Principal Component Extraction Research Articles

Near-optimal quantum kernel principal component analysis

Abstract Kernel principal component analysis (kernel PCA) is a nonlinear dimensionality reduction technique that employs kernel functions to map data into a high-dimensional feature space, thereby extending the applicability of linear PCA to nonlinear data and facilitating the extraction of informative principal components. However, kernel PCA necessitates the manipulation of large-scale matrices, leading to high computational complexity and posing challenges for efficient implementation in big data environments. Quantum computing has recently been integrated with kernel methods in machine learning, enabling effective analysis of input data within intractable feature spaces. Although existing quantum kernel PCA proposals promise exponential speedups, they impose stringent requirements on quantum hardware that are challenging to fulfill. In this work, we propose a quantum algorithm for kernel PCA by establishing a connection between quantum kernel methods and block encoding, thereby diagonalizing the centralized kernel matrix on a quantum computer. The query complexity is logarithmic with respect to the size of the data vector, $D$, and linear with respect to the size of the dataset. An exponential speedup could be achieved when the dataset consists of a few high-dimensional vectors, wherein the dataset size is polynomial in $\log(D)$, with $D$ being significantly large. In contrast to existing work, our algorithm enhances the efficiency of quantum kernel PCA and reduces the requirements for quantum hardware. Furthermore, we have also demonstrated that the algorithm based on block encoding matches the lower bound of query complexity, indicating that our algorithm is nearly optimal. Our research has laid down new pathways for developing quantum machine learning algorithms aimed at addressing tangible real-world problems and demonstrating quantum advantages within machine learning.

Sustainability or Technology - What Drives Consumer Purchase Behaviour in Digital Era?

Retail industry is undergoing transformation. Many changes are happening in this industry due to change in consumer behaviour. Every retailer is trying to understand, what drives the customers to select the retailer? It is technology, sustainability or both. Retailers are trying to identify the right technology for their business, which will delight customers and at the same trying to find out how to achieve sustainability in their business. The purpose of this paper is to find answers for these questions and help the retailers to invest their money wisely and satisfy their customers and remain relevant in the business. The outcomes of the study exhibit the shifting trend among customers which help the companies to cater the altering preferences of their customers. A well-structured questionnaire was developed and collected the primary data from 368 respondents adopting Convenient sampling method through online. Questionnaire was analysed by Factor extraction principal component analysis, Exploratory Factor Analysis, Eigen values and scree plot. The relation between the independent and dependent variables was established with multiple regression analysis. Each factor effect on sustainability and Advanced digitalisation independently was tested for its significance by adopting t – test statistic. The aggregate effect of all independent variables in each equation, in multiple regression are shown by the multiple correlation coefficient (R2). This multiple relation is also tested by adapting the F – test statistic.

Prediction of Maize Crop Yield Using Principal Component Analysis of Weather Parameters

The use of principal component analysis in the development of statistical models for crop yield forecasting has been demonstrated. Maize crop yield data for a period of 21 years (2001-2021) were drawn from the Dacnet website and the weather data were collected from the Meteorological Observatory, Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology Pantnagar, Uttarakhand. Maximum temperature, Minimum temperature, Relative Humidity A.M, Relative Humidity P.M, Total rainfall, Sunshine hours, Wind velocity and Evapotranspiration were the weather parameters considered for the study. Out of the 21-year data, 17-year data were used for training the model while remaining 4 years data were used for testing the model. Weekly data on weather variables was used to create weather indices [1]. This work involves developing forecasting models using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). Applied to extract principal components (PCs) from the correlation matrix of predictors. Five models (Model 1 to Model 5) are developed using MLR, with varying numbers of PCs as regressors which also include time trend and maize yield as dependent variable. The model performance was measured using Adjusted R-squared (adj R2) and Root Mean Square Error (RMSE) as goodness of fit criteria. On the basis of adj R2 and RMSE, model 1 which includes all the calculated weather indices, was found to be best suited model with high adj R2 (74.18 %) and least RMSE (276.36). Hence, this model can be used to forecast maize yield for the studied region.

Validity of the Physical activity scale for the elderly: results from the Neyshabur Longitudinal Study on Ageing

Background. Adequate physical activity is essential to healthy aging. This study aimed to examine the construct validity of the Persian version of the Physical Activity Scale for the Elderly (PASE) among community-dwelling adults, which would be used as a screening tool at the Neyshabur longitudinal study on the aging (NELSA) Center. Methods. This cross-sectional study was conducted in Neyshabur, Iran, as a pilot phase at the NELSA center between May and January 2019. The sampling method was a stratified random method. For explanatory factor analysis (EFA) and confirmatory factor analysis (CFA), 300 and 650 community-dwelling adults aged 50 and older were selected, respectively. EFA was used with the principal component extraction method, and Varimax rotation and CFA were performed with the maximum likelihood estimation method. Results. Of the 950 participants, 445 (46.8%) were male, and 505 (53.2%) were female. The mean age of participants was 63 ± 8.6 years. The mean physical activity score was 89.7 ± 70.7. According to the percentage of variance explained in EFA, all five extracted factors explained 62.7% of the total variance. The results of CFA showed that all item-scale relationships were significant (P < 0.05), except for household activity (Q8) and leisure time activity (Q2) (P > 0.05). The values of the fit indices showed that the model fits well with the data. Conclusion. The EFA and CFA confirmed the construct validity of the Persian version of PASE. This questionnaire is suitable for measuring the physical activity level of older Persian adults. Practical Implications. The Persian version of PASE is suitable for measuring the physical activity level of older Persian adults.

An adaptation and exploratory factor analysis of the smoking-related weight and eating episodes test (SWEET) for electronic cigarette users

IntroductionSmoking for weight control is a well-documented behavior. There is emerging evidence to suggest electronic cigarettes (e-cigarettes) may be used for similar motivations yet measure development for the use of e-cigarettes for weight control has received less attention. The objective of the current study was to adapt and explore the psychometric properties of The Smoking-Related Weight and Eating Episodes Test (SWEET) for e-cigarette users. MethodsYoung adult (N=1875) current cigarette, e-cigarette, and dual users completed the original SWEET (SWEET-C) and/or adapted SWEET for e-cigarette use (SWEET-EC) based on current tobacco product use. Demographics, associated e-cigarette characteristics, nicotine dependence, outcome expectancies, and eating disorder behaviors were also assessed. Participants were recruited online and measures were completed via self-report. ResultsFour exploratory factor analyses using principal components extraction and direct oblimin rotation methods were run to explore the SWEET-C and SWEET-EC. A one-factor solution explaining 66 % of the variance was retained for the SWEET-C, and a one-factor solution explaining 73 % of the variance was retained for the SWEET-EC. Both measures exhibited excellent internal consistency. Higher SWEET-EC scores were observed among dual users, and were associated with daily e-cigarette use, JUUL use, self-reported vaping for weight control, older age, higher body mass index, and problematic eating behaviors. ConclusionOur findings support the adaptation of the SWEET-EC to measure e-cigarette use for weight control. The SWEET-EC will help to better understand how individuals use e-cigarettes to curb eating behavior and for weight control.

A dimension reduction-based Kriging modeling method for high-dimensional time-variant uncertainty propagation and global sensitivity analysis

Surrogate models have been widely used in the uncertainty propagation and global sensitivity analysis of complex evaluation-expensive engineering problems. However, the construction of high-accuracy surrogate models for time-variant problems with high-dimensional inputs and outputs using a small number of training samples remains a challenge. To address this challenge, we propose a dimension reduction-based Kriging modeling (KMDR) method for high-dimensional time-variant uncertainty propagation and global sensitivity analysis. Singular value decomposition is performed on the original time-variant response to extract principal components from high-dimensional outputs. And the improved sufficient dimension reduction (ISDR) is performed on high-dimensional inputs to identify the latent input space with respect to each principal component of outputs. A ladle estimator with a rigorous mathematical definition is then employed to determine the number of principal components of the outputs and the dimensionalities of the latent input spaces. The ladle estimator considers variabilities in both eigenvalues and eigenvectors of a matrix and can determine the latent dimensionality more accurately and efficiently than existing approaches. Subsequently, Kriging models between high-dimensional inputs and each principal component of the outputs are constructed based on a newly devised Kriging kernel, which embeds the information of the ISDR into the kernel function and can achieve higher accuracy than directly constructing Kriging models between latent inputs and outputs. In addition, a generalized variance-based sensitivity index, which can quantify the effects of the inputs on the overall time-variant response, is defined and computed directly from Sobol’ sensitivity indices of the inputs at each time node. Finally, the surrogate model of the time-variant system constructed by the KMDR is directly adopted for efficient time-variant uncertainty propagation and global sensitivity analysis. Several examples demonstrate that the proposed approach can construct more accurate surrogate models and obtain more accurate time-variant uncertainty propagation and global sensitivity analysis results than existing methods with a small training set.

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection.

Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures.

Validation of the Internal Coherence Scale (ICS) in Healthy Geriatric Individuals and Patients Suffering from Diabetes Mellitus Type 2 and Cancer.

With increased life expectancy, the coexistence of functional impairment and multimorbidity can negatively impact life quality and coherence in geriatric individuals. The self-report 10-item Internal Coherence (ICS) measures how individuals cope with and make sense of disease-specific life challenges. The aim of this study was to validate the ICS in a sample of geriatric individuals. In a cross-sectional study, geriatric individuals with and without chronic diseases were recruited. A factor analysis with principal component extraction (PCA) and a structural equation model (SEM) was conducted to assess the ICS factor structure in a geriatric sample. To measure convergent validity, the following scales were used: Short Health Survey (SF-12), Karnofsky Performance Index (KPI), Trait autonomic regulation (Trait aR), Sense of Coherence Scale (SOC), and Geriatric Depression Scale (GDS). A sample of n = 104 (70-96 years of age) patients with Diabetes Mellitus Type 2 (n = 22), cancer diseases (n = 31) and healthy controls (n = 51) completed the ICS. PCA and SEM yielded the original two-factor solution: 1. Inner resilience and coherence and 2. Thermo coherence. Overall internal consistency for this cohort was satisfying (Cronbach's α with rα = 0.72), and test-retest reliability was moderate (rrt = 0.53). ICS scores were significantly correlated to all convergent criteria ranging between r = 0.22 * and 0.49 ** (p < 0.05 *; p < 0.01 **). Study results suggest that the ICS appears to be a reliable and valid tool to measure internal coherence in a geriatric cohort (70-96 years). However, moderate test-retest reliability prompts the consideration of potential age-effects that may bias the reliability for this specific cohort.

Method of feature extraction of abnormal communication signal in network based on nonlinear technology

Abstract Accurate extraction of abnormal communication signal features in the network is the basis to ensure the completion of network communication. Therefore, a method of feature extraction of abnormal communication signal in network based on nonlinear technology is proposed. In this method, wavelet transform is adopted to decompose the abnormal network communication signals in the high- and low-frequency bands. According to the distribution characteristics of noise and signal in the frequency band, the corresponding parameters are selected for phase space reconstruction and nonlinear dimension reduction of local tangent space mainstream shape recognition algorithm, and the decomposition coefficients of wavelet packet after noise reduction are reconstructed to realize the nonlinear noise reduction of abnormal signal; the denoised abnormal communication signal in network is mapped to the high-dimensional feature space. The principal component is analyzed in accordance with the nonlinear function in the mapped feature space, and the nonlinear function is solved by self-organizing neural network to output the principal component extraction result. According to test results, this method has a significant signal noise reduction effect, results are more than 92% for different abnormal communication signals, and the features of abnormal signals are accurately extracted.

Validity and reliability of the Romanian version of the Hill-Bone compliance to high blood pressure therapy scale.

Romania is considered a country with high cardiovascular risk, arterial hypertension and its complications accounting for about 60% of total deaths. The management of high blood pressure often involves a combination of both therapeutic regimens as well as lifestyle changes, to which patients have to be adherent. In order to assess patients adherence to professionals' recommendations, validated tools are needed. The aim of our study was to translate, culturally adapt and validate the Hill-Bone Compliance to High Blood Pressure Therapy Scale into Romanian. The study included 215 participants from Iasi, North-Eastern Romania. The internal consistency of the instrument was measured with Cronbach's alpha coefficient, while the construct validity was determined using exploratory factor analysis and principal component extraction with promax rotation. Sampling adequacy and appropriateness of data for factor analysis was measured using Kaiser-Meyer-Olkin (KMO) statistics and Bartlett's test of sphericity. Our statistical analysis revealed a Cronbach's alpha coefficient of 0.733 (73.3%) and a Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy of 0.697. The chi square test demonstrated that the overall perfect adherence was not significantly associated with the number of medications taken per day variable (p = 0.721). The Romanian version of the Hill-Bone Compliance to High Blood Pressure Therapy Scale demonstrated suitability for its use in evaluating adherence in the intended population.

Potato tuber yield and quality under potassium and magnesium addition on top of the nitrogen and phosphorus fertilization

The aim of the study to investigate the effect of individual or combined K and Mg addition on potato tuber yield and quality along the gradient of soil and environmental conditions, which was formed by the five experimental plots in the forest-steppe one of West Siberia. Location and time of the study. Microplot field experiment with moderately early potato cultivar Jelly with three fertilization treatments (N100P60, N100P60+K100 and N100P60+K100+Mg100) was setup in five replicates on five experimental plots with agricultural grey soils in the Novosibirsk Region in 2023. Geographical coordinates of the experimental sites varied from 55°15'40" to 54°47'09" NL and from 83°31'42" to 82º37' 56" EL. Methods. Instead of traditional temporal gradient (3–4 years of the experiment at a site) to account for weather variation, with the same purpose we used a spatial gradient, which in addition allowed to assess the effect of soil properties variation among the sites. Soil samples, collected prior to the experiment, were analyzed for organic carbon content, nitrate and ammonium nitrogen, exchangeable phosphorous, potassium and magnesium, as well as soil pH and electrical conductivity. In the course of the experiment air and soil (at 5 and 15 cm depth) temperatures was automatically recorded. At the end of the experiment potato tuber mass and number, as well as the aboveground phytomass, were measured at each microplot. After five months of tuber storage sensory properties (flavour, aroma, colour, mealiness and friability) of boiled tubers were assessed using a Hedonic scale. Analysis of variance, principal components extraction and multiple regression by partial least squares method were performed using Statistica v. 13.1. software. Results. Addition of K at the background NP fertilization (N100P60+K100 treatment) increased tuber yield by 30% and the aboveground phytomass by 40%. Addition of Mg (N100P60+K100+Mg100) did not affect yield properties, but markedly improved potato culinary quality. More than half of the variance of the potato yield properties was accounted for by the variation in soil and environmental conditions of the experimental fields location. Exchangeable Mg, ammonium N and soil pH were the main soil properties, allowing to forecast the current year tuber yield. The contribution of soil and environmental conditions of the experimental fields into the variance of the sensory properties, albeit small, was statistically significant (p≤0,05) for aroma and colour, with the similar trend for flavour and friability (0,05≤p≤0,10). Agrochemical properties, i.e. soil organic carbon, readily exchangeable P and nitrate N contents, as well as soil pH, were the main variables, determining tuber quality for consumers. Conclusions. The study performed on the agricultural grey soil in the forest-steppe zone of West Siberia confirmed the importance of optimizing potato mineral nutrition. Addition of NPK at the balanced rates significantly increased potato yield as compared with NP. Although addition of MG together with NPK did not increase the yield, it markedly improved sensory properties of potato. Some soil properties and temperature conditions, found to determine the yield (effective soil fertility), can be used in prognostic models for potato tuber yields and quality.

FDEPCA: A Novel Adaptive Nonlinear Feature Extraction Method via Fruit Fly Olfactory Neural Network for IoMT Anomaly Detection.

With the rapid development of 5G communication technology, the data in the Internet of Medical Things (IoMT) application systems exhibits complex characteristics such as large volume, high dimensionality, nonlinearity, and diversity, which significantly affect the efficiency and detection performance of anomaly detection tasks. How to efficiently extract nonlinear features from high-dimensional data in the context of the IoMT while minimizing information distortion in data objects are challenging problems in recent academic research. A novel adaptive nonlinear feature extraction method via fruit fly olfactory neural network (Fly dimension expansion projection and remain main components by PCA, FDEPCA) is proposed, where 1) the data are mean-centered; 2) a binary sparse random projection matrix is used for dimension expansion projection; and 3) PCA is used to extract principal component information. The proposed method overcomes the problems of present nonlinear feature extraction in the face of high-dimensional outliers where the intrinsic geometric structure of the data is severely distorted and computationally expensive. The dataset after nonlinear feature extraction by the FDEPCA algorithm is applied to specific anomaly detection models, using ROC curves and AUC as evaluation metrics for classification performance. Extensive comparison experiments are conducted on eight publicly available datasets, and experimental results show that compared with the popular nonlinear feature extraction algorithms, the FDEPCA algorithm has better classification performance and projection time advantage. When applied to proximity-based, probability-based, and ensemble-based different anomaly detection models respectively, the FDEPCA algorithm exhibits strong applicability in different types of anomaly detection classifiers.

ENGAGEMENT OR ENJOYMENT? ATTITUDES OF OLDER ADULTS IN GROUP LUNCH VERSUS. LIFELONG LEARNING PROGRAMS

Abstract Purpose This study examines aging attitudes among older adults who participated in the neighborhood lunch (NLP) and lifelong learning (LLP) programs. Methods Older adults from an NLP and an LLP in the same county in Florida participated. An aging attitudes measurement originally developed by German researchers was used. Results A total of 193 older adults participated, 43% from NLP. The mean age was 73.2 (SD=7.78) years. Data showed that LLP older adults endorsed higher on “would like to have responsibilities,” “have a task,” “do unpaid volunteer work,” and “help others” (p&amp;lt;.001). NLP participants, on the other hand, endorsed higher on “no longer have to contribute to society,” “want to enjoy life,” and “finally want to rest and relax” (all p&amp;lt;.001). Exploratory factor analyses (EFAs) using principal component extraction methods and varimax rotation with Kaiser normalization showed a clear balanced 2-factor pattern, with total variance explained 60% (30% each). These two factors were consistent with the theory representing engagement versus enjoyment aging attitudes. Reliabilities of the two-factor scales were satisfactory, with Cronbach’s alpha of .764 (CITC ranged: .457 ~ .660) for the engagement scale (4-item) and.634 (CITC ranged: .379~.499) for the enjoyment scale (3-item). Discussion Current findings showed that LLP participants endorsed higher engagement, while NLP participants endorsed higher enjoyment towards aging. This tested aging attitudes scale consists of 7 items with two clear theory-consistent factors among community-dwelling older adults. Results can help researchers quickly assess aging attitudes of diverse groups of older adults for tailored program and service development.

Using dual-task gait to recognize Alzheimer's disease and mild cognitive impairment: a cross-sectional study.

Gait is a potential diagnostic tool for detecting mild cognitive impairment (MCI) and Alzheimer's disease (AD). Nevertheless, little attention has been paid to arm movements during walking, and there is currently no consensus on gait asymmetry. Therefore, in this study, we aimed to determine whether arm motion and gait asymmetry could be utilized for identifying MCI and AD. In total, 102 middle-aged and elderly individuals were included in the final analysis and were assigned to the following three groups: AD (n = 27), MCI (n = 35), and a normal control group (n = 40). Gait and cognitive assessments were conducted for all participants. Gait detection included a single-task gait with free-speed walking and a dual-task gait with adding a cognitive task of successive minus seven to walking. Original gait parameters were collected using a wearable device featuring the MATRIX system 2.0. Gait parameters were shortened to several main gait domains through factor analysis using principal component extraction with varimax rotation. Subsequently, the extracted gait domains were used to differentiate the three groups, and the area under the receiver operating characteristic curve was calculated. Factor analysis of single-task gait identified five independent gait domains: rhythm symmetry, rhythm, pace asymmetry, arm motion, and variability. Factor analysis of the dual-task gait identified four gait domains: rhythm, variability, symmetry, and arm motion. During single-task walking, pace asymmetry was negatively correlated with MoCA scores and could distinguish between the AD group and the other two groups. Arm motion was not associated with MoCA scores, and did not exhibit adequate discrimination in either task. Currently, there is no reliable evidence suggesting that arm motion can be used to recognize AD or MCI. Gait asymmetry can serve as a potential gait marker for the auxiliary diagnosis of AD but not for MCI.

Loop Closure Detection Based on Compressed ConvNet Features in Dynamic Environments

In dynamic environments, convolutional neural networks (CNNs) often produce image feature maps with significant redundancy due to external factors such as moving objects and occlusions. These feature maps are inadequate as precise image descriptors for similarity measurement, hindering loop closure detection. Addressing this issue, this paper proposes feature compression of convolutional neural network output. The approach is detailed as follows: (1) employing ResNet152 as the backbone feature-extraction network, a Siamese neural network is constructed to enhance the efficiency of feature extraction; (2) utilizing KL transformation to extract principal components from the backbone network’s output, thereby eliminating redundant information; (3) employing the compressed features as input for NetVLAD to construct a spatially informed feature descriptor for similarity measurement. Experimental results demonstrate that, on the New College dataset, the proposed improved method exhibits an approximately 9.98% enhancement in average accuracy compared to the original network. On the City Center dataset, there is an improvement of approximately 2.64%, with an overall increase of about 23.51% in time performance. These findings indicate that the enhanced ResNet152 performs better than the original network in environments with more moving objects and occlusions.

Frequency analysis with multiple kernels and complete dictionary

In signal analysis, among the effort of seeking for efficient representations of a signal into the basic ones of meaningful frequencies, to extract principal frequency components, consecutively one after another or n at one time, is a fundamental strategy. For this goal, we define the concept of mean-frequency and develop the related frequency decomposition with the complete Szegö kernel dictionary, the latter consisting of the multiple kernels, being defined as the parameter-derivatives of the Szegö kernels. Several major energy matching pursuit type sparse representations, including greedy algorithm (GA), orthogonal greedy algorithm (OGA), adaptive Fourier decomposition (AFD), pre-orthogonal adaptive Fourier decomposition (POAFD), n-Best approximation, and unwinding Blaschke expansion, are analyzed and compared. Of which an order in reconstruction efficiency between the mentioned algorithms is given based on the detailed study of their respective remainders. The study spells out the natural connections between the multiple kernels and the related Laguerre system, and in particular, shows that both, like the Fourier series, extract out the O(n−σ) order convergence rate from the functions in the Hardy-Sobolev space of order σ>0. The existence of the n-Best approximation with the complete Szegö dictionary is proved and the related algorithm aspects are discussed. The included experiments form a significant integration part of the study, for they not only illustrate the theoretical results but also provide cross comparison between various ways of combination between the matching pursuit algorithms and the dictionaries in use. Experiments show that the complete dictionary remarkably improves approximation efficiency.

Preferences for Alternative Fuel Trucks among International Transport Companies

Fossil-fuel consumption in land freight is over 19%. Alternative fuel trucks (AFTs) help decrease greenhouse gas emissions. However, AFT preferences in international land transit have received little attention due to passing through various countries with different financial and regulation plans. This variety affects AFTs’ market share. This study analyzes factors influencing AFT preferences in international land transit. A questionnaire (designed in four sections) was distributed among international transport companies in Iran and Europe. A principal component analysis helped extract principal components composed of cognitive, environmental, policy, practical, and economic variables. The multinomial logit models include 26 meaningful variables. The marginal effect analysis shows that the service quality of the manufacturer, importance of greenhouse gas mitigation, and social responsibility do not significantly elevate AFT preferences. In addition, cargo type influences AFT choice. The results of this study help to identify the barriers affecting AFTs’ market share, which can positively impact air pollution.

Development of a systematic social observation tool for monitoring use of harm reduction supplies

BackgroundHarm reduction services such as safer injection supply distribution are essential to reducing morbidity and mortality among people who use drugs (PWUD); however, local use of harm reduction supplies (e.g., tourniquets, saline solution) is difficult to routinely and systematically monitor. The purpose of this study was to develop and validate a systematic social observation tool designed to assess use of harm reduction supplies at the street block level. MethodsData collection took place on a random sample of 150 blocks located throughout the Kensington neighborhood of North Philadelphia from November 2021 to January 2022. We measured inter-rater reliability by two-way mixed-effects intra-class correlation coefficients (ICC) with the consistency agreement definition and internal consistency reliability using Cronbach's alpha and McDonald's omega. Exploratory factor analysis with principal component extraction and promax rotation assessed internal consistency. We validated scales against locations of public syringe disposal boxes, a proxy measure for areas of concentrated drug use, using logistic regression. ResultsNaloxone canisters, syringe caps, saline and sterile water solution bottles showed the highest reliability (ICC≥0.7). Items also showed high internal consistency (alpha, omega>0.7). Exploratory factor analysis identified one, three-item scale with high internal consistency: syringe caps, vials, and baggies (alpha = 0.85; omega = 0.85)—all supplies used concurrently with drug injection but not discarded in syringe disposal boxes. Drug use (OR = 1.78, 95 % CI = (1.48, 2.23)), harm reduction (OR = 3.53, 95 % CI = (2.20, 6.12)), and EFA scales (OR = 1.85, 95 %CI = (1.51, 2.34)) were significantly and positively associated with being within walking distance (≤0.25 miles or 0.4 km) of a syringe disposal box. ConclusionThis study provides an efficient tool with high reliability and validity metrics to assess community uptake of harm reduction supplies designed for use by community organizations, policy makers, or other groups providing resources to PWUD.

Development and psychometric properties of Iranian Women’s Quality of Life Instrument (IWQOLI): mixed exploratory study

BackgroundTo determine the health needs and promote women's health, their quality of life should be investigated. For this purpose, a valid tool is needed, that has credible validity and reliability, and its concepts are clearly defined and culturally appropriate. This study aimed to develop and assess the psychometric properties of “Iranian Women’s Quality of Life Instrument (IWQOLI)”.MethodsThe items of “IWQOLI” were generated from themes extracted (150 items) from a content analysis approach with the participation of 40 women. Face validity of the questionnaire with the participation of 10 women and content validity by 10 experts was approved. To determine the domains of the questionnaire, exploratory factor analysis (principal component extraction method) was performed. Internal consistency and test—retest reliability methods with 14-day intervals (30 women) were used to assess the reliability of WQOLI.ResultsAfter performing the face and content validity, 32 items were deleted. S-CVI/Ave was obtained for the instrument (0.93). The factor structure of the inventory was identified by undertaking a principal component analysis in a sample of 590 women. Five factors were extracted with a total variance account of 56.24% and 28 items dropped at this point. The IWQOLI score was significantly correlated with the SF-36 (r = 0.717, p < 0.001). Reliability was demonstrated with Cronbach’s alpha coefficient of 0.919 for the entire scale (90-item). Consistency of the instrument was established with test–retest reliability with an interval of 2 weeks (intra-cluster correlation = 0.889, P < 0.001).ConclusionsThe Iranian women’s Quality of life Instrument “IWQOLI”, consisting of 90 items representing 5 domains (sense of peace in life, sense of security, health responsibility, pleasant communication, received comprehensive support), demonstrated excellent psychometric properties, so it may be used for measuring women’s QOL in practical research.

Study of Settlement Patterns in Farming–Pastoral Zones in Eastern Inner Mongolia Using Planar Quantization and Cluster Analysis

Settlements are comprehensive carriers of the material form expression and social appearance of human activities under specific geographical environmental choices. The analysis and preservation of their forms are important principles and strategies for rural settlement planning and construction. In this study, 28 settlements in the farming–pastoral zones in eastern Inner Mongolia were selected as the research objects. By combining fractal geometry and computer programming, the relationship between the boundary form, spatial structure, and architectural order of the settlements was quantitatively expressed, and quantitative indicators that better summarize the form of the settlements in the farming–pastoral zones in eastern Inner Mongolia were extracted. Then, factor analysis and cluster analysis were conducted using the Statistical Package for the Social Sciences software (SPSS26.0) to obtain the characteristic types of settlement forms. Finally, the classification results were combined with social and humanistic factors and the geographical environment to test the rationality of the results. The results show that (1) five form indicators effectively describe the settlement forms in the research area, among which the shape index, dimension of the public space, and architectural density play a crucial role in the spatial structure factors. (2) In this study, we mainly used a data collection and processing–principal component extraction and systematic clustering-type division method to complete scientific research on settlement form classification. (3) By combining the clustering results with the spatial form features and analysis mainly based on spatial structure factors, the settlement forms in the farming–pastoral zones in eastern Inner Mongolia are described as three typical types: multidirectional expanding settlement, settlement patterns extending at both ends, and centripetal development settlement. Furthermore, the characteristics of the human–land relationship implicit in each type of settlement form are explained, achieving a scientific representation and classification of the settlement forms. The research results provide useful quantitative guidance for rural revitalization, settlement form optimization, and preservation in the farming–pastoral zones in eastern Inner Mongolia