The protein extract of Ulva australis hydrolyzed with alcalase and flavourzyme investigated multi-functional properties, including total antioxidant capacity (TAC), collagenase inhibitory and antibacterial activities. The #5 fraction (SP5) and #7 fraction (SP7) of U. australis hydrolysate from cation-exchange chromatography showed significantly high TAC, collagenase inhibitory and antibacterial effects against Propionibacterium acnes, and only Q3 fraction from anion-exchange chromatography showed high multi-functional activities. The eight peptides among 42 peptides identified by MALDI-TOF/MS and Q-TOF/MS/MS were selected from the results to screen molecular docking on target proteins, and were synthesized. Thr-Gly-Thr-Trp (TGTW) showed ABTS [2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)] radical scavenging activity. Effect TAC as Trolox equivalence was dependent on the concentration of TGTW. The Asn-Arg-Asp-Tyr (NRDY) and Arg-Asp-Arg-Phe (RDRF) possessing collagenase inhibitory activity increased according to the increase of concentration and the IC50 values of them were 0.95 mM and 0.84 mM, respectively. Peptides, RDRF and His-Ala-Val-Tyr (HAVY) showed anti-P. acnes effects with IC50 values of 8.57 mM and 13.23 mM, respectively. These results suggest that the U. australis hydrolysate could be a resource for the application of effective nutraceuticals and cosmetics.

Background and Objectives: To assess femoral shaft bowing (FSB) in the coronal and sagittal planes and introduce the clinical implications of total knee arthroplasty (TKA) by analyzing a three-dimensional (3D) model with virtual implantation of the femoral component. Materials and Methods: Sixty-eight patients (average age: 69.1 years) underwent 3D model reconstruction, incorporating medullary canals, by importing computed tomography (CT) data into Mimics® software. A mechanical axis (MA) line was drawn from the midportion of the femoral head to the center of the intercondylar notch; proximal/distal straight centerlines (length, 60 mm; diameter, 1 mm) were placed in the center of the medulla canal. Acute angles between the two centerlines were measured as lateral and anterior bowing. The acute angle by distal centerline and MA line was measured as distal coronal and sagittal alignment in both anteroposterior (AP) and lateral views. The diameter of curve (DOC) of the medulla along the posterior border was measured. Results: The mean lateral bowing on the AP view was 3.71°, and the mean anterior bowing on the lateral view was 11.82°. The average DOC of the medullary canal was 1501.68 mm. The distal coronal alignment of all femurs was an average of 6.40°, and the distal sagittal alignment was 2.66°. Overall, 22 femurs had coronal bowing, 42 had sagittal bowing, and 15 had both. Conclusion: In Asians, FSB is present in coronal, sagittal, or both directions. Increased anterolateral FSB may lead to cortical abutment in the sagittal plane despite limited space in the coronal plane. During TKA, distal coronal alignment can guide distal femoral valgus cut angle, whereas distal sagittal alignment helps anticipate femoral component positioning, avoiding anterior notching. Osteotomy along the anterior cortical bone to prevent notching may result in outliers due to differences between distal sagittal alignment and distal anterior cortical axis.

Starting from an original survey conducted in eight countries in 2021 (Canada, China, Germany, Italy, Russia, Spain, UK, and USA), this research explores the relationship between household food waste and dietary habits in a cross-country comparative perspective. 8,000 questionnaires were recorded from samples representative of adult population of each country through an online survey conducted between the 13th and the 24th of August. The questionnaires were built on the work of Waste Watcher International Observatory on Food and Sustainability, an international observatory of social, behavioral and lifestyles dynamics behind household food waste. Relationship between per capita self-reported amount of food waste (expressed in kilocalories) and self-declared dietary habits (Traditional, Healthy and Sustainable, Vegetarian, Smart, Confused) was estimated using multiple linear regression models. Results show that Smart diets are associated with higher values of food waste in Canada, Spain, UK and USA. Vegetarian diets are associated to lower food waste values in China, Germany, UK and USA but not in Italy, Russia and Spain. Since the share of population adopting a Smart diet is on average 2.7% of the sample, interventions for food waste reduction should focus on this specific type of consumers, often associated to larger amounts of food waste.

Angiostrongylus cantonensis, a zoonotic parasite, can invade the human central nervous system (CNS) and cause acute eosinophilic meningitis or eosinophilic meningoencephalitis. Mice infected with A. cantonensis show elevated levels of pro-inflammatory cytokines, plasminogen activators, and matrix metalloproteinase-9, resulting in disruption of the blood-brain barrier (BBB) and immune cell infiltration into the CNS. Caveolin-1 (Cav-1) regulates the permeability of the BBB, which affects immune cells and cerebrospinal fluid. This intricate interaction ultimately fuels the progression of brain damage and edema. This study aims to investigate the regulatory role of Cav-1 in the pathogenesis of meningoencephalitis induced by A. cantonensis infection. We investigate pathological alterations by triphenyl-tetrazolium chloride, brain water content, BBB permeability, Western blot analysis, and gelatin zymography in BALB/c mice after A. cantonensis. The study evaluates the critical role of Cav-1 regulation through the TLR4 / MyD88 signaling pathway, modulates tight junction proteins, influences BBB permeability, and contributes to brain damage in A. cantonensis-induced meningoencephalitis.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

In the context of high-temperature water vapor treatment, Zn-Mg-Al alloy-coated steel sheets exhibit the emergence of a black surface. This study aims to explore the factors and mechanisms contributing to surface blackening by inducing black surfaces on Zn-Mg-Al alloy-coated steel sheets, which were fabricated through molten coating subjected to water vapor treatment at 150 degrees Celsius. The surfaced composition was predominantly identified as ZnO, a zinc oxide film, validated through X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Morphological analysis of the surface and cross-section post-water vapor treatment revealed a disrupted lamellar structure with diffused features, resulting in the formation of an oxide film. Optical properties analysis demonstrated an increased absorbance and a decreased bandgap energy after water vapor treatment, indicative of an augmented blackening effect. Consequently, the high-temperature water vapor treatment led to the formation of oxides on the surface, with the highly reactive Mg and Al extracting oxygen from the predominantly present ZnO surface. This process resulted in the creation of an oxygen-deficient oxide, ultimately causing surface blackening.

Button batteries pose a significant threat to young children and are found in many household items and children’s toys. Along with product manufacturers and retailers, parents and caregivers have a role in button battery ingestion prevention through vigilance regarding items containing button batteries in the home, and knowledge of button battery ingestion symptoms. There is limited information available about parent and caregiver perspectives towards button battery safety. Identifying the areas where increased education is required is important for informing safety campaigns and delivering targeted education. This study aimed to examine parent and caregiver elemental perspectives on button battery safety. One hundred and seventy-four parents and caregivers of children aged 0 to 5 completed a cross-sectional online survey. Distribution occurred via social media. The survey contained multiple-choice questions pertaining to button battery perspectives, and one open-ended question. Respondents recognized the dangers of button battery ingestion, yet only 37% reported being familiar with button battery ingestion symptoms. While 68% knew of household items containing button batteries, only 21% were aware of product recall information sources. Approximately 64% understood immediate post-ingestion steps and 17% found current safety labels unsatisfactory. In an open-ended question, parents and caregivers provided comments about button battery safety that revealed five broad themes; supervision and education; safe storage of button batteries in the home; ensuring children cannot access button batteries; disposing of button batteries safely; and avoiding purchasing items containing button batteries. This study provides a foundation for ongoing investigation of parent and caregiver perspectives on button battery home safety.

The onset of leaf senescence is triggered by external cues and internal factors such as phytohormones and signaling pathways involving transcription factors (TFs). Abscisic acid (ABA) strongly induces senescence and endogenous ABA levels are finely tuned by many senescence-associated TFs. Here, we report on the regulatory function of the senescence-induced TF OsWRKY5 TF in rice (Oryza sativa). OsWRKY5 expression was rapidly upregulated in senescing leaves, especially in yellowing sectors initiated by aging or dark treatment. A T-DNA insertion activation-tagged OsWRKY5-overexpressing mutant (termed oswrky5-D) promoted leaf senescence under natural and dark-induced senescence (DIS) conditions. By contrast, a T-DNA insertion oswrky5-knockdown mutant (termed oswrky5) retained leaf greenness during DIS. Reverse-transcription quantitative PCR (RT-qPCR) showed that OsWRKY5 upregulates the expression of genes controlling chlorophyll degradation and leaf senescence. Furthermore, RT-qPCR and yeast one-hybrid analysis demonstrated that OsWRKY5 indirectly upregulates the expression of senescence-associated NAC genes including OsNAP and OsNAC2. Precocious leaf yellowing in the oswrky5-D mutant might be caused by elevated endogenous ABA concentrations resulting from upregulated expression of ABA biosynthesis genes OsNCED3, OsNCED4, and OsNCED5, indicating that OsWRKY is a positive regulator of ABA biosynthesis during leaf senescence. Furthermore, OsWRKY5 expression was significantly suppressed by ABA treatment, indicating negative feedback regulation of OsWRKY5 expression by ABA. OsWRKY5 is a positive regulator of leaf senescence that upregulates senescence-induced NAC genes leading to expression of ABA biosynthesis and chlorophyll degradation genes.

Under the development mode of deep blending of emerging technologies, major projects are important for enhancing China's comprehensive national strength and independent innovation capability. Based on the social benefits of major projects, and considering the influence of reputation factors, an evolutionary game model composed of a general contractor and subcontractor of major projects is established. This paper studies the influence of various factors including the income distribution coefficient of collaborative innovation, and MATLAB simulation software is analyzed the influence of factors on an evolutionary path. The results show that a reasonable income distribution coefficient can promote the game behavior of both parties to the direction of active collaborative innovation; The reduction of innovation cost and the improvement of spillover technology absorption capacity can make innovation subjects tend to choose active collaborative innovation strategies. In addition, reputation loss above a specific threshold can effectively inhibit free-riding behavior and encourage innovation subjects to choose active collaborative innovation strategies. The research results provide reference for promoting major projects' technology innovation.

This study aimed to elucidate the antibacterial efficacy and unique chemical compositions generated from plasma-activated water (PAW) when utilized a novel vaginal cleansing device. We conducted an analysis of antibacterial activity against probiotics and several chemical compositions including ions under various operational parameters of PAW, including electrical power (12 and 24 V), treatment duration (1, 10, and 20 min), and stay duration (immediate, 30 and 60 min). Our findings revealed that as treatment duration increased, hypochlorous acid (HOCl), Ca2+, and Mg2+ concentrations increased and Cl−concentrations decreased. Higher electrical power and longer treatment duration resulted in increased HOCl levels, which acts to prevent the growth of general microorganisms. Notably, PAW exhibited no antibacterial effects against a type of probiotic, Lactobacillus reuteri that produces lactic acid for vaginal health. This result demonstrated the ability of the vaginal cleaning device to generate ions, primarily HOCl and some cations (Ca2+ and Mg2+), thereby providing vaginal mucosa protecting and cleansing effects with the vaginal environment.

As medical care aiming to maintain the quality of life for elderly patients is increases, implant treatment for elderly patients is increasing worldwide. Therefore, we aimed to investigate implant stability and the reliability of different measuring devices according to implant placement site and duration in patients aged over 65 years. The study evaluated 60 implants in 60 patients aged ≥ 65 years. The implant placement sites were divided into six evenly distributed sections (n=10) i.e., maxillary right- posterior, A; maxillary anterior, B; maxillary left-posterior, C; mandibular right-posterior, D; mandibular anterior, E; mandibular left-posterior, F. Participants visited the hospital seven times: preoperatively, 1V; implant surgery, 2V; stitch removal, 3V; 1-month follow-up, 4V; 2-months follow-up, 5V; before final-restoration delivery, 6V; and after final-restoration delivery, 7V. The implant stability was evaluated with the Osstell Mentor (ISQ), Periotest M (PTV), and Anycheck (IST). The mean values of ISQ, PTV, and IST were analyzed (α=.05). ISQ, PTV, and IST results of 5V and 6V were significantly higher than those of 2V (P<.05). The lowest ISQ results occurred in E location at 5V and 6V (P<.05). In all mandibular locations, IST results of 7V were significantly higher than those of 2V, 3V, 4V, and 5V (P<.05). ISQ results were negatively correlated with PTV and positively correlated with IST, and PTV was negatively correlated with IST. By considering various factors that affect implant stability, the appropriate implant load application time was determined. This could help increase the implant success rate in elderly patients. And as a diagnostic device for implant stability for evaluation of osseointegration in elderly patients, Anycheck was also able to prove its relative reliability compared to Osstell ISQ Mentor and Periotest M.

Third-generation biomass production utilizing microalgae exhibits sustainable and environmentally friendly attributes, along with significant potential as a source of physiologically active compounds. However, the process of screening and localizing strains that are capable of producing high-value-added substances necessitates a significant amount of effort. In the present study, we have successfully isolated the indigenous marine diatom Odontella aurita OAOSH22 from the East coast of Korea. Subsequently, we conducted a comprehensive analysis of its morphological, molecular, and biochemical characteristics. Afterwards, comprehensive analysis was conducted on the morphological, molecular, and biochemical characteristics, along with testing for optimal growth conditions. The morphological characteristics of the isolate were observed using optical and electron microscopes, and it exhibited typical features of O. aurita. Additionally, the molecular phylogenetic inference derived from the sequence of the small-subunit 18S rDNA confirmed the classification of the microalgal strain as O. aurita. This isolate has been confirmed to contain 7.1 mg g-1 dry cell weight (DCW) of fucoxanthin, a powerful antioxidant substance, in terms of dry cell weight (DCW). In addition, this isolate contains 11.1 mg g-1 DCW of eicosapentaenoic acid (EPA), which is one of the nutritionally essential polyunsaturated fatty acids. Therefore, this indigenous isolate exhibits a significant potential as a valuable source of bioactive substances for various bio-industrial applications.

We analyzed signal transduction of three constitutively activating mutants (M410T, L469R, and D590Y) and two inactivating mutants (D417N and Y558F) of the eel luteinizing hormone receptor (eel LHR), known to occur in human LHR. The objective of this study was to assess the functional effects of these mutations in signal transduction and cell surface loss of receptor. Mutant receptors were transiently expressed in Chinese hamster ovary (CHO-K1) cells. Eel LH-stimulated accumulation of cyclic adenosine monophosphate (cAMP) was measured by homogeneous time-resolved fluorescence (HTRF) assays. The loss of receptors from the cells surface was measured using an enzyme-linked immunosorbent assay (ELISA) in human embryonic kidney (HEK) 293 cells. The cAMP response in cells expressing the wild type eel LHR was increased in a dose-dependent manner using eel LH ligand stimulation. Compared with the wild type, cells expressing the activating mutants (M410T, L469R, and D590Y), exhibited a 4.0-, 19.1-, and 7.8-fold increase in basal cAMP response without agonist stimulation, respectively. Their maximal responses to agonist stimulation were approximately 65%, 52%, and 98%, respectively, of those of the wild type. The inactivating mutants (D417N and Y558F) did not completely impair signal transduction, and their maximal responses were only 33% and25 % of those of wild type. These data clearly showed that the eel LHR-L469R and D590Y, activating mutants enhanced the rate of the loss of cell surface receptors following treatment with eel LH. Thus, the loss of cell surface receptors in cells expressing mutant eel LHRs was consistent with the eel LH agonist-induced production of cAMP. Our results suggested that the activation of the eel LHR requires appropriate loss of LHR-ligand complexes from the cell surface.

In this study, we investigated the effects of Akebia quinata ethanol extract (AE), Akebia quinata sikhye (AS), and Akebia quinata water extract (AW) on alcohol-induced liver injury in rats. The hepatoprotective, anti-inflammatory, anti-apoptotic, and antioxidant effects of AE, AS, and AW were examined. Experimental animals were randomly divided into five groups: normal, ethanol, AE (10 mL/kg), AS (10 mL/kg), and AW (10 mL/kg). Each group was administered the respective treatment orally once per day for 21 days. CYP2E1 mRNA expression was significantly lower in the AE, AS, and AW groups than that in the ethanol group. Pro-inflammatory cytokines including cyclooxygenase-2, IL-6, and TNF-α increased significantly in the ethanol group but these increases were ameliorated with AE, AS, and AW treatment. Moreover, the expression of the apoptosis-associated proteins Bax, p53, procaspase-3, and PARP decreased after treatment with AE, AS, and AW. The expression of antioxidant enzymes including BCL-2, SOD, and GST slightly decreased in the ethanol group, but AE, AS, and AW treatment augmented their activities. AQ extracts and AS attenuated ethanol-induced increases in the levels of phosphorylated p-AKT, p-ERK, and p-JNK. These results demonstrate that AQ is a competence indicator for inhibiting alcoholic liver injury.

We evaluated oleanolic acid (OA)-induced anti-cancer activity, apoptotic mechanism, cell cycle status, and MAPK kinase signaling in DU145 (prostate cancer), MCF-7 (breast cancer), and U87 (human glioblastoma) cells. The IC50 values for OA-induced cytotoxicity were 112.57 in DU145, 132.29 in MCF-7, and 163.60 in U87 cells, respectively. OA (at 100 µg/mL) increased the number of apoptotic cells to 27.0% in DU145, 27.0% in MCF-7, and 15.7% in U87 cells, when compared to control cells. This enhanced apoptosis was due to increases in p53, cytochrome c, and Bax expression. OA-treated DU145 cells were arrested in G2 because of the activation of p-ERK and p21, and the decrease in cyclin B1 and cyclin E expression. Furthermore, OA-treated MCF-7 cells were arrested in G1 owing to the activation of p-JNK, p-ERK, p21, and p27, and the decrease in p-AKT, cyclin E, and CDK2. U87 cells also exhibited G1 phase arrest caused by the increase in p-ERK, p-JNK, p21, and p27, and the decrease in CDK2. Thus, OA arrests the cell cycle in different phases, and increases apoptosis in cancer cells. These results suggest that OA may alter the expression of cell cycle regulatory proteins in a cancer type-dependent manner.

This study aims to examine how the activation of nursing roles and professional identification as a nurse can influence moral judgments in terms of deontological and utilitarian inclinations. In Study 1, a priming technique was used to assess the impact of activating the nursing concept on moral reasoning. Participants were randomly assigned to either a nursing prime or neutral prime condition. By using a scrambled-sentences task, participants were prompted to think about nursing-related or neutral thoughts. Following the priming task, participants were asked to respond to 20 moral dilemmas. The process dissociation approach was employed to measure the degree of deontological and utilitarian tendencies in their moral reasoning. In Study 2, participants completed the nursing profession identification scale and the moral orientation scale before engaging in moral judgments similar to those in Study 1. The findings revealed that priming the concept of nursing roles resulted in an increase in deontological clinical inclinations, while having no significant effect on utilitarian inclinations. Additionally, a positive correlation was observed between identification with the nursing profession and deontological clinical inclinations, whereas a negative correlation was found with utilitarian inclinations. Deliberation orientation acted as a complete mediator in the relationship between nursing professional identification and deontological tendencies, and as a partial mediator for utilitarian tendencies.

This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.

Persons high in the manipulative, misanthropic trait Machiavellianism make use of various forms of deception to attain their goals. A recent study demonstrated that different facets of Machiavellianism account for different kinds of deception (Blötner & Bergold, 2023; https://doi.org/10.1111/bjso.12559). Intelligence could be another predictor of deception production. This research examined the interactions between different facets of Machiavellianism and verbal reasoning to predict the production of empty, vague pseudo-information that is supposed to help achieve desired states or prevent undesired ones (co-called “bullshit”). In a sample of 525 participants, and consistent with the hypotheses, individuals with high scores in the goal-oriented facet of Machiavellianism produced “bullshit” more frequently to achieve certain goals if they also scored high in verbal reasoning. Thus, and opposing common assumptions, Machiavellianism is not sufficient to explain engagement in deceptive behavior as it only reflects the motivation to do so. Especially in the case of verbally expressed deception, verbal skills are also required to be successful.

Luteinizing hormone receptor (LHR) is a member of the seven-transmembrane (TM) receptor family. Several mutations in LHR have been identified in many mammalian species, leading to either constitutive activation or inactivation of the receptor. Mutations in highly conserved regions of the TM domain have been reported. In this study, we analyzed signal transduction by three constitutively active mutants (designated M410T, L469R, and D590Y) and two inactivating mutants (D383N and Y546F) of eelLHR known as naturally occurring in human LHR . To directly assess the functional effects of these mutations, site-directed mutant receptors were transiently expressed in CHO-K1 cells and cAMP accumulation stimulated by recombinant eelLH (rec-eelLH) was measured by homogeneous time-resolved fluorescence (HTRF) assays. The cAMP response in cells expressing eelLHR wild-type (eLHR-WT) increased in a dose-dependent manner with rec-eelLH ligand stimulation. Cells expressing the activating eelLHR mutants, M410T, L469R, and D590Y, exhibited a 4.0-, 19.1-, and 7.8-fold increase in basal cAMP response, respectively. However, their maximal responses were approximately 73, 53, and 92%, respectively, of the maximal response of LHR-WT. The L469R mutant exhibited a particularly marked increase in cAMP production in the absence of agonist. The maximal responses of the inactivating mutants, D383N and Y546F, were 32 and 24% of LHR-WT, respectively. However, the inactivating mutations did not completely impair signal transduction. Thus, we report here the first characterization of activating and inactivating mutations in eelLHR and we show that these mutations have similar effects as those reported for mammalian LHRs. Moreover, eelLHR with activating mutations showed constitutive cAMP responses. These results provide important data on the signal transduction of constitutively active and inactive LHR mutants. Further studies should aim to identify the mechanism responsible for the significant increase in basal cAMP response in the constitutively activated eelLHR mutants.

This paper studied the solar PV frequency control in the U.S. Eastern Interconnection (EI) and Texas Interconnection (ERCOT) systems. The studied frequency control approaches include droop frequency control, inertia control, and droop-inertia-combined frequency control. The control effects of different frequency controls of PV in the EI and ERCOT are studied using actual high PV penetration interconnection grid models to provide suggestions to the future revision of future PV frequency control standards.

Health information technology has been widely used in healthcare, which has contributed a huge amount of data. Health data has four characteristics: high volume; high velocity; high variety and high value. Thus, they can be leveraged to i) discover associations between genes, diseases and drugs to implement precision medicine; ii) predict diseases and identify their corresponding causal factors to prevent or control the diseases at an earlier time; iii) learn risk factors related to clinical outcomes (e.g., patients’ unplanned readmission), to improve care quality and reduce healthcare expenditure; and iv) discover care coordination patterns representing good practice in the implementation of collaborative patient-centered care. At the same time, there are major challenges existing in data-driven healthcare research, which include: i) inefficient health data exchanges across different sources; ii) learned knowledge is biased to specific institution; iii) inefficient strategies to evaluate plausibility of the learned patterns and v) incorrect interpretation and translation of the learned patterns. In this paper, we review various types of health data, discuss opportunities and challenges existing in the data-driven healthcare research, provide solutions to solve the challenges, and state the important role of the data-driven healthcare research in the establishment of smart healthcare system.

Preprints are regularly cited in peer reviewed journal articles, books and conference paper. Are preprint citations somehow less important than citations to peer reviewed research papers? This study investigates citation patterns between 2017 and 2020 for preprints published in three preprint servers, one specializing in biology (bioRxiv), one in chemistry (ChemRxiv), and another hosting preprints in all disciplines (Research Square). As evaluation of scholarship continues to largely rely on citation-based metrics, this analysis and its outcomes will be useful to inform new research-based education in today’s scholarly communication.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

This letter introduces a frequency response characteristic (FRC) curve and its application in high renewable power systems. In addition, the letter presents a method for fast frequency response assessment and frequency nadir prediction without performing dynamic simulations using detailed models. The proposed FRC curve and fast frequency response assessment method are useful for operators to understand frequency response performance of high renewable systems in real time.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

This article illuminates the reception and transformation of Ippen一遍(1234-1289)’s religious tenets and practices by Enju Kato加藤円住 (1919-2021) and his temple Shōjōkō-ji清浄光寺. While Enju Kato asserts that his life has been dedicated to adhering and enacting Ippen's concept of "renunci-ation," he has reinterpreted this concept based on his personal experiences and comprehension. Second, Enju Kato and Shōjōkō-ji have inherited some of Ippen's religious practices yet adapted these to align with contemporary settings. This article presents a case study discussing the ac-ceptance and transformation of doctrines and practices of jishū時宗 in contemporary era, which has been overlooked by academia thus far.

Media thermal conductivity is important in various heat-transfer processes. Many conventional fluid conductors suffered low conductivity and environmental issues. Therefore, researchers developed efficient aqueous nanofluid conductors. Carbon nanotubes (CNTs) are widely studied to reach efficient, stable, low-cost and environmentally-friendly aqueous-nanofluids. This review provides a clear understanding of how CNTs improve thermal conductivity of aqueous nanofluids. A qualitative model is presented to explain literature mechanisms behind improvement. CNT type effects are discussed with other factors such as aspect ratio, Reynold number, dispersion quality, composition, temperature and additives. CNT functionalization is described. Relations to estimate nanofluid thermal conductivity are discussed. The model will help specialists to tailor CNT aqueous nanofluids as desired.

Introduction: In general, everyone believes that the smallest preterm infants should achieve normal intrauterine growth rates, but many thinks that this is not possible with current nutrition guidelines. There is resistance to giving enough nutrition for fear of "toxicity". The difference in weight Z-score between birth and a corrected gestational age (CGA) at discharge is assess in postnatal growth in our unit. Material and methods: An observational study was done between January 2018 and December 2020 where all cases that had ≤ 29 weeks of GA at birth and survived to 36 weeks corrected GA or that were discharged home. An aggressive nutrition protocol including parenteral as well as enteral nutrition was followed. Patients and their weight trajectory was plotted on the Fenton 2013 growth curve. The patients who had had a smaller WZP difference were also plotted. Results: A total of 32 cases were found. The median change in Z-score between birth and discharge of the whole group was -0.52 (IQR 0.53). Six of 32 (19%) had a more than one WZP, all of whom had severe pathologies. The median decline in Z score for this group with poor growth was 1.24 (IQR 0.22). There were 26 cases with a < 1 WZP (81%) and a median Z score fall of 0.39 (IQR 0.55). No important complications secondary to the ingested volumes or parenteral nutrition were reported. Conclusion: The group of cases with a > 1 WZP drop had severe pathologies. All the other cases had adequate growth parallel to normal weight growth charts and a few cases had some catch-up growth. The study showed that it is possible for many preterm infants to achieve normal intrauterine growth rates if they are given enough nutrition, but bigger multicenter studies are needed to confirm these findings.

Ongoing debates regarding the virtues and challenges of implementing open science for brain imaging research mirror those of the larger scientific community. The present commentary acknowledges the merits of arguments on both sides, as well as the underlying realities that have forced so many to feel the need to resist the implementation of an ideal. Potential sources of top-down reform are discussed, along with the factors that threaten to slow their progress. The potential roles of generational change and the individual are discussed, and a starter list of actionable steps that any researcher can take, big or small, is provided.

Low volatility ETFs have become popular in recent year because investors are starting to understand the advantages involved in investing in those ETFs compared to traditional index investment. In this research I have examined the performance of six popular low volatility ETFs, four of them invest in U.S stocks and the other two invest in other developed and emerging markets. Results have pointed out that not only five of the low volatility ETFs have exposed their investors to lower risk, four of them have also produced higher returns. The advantage of investing in low volatility ETFs is prominent in the U.S stock market. All three sizes of stocks categories (Large, Mid and Small Capitalization) low volatility ETFs have outperformed their indexes bench marks in terms of risk and returns. For the non U.S stock investing, while no advantage have been found for investing in low volatility ETFs for developed countries, significant lower risk was found for emerging markets.

In this study, cement is used as the component that provides the stabilizing effect in order to evaluate the hardness and stability of loess soil. To assess the strength characteristics of loess soil reinforced with cement, samples were created with four different cement contents and three different curing times. The materials were put through a series of tests to determine their flexural strength, direct shear strength, indirect tensile strength, and unconfined compressive strength. An appropriate cement dosage was found, in addition to a durability index that could be used to quantify the effect of water absorption investigations on cement-stabilized loess. Both of these discoveries were made simultaneously. Analyses of investigations such as scanning electron microscopy (SEM) and energy dispersive X-ray fluorescence spectrometry (XRF) examinations were carried out so that the fundamental mechanics of the materials could be comprehended. According to the findings, the cohesion of cement-stabilized loess is significantly more sensitive to structure than the friction angle of the material, and the cohesion is responsible for the increase in shear strength after remolding. To get the desired level of strength, it is necessary to adjust the cement's proportions. In addition, as the curing period progresses, we see an increase in the cement-stabilized loess's resistance and stiffness. This is because of the interactions that take place between the structure and the mineral composition. It is believed that this event was caused by the cementation that occurs naturally. As a consequence of this reaction, the production of new cementitious materials takes place. The cation exchange that causes the hydration and pozzolanic reaction that leads to the creation of aggregates and interparticle flocculation is responsible for their production. These findings suggest that cement may be utilized as a simple and effective method of loess stabilization, which will ultimately result in improved performance of the loess.

The “Crisis of Reproducibility” has received considerable attention both within the scientific community and without. While factors associated with scientific culture and practical practice are most often invoked, I propose that the Crisis of Reproducibility is ultimately a failure of generalization with a fundamental scientific basis in the methods used for biomedical research. The Denominator Problem describes how limitations intrinsic to the two primary approaches of biomedical research, clinical studies and pre-clinical experimental biology, lead to an inability to effectively characterize the full extent of biological heterogeneity, which compromises the task of generalizing acquired knowledge. Drawing on the example of the unifying role of theory in the physical sciences, I propose that multi-scale mathematical and dynamic computational models, when mapped to the modular structure of biological systems, can serve a unifying role as formal representations of what is conserved and similar from one biological context to another. This ability to explicitly describe the generation of heterogeneity from similarity addresses the Denominator Problem and provides a scientific response to the Crisis of Reproducibility.

This article explores the apologetic discourse towards the convergence between Pure Land Buddhism and science of the contemporary Chinese Buddhist monk Da An (1959- ). Faced with the formidable challenge posed by contemporary science towards public acceptance of Buddhism, Da An endeavors to employ science as a medium for propagating Pure Land teachings, seeking to accommodate the preferences of scientifically inclined individuals. He utilizes several theoretical physics concepts to validate that certain ostensibly miraculous accounts within Pure Land Buddhist texts are not incompatible with contemporary science. Further, he asserts that certain supernatural narratives therein indicate that some physical principles still residing in theoretical conjectures on Earth have already been validated and can be utilized at will by the celestial beings in Sukhāvatī, thus asserting the supremacy of Pure Land teachings over science. The article endeavors to examine the contemporary dialogue between Pure Land Buddhism and science, which has been overlooked in the current research realm of the interaction between Buddhism and science.

Scientific research in the study of Human-Robot Interactions (HRI) has advanced significantly in recent years. Social humanoid robots have undergone severe testing and have been implemented in a variety of settings, including educational institutions, healthcare facilities and senior care centers. Humanoid robots have also been evaluated across diverse population groups. However, the research on different children-groups is still scarce, especially deaf children. This feasibility study includes both hearing children and deaf children, and aim to assess the interaction between children and a humanoid robot without the use of sounds and voices. In addition, an experiment was conducted where the children were asked to watch a video and then assess whether the NAO humanoid robot responded to the emotions shown in the video in a congruent or incongruent way. The findings indicated that there was no difference between the children who were present-ed with congruent emotions in the NAO as opposed to the children who were presented with in-congruent emotions in the robot. Only the ability to predict emotion in videos and gender were significant predictors of guessing the correct emotion in robot. Although no significantly differ-ence was found between hearing and deaf children, this feasibility study is intended to lay the groundwork for future research in this unexplored field of study.

Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behavior. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics, and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here we review and clarify the key concepts of Connectivity, Computation, Criticality, and Coherence — the 4C’s — and outline a potential role for metastability as a common denominator across these propositions. We analyze and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging (fMRI), to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.

Inorganic polymeric materials react slowly at room temperature and as a result usually require high-temperature curing. This study used the Arrhenius equation to analyze the correlation between curing temperature and curing duration during high-temperature curing. The test results show that optimal values exist for each alkali equivalent of the activator (weight ratio of Na2O/glass powder), curing temperature, and curing duration. Extending the curing duration and increasing the curing temperature have positive effects when the alkali equivalent is lower than the optimal value. However, over-curing results in invisible cracking in the specimens. Furthermore, despite exhibiting high strength initially, the strength of specimens gradually diminishes after standing in air. To ensure the durability of glass-based geopolymer, the curing temperature should not exceed 70℃, and the curing duration should be less than one day.

Due to the high solar irradiance or energy price, certain regions in the U.S. may reach 100% PV penetration and experience degradation of frequency response greater than the interconnection as a whole. Therefore, in this section, the 100% PV penetration region in each interconnection is simulated to study the local high PV penetration effects. The study was performed by quantifying RoCoF, frequency nadir, and settling frequency at different regional PV penetration levels. The impact of high regional PV penetration on the compliance of grid code on frequency response is also studied.

The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, the problems such as knowledge loss still occur during the process of transfer learning to a new target dataset. To solve these problems, there are various regularization methods based on knowledge distillation techniques. In this paper, we propose a transfer learning regularization method based on feature map alignment used in the field of knowledge distillation. The proposed method is composed of two attention based submodules: self-pixel attention (SPA) and global channel attention (GCA). The self-pixel attention submodule utilizes both the feature maps of the source and target models, so that it provides an opportunity to jointly consider the features of the target and the knowledge of the source. The global channel attention submodule determines the importance of channels through all layers, unlike the existing methods that calculate these only within a single layer. Accordingly, transfer learning regularization is performed by considering both the internal of each single layer and the depth of the entire layer. Consequently, the proposed method using both of these submodules showed overall improved classification accuracy than the existing methods in classification experiments on commonly used dataset.

Artificial Intelligence based on Deep Learning is opening new horizons in Biomedical research and promises to revolutionize the Microscopy field. Slowly, it now transitions from the hands of experts in Computer Sciences to researchers in Cell Biology. Here, we introduce recent developments in Deep Learning applied to Microscopy, in a manner accessible to non-experts. We overview its concepts, capabilities and limitations, presenting applications in image segmentation, classification and restoration. We discuss how Deep Learning shows an outstanding potential to push the limits of Microscopy, enhancing resolution, signal and information content in acquired data. Its pitfalls are carefully discussed, as well as the future directions expected in this field.

In bilingual communities, social interactions take place in both single- and mixed-language con-texts. Some of the information shared in multilingual conversations is often required in conse-quent social encounters, like interlocutors’ personal information. In this study we explored whether the autobiographical information provided in a single-language context is better re-membered than in an equivalent mixed-language situation. More than 400 Basque-Spanish bilin-gual (pre-)teenagers were presented with new persons who introduced themselves by either using only Spanish or only Basque, or by inter-sententially mixing both languages. Different memory measures were collected immediately after the initial exposure to the new pieces of information (immediate recall and recognition) and on the day after (delayed recall and recognition). In none of the time points was the information provided in a mixed-language fashion worse remembered than that provided in a strict one-language context. Interestingly, the variability across partici-pants in their sociodemographic and linguistic variables had a negligible impact on the effects. These results are discussed considering their social and educational implications for bilingual communities.

.Purpose: This paper presents a comparative analysis of state-of-the-art DIR algorithms in the context of head and neck squamous cell carcinoma (HNSCC) radiation therapy. Materials and Methods: We used a dataset of head and neck cancer patients within the Cancer Image Archive, which includes 31 patients with 3D CT image data from pre-treatment, mid-treatment, and post-treatment stages of radiotherapy. We applied DIR algorithms to two datasets, one for pre-treatment to mid-treatment registration and another for mid-treatment to post-treatment registration, by aligning one CT image to another CT image. To quantitatively analyze the patient data, we focused on the spinal cord, optic nerve, brain stem, cochlea, and PTV as the OAR datasets. To verify the DIR algorithm, we used two non-learning-based methods, SyN and NiftyReg, as well as three deep learning-based methods, Voxelmorph, Cyclemorph, and Transmorph. We trained a deep learning model by adjusting the ratio of the training dataset, validation dataset, and test dataset to 7:1:2. Results: The average DSCs of SyN and NiftyReg, the non-learning-based methods, were 0.74±0.06 and 0.70±0.12, respectively. The deep learning-based methods, Voxelmorph, Cyclemorph, and Transmorph, had average DSCs of 0.72±0.08, 0.68±0.11, and 0.69±0.13, respectively. The deep learning DIR algorithm produced transformed outputs of OAR/PTV in a shorter time than other models, including commercial and conventional mathematical algorithms (At Inference(sec/images), Voxelmorph: 0.36, Cyclemorph: 0.3, Transmorph: 5.1, SyN: 140, NiftyReg: 40.2). Conclusions: In conclusion, our study provides a comprehensive comparison of commercial DIR algorithms with traditional and deep learning-based DIR methods. Our results demonstrate that commercial DIR algorithms outperform traditional DIR methods in terms of accuracy and efficiency. However, we also show that deep learning-based DIR methods have the potential to achieve similar performance to commercial algorithms with proper training and optimization. Our findings suggest that both commercial and deep learning-based DIR methods have their respective advantages and limitations, and the choice of method should depend on the specific requirements of the clinical application. Overall, our study contributes to ongoing efforts to improve the accuracy and efficiency of DIR methods for better patient outcomes.

As climate changes, our daily life has been much influenced by abnormal meteorological phenomena such as heavy rainfall, heat wave, heavy snowfall, and fine dust. Atmospheric air quality is worsening day by day and indoor air quality is also affected by interconnected daily activities throughout the inside and outside of buildings and houses. Nowadays, pollutants from various sources are emitted, transformed by sunlight, vapor, and ozone and transported into the city from country to country. Due to these reasons, there have been high demands to monitor the transportation of particulate matters and improve air quality. Monitoring of pollutants and identification of type and its concentration enables us to track and control its generation and consequently find out the solution. However, monitoring of pollutants, especially, particulate matter generation and its transportation is still not fully operated in atmospheric air due to its open nature and meteorological factors. Even though indoor air is relatively easy to monitor and control than outdoor in the aspect of specific volume and contaminant source, but it still needs to consider the meteorological parameters because indoor air is not fully separated from the outdoor air flow and contaminants transportation. In this study, optical approach using spectral sensor was attempted to reveal the feasibility of wavelength and chromaticity values of reflected light from specific particles. From the analysis of reflected light of various particulate matters according to different liquid additives, parameter studies were performed to investigate which experimental conditions can contribute to the enhanced selective sensing of particulate matters. Five different particulate matters such as household dust, soil, talc powder, gypsum powder and yellow pine tree pollen were utilized and observed to elucidate the relationship between property of particulate matter and detected light spectrum. Applicable approaches to assist current particle matter sensors and improve the selective sensing were suggested.

Convolution, recurrent and attention-based deep learning techniques have produced the most recent state-of-the-art results in multiple sensor-based human activity recognition (HAR) datasets. However, these techniques have high computing costs, restricting their use in low-powered devices. Different methods have been employed to increase the efficiency of these techniques; however, this often results in worse performance. Recently, pure MLP architectures have demonstrated competitive performance in vision-based tasks with lower computation costs than other deep-learning techniques. The MLP-Mixer is a pioneering pure MLP architecture that produces competitive results with state-of-the-art models in computer vision tasks. This paper shows the viability of the MLP-Mixer in sensor-based HAR. Furthermore, experiments are performed to gain insight into the Mixer modules essential for HAR, and a visual analysis of the Mixer’s weights is provided, validating the Mixer’s learning capabilities. As a result, the Mixer achieves an F1 score of 97%, 84.2%, 91.2% and 90% on the PAMAP2, Daphnet Gait, Opportunity Gestures and Opportunity Locomotion datasets, respectively, outperforming state-of-the-art models in all datasets except Opportunity Gestures.

Background/Aims: Conflicting results have been reported regarding the interaction between proton pump inhibitors (PPIs) and clopidogrel. We investigated whether concomitant PPI use influenced the risk of recurrence in patients with stroke and myocardial infarction (MI). Methods: This study used two databases for different designs, the Korean National Health Insurance Service (NHIS) database for a self-controlled case series design, and the national sample cohort of the NHIS database converted to the Observational Medical Outcomes Partnership-Common Data Model version for a cohort study. Results: In the PPI co-prescription group, recurrent hospitalization with stroke occurred in 17.6% of the 8,201 patients with history of stroke, and recurrent MI occurred in 17.1% of the 1,216 patients with history of MI during 1 year. According to the self-controlled case series, the overall relative risk (RR) of recurrent stroke was 2.09 [95% confidence interval (CI); 1.83-2.38], and the RR showed an increasing trend with PPI duration. In the cohort study, there was a higher incidence of recurrent stroke in the PPI co-prescription group [Hazard ratio (HR): 1.34, 95% CI: 1.01-1.76, P=0.04]. The overall RR of recurrent MI was 1.47 (95% CI; 1.02-2.11) in the self-controlled case series; however, there was no statistically significant difference in recurrent MI in the cohort study (HR:1.42, 95% CI:0.79-2.49, P=0.23). The impact of individual PPIs on stroke and MI showed different patterns. Conclusions: PPI co-prescription with clopidogrel was associated with recurrent stroke; however, its association with recurrent MI remains inconclusive. The influence of individual PPIs should be clarified in the future.

Macrophages exhibit impaired phagocytosis, adhesion, migration, and cytokine production in space, hindering their ability to elicit immune responses. Considering that the combined effect of spaceflight microgravity and radiation is multiscale and multifactorial in nature, it is expected that contradictory findings are common in the field. This theory paper reanalyzes research on the macrophage spaceflight response across multiple timescales from seconds to weeks, and spatial scales from the molecular, intracellular, extracellular, to the physiological. Key findings include time-dependence of both pro-inflammatory activation and integrin expression. Here, we introduce the time-dependent, intracellular localization of MRTF-A as a hypothetical confounder of macrophage activation. We discuss the mechanosensitive MRTF-A/SRF pathway dependence on the actin cytoskeleton/nucleoskeleton, microtubules, membrane mechanoreceptors, hypoxia, oxidative stress, and intracellular/extracellular crosstalk. By adopting a multiscale perspective, this paper provides the first mechanistic answer for a three-decade-old question regarding impaired cytokine secretion in microgravity—and strengthens the connection between the recent advances in mechanobiology, microgravity, and the spaceflight immune response. Finally, we hypothesize MRTF involvement and complications in treating spaceflight-induced cardiovascular, skeletal, and immune disease.

Improper disposal of waste plastic has caused serious ecological and environmental pollution problems. Transforming plastics into high value-added chemicals can not only achieve efficient recycling of waste plastics, but also an effective way to control white pollution. The catalyst selectively breaks the C-C bond of polyolefin plastic under heat treatment and converts it into liquid fuel, thus realizing sustainable recycling of plastics and has a good development prospect. This review provides a detailed overview of the current development of catalytic pyrolysis, catalytic hydrolysis, solvent decomposition and supercritical hydrothermal liquefaction for cracking plastics to make fuel oil. The reaction mechanism, influencing factors and promoting effects of catalysts in various degradation technologies are analyzed and summarized and the latest proposed tandem reaction for degrading plastics is briefly introduced. Finally, some optimization paths of waste plastic pyrolysis to fuel oil technology are proposed: synergies between mixed raw materials, in-depth exploration of catalysts, design and manufacture of reactors that match the pyrolysis technology. All these are important research directions for promoting the industrialization of plastic pyrolysis to fuel oil.

Atta capiguara is a grass-cutting ant species frequently found in Cerrado biome. However, little is known about the giant nest architecture of this ant. In this study, we investigated the architecture of three A. capiguara nests from the fragment of cerrado in Botucatu, São Paulo, Brazil. Casts were made of the nests by filling them with cement to permit better visualization of internal structures such as chambers and tunnels. After excavation, the depth and dimensions (length, width, and height) of the chambers were measured. The results showed the typical shape of Atta capiguara nests consisting of mounds of loose soil with unique features resembling a conic section. The fungus chambers were found outside the apparent main part of the nest and were spaced apart and distributed laterally at ground level. The waste chambers were located beneath the largest mound of loose soil. Both the fungus and waste chambers exhibited a sectoral distribution. Our study contributes to a better understanding of the so far unknown nest architecture of the grass-cutting ant A. capiguara.

3D Zernike moments based on 3D Zernike polynomials have been successfully applied to the field of voxelized 3D shape retrieval and have attracted more attention in biomedical image processing. As the order of 3D Zernike moments increases, both computational efficiency and numerical accuracy decrease. Due to this phenomenon, a more efficient and stable method for computing high-order 3D Zernike moments was proposed in this study. The proposed recursive formula for computing 3D Zernike radial polynomials combines the recursive calculation of spherical harmonics to develop a voxel-based algorithm for the calculation of 3D Zernike moments. The algorithm was applied to the 3D shape Michelangelo's David with a size of 150×150×150 voxels. As compared to the method without additional acceleration, the proposed method uses a group action of order sixteen orthogonal group and saving unnecessary iterations, the factor of speed-up is 56.783±3.999 when the order of Zernike moments is between 10 and 450. The proposed method also obtained an accurate reconstructed shape with the error rate (normalized mean square error) of 0.00 (4.17×10^-3) when the reconstruction was computed for all moments up to order 450.

With the increasingly online exposure of urban public spaces, the new concept of "Internet-celebrity cities" has emerged in China. The online internet-famous sites are the main characteristics of "Internet-celebrity cities". To fully understand online users' preferences in such kind of public spaces, this article took 27 typical riverfront internet-famous sites (RIFS) in Changsha city (China), as an example. Through the online images and review comments on social media platform "Xiaohongshu", the study analyzed users’ perception of RIFS specially on visual and emotional preferences. It revealed the following findings:(1) The popularity of RIFS had a significant head effect and there were far more positive emotions than neutral and negative emotions in review comments. (2) According to 5 different categories of RIFS: modern buildings with river views, restaurants with strong visual impact became the main attractions on commercial RIFS. The art exhibition RIFS attracted visitors to experience traditional culture and highlight their aesthetic pursuits. In the historical and cultural RIFS, people preferred to see the historical buildings with surrounding natural environment, as well as river and mountains views. Ecological Recreational RIFS showed the similar results with the uncultivated RIFS, for the main focus was on the ecological environment, a broader vision and the specific atmosphere. Moreover, the uncultivated RIFS showed more diversified attraction points. This study can provide a new perspective on the waterfront vitality and can offer a targeted and attractive manner for waterfront regeneration that are different from traditional methods.

Consumption of refined foods and beverages high in sugar make the teeth susceptible to the formation of biofilm, and lead to dental caries and diseases of the oral cavity such as periodontitis and periodontal disease. The aim of the present study was to determine the ability of selected probiotics to inhibit growth and biofilm formation by the cariogenic bacterium Streptococcus mutans in vitro. We screened strains of latic acid bacteria (LAB) (n=120) from the Bioresources Collection and Research Center (BCRC), saliva of healthy adults and infant stool. The antimicrobial activity of LAB in vitro was evaluated by agar spot culture and co-culture of the S. mutans strains. We determined the effect of heating and dilution factors (2- and 4-fold) on the antagonistic activity of LAB. Antagonistic substances in the spent culture suspensions (SCS) of LAB were precipitated by extraction with ammonium sulphate and chloroform to characterise the protein and lipophilic fractions. Results of co-culturing show that the SCS of the three LAB strains (Lactobacillus pentosus 13-1, 13-4 and Lactobacillus crispatus BCRC 14618) subjected to heat treatment showed significantly high antimicrobial activity. We found that substances produced by L. pentosus 13-4 which have the potential to exhibit antimicrobial properties might be lipophilic proteins. Additionally, we infer that the mechanism of reducing biofilm formation by Lactobacillus strains is associated with sucrose-dependent cell–cell adhesion and the gtfC level of glucosyltransferases (Gtfs) in the biofilm. Native LAB strains screened in our study may be used in chewing gums and other processed foods for preventing tooth decay.

The emergence of diverse infections worldwide, which is a serious global threat to human existence, necessitate the urgent development of novel therapeutic candidates that can combat these diseases with efficacy. Isatin, a secondary metabolite from tryptophan is considered a privileged scaffold and favorable pharmacophore with a unique structural moiety and varied chemical and biological properties resulting in its widespread applications by medicinal chemists. Molecular hybridization is one of the most common and important techniques use in the synthesis of their bioactive hybrids in drug discovery. This review containing published articles from 2005 to 2022 focuses on isatin hybrids which have been synthesized and reported in the literature alongside a discussion on their biological properties. The purpose of this review is to set up the direction for the design and development of isatin-hybrids with tailored biological properties as effective therapeutic candidates inspired by nature.

When you type” Iodine” in google scholar, you will end up with 2,200,000 articles that are titled with anything regarding iodine, except “what is it”. Here in this short note review, a summary of iodine that includes its origin, structure, types, sources, uses, concentration, toxicity, and iodine intake and how to measure it, was collected and infused in a nutshell.

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

In mesoscale eddies, chemical properties and biological composition are different from those in surrounding water, due to their unique physical processes. It is unclear how physical-biological coupling occurs in warm-core eddies, particularly since no study has examined the relationship between bacteria and viruses. The purpose of the present study was to examine the influence of anticyclonic warm eddy on the relationship between bacterial and viral abundances as well as viral activity (viral production) in different depths. In the core of warm eddy, bacterial abundance (0.48 to 2.82 ×105 cells mL−1) fluctuated less than that of the outside eddy (1.12 to 7.03×105 cells mL−1). In particular, there was four-folds higher of viral-bacterial abundance ratio (VBR) estimated within the eddy below DCM than the outside eddy. We suggest that prevailing physical forces with downwelling at the center of the anticyclonic warm eddy, viruses can also be transmitted into the deep ocean directly through adsorption on particulate organic matter during sinking. Overall, our findings provide valuable insights into the interaction between bacterial and viral communities and their ecological mechanism within the warm eddy.

This paper gives solutions to puzzles of double Wheeler’s delayed choice experiments, a photon’s passing two paths simultaneously and wave collapse velocity measure, and shows new quantum control, new quantum oscillation and new controlling photon oscillator (that can be arranged in quantum computer and quantum network as their classical correspondences) in experiments. Furthermore, this paper obtains the measured attractive state and the new useful key technology of altering state’s evolving paths for quantum systems. We discover the reason and the technological way for the present changes the past, further show their feasible experiments of new quantum control, new quantum oscillation and new quantum oscillator. Finally, this paper achieves the collapse response time t of wave, further concretely deduces the measured collapse velocity of quantum system. In the past, the collapse velocity cannot be objectively deduced and experimentally measured.

Semenov-Tian-Shansky has introduced the modified classical Yang-Baxter equation, which is called the modified $r$-matrix. Relevant studies have been extensive in recent times. This paper is devoted to study cohomology theory of modified Rota-Baxter pre-Lie algebras and its applications. First we introduce the concept and representations of modified Rota-Baxter pre-Lie algebras. We then develop the cohomology of modified Rota-Baxter pre-Lie algebras with coefficients in a suitable representation. As applications, we consider the infinitesimal deformations, abelian extensions and skeletal modified Rota-Baxter pre-Lie 2-algebra in terms of lower degree cohomology groups.

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.

In this paper, we define the cohomology of a modified $\lambda$-differential left-symmetric algebra with coefficients in a suitable representation. We also introduce the notion of modified $\lambda$-differential left-symmetric 2-algebra. We classify linear deformations and abelian extensions of modified $\lambda$-differential left-symmetric algebras using the second cohomology group and classify skeletal modified $\lambda$-differential left-symmetric 2-algebra using the third cohomology group as our propose cohomology applications. Moreover, we prove that strict modified $\lambda$-differential left-symmetric 2-algebras are equivalent to crossed modules of modified $\lambda$-differential left-symmetric algebras.

Objective: To study the factors affecting the time of umbilical cord shedding in premature infants.Methods: A total of 151 premature infants who were born in the obstetrics department of a Class III hospital in Hefei from November 2020 to June 2021 were selected as subjects. All premature infants and their families met the complete inclusion criteria. A total of 33 related factors from 3 aspects, including the birth situation of premature infants, the condition of premature infants in hospital and the condition of pregnant mothers, were collected for univariate analysis and multiple Logistic regression analysis. The relevant factors affecting the umbilical cord shedding time of premature infants were screened through the univariate analysis results, and the main factors affecting the umbilical cord shedding time of premature infants were screened through the multiple Logistic regression analysis results.it was found that the weight, gestural age, admission age, whether the premature infants were combined with NRDS, the use of antibiotics, the duration in the warm box, the initial temperature in the warm box, the duration of light therapy, the degree of Apgar for 1 minute, the maternal education level had an effect on the time of umbilical cord shedding of premature infants (P < 0.05). Through Logistic multiple regression analysis, it was screened that the duration of premature infants in the incubator was the main factor affecting the umbilical cord shedding time of premature infants (P < 0.05).Conclusion: Factors such as premature infants' weight, gestural age, admission age, whether they are combined with NRDS, antibiotic use, duration in the incubator, initial temperature in the incubator, duration of phototherapy, Apgar level of 1 minute, and maternal education level will affect the umbilical cord shedding time of premature infants. Among them, duration in the incubator is the main factor affecting the umbilical cord shedding time of premature infants.

Ehretia laevis Roxb. (Boraginaceae) has been extensively used as a traditional remedy for the treatment of a diverse range of ailments related to the respiratory system, the gastrointestinal tract, the reproductive system, and against several infections. This review critically assesses and documents, for the first time, the fragmented information on E. laevis, including its botanical description, folklore uses, bioactive phytometabolites and pharmacological activities. The goal is to explore this plant therapeutically. Ethnomedicinal surveys reveal that E. laevis has been used by tribal communities in Asian countries for the treatment of various disorders. Quantitative and qualitative phytochemical investigations of E. laevis showed the presence of important phytoconstituents such as pentacyclic triterpenoids, phenolic acids, flavonoids, fatty acids, steroids, alkaloids, aliphatic alcohols, hydrocarbons, amino acids, carbohydrates, vitamins and minerals. Fresh plant parts, crude extracts, fractions and isolated compounds have been reported to exhibit broad spectrum of therapeutic activities viz., antioxidant, antiarthritic, antidiabetic, anti-inflammatory, antiulcer, antidiarrheal, antidysenteric, wound healing and anti-infective activities. E. laevis is shown to be an excellent potential source of drugs for the mitigation of jaundice, asthma, dysentery, ulcers, diarrhea, ringworm, eczema, diabetes, fissure, syphilis, cuts and wounds, inflammation, liver problems, venereal and infectious disorders. Although few investigations authenticated its traditional uses but employed uncharacterized crude extracts of the plant, the major concerns raised are reproducibility of therapeutic efficacy and safety of plant material. The outcomes of limited pharmacological screening and reported bioactive compounds of E. laevis suggest that there is an urgent need for in-depth pharmacological investigations of the plant.

This paper uncovers that quantum uncertain principle makes the single particle with global property have no certain path, and then wave of quantum particle can simultaneously do pass the double slits. The two subwaves after passing Young’s double slits are entanglement, they may form interference of subwaves. Consequently, we find a kind of quantum probabilistic entanglements with Wheeler's delayed choice. Quantum particles such as photons, electrons, neutrons, protons etc mean that wave of the quantum particle can simultaneously do pass through Young's double slits, rather than individual quantum particle may pass through Young's double slits at the same time. When considering wave property, we cannot consider particle property (Just as in the photoelectric effect, considering the particle nature of the system, people cannot consider wave property, otherwise the photoelectric effect cannot appear). Therefore, this paper discovers that the ability of single photon to hit electrons out in photoelectric effect is complementarily equivalent to the ability of wave of the single photon to simultaneously pass through Young's double slits in wave-particle duality. Objective criteria for distinguishing classical and quantum particles are discovered and objectively give the applicable realm of quantum mechanics for the first time. The crisis of the single particle’s simultaneously passing through Young's double slits, which has been plaguing physicists in the whole world up to now for decades, is solved, in which the studies are classified as classical and quantum particles, the classical particle and quantum particle wave cannot and can pass the Young’s slits, respectively. This paper discovers both the new physics mechanism of passing the double slits of the wave with the amplitude of 4-dimensional momentum representation wave function reflecting particle nature and the principle of self-adaptive emergence of wave-particle duality, and then using the principle, this paper gives both direct explanations to the current experiments and new predictions of new some experiments for wave-particle duality. All the deduced results here are consistent with all relevant physics experiments.

A stable driver state is essential in the process of manually transition control, which inevitably occurs in Level 3 automated driving situations. To this end, this paper proposed a CNN algorithm-based driver state monitoring system that uses multi-sensor data such as driver's face image, biometric information, and vehicle behavior information as input. This system calculates the probability of drowsiness for each of the four time periods using a convolutional neural network (CNN) based on ToF camera-based eye blinking, ECG information (pulse rate) embedded in the steering wheel, and vehicle information (steering angle data). In order to build a reliable and high-quality training dataset (Ground Truth) for the CNN algorithm, a baseline was established by matching the driver's face image with the electrocardiogram (ECG) and electroencephalogram (EEG) changes in the drowsy and normal states. In a simulation test of the proposed CNN algorithm using more than 20,000 driver image data acquired using a driving simulator, the TNR was 94.8% and the accuracy was 94.2%. Our proposed method is expected to minimize human errors that may occur when switching control by monitoring inappropriate driver state (drowsiness) in real time.

Deep learning technologies for skin cancer detection have been dramatically advanced based on high resolution dermoscopic images. The low-cost approach based on clinical skin images of low resolution is promising but remains technically challenging due to their undermined image quality. In this paper, we propose a coarse-to-fine efficient super resolution transformer (CF-ESRT) network to reconstruct the dermoscopy-level high resolution skin image from a low resolution clinical image. By connecting the refinement network to the original super resolution transformers and applying perceptual and gradient losses, our framework noticeably improves the finer texture details of skin lesions in the super resolution (SR) images, and is effective to elevate the perceptual quality of the SR images. Quantitative and qualitative evaluations show that our method outperforms ESRT the basis model as well as the other state-of-the-art SR models.

In this paper, industrial structure distortion, two-way FDI and carbon emission intensity are brought into a unified research framework, and based on China's panel data from 2011 to 2020, empirical tests are conducted employing Exploratory Spatial Data Analysis (ESDA), spatial econometric model and intermediary effect test. The results show the following. Firstly, China's industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west, followed by the middle, and the lowest in the East. Secondly, the relationship between carbon emission intensity and economic development shows a "decoupling" effect and keeps decreasing year by year. The spatial disparity is remarkable, showing the pattern of "the east leading, the middle catching up and the west lagging ". At the provincial level, except in Xinjiang province, the carbon emission intensity of other provinces showed different degrees of decline. In terms of spatial distribution, the polarization characteristics of carbon emission intensity are significant, and the traditional spatial distribution pattern has been broken. Thirdly, there is a positive spatial correlation between China's industrial structure distortion, two-way FDI and carbon emission intensity. The distortion of industrial structure will not only lead to the increase of local carbon emission intensity but also produce reverse spillover to adjacent areas. IFDI and OFDI provide a strong driving force for the decline of carbon emission intensity. IFDI promotes the decline of carbon emission intensity in adjacent areas, while OFDI will increase the carbon emission intensity in surrounding areas. The interaction of IFDI and OFDI can significantly reduce the carbon emission intensity of local and adjacent areas. Fourthly, the results of intermediary effect analysis show that two-way FDI is the two channels of industrial structure distortion affecting carbon emission intensity. Industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI.

This study analyses 749 drawings of five female Bornean orang-utans (Pongo pygmaeus) at Tama Zoological Park in Japan. We searched for differences between individuals but also tried to identify possible temporal changes among the drawings of one individual, Molly, who drew almost 1,300 drawings from 2006 to 2016. An analysis of the drawings was carried out after collecting quantitative and qualitative variables. Our findings reveal evidence of differences in the drawing style of the five individuals as well as creative changes in Molly’s drawing style throughout her lifetime. Individuals differed in terms of the colours used, the space they filled but also the shapes (fan patterns, circles or loops) they drew. Molly drew less and less as she grew older, and we found a significant difference between drawings produced in winter, when orang-utans were kept inside and had less activity, and those produced during other seasons. Our results suggest that the drawing behaviour of these five orang-utans is not random and that differences among individuals might reflect differences of styles, states of mind but also motivation to draw.

Ecology is working to face its colonial roots and institutional inequities. As we build more diverse, equitable, and inclusive (DEI) institutions we must work to support new ecologists by empowering them with the knowledge and tools to succeed. Undergraduate research experiences (UREs) are critical for a student’s professional and interpersonal skill development and key for recruiting more diverse groups of students to ecology. Here, we highlight DEI dimensions of a URE in ecology, acknowledge safety considerations for field ecology, including harassment and assault, and provide tools to support the URE. This is written primarily for all URE students and secondarily for their advisors. We welcome students from underrepresented groups and encourage allyship from students from non-underrepresented groups. After reading this paper, we hope that all students feel more confident and excited about their URE and that advisors see how to improve DEI in their lab.

We study the Gaudin models with gl(1|1) symmetry that are twisted by a diagonal matrix and defined on tensor products of polynomial evaluation gl(1|1)[t]-modules, extending all the results of to the twisted case. Namely, we give an explicit description of the algebra of Hamiltonians (Gaudin Hamiltonians) acting on tensor products of polynomial evaluation gl(1|1)[t]-modules and show that there exists a bijection between common eigenvectors (up to proportionality) of the algebra of Hamiltonians and monic divisors of an explicit polynomial written in terms of the highest weights and evaluation parameters. In particular, our result implies that each common eigenspace of the algebra of Hamiltonians has dimension one. We also give dimensions of the generalized eigenspaces.

This paper derives measurement and identical principles, then makes the two principles into measurement and identical theorems of quantum mechanics, plus the three theorems derived earlier, we deduce the axiom system of current quantum mechanics, the general quantum theory no axiom presumptions not only solves the crisis to understand in current quantum mechanics, but also obtains new discoveries, e.g., discovers the velocities of quantum collapse and entanglement are instantaneously infinitely large. We deduce the general Schrȍdinger equation of any n particles from two aspects, and the wave function not only has particle properties of the complex square root state vector of the classical probability density of any n particles, but also has the plane wave properties of any n particles. Thus, the current crisis of the dispute about the origin of wave- particle duality of any n microscopic particles is solved. We display the classical locality and quantum non-locality for any n particle system, show entanglement origins, and discover not only any n-particle wave function system has the original, superposition and across entanglements, but also the entanglements are of interactions preserving conservation or correlation, three kinds of entanglements directly give lots of entanglement sources. This paper discovers, one of two pillars of modern physics, quantum mechanics of any n particle system is a generalization ( mechanics ) theory of the complex square root ( of real density function ) of classical statistical mechanics, any n particle system’s quantum mechanics of being just a generalization theory of the complex square root of classical statistical mechanics is both a revolutionary discovery and key new physics, which are influencing people’s philosophical thinking for modern physics, solve all the crisises in current quantum theories, quantum information and so on, and make quantum theory have scientific solid foundations checked, no basic axiom presumption and no all quantum strange incomprehensible properties, because classical statistical mechanics and its complex square root have scientific solid foundations checked. Thus, all current studies on various entanglements and their uses to quantum computer, quantum information and so on must be further updated and classified by the new entanglements. This and our early papers derive quantum physics, solve all crisises of basses of quantum mechanics, e.g., wave-particle duality & the first quantization origins, quantum nonlocality, entanglement origins & classifications, wave collapse and so on.Key words: quantum mechanics, operator, basic presumptions, wave-particle duality, principle of measurement, identical principle, superposition principle of states, entanglement origin, quantum communication, wave collapse, classical statistical mechanics, classical mechanics

The COVID-19 pandemic has caused ~ 2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interrogated at local Mexico City level by choropleth mapping. Our data show that the use of hexagonal cartograms is a better approach for spatial mapping of COVID-19 data in Mexico as it addresses bias in area size and population. We report sex/age-related spatial relationship with mortality amongst the Mexican states and a trend between health conditions and mortality at the state level. Within Mexico City, there is a clear south, north divide with higher mortality in the northern municipalities. Deceased patients in these northern municipalities have the highest pre-existing health conditions. Taken together, this study provides an improved presentation of COVID-19 mapping in Mexico and demonstrates spatial divergence of the mortality in Mexico.

As the natural resources are getting exhausted, the concept of sustainable development of region has received increasing attentions, especially for resource-dependent cities. In this paper an innovative method that emergy analysis and IPAT (Human Impact Population Affluence Technology) model were combined in order to analyze the quantitative relationship of economic growth and energy consumption and further to evaluate its overall sustainability level. Taiyuan, a traditional, resource-dependent city in China, is selected as the case study region. The main results show that total emergy of Taiyuan increased from 9.023× 1023 sej in 2007 to 9.116× 1023sej in 2014, with 38% reduction on non-renewable emergy and 125% growth on imported emergy. Regional emergy money ratio (RMB) was reduced by 48% from 5.31× 1013sej/$ in 2007 to 2.74× 1013sej/$ in 2014, indicating that the increasing speed of consuming resources and energy was faster than the increase of GDP, and that Taiyuan’s money purchasing power declined. The lower emergy sustainability index (ESI) indicates that Taiyuan was explored and produced large quantities of mineral resources, which puts more stress on the environment as a consequence, and that this is not sustainable in the long run. The IPAT analysis demonstrates that Taiyuan sticks to the efforts of energy conservation and environmental protection, in order to promote regional sustainable development, it is necessary to take an integrated effort. Policy insights suggest that resourceful regions should improve include energy and resource efficiency, optimizing energy and resourceful structure.

With the outbreak of coronavirus disease 2019 (COVID-19), there is a need for efficient management of patients with mild or no symptoms, which account for the majority. The aim of this study is to introduce the structure and operation protocol of a living and treatment support centre (LTSC) operated by Seoul National University Hospital in South Korea. The existing accommodation facility was converted into a 'patient centre' where patients was isolated. A few Medical staff here performed medical tests and responded to emergencies. Another part of the LTSC was 'remote monitoring centre'. In this center, patients’ self-measured vital signs and symptoms were monitored twice a day, and the medical staff staying here provided video-consultation via a smartphone. During the 3 weeks from March 5 to March 26, 2020, 113 patients were admitted and treated. LTSC could be an efficient alternative to hospital admission in pandemic situation like COVID-19.

This research provides a computationally efficient numerical tool for predicting 3-D turbulent flows over hydrofoils. It addresses a gap in existing numerical methods by extending the capabilities of the laminar VIScous Vorticity Equation (VISVE) solver to handle 3-D turbulent flow scenarios. The implementation integrates the k − ω SST model into the 3-D VISVE solver using the Finite Volume Method (FVM), extending its capability to handle turbulent flows. The turbulence model is also parallelized to improve computational efficiency. The application of the newly developed model to 3-D hydrofoil cases expands its scope and serves as preparatory work for future rotational propeller applications. Rigorous testing includes a convergence analysis concerning grid numbers and time step sizes, and validation against a Reynolds-Averaged Navier-Stokes (RANS) solver ensures the reliability and accuracy of the enhanced VISVE solver for turbulent flow simulations. The turbulent VISVE solver offers advantages such as reduced computational domain and costs through a vorticity-based approach. Turbulence concentration within boundary layers and free shear flows does not compromise the advantages of the small computational domain. The simplified meshing process, automatically generated by denoting the number of panels on the hydrofoil, makes the solver accessible for researchers and engineers.

Ensuring safe, high-quality food is an ongoing priority, yet consumers face heightened risk from foodborne pathogens due to extended supply chains and climate change in food industry. Nanomaterial-based assays are popular and have recently been developed to ensure food safety and high quality. This review discusses strategies for using nanomaterials in colorimetric biosensors. Localized surface plasmon resonance (LSPR) biosensors are commonly utilized for colorimetric biosensing. Several emerging technologies aimed at simple and rapid immunoassays for onsite applications have been introduced in the food industry. In the foreseeable future, field-friendly colorimetric biosensors could be adopted in food monitoring systems. The onsite and real-time detection of possible contaminants and biological substances in food and water is essential to ensure human health safety.

Quantitative trait loci (QTL) are genomic regions associated with phenotype variation of quantitative traits in a population. To date, a total of 267 QTL for 29 quantitative traits have been reported in 13 studies on flax. Of these, 200 QTL from 12 studies were identified based on genetic maps, scaffold sequences, or pre-released chromosome-scale pseudomolecules. Molecular markers for QTL identification differed across studies but were mainly based on simple sequence repeat (SSR) or single nucleotide polymorphism (SNP) markers. This article provides methods with software tools and database files to uniquely map SSR and SNP markers from different references onto the recently released chromosome-scale pseudomolecules. Using these methods, 195 QTL were successfully sorted onto the 15 flax chromosomes and grouped into 133 co-located QTL clusters. Mapping of QTL from different studies to the same reference enables comparisons and facilitates genome-wide QTL analysis, candidate gene scanning, and breeding applications.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Blood image intensity has been used to detect erythrocyte sedimentation rate (ESR). However, it does not give an information on biophysical properties of blood sample under continuous ESR. In this study, three physical properties, including, τ₀, ESR_τ, and AI_I, are suggested to quantify mechanical variations of blood under continuous ESR. To demonstrate the proposed method, blood sample is loaded into a driving syringe. The blood flow rate is set in a periodic on–off pattern. Blood sample is then supplied into a capillary chip, and microscopic blood images are captured at specific intervals. Blood shear stress is quantified from the interface of blood stream in the coflowing channel. Both τ₀ and ESR_τ are then obtained by analyzing the blood shear stress. Simultaneously, the AI_I is evaluated by analyzing the image intensity of blood flow. According to the experimental results, the τ₀ exhibits consistent trends with respect to hematocrit as well as diluent. The ESR_τ and AI_I showed a reciprocal relationship each other. Three suggested properties represented substantial differences for suspended blood samples (i.e., hardened red blood cells, different concentration of dextran solution and fibrinogen). In conclusion, the present method can detect variations of blood sample under continuous ESR effectively.

Interior permanent magnets motors are widely used in applications requiring high power den-sity and high efficiency due to their high torque generating capabilities. Recently, given the re-cent price fluctuations and unstable supply of rare-earth permanent magnets, alternative config-urations with reduced use of permanent magnets are being sought. Among the various candi-dates related to this, the consequent-pole type rotor structure can halve the number of perma-nent magnets used compared with conventional structures. However, in the no-load analysis, the waveform of the back electromotive force becomes asymmetric, generating a harmonic component. As a result, there is a disadvantage that the torque ripple increases. To overcome these shortcomings, we propose a novel rotor structure that applies a consequent-pole structure to an embedded permanent-magnet motor structure, wherein a number of permanent magnets are arranged in a flared structure to constitute a single polarity. In the proposed flared-structured magnet arrangement, it is possible to adjust the angle of the permanent magnet and the polar angle to mitigate the asymmetry of the back EMF waveform. The proposed struc-ture was optimized with a genetic algorithm and a prototype of the optimal model was con-structed and experimentally evaluated to verify its validity. Finally, the performance improve-ment and validity of the proposed structure were verified by comparing the analysis results of the optimal model with the experimental results.

This comprehensive study delves into the implementation and effectiveness of the flipped classroom model in engineering EMI (English as a Medium of Instruction) courses in Taiwan. Situated within the strategic framework of Taiwan's Bilingual Nation 2023 policy, this research aims to enhance the global competitiveness of Taiwan's semiconducting manufacturing engineering sector. The study’s primary focus is on understanding the perceptions and learning outcomes of EFL (English as a Foreign Language) students in engineering courses. Through a methodical approach using descriptive statistics and chi-square tests, the study analyzes data from 33 junior engineering students specializing in mechanical and mechatronic fields. The findings highlight a significant positive influence of the flipped classroom model on various aspects of student learning, including increased engagement, heightened creativity, and enhanced problem-solving skills. Additionally, the study emphasizes the critical role of timely L1 support in facilitating effective learning in EMI settings. These outcomes not only demonstrate the flipped classroom's viability as a sustainable educational approach but also provide crucial insights for educators and policymakers. By aligning cutting-edge teaching methods with the evolving needs of engineering education, this study contributes to the discourse on educational innovation. It ultimately guides the development of future engineering programs that cater to diverse student needs and fosters the cultivation of skilled engineering professionals.

Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

A road network represents road objects in a given geographic area and their interconnections, and is an essential component of intelligent transportation systems (ITS) enabling emerging new applications such as dynamic route guidance, driving assistance systems, and autonomous driving. As the digitization of geospatial information becomes prevalent, a number of road networks with a wide variety of characteristics coexist. In this paper, we present an area partitioning approach to the conflation of two road networks with a large difference in level of details. Our approach first partitions the geographic area by the Network Voronoi Area Diagram (NVAD) of low-detailed road network. Next, a subgraph of high-detailed road network corresponding to a complex intersection is extracted and then aggregated into a supernode so that a high matching precision can be achieved via 1:1 node matching. To improve the matching recall, we also present a few schemes that address the problem of missing corresponding object and representation dissimilarity between these road networks. Numerical results at Yeouido, Korea's autonomous vehicle testing site, show that our area partitioning approach can significantly improve the performance of road network matching.

This paper focuses on the non-destructive dielectric measurement for low-loss planar materials with a thickness of less than 3 mm using a large coaxial probe with an outer diameter of 48 mm. The aperture probe calibration procedure required only to make a measurement of the half-space air and three offset shorts. The reflection coefficient for the thin material is measured using a Keysight E5071C network analyzer from 0.3 MHz to 650 MHz and then converted to a relative dielectric constant and tangent loss via closed form capacitance model and lift-off calibration process. Measurement error of dielectric constant, Δε_r is less than 2.5 % from 1 MHz to 400 MHz and the resolution of loss tangent, tan δ measurement is capable of achieving 3×10^-3.

As the most serious of the many worse new pathological changes caused by diabetes, there are many risk factors for the occurrence and development of DR. It mainly includes hyperglycemia, hypertension, hyperlipidemia and so on. Among them, hyperglycemia is the most critical cause, which plays a key role in the pathological changes of DR.High-sucrose diets(HSDs) lead to elevated blood glucose levels in vivo, which, through oxidative stress, inflammation, the production of AGEs and VEGF, causes plenty of pathological damages to the retina and ultimately bring about loss of vision. The existing therapies for DR primarily target the terminal stage of the disease, when irreversible visual impairment has appeared. Therefore, early prevention is particularly critical. Early prevention of DR-related vision loss requires adjustments to dietary habits, mainly by reducing sugar intake. This article primarily discusses the risk factors, pathophysiological processes, and molecular mechanisms associated with the development of DR caused by HSDs. It aims to raise awareness of the crucial role of diet in the occurrence and progression of DR, promote timely changes in dietary habits, prevent vision loss, and improve the quality of life. The aim is to make people aware of the importance of diet in the occurrence and progression of DR. According to the dietary modification strategies we give, patients can change their poor eating habits timely, avoid the retinopathy that can be avoided theoretically, and obtain a excellent outcome.

This paper introduces in general the conservation work of the Nara Palace Site in Japan carried out by Japanese conservation experts. Concerning conservation theory, this paper focused on the international conventions in which the 1994 Nara Document on Authenticity plays an important role in contributing to Japanese cultural heritage conservation achievements with the expansion of Cultural diversity and Heritage diversity, the Values and Authenticity conceptions which proposed a new orientation in terms of awareness and openness broader vision in identifying architectural heritage values. Regarding, based on the 1994 Nara Document on Authenticity, the 1964 Venice Charter, and the 1999 Bura Charter, we try to propose a new concept of the so-called “Architectural Heritage DNA” and its potential structure, providing a simple formula for the authentic values appraisal of the heritage conservation works. In addition, we also try to supplement the concept of Inheritance between Conservation and Development which is the lack of international conventions of cultural heritage conservation. The viewpoints expressed in this paper are based on the most common concepts of international conventions but interpreted in a new-understandable, practical, and highly applicable way possible to the current socio-political situation and in the future, as well.

An easy to implement and cost-effective Fiber Bragg Grating (FBG) sensor interrogation technique based on a ring Erbium-Doped Fiber Laser (EDFL) topology is proposed and experimentally assessed. The FBG sensor is part of the EDFL cavity and must have a central wavelength located within the linear region of the EDF’s amplified spontaneous emission (ASE) spectrum which occurs between 1530 and 1540nm. In this manner, the wavelength-encoded response of the FBG under strain is converted to a linear variation of the laser output power, removing the need for spectrum analysis as well as any limitations from the use of external edge-filtering components. In addition, the laser linewidth is significantly reduced with respect to the FBG bandwidth thus improving the resolution of the system, whereas its sensitivity can be controlled through the pumping power. The performance of the system has been characterized by modeling and experiments for EDF's with different lengths, doping concentrations and pumping power levels. The influence of mode-hopping in the laser cavity on the resolution and accuracy of the system has also been investigated.

In this paper, a new axial-type magnetic gear with auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AFS-AMG, it has the higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterpart in RMGs, AFS-AMGs achieve small size. Then, the geometrical parameters of AFS-AMGs are optimized to obtain the better electromagnetic performances, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

The key issue in the post-genomic era is how to systematically describe the association between small molecule transcripts or translations inside cells. With the rapid development of high-throughput “omics” technologies, the achieved ability to detect and characterize molecules with other molecule targets opens up the possibility of investigating the relationships between different molecules from a global perspective. In this article, a Molecular Associations Network(MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attributes of the node itself (e.g. sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the manner of the node in the complex network (associations with other nodes). Random Forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieves a superb performance with 0.9735 AUC in 5-fold cross-validation, which show that the proposed method can provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment.

Parasitic diseases continue represent a threat on a global scale, particularly among the poorest countries in the world. This is particularly because of the absence of vaccines, and in some cases, resistance against available drugs, currently being used for their treatment. In this review emphasis is laid on natural products and scaffolds from African medicinal plants (AMPs) for lead drug discovery and possible further development of drugs for the treatment of parasitic diseases. In the discussion, emphasis has been laid on alkaloids, terpenoids, quinones, flavonoids and narrower compound classes of compounds with micromolar range activities against Schistosoma, Trypanosoma and Leishmania species. Suggestions for future drug development from African medicinal plants have also been provided.

Landiolol is an ultra-short-acting, selective ß1- adrenergic receptor blocker that was originally approved by Japan for the treatment of intraoperative tachyarrhythmias. It has gained attention for its use in the management of tachyarrhythmias and perioperative tachycardia, especially atrial fibrillation for both cardiac and non-cardiac surgeries. It can be the ideal agent for heart rate control due to its high β1‐selectivity, the potent negative chronotropic effect, a limited negative inotropic potential, and an ultrashort elimination half‐life (around 4 min); moreover, it may have a potential therapeutic effect for sepsis and pediatric patients. Landiolol seems to be superior to other short‐acting and selective beta‐blockers such as esmolol. This review aims to provide a comprehensive overview of landiolol, a new ultra-short-acting ß1 selective antagonist, including its pharmacology, clinical applications, efficacy, safety profile, and future directions in research and clinical data.

With the rise of the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), an abundant amount of trajectory data has made research on location prediction more popular. The check-in data shared through LBSN hides information related to life patterns, obtaining this information is helpful for location prediction. However, the trajectory data recorded by mobile devices is different from check-in data that has semantic information. In order to obtain the user’s semantic, relevant studies match the stay point to the nearest Point of Interest (POI), but location error may lead to wrong semantic matching. Therefore, we propose A Self-Attention for Next Location Prediction based on Semantic Mining to predict the next location. When calculating the semantic feature of a stay point, the first step is to search for the k nearest POI, then use the reciprocal of the distance from the stay point to the k nearest POI and the number of categories as weights. Finally, we use the probability to express semantic without losing other important semantic information. Furthermore, this research combined with sequential pattern mining can bring richer semantic features. In order to better perceive the trajectory, temporal features learn periodicity of time series by sine function. In terms of location features, we build a directed weighted graph and regard the frequency of users visiting locations as the weight, so the location features are rich in contextual information. We then adopt the Self-Attention model to capture long-term dependencies in long trajectory sequences. Experiments in Geolife show that the semantic matching of this study improved by 45.78% in TOP@1 compared with the closest distance search for POI. Compared with the baseline, the model proposed in this study improved by 2.5% in TOP@1.

Heat shock proteins (HSPs) are a family of molecular chaperones that regulate essential protein refolding and triage decisions to maintaining protein homeostasis. Numerous co-chaperone proteins directly interact and modify the function of HSPs, and these interactions impact the outcome of protein triage, impacting everything from structural proteins to cell signaling mediators. The chaperone/co-chaperone machinery protects against various stressors to ensuring cellular function in the face of stress. However, coding mutations, expression changes, and post-translational modifications of the chaperone/co-chaperone machinery can alter the cellular stress response. Importantly, these dysfunctions appear to contribute to numerous human diseases. Therapeutic targeting of chaperones is an attractive but challenging approach due to the vast functions of HSPs, likely contributing to the off-target effects of these therapies. Current efforts focus on targeting co-chaperones to develop precise treatments for numerous diseases caused by defects in protein quality control. This review focuses on the recent developments regarding selected HSP70/HSP90 co-chaperones, focusing on cardioprotection, neuroprotection, and cancer. We also discuss therapeutic approaches that highlight both the utility and challenges of targeting co-chaperones.

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to obtain the execution status, jump directions, and jump address of conditional branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of updates to the branch target buffer (BTB). When it exceeds the threshold, BTB is locked to prevent attackers from analyzing the execution status of branches based on the time difference of whether BTB is updated. Moreover, the hybrid physical unclonable function (PUF) circuit is designed to provide confidentiality protection for the jump directions, jump addresses, and their indexes, preventing attackers from stealing these critical data. If these mechanism fails and attackers successfully tamper with conditional branches, this paper proposes a control flow integrity (CFI) protection mechanism based on branch labels to timely detect tampering with instruction codes, jump addresses, and jump directions. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead.

In this paper, a semi-empirical model of air waves induced by falling rock is described. The model is composed of a uniform motion phase (velocity close to 0 m·s^-1) and an acceleration movement phase. The uniform motion phase was determined based on experimentally and the acceleration movement phase was derived by theoretical analysis. A series of experiments were performed to verify the semi-empirical model and elucidated the law of the uniform motion phase. The acceleration movement phase accounted for a larger portion with a greater height of the falling rock. Experimental results of different falling heights of the goaf showed close agreement with theoretical analysis values. The semi-empirical model could accurately and conveniently estimate the velocity of air wave induced by falling rock. Thus, the semi-empirical model can provide a reference and basis for estimating the speed of air waves and designing protective measures in mines.

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.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium-sulfur batteries in the second. These results provide insights into the establishment of R&D cooperation strategies by countries and pre-planning by companies in the battery industry.

Abstract: Molybdenum (Mo) thin films were sputtered from two kinds of Mo targets with different microstructures under the same sputtering process, and the effect of microstructure of Mo target on morphology, deposition rate and resistance of sputtered film were studied and discussed. The results show that morphological differences between Mo thin films sputtered by Mo targets with different microstructures are very small. The more uniform and finer the grain structures of Mo target, the better the uniformity on thickness and resistance of Mo sputtered film. Moreover, during sputtering process, when Mo target’s grain size is finer and the surface area of grain boundary is higher, the thickness reduction of the target is more homogeneous and the sputtering film has faster deposition velocity. The difference in microstructure of the Mo target has not obvious influence on the grain orientation of sputtering film.

The primary attraction of IaaS is providing elastic resources on demand. It becomes imperative that IaaS-users have an effective methodology for learning what resources they require, how many resources and for how long they need. However, the heterogeneity of resources, the diversity resource demands of different cloud applications and the variation of application-user behaviors pose IaaS-users big challenge. In this paper, we purpose a unified resource demand forecasting model suiting for different applications, various resources and diverse time-varying workload patterns. With the model, taking input from parameterized applications, resources and workload scenarios, the corresponding resources demands during any time interval can be deduced as output. The experiments configure concrete functions and parameters to help understanding the above model.

The “Pay-as-you-throw” scheme, PAYT, is an economic instrument of waste management that implements the “polluter pays” principle by charging inhabitants of municipalities according to the amount of residual, organic and bulky waste they send for third-party waste management. When combined with well-developed infrastructure to collect the different waste fractions (residual waste, paper and cardboard, plastics, bio waste, green cuttings, many recyclables) as well as with a good level of citizen’s awareness, its performance has frequently been linked to high collection rates of recyclables. However, the establishment and operation of PAYT systems can require significant resource inputs from municipalities. In this paper, PAYT is analysed through a case study from the German County of Aschaffenburg, covering nearly 20 years of implementation across 173,000 inhabitants. Key performance indicators applied include temporal trends in the county’s recyclables collection rate, waste treatment fees for residents and municipal waste management costs, benchmarked against German municipalities not implementing PAYT.