Assessment of heat stress contributing factors in the indoor environment among vulnerable populations in Klang Valley using principal component analysis (PCA).

Rising global temperatures can lead to heat waves, which in turn can pose health risks to the community. However, a notable gap remains in highlighting the primary contributing factors that amplify heat-health risk among vulnerable populations. This study aims to evaluate the precedence of heat stress contributing factors in urban and rural vulnerable populations living in hot and humid tropical regions. A comparative cross-sectional study was conducted, involving 108 respondents from urban and rural areas in Klang Valley, Malaysia, using a face-to-face interview and a validated questionnaire. Data was analyzed using the principal component analysis, categorizing factors into exposure, sensitivity, and adaptive capacity indicators. In urban areas, five principal components (PCs) explained 64.3% of variability, with primary factors being sensitivity (health morbidity, medicine intake, increased age), adaptive capacity (outdoor occupation type, lack of ceiling, longer residency duration), and exposure (lower ceiling height, increased building age). In rural, five PCs explained 71.5% of variability, with primary factors being exposure (lack of ceiling, high thermal conductivity roof material, increased building age, shorter residency duration), sensitivity (health morbidity, medicine intake, increased age), and adaptive capacity (female, non-smoking, higher BMI). The order of heat-health vulnerability indicators was sensitivity > adaptive capacity > exposure for urban areas, and exposure > sensitivity > adaptive capacity for rural areas. This study demonstrated a different pattern of leading contributors to heat stress between urban and rural vulnerable populations.

IMU Data-Driven and PCA-Based Approach to Establish Quantifiable and Practically Applicable Measures for V2 Technique Elements in Cross-Country Skiing.

Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)-driven by the variance produced by the technique extremes-resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.

Influence of the chemical composition of leachates on the results of ecotoxicity tests for different slag types.

In this study, four ecotoxicological tests on Vibrio fischeri bacteria, Sinapis alba L. (white mustard), Daphnia magna S. (daphnia's) and earthworms were performed for three types of aqueous slag (ladle, blast furnace and converter) leachates with two-grain sizes (<4 mm, <10 mm). Concentrations of toxic elements and concentrations of Cr(VI), Ca, Na, Al, and other ions were determined. The raw slags were analyzed using X-ray fluorescence spectroscopy (XRFS), and major substances were determined by X-ray powder diffraction (XRD). The aqueous slag leachates passed ecotoxicological tests and met the required criteria, showing no toxicity to Vibrio fischeri and complying with white mustard test criteria. According to the results of the ecotoxicity tests with daphnia, the blast furnace slag samples were not ecotoxic, while two other slag samples were found to be entirely compliant. Characterization of the slags showed that the effect of element/ion leachability and slag grain size is essential. Biplot principal component analysis (PCA) showed that grain size does not significantly affect the separation of individuals on the plane. A positive correlation on toxicity was found with pH, conductivity, calcium content, dissolved content, salinity and fluoride concentration, whereas a negative correlation was found with magnesium concentration, dissolved organic carbon and potassium concentration. The effective concentration at 50% inhibition (EC50) value for Vibrio fischeri correlated with the first dimension of bivariate assessment. In summary, it was found that the investigated slags can be effectively reused as they comply with regulations and do not endanger the environment.

Optimizing lung cancer prediction: leveraging Kernel PCA with dendritic neural models.

Lung cancer is considered a cause of increased mortality rate due to delays in diagnostics. There is an urgent need to develop an effective lung cancer prediction model that will help in the early diagnosis of cancer and save patients from unnecessary treatments. The objective of the current paper is to meet the extensiveness measure by using collaborative feature selection and feature extraction methods to enhance the dendritic neural model (DNM) in comparison to traditional machine learning (ML) models with minimum features and boost the accuracy, precision, and sensitivity of lung cancer prediction. Comprehensive experiments on a dataset comprising 1000 lung cancer patients and 23 features obtained from Kaggle. Crucial features are identified, and the proposed method's effectiveness is evaluated using metrics such as accuracy, precision, F1 score, sensitivity, specificity, and confusion matrix against other ML models. Feature extraction techniques including Principal Component Analysis (PCA), Kernel PCA (K-PCA), and Uniform Manifold Approximation and Projection (UMAP) are employed to optimize model performance. PCA evaluated the DNM accuracy at 96.50%, precision at 96.64% and 97.45% sensitivity. K-PCA explained the DNM accuracy of 98.50%, precision rate of 99.42%, and 98.84% sensitivity and UMAP elaborated the DNM accuracy of 98%, precision of 98.82%, and 98.82% sensitivity. The K-PCA approach showed outstanding performance in enhancing the DNM model. Highlighting the DNM's accurate prediction of lung cancer. These results emphasize the potential of the DNM model to contribute positively to healthcare research by providing better predictive outcomes.

Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach.

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm's robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability.

An SNR Enhancement Method for Φ-OTDR Vibration Signals Based on the PCA-VSS-NLMS Algorithm.

To improve the signal-to-noise ratio (SNR) of vibration signals in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, a principal component analysis variable step-size normalized least mean square (PCA-VSS-NLMS) denoising method was proposed in this study. First, the mathematical principle of the PCA-VSS-NLMS algorithm was constructed. This algorithm can adjust the input signal to achieve the best filter effect. Second, the effectiveness of the algorithm was verified via simulation, and the simulation results show that compared with the wavelet denoising (WD), Wiener filtering, variational mode decomposition (VMD), and variable step-size normalized least mean square (VSS-NLMS) algorithms, the PCA-VSS-NLMS algorithm can improve the SNR to 30.68 dB when the initial SNR is -1.23 dB. Finally, the PCA-VSS-NLMS algorithm was embedded into the built Φ-OTDR system, an 11.22 km fiber was measured, and PZT was added at 10.19-10.24 km to impose multiple sets of fixed-frequency disturbances. The experimental results show that the SNR of the vibration signal is 8.77 dB at 100 Hz and 0.07 s, and the SNR is improved to 26.17 dB after PCA-VSS-NLMS filtering; thus, the SNR is improved by 17.40 dB. This method can improve the SNR of the system's position information without the need to change the existing hardware conditions, and it provides a new scheme for the detection and recognition of long-distance vibration signals.

Capabilities Toward Intravenous Patient-Controlled Analgesia: A Cross-Sectional Study of Registered Nurses Working in Two Tertiary Hospitals in China and Thailand.

BACKGROUND: An increased interest has been observed in the wide use of intravenous patient-controlled analgesia (IV-PCA) to control acute postoperative pain in both China and Thailand. The safety and efficacy of IV-PCA in patient care requires competent and capable staff nurses. This study aimed to appraise the capabilities of Thai and Chinese registered nurses regarding IV-PCA as a guide to develop educational programs. METHOD: A descriptive cross-sectional survey was conducted with 203 Chinese and 270 Thai registered nurses. An anonymous self-report questionnaire addressing 6 domains of capabilities toward IV-PCA was used to collect the data. Descriptive and inferential statistics were employed to analyze the data. RESULTS: The study found that the mean percentage scores (MPS) of the overall capability on IV-PCA of the Thai and Chinese nurse participants were 55.5 (mean [M] = 57.3, standard deviation [SD] = 4.9) and 62.6 (M = 58.7, SD = 13.0), respectively, which indicated very low and low levels. Barriers to the use and care of patients receiving IV-PCA after surgery according to the Thai and Chinese nurse participants included a lack of knowledge and systematic training regarding IV-PCA and a lack of first-hand experience in providing care for IV-PCA patients. CONCLUSION: The study results call for intensive and effective training and education concerning all domains for registered nurses involved with patients receiving IV-PCA.

A Comprehensive Analysis of Fel Ursi and Its Common Adulterants Based on UHPLC-QTOF-MSE and Chemometrics.

BACKGROUND: As one of the four most valuable animal medicines, Fel Ursi, named Xiong Dan (XD) in China, has the effect of clearing heat, calming the liver, and brightening the eyes. However, due to the special source of XD and its high price, other animals' bile is often sold as XD or mixed with XD on the market, seriously affecting its clinical efficacy and consumers' rights and interests. In order to realize identification and adulteration analysis of XD, UHPLC-QTOF-MSE and multivariate statistical analysis were used to explore the differences in XD and six other animals' bile. METHODS: XD, pig gall (Zhu Dan, ZD), cow gall (Niu Dan, ND), rabbit gallbladder (Tu Dan, TD), duck gall (Yan Dan, YD), sheep gall (Yang Dan, YND), and chicken gall (Ji Dan, JD) were analyzed by UHPLC-QTOF-MSE, and the MS data, combined with multivariate analysis methods, were used to distinguish between them. Meanwhile, the potential chemical composition markers that contribute to their differences were further explored. RESULTS: The results showed that XD and six other animals' bile can be distinguished from each other obviously, with 27 ions with VIP > 1.0. We preliminarily identified 10 different bile acid-like components in XD and the other animals' bile with significant differences (p < 0.01) and VIP > 1.0, such as tauroursodeoxycholic acid, Glycohyodeoxycholic acid, and Glycodeoxycholic acid. CONCLUSIONS: The developed method was efficient and rapid in accurately distinguishing between XD and six other animals' bile. Based on the obtained chemical composition markers, it is beneficial to strengthen quality control for bile medicines.

Unravelling the Phytochemical Composition and Antioxidant Potential of Different Parts of Rumex vesicarius L.: A RP-HPLC-MS-MS/MS, Chemometrics, and Molecular Docking-Based Comparative Study.

Rumex vesicarius L. Polygonaceae is a wildly grown plant in Egypt, North Africa, and Asia with wide traditional uses. Several studies reported its biological activities and richness in phytochemicals. This research addresses a comprehensive metabolic profiling of the flowers, leaves, stems, and roots via RP-HPLC-QTOF-MS and MS/MS with chemometrics. A total of 60 metabolites were observed and grouped into phenolic acids, flavonoids, phenols, terpenes, amino acids, fatty acids, organic acids, and sugars. Principal component analysis and hierarchal cluster analysis showed the segregation of different parts. Moreover, the antioxidant capacity was determined via several methods and agreed with the previous results. Additionally, an in silico approach of molecular docking of the predominant bioactive metabolites was employed against two antioxidant targets, NADPH oxidase and human peroxiredoxin 5 enzyme (PDB ID: 2CDU and 1HD2) receptors, alongside ADME predictions. The molecular modelling revealed that most of the approached molecules were specifically binding with the tested enzymes, achieving high binding affinities. The results confirmed that R. vesicarius stems and roots are rich sources of bioactive antioxidant components. To our knowledge, this is the first comprehensive metabolic profiling of R. vesicarius giving a prospect of its relevance in the development of new naturally based antioxidants.

Ideotype based genotype selection in a multivariate dataset of sweet potato (Ipomoea batatas L.).

The dataset extensively examines the factors considered when choosing sweet potato genotypes, considering various characteristics. Notably, Moz1.15 demonstrated the highest marketable root yield at 46.46 t/ha, H5.ej.10 exhibited the highest beta-carotene level at 48.94 mg/100 g, and Moz1.9 recorded the highest vitamin C content at 23.89 mg/100 g. Moreover, there were significant correlations (ranging from 0.21 to 0.84) among the yield and quality traits studied in sweet potatoes. Principal component analysis (PCA) confirmed the connections among these traits, identifying four distinct clusters of genotypes, each characterized by specific significant combinations of traits. Factor analysis using the multi-trait genotype-ideotype index (MGIDI) highlighted the considerable impact of sweet potato traits across two growing seasons (2020-21 and 2021-22), facilitating the selection of genotypes with potential genetic gains ranging from 1.86 % to 75.4 %. Broad-sense heritability (h2) varied from 64.9 % to 99.8 %. The use of the MGIDI index pinpointed several promising genotypes, with BARI Mistialu-12 and H9.7.12 consistently performing well over both years. These genotypes exhibited both strengths and weaknesses.

Application of soil magnetometry and geochemical methods to investigate soil contamination with antimony.

The aim of the study was an assessment of the pollution level and identification of the antimony sources in soils in areas subjected to industrial anthropopressure from: transport, metallurgy and electrical waste recycling. The combination of soil magnetometry, chemical analyzes using atomic spectrometry (ICP-OES and ICP-MS), Sb fractionation analysis, statistical analysis (Pearson's correlation matrix, factor analysis) as well as Geoaccumulation Index, Pollution Load Index, and Sb/As factor allowed not only the assessment of soil contamination degree, but also comprehensive identification of different Sb sources. The results indicate that the soil in the vicinity of the studied objects was characterized by high values of magnetic susceptibility and thus, high contents of potentially toxic elements. The most polluted area was in the vicinity of electrical waste processing plants. Research has shown that the impact of road traffic and wearing off brake blocks, i.e. traffic anthropopression in general, has little effect on the surrounding soil in terms of antimony content. Large amounts of Pb, Zn, As and Cd were found in the soil collected in the vicinity of the heap after the processing of zinc-lead ores, the average antimony (11.31 mg kg-1) content was lower in the vicinity of the heap than in the area around the electrical and electronic waste processing plant, but still very high. Antimony in the studied soils was demobilized and associated mainly with the residual fraction.

Multivariate analysis of craniodental morphology in mouse-eared bats (Chiroptera, Vespertilionidae, Myotis) from Vietnam.

This study conducted biostatistical multivariate analyses on 23 craniodental morphological measurements from 209 specimens to study interspecific variations amongst 15 bat species of the genus Myotis in Vietnam. Univariate and multivariate analyses demonstrated that the studied species can be divided into four groups as follows: extra-large-sized species (M.chinensis), large-sized species (M.pilosus, M.indochinensis and M.annectans), medium-sized species (M.altarium, M.hasseltii, M.montivagus, M.horsfieldii, M.ater, M.laniger and M.muricola) and small-sized species (M.annamiticus, M.aff.siligorensis, M.rosseti and M.alticraniatus). Our data revealed that the main craniodental features contributing to the variations in distinguishing Myotis species are the width of the anterior palatal, least height of the coronoid process, length of the upper and lower canine-premolar, zygomatic width and width across the upper canines and lower premolar-molar length. Based on patterns of morphological differences, we conducted comparisons between morphometrically closely resembling species pairs and further discussed additional characteristics that are expected to support the taxonomy and systematics of Vietnamese Myotis bats.

Volatile Compounds for Discrimination between Beef, Pork, and Their Admixture Using Solid-Phase-Microextraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) and Chemometrics Analysis.

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

Long Noncoding RNAs CAT2064 and CAT2042 may Function as Diagnostic Biomarkers for Prostate Cancer by Affecting Target MicrorRNAs.

Prostate cancer (PCa) is the second most common cancer in men throughout the world, and the main cause of cancer death. Long noncoding RNAs (lncRNAs) act as crucial regulators in many human cancers. In this research, we measured the expression level of novel lncRNAs and their associated micro-RNAs (miRNAs) in PCa. In the present research, three lncRNAs were selected using the Mitranscriptome projec (CAT2064, CAT2042, and CAT2164.2). Samples of prostate tissue (20 PCa, and 20 BPH) and blood (14 PCa, and 14 BPH) were collected and the Real-time Quantitative Polymerase Chain Reaction (RT-qPCR) was used to measure the expression levels of the lncRNAs and their associated miRNAs. Based on our results, CAT2064 was significantly increased and CAT2042 was significantly decreased in human PCa tissue in comparison with BPH tissue. To discriminate PCa from BPH, CAT2064 (P < 0.05; 0.8750 AUC-ROC) showed a better potential as a diagnostic molecular biomarker compared to CAT2042 (P < 0.05; 0.8454 AUC-ROC). Furthermore, RT-qPCR results measured in blood samples from PCa patients showed a higher expression level of CAT2064 (P < 0.0001; AUC-ROC value of 0.8914) in comparison to CAT2042. CAT2064 and CAT2042 showed a positive correlation with the expression of miR-5095 and miR-1273a (r = 0.02885, 0.3202; P = 0.9413, 0.2266, respectively). CAT2064 and CAT2042 also had a negative correlation with miR-1304-3p and miR-1285-5p (r = - 0.3877, - 0.09330; P = 0.15, 0.7311, respectively). Collectively, CAT2064 and CAT2042 and their miRNA targets may constitute a regulatory network in PCa, and could serve as novel biomarkers. Supplementary Information: The online version contains supplementary material available at 10.1007/s12291-021-00999-6.

Knowledge, Attitudes and Practices Among Anesthesia and Thoracic Surgery Medical Staff Toward Ai-PCA.

Purpose: Artificial intelligence (AI) is increasingly influencing various medical fields, including anesthesiology. The Introduction of artificial intelligent patient-controlled analgesia (Ai-PCA) has been seen as a significant advancement in pain management. However, the adoption and practical application of Ai-PCA by medical staff, particularly in anesthesia and thoracic surgery, have not been extensively studied. This study aimed to investigate the knowledge, attitudes and practices (KAP) among anesthesia and thoracic surgery medical staff toward artificial intelligent patient-controlled analgesia (Ai-PCA). Participants and Methods: This web-based cross-sectional study was conducted between November 1, 2023 and November 15, 2023 at Jiangsu Cancer Hospital. A self-designed questionnaire was developed to collect demographic information of anesthesia and thoracic surgery medical staff, and to assess their knowledge, attitudes and practices toward Ai-PCA. Results: A total of 519 valid questionnaires were collected. Among the participants, 278 (53.56%) were female, 497 (95.76%) were employed in the field of anesthesiology, and 188 (36.22%) had participated in Ai-PCA training. The mean knowledge, attitude, and practice scores were 7.8±1.75 (possible range: 0-10), 37.43±4.16 (possible range: 9-45), and 28.38±9.27 (possible range: 9-45), respectively. Conclusion: The findings revealed that anesthesia and thoracic surgery medical staff have sufficient knowledge, active attitudes, but poor practices toward the Ai-PCA. Comprehensive training programs are needed to improve anesthesia and thoracic surgery medical staff's practices in this area.

Quantitative analysis of solid dosage forms of Atenolol by Raman spectroscopy.

OBJECTIVE: To develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance: For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol. METHODS: Various solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques, such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively. RESULTS: As the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model. CONCLUSIONS: Based on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.

Statistical methods for discrimination of STR genotypes using high resolution melt curve data.

Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen's principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

A bayesian spatio-temporal dynamic analysis of food security in Africa.

Exploring the factors influencing Food Security and Nutrition (FSN) and understanding its dynamics is crucial for planning and management. This understanding plays a pivotal role in supporting Africa's food security efforts to achieve various Sustainable Development Goals (SDGs). Utilizing Principal Component Analysis (PCA) on data from the FAO website, spanning from 2000 to 2019, informative components are derived for dynamic spatio-temporal modeling of Africa's FSN Given the dynamic and evolving nature of the factors impacting FSN, despite numerous efforts to understand and mitigate food insecurity, existing models often fail to capture this dynamic nature. This study employs a Bayesian dynamic spatio-temporal approach to explore the interconnected dynamics of food security and its components in Africa. The results reveal a consistent pattern of elevated FSN levels, showcasing notable stability in the initial and middle-to-late stages, followed by a significant acceleration in the late stage of the study period. The Democratic Republic of Congo and Ethiopia exhibited particularly noteworthy high levels of FSN dynamicity. In particular, child care factors and undernourishment factors showed significant dynamicity on FSN. This insight suggests establishing regional task forces or forums for coordinated responses to FSN challenges based on dynamicity patterns to prevent or mitigate the impact of potential food security crises.

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products.

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

Research on path planning for clear aligner flexible manufacturing.

In recent years, clear aligner can enhance individual appearance with dental defects, so it used more and more widely. However, in manufacturing process, there are still some problems, such as low degree of automation and high equipment cost. The problem of coordinate system mismatch between gingival curve point cloud and dental CAD model is faced to. The PCA-ICP registration algorithm is proposed, which includes coarse match algorithm and improve-ICP registration algorithm. The principal component analysis (PCA) based method can roughly find the posture relationship between the two point clouds. Using z-level dynamic hierarchical, the ICP registration can accurately find the posture between these two clouds. The final registration maximum distance error is 0.03 mm, which is smaller than robot machining error. Secondly, the clear aligner machining process is conducted to verify the registration effectiveness. Before machining, the path is generated based on the well registered gingival curve. After full registration, the tool path is calculated by establishing a local coordinate system between the workpiece and the tool to avoid interference. This path is calculated and generated as an executable program for ABB industrial robots. Finally, the robot was used for flexible cutting of clear aligners and was able to extract products, ensuring the effectiveness of the proposed research. This method can effectively solve the limitations of traditional milling path planning under such complex conditions.