse gas removals” dated May 27, 2022. Data are provided on the introduction of carbon in fertilizers for the period from 1966 to 2022. The area of arable land decreased from 732.0 thousand hectares in 1966 to 559.6 thousand hectares in 2022. The historical minimum and maximum were observed, respectively, for the area of arable land in 2004 (407 thousand hectares) and 1962–1988. (732 thousand hectares), for organic and mineral fertilizers in 2004 (644.1 tons) and in 1989 (37,830.6 tons). In 2022, the total amount of fertilizers applied was 65.1 tons for organic fertilizers, 3,620.6 tons for mineral fertilizers. The range of carbon values in fertilizers was 91.5–25,743.6 tons. There was a decrease in the amount of carbon supplied with fertilizers (from 9002.2 to 1133.1 tons). In 2022, an analysis of the gross values of carbon input with plant residues was carried out. In 2022, 1,501.8 t C ∙ year–1 was added to the arable land with plant residues. Most of them were introduced with wheat. Methodological recommendations were compiled to improve analytical reporting on carbon in agrocenoses, including support for farms implementing measures to accumulate carbon in the soil. It is proposed to create a carbon balance module for agroecosystems in a carbon sequestration database. The proposed solutions will allow monitoring the carbon balance of agroecosystems in the Republic of Khakassia.

Response Surface Methodology Optimization of Wear Rate Parameters in Metallic Alloys

The optimization of wear rate parameters in metallic alloys using Response Surface Methodology (RSM) has been experimentally performed. The wear rate, a critical factor affecting the durability and performance of metallic components, served as the response parameter, while track diameter, sliding speed, and mass difference were considered as independent variables. The Central Composite Design (CCD) experimental method systematically explored the response surface and optimizes the wear rate. A mathematical model was developed, revealing a significant p-value of 0.043 in the ANOVA table, indicating the collective influence of the independent variables on wear rate at a significance level of 0.05. Furthermore, the model demonstrates a substantial explanatory power, with R-squared of 69.45% and adjusted R-squared of 51.95%. The p-value calculated to be 0.60 for the statistical Lack of fit indicated a satisfactory model. These findings highlight the effectiveness of RSM in optimizing the experimental input values and offer valuable insights for enhancing the durability and performance of metallic alloys in various industrial applications. The obtained result addresses the problem of uncertainty inherent in optimal levels of input parameters wear experimentation.

Unexpected contributions by carbonates and organic matter in a silicate-dominated tropical catchment: An isotope approach

The understanding of global carbon has rarely extended to small-scale tropical river basins. To address these uncertainties, this study aims to investigate the importance of rock weathering and organic matter turnover in the carbon cycle in a terrain dominated by crystalline silicate rocks. The geochemical composition of the dissolved and particulate carbon phases (DIC, DOC and POC) and their stable carbon isotopes were studied in the Deduru Oya River in Sri Lanka. Dissolved inorganic carbon (DIC) was the most dominant carbon phase and its contribution to the total carbon pool varied between 67 and 89 %. Furthermore, the δ13CDIC values in the river varied between −1.1 and −16.5 ‰. The lithological characteristics and molar ratios between Ca2+, Mg2+ and HCO3− indicated rock weathering mainly by CO2 and carbonic acid. The δ13CDIC values for groundwater input were −15.9 ‰, while for carbonate weathering, mainly due to fertiliser input, they reached a value of −12.7 ‰. This input was fed into an isotope mass balance to determine the relative contributions. However, the isotope mass balance was only plausible after correcting for the effects on δ13CDIC caused by degassing and photosynthesis. Our study demonstrated that carbonate weathering and organic matter turnover are essential components of the river carbon cycle even in a silicate dominated catchment. They can represent up to 60 % of the DIC pool. Combined with the higher organic matter turnover and high pCO2 in the river water, it can be suggested that the Deduru Oya River acts as a net source of CO2 in the atmosphere. Our study shows that CO2 degassing and in-stream photosynthesis in tropical river systems need to be considered along with chemical weathering to account for carbon transport and turnover in tropical rivers.

Parkinson’s Disease Prediction Using Machine Learning Models

Parkinson's disease is a neurodegenerative condition that affects billions of persons worldwide. This abstract aims to shed light on the causes and consequences of this debilitating condition. The primary cause of Parkinson's disease is the progressive degeneration of dopaminergic neurons in the substantia nigra region of brain. This neuronal loss results in a depletion of dopamine, a crucial neurotransmitter responsible for regulating movement and coordination. Therefore, individuals with Parkinson's disease have symptoms like tremors, rigidity, bradykinesia, and postural instability. These signs profoundly impact the quality of life, causing difficulties with daily activities and reducing independence. In addition to motor symptoms, non-motor symptoms such as depression, cognitive impairment, and autonomic dysfunction often accompany the disease, further complicating the clinical picture. Research into the causes and consequences of Parkinson's disease is ongoing, with a focus on using efficient medications and refining the quality of life for those affected by this condition. Now by Using machine learning algorithms, we can predict whether a person has a specific disease based on input values like gender and age. These algorithms analyze patterns and relationships in data to get predictions about an individual's health status. This technology can assist in early disease detection and improve healthcare outcomes..

Copper - nickel electro-explosive powder feedstocks for extrusion-based additive manufacturing

The average sizes of copper and nickel nano- and microparticles formed at exploding wires in the range of inhomogeneous and homogeneous heating have been established. The average size of nanoparticles grows, and that of microparticles decreases when the value of energy input into wires reaching E ≈ 0,5Es (Es - sublimation energy). While E > 0.5Es the dependence of the average sizes of nano- and microparticles is weakly expressed. The viscosity of feedstocks increases as the proportion of nanoparticles in the powder increases and their size decreases. At equal dispersity and mass content of particles in the polymer, the feedstock viscosity has been found to depend on the adsorption behavior of metals. The analysis of temperature dependence of the feedstock viscosity based on copper and nickel powders has shown that the optimum values of E/Es for one-stage production of micro-nanoparticle powders by the exploding wires lie in the range of 0.4 … 0.7.