A number of epidemiological studies have implicated elevated iron levels with higher risks of cardiometabolic diseases, including the metabolic syndrome (MetS). MetS is a pathologic clustering of metabolic derangements characterized by central obesity, insulin resistance, hypertension, and dyslipidemia, which can all lead to increased cardiovascular disease morbidity and mortality. However, it remains unclear whether a causal association exists between higher iron status and MetS and its associated risk components. Using the data from the Taiwan Biobank (TWB) (19,012 males; 42,340 females) and of the European Whites from the UK Biobank (UKB) (128,549 males; 142,817 females), we first constructed ethnic- and sex-specific hemoglobin-polygenic risk scores (Hb-PRS) as instrumental variable (IV) for iron status with the Hb-associated SNPs we previously identified from genome-wide association studies (GWAS). We then conducted Mendelian randomization (MR) analyses to elucidate the causal role of elevated iron (either Hb-PRS quintiles or continuous Hb-PRS) on various metabolic components. MR-Egger regressions determined the potential pleiotropic effects of the constructed Hb-PRSs, while the causal relationship of Hb-PRS with anemia served as a control in the analyses. The mean Hb concentration range corresponding to the Hb-PRS quintiles is relatively narrow in both UKB (14.91-15.21 g/dL for males and 13.39-13.71 g/dL for females) and TWB (14.79-15.28 g/dL for males and 12.78-13.21 g/dL for females). In these windows of Hb-PRSs, we found differential associations of Hb-PRS with diabetes (T2D) or hyperglycemia (HGY) as compared to other MetS components. The highest quintiles of Hb-PRSs were causally associated with greater risks of hypertension (HTN) and hypertriglyceridemia (DLP) (i.e., [Taiwanese HC males: Q5 vs. Q1 ORHTN = 1.10 (1.00–1.21), p = 0.0473, Q4 vs. Q1 ORDLP = 1.10 (1.00–1.22), p = 0.0485; European White males: ORHTN = 1.05 (1.01–1.09), p = 0.0194, ORDLP = 1.04 (1.00–1.08), p = 0.0468]). Interestingly, we obtained a consistent negative causal association between increasing Hb-PRS quintiles and T2D or HGY risk across all sex subgroups (i.e., [Taiwanese HC females: ORHGY = 0.89 (0.84–0.95), 0.88 (0.83–0.94), 0.87 (0.82–0.93), 0.86 (0.81–0.92) from Q2 to Q5; European White females: ORHGY = 0.92 (0.87–0.98), 0.91 (0.86–0.97), 0.93 (0.87–0.98), 0.87 (0.82–0.93) from Q2 to Q5; all p < 0.05]. The positive causal relationships between Hb-PRSs and diastolic blood pressure (DBP) and triglycerides (TG) but inverse causal association with glucose (GLU) and glycated hemoglobin (HbA1c) further corroborated such findings. There is, however, a lack of causal association with central obesity (CO), while a U-shaped type of causality was observed for gout (GT) or hyperuricemia (HUA). Sensitivity analyses, which excluded subjects based on self-reports of health conditions or medications and hospital episode statistics data, revealed similar results. Our MR studies confirm a modest causal relationship between propensity of elevated iron status and increased risk of HTN and DLP. We have further observed that modestly lower iron levels causally contribute to the development of T2D. Further validation and elucidation of the underlying biological mechanisms are highly warranted.

In recent years, with the popularity of smart mobile devices, the interaction between devices and users, especially in the form of voice interaction, has become increasingly important. If smart devices can understand more users' emotional states through voice data, more customized services can be provided for users. This paper proposes a novel machine learning model for speech emotion recognition, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN), called CLDNN. To make the designed system can recognize the audio signal closer to the human auditory system does, this article uses the Mel frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal is extracted as the input of the model, and the feature values of the data are calculated using several local feature learning blocks (LFLB) composed of one-dimensional CNN. Because the audio signals are time-series data, the feature values obtained from LFLBs then input into LSTM layer to enhance the learning on time-series level. Finally, fully connected layers are used for classification and prediction. Three databases RAVDESS, EMO-DB and IEMOCAP are used for the experiments in this paper. The experimental results show that the proposed method can improve the accuracy compared to other related researches in speech emotion recognition.

The ZIPs (ZRT/IRT-like proteins) play critical roles in the absorption, transport, and intracellular balance of metal ions essential for various physiological processes in plants. However, little is known about the pan-genomic characteristics and properties of ZIP genes in cucumber (Cucumis sativus L.). In this study, we identified 10 CsZIP genes from the pan-genome of 13 C. sativus accessions. Among them, only CsZIP10 showed no variation in protein sequence length. We analyzed the gene structure, conserved domains, promoter cis-elements, and phylogenetic relationships of these 10 CsZIP genes derived from "9930". Based on phylogenetic analysis, the CsZIP genes were classified into three branches. Amino acid sequence comparison revealed the presence of conserved histidine residues in the ZIP proteins. Analysis of promoter cis-elements showed that most promoters contained elements responsive to plant hormones. Expression profiling in different tissues showed that most CsZIP genes were expressed at relatively low levels in C. sativus leave, stem, and tendril, and CsZIP7 and CsZIP10 were specifically expressed in root, indicating their potential involvement in the absorption and transport of metal ions. Transcriptomic data indicated that these 10 ZIP genes displayed responses to both downy mildew and powdery mildew, and CsZIP1 were significantly downregulated after both salt and heat treatments. In conclusion, this study deepens our understanding of the ZIP gene family and enhances our knowledge of the biological functions of CsZIP genes in C. sativus.

This paper proposed emotion recognition methods for consecutive facial images, and implements the inference of neural network model on a field-programmable gate array (FPGA). The proposed emotion recognition methods are based on a neural network model architecture that combines convolution neural networks (CNNs), long short-term memory (LSTM), and fully connected neural networks (FCNNs), called CLDNN or ConvLSTM-FCN. This type of neural network mod-el can analyze the local feature sequences obtained through convolution of data, making it suita-ble for processing time-series data such as consecutive facial images. In this paper, sequences of facial images are sampled from videos corresponding to various emotional state of subjects. The sampled images are then pre-processed with the processes includes facial detection, greyscale conversion, resize, and data augmentation if necessary. The 2-D CNN in ConvLSTM-FCN is used for feature extraction for these pre-processed facial images. These sequences of facial im-age features are time sequences with dependent properties between the elements within them. The LSTM is then used to model these time sequences followed by fully connected neural net-works (FCNNs) for classification. The proposed consecutive facial emotion recognition method achieves an average recognition rate of 99.51% on RAVDESS dataset, 87.80% on BAUM-1s dataset and 96.82% on eNTERFACE’05 data set, using 10-fold cross-validation on the PC. Some com-parisons of recognition accuracies between the proposed method and the other existing related works are conducted in this paper. According to the comparisons, the proposed emotion recog-nition methods outperform the existing related researches. The proposed emotion recognition methods are then implemented on an FPGA chip using the neural network model inference algo-rithms in this paper, and the accuracies of the experiments conducted on the FPGA chip are iden-tical to those obtained on the PC. This verifies that the proposed neural network model imple-mented on the FPGA chip performs well.

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to be a global threat due to its ability to evolve and generate new subvariants, leading to new waves of infection. Additionally, other coronaviruses like Middle East respiratory syndrome coronavirus (MERS-CoV, formerly known as hCoV-EMC) which first emerged in 2012, persists and continues to present a threat of severe illness to humans. The continued identification of novel coronaviruses, coupled with the potential for genetic recombination between different strains, raises the possibility of new coronavirus clades of global concern emerging. As a result, there is a pressing need for pan-CoV therapeutic drugs and vaccines. After extensive optimization of an HCV protease inhibitor screening hit, a novel 3CLPro inhibitor (MK-7845) was discovered and subsequently profiled. MK-7845 exhibited nanomolar in vitro potency with broad spectrum activity against a panel of clinical SARS-CoV-2 subvariants and MERS-CoV. Furthermore, when administered orally, MK-7845 demonstrated a notable reduction of viral burdens by >6 log orders in the lungs of transgenic mice infected with SARS-CoV-2 (K18-hACE2 mice) and MERS-CoV (K18-hDDP4 mice).

Simple Summary: 200 words Will biology, including humans, benefit or harm from new developments in information such as artificial intelligence (AI). First, are biology and information different? Life is an ordered form that can reproduce, maybe with variation; and any ordered arrangement, biological or not, potentially contains information. There have been ordered forms since earth was just a ball of chemicals, and some such chemicals can reproduce. So biological and non-biological information might be components of a unified process, ‘PanEvolution’ or ‘Pan-Evo’, based on the same four operations for living or non-living entities - Innovation; Transmission and replication; Adaptation; and Movement. This can produce separate groups called ‘species’. In a current Panspeciation event, information held in biology - especially our brains - is moving into its own environment. Harm to biology might be minimal if humans and AI both behave intelligently, because vastly different environments are optimal for humans and machines containing AI. This would be the first speciation-like event involving humans for millennia, but will not be particularly hostile to humans, if humans learn how to evaluate information and cooperate, to minimize the effect of both human stupidity and artificial simulated stupidity (ASS – a failure of AI). Abstract: 200 words Many people wonder whether biology, including humans, will benefit or harm from new developments in information such as artificial intelligence (AI). Biological and non-biological information might be components of a unified process, ‘Pan-Evolution’ or ‘Pan-Evo’, based on four basic operations – Innovation, Transmission, Adaptation, and Movement. Pan-Evo contains many types of variable objects, from molecules to ecosystems. Biological innovation includes mutations and behavioural changes; non-biological innovation includes naturally-occuring physical innovations, and innovation in software. Replication is commonplace in and outside biology, including autocatalytic chemicals, and autonomous software replication. Adaptation includes biological selection, autocatalytic chemicals and ‘evolutionary programming’ used in AI. Extension of biological speciation to non-biological information, creates a concept called ‘Panspeciation’. Panevolution might benefit or harm biology, but harm might be minimal if AI and the humans behave intelligently, because humans, and the machines in which AI resides, might split to the vastly different environments that suit them. That is a possible example of panspeciation, and would be the first speciation event involving humans for thousands of years. This event will not be particularly hostile to humans, if humans learn to evaluate information and cooperate better, to minimize both human stupidity and artificial simulated stupidity (ASS – a failure of AI).

This paper proposes two fully-automatic machine-extracted linguistic features from an unlimited text input for Mandarin prosody generation. One is the punctuation confidence (PC) which measures the likelihood of inserting a major punctuation mark (PM) at a word boundary. Another is the quotation confidence (QC) which measures the likelihood of a word string to be quoted as a meaningful or emphasized unit in text. Because a major PM in a text is highly correlated with a prosodic break, and a quoted word string plays an important role in human language understanding, the two features potentially could provide useful information for prosody generation. The idea is first realized by employing conditional random field (CRF)-based models to predict major PMs, quoted word string locations, and their associated confidences, i.e., the PC and the QC, for each word boundary. Then, the predicted punctuations and their confidences are combined with traditional contextual linguistic features to predict prosodic-acoustic features. Both objective and subjective tests showed that the prosody generation with the proposed linguistic features performed better than the one without the proposed features. So, the proposed PC and QC are promising features for Mandarin prosody generation.

A sensitive electrochemical sensor based on AuPtPd trimetallic nanoparticles functionalized multi-walled carbon nanotubes coupled with chitosan modified glassy carbon electrode (GCE/CS/MWCNTs-AuPtPd) for the rapid detection of bisphenol A (BPA) was proposed. AuPtPd trimetallic nanoparticles were first assembled on MWCNTs to obtain MWCNTs-AuPtPd nanocomposite. Then the MWCNTs-AuPtPd was further dispersed on the surface of chitosan modified electrode surface to fabricate the GCE/CS/MWCNTs-AuPtPd sensor. Due to the superior catalytic properties of MWCNTs-AuPtPd and the good film formation of chitosan, the constructed sensor displayed good performances for BPA detection. The structural morphology of CS/MWCNTs-AuPtPd was characterized by many methods, such as SEM, UV-vis and TEM. Under the optimal conditions, the designed sensor showed a linear range from 0.05 to 100 µM for BPA detecting with a low detection limit of 1.4 nM. The designed sensor was further used to realize the determination of BPA in real samples and the recoveries ranged from 94.4% to 103.6%. The results demonstrated that the electrochemical sensor designed by CS/MWCNTs-AuPtPd nanocomposites was reliable and could be a useful tool for the monitoring of food safety.

Light-colored and transparent polyimide (PI) films with good high-temperature dimensional stability are highly desired for advanced optoelectronic applications. However, in practice, the simultaneous achievement of good optical and thermal properties in one PI film is usually difficult due to the inter-conflicting molecular design for the polymers. In the present work, a series of PI-SiO2 nanocomposite films (ABTFCPI) were developed based on the PI matrix derived from hydrogenated pyromellitic anhydride (HPMDA) and an aromatic diamine containing benzanilide and trifluoromethyl substituents in the structure, 2,2'-bis(trifluoromethyl)-4,4'-bis[4-(4-aminobenzamide)]biphenyl (ABTFMB). The inorganic SiO2 fillers were incorporated into the nanocomposite films with the form of colloidal nanoparticles dispersed in the good solvent of N,N-dimethylacetamide (DMAc) for the PI matrix. The derived ABTFCPI nanocomposite films showed good film-forming ability, flexible and tough nature, good optical transparency, and good thermal properties with the loading amounts of SiO2 up to 30 wt% in the system. The ABTFCPI-30 film with the SiO2 content of 30 wt% in the film showed the optical transmittance of 79.6% at the wavelength of 400 nm (T400) with a thickness of 25 μm, the yellow index (b*) of 2.15, and the 5% weight loss temperatures (T5%) of 491 oC, which are all comparable to those the pristine ABTFCPI-0 matrix without filler (T400=81.8%; b*=1.77; T5%=492 oC). Meanwhile, the ABTFCPI-30 film exhibited obviously enhanced high-temperature dimensional stability with the linear coefficients of thermal expansion (CTE) of 25.4×10-6/K in the temperature range of 50 to 250 oC, which is much lower than that of the AMTFCPI-0 film (CTE=32.7×10-6/K).

There is an observed general low performance of Ethiopian Sport Governing Bodies (SGBs) in service delivery. Evidence shows that the trend can be corrected with proper understanding and insight into the process of organisational and management factors of these SGBs. This study aimed to investigate the contribution of organisational and management factors on effective sport service delivery in SGBs. For this study, a cross-sectional research design was employed. Purposive sampling was used to choose 350 samples. Pearson Product Moment Correlation Coefficient and multiple regression were used to analyse the data at the 0.05 significant level. The findings confirmed that organisational and management factor variables had a significant positive relationship with sport service delivery (organisational structure (r=0.515), organisational capabilities (r=0.701), communication (r=0.549), management practice (r=0.571), materials & resources (r=0.527) and stakeholders satisfaction (r=0.742). In addition, Management factors were found to be significant contributors for predicting effective sport service delivery (F(3, 329)= 165.868, R2= 0.612). Furthermore, organisational factors had a joint significant contribution to effective sport service delivery (F(3, 329) = 148.743, R2= 0.576). In addition, organisational and management factor variables jointly best predicted effective sport service delivery in Ethiopian SGBs (F(6, 326) =130.81, R2= 0.707). Therefore, this study suggests organisational and management factor variables can be used to predict effective sport service delivery.

Age-related Macular Degeneration (AMD) is the major cause of elders’ vision loss, early screening and treatment are the most efficient way to reduce the rate of blindness. AI-based methods based on ophthalmic images play great potential for AMD diagnosis. However, low levels of accuracy, robustness, and explainability are challenges for AI approaches applied in clinics. Thus, this study proposed a multi-type of data source fusion method and a multi-model fusion approach for AMD detection. Typical unsupervised (Hierarchical Clustering and K-Means), typical supervised (SVM, VGG-16, and ResNet) methods, and proposed methods (multi-source data fusion-based method and multi-model fusion-based approach) are compared based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular color fundus photography (CFP) and Ultra-Wide field Fundus (UWF) images. Data preprocessing and enhancements of each type of data are discussed. A feature extraction based on unsupervised ML models, feature combination and normalization, and multi-layer perception (MLP) algorithm are involved in the proposed multi-source data fusion-based method. Supervised ML and DL models and a voting mechanism are involved in the multi-model fusion-based approach. Findings show that the proposed methods present a high performance of accuracy and robustness. A real-world UWF database is involved from Shenzhen Aier Hospital. Practical and theoretical contributions are delivered. A reference value for medical diagnosis based on multiple digital images is contributed.

One method of estimating the evaporation rate (ER) is to use a variety of evaporation pans, such as the Class A standard evaporation pan (CASEP) and the Colorado Sanken standard evaporation pan (CSSEP). In this study, the rate of evaporation of CASEP and CSSEP have been investigated and compared with each other. This study was conducted in Semnan, Iran. CSSEP was used as a test pan, which was performed in an open space around the Faculty of Civil Engineering, Semnan University. Evaporation was recorded daily for 123 days. The evaporation of the CASEP pan was obtained from the synoptic station of Semnan, which is located at a distance of 2.39 km from the test site. Meteorological data were also obtained from the synoptic station of Semnan and compared with experimental evaporation data. The results of this study showed that the daily ER from CASEP and CSSEP in the tested time periods were not significantly different. Based on the Klomogorov-Sminrov method, the best statistical distributions for CASEP and CSSEP were calculated as Error and Gamma, respectively. The coefficient of determination (R 2 ) between the two pans was estimated to be about 93%. Also, by examining the ER with other meteorological data, it was observed that the ER has the highest correlation with the average daily air temperature.

To investigate the effect of Polyvinylpyrrolidone (PVP) addition and consequently porosity, two different sets of membranes are manufactured, since PVP is a widely used poring agent which has an impact on the mechanical properties of the membrane material. The first set (PAN 1) includes PAN and the necessary solvent while the second set (PAN 2) is made of PAN and PVP. These membranes are put through several characterisation processes including tensile testing. The obtained data are used to model the static behaviour of the membranes with different geometries, but similar loading and boundary conditions that represent their operating conditions. This modelling process is undertaken by using finite element method. The main idea is to investigate how geometry affects the load-carrying capacity of the membranes. Alongside membrane modelling, their materials are modelled with representative elements with hexagonal and rectangular pore arrays (RE) to understand the impact of porosity on the mechanical properties. Exploring the results, the best geometry is found as the elliptic membrane with the aspect ratio 4 and the better RE as the hexagonal array which can predict the elastic properties with an approximate error of 12%.

During the COVID-19 pandemic, antibody-based vaccines targeting the SARS-CoV-2 spike glycoprotein were the key focus for development because neutralizing antibodies were associated with protection against the SARS-CoV-2 infection in pre-clinical models and human trials. While deploying these spike-based vaccines saved millions of lives worldwide, it has become clear that the immunological mechanisms of protection against severe disease are multifaceted and involve non-neutralizing antibody components. Here, we describe a novel pan-sarbecovirus T-cell vaccine, ChAdOx1.COVconsv12, designed to complement and broaden the protection of spike vaccines. The vaccine immunogen COVconsv12 employs the two regions in the viral proteome most conserved among sarbecoviruses delivered by replication-deficient vector ChAdOx1. It focuses T cells towards epitopes shared among sarbecoviruses including evolving SARS-CoV-2 variants. Here, we show that ChAdOx1.COVconsv12 induced broad T-cell responses in the BALB/c and C57BL/6 mice. In the Syrian hamster challenge model, ChAdOx1.COVconsv12 alone did not protect against the SARS-CoV-2 infection, but when co-administered with 1/50th of the ChAdOx1 nCoV-19 spike vaccine protective dose, faster recovery and lower oral swab viral load were observed. Induction of CD8+ T cells may decrease COVID-19 severity and extend the T-cell response coverage of variants to match the known (and as yet unknown) members of the beta-coronavirus family.

The surface electromyogram (sEMG)--based gesture recognition to the development of intelligent prostheses has emerged as a promising avenue for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. Cross-session calibration and training data indicate the capacity to reduce this variation. The impact of varying calibration and training data amounts on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of training data volume on benchmark performance. Two amputees who had undergone amputations years prior were recruited and seven sessions of data were collected for analysis from each of them. The experimental results show that the calibration data improved the average accuracy per subject by 3.03% and 6.16%, respectively, compared to the baseline results. Moreover, it was found that to collect training data for modeling, boosting the number of training sessions proves more efficient than increasing the number of trials within a single session. Two potentially effective strategies are proposed in light of this research to enhance the cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, as well as offer effective strategies for maximizing the utilization of the entire dataset.

Iron overnutrition has been implicated with higher risk of developing metabolic and cardiovascular diseases, including metabolic syndrome (MetS), while iron deficiency anemia exacerbates many underlying chronic conditions. Hemoglobin (Hb) concentration in the blood, which reflects a major functional iron (i.e., heme iron) in the body, may serve as a surrogate of iron nutrition status. We conducted sex-specific observational association studies where we carefully titrated the association between Hb deciles and MetS and its components among the Taiwanese Han Chinese (HC) from the Taiwan Biobank and Europeans of White ancestry from the UK Biobank, representing two large ethnicities. Our data show that at higher-than-normal levels of Hb, increasing deciles of Hb concentration were significantly associated with MetS across all sex subgroups in both ethnicities, with the highest deciles resulting in up to three times greater risks than the reference group [Taiwanese HC: OR=3.17 (95% CI, 2.75-3.67) for Hb >16.5 g/dL in men, OR=3.11 (2.78-3.47) for Hb >14.5 g/dL in women; European Whites: OR=1.89 (1.80-1.98) for Hb >16.24 g/dL in men, OR=2.35 (2.24-2.47) for Hb >14.68 g/dL in women]. The association between stronger risks and increasing Hb deciles was similarly observed with all metabolic components except diabetes. Here we found that both the highest Hb decile groups and contrarily the lowest ones, with respect to the reference, were associated with higher odds of diabetes in both ethnic groups [e.g., Taiwanese HC men: OR=1.64 (1.33-2.02) for Hb >16.5 g/dL, OR=1.71 (1.39-2.10) for Hb <13.5 g/dL; European Whites women: OR=1.39 (1.26-1.45) for Hb >14.68 g/dL, OR=1.81 (1.63-2.01) for Hb <12.39 g/dL]. These findings confirm that elevated Hb concentrations, a potential indicator of iron overnutrition, may play a role in the pathophysiology of MetS and metabolic components.

A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area - water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the upper Mississippi River. The Otsu's automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each pre-defined polygon around each gauging station. In addition to the commonly used green-band based water indices, Landsat 8 and 9 OLI's ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadow), and AWEInsv (AWEI without shadow), where v represents the visible light band used in the water index. At each station, about 60-80 Landsat 8 or 9 images during 2013-2023 were used for assessing the performances of the twenty water indices through comparing the estimated WSEs with the measured WSEs. Results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices.

Milk fat globule membrane (MFGM) is a complex tri-layer membrane that wraps droplets of lipids in milk. In recent years, it has attracted widespread attention due to its excellent bioactive functions and nutritional value. The milk fat globule membrane contains a diverse array of bioactive lipids, including cholesterol, phospholipids, and sphingolipids, which play pivotal roles in mediating the bioactivity of the milk fat globule membrane. We sequentially summarize the main lipid types in milk fat globule membrane in this comprehensive review and outline the characterization methods used to employed them. In this comprehensive review, we sequentially describe the types of major lipids found in the milk fat globule membrane and outline the characterization methods employed to study them. Additionally, we compare the structural disparities among glycerophospholipids, sphingolipids, and gangliosides, while introducing the formation of lipid rafts facilitated by cholesterol. The focus of this review revolves around an extensive evaluation of current research on lipid isolates from the milk fat globule membrane, as well as products containing milk fat globule membrane lipids, with respect to their impact on human health. Notably, we emphasize clinical trials encompassing a large number of participants. The summarized bioactive functions of milk fat globule membrane lipids encompass regulation of human growth and development, influence on intestinal health, inhibition of cholesterol absorption, enhancement of exercise capacity, and anti-cancer effects. By offering a comprehensive overview, the aim of this review is to provide valuable insights into the diverse biologically active functions exhibited by lipids in milk fat globule membrane.

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive memory loss and a decline in activities of daily life. Ventricular enlargement has been associated with worse performance on global cognitive tests and AD. Our previous studies demonstrated that brain sulfatides, myelin-enriched lipids, are dramatically reduced in subjects at the earliest clinically recognizable AD stages via an apolipoprotein E (APOE)-dependent and isoform-specific process. Herein, we provided pre-clinical evidence that sulfatide deficiency is causally associated with brain ventricular enlargement. Specifically, taking advantage of genetic mouse models of global and adult-onset sulfatide deficiency, we demonstrated that sulfatide losses cause ventricular enlargement without significantly affecting hippocampal or whole brain volumes using histological and magnetic resonance imaging approaches. Mild decreases in sulfatide content and mild increases in ventricular areas were also observed in human APOE4 compared to APOE2 knock-in mice. Finally, we provided Western blot and immunofluorescence evidence that aquaporin-4, the most prevalent aquaporin channel in the central nervous system (CNS) that provides fast water transportation and regulates cerebrospinal fluid in the ventricles, is significantly increased under sulfatide-deficient conditions, while other major brain aquaporins (e.g., aquaporin-1) are not altered. In short, we unraveled a novel molecular mechanism that may contribute to ventricular enlargement in AD.

A convenient and fast multiresidue method for the efficient identification and quantification of 72 pesticides belonging to different chemical classes in red and white grape wines have been developed. The analysis was based on gas chromatography tandem quadrupole mass spectrometric determination (GC–MS/MS). An optimization strategy involved the selection of MWCNTs amount and cleanup procedure cycle times for m-PFC was performed to achieve ideal recoveries and reduce sample matrix compounds in the final extracts. The optimized procedure obtained consistent recoveries between 70.2 and 108.8% (70.2% and 108.8% for white wine, 72.3% and 106.0% for red wine) , with relative standard deviations (RSDs) generally lower than 8.3% at three spiking levels of 0.01, 0.05 and 0.2 mg/kg. Linearity was studied in the range between 0.005 and 0.2 mg/kg using pesticide standards prepared both in pure solvent and in the presence of matrix, showing coefficients of determination (R²) higher than 0.985 for all the pesticides. To improve accuracy, matrix matched calibration curves were used for calculating the quantification results. Finally, the method was used successfully in detecting pesticide residues in commercial gape wines.

The so-called “Chinese pyramids” are huge burial mounds covering the tombs of the Emperors of the Western Han dynasty. If we include also the mounds of the members of the royal families, these monuments sum up to more than 40, scattered throughout the western and the southern outskirts of modern Xi'an. They are mostly unexcavated and poorly known although, taken together, they form a fascinating sacred landscape, which was conceived as a perennial witness of one of the most magnificent Chinese dynasties. This sacred landscape is today encroached by the frenetic urban development of the Xi’an urban area. We discuss and elaborate here some of the results of a recent, new satellite-imagery survey of these monuments, highlighting the aspects which may contribute to solutions for a sustainable and compatible development within this important ancient landscape.

Wool quality and yield are important economic traits of livestock. In this study, the skin tissues of the upper scapula of Super Merino sheep (SM) and Small-Tailed Han sheep (STH) during the growing period were studied. Differentially expressed (DE) long non-coding RNAs (lncRNAs) and messenger RNAs (mRNA) were identified via histopathological observation and high-throughput RNA sequencing. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze differentially expressed genes. Reverse transcription quantitative Polymerase Chain Reaction (RT-qPCR) was used to verify nine selected DE lncRNAs and five DE mRNAs. Significant differences were found in the expression levels of LOC114113396 (KRTAP15-1), CRABP1, KRT10, KRTAP1-3, KRTAP4.3, KRT2, FGF1, IGF-1, and KIT in SM and STH skin tissues. These results indicate that these mRNAs may play an important role in the growth and development of hair follicles and the regulation of hair fiber traits. Differentially expressed lncRNA target genes and mRNAs were mainly enriched in biological processes and pathways related to skin development and fiber traits, which is consistent with previous studies. LncRNA MSTRG.9225.1, MSTRG.124320.1, MSTRG.96951.1, MSTRG.91685.1, MSTRG.122229.1, MSTRG.89255.1, MSTRG.98769.1, and MSTRG.84658.1 may indirectly participate in the growth and development of sheep hair follicles and the regulation of hair fiber diameter by regulating the expression of their respective target genes. These candidate lncRNAs and mRNAs may provide a basis for the study of sheep hair follicle growth and development and hair fiber traits.

Levels of synthetic musk fragrances (SMFs) and various personal care products (PCPs) were measured in the Han River and its tributaries in Seoul, Korea. The most abundant SMF in all river and PCP samples was 4,6,6,7,8,8-hexamethyl-1,3,4,7-tetrahydrocyclopenta[g]isochromene (HHCB), followed by 1-(3,5,5,6,8,8-hexamethyl-6,7-dihydronaphthalen-2-yl)ethanone (AHTN), musk ketone (MK), and 1,1,2,3,3-pentamethyl-2,5,6,7-tetrahydroinden-4-one (DPMI). There was a significant correlation between the SMF concentration in the PCPs and the Han River samples. Moving from upstream to downstream in the Han River, the median SMF concentration was 6.756, 2.945, 0.304, and 0.141 μg/L in the sewage treatment plant (STP) influent, effluent, tributaries, and mainstream, respectively, implying that effective SMF removal was achieved during the sewage treatment process, followed by dilution in the receiving water. Four STPs using advanced biological treatment processes had removal efficiencies of 55.8%, 50.6%, 43.3% for HHCB, AHTN, and MK, respectively. The highest SMF concentrations in the tributaries were observed at locations close to the STPs. Our study confirmed that the main source of SMFs in the receiving water were sewage effluent containing untreated SMFs, which are largely originated from household PCPs, especially hair care products (e.g., shampoo) and perfumes.

This paper presents a complete control strategy of the active return-to-center (RTC) control for electric power steering (EPS) systems. We first establish the mathematical model of the EPS system and analyze the source and influence of the self-aligning torque (SAT). Second, based on the feedback signals of steering column torque and steering wheel angle, we give the trigger conditions of a state switch between the steering assist state and the RTC state. In order to avoid the sudden change of the output torque for the driving motor when the state switches frequently between the steering assist state and the RTC state, we design an undisturbed state switching logic algorithm. This state switching logic algorithm ensures that the output value of the RTC controller is set to an initial value and increases in given steps up to a maximum value after entering the RTC state, and the output value of the RTC controller will reduce in given steps down to zero when exiting the RTC state. This therefore ensures smooth switch control between the two states and improves the driver’s steering feeling. Third, we design the RTC controller, which depends upon the feedback signals of the steering wheel angle and the angular velocity. In addition, the controller increases the auxiliary control function of the RTC torque based on vehicle speed. The experimental results show that the active RTC control method does not affect the basic assist characteristics, which effectively reduces the residual angle of the steering wheel at low vehicle speed and improves the RTC performance of the vehicle.

The aim of this paper is to compare the taxonomic composition and species diversity of the macrozoobenthos in Maritsa River (Bulgaria) and Han River (South Korea). Samples were collected at 15 selected sites in each river, including some of their main tributaries in 2020 and 2021. The number of the taxa recorded in Maritsa River was more than twice as great as in Han River: 192 taxa belonging to 19 systematic groups compared to 88 taxa belonging to 18 groups respectively. The order Ephemeroptera had the highest species richness: 31 taxa in the Bulgarian rivers and 26 taxa in the South Korean ones. The macrozoobenthic communities responded and adapted to the various conditions and impacts in the water environment with changes in the species composition and structure. The analysis of the similarity in the taxonomic composition showed low resemblance between all study sites but displayed distinct separations between the rivers and the two years. In general, the species structure of the macrozoobenthic communities in Maritsa River and its studied tributaries was better than in Han River. In both years, high species diversity was recorded at the reference sites in the rivers, characterized by conditions closely resembling natural environments. The species richness and the evenness of macrozoobenthos were very low at sites downstream subjected to considerable anthropogenic pressure. Some of the communities in Han River were almost destroyed completely.

Two major sample configurations are adopted in all the instruments for fabric hand measurement, flat sample as in KES and FAST types machines, and wrinkled sample as in PhabrOmeter. This paper compares the two sample types to examine which one if any offers a better coverage and reflection of the fabric sensory attributes. Fabrics have unique behaviors of drape, wrinkle and tactile sense which are entirely due to the simultaneous occurrence of both in-plane membrane deformation and out-of-plane bending deformation in multiple curvature. Such singular deformation mode cannot be detected by any machines using flat sample, whereas during a PhabrOmeter test, the fabric sample genuinely produces drape, wrinkle in addition to other related deformations. This paper then introduced the theoretical research pertaining to the measurement. Then a split sample experiment is conducted to demonstrate the importance of the internal connections in fabric during drape and wrinkle processes. As such fabric interconnection will be barely disclosed during tests using flat samples, another important advantage of PhabrOmeter is hence clearly shown.

Brassica yellows virus (BrYV) is a tentative species of the genus Polerovirus, which occurs widely and mostly damage Brassicaceae plants in East Asia. Since BrYV could not be transmitted mechanically, an insect transmission method is required for further virus research. Here, a reliable and unrestricted method was described, in which non-viruliferous aphids (Myzus persicae) acquired BrYV from transgenic Arabidopsis thaliana plants with virus full length genome germinated from seeds and frozen infected leaves were used to transmit the virus to healthy plants, and there was no significant difference in acquisition rate though transmission rate from frozen infected leaves was somehow lower compared to fresh infected leaves. This novel simple method could be applied to preservation of virus inocula, evaluation of variety resistance to BrYV, biological research on interaction among BrYV, aphid and host, which also provide a new idea on establishing a basic method using virus genomic transgenic plants or frozen infected leaves for other poleroviruses research.

Aiming at the problems of high labor intensity and high cost of manual picking, high loss rate of mechanical picking seeds, low mechanization level and difficult to guarantee the quality of picking plates in the process of picking plates of edible sunflower. Based on the principle of manual plate taking, a plate taking control system for sunflower harvesting table was designed. Through the research and analysis of the principle of taking the plate of the edible sunflower harvesting table. According to the actual operation requirements, the overall scheme of the sunflower plate control system is determined, and the control strategy of imitating artificial low loss harvesting is designed. To reduce the grain loss in the process of taking the plate and improve the control accuracy of the system on the movement speed and displacement of the key components in the process of taking the plate, a trapezoidal acceleration and deceleration control algorithm is designed as the control algorithm of taking the plate. To verify the working performance of the plate control system, with the absolute error, relative error and total loss rate of the harvest as objectives. The bench and field verification experiments were both carried out. The bench experiment showed that the speed error of the plate parts was not more than 0.028 m/s. In the bench experiments of the device, the maximum positioning error was 1.25 mm, the average relative error is only 0.94 %, and the grain loss rate was not more than 2.26 %. It result showed that the system algorithm was reliable, the positioning accuracy was high, and the taking plate operation can be completed well. The field verification experiment showed that the forward speed of the unit was in the range of 0.4~0.8 m/s, and the total loss rate of harvest was less than 5 %. When the forward speed is 0.6 m/s, the minimum harvest loss rate is 2.32 %, and which indicated the control system meets the requirements of sunflower harvesting operation.

This paper extends the application of the ROTASE model for the formation of spiral arms of disc galaxies, questions and confusions from readers about this model are addressed. The optical trail effect behind the spiral arm rotation is the natural consequence of the model. The morphologies of ring-galaxies are classified into four categories: type I: single ring; type II: 8-shaped double ring; type III: 8-shaped double ring wrapped by a larger outer ring; type IV: single ring without spiral and bar. All four types of ring galaxies can be described by the ROTASE model. The ROTASE model predicts that the false impression of spiral arm rotating ahead of the galactic bar in the galaxy MCG+00-04-051 will change with time, it will look like a normal galaxy with about 30° to 40° bar rotation in the future and the galactic bar ends will look like rotating ahead of the spiral arms with further 10 ° to 15 °bar rotation. The formation of one arm galaxies is due to X-matter at one side of supermassive black hole is much stronger than other side. More evidence is found to support the explanation of the formation and the evolution of the Hoag’s object. The possible evolution of spiral pattern of galaxies is illustrated by UGC 6093. The winding of the Milky Way could be tighter in the future based on the ROTASE model.

We suggest a deep learning based sensor signal processing method to remove chemical, kinetic and electrical artifacts from ion selective electrodes’ measured values. An ISE is used to investigate the concentration of a specific ion from aqueous solution, by measuring the Nernst potential along the glass membrane. However, application of ISE on a mixture of multiple ion has some problem. First problem is a chemical artifact which is called ion interference effect. Electrically charged particles interact with each other and flows through the glass membrane of different ISEs. Second problem is the kinetic artifact caused by the movement of the liquid. Water molecules collide with the glass membrane causing abnormal peak values of voltage. The last artifact is the interference of ISEs. When multiple ISEs are dipped into same solution, one electrode’s signal emission interference voltage measurement of other electrodes. Therefore, an ISE is recommended to be applied on single-ion solution, without any other sensors applied at the same time. Deep learning approach can remove both 3 artifacts at the same time. The proposed method used 5 layers of artificial neural networks to regress correct signal to remove complex artifacts with one-shot calculation. Its MAPE was less than 1.8% and R² of regression was 0.997. A randomly chosen value of AI-processed data has MAPE less than 5% (p-value 0.016).

Train fault detection primarily relies on the comparative analysis of collected images with standard reference images, making the accurate identification of the collected image types crucial for effective fault detection. Most current systems achieve indirect classification of carriage images by using Automatic Equipment Identification (AEI) devices to recognize carriage identification number information while capturing the carriage images. However, damaged RF tags or blurred characters can hinder these devices, obstructing further train fault analyses. The unique features of carriage linear array images, such as high resolution, extreme aspect ratios, and local non-linear distortions, pose significant challenges to recognition algorithms. This paper proposes a method specifically designed to recognize these types of images. First, we implement an object detection algorithm to locate the positions of key components in carriage linear array images. By leveraging the spatial layout mapping between these key components and the carriage type classification, we simplify the complex image recognition problem into a sparse point set alignment task. To ensure effective registration despite local distortions in the linear array images, we propose a weighted radial basis function. To address the issues of unknown matching relationships and quantity mismatches during point set alignment, the proposed objective function is designed to maximize the similarity between Gaussian mixtures of point sets, thereby determining the RBF weights. Extensive experiments have demonstrated that our algorithm achieves 100% recognition accuracy under local nonlinear scale distortions of less than 15%. The algorithm remains effective even in the presence of some false positives or missed detections in key component object detection results. It can accurately identify the target category from 79 image categories within 24 milliseconds on an i7 CPU without GPU support. It is of great significance to reduce system cost and promote automatic exterior fault detection for train rolling stock.

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Multimodal knowledge graph completion necessitates the integration of information from multiple modalities (such as images and text) into the structural representation of entities to improve link prediction. However, most existing studies have overlooked the interaction between different modalities. To address this issue, this paper proposed a Transformer-based knowledge graph link prediction model (MM-Transformer) that fuses multimodal features. Different modal encoders are employed to extract structural, visual and textual features, and hybrid key-value calculations are performed on features from different modalities based on the Transformer architecture. The similarities of textual tags to structural tags and visual tags are calculated and aggregated respectively, and multimodal entity representations are modeled and optimized to reduce the heterogeneity of the representations. Experimental results demonstrate that, compared to the current multimodal state-of-the-art methods, the proposed method achieves significant performance improvements in knowledge graph link prediction tasks. This proves that the proposed method effectively addresses the problem of multimodal feature fusion in knowledge graph link prediction tasks.

Process safety plays a vital role in the modern process industry. To prevent undesired accidents caused by malfunctions or other disturbances in complex industrial processes, considerable attention has been paid to data-driven fault detection techniques. To explore the underlying manifold structure, manifold learning methods including Laplacian eigenmaps, locally linear embedding, and Hessian eigenmaps have been utilized in data-driven fault detection. However, only the partial local structure information is extracted from the aforementioned methods. In this paper, these typical manifold learning methods are synthesized to find the underlying manifold structure from different angles. A more comprehensive local structure is discovered under a unified framework by constructing an objection optimization function for dimension reduction of the process data. The proposed method takes advantage of different manifold learning methods. Based on the proposed dimension reduction method, a new data-driven fault detection method is developed. Hotelling’s T2 and Q statistics are established for the purpose of fault detection. Experiments on an industrial benchmark Tennessee Eastman process and a real blast furnace ironmaking process are carried out to demonstrate the superiority and effectiveness of the proposed method.

The study of notable people as agents for increasing cancer awareness started just in the last decades of the 20th century. This study aimed to identify Pan-American notable people with head and neck cancer (HNC) and to assess HNC’s professional perspective on communicating stories of notable patients with HNC to promote prevention. Methods: This cross-sectional study was conducted by applying a RedCap questionnaire to oral medicinists, oral pathologists, head and neck surgeons, and clinical oncologists with experience in HNC research, education, or treatment in Pan-American countries. Additionally, a structured search was performed on PubMed, Scopus, EMBASE, Web of Science, LILACS, and gray literature Results: We identified 42 notable people from the United States, Brazil, Argentina, Mexico, El Salvador, Chile, Colombia, and Peru who were mainly actors, athletes, and musicians. Participants agreed that stories of notable patients with HNC can positively impact the population and promote prevention if communicated by the internet, social media, and television. Conclusion: In the Pan-American region, notable individuals who have suffered from HNC may be powerful advocates for increasing cancer awareness and promoting prevention strategies. The professionals involved in the study expressed a positive inclination towards using the stories of these notable HNC survivors as an effective prevention strategy.

G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan GPCR involving HIV infection, colonic inflammation and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability of inhibiting cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. Using large-scale publicly available data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we found that GPR15 expression is significantly lower in colon adenocarcinoma (COAD) than in normal tissues. And among 33 cancer types, GPR15 expression is significantly correlated with the prognoses of COAD, neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD) positively and stomach adenocarcinoma (STAD) negatively. This study also revealed that commonly upregulated gene set in the high GPR15 expression group (stratified via median) of COAD, HNSC, LUAD and STAD are enriched in immune systems, indicating that GPR15 might be considered as a potential target for cancer immunotherapy. Furthermore, we modelled the 3D structure of GPR15 and conducted the structure-based virtual screening. The top 8 hits compounds were screened and then subjected to molecular dynamics (MD) simulation for stability analysis. Our study provided novel insights into the role of GPR15 in a pan-cancer manner and discovered a potential hit compound for GPR15 antagonists.

As an emerging model, self-service fitness centers are reshaping the consumer fitness experience globally. Given the crucial role of customer loyalty in the sustainable operation and management of fitness service enterprises, this study constructs a structural model incorporating service quality, service convenience, customer satisfaction, word-of-mouth intentions, and repatronage intentions. The model is designed to elucidate the factors and mechanisms impacting customer loyalty at self-service fitness centers and further investigates how these relationships are affected by service methods, either with staff or without staff. The study employs PLS-SEM to empirically analyze 552 customer questionnaires from four self-service fitness centers in first-tier cities in China. The results reveal positive correlations among the constructs in the structural model, emphasizing the important position of raising service quality standards and reducing the time and effort expended by customers in the management of customer loyalty. Concurrently, service convenience was found to play a significant driving role in customers' perceived service quality. Through the chain-mediating effect of service quality and customer satisfaction, service convenience exerts a considerable positive impact on word-of-mouth intentions and repatronage intentions. Moreover, the study reveals significant differences in these relationships when considering service methods, either with staff or without staff.

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct Activities of Daily Living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with ADLs to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADL.s while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as – the lack of functionalities to detect both falls and indoor locations, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an Ambient Assisted Living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real world. Proof of concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparison studies with prior works in this field are also presented to uphold the novelty of this work. The first comparison study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparison study shows that the cost for the development of this system is the least as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.

Large terrestrial carnivores play a crucial role in top-down control within terrestrial ecosystems, maintaining ecosystem stability and biodiversity. However, intense interspecific competition often arises among sympatric large carnivores, leading to population reductions or even extinctions. Spatial partitioning through divergent habitat selection helps mitigate such competition. In Xinlong County, Sichuan Province, we used 293 infrared cameras for monitoring from September to May 2016 and March to October 2022. By employing the Generalized Linear Model (GLM) and the Maximum Entropy Model (MaxEnt), we developed an ensemble model predicting the suitable habitat distribution of leopards (Panthera pardus) and wolves (Canis lupus). We analyzed the main environmental factors influencing habitat selection and the fragmentation of suitable habitats. We found that suitable habitat distribution differed significantly between them. Both species preferred areas with gentle slopes close to settlements. While leopards' habitat selection primarily depended on the distance from settlements, the slope was predominant for wolves. Suitable habitats displayed aggregation, yet wolves exhibited higher fragmentation and more complex patch shapes, indicating greater susceptibility to human activities. These results suggest that sympatric large carnivores, such as leopards and wolves, can reduce spatial competition intensity and promote spatial partitioning by selecting divergent suitable habitats, thereby facilitating species coexistence.

Alternative splicing can produce transcripts that affect cancer development and shows potential for cancer diagnosis and treatment. However, intron retention (IR), a type of alternative splicing, has been less systematically studied in cancer biology research. Here, we generated a pan-cancer IR landscape for more than 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). We characterized differentially retained introns between tumor and normal samples and identified retained introns associated with survival. We discovered 988 differentially retained introns in 14 cancers, some of which demonstrated diagnostic potential in multiple cancer types. We also inferred a large number of prognosis-related introns in 33 cancer types, and the associated genes included well-known cancer hallmarks such as angiogenesis, metastasis, and DNA mutations. Notably, we discovered a novel intron retention event inside 5′UTR of STN1 that is associated with the survival of lung cancer patients. The retained intron reduces translation efficiency by producing upstream open reading frames (uORFs) and thereby inhibits colony formation and cell migration of lung cancer cells. Besides, the IR-based prognostic model achieved good stratification on certain cancers, as illustrated in acute myeloid leukemia. Taken together, we performed a comprehensive IR survey at a pan-cancer level, and the results implied that IR has the potential to be diagnostic and prognostic cancer biomarkers, as well as new drug targets.

BACKGROUND AND OBJECTIVE: The use of biomass for cooking carries significant health implications and can lead to adverse environmental issues such as deforestation, greenhouse gas emissions, and erosion. Despite these concerns, there is a lack of information about the nature and extent of cooking emissions in many developing countries. To address this gap, a study was conducted to assess cooking emission levels and their associated health effects among households in Dodoma City, Tanzania. METHODS: A descriptive cross-sectional design was conducted, involving 285 households in the peri-urban area of Dodoma City, Tanzania. A combination of questionnaires, observational checklists, and sampling devices (Aeroqual series 500 for Carbon Monoxide and Temptop PMD 351 for Particulate Matters) were employed for data collection, adhering to established protocols. Additionally, lung function tests were conducted with consent from the primary household cooks. Collected data were entered into an Excel spreadsheet and analyzed using STATA 17 software. FINDINGS: Out of 285 participants, 74.7% predominantly use charcoal for cooking, with 68.4% having outdoor kitchen setups. Significant differences in pollutant concentrations were observed across various fuel types (P=0.0001), with firewood emitting higher levels of particulate matter and carbon monoxide. Carbon monoxide concentrations during cooking were 6 times higher for firewood, 5 times higher for charcoal-gas, and 3 times higher for charcoal compared to WHO daily average limits. Additionally, a positive correlation between pollutant concentrations was noted (P-value = 0.0000). Lung problems 17.8% followed by eye irritation 15.7% were mentioned by those who claimed to know the health effects associated with cooking emissions. In addition, the spirometry test indicated that the majority of the participants had % predicted >80 while only 19% had % predicted below 50. CONCLUSION: Charcoal and firewood remain the primary cooking fuels in the study area despite their significant negative impacts. Our findings highlight a lack of awareness among community members regarding the health effects of biomass cooking emissions and insufficient guidance on constructing healthy kitchens. To address this, enforcing building standards, raising health awareness, and ensuring affordable access to modern fuels are crucial steps. These measures are essential for promoting healthier cooking practices and mitigating the adverse effects on households.

At secondary healthcare, Carbapenem-Resistant Enterobacterales (CREs) such as observed in Klebsiella pneumoniae are a global public health priority with significant clinical outcomes. In this study, we describe clinical, phenotypic, and genotypic characteristics of three pan-drug resistant (PDR) isolates that demonstrated extended resistance to conventional and novel antimicrobials. All patients had risk factors for acquisition of multidrug resistant organisms while microbiological susceptibility testing showed resistance to all conventional antimicrobials. Advanced susceptibility testing demonstrated resistance to broad agents such as ceftazidime-avibactam, ceftolozane-tazobactam, and meropenem-vaborbactam. Nevertheless, all isolates were susceptible to cefiderocol, one of the novel antimicrobials that demonstrated potent in vitro activity against resistant Gram-negative bacteria including CREs pointing towards potential therapeutic role for PDR pathogens. Expanded genomic studies revealed multiple antimicrobial resistant genes (ARGs), including blaNMD-5 and blaOXA derivative types as well as a mutated outer membrane porin protein (OmpK37).

Recent studies suggest that PEBP1/RKIP and YY1, despite having distinct molecular functions, may interact and mutually influence one another's activity. They exhibit reciprocal control over each other's expression through regulatory loops, prompting the hypothesis that their interplay could be pivotal in cancer advancement and resistance to drugs. To delve into this interplay's functional characteristics, we conducted a comprehensive analysis using bioinformatics tools across a range of cancers. Our results confirm the association between elevated YY1 mRNA levels and varying survival outcomes in diverse tumors. Furthermore, we observed differing degrees of inhibitory or stimulatory effects of these two genes in various cancer pathways, along with correlations between their mRNA expression and immune infiltration. Additionally, YY1/PEBP1 expression (or methylation) displayed connections with genomic alterations across cancer types. Notably, we uncovered links between YY1/PEBP1 and different indicators of immunosuppression, such as immune checkpoint blockade response, and T-cell dysfunction/exclusion levels, across different patient groups. Overall, our findings underscore the significant role of the interplay between YY1 and PEBP1 in cancer progression, influencing genomic changes, tumor immunity or the tumor microenvironment. Additionally, these two gene products appear to impact the sensitivity of anticancer drugs, opening new avenues for cancer therapy.

Evaluation of the effectiveness of protected areas is critical for forest conservation policies and priorities. To evaluate their effectiveness, we used 30-m resolution forest cover change data between 1990 and 2010 for ~4,000 protected areas and analyzed the relationships of the effectiveness of protected areas with socio-economic variables. Our results show that protected areas in the Tropics avoided 83,500 ± 21,200 km2 of deforestation during the 2000s. Brazil’s protected areas have the largest amount of avoided deforestation of 50,000 km2. We also show the amount of international aid received by tropical countries compared to the effectiveness of protected areas. International aid had major benefits in Latin America led by Brazil while tropical Asian countries used the resource ineffectively. Our results demonstrate that protected areas have been relatively more efficient in countries where deforestation pressures were increasing, and governance and forest change monitoring capacity are important factors for enhancing the efficacy of international aid.

Our paper examines the place of Pan-Africanism as an educational, political, and cultural movement which had a lasting impact on the on the relationship between liberation and people of African descent, in the continent of Africa and the Diaspora. We also show its evolution, beginning with formerly enslaved Africans in the Americas, to the colonial borders of the 1884 Berlin Conference, and conclude with the independence movements in Africa. For formerly enslaved Africans, Pan-Africanism was an idea that helped them see their commonalities as victims of racism. That is, they realized that they were enslaved because they came from the same continent and shared the same racial heritage. They associated the continent of Africa with freedom. The partitioning of Africa at the Berlin Conference (colonialism) created pseudo-nation states out of what was initially seen as an undivided continent. Pan-Africanism provided an ideology for rallying Africans at home and abroad against colonialism, and the creation of colonial nation-states did not erase the idea of a united Africa. As different African nations gained political independence, they took it upon themselves to support those countries fighting for their independence. The belief, then, was that as long as one African nation was not free, the continent could not be viewed as free. The existence of nation-states did not imply the negation of Pan-Africanism. The political ideas we examine include those of Marcus Garvey, W.E.B. Du Bois, Kwame Nkrumah, and Thabo Mbeki.

Quantification of the trade-offs among greenhouse gas (GHG) emissions, yield and farmers’ incomes is essential for proposing economic and environment nitrogen (N) management strategies for optimizing agricultural production. A four-year (2017–2020) field experiment (including four treatments: control experiment (CK), suitable utilization of fertilization (SU), emission reduction treatment (ER), and high fertilization (HF)) was conducted on maize (Zea mays L.) in the North China Plain. The Life Cycle Assessment (LCA) method was used in this study to quantify the GHG emissions and farmers’ incomes during the whole maize production process. The total GHG emissions of CK, SU, ER and HF treatments in the process of maize production are 10,755.2, 12,908.7, 11,950.1, and 14,274.5 kg CO2-eq ha−1 respectively, of which the direct emissions account for 84.8%, 76.8%, 74.9%, and 71.0%, respectively. Adding inhibitor significantly reduced direct GHG emissions, and the N2O and CO2 emissions from the maize fields in the ER treatment decreased by 30.0% and 7.9% compared to those in the SU treatment. Insignificant differences in yield were found between the SU and ER treatments, indicating that adding fertilizer inhibitors did not affect farmers’ incomes while reducing GHG emissions. The yield for SU, ER and HF treatments all significantly increased by 12.9%–24.0%, 10.0%–20.7% and 2.1%–17.4% compared to CK, respectively. In comparison with CK, both SU and ER significantly promoted agricultural net profit (ANP) by 16.6% and 12.2% with the mean ANP values of 3,101.0 USD ha-1 and 2,980.0 USD ha-1, respectively. Due to the high agricultural inputs, the ANP values in the HF treatment was 11.2%, 16.6% and 12.4% lower than those in the SU treatment in 2018–2020. In conclusion, the combination of N fertilizer and inhibitors was proved to be an environmentally friendly, high-profit and low-emissions production technology while sustaining or even increasing maize yields in the North China Plain, which was conducive to achieving agricultural sustainability.

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

This paper aims to explore the development of the field of Intangible Cultural Heritage Design. It seeks to understand the global trends in this field and provide insights for its growth and development. The objectives are achieved through a mixed-methods approach that combines bibliometric methods and content analysis. This approach allows for a quantitative and qualitative analysis of the scientific literature related to Intangible Cultural Heritage Design. The findings of the work include an analysis of the most productive countries/regions, institutions, journals, and authors in the field of Intangible Cultural Heritage Design. It also includes citation and co-citation analysis, which helps identify influential scholars and popular journals in the field, as well as patterns of col-laboration within the field. The outcomes and implications for practice include a better understanding of the global trends in Intangible Cultural Heritage Design and insights for its growth and development. The research find-ings have important theoretical and practical implications for the field, and future research directions are pro-posed. The value of the paper lies in its contribution to a better understanding of the global trends in Intangible Cultural Heritage Design. It provides insights and assistance for its growth and development, mak-ing it valuable to researchers and practitioners in the field.

As matrix representations of magic labelings of related hypergraphs, magic squares and their various variants have been applied to many domains. Among various subclasses, trimagic squares have been investigated for over a hundred years. The existence problem for trimagic squares with singly even orders and orders 16n has been solved completely. However, very little is known for the existence of trimagic squares with other even orders except for the only three examples and three families. We construct normal trimagic squares by using product constructions, row-square magic rectangles, and trimagic pairs of orthogonal diagonal Latin squares. We give a new product construction: For positive integers p, q, and r having the same parity other than 1, 2, 3, 6, if normal p × q and r × q row-square magic rectangles exist, then a normal trimagic square with order pqr exists. As its application, we construct normal trimagic squares of orders 8q^3 and 8pqr for all odd integers q not less than 7 and p,r ∈ {7, 11, 13, 17, 19, 23, 29, 31, 37}. Our construction can easily be extended to construct multimagic squares.

This paper serves as a seamless continuation of the previous work titled "Special Spirals are Produced by the ROTASE Galactic Spiral Equations with the Sequential Prime Numbers." Upon revisiting the data, a noteworthy observation emerged: the special spirals can be meticulously aligned with the prime spiral on a point-by-point basis through careful scaling and rotation for the parameter ρ remaining a constant greater than one. As ρ increases, both the scaling factor and rotation angle exhibit a corresponding augmentation, albeit with an intriguing caveat—the rotation angle asymptotically approaches its limit of 90°. Notably, the galactic spiral can be perceived as a remarkably close approximation of the Archimedean spiral, with the degree of proximity increasing as ρ grows larger. Furthermore, an intriguing correspondence arises when ρ takes on a value of 1; at this value, the galactic spiral bears a striking resemblance to the Fermat spiral, the space between two consecutive spiral loops undergoes a continuous reduction as the loop's radius expands, yet it never diminishes to zero; simultaneously, the area between two successive loops experiences an augmentation with the value of ρ.

With the galaxy J101652.52 as a model galaxy, this paper studied on the temporal duration of optically observed spiral patterns in disc galaxies is based on the finite reservoir of hydrogen available without replenishment following the initial formation of these well-isolated galaxies. The galaxy J101652.52 exhibits a grand design spiral pattern of two wide open spiral arms with perfect central symmetry, has only rear side trail effect, which clearly indicates that there is no corotation circle, therefore, the Density wave theory proposed for the spiral arm formation may not be applicable in this galaxy. The results reveal a gradual decline in observable spiral arms regardless of any spiral arm formation mechanism. This reduction occurs after half-loop rotation (as defined in the paper) as hydrogen reserves deplete, hindering new star formation. Ultimately, spiral patterns may possibly become optically invisible at normal conditions after a full-loop rotation (as defined in the paper) with hydrogen depletion, even if the formation mechanism remains active. The spiral galaxies with spiral arms decoupled from galactic bars or having soft spiral structures shall have similar results as long as that the spiral pattern rotation speed is significant different from the galactic matter rotation speed. Most of observed spiral galaxies have soft spiral structures, their spiral patterns dynamically change constantly. The results of this study support the idea of short-lived spiral patterns compared to the lifespan of the spiral galaxies. The spiral arm formation may occur once like the onetime firework show in a galaxy’s life based on the limited availability of hydrogens. A concise, universally applicable mechanism for spiral arm formation across various galaxies should exist which provides a single mathematical formula to describe the broad range of morphologies of the spiral arms with few adjustable parameters. Some exceptions may be explained within this framework. Furthermore, the observed data on spiral pattern and galactic bar rotation speeds in the Milky Way require rigorous verification, particularly concerning the winding problem.

The Density Wave Theory, which aims to explain the formation of spiral arms in disc galaxies, has been subject to discussion. According to this theory, at the co-rotation circle, the rotational velocity of the density waves or spiral arms matches that of the galactic matter. Consequently, this alignment should impede the formation of new stars within this co-rotation circle, leading to the presence of dark or dim gaps within the spiral arms. Additionally, the theory predicts the occurrence of the front side trail effect in the inner region of the co-rotation circle and the rear side trail effect in the outer region. However, despite these expectations, no such spiral patterns have been observed, prompting the need for revision or reconsideration of the Density Wave Theory.

This paper illustrates that the ROTASE model proposed by the author for the formation of spiral arms of galaxies is nicely demonstrated by the spirals generated by rockets venting fuel during free fall. The patterns of spirals by the falling rockets strongly support that the spiral galaxies with perfect central symmetry have to be originated from the galactic centers, and the spiral arms and the galactic bars are bound together as “rigid” body. The exclusive presence of a rear side trail effect in galaxies indicates the absence of co-rotation, and the rotation of spiral arms is faster than the galactic matters. The exact nature of X-matter remains an open question.

This short paper demonstrates that the galactic spiral equations developed from ROTASE model can be used to morphologically simulate the phyllotactic spiral-ring patterns of plants. It is also noticed during this project that although the numbers in the Fibonacci sequence dominate the phyllotactic numbers, there are still significant phyllotactic numbers are not in the Fibonacci sequence, however, most of those numbers are well fit into a Fibonacci sibling sequence starting with numbers 0 and 2. Many plants show the phyllotactic number pairs of 10-16, 16-26 and 26-42 from the Fibonacci sibling sequence, which is the second dominated sequence in the phyllotaxis.

In this paper, the sequential prime numbers are used as variables for the galactic spiral equations which were developed from the ROTASE model. Special spiral patterns are produced when prime numbers are treated with the unit of radian. The special spiral patterns produced with the first 1000 prime numbers have 20 spirals arranged in two groups. The two groups have perfect central symmetry with each other and are separated with two spiral gaps. The special spiral pattern produced with natural numbers from 1 to 7919 shows 6 spirals in the central area and 44 spirals in the outer area. The whole 7919 spiral points can be plotted with either 6-spiral pattern or 44-spiral pattern. For the spirals only produced by the prime numbers in the 6-spiral pattern plotting, the spiral 2 and spiral 3 each has only one spiral point produced by prime number 2 and 3, respectively, all other spiral points produced by other prime numbers are located on the spiral 1 and spiral 5. The special spiral pattern is well explained with careful analysis, it is concluded that all prime numbers greater than 3 must meet one of the equations: P₁ = 1 + 6 * n (n > 0) P₅ = 5 + 6 * n (n ≥ 0) In other words, every prime number greater than 3 is either a P₁ prime number or a P₅ prime number, no exception. Matching one of the equations is a necessary condition for a number to be a prime number, not a sufficient condition. Hope such sufficient condition can be found in the future. The number of P₁ prime numbers roughly equal the number of P₅ prime number in the first 2 billion prime numbers. The galactic spiral equations with golden angle can duplicate Vogel’s result for the simulation of sunflower seed head pattern, and a pinwheel pattern can be produced also with galactic spiral equations and 1 degree more than golden angle.

Sensory evaluation has been widely applied in assessing the quality of many consumer products that directly serve human needs such as food, beverages, clothing, etc. This paper first examined the inherent deficiencies in this approach, due mainly to the essentially subjective nature of human sensory preference. It then argued that instinctively designating certain materials attributes as sensory perceptions is unnecessary; considering every scientific concept is in essence the processed results of our sensory organs/brains, i.e., all quantities were initially human perceptions. Scientific advances have inevitably generated gradual transformation of such human perceptions as warmth and heaviness into objective parameters measurable in physical quantities like temperature and kilograms. Then using existing successful examples, it demonstrated firmly how the sensory attributes can be assessed by more reliable instrumental methods, and envisioned the key steps to turning a perception into scientifically measurable parameter(s).

Cloth wearing seems so natural that everyone is self-deemed knowledgeable and has some expert opinions about it. However to clearly explain the physics involved, and hence to make predictions for clothing design or selection, turns out as demonstrated below to be quite challenging even for experts. Cloth is a multiphased, porous and anisotropic material system and usually in multilayers. Unlike ordinary engineering heat transfer problems, the human body acts as an internal heat source in a clothing situation, thus forming a temperature gradient between body and ambient, and the sign of this gradient often changes as the ambient temperature varies. Our body also perspires and the sweat evaporates, an effective body cooling process via phase change. To bring all the variables into analysis quickly escalates into a formidable task. This work attempts to unravel the problem from a physics perspective, focusing on several rarely noticed yet critically important mechanisms involved so as to offer a clear and accurate depiction of the principles in clothing thermal comfort.

This study, based on the AISAS model, explores the impact of the interaction effect between visual and auditory signals on consumer purchase behavior. Using experimental methods, 120 participants were randomly assigned to four different visual and auditory signal combinations, and their purchase intentions and actual purchase behavior were measured. The results show that the interaction effect between visual and auditory signals has a significant impact on both purchase intentions and actual purchase behavior, and there is a significant positive relationship. Specifically, when visual and auditory signals are mutually consistent, consumers have the highest purchase intentions and actual purchase behavior; when both visual and auditory signals are absent, consumers have the lowest purchase intentions and actual purchase behavior; when either the visual or auditory signal is missing, consumers' purchase intentions and actual purchase behavior are in between the two extremes. This study provides a new perspective for understanding consumers' decision-making processes in multi-sensory environments and offers valuable insights for the development of marketing strategies.

In order to achieve the dual carbon goal and simplify the decarbonization process, a CO2 recovery process of the fire-flooding exhaust was designed. The process adopted the two-stage membrane separation method to recover CO2 and obtain high purity CO2 rich flow, and then through the mixed refrigerant liquefaction and rectification column, food grade liquid CO2 was obtained. HYSYS process simulation shows that for the fire-flooding exhaust with a flow rate of 1000 kg/h and a given composition, the process can achieve a CO2 recovery of 69.02%, total product sales S=2086.8 yuan/h, and specific power consumption of 2.818 kW∙h/kg. On this basis, a hybrid simulation platform of HYSYS & Matlab was established. Taking the specific power consumption of the product as the objective function, sequential quadratic programming (SQP) algorithm was adopted to optimize the process, and the optimized specific power consumption was reduced by 18.8%. Finally, by adjusting N2/CO2 components, the adaptability of the optimization condition was analyzed.

Motivated by South Africa's need in the transition to a net-zero economy; this study 10 investigates the integration of renewable energy sources (RES) into oil refineries, considering 11 therein unique challenges and opportunities. The research focuses on optimizing RES allocation 12 using Particle Swarm Optimization (PSO), a data-driven approach that adapts to real-time 13 operational conditions. Traditional energy management systems often struggle with the inherent 14variability of RES, leading to suboptimal energy distribution and increased emissions. Therefore, 15 this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil 16 refineries. It considers factors like the levelized cost of energy, geographical location, and available 17 technology. The methodology involves formulating the optimization problem, developing a PSO 18 model, and implementing it in a simulated oil refinery environment. The results demonstrate 19 significant convergence of the PSO algorithm, leading to an optimal configuration for integrating 20 RES and achieving cost reductions and sustainability goals. The optimization result of 4,457,527.00 21 Rand achieved by iterations is much better than the result of 4,829,638.88 Rand acquired using 22Linear Programming as the baseline model. The mean cost, indicating consistent performance, has 23 remained at its original value of 4,457,527.00 Rand, highlighting the convergence. Key findings 24 include the average distance measure decreasing from 4.2 to 3.4, indicating particle convergence; 25 swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; 26 average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the 27 optimum cost function achieved at 4,457,527 Rand with zero standard deviation, highlighting 28 stability and optimal solution identification. This research offers a valuable solution for oil 29 refineries seeking to integrate RES effectively, contributing to South Africa's transition to a 30sustainable energy future.

In order to quickly develop a multi ship collaborative collision avoidance plan in multi ship encounter scenarios, a multi ship collision avoidance decision model is proposed in accordance with the requirements of the International Maritime Collision Avoidance Regulations, taking into account the collision avoidance operations in multi ship encounter scenarios. Firstly, the collision risk between ships is determined by the closest encounter distance (DCPA) and closest encounter time (TCPA). Secondly, a decision objective function is established based on rules, degree of ship deviation, and ship risk. Finally, the prediction mechanism is used to calculate the minimum value of the objective function and complete the collision avoidance decision. The simulation results show that in the scenario of multiple ship encounters, the collision avoidance model can achieve safe collision avoidance with a smaller steering frequency and a shorter col-lision avoidance time. The proposed multi ship collision avoidance model can provide reference for collision avoidance decision-making in multi ship encounter scenarios, which helps to im-prove the collision avoidance efficiency in multi ship collision avoidance

This work compared the performance of three methods for constructing a regional ensemble prediction system (EPS) for wind speed forecasts: dynamical downscaling, breeding of growth modes (BGM), and blending method. The Weather Research and Forecasting (WRF) model was used to downscale the European Centre for Medium-range Weather Forecast (ECMWF) EPS. In addition, as the BGM method needs observation data for generating scaling factors, an alternative method for generating scaling factors was proposed to eliminate dependence on observation data. One-month tests between October 1st and October 30th, 2020, were implemented to evaluate the performance of three methods in the Gansu province of China. The results demonstrate that the blending method outperforms the other two methods. Furthermore, the difference in performance is evident mainly in early forecast lead time and becomes negligible as forecast time increases.

In this paper, we consider an economy, in which consumers exhibit unrealistic optimism, and show that such an economy is more likely to undergo early recessions and underinvestment rather than experiencing bubbles or overinvestment. In this model, each consumer incorrectly believes that a negative aggregate preference shock will hit all other consumers, but not herself. When consumers have such an unrealistic optimism, the output price must fall prematurely before the aggregate shock occurs. The reason is that if the price does not plummet today, all consumers would postpone making purchases by falsely expecting that an arbitrage opportunity will arise in the future when the aggregate shock hits the economy, in which case, the output market cannot clear. We also show that the government has the potential to improve welfare of the economy by providing subsidies, if the government judiciously selects the right timing for intervention.

The aim of this study was to examine oxidative stress and low level of alpha-1-antitrypsin (A1AT) in primary Sjögren's syndrome (pSS), and evaluate the associated autoreactivity against unmodified and their 4-hydroxy-2-nonenal (HNE)-modified peptides with pSS. Two differentially expressed proteins, alpha-1-acid glycoprotein 1 (A1AG1) and A1AT, exhibited 2-fold differences, and their HNE modifications were identified by depleted-albumin and immunoglobulin G (IgG) serum protein, in-solution digestion, in-gel digestion, and nano-LC-MS/MS from pSS patients and age-matched healthy controls (HCs). Furthermore, levels of proteins, confirmation of HNE modifications, HNE-protein adducts and autoreactivity against unmodified and their HNE-modified peptides were further validated. Levels of the HNE-protein adduct and A1AG1 were significantly higher in pSS patients than HCs, but levels of A1AT were significantly lower in pSS patients compared to HCs. Only the HNE modification of A1AT was confirmed. Further, concentrations of anti-A1AT^50-63 IgG and anti-A1AT^50-63 HNE IgA were significantly lower in pSS patients than HCs. Our study suggests that elevated HNE-protein adduct, oxidative stress, level [odds ratio (OR) 4.877, p = 0.003], lowered A1AT level (OR 3.910, p = 0.010) and a decreased level of anti-A1AT^50-63 IgG (OR 3.360, p = 0.010) showed an increased risk in pSS patients compared to HCs, respectively.

Mammalian seminal plasma contains a multitude of bioactive components, including lipids, glucose, mineral elements, metabolites, proteins, cytokines and growth factors, with various functions during insemination and fertilization. The seminal plasma protein PDC-109 is one of the major soluble components of the bovine ejaculate and is crucially important for sperm motility, capacitation and acrosome reaction. A hitherto underappreciated function of seminal plasma is its anti-microbial and anti-viral activity, which may limit sexual transmission of infectious diseases during intercourse. We have recently discovered that PDC-109 inhibits the membrane fusion activity of influenza virus particles and significantly impairs viral infections at micromolar concentrations. Here we investigated whether the antiviral activity of PDC-109 is restricted to Influenza or if other mammalian viruses are similarly affected. We focused on Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 19 (COVID-19), thoroughly assessing PDC-109 inhibition with SARS-CoV-2 Spike (S)-pseudotyped reporter virus particles, but also live-virus infections. Consistent with our previous publications we found significant virus inhibition, albeit accompanied by substantial cytotoxicity. Using time-of-addition experiments however, we discovered treatment regimen that enable virus suppression without affecting cell viability. We furthermore demonstrated that PDC-109 is also able to impair infections mediated by the VSV glycoprotein (VSVg) thus indicating a broad pan-antiviral activity against multiple virus species and families.

Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had the most accurate performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.

Species have shown their habital variations in responding to climate change, especially during the spring and summer spawning seasons. The species distribution model (SDM) is considered the most favorable tool to study the potential effects of climate change on species distribution. Therefore, we developed the ensemble SDM to predict the changes in species distribution of Portunus trituberculatus among different seasons in 2050 and 2100 under the climate scenarios RCP4.5 and RCP8.5. The results of SDM indicate that the distribution of this species will move northward and have obviouse seasonal variations. Meanwhile, the suitable habitat for the species will be significantly reduced in summer, with loses rates ranging from 45.23% (RCP4.5) to 88.26% (RCP.8.5) by 2100s. Habitat reduction will mainly occur in the East China Sea and southern part of the Yellow Sea, while there will be a small increase in the northern Bohai Sea. These findings will be important to manage the ecosystem and fishery, provide an information forecast of this species in the future, and maintain species diversity if the seawater temperature rises.

During the high-speed flight of a vehicle in the atmosphere, surface friction with the air generates aerodynamic heating. The aerodynamic heating phenomenon can create extremely high temperatures near the surface. These high temperatures impact material properties and the structure of the aircraft, so thermal deformation measurement is essential in aerospace engineering. This paper revisits high-temperature deformation measurement using the digital image correlation (DIC) technique under high-intensity blackbody radiation with a precise speckle pattern fabrication and a heat haze reduction method. The effect of speckle pattern on the DIC measurement has been thoroughly studied at room temperature, but high-temperature measurement studies have not reported such effects so far. We found that the commonly used methods to reduce the heat haze effect could produce incorrect results. Hence, we propose a new method to mitigate heat haze effects. An infrared radiation heater was employed to make an experimental setup that could heat a specimen up to 950 ℃. First, we mitigated image saturation using a short-wavelength bandpass filter with blue light illumination, a standard procedure for high-temperature DIC deformation measurement. Second, we studied how to determine the proper size of the speckle pattern under a high-temperature environment. Third, we devised a reduction method for the heat haze effect. As proof of the effectiveness of our developed experimental method, we successfully measured the deformation of stainless steel 304 specimens from 25 ℃ to 800 ℃. The results confirmed that this method can be applied to the research and development of thermal protection systems in the aerospace field.

Surface water plays a crucial role in climate change, human production, and life, making accurate monitoring and observation of surface water particularly important. However, due to the significant diversity and complexity of water distribution in surface space, accurate mapping of surface water faces considerable challenges. When extracting water bodies from medium-resolution satellite remote sensing images, CNN methods may suffer from limitations in receptive fields and insufficient context modeling capabilities, leading to the loss of water body boundary details and poor fusion of multiscale features. Currently, there is relatively little research on this issue; therefore, it is necessary to explore new combinations of deep learning networks to address these challenges. The purpose of this study is to address the above issues. We propose a new combination of deep learning networks that fully utilize multiscale information to enhance water features. Specifically, we first combine deformable convolutions with the Swin Transformer to increase effective receptive fields while better integrating global semantic information. This combination can capture features of water bodies at different scales, improve the accuracy and integrity of water extraction, and provide reliable technical support for detailed water body extraction. We tested the newly constructed model using Sentinel-2 satellite images. Our model achieved results of over 90%, with an average accuracy of 97.89%, average precision of 94.98%, average recall of 90.05%, and an average F1 score of 92.33%. In addition, our model achieved an accuracy of 98.03% in mountainous areas. Our experiments and results validate the potential of combining the Swin Transformer and deformable convolutions in detailed water body extraction.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

Pretilachlor is a chloroacetamide herbicide, mainly used for the control of weeds and broadleaf weeds in rice, and widely used in China. For the detection of residues of pretilachlor in the en-vironment and food, a highly sensitive and specific monoclonal antibody against pretilachlor was prepared, and the half maximum inhibitory concentration (IC50) of the monoclonal antibody was validated to be 31.47±2.35 μg/L. An indirect competitive ELISA (ic-ELISA) based on the antibody with a linear range of 6.25~100 μg/L was developed for the detection of pretilachlor residues in the environment and food crops. The specificity of the antibody was explained by computer simula-tions and experimental validation. No cross-reactivity of this monoclonal antibody to alachlor, acetochlor and metalaxyl, and it cross-reacted less than 3.0% to both butachlor and propisochlor. The limits of detection (LOD) for pretilachlor in lake, rice, and soil samples were 4.83~5.23 μg/L. The recoveries of all samples were 78.3%~91.3%. The ic-ELISA method was validated by high-performance liquid chromatography, thus, it can be used for residue detection of pretilachlor in the environment and grains.

The availability of multiple sequenced genomes from a single species made it possible to explore intra- and inter-specific genomic comparisons at higher resolution and build clade-specific pangenomes of several crops. The pan-genomes of crops constructed from various cultivars/accessions, landraces, and wild ancestral species represent a compendium of genes and structural variations and allow researchers to search for the novel genes and alleles that were inadvertently lost in domesticated crops during the historical process of crop domestication or in the process of extensive plant breeding. Fortunately, many valuable genes and alleles associated with desirable traits like disease resistance, abiotic stress tolerance, plant architecture, and nutrition qualities exist in landraces, ancestral species, and crop wild relatives. The novels genes from the wild ancestors and landraces can be introduced back to high-yielding varieties of modern crops by implementing classical plant breeding, genomic selection, and transgenic/gene editing approaches. Thus, pan-genomic represents a great leap in plant research and offers new avenues for targeted breeding for mitigating the impact of global climate change. Here we summarize the tools used for pangenome assembly and annotations, web-portals hosting plant pangenomes. Furthermore, we highlight a few discoveries made in crops using the pan-genomic approach and its future potential.

Aim: We estimated the correlation between serum bilirubin concentrations and insulin resistance in advanced maternal age without diabetes mellitus. Method: The cross-sectional study involved consecutive 50 women of advanced maternal age without diabetes mellitus, and insulin resistance (IR) was diagnosed according to the homeostatic model assessment insulin resistant (HOMA-IR) formula and the cut-off point was set to more than 2.5. Serum bilirubin and HOMA-IR were compared between the IR (+) and IR (-) groups. Results: We found that serum bilirubin levels in the IR subjects were significantly decreased compared to those without IR (p = 0.0252). Serum bilirubin concentrations were negatively correlated with the weight and the fasting insulin concentrations in all subjects (r=−0.282, p=0.047, r=-0.424, p=0.002). Additionally, serum bilirubin had a negatively correlation with HOMA-IR (r = -0.410, p = 0.003) in all subjects. The multiple linear regression analysis found that serum total bilirubin levels were independently correlated with HOMA-IR (beta = -0.392, p= 0.015) in these subjects. Conclusions: The study demonstrated that serum bilirubin concentrations had negatively correlation with insulin resistance in advanced pregnant women without diabetes mellitus, and decreased serum bilirubin may forebode increased gestational diabetes mellitus risk in our study population.

This study analyzes the effect of economic growth and income distribution on health inequality using data from the Organization for Economic Cooperation and Development (OECD). A panel analysis was conducted by combining 21 years of data (from 2000 to 2020) from 38 OECD countries. Life expectancy and avoidable mortality were set as the dependent variables; gross domestic product and poverty gap were set as the independent variables; body mass index, consumption, smoking rate, healthcare personnel, number of beds in health service facilities, national medical expenses, and unemployment were set as control variables. First, economic growth and poverty gap significantly affect life expectancy and avoidable mortality. It has been confirmed that to resolve health inequality, growth during growth and distribution must be prioritized. Second, it has been confirmed that as the poverty gap increases, life expectancy increases and the avoidable mortality rate decreases. A mechanism different from the existence of a medical security system targeting the lowest class or an increase in the welfare of society worked. In conclusion, as a policy proposal to alleviate health inequality, this study suggests that policy intervention is necessary to block or alleviate the negative impact of income inequality in health care policies.

Evaporation from surface water plays a key role in water accounting of basins, water resources management, and irrigation systems management, so simulating evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. In the second method, the best overall relationship obtained in the first method was used as the basic relationship, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation was drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

The critical period (CP) phenomenon in language development ranks among the 125 conundrums facing scientists in the 21st century, according to Science. While the phenomenon itself has been noncontroversial in first language acquisition, it still warrants an adequate explanation. Predicated on language acquisition as a complex process, questions among the first to be raised include: How do children accomplish this remarkable feat in such a short amount of time? And how do nature and nurture come together to influence language learning? In second language acquisition, however, both the notion of CP, albeit popular, and its empirical evidence have remained contested to this date - among the questions, whether the observed evidence counts as CP-specific and/or whether or not it warrants an isomorphic attribution to maturational constraints. Entwined in this debate are two well-established facets of inter-learner differential attainment. The first is that there exists a stark difference in ultimate attainment between younger and older learners. A second facet is the vast differences in ultimate attainment among older learners. In this article, adopting a social physics approach, we mathematically establish both the relationship between nature and nurture contributions and the presence of a critical period, and, at once, tender a parsimonious and probable theory for the twin phenomena of inter-learner differential attainment.

Climate change represents a prevalent challenge confronted by humanity, and with cities being one of the regions most heavily impacted by such change, enhancing their climate resilience is paramount in adapting to and mitigating the associated risks and losses. This article evaluates the climate resilience of 270 major cities in China by creating an urban climate resilience indicator system centered on exposure, vulnerability, and resilience. The article analyses the spatial and temporal evolution trend of climate resilience in Chinese cities, as well as the source of regional differences using Kernel density analysis and the Dagum Gini coefficient decomposition. The article then explores the influence of financial investments on urban climate resilience, considering the moderating effect of urbanization rate.

This paper reviews the requirements for the production, quality, and quality assurance of solid recovered fuels (SRF) that are increasingly used for the substitution of fossil fuels in the indus-trial utilities. The economical, technological and environmental aspects should be considered before using SRF as an alternative fuel. In general, the most important factors determining the commercial production of SRF referred to the calorific values and their available amounts adopted in the starting materials. Furthermore, the low-melting impurities and undesired constituents/elements, including chlorine, sulfur, potassium and sodium, may pose negative impacts on air pollutant emission and slagging & fouling in the process utilities. In this regard, the vent emitted from the industrial utilities must comply with the effluent standards of station-ary sources like particulate, sulfur oxides, nitrogen oxides, heavy metals (e.g., lead, cadmium, and mercury) and dioxins. For these reasons, this work will summarize the legal or regulatory requirements under development in Asian countries (i.e., Japan, South Korea, and Taiwan) and European Union (EU). Finally, the outlook of solid recovered fuel (SRF) in the waste manage-ment and industrial sectors will be addressed to echo the sustainable development goals (SDGs).

In the ground-based infrared optical remote sensing system, the target signal is very weak due to the dynamic strong light background and the movement of small and small targets. In order to improve the limit detection capability, the background suppression and target enhancement methods are required to be more suitable for this scene. To solve this problem, we first analyze the image features in the current scene, and propose a more perfect point target and noise model. Then, we propose a new WY distribution function based on the Fermi Dirac distribution function, and propose an anisotropic filtering method based on this function, and further suppress the back-ground through the difference results of two steps. Then, based on the distribution function, we further designed an energy concentration accumulation strategy in 9 scale directions, through which the SNR of the target is effectively improved and the suppression ability of the background is enhanced. Through quantitative and qualitative experimental analysis, compared with the same type of algorithms, the proposed method has better robustness against extremely weak targets and dynamic backgrounds.

The effects of reaction temperature, reaction time, heating rate, nitrogen flow rate and catalyst quality on the oil yield and oxygen content in the oil were investigated by using the single factor principle. Too high and too low reaction temperature, nitrogen flow rate, heating rate and reaction time have improved the yield of bio-oil, but the oxygen content in bio-oil is higher. The reduction of catalytic dose is beneficial to the increase of oil yield but not conducive to the increase of aromatic compound content. The optimal reaction conditions were observed as reaction temperature of 500℃, nitrogen flow rate of 110 mL/min, heating rate of 20℃/min, reaction time of 15min, mass ratio of catalyst to coconut coat powder was 1, and the oil yield was 4.97%, aquatic yield was 35.80%, and solid yield was 30.78%. The gas yield was 28.45%, the oxygen content was 10.3%, and the aromatic compound content was 89.7%.The analysis of catalytic cracking mechanism shows that most of the oxygen-containing compounds in the bio-oil are generated into water and aromatic compounds by catalytic cracking reaction at high temperature, thus removing oxygen.

Concrete cracks and local damage can affect the bond performance between concrete and steel bars, thereby reducing the durability of reinforced concrete structures. Compared with general concrete crack repair methods, biomineralization repair not only has effective bonding capabilities but is also particularly environmentally friendly. In view of this, this study aimed to apply biomineralization technology to repair damaged fiber-reinforced lightweight aggregate concrete (LWAC). Two groups of LWAC specimens were prepared. The control group used lightweight aggregates (LWAs) without bacterial spores and nutrient sources, and the experimental group used LWAs containing bacterial spores and nutrient sources. These specimens were first subjected to compression tests and pull-out tests, respectively, and thus were damaged. After the damaged specimen healed itself in different ways for 28 days, secondary compression and pull-out tests were conducted. The self-healing method of the control group involved placing the specimens in an incubator. The experimental group was further divided into experimental group I and experimental group II. The self-healing method of experimental group I was the same as that of the control group. The self-healing method of experimental group II involved soaking the specimen in a mixed solution of urea and calcium acetate for two days, and then taking it out and placing it in an incubator for two days, with a cycle of four days. The test results showed that, compared with the control group specimens, the relative bond strength ratio of the experimental group II specimens increased by 17.9%. Moreover, the EDS and XRD analysis results confirmed that the precipitate formed at the crack was calcium carbonate, which improved the compressive strength and bond strength after self-healing. This indicates that the biomineralization maintenance method used in experimental group II is more effective.

Until now, crystalline NiCo2S4 and its composites have demonstrated improve performances in supercapacitor applications, compared to their oxide analogous, due to their relatively higher electrical conductivity, and multifaceted redox reaction. However, amorphous phase materials have recently shown promise in electrochemical energy storage systems. In this work, urea-assisted amorphous NiCo2S4 on nickel foam (referred to as aNCS) with high nitrogen atom% content, was obtained via hydrothermal method. In comparison to the crystalline NiCo2S4 (referred to as cNCS), aNCS exhibited superior electrochemical performance with specific capacitance of ~3506 F g-1 which is higher than cNCS specific capacitance of ~2185 F g-1 at 2 A g-1. Additionally, aNCS in two-electrode asymmetry supercapacitor exhibited specific capacitance and energy density of ~196 F g-1 and 56 Wh kg-1, respectively.

Radio-dynamic therapy (RDT), as an emerging cancer treatment method, has attracted attention due to its remarkable therapeutic efficacy using low-dose, high-energy radiation (such as X-rays), and has shown significant potential in cancer treatment. The RDT system typically consists of scintillators and photosensitizers. Scintillators absorb X-rays and convert them to visible light, activating nearby photosensitizers to generate cytotoxic reactive oxygen species (ROS). Challenges faced by the two-component strategy, including low loading capacity and inefficient energy transfer, hinder its final effectiveness. In addition, the tumor microenvironment (TME) with hypoxia and immunosuppression limits the efficacy of RDTs. Recent advances introduce one-component RDT systems based on nanomaterials with high-Z metal elements, which effectively inhibit deep-seated tumors. These novel RDT systems exhibit immune enhancement and immune memory, potentially eliminating both primary and metastatic tumors. This review comprehensively analyzes recent advances in the rational construction of RDTs, exploring their mechanisms and its application in the treatment of deep-seated tumors. Aimed at providing a practical resource for oncology researchers and practitioners, the review offers new perspectives for potential future directions in RDT research.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

Probiotics, known for regulating gut microbiota, may aid those with overweight or obesity, but their mechanisms require more research. This study involved 75 overweight or obese young adults, randomly assigned to either a Bifidobacterium brevis BBr60 group or a placebo group. Both groups received diet guidance and took either BBr60 (1×1010 CFU/day) or a placebo for 12 weeks. Re-searchers analyzed body composition, serum glucose, lipids, liver and kidney function, comprehensive metabolome, and intestinal homeostasis before and after the intervention. After 12 weeks, BBr60 significantly reduced weight and BMI compared to pre-treatment levels and outperformed the placebo. The BBr60 group also showed improved blood biochemistry, with notably lower fasting blood glucose (FBG) levels than the placebo group (p < 0.05). Additionally, BBr60 influenced vital serum and fecal metabolites related to three amino acid metabolic pathways and regulated the bacteria Dialister, Klebsiella, and Bacteroides, which correlated strongly with serum metabolites. These findings indicate that BBr60 can safely and effectively regulating BMI, body weight, serum glucose, lipids, and liver function markers, which may involve BBr60's impact on key gut bacteria, which influence metabolites related to the valine, leucine, and isoleucine biosynthesis; glycine, serine, and threonine metabolism; and alanine, aspartate, and glutamate metabolism.

Since large articulated vehicles have uncertainties in trailer articulation angle as well as dynamic complexity, it is not easy to accurately establish a reliable motion plan. In this paper, two geometric path plans constructed based on the empirical rules of driving experts are presented so that articulated vehicles can automatically perform perpendicular parking on a reverse path. By analyzing the empirical parking methods of professional drivers, these path plans were constructed by appropriately combining several standardized simple basic motions to facilitate implementation in real vehicles. In addition, path plans included appropriate complementary motions to effectively respond to uncertainties arising from articulation angles, etc. The complementary motions developed in this study are based on the results of qualitative analysis on the behavior of articulated vehicles. The usefulness of the proposed articulated vehicle parking method has been proven through hundreds of experimental tests using a 1:10 ratio model automated vehicle.

The security of several full homomorphic encryption (FHE) schemes depends on the hardness of the approximate common divisor (ACD) problem. The analysis of attack and defense against the system is one of the frontiers of cryptography research. In this paper, the performance of existing algorithms, including orthogonal lattice, simultaneous diophantine approximation, multivariate polynomial and sample pre-processing are reviewed and analyzed for solving the ACD problem. Orthogonal lattice (OL) algorithms are divided into two categories (OL-$\land$ and OL-$\vee$) for the first time. And an improved algorithm of OL-$\vee$ is presented to solve the GACD problem. This new algorithm works well in polynomial time if the parameter satisfies certain conditions. Compared with Ding and Tao's OL algorithm, the lattice reduction algorithm is used only once, and when the error vector $\mathbf{r}$ is recovered in Ding et al.'s OL algorithm, the possible difference between the restored and the true value of $p$ is given. It is helpful to expand the scope of OL attacks.

Demand for development of the convergence industry, research studies on electrical conductivity, thermal characteristics, semiconductors, motors, and batteries using special materials have come to the fore. Meanwhile, molybdenum (Mo) exhibits relatively small inorganic qualities, and the thermal conductivity rate is applied to various fields. In this study, in-depth characteristics were considered regarding the concentration of thermal characteristics, IR car terminal characteristics, and IR ve-hicles. This study calculated each phase temperature of the molybdenum sputtered specimens in the steady state according to the heat transfer theory. When the molybdenum-sputtered fabric’s metal layer pointed to the outside air, the heat transfer rate (Q) was high at 5748.3W, In contrast, if the molybdenum sputtered film’s metal layer of the pointed toward the heat source, the heat transfer rate (Q) was low at 187.1W. As a result of measuring the IR transmittance, the infrared transmit-tance of the molybdenum sputtering-treated sample was significantly reduced compared to the untreated sample. In the case of untreated samples, the transmittance ranged from 92.7 to 42.0%. When only the cross part was treated with molybdenum sputtering and the molybdenum surface was directed toward the IR irradiator, the IR transmittance was 66.8~0.7%. It is believed that the molybdenum sputtering polyamide samples produced in this study can be applied to multifunc-tional military wear, biosignal detection sensors, semiconductor products, batteries, etc., by utilizing excellent electrical properties, stealth functions, IR blocking properties, and lightness for infrared thermal imaging detectors.

Melatonin, a naturally occurring chemical, has pleiotropic effects across various species. It plays a vital role in animals by regulating the body clock, while in plants, it is known to resist aging. Melatonin is associated with various plant physiological processes, including stress response, plant growth, and flowering time. It has prospects in enhancing and promoting plant growth and development, as well as in strengthening crop yield and increasing sustainability in agriculture. This review summarized the potential roles of melatonin in several plant species, as well as its biosynthetic modes. In addition, we described the recently uncovered melatonin features in plants, summarized the issues and challenges facing plant melatonin research and some feasible solution ideas are proposed.

To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEI_ns, and AWEI_s) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEI_ns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEI_ns and AWEI_s are better than NDWI, MNDWI and MNDWI2. AWEI_ns works better than AWEI_s under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEI_ns.

Background: Boron Neutron Capture Therapy (BNCT) is an emerging radiotherapy. There are ongoing efforts to develop a Canadian accelerator-based BNCT center. However, it remains unclear how Canadian radiation oncologists (RO), medical physicists (MP), and their trainees perceive BNCT and its impact on radiation oncology as a discipline. Methods: A survey was created to explore knowledge of BNCT, its clinical role, and support for Canadian research. It was distributed through the Canadian Association of Radiation Oncology (CARO) and Canadian Organization of Medical Physicists (COMP). Results: We received 118 valid responses from all 10 provinces, from 70 RO (59.3%) and 48 MP (40.7%), including 9 residents. Most knew of BNCT and its indications (60.2%). Although many were unaware of reasons behind early failures (44.1%), common reasons were lack of clinical trials and inaccessibility of neutron sources (42.4%) and reactor unsuitability (34.7%). Additionally, 90.6% showed definite (66.9%) or possible (23.7%) support for Canadian BNCT research, while 89% indicated definite (56.8%) or possible (32.2%) willingness for BNCT referrals. Conclusions: Most RO and MP supported Canadian BNCT research and would refer patients. However, limited awareness and lack of experiences remain a challenge. Educational sessions are needed to realize this innovative cancer treatment in Canada.

The increasing use of whole-body computed tomography (WBCT) examinations, also known as panCT, in emergency departments for trauma patients has raised concerns about potential over-use and the associated risk of unnecessary radiation exposure. The purpose of this study was to examine the utilization patterns and findings of panCT scans performed over one year at a major academic hospital. This retrospective cohort study included 599 trauma patients (adults and pe-diatrics) who underwent panCT scans in 2023. De-identified data for each patient, including the radiology report, age, gender, and dose-length product (DLP), were retrieved and reviewed. Ra-diology reports were classified as negative (no acute traumatic injuries) or positive, with posi-tive cases further subclassified based on injury location. Injury severity scores (ISS) were also calculated based on the findings of the radiology reports. Statistical analysis was performed us-ing Python programming language to assess predictors of report findings. Overall, 56% of panCT scans were negative. Logistic regression showed that age significantly impacted the like-lihood of negative findings (p=0.009), while gender did not (p=0.182). One-third of positive cases showed injuries either in head and neck (H&N) or chest-abdomen-pelvis (CAP) regions. Most cases (70% adults, 65% pediatrics) had an ISS between 1 and 8. Median DLP values were 2264 mGy.cm in pediatrics and 2666 mGy.cm in adults. The study showed a high rate of negative panCT scans, suggesting potential overuse of panCT. These findings highlight the need for more selective CT imaging approaches, evidence-based guidelines, and decision-support tools to promote appropriate utilization of panCT scans, reducing unnecessary radiation exposure while ensuring high-risk patients receive appropriate imaging.

This paper considers the direction of arrival (DOA) estimation problem of phase ambiguity for unfolded coprime linear array (UCLA). The existing most common stacking subarray-based method can tackle the phase ambiguity problem. However, the method is not always true. For a given DOA, it has its corresponding steering vector, but if there are two DOAs which have the same steering vectors with the given DOA for different subarrays, the phase ambiguity problem still arises. A modified method, which defines a decision variable and uses the classic beamforming technique to estimate the DOAs is proposed. However, this method needs additional algorithm to distinguish the true DOAs besides multiple signal classification (MUSIC) spectrum. And sometimes this method is not reliable if the decision variable is not chosen appropriately. Therefore, based on the UCLA, we reconstruct the array to design an improved UCLA called IUCLA. With moving the reference element, the linear relationship among the directional vectors can be broken. In this way, we can directly utilize the MUSIC algorithm to estimate with no additional algorithm. The proposed method can solve the phase ambiguity problem and it does not demand other technique to decrease the complexity. Moreover, the Cramer-Rao Bound (CRB), as the lower bound of the unbiased estimation, is provided and numerical simulations are given to demonstrate the effectiveness and superiority of the proposed method.

Dielectric elastomer actuators (DEAs) offer robust, high energy density solutions for soft robotics. The proposed end effector consists of three spring roll configuration DEAs, each acting as a robotic finger, using a 3M VHB-F9473PC adhesive membrane. The lateral force, bending angle and response time of the actuator are measured experimentally and compared with the predictions of an analytical model. The cylindrical actuator measures 70 mm in length and 15 mm in diameter and achieved a lateral force of 30 mN, a bending angle of 6.8 ° and a response time of ≈ 1 s. Spring roll configuration DEAs are shown to reduce the effects of viscoelasticity seen in the membrane, making the actuator more controllable at higher voltages. The dielectric constant of the membrane is shown to be a limiting factor of actuation, with a decrease in dielectric constant resulting in larger actuation. The end effector successfully gripped numerous light objects for extended periods, showing the applicability of spring roll DEAs for soft end effectors.

The purpose of this study is to investigate and validate the influencing factors of audiences’ attitude toward virtual concerts. To address this issue, the current study proposes a conceptual model integrating player experiences’ factors (autonomy, relatedness, and engagement) and the technology acceptance model (perceived usefulness, perceived ease of use, and perceived enjoyment). An online questionnaire on virtual concert experiences was distributed to Chinese audiences who had attended virtual concerts. Structural equation modeling was then used to establish the relationship between variables. The results suggested autonomy, relatedness and engagement positively impacted perceived usefulness, perceived ease of use, and perceived enjoyment. Furthermore, the perceived usefulness, perceived ease of use, and perceived enjoyment were significant predictors of audiences’ attitude. The findings of this study can provide a reference for relevant virtual entertainment providers and can also serve as a point of development and exploration for technology acceptance model and player experience in the field of virtual concerts.

With the continuous improvement of people's quality of life and aesthetics, furniture products have higher requirements. Excellent solid wood chairs are the most representative products in furniture to enhance space and taste and whose design may significantly impact consumers' emo-tional experience and purchase decisions. This study aims to evaluate how the modeling imagery of solid wood chairs affects user preferences and emotional experiences. The development of the current era is inseparable from the analysis of big data. Firstly, a representative sample was ob-tained by multidimensional scaling (MDS), and the sample was analyzed and evaluated by fac-tor analysis. Moreover, five groups of adjective vocabulary were selected to describe the model-ing imagery of solid wood chairs, such as "balanced and coordinated", "unique and novel", "prac-tical and simple", "quality and detailed" and "traditional and plain". Further, the triangular fuzzy theory was applied to analyze and discuss the 12 types of solid wood chairs in the five groups of adjective vocabulary. Then the study verified that the differences in the evaluations of the 12 sam-ples in the "unique and novel" and "quality and detailed" groups were significant, but more minor differences in the groups of "traditional and plain", "balanced and coordinated" and "practical and simple". Through comprehensive comparison, five groups with similar modeling imagery were created, and solid wood chairs with different modeling imagery should be placed in suita-ble spaces. According to the results of this study, the evaluation of the modeling imagery of solid wood chairs can not only rely on subjective judgments but can be reasonably described through data analysis and mathematical algorithms. It can scientifically and effectively reflect the poten-tial perception needs of consumers on the modeling imagery of solid wood chairs, as well as help to improve the design efficiency of the furniture product development stage.

In this letter we investigate if SARS-CoV-2 RNA is involved in the increased ageing of alveolar cells. Our in silico study is explorative. With the results we are able to outline experiments with AEC2 repair of bleomycin damaged alveolar cells. If AEC2 repair capability is diminished by spike RNA then perhaps this result provides a first step on a route to treat immortal lung cancer cells.

Modified ammonium polyphosphate (MAPP) as a novel mono-component intumescent flame retardant (IFR) was prepared via the ionic exchange between ammonium polyphosphate (APP) and piperazine sulfonate, which is synthesized by self-assembly using 1-(2-Amioethyl) piperazine (AEP) and p-amino benzene sulfonic acid (ASC) as raw materials. This all-in-one IFR integrating three functional elements (carbon, acid, and gas source) showed more efficient flame retardancy and excellent smoke suppression as well as better mechanical properties than the conventional APP. The incorporation of 22.5 wt.% MAPP into polypropylene (PP) eliminated the melt dripping phenomenon and passed the UL-94 V-0 rating. The results of the cone calorimetry test (CCT) revealed that the release of heat, smoke, and CO is significantly decreased, demonstrating that this novel IFR endows PP with excellent fire safety more effectively. For PP/MAPP composites, a possible IFR mechanism was proposed based on the analysis of the pyrolysis gas and char residues.

The switched inertance hydraulic system (SIHS) is a novel high-bandwidth and energy-efficient digital device which can adjust or control flow and pressure by a means that does not rely on throttling the flow and dissipation of power. An SIHS can provide an efficient step-up or step-down of pressure or flow rate by using a digital control signal. In this article, analytical models of an SIHS in a four-port high-speed switching valve configuration are proposed, and the system dynamics and performance are investigated theoretically and experimentally. The flow responses, system characteristics and power consumption can be predicted effectively and accurately by using the proposed models, which were validated by comparing with experiments and with numerical simulation. The four-port configuration is compared with the three-port configuration, and it is concluded that the former one is less efficient for valves of the same size, but provides a bi-direction control capability. As bi-direction control is a common requirement, this constitutes an important contribution to the development of efficient digital hydraulics.