Weight Of Factors Research Articles

Systematic Review Protocol: Anhedonia in Youth and the Role of Internet-Related Behavior

Background: Depression is a serious and debilitating condition with a rising prevalence. Anhedonia, a core symptom of depression, is notably significant and the second most weighted factor among the non-somatic concerns of depression, following depressed mood. The interaction between technology use, mood, emotions, depression, and anhedonia is a critical area of investigation. Aim: This study aims to develop a comprehensive Systematic Review Protocol to examine the emotional effects of Internet-related behavior in young people. Methods: A systematic review protocol was developed following PRISMA guidelines for systematic reviews. The research question was formulated according to the PICOS framework. The search was conducted using PubMed/Medline, Cochrane Library, Embase, Scopus, and PsycInfo, supplemented by gray literature sources via Google Scholar. The methodological quality and risk of bias was assessed using the Critical Appraisal Skills Programme (CASP) framework. This systematic review protocol was registered on the Open Science Framework with the registration DOI: 10.17605/OSF.IO/SHNJU. Conclusions: The findings of this systematic review are expected to provide new evidence on the correlations between depression, Internet addiction, and anhedonia, contributing to the development of targeted intervention strategies and improving the understanding of young peoples’ emotional well-being.

Safer operating areas (SOA) of cylindrical lithium-ion battery – A probabilistic approach

This study introduces a real-time probabilistic safety assessment of a 18650 cylindrical battery. The physics-based failure scenarios from battery abuse are mapped onto Bayesian Network framework, enabling a quantitative estimation of monitoring parameters, health indicators and loss of control events. The model (encompassing multiple states, weightage factors, interdependencies, and uncertainties) is validated with 70 experimental data points of the same geometry, comprising 4 categories of abuses (surface heating, fast overcharging, external short circuit, and internal deformation). Through testing, probabilistic safety thresholds are established for 3 observed events: gas formation, venting, and fire and explosion.Sensitivity analysis reveals state of charge (at SOC > 50 %) and surface temperature (at Tsurf > 90°C) as the most influential parameters while inadequate cooling system and current interrupt device (CID) as influential safeguards for thermal runaway and fire to occur. Moreover, the identified safety thresholds are subjected to validation and testing against multiple health indicators to draw important insights. Probability, P(fire)deformation>P(fire)surface heat as internal deformation lowers onset to thermal runaway. In culmination, a safer operating area (SOA) is proposed, leveraging the identified thresholds and most sensitive parameters. The proposed SOA with 4 distinct boundaries is not only consistent with the temporal variations in cell temperature during thermal abuse test, but also provides a robust approach to monitor the safety of a battery.

Research on the Assessment Method of College Students' Financial Difficulty Index in the Context of Big Data Based on AHP Algorithm

The emergence of big data has allowed researchers to study people's financial struggles in greater detail. Among various kinds of difficulties, the financial barriers preventing low-income children from enrolling in higher education have received significant attention. Therefore, this paper uses the AHP algorithm and proposes a college student poverty index assessment method, aiming to provide a reference basis for the normal enrollment of children from low-income families by comprehensively analyzing various factors. This paper first constructs a hierarchical model to decompose the college student poverty problem into multiple influencing factors. Then, the AHP algorithm was utilized to calculate each factor's weights and consistency test, and each factor's relative importance was derived. As a result, each college student's poverty index was calculated using the comprehensive score method. The results show that the assessment method can reflect the degree of economic and social difficulties of college students from low-income families with better accuracy, providing a reliable reference value for promoting them in regular college enrollment. At the same time, the method can also be used in the government's formulation of poverty alleviation policies and the school's educational assistance work.

Soil Quality Assessment and Its Spatial Variability in an Intensively Cultivated Area in India

Intensive agricultural practices lead to a deterioration in soil quality, causing a decline in farm productivity and quality, and disturbing the ecosystem balance in command areas. To achieve sustainable production and implement effective soil management strategies, understanding the state and spatial variability of soil quality is essential. The study aims to enhance the understanding of soil quality variability and provide actionable insights for sustainable soil management. In this regard, principal component analysis (PCA) and digital soil mapping were used to assess and map the spatial variability of the soil quality index (SQI) in the Cauvery command area, Mandya district, Karnataka, India. A total of 145 georeferenced soil samples were drawn at 0–15 cm depth and analyzed for physico-chemical properties. PCA was used to reduce the dataset into a minimum dataset as eight important soil indicators and to determine relative weightage factors, which were used for assessing SQI with linear and non-linear scoring methods. For spatial assessment of SQI, the random forest algorithm with environmental covariates was used to map eight soil indicators selected in the minimum dataset. The soil property maps were subjected to linear and non-linear scoring, followed by multiplying with corresponding weightage factors and summation to produce SQI maps. Results reveal that values of SQI calculated using linear scoring, range from 0.10 to 0.64, with a mean of 0.39, while non-linear scoring exhibits a wider range of 0.12 to 0.78 and a mean of 0.48. With a slight higher sensitivity index of 6.5, non-linear scoring proved to be the better scoring method compared to linear scoring. Spatial assessment shows that the R2 and LCC between the calculated and predicted SQI were higher for non-linear scoring (0.66 and 0.66) compared to linear scoring (0.60 and 0.65). The SQI maps reveal high spatial variability with more than 40 percent of soils classified as moderate-to-low index. The soils with low SQI were distributed in eastern parts, whereas western parts exhibited high-to-very-high soil quality. To achieve production goals and improve soil quality in the eastern region, sustainable soil and crop management strategies must be developed, and their effects on soil quality should be assessed.

Optimization Study of the Production Layout in the Trouser Workshop of GR Company

GR Company, as a traditional clothing manufacturing enterprise, the suit trousers production workshop has the problems of irrational facility arrangement, poor space utilization leading to long material handling routes, inconvenient employee pick-up and space wastage. Considering the current development, this paper, based on SLP theory, draws a comprehensive relationship map of operations by collating and analyzing the interrelationships between logistics and non-logistics of the production process and operation units, and then re-plans and designs the workshop in a reasonable way. The results show that: the location of the operating units is drawn out, combined with the actual demand for the area of each operating unit, the preliminary design of the two layout schemes; the weighted factor method and scoring method are used to select the optimal layout of the more reasonable scheme 1. The distance of material handling is shortened between the various operating units to reduce the cost, improve the production efficiency and space, so as to enhance the competitiveness and sustainable development of the enterprise.

Weighted CTDI Equation for 3D Rotational Angiography: A Monte Carlo Study

This study aims to verify the weighted Computed Tomography Dose Index (CTDIw) coefficients of 3D rotational angiography (3DRA) procedure using Monte Carlo simulation. The Monte Carlo simulation EGSnrc usercode was employed for 3D dose simulations of the rotational angiography procedure. A virtual phantom resembles the head CTDI phantom was constructed, with a diameter of 16 cm and a density resembling polymethyl methacrylate (1.13 g/cm3). A series of virtual phantoms consisting of 5 images with ionization chamber detectors at the center position, 12 o'clock, 9 o'clock, 6 o'clock, and 3 o'clock were acquired. Simulations were performed with photon sources of 70 and 109 kVp for 200-degree x-ray tube rotation. The field of view was divided into narrow, wide, and full beam with diameters of 1.7 cm; 4.9 cm; and 8.6 cm, respectively. The simulated doses at the ionization chamber were processed into weighting factor for weighted CTDI and compared with direct measurements. The dose ratio between peripheral and center positions for 360° CBCT and 200° 3DRA was 1:1 and 1:3 in this study. The weighting factors for 3DRA were determined as CTDIcenter = ¼ and CTDIperiphery = ¾. The measured average percentage difference of CTDIw between our weighted factor and conventional CTDIw was 1.75 % (-3.99 % to 6.08 %). The x-ray tube position of 3DRA impacted the accuracy of weighting factor of CTDIw, with implications for the proposed weighting factor (Wcenter = ¼ and Wperiphery = ¾) when using a 3DRA machine.

Adaptive control for shape memory alloy actuated systems with applications to human-robot interaction.

Shape memory alloy (SMA) actuators are attractive options for robotic applications due to their salient features. So far, achieving precise control of SMA actuators and applying them to human-robot interaction scenarios remains a challenge. This paper proposes a novel approach to deal with the control problem of a SMA actuator. Departing from conventional mechanism models, we attempt to describe this nonlinear plant using a gray-box model, in which only the input current and the output displacement are measured. The control scheme consists of the model parameters updating and the control law calculation. The adaptation algorithm is founded on the multi-innovation concept and incorporates a dead-zone weighted factor, aiming to concurrently reduce computational complexities and enhance robustness properties. The control law is based on a PI controller, the gains of which are designed by the pole assignment technique. Theoretical analysis proves that the closed-loop performance can be ensured under mild conditions. The experiments are first conducted through the Beckhoff controller. The comparative results suggest that the proposed adaptive PI control strategy exhibits broad applicability, particularly under load variations. Subsequently, the SMA actuator is designed and incorporated into the hand rehabilitation robot. System position tracking experiments and passive rehabilitation training experiments for various gestures are then conducted. The experimental outcomes demonstrate that the hand rehabilitation robot, utilizing the SMA actuator, achieves higher position tracking accuracy and a more stable system under the adaptive control strategy proposed in this paper. Simultaneously, it successfully accommodates hand rehabilitation movements for multiple gestures. The adaptive controller proposed in this paper takes into account both the computational complexity of the model and the accuracy of the control results, Experimental results not only demonstrate the practicality and reliability of the controller but also attest to its potential application in human-machine interaction within the field of neural rehabilitation.

GIS based Site Suitability Analysis for Wastewater Treatment Plant in Upper Ponnaiyar Watershed, South India

The management of water resources is a major challenge for most of the dry land areas, as the demand for water are increasing and the quality are being compromised due to a number of natural and economic factors. If the proper location for the treatment plant is not selected, then it may lead to soil degradation and groundwater pollution. This study was conducted by using GIS techniques for selecting suitable wastewater treatment plant sites. There are seven parameters considered in the analysis consists of land use/land cover, elevation, road proximity, a slope of the ground, drainage density, geology, and soil. The weighted index overlay analyses of the final map with final weighted factor map were integrated and produced the final suitable wastewater treatment plant site map using ArcGIS Spatial Analyst tools. As a result, 73.88 km2 (6.90%), 359.55km2 (33.59%), 441.08km2(41.21%), 180.71km2 (16.88%), 15.05 km2 (1.41%) of the total study area was found to be unsuitable, low suitable, moderate suitable, high suitable and very high suitable respectively. The area of very high suitable is preferable for wastewater treatment plant sites, because of their minimum effect on the environment, public health and cost effective than other parts of the study area. Therefore, the study will help the concerned authorities to formulate their development strategies according to the selected suitable wastewater treatment plant site available to the area.

Identification of time-varying inertia tensor parameters of a space solar power satellite based on a robust weighted recursive algorithm under impulsive disturbance

Considering changes in the structural size and mass caused by the on-orbit assembly process in a planar space solar power satellite, this study proposes an improved robust recursive algorithm to identify the time-varying inertia tensor parameters of the system. In this algorithm, to decrease the effect of impulsive disturbance on inertial parameter identification during the on-orbit assembly and improve computational accuracy, robust weighted factor and multi-innovation theory are involved. Then, the time-varying inertia tensor parameters of this large flexible structure are identified based on the distributed measurement results of multiple attitude sensors. The simulation results indicate that the proposed algorithm improves the identification accuracy and has better robustness than the original method under the impulsive disturbance. The relative values of the average relative error caused by the impulsive disturbance under the proposed algorithm are approximately 27.8–73.0% less than those under the original method. Additionally, the results also demonstrate that the proposed algorithm has higher noise immunity than the classical recursive least-squares method in a time-varying system. When the impulsive disturbance and zero-mean Gaussian white noise are applied to the system, compared with the conventional recursive least-squares method, the proposed algorithm yields lower absolute and relative values of the average relative error by approximately 4.2% and 44.9%, respectively.

Groundwater Assessment Using MIF Technique in a Small Basin of Malwa Region, Madhya Pradesh, India

Water, a vital natural resource and essential component for all living organisms, exists in two primary forms: surface water and groundwater. Groundwater, a significant global resource, is unevenly distributed worldwide. Enhancing groundwater recharge is a fundamental hydrological parameter for assessing, managing, and modelling groundwater resources. However, determining recharge rates is a complex and challenging task, despite its crucial role in recharge assessments. The current study has been conducted in northwest part of Madhya Pradesh. The Neemuch and Mandsaur district comes under semi-arid zones and faces the problem of scarcity of water annually. The primary objective of this study is to evaluate groundwater potential zones using an integrated approach using geospatial tools. Various thematic parameters i.e., slope, rainfall, geology, drainage density lineaments, geomorphology and soil were considered for identifying evaluate potential zones. Thematic maps were generated using SRTM DEM, Sentinel-2 Imagery, and Survey of India (SOI) toposheets at a scale of 1:50000. The selected thematic factors were integrated for the determination of the groundwater potential of the study area, with appropriate weightage factors given to different themes by means of the multi-influencing factor (MIF) approach. This process was repeated for other layers, resulting in reclassified layers. These layers were then combined to delineate zones classified as “very good”, “good”, “moderate”, “poor”, and “very poor” for groundwater potential. The assessment of groundwater potential information using geospatial techniques can aid in identifying suitable locations for providing safe drinking water to rural populations. This comprehensive integrated approach of RS and GIS in assessing groundwater potentiality offers valuable insights for effective identification of suitable areas to meet the potable water needs of rural communities.

AGILE ESTIMATIONS: EMPIRICAL STUDY USING WEIGHTED COMPLEXITY FACTORS

Software time and cost estimates are always challenging due to the quickly changing software process. Especially Agile project estimations are complex, and the story points differ from person to person. Due to the person-to-person variations, project managers have difficulty arriving at the project cost for development projects with different sizes or complexity of systems/software. This paper proposes a new estimation model named the WEighted COmplexity Factor Estimation (WECOFE) Model to reduce the complexities of Agile story point estimations. Weighted complexities factors identify Low, Moderate, and High Project Level Factors and Story Level Factors. For each level, multiple factors were defined. Derived Weighted complexity based on Project, Requirement, and Resource Level factors. Each story's point estimation is explained based on weighted complexity defined and assigned factors values. Conducted study on various industry projects, showing that the WECOFE approach is promising by observing the Magnitude of Relative Error (MRE) and Mean Magnitude of Relative Error (MMRE). Also, performed a t-test for one sprint project with 51 stories, and paired sample means are equal for WECOFE with actual story points spent by the team.

Enhanced Whale Optimization Algorithm for Improved Transient Electromagnetic Inversion in the Presence of Induced Polarization Effects

The transient electromagnetic (TEM) method is a non-contact technique used to identify underground structures, commonly used in mineral resource exploration. However, the induced polarization (IP) will increase the nonlinearity of TEM inversion, and it is difficult to predict the geoelectric structure from TEM response signals in conventional gradient inversion. We select a heuristic algorithm suitable for nonlinear inversion—a whale optimization algorithm to perform TEM inversion with an IP effect. The inverse framework is optimized by opposition-based learning (OBL) and an adaptive weighted factor (AWF). OBL improves initial population distribution for better global search, while the AWF replaces random operators to balance global and local search, enhancing solution accuracy and ensuring stable convergence. Tests on layered geoelectric models demonstrate that our improved WOA effectively reconstructs geoelectric structures, extracts IP information, and performs robustly in noisy environments. Compared to other nonlinear inversion methods, our proposed approach shows superior convergence and accuracy, effectively extracting IP information from TEM signals, with an error of less than 8%.

Optimum energy management strategy with NEM-ETOU for campus buildings installed with solar PV using EPSO

In campus facilities, classrooms and labs often use a large amount of energy due to their variable consumption pattern and lack of use management. Installing solar photovoltaic (PV) rooftop in the campus building through net-energy-metering (NEM) scheme has recently increased to reduce energy usage as well as generate income from the sales of excess solar energy to the grid. Meanwhile, the Malaysian government has introduced the Enhanced Time of Use (ETOU) tariff to encourage consumers to shift their electricity usage to reduce their bills through demand response. Despite offering different rates at various times of the day, the ETOU tariff may lead to an increase in electricity expenses if consumers do not effectively shift their consumption to lower rate hours and eventually using more during peak hours. Additionally, there has been lack of quantitative analysis conducted to determine how effective the compensation scheme provided by NEM. Therefore, considering a university building that holds a commercial electricity tariff, this study aims to develop an optimum energy management strategy integrating demand response and solar energy rooftop using improved Evolutionary- Particle Swarm Optimisation (EPSO). To determine the best load shifting strategy, the model was used to evaluate two Universiti Teknologi MARA (UiTM) campuses where NEM solar PV rooftop systems were installed. The analysis involved testing various weightage factors for controlling the loads, ranging from 5% to 20% under 2 different electricity tariffs offered in Malaysia. Results show the electricity cost under EPSO provides a significant cost reduction compared to PSO for both tariffs with a lesser computational time. Furthermore, EToU tariff with a weightage factor of 20% gives a higher energy and cost saving compared to fixed tariff. The findings from this research will assist the energy users in managing the load and generation from solar, whilst supporting the policymakers in designing effective compensation schemes.

Parameter and order estimation algorithms and convergence analysis for lithium‐ion batteries

AbstractThe fractional‐order equivalent circuit model can reflect the internal reaction mechanism of a lithium‐ion battery well. This article aims to design an effective model and optimization method to describe and analyze the operating characteristics of the lithium‐ion battery based on online measurement data. The fractional‐order controlled autoregressive model is derived by exploiting the memory superiorities of the fractional‐order, which comprises the electrochemical impedance spectroscopy and the ‐RC equivalent circuit model as the special cases. The utilization of polynomial properties reduces the difficulty of identification while preserving the ability of the controlled autoregressive model to fit the characteristics of the battery. To realize simultaneous parameter and the order estimation, a weighted gradient descent algorithm is proposed. The approach designs a new gradient direction and fully utilizes the data from the lithium‐ion battery by adding a suitable weighted factor. In addition, the forgetting factor is introduced to speed up convergence and produce more accurate parameter estimation. Furthermore, in order to analyze the convergence of the proposed algorithms, the convergence properties are proved by using martingale convergence theory and stochastic principle. Finally, the experimental simulation result shows the performance of the proposed algorithms.

Distance Protection for Lines Connecting Converter Interfaced Renewable Power Plants: Adaptive to Grid-end Structural Changes

Typical fault current characteristics of converter interfaced renewable power plants have significant impact on the reliability of traditional line distance protection. Large degrees of system non-homogeneity introduce substantial error in the apparent impedance calculation. In this paper, an adaptive distance relaying method for lines connected to converter-interfaced renewable power plants is proposed. A range of fault current phase angles is identified using the local and remote equivalent source impedances, which are calculated using only local measurements. Corresponding to the lower and upper bound of the identified range of fault current phase angle, two apparent impedances are calculated based on priority to dependability and security of protection operation. The trip decision is made based on the weighted sum of the first and second apparent impedances. Tuning of the dependability and security weightage factors based on the calculated remote grid-end source impedance, ensures adaptability and reliability of operation, even after structural changes in the grid. The proposed method is verified using IEEE 39 bus system and validated using field data. Comparative assessment with practically proven distance and modified distance protection method for lines connected with renewables is also provided.

Array Optimization Based on Weighted and Hilbert–Schmidt Schemes of Multisensor Detection System

This paper presents a novel sensor array optimization scheme for multisensor electronic nose detection system. A system architecture with multisensor is first proposed to implement the medical detection, including the bacterial culture medium detection and animal wound infection detection. The system efficiency is evaluated by comparing with the field asymmetric ion mobility spectrometry (FAIMS) system. To further improve the detection effect and reduce the number of sensors of the electronic nose system, we then derive two sensor array optimization procedures based on factor analysis and Hilbert-Schmidt independence criterion, respectively. Specifically, the weighted factor analysis method and non-weighted factor analysis method are proposed via factor analysis. Besides, the Hilbert-Schmidt independence criterion optimization design of linear kernel function and Gaussian kernel function are also exploited. The experiment results highlight that compared with the existing approaches, the proposed weighted factor analysis optimization method and Hilbert-Schmidt independence criterion optimization method (Gaussian kernel function) can achieve a significant system performance.

Push and pull in the sustainable development of ecological landscape and ecological resources: A dual perceptions of tourists and service staff

AbstractThe protection of the ecological landscape and the development of ecological resources are vital in the development of the global green economy, Their win‐win coordinated development is influenced by factors such as nature, culture, and economy, and more attention should be placed on tourist perception and sustainable resource development. Previous studies have mainly focused on a single perspective and failed to comprehensively consider the coordinated relationship between ecological landscape and ecological resources. This research combined push‐and‐pull theory and applied empirical analysis to verify the structure of tourists' perceptions and the law of sustainable development of resources related to the development of ecological landscape in the new era. It extracted and analyzed 34 influencing factors and applied the analytic network process (ANP) to analyze each factor's weights, to propose an evaluation system for eco‐tourism sustainability development that can help the construction of ecological landscape in the future. The reconstruction and further development of eco‐tourism in the new era is reflected in the ideological and practical dimensions; from single‐perspective linear thinking to multi‐dimensional integrated governance; from conflict between resources conservation and development to collaboration; from isolated management to building a community with a shared future; and showing the transformation and innovation at multiple levels of individual, community, government and national governance.

Factors Influencing Hospitals' Disaster Preparedness in the Eastern Province of Saudi Arabia.

The study aimed to identify the factors that influence the disaster preparedness of hospitals and validate an evaluation framework to assess hospital disaster preparedness (HDP) capability in the Eastern Province of Saudi Arabia. A cross-sectional survey of all hospitals (n = 72) in the Eastern Region of Saudi Arabia was conducted. A factor analysis method was used to identify common factors and validate the evaluation framework to assess HDP capacity. Sixty-three (63) hospitals responded to the survey. A 3-factor structure was identified as key predicators of HDP capacity. The first factor was the most highly weighted factor, which included education and training (0.849), monitoring and assessing HDP (0.723), disaster planning (0.721), and command and control (0.713). The second factor included surge capacity (0.708), triage system (0.844), post-disaster recovery (0.809), and communication (0.678). The third factor represented safety and security (0.638) as well as logistics, equipment, and supplies (0.766). The identified 3-factor structure provides an innovative approach to assist the operationalization of the concept of HDP capacity building and service improvement, as well as serve as a groundwork to further develop instrument for assessing HDP in future studies.

DGDNet: Deep Gradient Descent Network for Remotely Sensed Image Denoising

Gradient descent strategy, viewed as an important model optimization method, has been widely used for various tasks (such as model-based image denoising) of computer vision. In the gradient descent denoising model, the learning rate (LR) and residual component are two important parts to be adaptively estimated for its stable point. This letter proposes a deep gradient descent network (DGDNet), including two key points: one is that the LR is designed with eigenvalues of Hessian matrix of remotely sensed images (RSIs) and their local weighted factor (LWF), which can recognize structures from RSIs degraded by additive white Gaussian noise (AWGN). The other is that the residual part is calculated by an U-shaped network (USNet) to speed up the DGDNet convergent to a fixed point. Finally, the two components are plugged into the gradient descent scheme and contribute to an enjoyable result with a few iterations. Quantitatively and qualitatively experimental results demonstrate that the proposed DGDNet can obtain a stable solution efficiently, and produce competitive denoising performance which is even better than that yielded by the state-of-the-art noise reduction methods.

AEECA for Reliable Communication to Enhance the Network Life Time for WSN

Nowadays, wireless sensor networks play a vital role in our day to day life. Wireless communication is preferred for many sensing applications due its convenience, flexibility and effectiveness. The sensors to sense the environmental factor are versatile and send sensed data to central station wirelessly. The cluster based protocols are provided an optimal solution for enhancing the lifetime of the sensor networks. In this paper, modified K-means ++ algorithm is used to form the cluster and cluster head in an efficient way and the Advanced Energy-Efficient Cluster head selection Algorithm (AEECA) is used to calculate the weighted factor of the transmission path and effective data collection using gateway node. The experimental results show the proposed algorithm outperforms the existing routing algorithms.