The objective of this study was aimed to study the sensory processes of the “human-computer interaction” model when classifying visual images with an incomplete set of signs based on the analysis of early, middle, late and slow components of event-related potentials (ERPs). 26 healthy subjects (men) aged 20-22 years were investigated. ERPs in 19 monopolar sites according to the 10/20 system were recorded. Discriminant and factor analysis were applied. The component N450 is the most specialized indicator of the perception of unrecognizable (oddball) visual images. The amplitude of the ultra-late components N750 and N900 is also higher under conditions of presentation of the oddball image, regardless of the location of the registration points. The results of the study are discussed in the light of the paradigm of the P300 wave application in brain-computer interface systems, as well as with the peculiarities in brain pathology. Promising directions for the development of studies of the “Brain Computer Interface” (BCI) P300 systems are to increase the throughput of information flows. To extend the application of the P300 ERPs to multiple modalities, the underlying physiological mechanisms and responses of the brain for a particular sensory system and mental function must be carefully examined.

Floods and droughts are driven, in part, by spatial patterns of extreme rainfall. Heat waves are driven by spatial patterns of extreme temperature. It is therefore of interest to design statistical methodologies that allow the identification of likely patterns of extreme rain or temperature from observed historical data. The standard work-horse for identifying patterns of climate variability in historical data is Principal Component Analysis (PCA) and its variants. But PCA optimizes for variance not spatial extremes, and so there is no particular reason why the first PCA spatial pattern should identify, or even approximate, the types of patterns that may drive these phenomena, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as `sum of all points in the spatial field'. We compare the first PCA and DCA spatial patterns for US rainfall anomalies on a 6 month timescale, using the sum metric for the definition of DCA in order to focus on total rainfall anomaly over the domain, and find that they are somewhat different. The definitions of PCA and DCA result in the first PCA spatial pattern having the larger explained variance of the two patterns, while the first DCA spatial pattern, when scaled appropriately, has a higher likelihood and greater total rainfall anomaly, and indeed is the pattern with the highest total rainfall anomaly for any given likelihood. In combination these two patterns yield more insight into rainfall variability and extremes than either pattern on its own.

Analyzing big data poses a great challenge for numerous researchers to explore the data structure. Dimension reduction methods can be used to reduce data dimensionality, taking it from occupying a high-dimensional space to existing in a lower-dimensional space while retaining as much information as possible. Principal Component Analysis is one of the most popular used to reduce the dimensional space. The bootstrap sample is obtained by randomly sampling n times with replacement from the original sample, the method provides easy tool to understand the interactive component and develop the process. There are not enough researches discussed the stability of PCA method using bootstrap method. In this paper, the bootstrap method is used to analyze the stability of PCA results and to estimate the number of PCA in efficient way. The method is used to estimate the number of PCA that needed to classify the data set, and the effectiveness of the discussed techniques is demonstrated through real data sets.

The larvae of some species of the subgenus Orthocladius s. str. (Diptera, Chironomidae) are here described for the first time with corrections and additions to the descriptions of adult males and pupal exuviae. The identification of larvae is generally not possible without association with their pupal exuviae and/or adult males, so the descriptions here are based only on reared material or on pupae with the associated larval exuviae. Usually, Chironomidae larvae can be separated on the basis of morphometric characters, and the most discriminant characters ones are: 1- the ratio between the width of median tooth of mentum (Dm) and the width of the first lateral tooth (Dl) = mental ratio (DmDl), 2- the ratio between the length of the first antennal segment (A1) and the combined length of segments 2-5 (A(2-5)2-5) = antennal ratio (AR). The shape of mandible, maxilla, and other body parts are almost identical in all the species considered in this study. The larva of Orthocladius (Symposiocladius) lignicola is very characteristic and can be separated by the shape of mentum and the larvae of all the known species of Symposiocladius are characterized by the presence of large Lauterborn organs on antennae and of tufts of setae on abdominal segments. The larvae of Orthocladius (Orthocladius) oblidens and Orthocladius (Orthocladius) rhyacobius can be distinguished from other species basing on their large Dm and to each other by AR. A principal component analysis was carried out using 5 characters: 1- Dm, 2- Dl, 3- length of A1, 4- width of A1 (A1W), 5- combined length of segments 2-5 (A2-5). The most discriminant characters were Dm and A1, confirming that DmDl and AR can be used to separate species at larval stage, but the large superposition of morphometric characters in different species confirms that association with pupal exuviae is in any case needed to identify larvae. In future perspective, the development of reference DNA barcodes from specimens identified by specialists is recommended since possibly the best tool for larvae identification, but association of barcodes with morphotypes is in any case fundamental.

Principal component analysis (PCA) is used to reduce the dimensionalities of high dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high dimensional space. PCA and many PCA-related methods have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods, and their applications for investigating protein dynamics.

In light of the prevalent pest and disease issues faced by greenhouse cucumbers, a staple vegetable during winter, this study introduces a detection method based on the enhanced YOLOv8s model. This method aims to provide technical support for detecting and classifying pests and diseases in cucumber agricultural production. The model integrates the 'MultiCat' module for multiscale feature fusion and employs the 'C2fe' and 'ADC2f'modules to strengthen spatial and channel attention. The 'Block2d' function also facilitates the choice between average pooling and attention-based spatial pooling. Channel fusion is achieved through additive and multiplicative operations, allowing the model to delve deeper into feature learning. Experimental results confirm that our approach outperforms the original YOLOv8s model in pest detection, particularly excelling in the identification of small-scale and overlapping afflictions.

The aim of the presents study is to further expand the model of translational cross-validation by investigating the brain circuits or networks which underpin the same tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesized that there will be neural network patterns of activation and deactivation, which correspond to real time performance on clinical self-evaluation scales. We recruited 42 subjects: 20 healthy controls and 22 patients with major depressive episode. All subjects underwent functional MRI scanning using paradigm comprised of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping. The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for major depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with healthy controls. It is for the first time when potential neuroimaging state dependent biomarker has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the ability of both methods to concordantly distinguish groups by means of incremental translational cross-validation.

The many strains of Cannabis spp. are associated with many effects on users and contain many different potentially psychoactive metabolites, but the links between metabolite profiles and user effects are unclear. Here we take a statistical approach to linking cause (i.e. metabolites) to effects in Cannabis spp. through the prism of strains, using quantitative data for metabolite composition and user effects. We find that species (indica vs. sativa) explains <2% of the variability in metabolite profiles, while strain explains 1/3 of variability, indicating species is nonindicative of metabolite composition, while strain is approximately indicative. Using random forests we generate a table of potential metabolite-effect links. We also find that effect-weighted metabolite composition can effectively be described in terms of four values representing the concentrations of pairs or triplets of particular compounds.

We consider mixture experiments in which the proportions of the components must be non-negative and their sum must equal one. Thus, the experimental region for a mixture of components is a simplex. Li and Zhang (2017) made the conjecture that the pseudo component transformation of the lattice points in the simplex has a special property. In this paper, we show that this conjecture is not true in general. Furthermore, we refine this conjecture and prove the refined conjecture.

This study assesses attitudes of young adults' (18-30 years old) consumption on local and traditional products in7 European countries. A clustered sample (n=836) from natives of Greece, Bulgaria, Romania, Slovenia, Croatia, Denmark and France was collected, by distributing questionnaires through social media and university mail services. Sample was examined by implementing Principal Component Analysis (PCA) in three different samples; overall and two subgroups, Eastern and Western European countries. Six major factors revealed: consumer behavior, health issues, cost, influence from media and close environment and availability on store. As a result, young adults have a positive attitude to local and traditional food products but they express insecurity for health issues. Cost factor influences less people from Eastern European countries than those from the overall sample (3rd and 5th factor accordingly). Influence of close environment is a different factor in Eastern countries comparing to Western ones that it common with influence from media. Females and older people (25-30 years old) doubt less about TFPs, while media have high influence on consumers’ decisions. Aim of this survey is to create consumer profiles of young adults and create different promotion strategies of local and traditional products among the two groups of countries.

Strategic researches on green mine construction are of great theoretical and practical significance to the sustainable development of China's mining industry as well as the great-leap-forward development strategies of China. Strategies of green mine construction in China are methods summarized to solve all potential problems from mine production to ecological restoration. At present, strategies of green mine construction in China are not fully evaluated and studied yet. Therefore, on the basis of green mine construction related literatures carried out by researchers in China and abroad, this study took the green mine of Yongcheng City in China as the research object to evaluate the current situation of green mine construction in Yongcheng City and put forward corresponding countermeasures. First of all, driving force-pressure-state-impact-response (DPSIR) model was introduced for the construction of evaluation index system; construction principles and selection methods of indexes and the index system based on driving force, pressure, state, impact and response were constructed. Secondly, principal component analysis (PCA) was adopted to calculate and evaluate data of green mine of Yongcheng City in recent years, and construction state of green mine in Yongcheng City was analyzed concretely according to the evaluation results. Empirical results showed that, the evaluation system constructed in this study was feasible, which could be applied to evaluate construction of green mine effectively.

In the field of Thermal Non-Destructive Testing and Evaluation (TNDT&E), active thermography gained popularity due to its fast, wide area monitoring and remote inspection capability to assess materials without compromising their future usability. Among the various active thermographic methods, pulse compression favourable frequency-modulated thermal wave imaging stands out for its enhanced detectability and depth resolution. In this study, experimental investigation has been carried out on a hardened steel sample used in ship building industry with a flat-bottom hole simulated defect using the Frequency Modulated Thermal Wave Imaging (FMTWI) technique. And the defect detection capabilities of FMTWI have been investigated from various statistical post processing approaches and compared by taking Signal to Noise Ration (SNR) as a figure of merit. Among various adopted statistical post-processing techniques, the pulse compression has been carried out using different methods: offset removal with polynomial curve fitting, and Principal Component Analysis (PCA) which is an unsupervised learning approach for data reduction and offset removal with median centering for data standardization. The performance of these techniques was assessed through experimental investigations on hardened steel specimen used in ship building to provide valuable insights into their effectiveness in defect detection capabilities.

This article presents a study conducted within a wind tunnel to enhance understanding of the excitation mechanism of stay cable vibration under arid conditions. Numerous wind tunnel tests were meticulously analyzed. Initially, the vibration of the stay cable was measured under steady flow conditions at a flow angle of 45 degrees and an inclination of 25 degrees, while varying wind velocities were applied. Additionally, an investigation into the flow field surrounding the stay cable was conducted in both vertical and horizontal directions. By utilizing two hot wire anemometers in the cable wake, an extensive database of flow field measurements was obtained. The experimental results revealed that the vibration characteristics of the stay cable under the arid conditions considered in this study aligned with findings reported in existing literature. Notably, a deeper comprehension of the excitation mechanism of a stay cable in a dry state was attained. This mechanism is closely associated with the inhibition of Karman vortices and the development of low-frequency vortices. At low wind speeds, Karman vortices predominated, resulting in small-amplitude vibrations. However, as the wind speed increased, the influence of Karman vortices diminished progressively, while the low-frequency vortices grew stronger. These low-frequency vortices exhibited high energy and a significant correlation with shedding along the stay cable, thereby inducing cable vibration in a dry environment.

The mutual information between the state of a neural network and the state of the external world represents the amount of information stored in the neural network that is associated with the external world. In contrast, the surprise of the sensory input indicates the unpredictability of the current input. In other words, this is a measure of inference ability, and an upper bound of the surprise is known as the variational free energy. According to the free-energy principle (FEP), a neural network continuously minimizes the free energy to perceive the external world. For the survival of animals, inference ability is considered to be more important than simply memorized information. In this study, the free energy is shown to represent the gap between the amount of information stored in the neural network and that available for inference. This concept involves both the FEP and the infomax principle, and will be a useful measure for quantifying the amount of information available for inference.

Trade-offs in ecosystem services (ES) manifest when the enhancement of one service leads to the diminishing of another. These trade-offs pose a notable challenge, impacting the sustainability of particular socio-ecological systems peri-urban landscapes (PULs). This issue arises from the dynamic processes associated with peri-urbanization, posing threats to natural ecosystems and their services in peri-urban areas. Additionally, the escalating demand for ecosystem services in PULs contributes to these trade-offs. Policy making and planning concerning ecosystem ES trade-offs in PULs should prioritize the promotion of a balance between conflicting services and foster synergies among them. However, it is noteworthy that ES trade-offs in PULs are not given high priority on policy and planning agendas. Knowledge regarding policy development and planning for ES trade-offs in PULs often remains concealed within specific country and regional case studies. Consequently, this research seeks to characterize the ES trade-offs in the selected PUL case studies, with the objective of identifying potential commonalities among them. Furthermore, the study aims to identify: (i) the factors driving ES trade-offs, (ii) challenges pertaining to how policy-making and planning address ES trade-offs in PULs, and (iii) recommendations for enhancing governance practices to better manage peri-urban ES trade-offs. We designed a semi-quantitative survey and collected information about 23 different case studies, located across the world. Answers from this survey were analyzed with the help of Principal Component Analysis approach. The results showed that the most common trade-off occurred between “cultural and provisioning” and “regulating and provisioning” ES. It was found that urban development is the basic driver behind emerging of the examined trade-offs. To tackle this issue at the governance level, the study recommends establishing mechanisms to facilitate collaboration among stakeholders. This should be accompanied by robust dissemination efforts and the promotion of awareness among actors regarding the fundamental concepts of ES and PULs.

Principal Component Analysis (PCA) is a method that identifies common directions within multivariate data and presents the data in as few dimensions as possible. One of the advantages of PCA is its objectivity, as the same results can be obtained regardless of who performs the analysis. However, PCA is not a robust method and is sensitive to noise. Consequently, the directions identified by PCA may deviate slightly. If we can teach PCA to account for this deviation and correct it, the results should become more comprehensible. The methods for doing this and an issue with this are presented.

In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring Volatile Organic Compounds (VOCs) using a Photoionization Detector (PID). This paper analyzes the reasons for the low accuracy of the traditional Support Vector Machine (SVM) regression method. To address the issue, the PID signal is subjected to feature extraction and Principal Component Analysis (PCA) to reduce the data dimensionality. Moreover, the optimal SVM parameters are selected using a Genetic Algorithm (GA), and a combined approach of SVM regression with PCA and GA is utilized for PID signal regression analysis. And the effectiveness of the method is validated through extensive experiments and simulations. Furthermore, the influence of the sample quantity on the regression accuracy is analyzed, enabling accurate monitoring of VOCs concentration in a moist environment.

Software testing is an integral yet time-consuming component of software development, and even with capable software testing resources (technology and people), it is still regarded as problematic. Test automation is widely being utilised within the software industry to provide increased testing capacity to ensure high product quality and reliability. In this paper, we are specifically addressing automated testing whereby test cases are manually written. Test automation has its benefits, drawbacks, and impacts on different stages of development. Furthermore, there is often a disconnect between non-technical and technical roles, where non-technical roles (e.g., management) predominantly strive to reduce costs and delivery time whereas technical roles are often driven by quality and completeness. Although it is widely understood that there are challenges with adopting and using automated testing, there is a lack of evidence to understand the different attitudes toward automated testing (AtAT), focusing specifically on why it is not adopted. In this paper, the authors have surveyed practitioners within the software industry from different roles to determine common trends and draw conclusions. A two-stage approach is presented, comprising a comprehensive descriptive analysis and the use of Principle Component Analysis. In total, 81 participants were provided with a series of 22 questions and their responses are compared against job role types and experience levels. In summary, 6 key findings are presented covering expertise, time, cost, tools and techniques, utilisation, organisation, and capacity

This paper focuses on the application of principal component analysis (PCA) to conduct a source apportionment of atmospheric aerosols from 8 sampling locations along the Marilao-Meycauayan-Obando River System (MMORS). Aerosols were collected on May 2016 during the same time that water samples were collected. Elemental analysis was conducted using a scanning electron microscope coupled with energy dispersive x-ray (SEM-EDX). Carbon (C), nitrogen (N), oxygen (O), sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), titanium (Ti), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), bromine (Br), niobium (Nb), barium (Ba), mercury (Hg), and lead (Pb) concentrations were measured and used as inputs in Principal Component Analysis (PCA). The aerosol samples showed the presence of heavy metals Pb and Hg, elements that were also detected in trace amounts in the water measurements. Concentrations of heavy metals Fe, Pb, Hg in the aerosols were attributed to industrial sources. However, it was determined that the primary source of aerosols in the area were traffic and crustal emissions (C, N, O, Si, Al, Ca). Thus, control of traffic emissions would be more beneficial in reducing aerosol emissions in Meycauayan.

The post-harvest processes of coffee are widely accepted as key factors in determining the quality of the product. In the Cauca department, Southwestern Colombia, this stage is carried out empirically by farmers in the region, using old methods that do not assure consistent quality. This study proposes to determine the best post-harvest temperature and time conditions for coffee produced in the region. For this purpose, we carried the fermentation and honey process out on different coffee samples of the Coffea Arabica species of the Castillo variety. Subsequently, the cup profile quality of the coffee samples was determined by a sensory evaluation by experts. Finally, we applied descriptive statistical techniques to the resulting data and principal component analysis and hierarchical cluster analysis to find similarities between the samples. The results suggest that the honey process gets better evaluations in the cup profile over any condition of temperature and fermentation time.

Light-gated ion channel and ion pump rhodopsins are widely used as optogenetic tools and these can control the electrically excitable cells as: (1) they are a single-component system i.e., their sensor and effector functions are encoded by the 7-transmembrane domains and (2) they show fast kinetics with small dark-thermal recovery time. In cellular signaling, a signal receptor, modulator and effector components are involved for attaining synchronous multicomponent regulation. Optical modulation of this network requires either receptor to effector encoded in a single ORF or direct modulation of the effector domain through bypassing all upstream players. Recently discovered modular rhodopsins like rhodopsin guanine cyclase (RhoGC) and rhodopsin phosphodiesterase (RhoPDE) paves the way to establish proof of concept. Light sensor coupled modular system could be expressed in a precise cell type and which holds great potential in the advancement of optogenetics 2.0. It would enable manipulating entire relevant cell signaling system. Here, we had identified 50 novels modular rhodopsins with variant rhodopsins domain and its diverse cognate signaling cascades encoded in a single ORF, which are associated with specialized functions in the cells. These novel modular algal rhodopsins have been characterized functionality based on their sequence and structural homology with previously characterized rhodopsins. Presented novel modular rhodopsins with various effector domains hold potential to expand optogenetics tool kit to regulate various cellular signaling pathways across the diverse biological model systems.

The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric Synthetic Aperture Radar (PolSAR) images. This manuscript addresses two important aspects when applying such methods on real data, namely speckle filtering and statistical classification with ICA. A novel PolSAR data processing framework is introduced by adjusting the Lee's sigma filter to the particular nature of the Touzi's polarimetric decomposition. In its current form, it allows the use of the ICA mixing matrix in the derived speckle filter. An extension of the Fromont at al. iterative segmentation is introduced, equally. This proposed framework is tested using P band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.

Over the past decade and a half, dynamic functional imaging has revolutionized the neuroimaging field. Since 2009, it has revealed low dimensional brain connectivity measures, has identified potential common human spatial connectivity states, has tracked the transition patterns of these states, and has demonstrated meaningful alterations in these transition and spatial patterns in neurological disorders, psychiatric disorders, and over the course of development. Recently, researchers have begun to analyze this data from the perspective of dynamic system and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Consciousness, in particular, appears to be strongly linked to entropy production in the brain. Little attention has been paid to the effects of psychiatric disease on entropy production in the human brain, however. Even disorders characterized by substantial changes in conscious experience have not been widely analyzed from this perspective. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls, and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients’ brain function.

This study introduces an integrated methodology that incorporates vessel motion dynamics into the evaluation of operations and maintenance (O&M) costs for floating offshore wind turbines (FOWTs). By combining UWiSE, a discrete-event simulation tool, with SafeTrans, a voyage simulation software, the methodology accounts for vessel motion effects during offshore operations. The approach was demonstrated through a numerical case study at two wind farm sites, Marram Wind and Celtic Sea C, with a fictive wind farm layout of 100 × 15 MW NREL turbine on a UMaine VolturnUS-S platform. Three Major Component Replacement (MCR) strategies were assessed: Tow-to-Port (T2P), Floating-to-Floating (FTF), and Self-Hoisting Crane (SHC). The T2P strategy resulted in the highest O&M costs—94 k€/MW/year at Marram Wind and 97 k€/MW/year at Celtic Sea C—due to extended MCR durations (90-180 days), leading to lower availability (90%-94%). In contrast, the FTF and SHC strategies, which are still under development, demonstrate significantly lower costs and reduced downtime. The SHC strategy, in particular, has proven to be the most cost-effective, achieving up to a 64% reduction in costs while increasing availability to 97%-98%. The integrated approach incorporates vessel dynamics, accounting for factors such as wave direction, wave period, and vessel response to varying sea states. This allows for greater flexibility in setting operational limits, potentially permitting higher limits in favorable conditions where vessel motion impact is reduced. However, in scenarios where dynamic vessel responses lead to increased motions—such as when waves approach from the side or when the wave period is close to the vessel's natural roll period—more restrictive limits may be necessary, even if significant wave heights are lower. This flexibility or restriction highlights the importance of incorporating motion-based dynamics for emerging technologies in the evolving FOWT O&M market.

Background: The aim of this systematic review was to evaluate diagnostic accuracy of molecular-based LTPs serum IgE for the diagnosis of food allergies in patients with suspected allergy to one of the LTPs-containing food. Methods: cohort, prospective or retrospective cross- sectional studies were considered for inclusion in the review. Oral food challenge (both open and double-blind placebo-controlled) was the reference standard for the diagnosis. Pubmed (MEDLINE), Web of Science, Scopus, and ClinicalTrial.org were searched for relevant papers. Risk of bias was assigned by QUADAS-2 tool. Data were reported as sensitivity and specificity. Study protocol was registered in the PROSPERO database (CRD42022321985). Results: Fifteen articles, including 2395 individuals, were included. Sensitivity of Ara h 9 for peanut allergy diagnosis ranged from 6 to 61%; specificity ranged from 57 to 100%. Cor a 8 sensitivity ranged from 11 to 43%, with specificity ranging from 59 to 94%. Conclusions: Ara h9 and Cor a 8 serum IgE may be useful to confirm diagnosis of peanut and hazelnut allergy in symptomatic patients, although diagnostic accuracy is limited by low sensitivity. More investigation on other LTPs and in adult population may be important to define the clinical role of this test in food allergies diagnostics.

There are various types of charged particles in the space environment, which can cause different types of radiation damage to materials and devices, leading to on-orbit failures and even accidents of spacecraft. Developing lightweight and efficient radiation shielding materials is an effective approach to improve the inherent protection of spacecraft. The protective performance of different materials against proton and electron spectra in the Earth’s radiation belts is evaluated by Geant4 simulation. Based on the simulation results, suitable hardening components are selected to design composite materials, and B4C/PEEK composites with different B4C content were successfully prepared. Experimental results demonstrate that the simulated and experimental results for the electron, proton and neutron shielding performance of B4C/PEEK composites are consistent. These composites exhibit excellent radiation shielding capabilities against electrons, protons and neutrons, and the radiation protection performance improves with increasing B4C content in the B4C/PEEK composite materials.

Electricity price signal is a direct reflection of the attributes of electric power commodities, but due to the influence of market supply and demand, power producers' offer, line capacity and other factors, electricity price signals often show a variety of abnormal forms. The identification and traceability of abnormal tariff signals is an important daily work of power trading centers at all levels. However, at present, it is common to rely on manual experience to analyze the causes of abnormal tariffs, which is inefficient and difficult to ensure the objective and scientific traceability of abnormal tariff causes. Since different types of abnormal tariff signals are caused by different dominant factors, if the key features of abnormal tariffs can be classified and matched, the identification efficiency can be effectively improved. For this reason, this paper proposes a traceability method of abnormal tariffs based on the comparison analysis of key features. First of all, based on the characteristics of historical tariff data, this paper completes the classification of tariff spike magnitude anomaly and tariff mean value anomaly, further establishes the key features of each type of abnormal tariff signal based on principal component analysis, and finally calculates the influence degree of each element within the key features one by one based on alternative algorithms, and then realizes the screening and traceability of the causes of abnormal tariffs based on the importance of the degree of influence ordering. The effectiveness of the proposed method has been verified in a large number of examples constructed based on the actual data of the electricity market, and the average correctness rate of the proposed method for the traceability of the causes of the average value and spike anomalies of the electricity price reaches more than 85%, which can reduce the labor cost in the process of the traceability of the causes of the anomalous electricity price.

We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as the group orthogonal greedy algorithm and the high-dimensional Akaike information criterion to mitigate the impact of irrelevant predictors. Moreover, a novel technique, called peeling, is used to boost the variable selection procedure so that many factor-relevant predictors can be included in prediction. Finally, the supervised dynamic principal component analysis (sdPCA) method is adopted to account for the dynamic information in factor recovery. In simulation studies, we found that the proposed method adapts well to unknown degrees of sparsity and factor strength, which results in good performance even when the number of relevant predictors is large compared to the sample size. Applying to economic and environmental studies, the proposed method consistently performs well compared to some commonly used benchmarks in one-step-ahead out-sample forecasts.

(1)Background：To date, no studies have epidemiologically examined whether the concentration of particulate matter (PM) 2.5 and its ionic components is related to the prevalence of skin symptoms. Therefore, this study aimed to clarify this association in patients with allergic diseases. (2)Methods: From February 1 to May 31, 2020, we evaluated the daily prevalence of skin symptoms in outpatients with allergic diseases being treated at Fukuoka Hospital, Fukuoka, Japan, and measured the concentration of PM2.5 and its ionic components. （3）Results: Univariate analysis showed a statistically significant association between skin symptoms and the concentration of PM2.5 and the ionic components SO42-, NH4+, K+, and Mg2+, and multivariate analysis showed a statistically significant association between the daily prevalence of skin symptoms and the concentration of the ionic components SO42- or Mg2+. (4)Conclusions: Our findings indicate that the concentration of some PM2.5 ionic components may affect skin symptoms in patients with allergic diseases.

Wheat, a component of the staple diet globally, is a common food allergen in children. The symptoms of wheat allergy (WA) range from skin rash to shortness of breath, significantly impairing quality of life. Following initial clinical suspicion, individuals may undergo wheat allergen-specific skin prick test (SPT), blood test for serum immunoglobulin E (sIgE) levels or oral food challenge. Conventional management of WA lies in wheat avoidance, yet accidental consumption may be inevitable owing to the ubiquity of wheat in various food products. This article aims to provide an overview of the immunologic pathway of WA, followed by its novel diagnostic methods, namely alcohol-soluble SPT extracts, component-resolved diagnosis and basophil activation test (BAT). The mechanisms underlying wheat allergen-specific oral immunotherapy (OIT) as well as a summary of the efficacy, tolerability and safety of related clinical trials will then be discussed.

This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). Previous exploration of the topographic variables revealed high correlation and multicollinearity among some of them, which led to dimensionality reduction through principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being an El Niño phenomenon and the second being an earthquake exceeding 8.8Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of &gt;0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8Mw, approximately 40% of the NLC area would be exposed to the highest hazard levels. It also highlights the importance of integrating methodologies to reduce model uncertainty, such as comprehensive analysis of variables supported by high quality data and organized workflows. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies.

Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to device suitable applications. This article aims to propose for the first time an approach to use quantum logic in image processing at hand of a process for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables to initiate the principal component analysis. At hand of experiments, we explore the possibility to distinguish between different geometrical objects and discuss the results in terms of quantum logical interpretation.

The valve controlled cylinder drive system is the most common type among hydraulic applications. The nonlinear behaviour in such system is inevitable when the valve spool around its null position [1] , Leaney [2] utilised component linking method to investigate the nonlinearities in a Moog valve controlled asymmetric cylinder drive system by simulation in Fortran, in which a generalised concept is introduced and validated by comparing to the experimental results. An factor is proposed in the generalised concepts to describe the asymmetric cylinder state, which is a constant when the cylinder in extending or retracting, but numerically calculated when the valve spool in the underlap region [2]. This paper utilises the component linking method to simulate the Moog valve controlled asymmetric cylinder drive system in Matlab Simulink, and proposed an analytical solution for factor when the valve spool in the underlap region.

With the deterioration of the bridge performance and ever-increasing amount of traffic, the bridge safety is becoming a concern for engineering community. A method that can assess the bridge's condition in real-time is urgently needed. The main factors that hinder the bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA) based method is developed for structural damage detection of bridges in operational environments in this paper. Two main operational environmental factors: the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate to assess the bridge condition in operational environments.

To ensure sustainability of the future power grid, the rate of expansion of distributed renewable energy resources (DRER) has introduced operational challenges. These includes managing transmission constraints with DRER power injection, dispatching DRER efficiently, managing system frequency and ensuring sufficient reactive power for voltage support. Coupled with the intensification of wildfires, power infrastructure across the United States faces challenges to minimize the impact to maintain system reliability and resiliency. This research embarks on a comprehensive evaluation, beginning with an in-depth historical analysis to delineate regions most susceptible to wildfires. Utilizing a multidimensional approach, the study assesses wildfire-induced risks to power grids, by integrating historical wildfire occurrences, real-time wildfire proximities, Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation metrics, and system parameters, by employing Principal Component Analysis (PCA) based optimal weights, leading to the formulation of a novel risk factor model. This risk factor has the potential to be the key to ensuring the resilience of a renewable-rich smart grid when faced with a severe weather event. Our model’s applicability was further verified through an empirical assessment, selecting representative networks from diverse regions, providing insights into the geographical variability of risk factors. Ultimately, this study offers stakeholders and policymakers a comprehensive tool-set, empowering decisions regarding infrastructure investments, grid reinforcements, and strategic power rerouting to ensure consistent energy delivery using risk assessment against wildfire.

Objectives: The purposes of this study were to 1) analyze the disruption of intracardiac flow by flow components and kinetic energy (KE) parameters in single ventricle physiology patients and compare with controls; 2) investigate left ventricular diastolic dysfunction in terms of 4D flow parameters in repaired Fontan (rFontan) group; 3) compare intracardiac flow parameters in morphologic LV and RV rFontan patient groups. Methods: Twenty-five rFontan patients (age: 10±3, M/F: 15/10) and fourteen controls (age: 10±2, M/F: 8/6) were prospectively recruited. All underwent cine and 4D flow cardiovascular magnetic resonance (CMR) on 3.0T scanner. Cardiac function and inter-ventricular mechanical dyssynchrony were analyzed using cine images. From 4D flow CMR, ventricular flow components were assessed: direct flow, retained inflow, delayed ejection flow and residual volume. Global and regional blood flow KE parameters, normalized to end-diastolic volume (EDV) were analyzed for functional single ventricle (FSV). Left ventricular (LV) diastolic dysfunction was assessed on echocardiography guidelines. Results: In comparison of rFontan vs. controls, median FSV residual volume (28% vs. 23%, P= 0.034) were higher in rFontan; median FSV direct flow (32% vs. 40%, P=0.005) and delayed ejection flow (17% vs. 24%, P=0.024) were lower in rFontan. FSV KEiEDV parameters were all lower in rFontan (all P<0.05). No significant differences were observed for flow components and KE parameters between patients with and without inter-ventricular dyssynchrony. FSV direct flow (AUC=0.76, Sensitivity=86%, Specificity=70%) was an independent predictor of LV diastolic dysfunction. Residual volume and E-wave KEiEDV were significantly different between morphologic RV and morphologic LV patient group. Conclusions: The changed flow pattern and decreased KE may be hemodynamic disturbances and impaired ventricular filling in rFontan patients. Reduced direct flow is associated with LV diastolic dysfunction. Morphologic right ventricular subgroup is worse than morphologic left ventricular group in terms of the intracardiac hemodynamics.

The digital economy is a fundamental feature of sustainability. The economic, social, and environmental dimensions of sustainability stand up for digitalization and digital transformation projects. In order to understand and practice the digital economy, the goal of our paper is to evaluate accurate variables regarding fintech scope for discussing the efficient implication of these projects. The principal component analysis is used to investigate and evaluate the sustainable digital economy indicators. An empirical case from health care services in Africa from World Bank projects discusses this challenge. Results help produce an effective understanding of how the digital economy enables facing fintech challenges in Africa.

A major problem in the post-inversion geophysical interpretation is the extraction of geological information from inverted physical property models, which do not necessarily represent all underlying geological features. No matter how accurate the inversions are, each inverted physical property model is sensitive to limited aspects of subsurface geology and is insensitive to other geological features that are otherwise detectable with complementary physical property models. Therefore, specific parts of the geological model can be reconstructed from different physical property models. To show how this reconstruction works, we simulated a complex geological system that comprises an original layered earth model that has passed several geological deformations and alteration overprints. Linear combination of complex geological features comprised three physical property distributions: Electrical resistivity, induced polarization chargeability, and magnetic susceptibility models. This study proposes a multivariate feature extraction approach to extract information about the underlying geological features comprising the bulk physical properties. We evaluated our method in numerical simulations and compared three feature extraction algorithms to see the tolerance of each method to the geological artifacts and noises. We show that the fast-independent component analysis (fast-ICA) algorithm by negentropy maximization is a robust method in the geological feature extraction that can handle the added unknown geological noises. The post-inversion physical properties are also used to reconstruct the underlying geological sources. We show that the sharpness of the inverted images is an important constraint on the feature extraction process. Our method successfully separates geological features in multiple 3D physical property models. This methodology is reproducible for any number of lithologies and physical property combinations and can recover the latent geological features, including the background geological patterns from overprints of chemical alteration.

In this paper, the Group Method of Data Handling (GMDH) type of neural networks is used for the inductance calculation of variable inductors. The relation between the inductance of the inductor in the linear and nonlinear regions is investigated, and parameters such as the voltage across the inductor, bias current, and ac current are taken into account. The experimental setup is used for generating the data needed for training the neural network. Over 800 experiments were conducted and were used for training and validation of the neural network results. The results are compared with the reluctance equivalent circuit method, and they show a much better accuracy. The proposed method can be used for the calculation of various magnetic components, and it is not limited to variable inductors.

Citrus essential oils (EOs) have various bioactivities like antioxidants, with many applications. Antioxidant activities depend on the chemical compositions of the EOs, which are affected by climate, soil, and geographical region. Thus, investigations on chemical compositions and antioxidant activities of Citrus EOs in different countries are valuable. In this study, we distilled EOs from peels of Indonesian-grown Citrus, including C. nobilis, C. limon, C. aurantifolia, C. amblycarpa, and Citrus spp.Chemical compositions of EOs were analyzed using Gas Chromatography-Mass Spectrometer (GC-MS), whereas the antioxidant activities were determined by employing 2,2-diphenyl-2-picrylhydrazyl (DPPH) method. Furthermore, principal component analysis (PCA) was applied to elucidate the main contributing compounds for antioxidant activity. The results show that all EOs possess unique chemical characteristics, with limonene as the majority constituent. For antioxidant activities, C. limon and C. amblycarpa EOs are the two strongest, IC50 values below 7.00 μL/mL. PCA approach suggests that -terpinene mainly contributes to the high antioxidant activities of C. limon and C. amblycarpa. Moreover, o-cymene, thymol, p-cymene, and α-pharnesene may also be responsible for the antioxidant activity of C. limon EO. These results are valuable information for the applications of Citrus EOs as antioxidant sources.

Hope is important in the rehabilitation of persons with schizophrenia, through scales to measure hope are not appropriate for this population. The purpose of this cross-sectional study was to identify the psychometric properties of the Schizophrenia Hope Scale-9 (SHS-9) using data from 83 people with schizophrenia in four mental health centers and 762 healthy persons from two universities in South Korea. The mean (standard deviation) SHS-9 score of the participants with schizophrenia and healthy participants was 11.24 (4.90) and 14.83 (3.10), respectively. Lower scores indicate a lower level of hope. The internal consistency alpha coefficient was 0.92 with a 4-week test-retest reliability of 0.89. Criterion-related construct validity was established by examining the correlation between the SHS-9 and the State-Trait Hope Inventory scores. Divergent validity was identified through a negative relationship of SHS-9 with the Beck Hopelessness Scale. The construct validity of the SHS-9 was confirmed through principal component analysis with extraction methods, which resulted in a one-factor solution, accounting for 49–60% of the total item variance.. This study provided evidence for the validity and reliability of the SHS-9; therefore, it could be used to measure hope in people with schizophrenia.

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

This study investigated the phenolic compounds of 15 Chrysanthemum morifolium Ramat cv. ‘Hangbaiju’, including 6 ‘Duoju’ and 9 ‘Taiju’ using high performance liquid chromatography. The antioxidant activities of these ‘Hangbaiju’ were estimated by DPPH, ABTS and FPAR assays. Results showed that a total of 14 phenolic compounds were detected in these flowers, including 3 mono-caffeoylquinic acids, 3 di-caffeoylquinic acids, 1 phenolic acid and 7 flavonoids. ‘Duoju’ and ‘Taiju’ possessed different concentration of phenolic compounds, and ‘Taiju’ exhibited higher caffeoylquinic acids and stronger antioxidant activities than ‘Duoju’. Caffeoylquinic acids showed a strong correlation with the antioxidant activities of the samples. Principal component analysis revealed an obvious separation between ‘Duoju’ and ‘Taiju’ using phenolic compounds as variables. Apigenin-7-O-glucoside, 3,5-di-O-caffeoylquinic acid, luteolin and acacetin were found to be the key phenolic compounds to differentiate ‘Duoju’ from ‘Taiju’.

The Inefficient water use, varying and low productivity in Kenya public irrigation schemes is a major concern. It is therefore necessary to periodically monitor and evaluate the performance of public irrigation schemes. The performance of public irrigation in western Kenya was assessed by combining benchmarking methodology and principal component analysis. The aim was to quantify and rank the performance of pumped public irrigation schemes in Kenya. Eleven benchmarking indicators were computed for the period from 2012 to 2016 and compared to global benchmark values. The indicators used fall under agricultural productivity, water supply and financial performance categories. The computed agricultural productivity was 36%–51% in Ahero, 23%–42% in West Kano and 26%–50% Bunyala irrigation scheme. Water supply performance in Ahero, West Kano and Bunyala irrigation schemes varied from 24% to 58%, 3% to 49% and 19% to 43% respectively. Financial performance varied from 46% to 54% in Ahero, 25% to 32% in West Kano and 54%–56% in Bunyala irrigation scheme. An average overall performance efficiency of 46%, 39% and 31% was obtained in Ahero, Bunyala and West Kano irrigation schemes respectively. The performance of the irrigation schemes is very poor and measures on improving performance are needed.

Human adaptive responses to climate change occur at the local level, where climatic variability is experienced. Therefore analyzing vulnerability at the local level is important in planning effective adaptation options in a semi-arid environment. This study was conducted to assess vulnerability of Maasai pastoralist communities in Kajiado County, Kenya to climate change by generating vulnerability index for the communities. Data was collected using questionnaires that were administered to 305 households in the five different administrative wards (Oloosirkon/Sholinke, Kitengela, Kapetui North, Kenyawa-Poka and Ilmaroro) in Kajiado East. Vulnerability was measured as the net effect of adaptive capacity, sensitivity and exposure to climate change. Principal Component Analysis (PCA) was used to assign weights to the vulnerability indicators used for the study and also to calculate the household vulnerability index. A vulnerability map was produced using the GIS software package ArcGIS 10.2. Results showed that gender of household head, age of household head, educational level, access to extension agents, herd size, livestock diversity and access to credit facility influenced vulnerability of the Maasai pastoralists to climate change in Kajiado East. The result showed that the most vulnerable communities with the highest negative vulnerability index value are Ilpolosat (-2.31), Oloosirikon (-2.22), Lenihani (-2.05), Konza (-1.81) and Oloshaiki (-1.53). The communities with the highest positive vulnerability index values were Kekayaya (4.02), Kepiro (3.47), Omoyi (2.81), Esilanke (2.23), Kisaju (2.16) and Olmerui (2.15). We conclude that provision of basic amenities such as good roads and electricity; access to extension agents, access to credit facilities and herd mobility will reduce vulnerability of Maasai pastoralists in Kajiado east to climate change and variability.

Modern companies accumulate a vast amount of customer data that can be used for creating a personalized experience. Analyzing this data is difficult and most business intelligence tools cannot cope with the volume of the data. One example is churn prediction, where the cost of retaining existing customers is less than acquiring new ones. Several data mining and machine learning approaches can be used, but there is still little information about the different algorithm settings to be used when the dataset doesn't fit into a single computer memory. Because of the difficulties of applying feature selection techniques at a large scale, Incremental Probabilistic Component Analysis (IPCA) is proposed as a data preprocessing technique. Also, we present a new approach to large scale churn prediction problems based on the mini-batch Stochastic Gradient Decent (SGD) algorithm. Compared to other techniques, the new method facilitates training with large data volumes using a small memory footprint while achieving good prediction results.

Previous research has indicated that ultra-high pressure (UHP) technology is useful in speeding up the extraction process of cold brew (CB) coffee. However, the impact of different degrees of roasting on UHP CB coffee is still unclear. Therefore, this study aimed to investigate the effects of three different degrees of roasting (light, medium, and dark) on the physicochemical and flavor characteristics of UHP CB coffee. The results of this study showed that most physicochemical characteristics of UHP coffee were similar to those of conventional CB coffee, regardless of the degree of roasting. However, the majority of physicochemical characteristics, non-volatile components, and sensory evaluation significantly increased with an increase in roasting degree. The HS-SPME-GC/MS analysis showed that most volatile components, such as pyrazines, furans (except furfural), esters, pyrrole, pyridine, and most aldehydes, increased from light to dark roasting, and this was significantly evident in CB coffee compared with UHP. The content of volatile components in UHP coffee was significantly (P<0.05) higher than that in CB coffee at the same degree of roasting, especially with dark roasting. The combination of orthogonal partial least squares-discriminant analysis (OPLS-DA) and principal component analysis (PCA) indicated that the distance between coffee samples at different degrees of roasting under CB coffee was greater than that under UHP coffee. However, the distance between these two extraction methods at medium roasting was evidently closer than that at light and dark roasting. Moreover, 15 representative differential compounds, such as were Hazelnut pyrazine, linalool, butane-2,3-dione, 3- methylbutanal found by the calculation of odor-active values (OAVs) indicating the main contributing compounds to odor. Overall, the effect of roasting degrees on UHP coffee was less than that on CB coffee, which was consistent with the results of physicochemical characteristics, volatile components, and sensory evaluation.

In genomics analysis, with the development of sequencing technology and the rapid growth of data volume, how to effectively identify and analyze important information in genomic data from massive data has become an important challenge. Identify key patterns and features in genomic data through advanced community testing methods. First, genomic data often contains a lot of noise and redundant information that needs to be processed through steps such as normalization, filtering, and dimensionality reduction. Normalization makes data at different scales comparable, filtering out data points with low quality or missing values, and dimensionality reduction reduces data dimensions through principal component analysis (PCA) and retains the main information. The gene co-expression network establishes the connection relationship between genes by calculating the expression similarity matrix between genes, and applies the Louvain algorithm to maximize the modularity of the network, aggregate nodes layer by layer, form a community structure, and identify the complex relationship and potential structure between genes. Finally, we utilize the cancer dataset to evaluate the proposed model. From our extensive experimental results, we can observe that Louvain's algorithm indicates outperformances and accuracy in the recognition and analysis of genomic data.

This paper investigates five different GARCH models (GARCH, EGARCH, IGARCH, Component GARCH (CGARCH) and GJR-GARCH) along with six distributions (Normal, Student’s t, GED and their skewed forms), which are used to forecast the volatility for the Bulgarian Stock index SOFIX. We use a GARCH model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data covers the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at forecasting the volatility of SOFIX and which distribution is most appropriate. The results, based on BIC and AIC information criteria, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student t-distribution is preferred for forecasting. With the help of 5000 simulations, it is estimated that the chances of Index SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.

In this study, we primarily assess the relationship between social safeguards and the living standards of local communities in different ecological zones of Ghana. This research aims to assist policymakers and stakeholders to make informed and appropriate decisions when developing social safeguards for emission reductions projects. Principal component analysis was employed to develop a living condition index, after which we generated a second construct based on the number of factors affecting living conditions. The results showed that access to sharecropping, literacy, extension services, financial institutions, a mobile phone network, and markets have significant positive relationships with the living conditions of local communities in Ghana. In addition, 78% of the social safeguards described in Ghana’s REDD+ Strategy, Forest and Wildlife Policy, Benefits Sharing Mechanism, and Forest Plantation Development Strategy are aligned with the determinants of local communities’ living conditions. The findings suggest a need to enforce benefit-sharing mechanism, better forest governance, and tenancy reforms in order to prevent the overexploitation of landowners and avoid elite capture. Furthermore, farmers must be linked to financial institutions in order to obtain credits. Additionally, we must seek to provide local communities with better access to markets and mobile phone networks in a way that allows them to carry out their livelihood activities effectively and efficiently. These measures help to reduce the risk of REDD+ actions and ensure sustainability.

Soybean is one of the world’s most economically significant crops and is widely utilized as an essential source of vegetable protein and edible oil. Cultivated soybean is domesticated from its annual counterpart, wild soybean, which is considered valuable germplasm for soybean breeding. However, wild soybean accessions generally produce seeds with impermeable coats, a trait known as hard seededness (HS), which is beneficial for long-term seed survival but is undesirable for the uniform water absorption and germination of seeds, thus limiting the utilization of wild soybeans in breeding. However, moderate HS can isolate the embryo from the surrounding environment and is thus beneficial for long-term seed storage and germplasm preservation. The HS trait is primarily associated with the structure and chemical composition of the seed coat. However, its development is also influenced by various environmental conditions, such as water and temperature. Genetic analysis has revealed that HS of soybean is a complex quantitative trait controlled by multiple genes or minor quantitative trait loci (QTL), with many QTLs and several causal genes currently identified. Investigating the physiological and molecular mechanisms underlying this trait is crucial for soybean breeding, production, and food processing. For this article, the literature was reviewed and condensed to create a well-rounded picture of the current understanding of internal and external factors, QTLs, causal genes, and the regulatory mechanisms related to the HS of soybean, with the aim of providing reference for future research and utilization of this trait.

Since 2019, the SARS-CoV-2 virus has caused a global pandemic, resulting in widespread infections and ongoing mutations. Analysing these mutations is essential for predicting future impacts. Unlike influenza mutations, SARS-CoV-2 mutations displayed distinct selective patterns that were concentrated in the spike protein and small ORFs. In contrast to the gradual accumulation seen in influenza mutations, SARS-CoV-2 mutations lead to the abrupt emergence of new variants and subsequent outbreaks. This phenomenon may be attributed to their targeted cellular substances; unlike the influenza virus, which has mutated to evade acquired immunity, SARS-CoV-2 appeared to mutate to target individuals who have not been previously infected. The Omicron variant, which emerged in late 2021, demonstrates significant mutations that set it apart from previous variants. The rapid mutation rate of SARS-CoV-2 has now reached a level comparable to 30 years of influenza variation. The most recent variant, JN.1, exhibits a discernible trajectory of change distinct from previous Omicron variants.

A compositional data vector is a special type of multivariate observation in which the elements of the vector are non-negative and sum to a constant, usually taken to be unity. A compositional data does not have zero and only retains relative information. Furthermore, comparisons can only be made between compositional data of the same component. The relevant sample space is the standard simplex. A simplex space is a space that is a generalized form of a triangle. For compositional data, many of the operations defined in Euclidean space are meaningless. Microbiome analyzes have become popular in recent years. Operational Taxonomic Units (OTU) and Amplicon Sequence Variants (ASVs) used in microbiome analysis are one type of compositional data. The microbiome data are counts of different species within a sample and it is compositional. Although there is an order of bacterial abundance within a sample, there is no order of bacterial abundance between samples. Firmicutes and Bacteroidetes, the major phyla in the colon, have been observed in humans worldwide. Gut microbiome analyses often use the Firmicutes/Bacteroidetes ratio and principal coordinates analysis (PCoA). Alpha and beta diversities are used as indicators of bacterial flora diversity. However, misinformation is pervasive in the human microbiome literature and analysis. There is a lot of misunderstanding regarding compositional data and its analysis. An attempt was made to demonstrate how to analyze using the “National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN JMD) (Public Data).” The results showed that PCoA did not work, and principal component analysis (PCA) was useful for analyzing the gut microbiome relative diversity.

In large-scale parallel computing and communication systems, an interconnect network structure is usually modeled as a graph, in which vertices and edges correspond to processors and communication links, respectively. In a graph, the vertices and edges are likely to fail, so we must think about the fault tolerance of a graph. Connectivity is an important parameter in the study of faulty tolerance for a graph. In this paper, we study a special class of connectivity: $m$-component connectivity, this is a natural generalization of the classical connectivity of graphs defined in terms of the minimum vertex-cut. Let $F$ is a vertex set of $G$ $(i.e, F\subseteq V(G))$, if the following conditions are satisfied, we say $F$ is a $m$-component cut: $(1)$ $G-F$ is disconnected; $(2)$ the number of components in $G-F$ is greater than or equal to $m$. In another word, the $m$-component connectivity $c\kappa_{m}(G)$ is defined as $min\{|F|~|~F\subseteq V(G)$ and $F$ is a $m$-component cut$\}$. Determining $m$-component connectivity is still unsolved in most interconnection networks even for small $m'$s. Leaf-sort graph is a special Cayley graph, it has many special properties that are different from other Cayley graphs. So we need to pay attention to some of it's special properties when we study it. In this paper, we can get the values: $c\kappa_{3}(CF_{n})=3n-6$ $(n$ is odd$)$ and $c\kappa_{3}(CF_{n})=3n-7$ $(n$ is even$)$ for $n\geq3$; $c\kappa_{4}(CF_{n})=\frac{9n-21}{2}$ $(n$ is odd$)$ and $c\kappa_{4}(CF_{n})=\frac{9n-24}{2}$ $(n$ is even$)$ for $n\geq4$; $c\kappa_{5}(CF_{n})=6n-16$ $(n$ is odd$)$ and $c\kappa_{5}(CF_{n})=6n-18$ $(n$ is even$)$ for $n\geq5$.

The flower of Syringa pubescens Turcz. is a Chinese folk medicine and also used as flower tea for healthcare. The effects of five drying methods on the active compound contents, the antioxidant abilities, anti-inflammatory properties and enzyme inhibitory activities were evaluated. The plant materials were treated using shade-drying, microwave drying, sun-drying, infrared drying and oven drying. The seven active compounds were simultaneously determined by an HPLC method. Furthermore, the chemicals differences were assessed using Scanning electron microscopy, UV spectroscopy and infrared spectroscopy. The antioxidant capacities and protective effects on L02 cells induced by hydrogen peroxide were measured. The anti-inflammatory effects on lipopolysaccharide-induced RAW264.7 cells were investigated. The enzyme inhibitory activities were determined against α-amylase, α-glucosidase cholinesterases and tyrosinase. The results indicated that drying methods had significantly influences on the active compound contents and biological properties. In consideration of the contents of bioactive compound, the suitable drying methods were SHD, SD, OD and MD. The SHD sample exhibited stronger radical scavenging ability on DPPH, ABTS, hydroxyl and superoxide anions and higher reducing power compared with other samples prepared by SD, OD, MD and IRD. Moreover, SHD sample had strong protective effects against L02 cells damage induced by H2O2. SPF samples, especially SHD sample, significantly inhibited inflammatory factor secretion in LPS-stimulated RAW 264.7 cell. All studied SPF sample exhibited evidently inhibitory effects on the four enzymes. SHD sample possessed the highest inhibitory activity on α-glucosidase and α-amylase. While SHD and SD samples had the potential inhibitory effects on cholinesterases and tyrosinase. In consideration of active compound contents and biological activities, it was recommended that SHD and SD were applied for drying SPF at an industrial scale.

Red color conversion materials have often been used in conventional white LEDs (light-emitting diodes) to enhance the insufficient deep red component and thus improve the color rendering property. Quantum dots (QDs) are one of the candidates due to their flexibility in controlling the emission wavelength, which is attributed to the quantum confinement effect. Two types of remote QD components, i.e., QD films and QD caps, were prepared and applied to conventional white LED illumination to improve the color rendering properties. Thanks to the red component near 630 nm caused by the QD components, the color rendering indices(CRIs) of both Ra and R9 could be increased to over 95. It was found that both the diffusing nature of the reflector and the light recycling process in the vertical cavity between the bottom reflector and the top optical films play important roles in improving the color conversion efficiency of remote QD components. The present study showed that the proper application of remote QDs combined with a suitable optical cavity can control the correlated color temperature of the illumination over a wide range, thus realizing different color appearances of white LED illumination. In addition, a high CRI of over 95 could be achieved due to sufficient excitation from fewer QDs due to the strong optical cavity effect.

This study evaluated eight herbal and fruit teas to produce kombucha beverages. Besides, the effect of initial sucrose concentration (10, 40, and 70% w/v) on kombucha fermentation from tea extracts was investigated. Kinetic characterization and modeling of kombucha fermentation were also performed. Results showed that kombucha beverages' characteristics and chemical content changed according to tea type and initial sucrose concentration. It was determined that as initial sucrose concentration increased, the consumed substrate concentration increased. The highest total biomass values were determined when green tea and pomegranate tea were supplemented with 70 g/L sucrose. The highest total phenolic content and antioxidant activity were achieved with green tea samples. The total acidity and phenolic substance concentration increased as fermentation progressed in general. The kinetic parameters including maximum consumption rate, sugar utilization, yield, biomass yield, maximum biomass, total acidity, phenolic production rate, and the phenolic result were computed depending on changing substrate concentration. Concerning kinetic modeling, proposed models (Logistic and Luedeking-Piret models) estimated satisfactorily experimental substrate consumption and total acidity values. Besides, total acidity was associated with the substrate consumption.

This paper presents a novel high-power rotary waveguide phase shifter based on circular polarizers specifically engineered for high-power microwave (HPM) applications. The phase shifter is capable of performing a precise 360° linear phase shift through rotation and consists of three parts: a linear polarized to left-handed circular polarized (LP-LHCP) mode converter, a left-handed to right-handed circular polarized (LH-RHCP) mode converter, and a linear polarized to right-handed circular polarized (LP-RHCP) mode converter. The paper analyzes the phase shifting principle, optimizes the three parts of the X-band rotary waveguide phase shifter, and conducts simulation studies on the entire phase shifter made of aluminum. The results show that the reflection is less than -20dB and the transmission efficiency is over 95% within 9.5GHz to 10.2GHz. The phase shift has a linear relationship with the rotation angle by a factor of two within this frequency range. Specifically, the phase shifter can achieve a linear phase shift of 360° when rotated from 0° to 180°, with a maximum deviation of less than 1.2°. Moreover, the power-handling capacity of the phase shifter in vacuum exceeds 242mW.

This study introduces a new procedure for three-component spectral matching of seismic ground acceleration records. The procedure involves preserving the phase angle and varying the Fourier amplitudes to match the spectra of the recorded records with a provided target spectrum that represents the spectral values of ground motions in three orthogonal directions. Several three-component recorded records were matched against two target response spectra to test the method. An accurate convergence was achieved in the velocity and displacement records. In addition, the scales among the components’ magnitude were maintained, creating realistic three-component ground motions. Such ground motion inputs are important to qualify critical structures sensitive to three-component motions. Our proposed procedure is novel because the present practices are mostly created based on one-component ground motions.

Previous studies have mostly examined how sustainable cities try to promote non-motorized travel by creating a walking-friendly environment. Such existing studies provide little research that identifies how the built environment affects pedestrian volume in high-density areas. This paper presents a methodology that combines person correlation analysis, stepwise regression, and principal component analysis for exploring the internal correlation and potential impact of built environment variables. To study this relationship, cross-sectional data in the Melbourne central business district were selected. Pearson’s correlation coefficient confirmed that visible green index and intersection density were not correlated to pedestrian volume. The results from stepwise regression showed that land-use mix degree, public transit stop density, and employment density could be associated with pedestrian volume. Moreover, two principal components were extracted by factor analysis. The result of the first component yielded an internal correlation where land-use and amenities components were positively associated with the pedestrian volume. Component 2 presents parking facilities density, which negatively relates to the pedestrian volume. Based on the results, existing street problems and policy recommendations were put forward to suggest diversifying community service within walking distance, improving the service level of the public transit system, and restricting on-street parking in Melbourne.

To characterize the mechanisms by which the highly-conserved exocyst trafficking complex regulates eye physiology in zebrafish and mice, we focused on exoc5 (aka sec10), a central exocyst component. We analyzed both exoc5 zebrafish mutants and retinal pigmented epithelium (RPE)-specific Exoc5 knockout mice. Exoc5 is present in both the non-pigmented epithelium of the ciliary body and in the RPE. In this study we set out to establish an animal model to study the mechanisms underlying the ocular phenotype and to establish if loss of visual function is induced by postnatal RPE Exoc5-deficiency. Exoc5-/- zebrafish showed smaller eyes, with decreased number of melanocytes in the RPE and shorter photoreceptor outer segments. At 3.5 days post fertilization, loss of rod and cone opsins were observed in zebrafish Tg:exoc5 mutants. Mice with postnatal RPE-specific loss of Exoc5 showed retinal thinning associated with compromised visual function, and loss of visual photoreceptor pigments. This retinal phenotype in Exoc5-/- mice was present at 20-weeks, and the phenotype was more severe at 27-weeks, indicating progressive disease phenotype. We previously showed that the exocyst is necessary for photoreceptor ciliogenesis and retinal development. Here, we report that exoc5 mutant zebrafish and mice with RPE-specific genetic ablation of Exoc5 develop abnormal RPE pigmentation, resulting in retinal cell dystrophy and loss of visual pigments associated with compromised vision. As RPE cells are “downstream” of photoreceptor cells in the visual process, these data suggest exocyst-mediated retrograde communication and dependence between the RPE and photoreceptors.

The testing of high-dimensional normality has been an important issue and has been intensively studied in literatures, it depends on the Variance-Covariance matrix of the sample, numerous methods have been proposed to reduce the complex of the Variance-Covariance matrix. The principle component analysis(PCA) was widely used since it can project the high-dimensional data into lower dimensional orthogonal space, and the normality of the reduced data can be evaluated by Jarque-Bera(JB) statistic on each principle direction. We propose two combined statistics, the summation and the maximum of one-way JB statistics, upon the independency of each principle direction, to test the multivariate normality of data in high dimensions. The performance of the proposed methods is illustrated by the empirical power of the simulated data of normal data and non-normal data. Two real examples show the validity of our proposed methods.

Prostate cancer (Pca) is a highly heterogeneous disease and the second more common tumor in males. Molecular and genetic profiles have been used to identify subtypes and guide therapeutic intervention. However, roughly 26% of primary Pca are driven by unknown molecular lesions. We use Principal Component Analysis (PCA) and custom RNAseq-data normalization to identify a gene expression signature which segregates primary PRAD from normal tissues. This Core-Expression Signature (PRAD-CES) includes 33 genes and accounts for 39% of data complexity along the PC1-cancer axis. The PRAD-CES is populated by protein-coding (AMACR, TP63, HPN) and RNA-genes (PCA3, ARLN1) sparsely found in previous studies, validated/predicted biomarkers (HOXC6, TDRD1, DLX1), and/or cancer drivers (PCA3, ARLN1, PCAT-14). Of note, the PRAD-CES also comprises six over-expressed LncRNAs without previous Pca association, four of them potentially modulating driver’s genes TMPRSS2, PRUNE2 and AMACR. Overall, our PCA capture 57% of data complexity within PC1-3. GO enrichment and correlation analysis involving major clinical features (i.e., Gleason Score, AR Score, TMPRSS2-ERG fusion and Tumor Cellularity) suggest that PC2 and PC3 gene signatures might describe more aggressive and inflammation-prone transitional forms of PRAD. Of note, surfaced genes may entail novel prognostic biomarkers and molecular alterations to intervene. Particularly, our work uncovered RNA genes with appealing implications on Pca biology and progression.

In this study, we used statistical models to analyze nonlinear behavior links with atmospheric teleconnections between hydrometeorological variables and Indian Ocean Dipole (IOD) mode over the East Asia (EA) region. The analysis of atmospheric teleconnections was conducted using principal component analysis and singular spectrum analysis techniques. Moreover, the nonlinear lag-time correlations between climate indices and hydrological variables were calculated using mutual information (MI) techniques. The teleconnection-based nonlinear correlation coefficients (CCs) were higher than the linear CCs in each lag time. Additionally, we documented that the IOD has a direct influence on hydro-meteorological variables, such as precipitation within the Korean Peninsula (KP). Moreover, during the warm season (June to September) the variation of hydro-meteorological variables in the KP demonstrated significantly decreasing patterns during positive IOD years and they have neutral conditions during negative IOD years in comparison with long-term normal conditions. Finally, the revealed relationship between climate indices and hydro-meteorological variables and their possible changes will allow better understanding of stakeholder decision-making regarding to manage of freshwater management over the EA region. It can also provide useful data for long-range water resources prediction, to minimize hydrological uncertainties in a changing climate.

This research seeks to interpret the component analysis of an innovative bio-asphalt binder using guayule resin in addition to crumb rubber modifier (CRM) at high concentrations. Such asphalt modification aims to minimize the dependency on virgin asphalt binder and provide new solutions concerning sustainable, flexible pavement industry. Guayule resin is a promising bioresource for asphalt binder replacement. By now, it could be considered a no value byproduct extracted during the guayule natural rubber production. CRM is a recycled material derived from scrap tires. The provided interpretation could help in understanding the asphalt-rubber-guayule interaction mechanism. Fourier transform infrared spectroscopy (FTIR), supported by thermo-gravimetric analysis (TGA), was used to investigate the component analyses of guayule resin composition, asphalt guayule interaction, and asphalt rubber guayule interaction, compared to corresponding asphalt rubber interaction. Additionally, the rheological properties at elevated temperatures were provided to link the microscale properties with the final product performance. The study clarified the distinct carbon and hydrogen compositional elements of guayule resin. Asphalt and guayule resin have similarities in chemical composition and rheological behavior with temperature susceptibility. The asphalt guayule binder had physical interaction. However, when both interacted with rubber, a chemical interaction was attributed, with no difference in rubber dissolution tendency, in asphalt rubber guayule, compared to asphalt rubber. A bio-binder composed of 62.5% asphalt, 25% guayule and 12.5% CRM had the potential to provide rheological properties better than base asphalt. Such behavior was interpreted by a high release of rubber components.

In this study, a novel theory to investigate the mass oscillation of particles is proposed. It has been proven that, at high-energy conditions, the fermion field described by Dirac’s Lagrangian interacts with the half-integer spin tachyon field with negative energy, causing the formation of composite particles whose mass depends on the total angular momentum. The proposed theory is based on a new interpretation of the Majorana equation for particles with arbitrary spin and shows that mass oscillation is a phenomenon in which the component of particle decay prevails over that of mixing mass states. Using the kinematic of Lemke for spacelike particle decay, we propose a mechanism able to explain the neutrino flavour change. The proposed mechanism is also investigated concerning the shape of its spectrum. Finally, the Lagrangian field of composite particles is formulated.

Fiber optic sensors cannot measure damage; for getting information about damage from strain measurements, additional strategies are needed, and several alternatives have been proposed. This paper discuss two independent concepts: the first one is based on detecting the new strains appearing around a damage spot; the structure does not need to be under loads; the technique is very robust, damage detectability is high, but it requires sensors to be located very close to the damage, so it is a local technique. The second approach offers a wider coverage of the structure, it is based on identifying the changes caused by the damage on the strains field in the whole structure for similar external loads. Damage location does not need to be known a priori, detectability is dependent upon the sensors network density, damage size and the external loads. Examples of application to real structures are given.

A monocular vision pose estimation and identification algorithm used on a small spacecraft for future orbital servicing is studied in this paper. A tracker spacecraft equipped with a short-range vision system is proposed to recover the 3D structural model of a space target in orbit and automatically identify its solar panels and main body using only visual information from an onboard camera. The proposed reconstruction and identification framework is tested using structure-from-motion and point cloud identification methods. The Efficient Perspective-n-Points (EPnP) descriptor is used for pose estimation. Triangulated points are used for component segmentation by means of orientation histogram descriptors. Experimental results based on laboratory images of a spacecraft model show the effectiveness and robustness of our approach.

In this paper, the quantum theory of the infinite-component Majorana field for the fermionic tower is formulated. This study proves that the energy states with increasing spin are simply composite systems made by a bradyon and antitachyons with half-integer spin. The quantum field describing these exotic states is obtained by the infinite sum of four-spinor operators, which each operator depends on the spin and the rest mass of the bradyon in its fundamental state. The interaction between bradyon-tachyon, tachyon-tachyon and tachyon-luxon has also been considered and included in the total Lagrangian. The obtained theory is consistent with the CPT invariance and the spin-statistics theorem and could explain the existence of new forms of matter not predictable within the standard model.

The European Commission has been striving to achieve sustainable development in its rural areas for more than 25 years through funds aimed at modernizing the agricultural and forestry sectors, protecting the environment and improving the quality of life. But is sustainable rural development really being accomplished? This study sets out to answer this question in the case of Extremadura, a Spanish territory with Low Demographic Density and a Gross Domestic Product still below 75 % of the European average. Both qualitative and quantitative methodology have been employed, using a Principal Component Analysis the result of which has provided us with a model which shows how various behaviors coexist in the region in view of the distribution of current funding from the EAFRD. The most dynamic areas have received the largest amounts of funding and these are linked to the agricultural sector and to the protection of the environment, leaving aside the more depressed areas and the implementation of the LEADER Approach as well. Therefore, we have come to the conclusion that the current rural development in Extremadura is not sustainable enough.

Ionospheric delay has been a critical issue that limits the accuracy of GNSS precise positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where irregularity of ionosphere is often significant. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on TEC semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospherical delay. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The results have shown that the interpolation accuracy of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

We investigate soccer analytics from supervised learning, unsupervised learning, and reinforcement learning perspectives. With the increasing availability of player tracking data and event logs, machine learning techniques have become essential for uncovering patterns in player and team performance. In this paper, we examine how supervised learning models are applied to tasks such as match outcome prediction and player rating systems, while unsupervised learning is utilized for player clustering, tactical analysis, and the discovery of hidden patterns in game data. Reinforcement learning, on the other hand, plays a key role in optimizing decision-making during matches by learning optimal strategies and tactics through trial and error. By providing a comprehensive overview of these approaches, we aim to highlight the transformative potential of machine learning in modern soccer analytics and how it continues to shape the sport. We also provide summary of other soccer analytics research in this work.

In this work, a method for measuring tow angles in carbon fiber components, based on the use of a polarized camera, is analyzed from a metrological point of view. Carbon fibers alter the direction of the reflected light electrical field, so that in each point of the surface of a composite piece the angle of polarization of reflected light matches the fiber orientation. A statistical analysis of the angle of linear polarization (AoLP) in each pixel of each examined area allows to evaluate the average winding angle. An evaluation of the measurement uncertainty of the method on a cylinder obtained by a filament winding process is carried out, and the result appears adequate for the study of the distribution of angles along the surface of the piece, in order to optimize the process.

Abrus precatorius is a medicinal herb originated from Southeast Asia and native to India. The Hindustani centre of origin was found to possess lot of variability especially with respect to seed color. However, no previous researchers have focused on studying the variability by including the entire variability of the region. A total of 99 accessions were analyzed for its biochemical components like moisture content, total phenols, antioxidants, ash content, protein content, total monomeric anthocyanin and flavonols. Principal component analysis (PCA) shown that first three components viz., seed color, phenols and antioxidants are major principal components that contribute to 80.86% of total variability in the germplasm. The dendrogram constructed based on the biochemical data and by using the software Darwin version 6.0. formed three main clusters i.e. cluster 1, cluster 2 and cluster 3 which contain 55 accessions, 40 accessions and 1 accession respectively. Seed color was found to be the main criteria based on which the Abrus accessions were grouped. Accessions from different clusters also differ for their mean phytochemical constituents. Germplasm curators / plant breeders can select such elite genotypes and utilize them in crop improvement programmes.

PgRMC1 (progesterone receptor membrane component 1) is a progesterone-binding protein highly expressed in various cancer cells. Its expression has been associated with cancer development, chemoresistance, and poor prognosis in breast cancer through the regulation of drug and sterol metabolism, cell cycle, and apoptosis. However, the precise functions of PgRMC1 remain unclear. We examined PgRMC1 expression in breast cancer tissues and analyzed its correlation with clinicopathological characteristics and treatment response (n=112). PgRMC1 expression was detected in breast cancer tissue, while no immunoreactivity for PgRMC1 was observed in normal breast tissue. Notably, PgRMC1 expression levels were high in the HER2 subtype. Among patients treated with neoadjuvant chemotherapy, those with a poor pathological response had significantly higher PgRMC1 expression than those with a good response. PgRMC1 overexpression in breast cancer cell lines reduced chemosensitivity. Real-time qPCR analyses revealed that PgRMC1 induced the expression of differentiation markers, including CDH1 and KRT19, indicating that PgRMC1 is involved in the suppression of epithelial-mesenchymal transition and mammary luminal epithelial differentiation. Our findings suggest that PgRMC1 may contribute to chemoresistance and serve as a putative biomarker for assessing neoadjuvant chemotherapy sensitivity.

Immunohistochemistry is a powerful technique, widely used in biomedical research and clinics, that allows to determine the expression levels of some proteins of interest in tissue samples by means of the intensity of color due to the expression of biomarkers using specific antibodies. As such, immunohistochemical images are complex and problematic to be quantified. Recently we proposed a novel method including a first separation stage based on nonnegative matrix factorization (NMF) that achieved good results. However, that method was highly dependent on sparseness and non-negativity parameter choice and on algorithm initialization. Furthermore, the previously proposed method needed a reference image as starting point for the NMF algorithm. In the present work, we propose a new, simpler and robust method for the automated unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest). The new proposed method represents a simpler approach that, in one side, avoids the use of NMF for the separation stage, and in the other side, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest by means of principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing the finding of color vectors by considering the peaks of density of points. The method also develops a new scoring stage by avoiding, again, reference images, making the procedure more robust and less dependent on the parameters. Experiments for the semi-quantitative scoring of images in five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.

Ultra-high temperature oxidation-resistant alloys (UTORAs) have received a lot of attention due to the increased research demand for deep space exploration around the world. But UTORAs have the disadvantages of easy oxidation and chalking. So, in this study, a UTORA was prepared by hot-press sintering on VZrHfNbTa (HEA) substrate coated with hafnium. The bonding mechanism, resistance to high-temperature oxidation, and hardness of the sample tests were carried out. The results show that zirconium in the matrix will diffuse into the hafnium coating during the high-temperature sintering process and form the HfZr alloy transition layer, the coating thickness of the composite is about 120 μm, and the diffusion distance of zirconium in the hafnium coating is about 60 μm, this transition layer chemically combines the hafnium coating and the HEA substrate into a monolithic alloy composite. The results of high-temperature oxidation experiments show that the oxidation degree of the hafnium-coated VZrHfNbTa composite material was significantly lower than that of the VZrHfNbTa HEA after oxidation in air at 1600 °C for 5 h. The weight gain of the coated sample after oxidation is 56.56 mg/cm2, which was only 57.7 % compared to the weight gain of the uncoated sample (98.09 mg/cm2 for uncoated), and the surface of the uncoated HEA showed obvious dents, oxidation, and pulverization occurred on the surface and interior of the sample. In contrast, the coated composite alloy sample mainly underwent surface oxidation sintering to form a dense HfO2 protective layer, and the internal oxidation of the hafnium-coated VZrHfNbTa composite alloy was significantly lower than that of the uncoated VZrHfNbTa HEA.

Panax ginseng C. A. Meyer (P. ginseng), has been widely used in traditional Chinese medicine (TCM). It contains a number of chemical components and possesses a variety of pharmacological activities. Alzheimer's disease (AD) is neurodegenerative disease that creates a huge burden on the lives and health of individuals. In recent years, studies have indicated that the chemical components of P. ginseng, especially ginsenosides, play a pronounced positive role in the prevention and treatment of neurological diseases. This review aimed to summarize currently studies on chemical components and the mechanisms action in AD intervention treatment of P. ginseng, especially ginsenosides. In this review, the components of P. ginseng and their respective active effects were first introduced. Then the key molecular mechanisms and signaling pathways of P. ginseng were introduced, and its different active ingredients in the prevention and treatment of Alzheimer's disease-related pathogenesis were also summarized from the pathogenesis of AD Aβ generation and aggregation, hyperphosphorylation of tau protein，oxidant stress，neuroinflammation，mitochondrial damage，disorder of neurotransmitter and gut microbiota. Signaling pathway networks related to the action of ginseng active ingredients was constructed, which could serve as a therapeutic target for Alzheimer's disease. In addition to a detailed report on P. ginseng in improving Alzheimer's disease-related pathogenesis, the application of the current technology, spatial metabonomics in AD therapeutics and diagnostics were later discussed. Spatial metabonomics could be applied to investigate the multi-target intervention of P. ginseng on Alzheimer's disease. Research perspectives for the study of P. ginseng in the treatment of Alzheimer's disease were provided for further studies.

Rapeseed meal (RM) is an important agroindustrial by-product produced in large quantities by oil extraction from seeds. However, the efficient utilization of RM as animal feed is limited by its low metabolizable energy, poor palatability, and high levels of fiber and anti-nutritional components. Here, we investigate the potential of enriching RM with single-cell protein by fermentation with conventional and unconventional yeasts, to make a nutritionally improved feed component. The process of simultaneous saccharification and fermentation improved the parameters of the waste biomass, especially the protein content, the amount of crude fiber, and the degree of biotransformation of isoflavone compounds present in the waste material. Fermentation yielded the highest protein gain for the Saccharomyces cerevisiae Ethanol Red strain (ΔN=2.38%) at a biomass load of 12.5 g and for Scheffersomyces stipitis (ΔN=2.34%) at an enzyme dose of 0.125ml/10g DM. Crude fiber content (CF) was reduced by 2.55–7.18%. The SSF process resulted in the conversion of isoflavones to forms with fewer adverse effects and lower estrogenic activity.

(1) Background: In order to study the effects of different electric cookers on the nutritional components of millet porridge, five different electric cookers were selected to cook millet porridge, and sensory and nutritional components in millet porridge, millet soup, and millet grains were analyzed. (2) Methods: Using principal component and cluster analysis, a variety of nutritional components were comprehensively compared. (3) Results: The results showed that among the different cooked samples, the content of amylose and reducing sugar was the highest in the samples cooked by electric cooker no. 3. The electric cooker no. 4 samples had the highest sensory evaluation score, crude fat, and protein content. The contents of ash, fatty acids, bound amino acids, and minerals were the highest in the electric cooker no. 5 samples. The sensory evaluation score and content of crude fat, ash, reducing sugar, direct starch, and Cu were higher in millet grains than in millet soup or porridge. The content of fatty acid, protein, amino acid, Zn, Fe, Mg, Mn, and Ca was highest in millet soup. Different electric cookers produced millet porridge with varying nutritional levels. (4) Conclusions: This study provides a reference for the further development of new electric cookers.

Waste from fish cutting (heads, swim bladders, fins, skin, bones) is a high-value technological raw material for obtaining substances and products based on them with a wide range of properties. The possibility of using waste from cutting fish of the Gadidae family: the Alaska pollock (Gadus chalcogrammus) and the Pacific cod (Gadus macrocephalus), processed in the coastal zone, is scientifically substantiated. In this work, a technology has been developed for processing accumulated waste from fish cutting in order to obtain fish gelatin, which is characterized by high protein content (more than 80.0%) and a full set of essential and nonessential amino acids. We studied the quality of fish gelatin obtained from wastes from cutting the fish of the Gadidae family. The possibility of using fish gelatin as a component of fish products is shown; the dose of its introduction into the fish products is substantiated. The data obtained made it possible to recommend the use of fish processing waste products as a gelling component and a source of amino acids in multicomponent food systems.

Organizations with proper human resources (HR) practices play an exemplary role in developing their employees’ innovation. Though there is extensive literature on managing organizational innovation, even in today’s scenario some organizations stand as a barrier for employees’ growth and innovation at the workplace. This study aimed to holistically explore the organizational factors affecting employee innovation using principal component analysis (PCA) and condense the dimensionalities for a better focus of organizational development. The study executed a survey questionnaire and collected useful data from one hundred and ninety-five (195) respondents of various Indian companies. The study identified forty-six sub-factors and evolved into nine major organizational factors influencing employee innovation namely organization structure, organization culture and environment, corporate strategy, innovation process, employee, technology, resources, knowledge management and management and leadership. The study recommended that any firm must focus on these factors to encourage employee innovation leading to overall organizational success. It also provides broad implications to HR managers, firm policymakers and top management to reassess and formulate the best organizational strategies to promote innovation culture in the organization.

A characteristic feature of lightweight constructions is their low thermal mass which causes high internal temperature fluctuations that require high heating and cooling demand throughout the year. Phase Change Materials (PCMs) is effective in providing thermal inertia to low thermal mass buildings. The aim of this paper is to analyse the thermal behaviour of two proposed lightweight buildings designed for homeless people and to investigate the potential benefit achievable through the use of different types of PCM in the temperate climatic conditions of Christchurch, New Zealand. For this purpose, over 300 numerical simulations have been conducted using the simulation software DesignBuilder®. The bulk of the simulations were carried out under the assumption that the whole opaque building envelope is equipped with PCM. The results showed significant energy saving and comfort enhancement through the application of PCMs. Thereby, annual energy saving of over 50 % was reached for some of the PCMs considered. Additionally, the effectiveness of single, PCM-equipped structure components was investigated and substantial benefits between 19 and 27 % annual energy saving were achieved. However, occupant behaviour in terms of ventilation habits, occupancy of zones etc. remains one of the biggest challenges in any simulation work due to insufficient data.

In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-Wavelet-ANN models are compared with those obtained from Wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid Wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the Wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-Wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicators in rivers.

As entrepreneurial interest is believed to represent a causal factor increasing entrepreneurship, research has begun to explore how family systems affect youth entrepreneurial interests. In the present study, we attempt to identify different types of family influence on the entrepreneurial interests of young people. A questionnaire was used to obtain data from 1,633 Spanish youths, who were 15 to 18 years old, and another questionnaire was used to obtain data from 839 parents. Principal Component Analysis identified unique family types and revealed that they have differential associations to entrepreneurial interest among youths. These findings reaffirm the influence of family on the entrepreneurial ecosystem and the promotion of an entrepreneurial family cuture. This study further suggests that early attention should focus on the detection of entrepreneurial interest among youths so that actions can be implemented in the families of low-interest youths to incentivize an entrepreneurial family culture.

Extraction of high-resolution surface waves is essential in surface-wave survey. Because reflections usually interfere with surface waves on X component in a multicomponent seismic exploration, it is difficult to extract dispersion curves of surface waves. The situation goes more serious when the frequencies and velocities of higher-mode surface waves are close to those of PS-waves. A method for surface-wave extraction is proposed based on the morphological differences between reflections and surface waves. Frequency-domain high-resolution linear Radon transform (LRT) and time-domain high-resolution hyperbolic Radon transform (HRT) are used to represent surface waves and reflections respectively. Then, the sparse representation problem based on the morphological component analysis (MCA) is built and optimally solved to obtain high-fidelity surface waves. An advantage of our method is its ability to extract surface waves when their frequencies and velocities are close to those of reflections. Furthermore, results of synthetic and field examples confirm that the proposed method can attenuate the distortion of surface-wave dispersive energy caused by reflections, which contributes to extracting accurate dispersion curves.

There is a great interest in finding antioxidants and products with antimicrobial activities from natural sources. The aim of this study was to obtain and identify the phenolic components and organic acids from Camellia oleifera cake. In addition, their antioxidant and antimicrobial activities were also investigated. The purity of phenolic components and organic acids obtained were 94.1 ± 0.5% w/w and 96.0 ± 0.3% w/w, respectively. There are 15 phenolic components have been detected and identified by HPLC-ESI-MS. Oxalic, citric, acetic, malic, and succinic acids are found to be major organic acids. The phenolic components and organic acids both had good antioxidant capacity, evaluated by 4 antioxidant activity assays (hydroxyl radical scavenging, superoxide radical scavenging, 1,1-diphenyl-2-picryl-hydrazyl radical scavenging and lipid peroxidation inhibition). In addition, the phenolic components and organic acids exhibited significant inhibitory activity against bacteria Staphylococcus aureus, Escherichia coli, Bacillus subtilis, and fungi Mucor racemosus, Aspergillus oryzae, Rhizopus stolonifer. The phenolic components and organic acids from C. oleifera cake both showed good antioxidant capacity and exhibited antimicrobial activities. These results may be useful for the future use of phenolic components and organic acids from C. oleifera cake.

Clinical supervision is an essential element in trainees, preventing burnout and ensuring the effectiveness of their intervention. AI technologies offer increasing possibilities for the development of clinical practices, among it supervision appears to possess the ideal characteristics for automation processes. In this study we test the capabilities of ChatGPT-4 to provide supervisory feedback by then placing them in comparison with feedback produced by a qualified supervisor. Two ChatGPT-4-generated feedbacks (the first one produced by a naïve identity and the second one by a trained identity) and one human-produced feedback were evaluated by means of a liking questionnaire, which was filled out to a group of gestalt psychotherapy trainees. Principal Component Analysis (PCA) highlighted 4 components of the questionnaire: relational and emotional dimensions (C1), didactic and technical quality (C2), treatment support and development (C3), and professional orientation and adaptability (C4). The ratings of satisfaction, obtained from the three supervisory feedbacks, were compared by applying 1-way analysis of variance (ANOVA). Statistical evaluations were performed using the statistical package SPSS version 25. The feedback generated by pre-trained AI (f2) was rated significantly higher than the other two (untrained AI feedback (f1) and human feedback (f3) in C4; in C1 the superiority of f2 over f1 but not over f3 appears significant. These results suggest that the use of pre-trained AI may be an appreciable option for increasing the effectiveness of clinical supervision in some specific areas, including especially career guidance.

Different researchers analyzed effective computational methods that maintain the precision of Allen-Chan (AC) equations and their constant security. This article presents a method known as reduced order model technique by utilizing kernel principle component analysis (KPCA), a nonlinear variation of traditional principal component analysis (PCA). KPCA is utilized on the data matrix created using discrete solution vectors of the AC equation. In order to achieve discrete solutions, small variations are applied for dividing up extraterrestrial elements, while Kahan’s method is used for temporal calculations. Handling the process of backmapping from small-scale space involves utilizing a non-iterative formula rooted in the concept of the multi-dimensional scaling (MDS) method. Using KPCA, we show that simplified sorting methods preserve dissipation of energy structure. The effectiveness of simplified solutions from linear PCA and KPCA, the retention of invariants, and computational speeds are shown through one-, two-, and three-dimensional AC equations.

The susceptibility of seven species of chestnuts to the orchid leafminer was studied in the arboretum of the Main Botanical Garden of the Russian Academy of Sciences (MBG RAS), taking into account their interspecific characteristics. Using pheromone delta traps, the highest number of moths was shown for A. hippocstanuam and the lowest for A. chinensis. A number of anatomical parameters of leaves were investigated, such as the thickness of the epidermal cell wall and the thickness of the palisade and spongy parenchyma layers. As a result, it was shown that the most infected chestnut species had a greater thickness of the nutritious parenchyma tissue. No de-pendence was found between the degree of susceptibility to the Ohrid leaf miner and such indi-cators as the content of chlorophyll a + b and carotenoids in the leaves of seven species of chestnuts. Nevertheless, resistance of different species of the genus Aesculus to C. ohridella under increased tannin content in leaves has been shown. Evaluation of phenolic compounds and flavonoids has not established their reliable role as repellents. The high levels of carbohydrates found during the study contributed to increased susceptibility to the Ohrid leaf miner.

The fixed and random effects models are popular methods for identifying the causal effects of treatment. First, I introduce the two-way fixed effects model and show a class of the estimators for the random effects models. Misidentification may occur in these models due to the treatment effect heterogeneity. Then, I propose the differential error component models to address such problems while the models exclude the fixed effects of units and time periods and consider the random effects of units and time periods. Meanwhile, I present a simulation of a staggered design and revisit the application in (De Chaisemartin and D'Haultfoeuille, 2020). The results demonstrate that one additional newspaper increases the 0.31% average effect of the presidential turnout and the 1.05% average effect when the newspapers’ changes are non-negative, where the proposed estimators are robust and efficient.

The increasing concern regarding climate change continues to motivate research in the Arctic. Within the delicate cycle of the cryosphere, leads are an important kinematic feature that regulate heat balances in the Arctic. Therefore, it is necessary to quantify the genesis of leads over time to identify when and where changes are occurring. The use of a principal component analysis (PCA) is one such tool that is used to identify the characteristics of seasonal ice variability. The PCA not only identifies “normal” or “dominant” conditions or features, but also extract anomalous spatial features from an archive of long-term sequence of images.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Use of probiotic lactobacilli has been proposed as a strategy to mitigate damage associated to exposure to toxic metals. Their protective effect against cationic metal ions, such as those of mercury or lead, is believed to stem from their chelating and accumulating potential. However, their retention of anionic toxic metalloids, such as inorganic arsenic, is generally lower. Through the construction of mutants in phosphate transporter genes (pst) in Lactiplantibacillus plantarum and Lacticaseibacillus paracasei strains, coupled with arsenate [As(V)] uptake and toxicity assays, we determined that the incorporation of As(V), which structurally resembles phosphate, is likely facilitated by phosphate transporters. Surprisingly, inactivation in Lc. paracasei of PhoP, the tran-scriptional regulator of the two-component system PhoPR, a signal transducer involved in phosphate sensing, led to an increased resistance to arsenite [As(III)]. In comparison to the wild type, the phoP strain exhibited no differences in the ability to retain As(III), and there were no observed changes in the oxidation of As(III) to the less toxic As(V). These results reinforce the idea that specific transport, and not unspecific cell retention plays a role in As(V) biosorption by lactobacilli, while they reveal an unexpected phenotype for the lack of the pleiotropic regulator PhoP.

This study employs Principal Component Analysis to examine cranial measurements obtained from both extant and fossil specimens spanning 38 species and comprising over 300 individuals within the subfamily Cervinae. Our findings elucidate craniometric distinctions among cervids characterized by varying body sizes and certain evolutionary adaptations. While our results generally corroborate previous assertions regarding the limited craniometric variability among plesiometacarpal deer, they also unveil specific cranial adaptations within certain genera and species. The Principal Component Analysis of craniometric data revealed that giant and large-sized deer display significantly broader ecomorphological diversity in cranial shape compared to small-sized deer. Secondly, small-sized deer exhibit greater uniformity in their cranial shape, appearing densely clustered on the factorial map. Thus, we conclude that body size imposes ecological constraints, limiting the available niches due to eco-physiological factors. The study demonstrates that endemic insular deer do not evolve consistent craniometric features attributable to insular isolation, while the cranial proportions of medium-to-small sized deer delineate a ubiquitous morphological archetype characteristic of numerous cervid taxa spanning diverse phylogenetic lineages and sharing comparable body sizes. This group of "Dama-like" deer, characterized by similar body size, metabolic rates, ecological needs, and cranial morphometry, is referred to here as the fundamental eco-physiological type, typical of warm regions within the Palearctic and Oriental biogeographic realms..

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

Since the outbreak of the COVID-19 pandemic, patients infected with COVID-19 may experience abnormal heart rates, posing potential health risks. This study proposes a non-contact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in more accurate diagnosis and treatment. In this study, except for the different image acquisition method, other research processes and methods are similar to the research methods in [15]. The image acquisition method is changed from forehead image to palm image. The new non-contact measurement performance of the proposed method can effectively not only avoid infection concerns but also obtain heart rate and HRV quickly and conveniently, providing higher accuracy for physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to those in recently published papers.

To mitigate the interference of waves on offshore operation vessels, heave compensation systems find widespread application. The performance of heave compensation systems significantly influences the efficiency and safety of maritime operations. This paper establishes a mathematical model for winch-based active heave compensation system. It introduces a three-loop active disturbance rejection control (ADRC) strategy encompassing piston position control, winch speed control, and load displacement control, enabling real-time estimation and compensation of system disturbances, thereby enhancing the performance of the heave compensation system. To assess the effectiveness of this control strategy, the study employs Matlab/Simulink and AME Sim to construct a co-simulation model and conducts a comparative analysis with traditional PID control systems. The research findings indicate that the three-loop ADRC position control strategy consistently delivers superior compensation performance across various operational scenarios.

Background The Delta Neutrophil Index (DNI), a prodiagnostic marker for sepsis, utilizes leukocyte count. Platelet activation, similar to leukocytes, plays a crucial role in host defence against pathogens and may serve as a predictor of sepsis outcome. However, the combined evaluation of Mean Platelet Component (MPC) and DNI in assessing sepsis has been rarely used. Methods To assess the prodiagnostic validity of simultaneous utilization of DNI and MPC in cases of human febrile sepsis, we conducted measurements of cellular indices, including DNI and MPC, as well as molecular biomarkers such as procalcitonin (PCT) and C-reactive protein (CRP). This study was carried out on patients admitted to the emergency department with suspected sepsis. Results Using a cutoff value of 2.65%, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DNI in sepsis were found to be 69%, 73.9%, 77.9%, and 64.1%, respectively. Furthermore, significant differences in DNI and MPC levels were observed between the sepsis and non-sepsis groups (6.7±7.8% versus 2.1±2.2%, P=0.000 and 26.0±1.9 g/dl versus 26.8±1.4 g/dl, P=0.002), respectively. Notably, there was a negative correlation between DNI and MPC, with the strength of the correlation varying based on the cause of sepsis. By setting the cutoff value for DNI at 6.2%, the sensitivity, specificity, and NPV improved to 100%, 80.3%, and 100%, respectively, although the PPV remained at 10.6%. Importantly, the DNI≥6.2 group exhibited a higher incidence of death (10.9%, 5/46) and multiorgan failure (19.6%, 9/46) compared to the DNI<6.2 group (0.0%, 0/46 and 0.0%, 0/167, respectively) (P=0.000 and P=0.000, respectively). Conclusions In our study, DNI demonstrates superior effectiveness compared to other molecular biomarkers such as CRP and procalcitonin in distinguishing febrile septic patients from febrile nonseptic patients and predicting prognosis. Additionally, there exists a negative correlation between MPC and DNI, making MPC a valuable marker for differentiating the etiology of sepsis. These findings hold significant clinical implications as DNI/MPC is a cost-effective and readily applicable approach in various impending sepsis scenarios. Notably, this study represents the first examination of the prodiagnostic validity of simultaneously employing DNI and MPC in human cases of febrile sepsis.