A diffusion welding of coarse-grained and ultrafine-grained (UFG) specimens of titanium near-α alloy Ti-5Al-2V used in nuclear power engineering was made by Spark Plasma Sintering. The failure of the welded specimens in the conditions of hot salt corrosion and of electrochemical corrosion were shown to have preferentially intercrystalline character. In the case of presence of macrodefects, crevice corrosion of the welded joints was observed. The resistance of the alloys against the intercrystalline corrosion was found to be determined by the concentration of vanadium at the titanium grain boundaries, by the size and volume fraction of the β-phase particles and by the presence of micro- and macropores in the welded joints. The specimens of the welded joints of the UFG alloy have higher hardness, hot salt corrosion resistance and the electrochemical corrosion.

Uncultured, unmodified, autologous, adipose-derived regenerative cells (UA-ADRCs) are a safe and effective treatment option for various musculoskeletal pathologies. However, it is unknown whether the composition of the final cell suspension systematically varies with the subject's individual age, sex, body mass index and ethnicity. UA-ADRCs were isolated from lipoaspirate from n=232 subjects undergoing elective lipoplasty using the Transpose RT system (InGeneron, Inc.; Houston, TX, USA). The UA-ADRCs were assessed for the number of nucleated cells, cell viability and the number of viable nucleated cells per gram of adipose tissue harvested. Cells from n=37 subjects were further characterized using four-channel flow cytometry. The present study shows, for the first time, that key characteristics of UA-ADRCs can be independent of the subject's age, sex, BMI and ethnicity. This result has important implications for the general applicability of UA-ADRCs in regeneration of musculoskeletal tissue. Future studies must determine whether in-dependence of key characteristics of UA-ADRCs of the subject's individual age, sex, BMI and eth-nicity only applies to the system used in the present study, or also to others of the more than 25 different experimental methods and commercially available systems used to isolate UA-ADRCs from lipoaspirate that have been described in the literature.

Worldwide, depressive disorder is one of the leading determinants of disability-adjusted life years. Although the benefits associated with physical activity (PA), there is a lack of information related to depression, especially in countries like Czechia, where modern approaches to mental health care only recently emerged. The PA levels were associated with aspects of depression such as clinician-diagnosed history; different severities; continuous depression scores; and specific symptoms that characterize the depression. The multivariable-adjusted Poisson regression models were carried out on 2123 participants (45.3% men, median 48 years). Compared to subjects with insufficient PA, the moderate and high PA levels were inversely associated with clinician-diagnosed depression history (respectively, prevalence rate [PR]= 0.84; 95% CI 0.66-0.82 and PR=0.50; 95% CI 0.36-0.67); and with continuous depression scores (PR=0.85; 95% CI 0.75-0.97; and PR=0.79; 95%CI 0.70-0.90).; but only high PA showed association with depression categories (PR=0.75; 95%CI 0.60-0.95). Depressed mood and worthlessness were the symptoms associated with moderate and high PA. Tiredness, change in appetite, and problems with concentration only with high PA. Although only high PA was sufficient for people intending changes among depression categories, the moderate PA may be enough for slight changes in depressive symptoms, and a good strategy when starting.

(1) Background: When the Omicron variant of SARS-CoV-2 first emerged in Germany in January 2022, data on related disease severity among children and adolescents was not yet available. Given Omicron’s high transmissibility, the ability to assess its impact on admission and hospitalization rates in children’s hospitals is critical for the purpose of understanding the scope of its burden on the German health care system. (2) Methods: From January 24, 2022 to July 31, 2022, SARS-CoV-2 cases admitted to German pediatric hospitals were monitored via a national, clinician-led reporting system (CLRS) established by the German Society for Pediatric Infectious Diseases (DGPI). Cases treated on general wards and intensive care units, as well as patient age and need for respiratory support were recorded. (3) Results: From January to July 2022, a median of 1.7 cases (range 0.4–3) per reporting pediatric hospital per day were hospitalized on general wards, whereas a median of 0.1 cases (range 0–0.4 cases) were on intensive care units. Of all hospitalized patients, 4.2% received respiratory support. (4) Conclusions: Despite the high incidence rates documented in connection with the Omicron variant in early 2022, the number of pediatric hospital admissions, and especially the number of cases with need for intensive care treatment and respiratory support due to a symptomatic SARS-CoV-2 infection, remained relatively low. Higher Omicron incidence rates had only a modest impact on SARS-CoV-2-related admissions and hospitalization in German children’s hospitals.

We assessed true bugs among above-groundand and soil inhabitants of four different biocoenosis of pine forests representing the intra-zone areas of flora and fauna of the Kazakh upland. 15 species of three families were listed for the Central Kazakhstan region for the first time. Species Eremocoris podagricus was not registered for the territory of Kazakhstan earlier. Fifteen species (50 %) were true above-ground inhabitants or ones living on grasses, whereas other species use soil surface and ground litter as temporary habitats. Drymus brunneus and Eremocoris fenestratus play a key role in the structure and function of the true bugs assemblages in the in biotopes studied. The ecological success of typically forest and boreal inhabitants in biotopes of arid region is explained by relict nature of forests and ancient connections with the taiga zone of Western Siberia and mountain forests of the Urals and Altai. The results of a comparative analysis of the similarities and differences of the dominant and ecological structures of Heteroptera complexes of aboriginal studied forests and derived type of forest are presented.

Background: In the present study, we assessed the feasibility and success outcomes of voice prosthesis (VP) changes when performed by speech-language pathologist (SLP). Methods: Patients treated with total laryngectomy (TL) from January 2020 to December 2020 were prospectively recruited from our medical center. Patients benefited from tracheoesophageal puncture. The VP changes were performed by the senior SLP and the following data were collected for each VP change: date of placement; change or removal; VP type and size; reason for change or removal; and use of a washer for periprosthetic leakage. A patient-reported outcome questionnaire including 6 items was proposed to patients at each VP change (Appendix 1). Items were assessed with a 10-point Likert-scale. Results: Fifty-two VP changes were performed by the senior SLP during the study period. The mean duration of the SLP consultation, including patient history, examination and VP change procedure was 20 min (range: 15-30). The median prosthesis lifetime was 88 days. The main reasons for VP changes were transprosthetic (N=34; 79%) and periprosthetic (N=7; 21%) leakages, respectively. SLP successfully performed all VP changes. He did not change one VP but used a periprosthetic silastic to stop the periprosthetic leakages. In two cases, SLP needed the surgeon examination to discuss about the following indication: implant mucosa inclusion and autologous fat injection. The patient satisfaction was high according to the speed and the quality of care by the SLP. Conclusion: The delegation of VP change from the otolaryngologist-head and neck sur-geon to the speech-language pathologist (SLP) may be done without significant complications. The delegation of VP change procedure to SLP may be interesting in some rural regions with otolaryngologist shortage.

Ovarian cancer is a deadly disease attributed to late-stage detection as well as recurrence and development of chemoresistance. Ovarian cancer stem cells (OCSCs) are hypothesized to be largely responsible for emergence of chemoresistant tumors. Although chemotherapy may initially succeed at decreasing the size and number of tumors, it leaves behind residual malignant OCSCs. In this study, we demonstrate that Aldehyde dehydrogenase 1A1 (ALDH1A1) is essential for the survival of OCSCs. We identified a novel ALDH1A1 inhibitor, compound 974, and used 974 as a tool to decipher the mechanism of stemness regulation by ALDH1A1. Treatment of OCSCs with 974 significantly inhibited ALDH activity, expression of stemness genes, spheroid, and colony formation. In vivo limiting dilution assay demonstrated that 974 significantly inhibited CSC frequency. Transcriptomic sequencing of cells treated with 974 revealed significant downregulation of genes related to stemness and chemoresistance as well as senescence and senescence associated secretory phenotype (SASP). We confirmed that 974 inhibited senescence and stemness induced by platinum-based chemotherapy in functional assays. Overall, these data establish that ALDH1A1 is essential for OCSCs survival and ALDH1A1 inhibition sup-presses chemotherapy induced senescence and stemness. Targeting ALDH1A1 using small molecule inhibitors in combination with chemotherapy therefore presents a promising strategy to pre-vent ovarian cancer recurrence and has potential for clinical translation.

We report on the application of time-resolved inline digital holography to study the nonlinear optical properties of quantum dots deposited onto the sample glass. Fresnel diffraction patterns of the probe pulse due to noncollinear degenerate phase modulation induced by the femtosecond pump pulse were extracted from the set of inline digital holograms and analyzed. Absolute values of the nonlinear refractive index of both sample glass substrate and the deposited layer of quantum dots were evaluated using the proposed technique. To characterize the inhomogeneous distribution of samples’ nonlinear optical properties we proposed to plot an optical nonlinearity map calculated as a local standard deviation of diffraction patterns intensity induced by noncollinear degenerate phase modulation.

Access and Benefit Sharing (ABS) is a framework which refers to a relatively recent type of legal requirements for access to and/or use of “genetic resources”. They are based on diverse national and regional laws and regulations, which mostly result from the implementation of the United Nations’ Convention on Biological Diversity (CBD) and its supplementary agreement, the Nagoya Protocol. Their ambition is to achieve fair and equitable sharing of benefits arising out of the use of genetic resources as an incentive to conserve and sustainably use them. This paper describes the experiences, practical constraints and complexities encountered by users of genetic resources when dealing with ABS legislation, with a focus on users from the private sector. We provide insights on how ABS laws have fundamentally changed the way of working with genetic resources in the hope that it inspires re-thinking of the ABS framework, to better support the overall objectives of the CBD.

Reef-dwelling calcifiers face numerous environmental stresses associated with anthropogenic carbon dioxide emissions, including ocean acidification and warming. Photosymbiont-bearing calcifiers, such as large benthic foraminifera, are particularly sensitive. To gain insight into their resistance and adaptive mechanisms to climate change, Amphistegina lobifera from the Gulf of Aqaba were cultured under elevated pCO2 (492, 963, and 3182 ppm) fully-crossed with elevated temperature (28°C and 31°C) for two months. Differential protein abundances in host and photosymbionts amongst treatments were investigated alongside physiological responses and microenvironmental pH variations. Over 1000 proteins were identified, of which one-third varied significantly between treatments. Thermal stress induced protein depletions, along with reduced holobiont growth. Elevated pCO2 caused only minor proteomic alterations and color changes. However, combined stressors reduced pore sizes and increased microenvironmental pH, indicating adaptive modifications to gas exchange. Notably, substantial proteomic variations at moderate-pCO2 and 31°C indicate cellular stress, while stable physiological performance at high-pCO2 and 31°C is scrutinized by putative decreases in test stability. Our experiment shows that the effects of climate change can be missed when stressors are assessed in isolation, and that physiological responses should be assessed across organismal levels to make more realistic predictions for the fate of reef calcifiers.

This study evaluated the impact of didactic videos and service user testimonial videos on mental illness stigma among medical students. Two randomized controlled trials were conducted in Nepal. Study 1 examined stigma reduction for depression. Study 2 examined depression and psychosis. Participants were Nepali medical students (Study 1:n=94, Study 2:¬n=213) randomized to three conditions: a didactic video based on the mental health Gap Action Programme (mhGAP), a service user video about living with mental illness, or a control condition with no videos. In Study 1, videos only addressed depression. In Study 2, videos addressed depression and psychosis. In Study 1, both didactic and service user videos reduced stigma compared to the control (F2,91=6.37, p=0.003). In Study 2 (depression and psychosis), there were no differences among the three arms (F2,210=2.07, p=0.13). When comparing Study 1 and 2, there was greater stigma reduction in the service user video arm with only depression versus service user videos with depression and psychosis (t(31)=-3.04, p=0.005). In summary, didactic and service user videos were associated with decreased stigma when content addressed only depression. However, no stigma reduction was seen when including depression and psychosis. This calls for different strategies based on types of mental illnesses. ClinicalTrials.gov identifier: NCT03231761

Aedes scapularis is a neotropical mosquito known to transmit pathogens of medical and veterinary importance. Its recent establishment in southeastern Florida has potential public health implications. We used an ecological niche modeling approach to predict the abiotic environmental suitability for Ae. scapularis across much of the Americas and Caribbean Islands. Georeferenced occurrence data obtained from the Global Biodiversity Inventory Facility and recent collection records of Ae. scapularis from southern Florida served as input for model calibration. Environmental layers included bioclimatic variables provided in 2000 to 2010 average Modern Era Retrospective-analysis for Research and Applications climatic (MERRAclim) data. Models were run in the software program Maxent. Isothermality values found often in costal environments contributed strongest to model performance. Model projections suggested areas predicted suitable for Ae. scapularis across portions of the Amazon Basin, the Yucatán Peninsula, the Florida Peninsula, and multiple Caribbean Islands. Additionally, model predictions suggested connectivity of highly suitable or relatively suitable environments spanning the United States Gulf Coast, which may facilitate geographic expansion of this species. At least sixteen Florida counties were predicted highly suitable for Ae. scapularis, suggesting vigilance is needed by vector control and public health agencies to recognize further spread of this vector.

Meprin α and β are zinc-dependent proteinases implicated in multiple diseases including cancers, fibrosis, and Alzheimer’s. However, until recently, only a few inhibitors of either meprin were reported and no inhibitors are in pre-clinical development. Moreover, inhibitors of other metzincins developed in previous years are not effective in inhibiting meprins suggesting the need for de novo discovery effort. To address the paucity of tractable meprin inhibitors we developed ultra-high throughput assays and conducted parallel screening of >650,000 compounds against each meprin. As a result of this effort, we identified 5 selective meprin α hits belonging to three different chemotypes (triazole-hydroxyacetamides, sulfonamide-hydroxypropanamides, and phenoxy-hydroxyacetamides). These hits demonstrated a nanomolar to micromolar inhibitory activity against meprin α with low cytotoxicity and >30-fold selectivity against meprin β and other related metzincincs. These are the most selective inhibitors of meprin α to date.

COVID-19 has emerged as deadly pandemic worldwide with no vaccine or suitable antiviral drugs to prevent or cure the disease. Because of the time-consuming process to develop new vaccines or antiviral agents, there has been a growing interest in repurposing some existing drugs to combat SARS-CoV-2. Vitamin D is known to be protective against acute respiratory distress syndrome (ARDS), pneumonia and cytokine storm. Recently it has been used as a repurposed drug for the treatment of H5N1 virus-induced lung injury. Circumstantial evidences indicate that people with low level of vitamin D are more susceptible to SARS-CoV-2. Although, vitamin D was suggested to interfere with viral replication, its interaction with any SARS-CoV-2 protein is unexplored yet. Beside this, ivermectin, a well-known anti-parasitic agent, exhibits potent anti-viral activities in vitro against viruses such as HIV-1 and dengue. Very recently, ivermectin has been found to reduce viral load of SARS-CoV-2 in vitro. We have analyzed available structures of SARS-CoV-2 proteins to identify probable binding partner(s) of vitamin D and ivermectin through knowledge-based docking studies and figured out possible implication of their binding in SARS-CoV-2 infection. Our observations suggest that the non-structural protein nsp7 possesses a potential site to house 25-hydroxyvitamin D3 (VDY) or the active form of Vitamin D, calcitrol. Binding of vitamin D with nsp7 likely to hamper the formation of nsp7-nsp8 complex which is required to bind with RNA dependent RNA polymerase (RdRP), nsp12 for optimal function. On the other hand, potential binding site of ivermectin has been identified in the S2 subunit of trimeric spike(S) glycoprotein of SARS-CoV-2. We propose that deeply inserted mode of ivermectin binding at three inter-subunit junctions may restrict large scale conformational changes of S2 helices which is necessary for efficient fusion of viral and host membrane. Our study, therefore, opens up avenues for further investigations to consider vitamin D and ivermectin as potential drugs against SARS-CoV-2.

Coronaviruses (CoVs) are positive-stranded RNA viruses that infect humans and animals. Infection by CoVs such as HCoV-229E, -NL63, -OC43 and -HKUI1 leads to the common cold, short lasting rhinitis, cough, sore throat and fever. However, CoVs such as Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and the newest SARS-CoV-2 (the causative agent of COVID-19) lead to severe and deadly diseases with mortality rates ranging between ~1 to 35% depending on factors such as age and pre-existing conditions. Despite continuous global health threats to human, there are no approved vaccines or drugs targeting human CoVs, and the recent outbreak of COVID-19 emphasizes an urgent need for therapeutic interventions. Using computational and bioinformatics tools, here we present the feasibility of reported broad-spectrum RNA polymerase inhibitors as anti- SARS-CoV-2 drugs targeting its main RNA polymerase, suggesting that investigational and approved nucleoside RNA polymerase inhibitors have potential as anti-SARS-CoV-2 drugs. However, we note that it is also possible for SARS-CoV-2 to evolve and acquire drug resistance mutations against these nucleoside inhibitors.

This paper investigates an alternative use of sterile aggregate materials which may arise from various construction applications in conjunction with other low-cost mineral raw materials to remediate the acid mine drainage phenomenon. This study is based on the combination of unprocessed mineral raw materials as well as on the basic concept of the cyclic economy where the conversion of a waste into a raw material for another application can be achieved. In this way, the value of mineral raw materials can be prolonged for as long as possible, waste generation and exploitation of natural resources are minimized and resources are kept as far as possible within the existing economy. In this study, an electrically continuous flow driven forced device proposed and demonstrated for the remediation of waste water in lab-scale by using certain mixes of mineral raw materials (serpentinite, andesite, magnesite, peat and biochar). Our results focus on the impact of the studied mineral raw materials and especially on their synergy on the water purification potential under continuous water flow operation. Using the proposed 7-day experimental electrically continuous flow driven forced device with the certain mixes of mineral raw materials, the increase of pH values from 3.00 to 6.82 as well as significant removal of Fe, Cu and Zn was achieved.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social communication and repetitive or stereotypic behaviours. In utero exposure to environmental chemicals, such as polychlorinated biphenyls (PCBs), may play a role in the etiology of ASD. We examined the relation between plasma PCB concentrations measured during pregnancy and autistic behaviours in a subset of children aged 3–4 years old in the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pregnancy and birth cohort of 546 mother-infant pairs from Canada (enrolled: 2008-2011). We quantified the concentrations of 6 PCB congeners that were detected in >40% of plasma samples collected during the 1st trimester. At age 3–4 years, caregivers completed the Social Responsiveness Scale-2 (SRS), a valid and reliable measure of children’s reciprocal social and repetitive behaviors and restricted interests. We examined SRS scores as both a continuous and binary outcome, and we calculated Bayesian predictive odds ratios for more autistic behaviours based on a latent variable model for SRS scores >60. We found no association between plasma PCB concentrations and autistic behavior, However, we found small and imprecise increases in the mean SRS score and odds of more autistic behaviour for the highest quartile of plasma PCB concentrations compared with the lowest quartile; for instance, an average increase of 1.1 [95%PCI: −0.5, 2.6] in the mean SRS (exposure contrast 4^th versus 1^st quartile) for PCB138 translated to an odds ratio of 1.6 [95%PCI: 1.0, 2.5]. Our findings illustrate the importance of measuring associations between PCBs and autistic behaviour on both continuous and binary scales.

The study is based on the results of long-term field studies, satellite and aerial data analysis. In the maximum of the Little Ice Age (LIA) 243 glaciers with a total area of 353.4 km2 were reconstructed. By the results of interpretation of Corona images by 1968 the number of glaciers increased (236), the total area reduced to 242 km2. In 2010 glaciation was represented by 237 glaciers with a total area of 201 km2. Thus, from the maximum of the LIA, the glaciation of the Tavan-Bogd mountains decreased by 43%, which is somewhat less than the neighboring glacial centers (Ikh-Turgen, Tsambagarav, Tsengel-Khayrkhan and Mongun-Taiga mountains). The probable cause is the predominance of larger glaciers relatively resistant to warming and higher altitudes. The effect of glacier size on their stability is supported by differences in the relative shrinkage of glaciers in different parts of the Tavan-Bogdo-Ola massif: the smallest decline occurred in the basins of the Tsagan-Gol (31.7%) and Sangadir (36.4%) rivers where the largest glaciers are located. On the contrary, on the lower periphery of the massif, where small glaciers predominate, the relative reduction was large (74-79%). On the background of the general retreat trend large valleys glaciers retreated faster in 1968-1977 and after 2010. In 1990-s the retreat was slow. After 2010 the glacial retreat was extremely fast. The retreat of glaciers in the last 50-60 years was caused by a trend towards a decrease in the amount of precipitation until the mid-1970s and a sharp warming in the 1990s and early 2000s.

Two new zoanthamine alkaloids, namely 3-acetoxynorzoanthamine (1) and 3-acetoxyzoanthamine (2), have been isolated from the zoantharian Zoanthus cf. pulchellus collected off the coast of the Peninsula of Santa Elena – Ecuador, together with three known alkaloids zoanthamine, norzoanthamine and 3-hydroxynorzoanthamine. The chemical structures of 1 and 2 were determined by interpretation of their 1D and 2D NMR data and comparison with literature data. This is the first report of zoanthamine-type alkaloids from Zoanthus cf. pulchellus collected in the Tropical Eastern Pacific. The neuroinflammatory activity of all the isolated compounds were evaluated in microglia BV-2 cells and high inhibitory effects were observed in ROS and NO generation.

There is growing interest in characterizing the viromes of diverse mammalian species, particularly in the context of disease emergence. However, little is known about virome diversity in aquatic mammals, in part due to difficulties in sampling. We characterized the virome of the exhaled breath (or blow) of the Eastern Australian humpback whale (Megaptera novaeangliae). To achieve an unbiased survey of virome diversity a meta-transcriptomic analysis was performed on 19 pooled whale blow samples collected via a purpose-built Unmanned Aerial Vehicle (UAV, or drone) approximately 3km off the coast of Sydney, Australia during the 2017 winter annual northward migration from Antarctica to northern Australia. To our knowledge, this is the first time that UAVs have been used to sample viruses. Despite the relatively small number of animals surveyed in this initial study, we identified six novel virus species from five viral families. This work demonstrates the potential of UAVs in studies of virus disease, diversity, and evolution.

The launch of the RadioAstron space radio telescope provides a unique opportunity to study the extreme high brightness temperature of Active Galactic Nuclei (AGNs) with unprecedented long baselines of up to 28 Earth diameters. A coordinated ground-based flux density monitoring of RadioAstron targets is essential to determine the effect of interstellar scintillation (ISS) on the Space Very Long Baseline Interferometry (SVLBI) visibilities. Moreover, a combination/comparison of scintillation with SVLBI observations is expected to reveal the relative influence of source brightness temperature, compactness, and properties of the interstellar medium on the observed variability at centimeter wavelengths. In 2014 we started a RadioAstron target triggered flux monitoring with the Effelsberg 100-m radio telescope in support of this SVLBI mission. A total of 112 targets were observed during the five-session monitoring performed so far. In this paper we present a statistical study on the short-term flux density variability of the sample, which is focused on the variability characteristics and derived physical properties of the observed sources.

Previous study demonstrated the reconstituted type I collagen matrix extracted from rabbit tendons enabled to regenerate the TMJ disc in the rabbit. The aim of this study was to investigate changes in the extracellular matrix (ECM) and mechanisms of regeneration in TMJ disc. In 36 New Zealand rabbits that underwent a partial discectomy, discs were replaced with reconstituted collagen templates for 3 months. A histological analysis showed that moderate to severe degeneration appeared in partially discectomized joints without implantation. In contrast, discs that received the reconstituted collagen template regenerated, and returned to normal to protect the joint. Cells in the regenerative tissue expressed ECM, and fibers became regular and compact due to tissue remodeling over time. Reparative cells differentiated into chondroblasts, and showed highly dense pericellular fibers. The morphology and collagen composition of the disc and condyle in the 3-month experimental group were similar to those of normal tissues. In conclusion, the reconstituted collagen template facilitated the regeneration of surgically discectomized discs. Type I and type II collagens play a crucial role in the regeneration of articular discs.

A reduction in population sodium (as salt) consumption is one of the most cost-effective strategies to reduce the burden of cardiovascular disease and it is a global health priority. High potassium intake is also recommended to reduce cardiovascular disease. To establish effective policies for setting targets and monitoring effectiveness within each country, the current level of consumption should be known. Greece lacks data on actual sodium and potassium intakes. The aim of the present study was therefore to assess dietary salt (using sodium as biomarker) and potassium intakes in a sample of healthy adults in northern Greece and to determine whether adherence to a Mediterranean diet is related to different sodium intakes or sodium-to-potassium ratio. A cross-sectional survey was carried out in Thessaloniki greater metropolitan area (northern Greece) (n=252, aged 18-75 years, 45.2% males). Participants’ dietary sodium and potassium intakes were determined by 24h urinary sodium and potassium excretions. In addition, we estimated their adherence to Mediterranean diet by the use of an 11-item MedDietScore (range 0-55). The mean sodium excretion was 175 (SD 72) mmol/day, equivalent to 4,220 (1,745) mg of sodium or 10.7 (4.4) g of salt per day and potassium excretion was 65 (25) mmol/day, equivalent to 3,303 (1,247) mg/day. Men had higher sodium and potassium excretions compared to women. Only 5.6% of the sample had salt intake <5g/d, which is the target intake recommended by the WHO. Mean sodium-to-potassium excretion ratio was 2.82 (1.07). There was no significant difference in salt or potassium intake or their ratio across MedDietScore quartiles. No significant relationships were found between salt intake and adherence to Mediterranean diet, suggesting that the perception of the health benefits of the Mediterranean diet does not hold when referring to salt consumption. These results suggest the need for a larger nation-wide survey on salt intake in Greece and underline the importance of continuation of salt reduction initiatives in Greece.

Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR recorded data allows power quality forensics and event analysis, but this capability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations and without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR recorded data storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency, and Root Means Squared (RMS) histograms, which alleviate current high data storage requirements and provides effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the date storage requirement by up to 99.58%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR recorded event.

Carbon tax policies (CTP) are globally acknowledged for curbing energy consumption and emissions. Despite global adoption, China's reluctance to embrace a carbon tax, possibly due to effectiveness and public acceptance concerns, is notable. This study employs Choice-Based Conjoint Analysis to gauge Shanghai residents' preferences on four CTP attributes. Among Shanghai's populace, the annual cost of the carbon tax policy ranks highest (48.87%), followed by implementation transparency (24.72%), revenue utilization (16.68%), and policy implementers (9.73%). Notably, Shanghai residents exhibit an average annual willingness to pay of 1435.28 CNY, indicating increased acceptance. Enhanced public awareness of carbon tax policies significantly boosts willingness to pay, augmenting carbon tax revenues and fostering low-carbon technology development. Additionally, transparency improvements in carbon tax policy yield notable increases in willingness to pay, underscoring the link between understanding CTP and its perceived importance. These findings highlight the potential of carbon tax policies in advancing sustainability and transitioning to low-carbon economies.

A reduced-dimension (RD) method based on the proper orthogonal decomposition (POD) technology and the linearized Crank-Nicolson mixed finite element (CNMFE) scheme for solving the 2D nonlinear Extended Fisher-Kolmogorov (EFK) equation is proposed. The method reduces CPU runtime and error accumulation by reducing the dimension of the unknown CNMFE solution coefficient vectors. For this purpose, The CNMFE scheme of the EFK equation is established and the uniqueness, stability and convergence of the CNMFE solutions are discussed. Subsequently, the matrix-based RDCNMFE scheme is derived by applying the POD method. Furthermore, the uniqueness, stability and error estimates of the linearized RDCNMFE solution are proved. Finally, numerical experiments are carried out to validate the theoretical findings. In addition, we contrast the RDCNMFE method with the CNMFE method, highlighting the advantages of the dimensionality reduction method.

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files ( greater than 50 MB) or Big data file ( grater than 1 TB) computational complexity is more we leverage on sharding concept to enhance both space and time complexity in cloud storage, hence the large files are divided into shards and stored in different database servers. We also employed blockchain smart contract to enhance secure retrieval of the file and also a secure access method which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data’s authenticity is shared with the verifier at the destination cloud, rather than the actual data itself. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies require to store data. As the environment maintains privacy of the user data and by not storing the raw data of the user anywhere, the entire environment is setup as a Zero trust model.

(1) Background: The rapid evolution of the internet and technological infrastructure has led to a surge in data generation across various contexts, increasing the use of machine learning tools to extract valuable information. Clustering, particularly using the K-means algorithm, is a common technique. However, determining the optimal number of clusters in K-means is challenging, as traditional methods like the elbow method can be imprecise and subjective. This study proposes a more accurate and objective method to identify the optimal number of clusters. (2) Methods: The proposed method utilizes the numerical derivative of cluster inertias and the maximum value of the ratio between contiguous derivatives. Implemented in Python using sklearn, numpy, and matplotlib, the method was validated with synthetic datasets generated by artificial intelligence, where cluster numbers are clearly distinguishable. (3) Results: The method proved to be more precise and less subjective than the traditional elbow method, accurately identifying the optimal number of clusters in all tested synthetic datasets. Additionally, it demonstrated computational efficiency with minimal RAM usage and execution time, making it suitable for practical data analysis applications. (4) Conclusions: This new mathematical and computational method significantly improves the determination of the optimal number of clusters in K-means, offering a more accurate and objective alternative to traditional techniques. Future work will extend this method to hierarchical clustering and develop a cloud service for wider accessibility.

Procedural and reporting guidelines frame the process of scientific practices and communications among researchers and the community at large. In the pursuit of fostering reproducibility, several methodological frameworks have been proposed by several initiatives. Nevertheless, recent studies indicate that data leakage and reproducibility are still prominent challenges. Recent studies have shown the transforming potential of incorporating the FAIR (Findable, Accessible, Interoperable and Reusable) principles in the work-flow of different context such as software and machine learning model development stages to cultivate open science. In this work, we introduce a framework to calibrate reporting guidelines against the FAIR principles in order to foster reproducibility and open science. We adapted the “Best fit” framework synthesis approach to develop the calibration framework. We propose a series of defined workflows to calibrate reporting guidelines with FAIR principles and a use case to demonstrate the process. By integrating FAIR principles with established reporting guidelines, the proposed framework bridges the gap in accommodating both FAIR metrics and reporting guidelines and benefits from advantages of these major integrated components.

Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries. To address these challenges, we propose PDCNER, a Pig Disease Chinese Named Entity Recognition method leveraging lexicon-enhanced BERT and contrastive learning. Firstly, we construct a domain-specific lexicon and pre-train word embeddings in the pig disease domain. Secondly, we integrate lexicon information of pig diseases into the lower layers of BERT using a Lexicon Adapter layer, which employs char-word pair sequences. Thirdly, to enhance feature representation, we propose a lexicon-enhanced contrastive loss layer on top of BERT. Finally, a Conditional Random Field (CRF) layer is employed as the model's decoder. Experimental results show that our proposed model demonstrates superior performance over several mainstream models, achieving a precision of 87.76%, a recall of 86.97%, and an F1-score of 87.36%. The proposed model outperforms BERT-BiLSTM-CRF and LEBERT by 14.05% and 6.8%, respectively, with only 10% of the samples available, showcasing its robustness in data scarcity scenarios. Furthermore, the model exhibits generalizability across publicly available datasets. Our work provides reliable technical support for the information extraction of pig diseases in Chinese and can be easily extended to other domains, thereby facilitating seamless adaptation for named entity identification across diverse contexts. The codes have been open-sourced at https://github.com/tufeifei923/pdcner.

Proton Exchange Membrane Fuel Cell (PEMFC) is a fast-developing battery technology, and the key to its reliability and lifespan improvement lies in the accurate assessment of durability. However, the degradation mechanism of PEMFC is hard to determine and its internal parameters are highly coupled. Thus, the development of the more accurate life prediction model that meets the actual scenarios is urgent to investigate. To solve this problem, a multi-feature fusion life prediction method based on Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) is proposed. A TCN algorithm is used as the prediction base model, and two GRU modules are included to the model to strengthen the model expression ability and improve its predictive accuracy. Two widely recognized datasets and two operating conditions are utilized for model training and prediction, respectively. Comparisons are made with single-feature parameter models in terms of Root Mean Square Error (RMSE) and Determination Coefficient (R2). The results show that the prediction accuracy of the TCN-GRU multi-feature fusion model is higher than that of the single-feature models in terms of stability and anti-interference under both operating conditions. Furthermore, with the increase of input feature parameters, the TCN-GRU model is closer to the real value, which proves once again that the proposed model can meet the accuracy requirements of the life prediction of PEMFC.

Current sampling methods for detecting the presence of the invasive freshwater hydrozoan Craspedacusta sowerbii rely mainly on visual confirmation of the medusa stage. Confirming the presence of the polyp stage is equally important to observing medusae since typical late summer/early fall occurrences or observations of medusae are sporadic though becoming more frequent. The polyp stage is important as it is the organism’s primary stage and is present throughout the year depending on water temperatures. Therefore, sampling methods for the polyp stage are commonly the collection of substrates such as rocks, plants or pieces of wood in a given body of water and can be cumbersome to examine. Based on preliminary culturing of the polyp stage on glass and plastic microscope slides in the lab, we designed a novel sampling methodology based on submerging 4 substrate types (small glass petri dishes, Hester-Dendy plates, plastic and glass microscope slides) to confirm the presence of C. sowerbii polyps in the field. We tested this method in various lakes in the Illinois-Indiana region (USA). The sampling method we designed was effective in that C. sowerbii polyps (and sometimes colonies) were found on both plastic and glass slides. While this method can be sufficient for detection of the polyp stage, it also shows significant potential for improvement; we highlight water flow and competition and predation from other organisms as significant factors influencing the collection of C. sowerbii polyps for future methodologies.

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

Karst peaks and depressions are scattered in karst zones with strong spatial heterogeneity and fragile ecological environments and are crucial for determining the degree of karst geomorphologic development. However, realizing automatic depiction and extracting depressions with high accuracy is difficult because of their complex morphology. Herein, based on 12.5-m resolution DEM data, six typical karst peaks from depressions in southwest China were selected as the study areas and a revised terrain openness index method based on slope mutation points (ROBSMPs) was used to determine the degree of karst geomorphologic development and the boundary of karst depressions. The extent of depressions extracted by ROBSMPs and the terrain openness index method with the extent of depressions hand-drawn based on remote sensing images was compared and analyzed. The results show that compared with the topographic openness index method, the overall accuracy of karst depression extracted by ROBSMPs was improved and the perimeter, area, and raster displacement error indexes were reduced. ROBSMPs realized high-precision extraction of depressions, thereby strengthening the applicability of the topographic openness index method to karst peak zones. This study offers a new perspective and path toward the expansion of digital terrain analysis technology in karst mountainous areas and is expected to play a vital role in the extraction of similar geomorphic units in karst zones.

Brushless Direct Current Motors are significantly improved for using many electrical applications. The design investigation is increased year by year and most of the motor design researchers find out the superior efficiency of the BLDC motor. The traditional motor consumed more electrical power than BLDC motor. The design of BLDC motors that are compact, small, easy to control, save energy and light weight. Due to this fact, the design of BLDC motor should be analyzed to improve the motor characteristics performance. The research focuses on the design of BLDC motor performance using Taguchi Method and Response Surface Methodology (RSM) for Finite Element Method (FEM). Firstly, the Taguchi Method is applied to identify the optimization point of design parameters and consider using the reference model of actual BLDC electric fan motor with a rated power of 26Watts design. Based on the Taguchi Method to create the RSM is used to two-step optimization design performance using in FEM analysis. The performances of motor design proposed approach to provide the effeteness for motor design investigator. In this investigation aims the better structural performances in output power, torque, efficiency and torque ripple. The final design of proposed model achieved an energy efficiency is 10% higher than the reference design of prototype modes, the output power result is 10W higher this reference design. We can reduce the loss of the proposed motor design. Furthermore, the maximum torque can also increase the 0.032Nm of that design. The design simulation results were produced and studied by the FEM analysis of JMAG software. From the evaluation results, the configurations of reference model design are compared with the proposed design by using the three methods of motor design optimization showed the best characteristics for performance of motor investigation design.

This paper proposes a lightweight joint with bamboo culm cross connection, which is constructed by screwing one side of the screw into the other side of the wooden plug, its simple construction makes it easy to manufacture and cost-effective. This form connects adjacent bamboo culms to form corner joints, and bolt connects adjacent bamboo culm frames to form edge joints, to explore the mechanical properties of the combination joints formed by corner joints and edge joints, by combining experiments and numerical simulations, the method discovered and explained the relationship and mechanism between the number and position of bolts and the stiffness of edge joints. A calculation model for frame-unit bamboo culm members considering semi-stiffness of joints is proposed, and the conversion equations between linear stiffness of corner and edge joint and their rotational stiffnesses are derived. By establishing the series and parallel relationship between the corner and edge joints in the assembled members, a generalized relational equation between the total stiffness of the assembled joint and the stiffness of the corner and edge joints is derived. A numerical simulation method for frame-unit bamboo culm members with semi-rigid joints is proposed, which can obtain the stiffness calculation formula of edge joints and the average rotational stiffness value of semi-rigid corner joints. A finite element analysis method based on semi-rigid joints is proposed for frame-unit bamboo culm members, and the internal force sketches and calculation methods for bamboo culms and joints are presented, which can be used for the internal force analysis and design of frame-unit bamboo culm lattice structures.

Over the last decades, an important number of regional and global Digital Elevation Models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs require to be expressed in the same Coordinate Reference System (CRS), sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention), and with heights relative to the same Vertical Reference System (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements, or disparities, may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect or curvature. Therefore, the control of DEM planimetric accuracy is certainly the most important task to perform at first before any comparison. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy, by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have been already computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been performed in a 11x11 grid with a 0.1-pixel step in both directions. The Copernicus DEM GLO-30 has been interpolated by a “classical” bicubic to achieve these 11x11 displacements. This validation method has been applied in four 10 km x 10 km DEMIX tiles showing different height distributions. Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with with typical errors (e_b) ranging from 12 to 20 % of the pixel size (worst case in Croatia).These errors in displacement retrievals are not constant in the 11x11 grid and the overall error E_b depends on the roughness (altimetry variations) encountered in the different tiles. The second idea of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d=1 of the interpolated point) on the accuracy of the displacement retrieval. By considering E_b as a quality indicator, tests have been performed in the four DEMIX tiles making the bicubic parameter vary between -1.5 and 0.0 by step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four E_b minimal values. This BBC parameter b* is low for flat areas (around -0.95) and higher in mountainous areas (around -0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r=0.717). The logarithmic regression formula estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC.

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is of course exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology, (FRAM) which did allow the analyst in-sights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative ap-proach. In the last 10 years, this original approach developed to model systems as sets of interactive, interdependent “functions”, (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, and can pull it all together into dynamic models, (as opposed to static, fixed System Theoretic Process Accimaps), from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.

In this paper, we propose an efficient numerical method to solve the diffusive logistic model with a free boundary which is often used to simulate the spreading of the new or invasive species. The boundary movement is tracked by the level-set method, where the Hamilton-Jacobi weighted essentially nonoscillatory (HJ-WENO) scheme is utilized to capture the boundary curve embedded by the Cartesian grids via the embedded boundary method. Then the radial basis function-finite difference (RBF-FD) method is adopted to the spatial discretization and the IMEX scheme is considered for time integration. A variety of numerical examples are carried out to demonstrate the evolution of diffusive logistic model in different initial boundaries.

A novel approach was developed that combined LP-based row generation with optimization-based sorting to tackle computational challenges posed by budget allocation problems with combinatorial constraints. The proposed approach dynamically generated constraints using row generation and prioritized them using optimization-based sorting to ensure a high-quality solution. Computational experiments and case studies revealed that as the problem size increased, the proposed approach outperformed simplex solutions in terms of solution search time. Specifically, for a problem with 50 projects (N = 50) and 2,251,799,813,685,250 constraints, the proposed approach found a solution in just 1.4 seconds, while LP failed due to the problem size. The proposed approach demonstrated enhanced computational efficiency and solution quality compared to traditional LP methods.

In view of the limitations of the existing eccentricity error separation method of the rotary table, an eccentricity error separation method based on the phase feature of the moiré signal of single reading head is proposed herein. A grating pair transmission model is established based on the analysis of the principle of the rotary table, thereby the influence of eccentricity error on the phase feature of the moiré signal in the rotation course of the rotary table is clarified, and the corresponding model between the phase feature spectral components and the eccentricity error is established. The verification experiments of the proposed method are carried out based on the laboratory-made circuit system. After verifying the accuracy of the data acquisition of the laboratory-made circuit board, the verification experiments of the eccentricity error separation effect of the proposed method are carried out. The experimental data are compared with those of the traditional method and the results show that the error between the two methods is 2.34μm and the relative error is 2.3%.

Introduction: The serological support to early diagnosis and differential diagnosis of inflammatory bowel diseases (IBD) is actually very limited. In this study we evaluated the performance of a promising multiparametric method including either well established and newly developed biomarkers. Methods: This multicenter retrospective observational study finally enrolled 156 patients with IBD, 100 affected by Crohn’s disease (CD) and 56 by ulcerative colitis (UC) recruited at the Gastroenterology Units of Udine (Italy), Rijeka (Croatia) and Belgrade (Serbia). Twently age-sex matched blood donors (BD) were included as controls. Autoantibody profiles were determined using a mosaic cell and tissue-based indirect immunofluorescence (IIF) method simultaneously investigating: anti-saccharomyces cerevisiae antibodies (ASCA), anti-atypical perinuclear neutrophilic antibodies (P-ANCA), anti-pancreatic antigens antibodies (PAB) and anti-goblet cells antibodies (GAB). Results: PAB (anti-CUZD1 and/or anti-GP2 antibodies) were present in 24 CD patients versus none of UC patients or BD (24% sensitivity, 100% specificity). As regards CD patients, combined positivity of PAB and ASCA (sensitivity 84%, specificity 71.4%) performed better than ASCA alone. Colon involvement (87.5% vs. 60.5%; p=0.014), deep mucosal lesions (58.3% vs. 25.0%; p=0.002) and need for biologic therapies (79.2% vs. 46.1%; p=0.005) were significantly more prevalent in PAB-positive than in PAB-negative CD patients. Multivariate analysis identified PAB positivity (OR=3.67; 95%CI=1.29-10.46) and anti-CUZD1 in particular (OR=3.54; 95%CI=1.08-11.63) as significant risk factors for deep mucosal lesions development in CD. Conclusion: A multiparametric diagnostic approach appears very useful to better characterize IBD patients. PAB, isolated or combined with other autoantibodies, may support both differential diagnosis and selection of CD patients at risk for more severe disease.

A method of measuring the absolute pose parameters of a moving rigid body using a monocular camera is proposed, aiming at addressing calibration difficulties and inconsistencies of repeated measurements of the rigid-body pose for a camera having a varying focal length. The proposed method does not require calibration beforehand. Using more than six non-coplanar control points symmetrically arranged in the rigid-body and world coordinate systems, the matrices of rotation and translation between the camera and two coordinate systems are obtained and the absolute pose of the rigid body measured. In this paper, formulas of the absolute pose measurement of a moving rigid body are deduced systematically and the complete implementation is presented. Position and attitude measurement experiments carried out on a three-axis precision turntable show that the average absolute error in the attitude angle of a moving rigid body measured by an uncalibrated camera at different positions changes by no more than 0.2 degrees. Analysis of the three-dimensional coordinate errors of the centroid of a moving rigid body shows little deviation in measurements made at three camera positions, with the maximum deviation of the average absolute error being 0.53 cm and the maximum deviation of the standard deviation being 0.66 cm. The proposed method can measure the absolute pose of a rigid body and is insensitive to the position of the camera in the measurement process. This work thus provides guidance for the repeated measurement of the absolute pose of a moving rigid body using a monocular camera.

This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for reducing or mitigating environmental pollutions and climate change. To be precise, CVML is an innovative hybrid machine-learning model, and it can leverage a limited amount of survey data for prediction and data enrichment purposes. The model comprises of two interconnected modules: Module I, an unsupervised learning algorithm, and Module II, a supervised learning algorithm. Module I is responsible for grouping the data into groups based on common characteristics, thereby grouping the corresponding dependent variable, whereas Module II is in charge of demonstrating the ability to predict and the capacity to appropriately assign new samples to their respective category based on input attributes. Take a survey on the topic of air pollution in Hanoi in 2019 as an example, we found that CVML can predict households’ willingness– to– pay for polluted air mitigation at a high degree of accuracy (i.e., 98%). We found that CVML can help users reduce costs or save resources because it makes use of secondary data that is available on many open data sources. These findings suggest that CVML is a sound and practical method that could be widely applied in a wide range of fields, particularly environmental economics and sustainability science. In practice, CVML could be used to support decision-makers in improving the financial resources to maintain and/or further support many environmental programs in years to come.

Titanium (Ti) and Ti-based alloys are commonly used in dental implants, and surface modifications of dental implants are important for achieving osseointegration (i.e., direct connection between the implant surface and bone). This study investigated the effect of an eco-friendly etching solution—a hydrogen peroxide–sodium bicarbonate mixture—on the surface properties and contact angles of and osteoblast adhesion and proliferation on Ti surfaces. Disk-shaped Ti specimens were prepared using different surface treatments (machining, sandblasting, and sandblasting/acid-etching), and they were immersed in the etching solution and then ultrasonically cleaned. Surface characterization was performed using scanning electron microscopy, digital microscopy, contact angle analysis, and X-ray photoelectron spectroscopy. MG-63 osteoblasts were cultured on the specimens, and their adhesion to the specimen surface and their proliferation were examined using staining and the MTT assay, respectively. Additional etching with the etching solution caused the formation of nano/micro hierarchical structures, increased the surface roughness, and enhanced the hydrophilicity. Osteoblast adhesion and proliferation were found to improve on the modified surfaces. The eco-friendly etching method has the potential to enhance the biological properties of Ti implant surfaces and to thereby improve the dental implant performance.

The hard bottom layer of paddy field has a great influence on the driving stability and operation quality and efficiency of intelligent farm machinery, and the continuous improvement of unmanned precision operation accuracy and operation efficiency of paddy field operation machin-ery is the support to realize unmanned rice farm. In this paper, in view of the complicated hard bottom layer situation of unmanned operation farm machinery driving is difficult to realize to quantify the local characteristics of hard bottom layer of paddy field, the unmanned rice direct seeding machine chassis is used to operate the operation field and collect the hard bottom layer information simultaneously, and the data processing method of automatic calibration of sensor installation error, abnormal value rejection and 3D sample curve denoising of contour trajectory is designed; a hard bottom layer surface profile evaluation method based on the local sliding surface roughness is proposed. The local characteristics of the hard bottom layer were quantified, and the quantified results of the local characteristics of the hard bottom layer in the test plots showed that the mean value of the local roughness was 0.0065, 68.27% was distributed in the variation range of 0.0042~0.0087, and 99.73% was distributed in the variation range of 0~0.0133. Based on the test field data, the surface roughness features are verified to describe the variability of representative working conditions such as transport, downfield, operation and trapping of unmanned operation of intelligent farm machinery. The method of quantifying the hard-bottom local features of farm machinery driving can provide feedback on the local environmental features of intelligent farm machinery driving at the current position, and provide a reference basis for the design optimization of unmanned system for improving the quality of intelligent farm machinery operation.

Three-Dimensional reconstruction of the corneal surface provides a powerful tool for managing corneal diseases. This study proposes a novel method for reconstructing the corneal surface from elevation point clouds, using modal schemes capable of reproducing corneal shapes using surface polynomial functions. The multivariable polynomial fitting was performed using a non-dominated sorting multivariable genetic algorithm (NS-MVGA). Standard reconstruction methods using least-squares discrete fitting (LSQ) and sequential quadratic programming (SQP) were compared with the evolutionary algorithm-based approach. The study included 49 eyes of 49 patients (ages 11-63) sorted in two groups: control (33 eyes) and keratoconus (KC) (16 eyes). Tomographic information (Sirius, Costruzione Strumenti Oftalmici, Italy) was processed using Matlab. The goodness of fit for each method was evaluated using mean squared error (MSE), measured at the same nodes where the elevation data were collected. Polynomial fitting based on NS-MVGA improves MSE values by 86% compared to LSQ-based methods in healthy patients. Moreover, this new method improves aberrated surface reconstruction by 43% for Amsler-Krumeich (AK) Grade 1 keratoconus patients. Finally, significant improvements were also found in morpho-geometric parameters, such as asphericity and corneal curvature radii.

Proteasomes critically regulate proteostasis via protein degradation. Proteasomes are multisubunit complexes composed of the 20S proteolytic core particle (20S CP) that, in association with one or two 19S regulatory particles (19S RPs), generate the 26S proteasome. 26S is the major proteasomal complex in the cells. Native gel protocols are used to investigate the 26S/20S ratio. However, a simple method for detecting these proteasome complexes in the cells is missing. To this end, using CRISPR technology, we YFP tagged the endogenous PSMB6 (b1) gene, a 20S CP subunit, and co-tagged endogenous PSMD6 (Rpn7), a 19S RP subunit, with mScarlet fluorescent protein. We observed colocalization of YFP and mScarlet fluorescent proteins in the cells, with higher nuclear accumulation. Nuclear proteasomal granules are formed under osmotic stress, and all were positive for YFP and mScarlet. Previously we have reported that PSMD1 knockdown, one of the 19 RP subunits, gives rise to a high level of “free” 20S CP. Intriguingly, under this condition, the 20S-YFP remained nuclear, whereas the PSMD6-mScarlet is mostly in cytoplasm, demonstrating the distinct subcellular distribution of uncapped 20S CP. Lately, we have shown that the PSMA3 (a7) C-termininus, a 20S CP subunit, binds multiple intrinsically disordered proteins (IDPs). Remarkably, truncation of the PSMA3 C-terminus is phenotypically reminiscent of PSMD1 knockdown. These data suggest the PSMA3 C-terminal region is critical for the 26S proteasome integrity.

Plant Factory Transplanter (PFT) is a key component of the plant factory system. Its operation status directly affects the quality and survival rate of planted seedlings, which in turn affects the overall yield and economic efficiency. To monitor the operation status and transplanting quality of a transplanting machine in a timely manner, the primary task is to use a computerized and easy-to-use method to monitor the transplanting units. Inspired by the latest developments in augmented reality and robotics, a DT model-based and data-driven online monitoring method for plant factory transplanting equipment is proposed. First, a data-driven and virtual model approach is combined to construct a multi-domain digital twin of the transplanting equipment. Then, taking the vibration frequency domain signal above the transplanting manipulator and the image features of the transplanting seedling tray as input variables, the evaluation method and configuration method of the PFT digital twin system are proposed. Finally, the effect of the transplanter is evaluated, and the cycle can be repeated to optimize the transplanter to achieve optimal operation parameters. The results show that the digital twin model can effectively use the sensor data to identify the mechanical vibration characteristics and avoid affecting transplanting quality due to mechanical resonance. At a transplanting rate of 3000 plants/h, the transplanting efficiency can be maintained at a high level and the vibration signal of X, Y, Z-axis above the transplanting manipulator is relatively calm. In this case, Combined the optimal threshold method with the traditional Wiener algorithm, the identification rate of healthy potted seedlings can reach 94.3%. Through comprehensively using the optimal threshold method and 3D block matching filtering algorithm for image threshold segmentation and denoising, the recognition rate of healthy seedlings has reached over 96.10%. In addition, the developed digital twin can predict the operational efficiency and optimal timing of the detected transplanter, even if the environmental and sensor data are not included in the training. The proposed digital twin model can be used for damage detection and operational effectiveness assessment of other plant factory equipment structures.

Evaluating the efficacy of the traceability systems (TS) plays an important role not only for planning system implementation before development, but also for analyzing system performance once the system is in use. In the present work, we evaluate the traceability granularity using a comprehensive and quantifiable model and try find its influencing factors via an empirical analysis with 80 vegetable companies in Tianjin city, China. Granularity indicators were collected mostly by the TS platform to ensure the objectivity of the data, and the granularity score was evaluated by using a TS granularity model. The results show a clear imbalance in the distribution of companies as a function of score. The number of companies (21) scoring in the range [50,60] exceeded the number in the other score ranges. Furthermore, the influencing factors on traceability granularity were analyzed by using a rough set method based on nine factors pre-selected by using a published method. The results show that the factor “number of TS operation staff” is deleted because it is unimportant. The remaining factors rank according to importance as follows: Expected revenue &gt; Supply chain (SC) integration degree &gt; Cognition of TS &gt; Certification system &gt; Company sales &gt; Informationization management level &gt; System maintenance investment &gt; Manager education level. Based on these results, the corresponding implications are given with the goal of (i) establishing the market mechanism of high price with high quality, (ii) increasing government investment for constructing the TS, and (iii) enhancing the organization of SC companies.

In recent years, there has been a growing interest in the preoperative modeling of fluid-structure interaction in the treatment of cerebral aneurysms. In this study, we investigate two cases involving laminar incompressible fluid flow interacting with hyperelastic materials (a vertical flap and aneurysm walls) under the effect of fluid flow. We present a finite element method (FEM) for these prototypical two-dimensional ($2D$) configurations, taking into account the complex flows and deformation of the variant's structure models. We use an Arbitrary Lagrangian-Eulerian (ALE) formulation in a continuum, fully monolithic coupled way, and discretize the fluid and solid domains using the quadratic LBB stable $P_2P_1$ finite element pair to approximate the displacement, velocity, and pressure spaces independently. The resulting discretized form of the nonlinear algebraic system is linearized using a variant of Newton's procedure, and the Jacobian matrices are approximated via the divided difference method. The resulting linear systems are solved using a direct solver MUMPS. We evaluate hydrodynamic forces such as drag and lift coefficients for nonlinear elastic material models, including Saint-Venant Kirchoff, Neo-Hookean, and Mooney-Rivlin (2 parameters) separately. To gain more physical insight into the problem, we verify the computed results by comparing the velocity, viscosity, and pressure fields. Our aim is to qualitatively analyze the changes in the mechanics of the wall behavior of the elasticity of the flap vs. the complexity of deformation, interface, details for prototypical flow situations, corresponding displacement, and deformation. We also provide a physical interpretation of blood flow magnitude and basic hemodynamic phenomena of wall shear stresses (WSS) in aneurysma and the range of deformation. Overall, this study presents a comprehensive approach to modeling fluid-structure interaction in cerebral aneurysms, which could help in the development of more effective treatment strategies.

An HPLC method for indicating stability was developed, created, and confirmed for the quantification measurement of recombinant human erythropoietin in large quantities as well as dosage forms. An isocratic separation was accomplished using a Thermo Biobasic C18 (250 mm 4.6 mm i.d., 300Ao, 5 m) column with a flow rate of 1.0 mL min-1 as well as a UV detector to monitor the eluate at 278 nm. Acetonitrile, 0.05 mM potassium dihydrogen phosphate (80:20 v/v), and orthophosphoric acid adjusted to 4.0 made up the mobile phase. The drug was exposed to oxidative stress, hydrolysis, and photodegradation to simulate stress conditionsRecombinant Human Erythropoietin, the parent compound, was eluted at roughly 6.675 minutes, as well as all byproducts have been entirely disconnected inside an overall analytical run time of about 15 minutes. To identify quantification limits of 0.05 and 0.2 g mL-1, respectively, the method was linear over a concentration range of 1-6 g mL-1 (r = 0.9989). Recombinant Human Erythropoietin can be measured accurately, selectively, sensitively, and precisely using this method both in dosage form and in bulk. Stress-induced degradation products did not interact with the identification of Recombinant Human Erythropoietin, indicating that the assay is stable.

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

The deformation monitoring information of concrete dams contains some high-frequency com-ponents, and the high-frequency components are strongly nonlinear, which reduces the accuracy of dam deformation prediction. In order to solve such problems, this paper proposes a concrete dam deformation monitoring model based on empirical mode decomposition (EMD) combined with wavelet threshold noise reduction and sparrow search algorithm (SSA) optimization of long short-term memory network (LSTM). The model uses EMD combined with wavelet threshold to decompose and denoise the measured deformation data. On this basis, the LSTM model based on SSA optimization is used to mine the nonlinear function relationship between the reconstructed monitoring data and various influencing factors. The example analysis shows that the model has good calculation speed, fitting and prediction accuracy and it can effectively mine the date char-acteristics inherent in the measured deformation, and reduce the influence of noise components on the modeling accuracy.

Li4Ti5O12 (LTO) exhibits zero-strain behavior, exceptional cycle stability, low cost, and high safety. However, it is still low in electronic and ionic conductivity. Incorporating Sn into LTO materials can increase electronic conductivity and specific capacity. However, Sn still experiences volumetric expansion during the charging/discharging process. Adding activated carbon (AC) into the LTO/Sn composite can help improve the expansion resistance and electronic conductivity. In this work, the AC was first synthesized from charcoals through the carbon activation process and mixed with LTO precursors through the sol-hydrothermal method followed by mixing with Sn through the mechanochemical process to produce LTO@AC/Sn composites. The Sn content was fixed at 15 wt.%, while the AC contents were varied at 1 wt.%, 3 wt.%, and 5 wt.%. The AC specific surface area is increased by more than 100% compared to the non-activated one. The best effects of AC on grain morphology and distribution were found in the LTO/Sn contained 3 wt.% of AC, leading to transfer resistance, ohmic resistance, specific capacity, and coulombic efficiency were found to be 48.1 Ω, 8.5 Ω, 138 mAhg-1, and near 100%, respectively. The result suggests that the LTO@AC/Sn could be a favorable anode active material in lithium-ion batteries.

This paper presents an approach to the elastic analysis of beams subjected to Saint-Venant torsion using Green’s theorem and the finite difference method (FDM). The Saint-Venant torsion of beams, also called free torsion or unrestrained torsion, is characterized by the absence of axial stresses due to torsion; only shear stresses are developed. A solution to this torsion problem consists of finding a stress function that satisfies the governing equation and the boundary conditions. The FDM is an approximate method for solving problems described with differential equations; it does not involve solving differential equations, equations are formulated with values at selected nodes of the structure. In this paper, the beam’s cross-section was discretized using a two-dimensional grid and additional nodes were introduced on the boundaries. The introduction of additional nodes allowed us to apply the governing equations at boundary nodes and satisfy the boundary conditions. Beams with solid sections as well as multiply connected cross-sections were analyzed using this model; shear stresses and localized stresses at reentrant corners, torsion constant, and warping displacements were determined. Furthermore, beams with thin-walled closed sections, single-cell or multiple-cell, were analyzed using the Prandtl stress function whereby a linear distribution of the shear stresses over the thickness was considered; closed-form solutions for shear stresses and torsion constant were derived. The results obtained in this study showed good agreement with the exact results for rectangular cross-sections, and the accuracy was increased through a grid refinement. For thin-walled closed sections, the shear stresses obtained at the centerline using the closed-form solutions were in agreement with the values using Bredt’s analysis but the maximal values in the cross-section, which did not necessarily occur at the position with the smallest thickness, were higher; in addition, the results using the closed-form solutions were in good agreement with those using FDM.

Three-phase motors are commonly adopted in several industrial contexts and their failures can result in costly downtime causing undesired service outages; this way, motor diagnostics is an issue that assumes great importance. To prevent their failures and timely face the considered service outages, a non-invasive method to identify electrical and mechanical faults in three-phase asynchronous electric motors is proposed in the paper. In particular, a measurement strategy along with a machine learning algorithm based on Artificial Neural Network is exploited to properly classify failures. In particular, digitized current samples of each motor phase are first processed by means of FFT and PSD in order to estimate the associated spectrum. Suitable features (in terms of frequency and amplitude of the spectral components) are then singled out to either train or feed a neural network acting as a classifier. The method is preliminary validated on a set of 28 electric motors, and its performance is compared with common state-of-art machine learning techniques. The obtained results show that the proposed methodology is able to reach accuracy levels greater than 98\% in identifying anomalous conditions of three-phase asynchronous motors.

Wireless cellular communication technology has developed into a very resourceful commodity worldwide. Today, people of all races can hardly live without means of voice and data cellular communication technology. Imprecise propagation loss estimation leads to high power waste, high co-channel interference and poor service quality in cellular communication system networks. This paper proposes a realistic adaptive fine-tuning method for distinctive propagation loss estimation over a microcellular communication radio links based on signal power measurements from Long Term Evolution radio broadband networks, taking non-line of sight (NLOS) and line of sight (LOS) environments into consideration. The methodology is verified by measurements taken in non-line of sight and line of sight signal propagation scenarios. The results showed that the estimated propagation losses using the proposed realistic adaptive tuning models were more accurate than the existing Cost -231 modelling estimation approach

Wireless cellular communication technology has developed into a very resourceful commodity worldwide. Today, people of all races can hardly live without means of voice and data cellular communication technology. Imprecise propagation loss estimation leads to high power waste, high co-channel interference and poor service quality in cellular communication system networks. This paper proposes a realistic adaptive fine-tuning method for distinctive propagation loss estimation over a microcellular communication radio links based on signal power measurements from Long Term Evolution radio broadband networks, taking non-line of sight (NLOS) and line of sight (LOS) environments into consideration. The methodology is verified by measurements taken in non-line of sight and line of sight signal propagation scenarios. The results showed that the estimated propagation losses using the proposed realistic adaptive tuning models were more accurate than the existing Cost -231 modelling estimation approach.

In this paper, the classical least square (CLS) method with molecular absorption spectrophotometric measurement was used to determine simultaneously of paracetamol (PAR), ibuprofen (IBU), and caffeine (CAF) in tablets. The absorbance spectra of the standard solutions and samples were measured over a wavelength from 220 to 300 nm with a 0.5 nm step. The concentration of PAR, IBU and CAF in the sample solutions were calculated by using a program called CLS-Excel written in Microsoft Excel 2016 and Visual Basic for Applications (VBA). The method and CLS-Excel program were tested on mixed standard laboratory samples with different PAR, IBU, and CAF concentration ratios, and they showed small errors and satisfying repeatability. An analytical procedure for tablets containing PAR, IBU, and CAF was developed. The reliability of the procedure was proved via the recovery and repeatability of the analysis results with an actual tablet sample and comparing the mean contents of active substances in the tablets obtained from the analytical procedure with the from the HPLC method. The procedure is simple with a reduced cost compared with the HPLC standard method.

Since its initial development in the 1970’s by Phil Burke and his collaborators, the R-matrix theory and associated computer codes have become the de facto approach for the calculation of accurate data for general electron-atom/ion/molecule collision and photoionization processes. The use of a non-orthonormal set of orbitals based on B-splines, now called the B-spline R-matrix (BSR) approach, was pioneered by Zatsarinny. It has considerably extended the flexibility of the approach and improved particularly the treatment of complex many-electron atomic and ionic targets, for which accurate data are needed in many modelling applications for processes involving low-temperature plasmas. Both the original R-matrix approach and the BSR method have been extended to the interaction of short, intense electromagnetic (EM) radiation with atoms and molecules. Here we provide an overview of the theoretical tools that were required to facilitate the extension of the theory to the time domain. As an example of a practical application, we show results for two-photon ionization of argon by intense short-pulse extreme ultraviolet radiation

Bearings are widely used in various types of electrical machinery and equipment. As their core components, failures will often cause serious consequences . At present, most methods of parameter adjustment are still manual adjustment of parameters. This adjustment method is susceptible to prior knowledge and easy to fall into the local optimal solution, failing to obtain the global optimal solution and requires a lot of resources.Therefore, this paper proposes a new method of bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by simulated annealing algorithm.The experimental results show that the method proposed in this paper has a more accurate effect in feature extraction and fault classification compared with traditional bearing fault diagnosis methods. At the same time, compared with the traditional artificial neural network parameter adjustment, this paper introduces the simulated annealing algorithm to automatically adjust the parameters of the neural network, thereby obtaining an adaptive bearing fault diagnosis method. To verify the effectiveness of the method, the Case Western Reserve University bearing database was used for testing, and the traditional intelligent bearing fault diagnosis method was compared. The results show that the method proposed in this paper has good results in bearing fault diagnosis. Provides a new way of thinking in the field of bearing fault diagnosis in parameter adjustment and fault classification algorithms

An approach to estimating the objective function value of minimization maximum lateness problem is proposed. It is shown how to use transformed instances to define a new continuous objective function. After that, using this new objective function, the approach itself is formulated. We calculate the objective function value for some polynomially solvable transformed instances and use them as interpolation nodes to estimate the objective function of the initial instance. What is more, two new polynomial cases, that are easy to use in the approach, are proposed. In the end of the paper numeric experiments are described and their results are provided.

Socio-ecologic, socio-economic, and socio-technical transitions are opportunities that require fundamental changes in the system. These will encounter matters associated with security, service adoption by end-users, infrastructure and availability. The purpose of this study is to examine and overcome the risks to take advantage of opportunities through the novel Risky-Opportunity Analysis Method (ROAM). A novel quantitative method is designed to determine when, after making some changes, the risks become acceptable so that the opportunity does not deviate from the objectives. The approach provided a quantitative evaluation of the possible changes in parallel with digitization, towards providing a green Service Supply Chain (SSC). The result of ROAM shows that the most cost-effective change to increase the resilience of the system is a solution (SMS) which is different from that identified by a TOPSIS multi-criteria method. Real-word decisions in change management should tackle the complexity of systems and uncertainty of events during and after transition through a careful analysis of the alternatives. A case-study was carried out to evaluate the alternatives of an ancillary service in the Payment Service Providers (PSP). The comparison of the ROAM results with the traditional TOPSIS of the case-study unveils the priority of the ROAM in practice when the alternatives are Risky-Opportunities. The existing risk assessment tools do not take advantage of risky opportunities. To this aim, the current article introduces the term Risky-Opportunity, and two indexes Stress and Strain of the alternatives that are designed to be employed in the new quantitative ROAM approach.

This paper presents an approach to the Euler-Bernoulli beam theory (EBBT) using the finite difference method (FDM). The EBBT covers the case of small deflections, and shear deformations are not considered. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected points of the structure. Generally, the finite difference approximations are derived based on fourth-order polynomial hypothesis (FOPH) and second-order polynomial hypothesis (SOPH) for the deflection curve; the FOPH is made for the fourth and third derivative of the deflection curve while the SOPH is made for its second and first derivative. In addition, the boundary conditions and not the governing equations are applied at the beam’s ends. In this paper, the FOPH was made for all of the derivatives of the deflection curve, and additional points were introduced at the beam’s ends and positions of discontinuity (concentrated loads or moments, supports, hinges, springs, etc.). The introduction of additional points allowed us to apply the governing equations at the beam’s ends and to satisfy the boundary and continuity conditions. Moreover, grid points with variable spacing were also considered, the grid being uniform within beam segments. First-order analysis, second-order analysis, and vibration analysis of structures were conducted with this model. Furthermore, tapered beams were analyzed (element stiffness matrix, second-order analysis). Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. The results obtained in this paper showed good agreement with those of other studies, and the accuracy was increased through a grid refinement. Especially in the first-order analysis of uniform beams, the results were exact for uniformly distributed and concentrated loads regardless of the grid. Further research will be needed to investigate polynomial refinements (higher-order polynomials such as fifth-order, sixth-order…) of the deflection curve; the polynomial refinements aimed to increase the accuracy, whereby non-centered finite difference approximations at beam’s ends and positions of discontinuity would be used.

Background: The dairy industry heavily relies on fermentation processes driven in high proportion by Lactococcus lactis. The fermentation process can be perturbed or even stopped by bacteriophage activity leading to complete loss of fermentation batch or decreased quality product. Monitoring of the phage diversity and dynamics in the process allows to implement protective measures (e.g. starter rotation) in order to maintain unperturbed production.; Methods: Universal primers were used to amplify sequences of the 936, c2, and P335 Lactococcus phage types. The amplicons were sequences with Sanger method and obtained degenerate sequences were analyzed using simple bioinformatic pipeline in R environment.; Results: The most prevalent phage type is 936, followed by P335, whereas c2 type is less frequent.; Conclusions: Curd cheeses prepared on non-pasteurized milk based on native milk microbiota had higher diversity of phages distinct of these found in dairy plants. Sanger sequencing of heterogenous amplicons generated on metagenome DNA can be used to asses low-complexity microbiota diversity.

The paper formulates requirements for a polymer material for molding products from it by fused deposition modeling. A methodology for evaluating the suitability of a polymer or composite of thereof in 3D printing technology has been developed. A graphic representation of the developed methodology in the form of a temperature-shear rate logarithm diagram is proposed. Application of the proposed methodology makes it possible to simplify the development of new materials for 3D printing by fused deposition modeling both at the stage of selecting the components of the polymer composition and at the stage of its subsequent approbation.

The energy transition requires integration of different energy carriers, including electricity, heat, and transport sectors. Energy modeling methods and tools are essential to provide a clear insight into the energy transition. However, the methodologies often overlook the details of small-scale energy systems. The study states an innovative approach to facilitate sub-national energy systems with 100% renewable penetration and sectoral integration. An optimization model, OSeEM-SN, is developed under the Oemof framework. The model is validated using the case study of Schleswig-Holstein. The study assumes three scenarios representing 25%, 50%, and 100% of the total available biomass potentials. OSeEM-SN reaches feasible solutions without additional offshore wind investment, indicating that they can be reserved for supplying other states’ energy demand. The annual investment cost varies between 1.02 bn – 1.44 bn €/yr for the three scenarios. The electricity generation decreases by 17%, indicating that with high biomass-based combined heat and power plants, the curtailment from other renewable plants can be decreased. Ground source heat pumps dominate the heat mix; however, their installation decreases by 28% as the biomass penetrates fully into the energy mix. The validation confirms OSeEM-SN as a beneficial tool to examine different scenarios for sub-national energy systems.

Remote sensing technology provides a new way to explore the earth for geoscience applications. In the context of continuous deepening of geological research and the vigorous development of geospatial information science, 3D geological modeling technology has become a research hotspot in the intersection of earth science and information science. The traditional 3D geological modeling technology refers to underground 3D modeling using geological data. This modeling method can only display the underground scene in 3D visualization, but cannot display the surface in detail. In this study, remote sensing technology is adopted to improve the traditional 3D geological modeling method, so that geological modeling can be integrated with remote sensing image, and a new 3D geological modeling method based on remote sensing technology is developed. This method can perform integrated 3D visualization of underground and aboveground scenes, which provides a new way for disaster prevention and reduction, geological prospecting and tectonic interpretation. The new 3D geological modeling method is mainly applied in the geological field, and has made new progress in multi-source heterogeneous geological data fusion, large-scale high-precision modeling, geological grid subdivision, attribute modeling technology, remote sensing image fusion and other aspects. Taking the Ya'an area as an example, this paper makes use of the new generation of 3D geological modeling technology to carry out 3D geological modeling and visual display. The 3D visualization in Ya'an area verifies the feasibility and effectiveness of the new 3D geological modeling method based on remote sensing technology in the current data environment.

Coronavirus Disease 2019 (COVID-19) has become a global health threat and has placed an extraordinary demand for healthcare workers around the world. In this study, we aim to examine the prevalence of burnout, its associating factors, and experience among Malaysian healthcare workers through an embedded mixed-method study design. We found that more than half of Malaysian health care workers in this sample experienced burnout. Direct involvement in COVID-19 screening or treatment, having a medical condition, and less psychological support in the workplace emerged to be the significant factors for personal-, work- and patient-related burnout. Participants described workload, uncertainties from the pandemic, challenged work-family balance and stretched workplace relationships as the sources of burnout. Exhaustion appeared to be the major symptom and many participants utilized problem-focused coping to deal with the adversities experienced during the pandemic. Participants reported physical, occupational, psychological, and social-related negative impacts emanating from burnout. As the pandemic trajectory is yet unknown, the findings provide early insight and guidance for possible interventions.

Rock salt is characterized by specific geomechanical and rheological properties. Layers of rock salt on depth over 900 m cause problems with shaft lining deformation. Methods of shaft lining protection used so far (e.g. in Sieroszowice mine) have not been effective enough. The research presents a patented and copyright protected concept of a shaft lining construction that can be used in rock masses with strong rheological properties and susceptible to leaching. In the case of salt layers, especially at significant depths the relative convergence of the heading contour may be about 40 ‰/year. That results in the fact that any other method of securing the shaft lining, e.g. by making it flexible, is not sufficient to ensure the stability of the shaft guidance geometry. In the new shaft lining concept, the excessive rock creep into the outbreak inside the shaft diameter is removed by local and controlled leaching of the shaft cheeks by means of fresh water through a porous medium at the contact layer behind the watertight tubing lining. The article presents the methodology of performing tests on a special device and the test results.

The four solvent extractives obtained from aerial parts of Leucophyllum frutescens were evaluated for their Total Antioxidant Activity (TAA) by ammonium molybdate method, scavenging potential by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and Trolox-Equivalent Antioxidant Capacity (TEAC) assays, metal-reducing potential by Cupric Reducing Antioxidant Capacity (CUPRAC) and Ferric Reducing Antioxidant Power (FRAP) assays, Total Phenolic Content (TPC), Total Flavonoid Content (TFC) and their biological activities. The study concluded that BULE exhibited total antioxidant activity (226.235±1.222 mg AA.Eq.gm-1 DE±S.D) by molybdate method, CHLE exhibited more scavenging potential (DPPH 209.589±8.500 mg trolox Eq.gm-1 DE±S.D and TEAC 210.166±7.954 mg trolox Eq.gm-1 DE±S.D) and reducing potential (CUPRAC 646.889±16.889 mg trolox Eq.gm-1 DE±S.D & FRAP 472.981±15.625 mg trolox Eq.gm-1 DE±S.D). Phytochemical quantification concluded high TPC by BULE (189.369±1.393 mg GA.Eq.gm-1 DE±S.D) and high TFC by CHLE (232.458±1.589 mg Qu.Eq.gm-1 DE±S.D). Strong inhibition of α-glucosidase and urease enzymes was observed by HELE (IC50 0.3321±0.007 mg.ml-1±SD) and BULE (IC50 4.09±0.357 mg.ml-1±SD) extractives, respectively. The hemolytic effect shown by hexane extract (HELE) was higher with HA50 25.545±0.927 ug.ml-1±SD whereas methanol (MELE), chloroform (CHLE), and butanol (BULE) exhibited hemolytic effects at higher concentration with HA50 400.067±1.364, 321.394±1.332, and 332.957±0.465 µg.ml-1±SD, respectively. GC-MS profiling of HELE of L. frutescens was performed for qualitative analysis. The principal phytochemicals tentatively identified by GC-MS analysis of HELE accounts for fatty acids (60.221%), lignans (17.687%), ketones (3.358%), phenols (2.584%), sesquiterpenes (1.265%), and aldehydes (0.345%).

The contact materials in high-voltage vacuum interrupter require properties such as high conductivity, high density, and high hardness to minimize arc heat damage. In this study, copper–molybdenum-chromium alloys contact materials are examined for a high voltage contact material. Ball milling process was carried out after analyzing the raw materials of copper, chromium, and molybdenum powders. A green compact was produced using a high press with the mixed powder. Afterwards, composite was produced by sintering method according to temperature and infiltration method according to Cu content in green compact. The composite of sintering method showed a density of 8.55 g/cm3 (relative density 93%) a hardness of 217 HV, and an electrical conductivity of 40.7 IACS% at 1200 °C. The composite of 10 wt.% Cu produced by the Cu infiltration method showed a density of 8.7 g/cm3 (relative density 94%), Hardness of 274HV and electrical conductivity of 39 IACS% at 1300 °C. The measurements of physical properties showed the new possibility of using the Cu–Cr–Mo alloy as a contact material for high-voltage vacuum interrupters.

In this study, the elastic properties of composite materials are investigated, considering the effects of separation of fiber-matrix joint boundary and matrix failure. In this method, by assuming periodic microstructure and using a linear approximation of the displacement field by applying continuity and equilibrium conditions, the composite fiber composite relation is determined. The effect of separation is assumed by introducing tangential and normal scalar parameters in the equations by assuming the displacement field jump at the common boundary. In order to express the effect of matrix micro-cracks, the fracture mechanics framework of continuous environments was used and the micro-cracks parallel to the fibers, perpendicular to the fibers and in the thickness direction with scalar parameters were expressed. At the end of the effect of these parameters the results are presented in graphs. The results show that the presence of defective joint at the joint boundary and the matrix micro-components reduce the hardness of the composite and thus it’s bearing load, which is more significant at the defective joint state.

The advance of the medical field in the 21st century has influenced the development of many innovations, including in medical education. PBL (problem-based learning) is one of the pedagogical approaches that has been studying and implementing worldwide to attract medical students’ attention with their learning style and learning environment. China is the most significant “receiver” country of foreign students in Asia, and clinical medicine is one of the most chosen majors for undergraduate. In China, PBL has been implementing in some schools, but mostly with the local students and only a few implementations to the international students. International medical students in China faced a variety of challenges during their studies. In this review paper, we give the solution to adapt to the challenges by focusing on the implementation the integrated organ/system curriculum/syllabus with the PBL method to the school system with the support of the school stakeholders and students’ role.

The radio activity in Wireless Sensor Networks (WSN) and Internet of Things (IoT) applications are the most common reason for power consumption. However, recognizing and controlling the factors affecting radio activity can be valuable for managing node power. ContikiMAC is a low-power Radio Duty-Cycle protocol in Contiki OS that uses Clear Channel Assessments (CCA) to check radio status periodically. The time taken to check the radio in receive mode WakeUp, is one of the most important reasons for power consumption which in most the cases can lead to negative WakeUp in Radio Duty-Cycles and ContikiMAC especially. Here, we present a detailed analysis of idle listening time factors on the ContikiMAC. Then, we propose Light Weight CCA(LW-CCA) as an extension to ContikiMAC to reduce power consumption by reducing the radio check time in receive mode WakeUp’s, while maintaining up to 99% the Packet Delivery Rate (PDR). The simulation results show that the proposed method reduces significantly energy consumption in nodes compared to ContikiMAC and thus it helps maintain a high network performance.

Monitoring coffee quality as a means of detecting and preventing economically motivated fraud is an important aspect of international commerce today. Therefore, there is a compelling need for rapid high throughput validated analytical techniques such as quantitative proton NMR spectroscopy for screening and authenticity testing. For this reason, we sought to validate an NMR spectroscopic method for routine screening of coffee for quality and authenticity. A factorial experimental design was used to investigate the influence of NMR device, extraction time and nature of coffee on the content of caffeine, 16-O-methylcafestol (OMC), kahweol, furfuryl alcohol and 5-hydroxymethylfurfural (HMF) in coffee. The method was successfully validated for specificity, selectivity, sensitivity and linearity of detector response. The proposed method produced satisfactory precision for all analytes in roasted coffee, except for kahweol in canephora (robusta) coffee. The proposed validated method may be used for routine screening of roasted coffee for quality and authenticity control, as its applicability was demonstrated during the recent OPSON VIII Europol-Interpol operation on coffee fraud control.

China is not only short of fresh water resources per capita, but also faces a serious problem of water pollution in recent years, with 190 million people suffering from excessive levels of harmful substances in their drinking water. Such as arsenic poisoning and fluorosis and other endemic water diseases high incidence. As a series of water pollution prevention plan of action by the Chinese government announced that, this paper uses the modified Undesirable Dynamic Network model empirical analysis of China's 31 provincial administrative region economic growth, wastewater treatment, and water disease control efficiency between 2013 and 2017. The results show that the efficiency of water pollution disease in all four regions of China and the total efficiency in the three regions of east, west and central China all show a decreasing trend, and the efficiency scores and rankings of all provinces and cities within the region fluctuate greatly. The eastern region with the most developed economy has the best overall performance, with higher efficiency in Water consumption and Water disease control. However, the efficiency of wastewater treatment in northeast China is stable and better. Given the high level of economic development in China and the results of the above mentioned efficiency in water pollution and water diseases, improving the efficiency and quality of wastewater treatment in China is regarded as an important factor in achieving the strategic goal of green growth.

We propose an improved earlier described “mirror” method [1] for detecting in cell nuclear extracts mutations that arise in DNA during its replication due to misincorporation of deoxyadenosine-5’-monophosphate (dAMP) opposite 7,8-dihydro-8-oxoguanine (8-oxoG). The method is based on the synthesis of a complementary chain (“mirror”) by nuclear extracts of different mice organs on a template containing 8-oxoG inside and dideoxycytidine residue (ddC) at the 3’-end. The “mirror”was amplified by PCR using primers part of which was non-complementary to the template. It allowed obtaining the “framed mirror” products. The misincorporation of dAMP in “framed mirror” products forms an EcoRI restriction site. The restriction analysis of double-stranded “framed mirror” products allows a quantification of the mutation frequency in nuclear extracts. The data obtained showed that the mutagenic potential of 8-oxoG markedly varied in different organs of adult mice and embryos.

Management of demand and capacity for airport operation is important. The effect of capacity setting on slot adjustment, maximum adjustment and flight delay has been extensively investigated and discussed in previous slot adjustment models. But its effect on characteristics of flight waves has not been studied deeply. At present, in order to resolve the problem that Linear Integer Programming (LIP) is limited in slot allocation due to its fast dimension growth and large memory consumption, the general practice is to optimize the slot application according to data-splitting technology. However, the relationship among data splitting method, capacity setting and characteristics of slot wave has not been investigated in detail, which limits the application of LIP in slot allocation. Through the detailed analysis and testing, we found that the waveform and amplitude of slot wave can be controlled and designed by appropriate virtual capacity settings, while the number of capacity constraints and the computing time is reduced by adopting appropriate data feeding method. Our research also provides clues for further research on the construction of the flight waves for airlines operating at specific airports in order to establish direct or indirect connectivity, and increase hit rate by using our approaches.

The objective of this study is to evaluate the effects of the 3D point cloud density derived from unmanned aerial vehicle (UAV) photogrammetry and structure from motion (SfM) and multi-view stereopsis (MVS) techniques, the interpolation method for generating a digital terrain model (DTM), and the resolution (grid size) of the derived DTM on the accuracy of estimated heights in small areas, where a very accurate high spatial resolution is required. A UAV-photogrammetry project was carried out on a bare soil of 13 × 13 m with a rotatory wing UAV at 10 m flight altitude (equivalent ground sample distance = 0.4 cm). The 3D point cloud was derived, and five sample replications representing 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80 and 90% of the original cloud were extracted to analyze the effect of cloud density on DTM accuracy. For each of these samples, DTMs were derived using four different interpolation methods (Inverse Distance Weighted (IDW), Multiquadric Radial Basis Function (MRBF), Kriging (KR), and Triangulation with Linear Interpolation (TLI)) and 15 DTM grid size (GS) values (20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0.67, 0.5, and 0.4 cm). Then, 675 DTMs were analyzed. The results showed, for each interpolation method and each density, an optimal GS value (most of the cases equal to 1 cm) for which the Root Mean Square Error (RMSE) is minimum. IDW was the interpolator which yielded best accuracies for all combination of densities and GS. Its RMSE, considering the raw cloud, was 1.054 cm. The RMSE increased 3% when a point cloud with 80% extracted from the raw cloud was used to generate the DTM. When the point cloud included the 40% of the raw cloud, RMSE increased 5%. For densities lower than 15%, RMSE increased exponentially (45% for 1% of raw cloud). The grid size minimizing RMSE for densities of 20% or higher was 1 cm, which represents 2.5 times the ground sample distance of the pictures used for developing the photogrammetry project.

In this study, the field measurement-based validation of a novel sediment transport calculation method is presented. River sections with complex bed topography and inhomogeneous bed material composition highlight the need for an improved sediment transport calculation method. The complexity of the morphodynamic features can result in the simultaneous appearance of the gravel and finer sand dominated sediment transport (e.g. parallel bed armoring and siltation) at different regions within a shorter river reach. For the improvement purpose of sediment transport calculation in such complex river beds, a novel sediment transport method was elaborated. The base concept of it is the combined use of two already existing empirical sediment transport models. The method was already validated against laboratory measurements. The major goal of this study is the verification of the novel method with a real river case study. The combining of the two sediment transport models is based on the implementation of a recently presented classification method of the locally dominant sediment transport nature (gravel or sand transport dominates). The results are compared with measured bed change maps. The verification clearly refers to the meaningful improvement in the sediment transport calculation by the novel manner in case of spatially varying bed content.

Collisions of low energy electrons with molecules are important for understanding many aspects of the environment and technologies. Understanding the processes that occur in these types of collisions can give insights into plasma etching processes, edge effects in fusion plasmas, radiation damage to biological tissues and more. A radical update of the previous expert system for computing observables relevant to these processes, Quantemol-N, is presented. The new Quantemol Electron Collision (QEC) expert system simplifyies the user experience, improving reliability and implements new features. The QEC GUI interfaces the Molpro quantum chemistry package for molecular target setups and to the sophisticated UKRmol+ codes to generate accurate and reliable cross-sections. These include elastic cross-sections, super elastic cross-sections between excited states, electron impact dissociation, scattering reaction rates, dissociative electron attachment, differential cross-sections, momentum transfer cross-sections, ionization cross sections and high energy electron scattering cross-sections. With this new interface we will be implementing dissociative recombination estimations, vibrational excitations for neutrals and ions, and effective core potentials in the near future.

The rapid development of multicopters has led to many security problems. In order to prevent multicopters from invading restricted areas or famous buildings, an Anti-UAV Defense System (AUDS) has been developed and become a research topic of interest. Topics under research in relation to this include electromagnetic interference guns for unmanned aerial vehicles (UAVs), high-energy laser guns, US military net warheads, and AUDSs with net guns. However, these AUDSs use either manual aiming or expensive radar to track UAVs. This paper proposes a dual-axis rotary platform with UAV automatic tracking. The tracking platform uses visual image processing technology to track and lock the dynamic displacement of a UAV. When a target UAV is locked, the system uses a nine-axis attitude meter and laser rangefinders to measure its flight altitude and calculates its longitude and latitude coordinates through sphere coordinates to provide UAV monitoring for further defense or attack missions. Tracking tests of UAV flights in the air were carried out using a DJI MAVIC UAV at a height of 30 meters to 100 meters. It was set up for UAV image capture and visual recognition for tracking under various weather conditions by a thermal imager and a full-color camera respectively. When there was no cloud during the daytime, the images captured by the thermal imager and full-color camera provide a high-quality image recognition result. However, under dark weather, black clouds will emit radiant energy and seriously affect the capture of images by a thermal imager. When there is no cloud at night, the thermal imager performs well in UAV image capture. When the UAV is tracked and locked, the system can effectively obtain the flight altitude and longitude and latitude coordinate values.

The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

Fault line detection timely and accurately when single-phase-to-earth fault occurs in resonant grounding system is still a focus of research. This paper presents a new approach for fault detection based on data fusion and it has non-artificial setting. Firstly, the fault criterion for interphase difference energy ratio and time-frequency correlation coefficient of each line is proposed. Subsequently, the paper establish a coordinate system with the interphase difference energy ratio as X axis and the time-frequency correlation coefficient as Y axis, and it uses the Euclidean distance algorithm to get the characteristic distance of each line by fusing two-dimensional information. Finally, comparing the sound distance and the fault distance of each line to discriminate the fault line. Electromagnetic Transients Program (EMTP) simulation results and adaptability analysis have confirmed the effectiveness and reliability of the proposed scheme.

A field experiment was conducted in Ninghe, Tianjin, China, using 15N isotope method to evaluate the application of organic fertilizer on N distribution patterns of labelled and unlabeled N fertilizer, ammonium sulfate -15N uptake by rice, N use efficiency (NUE), and the fate of (15NH4)2SO4 applied. The experiment included eight treatments: CK-N (control + no-duck), CK-D (control + ducks), CF-N (chemical fertilizer + no-ducks), CF-D (chemical fertilizer + ducks), CM-N (chemical fertilizer + organic fertilizer + no-ducks), CM-D (chemical fertilizer + organic fertilizer + ducks), CD-N (chemical fertilizer 30% off + organic fertilizer + no-ducks), and CD-D (chemical fertilizer 30% off + organic fertilizer + ducks). The results showed that the application of organic fertilizer whether CM or CD significantly increased N and P concentrations over control (CK) and chemical fertilizer (CF). Moreover, no-significant differences were found in 15N fresh grain and husk concentration. Both organs ranged of 14.2-14.4 g kg-1 and 6.2-6.3 g kg-1, respectively. N derived from the fertilizer and soil significantly affected fresh grain compared to fresh husk. However, N uptake and N use efficiency did not show any differences. We concluded that organic fertilizer has a significant influence on rice growth and promote crop productivity.

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aim to understand their preferences to the related items. Example of questions may include "do you like this book?" and the users answer,"yes", "no", "I have not read it (unknown)", will reflect the degree of interest for the item by the users. As a consequence, the system can learn the users' preferences from these answers. The goal of active learning is to correctly choose the questions (items) for users. Thus it is necessary to personalize the questionnaires to retrieve the maximum information by avoiding "unknown" answers. In this paper, we propose an active learning technique that exploits past users' interests and past users' predictions in order to identify the best questions to ask.

The article discusses the validation process of a certain method of balancing gas contained in the pore space of rocks. The validation was based upon juxtaposition of the examination of rocks' porosity and the effects of comminution in terms of assessing the possibility of opening the pore space. The tests were carried out for six dolomite samples taken from different areas of the 'Polkowice-Sieroszowice’ copper mine in Poland. Before the rock material was ground, it was examined using the porosimetric method and subsequently subjected to the SEM analysis. After grinding, the research material was subjected to the granulometric analysis. Prior to the grinding process, the rocks' porosity fell in the range of 0.3-14.8%, while the volume of the open pores was included in the 0.01-0.06 cm3/g range. The research material was also characterized by inhomogeneous distribution of the pore volume. The grinding process was performed using an original device – the GPR analyzer. The ground samples were characterized by similar particle size distribution and mean diameters D_3.2 of about 4.0-4.5 µm. The SEM analysis revealed pores of various size and shape on the surface of the rock cores, while at the same time demonstrating lack of pores following the grinding process. The grain size distribution curves were compared with the cumulative pore volume curves of the cores before grinding. The resulting intersection points of both curves were seen as testifying to the presence of openings in the rock pores and release of the accumulated gas. The opening percentage of the pore space was associated with the coordinates of the curves' intersections. In order to confirm the argument put forward in this paper – i.e. that comminution of a rock to grains of a size comparable with the size of the rock's pores results in the release of gas contained in the pore space – the amount of gas released as a result of the comminution process was studied. The results of gas balancing demonstrated that the pore space of the investigated dolomites was filled with gas in amounts from 3.19 cm3/kg to 45.86 cm3/kg. The obtained results of the rock material comminution to grains comparable – in terms of size – to the size of the pores of investigated rocks, along with asserting the presence of gas in the pore space of the studied dolomites, were regarded as a proof that the method of balancing gas in rocks via rock comminution is correct.

Various natural disasters such as floods, fires, earthquakes, etc. have affected human life. Detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to Earth scientist. One of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. Based on latest achievements of Data Mining international institutions such as Rexer-KDnugget-Gartner and also newest authentic articles, SVM, KNN, C4.5, MLP are from most important and popular and leading classifiers in data world.Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ، C4.5 decision tree and KNN near neighborhood have been utilized in a combined form to detect and categorize micro seismic events. In general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting preprocessing and separating training and testing samples, 4) generating relevant models with training samples and detecting and categorizing test samples and 5) extracting a cluster with the maximum candidate using boost learning. After simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. Accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively.

Observation of the ambient aerosol surface area concentrations is important to understand the aerosol toxicity because an increased surface area may be able to act as an enhanced reaction interface for certain reactions between aerosol particles and biological cells, as well as an augmented carrier surface for co-pollutants. In this study, the concentration of aerosol surface area was measured from April 2015 to March 2016 in Fukuoka, Japan. We investigated the monthly and diurnal variations in the correlations between the aerosol surface area and black carbon (BC) and sulfate concentrations. Throughout the year, aerosol surface area concentration was strongly correlated with the concentrations of BC, which has relatively large surface area since BC particles are usually submicron agglomerates consisting of much smaller (tens of nanometers) sized primary soot particles. The slopes of the regression between the aerosol surface area and BC concentrations was highest in August and September 2015. This appears to have been the results of an increase in the proportion of the airmass that originated on the main islands of Japan. This may enhance the introduction of the BC from the main islands of Japan that is relatively fresh (or “uncoated”), thereby maintaining its larger surface area.

The rapid development of China’s textile industry leads to consumption and pollution of large volumes of water. Therefore, the textile industry has been the focus of water conservation and waste reduction in China’s 13th Five-Year Plan (2016–2020). The premise of sustainable development is to achieve decoupling of economic growth from water consumption and wastewater discharge. In this work, changes in blue water (water consumption), grey water (water pollutants), and water footprints of the textile industry from 2001 to 2014 were calculated. The relationship between water footprint and economic growth was then examined using the Tapio decoupling model. Furthermore, factors influencing water footprint were determined through logarithmic mean Divisia index (LMDI) method. Results show that the water footprint of China’s textile industry has strongly decoupled for five years (2002, 2006, 2008, 2011, and 2013) and weakly decoupled for four years (2002, 2007, 2009, and 2010). A decoupling trend occurred during 2001–2014, but a steady stage of decoupling has not been achieved yet. Based on the decomposition analysis, the total water footprint is mainly increased by production scale and inhibited by the technology. In addition, the effect of industrial structure adjustment is relatively weak.

The three dimensional conformable time fractional Kadomtsev-Petviashvili and the conformable time fractional modified Kawahara equations are solved by implementing the Kudryashov's procedure. The corresponding wave transformation reduces both equations to some ODEs. Balancing the nonlinear and the highest order derivative terms gives the structure of the solutions in the finite series form. The useful symbolic tools are used to solve the resultant algebraic systems. The solutions are expressed in explicit forms.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

The preparation of gold nanoparticles (AuNPs) involves a variety of chemical and physical methods. These methods use toxic and environmentally harmful chemicals. Consequently, the synthesis of AuNPs using green chemistry has been under investigation to develop eco-friendly nanoparticles. One method to achieve this is the use of plant-derived phytochemicals capable of reducing gold ions to produce AuNPs. The aim of this study was to implement a facile microtitre-plate method to screen a large number of aqueous plant extracts to determine the optimum concentration (OC) to bio-synthesize the AuNPs. Several AuNPs of different sizes and shapes were successfully synthesized and characterized from seventeen South African plants. The characterization was done using Ultra Violet-Visible Spectroscopy, Dynamic Light Scattering, High Resolution Transmission Electron Microscopy and Energy-Dispersive X-ray Spectroscopy. We also studied the effects of temperature on the synthesis of the nanoparticles and measured its effect on the particle size of the synthesized AuNPs and the data showed that changes in temperatures affect the size and dispersity of the generated AuNPs. Further, some of the synthesized AuNPs were stable upon incubation with different biological solutions in vitro.

The present study aims to develop an efficient numerical method for computing the diffraction and radiation of water waves with horizontal long cylindrical structures, such as floating breakwaters. A higher-order scheme is used to discretize geometry of the structure as well as the relevant physical quantities. As the kernel of this method, Wehausen’s free-surface Green function is calculated by a newly-developed Gauss-Kronrod adaptive quadrature algorithm after elimination of its Cauchy-type singularities. To improve computational efficiency, a Chebyshev approximation approach is applied to a fast calculation of the Green function that needs evaluation thousands of times. In addition, OpenMP parallel technique is used to the formation of influence coefficient matrix, which significantly reduces CPU time. Finally, computations are performed on wave exciting forces and hydrodynamic coefficients for the long cylindrical structures, either floating or submerged. Comparison with other numerical and analytical methods demonstrates good performance of the present method.

Based on the engineering background of Daliuta mining area, the fracture distribution and water movement within the weakly cemented overlay strata are studied through liquid-solid coupling physical simulation and COMSOL numerical simulation test. 3D fracture network of overburden strata in mining is plotted based on the result of the physical simulation test. The results showed that the ‘fractured zone’ of the weakly cemented overburden strata in the western area develops more evidently than that in the eastern area ---- wide-opened fractures form at both ends of the working area and this leads to a wide distribution of fractures in the overlying strata.After the initial caving of the roof, height of the penetrating fractures increases rapidly and the separated strata space becomes the main channel for water flow. Under the action of water flow and rock strata movement, mining-induced fractures in overlying rock display cyclic change of expansion, penetration and closure. When the working face is fully mined, the penetrating fractures in the overlying strata and mining-induced fractures up in the working face become the main passages for water flow. The experimental result provides a reference for underground reservoirs layout and maintenance and water-conserved mining in western China.

Whether the universe is a computer simulation, or whether we wish to efficiently model our universe in a computer simulation, there would be benefits to modeling it in a fashion analogous to computer spreadsheet, each lattice cell can be conceived as containing all the mathematical formula necessary to continuously compute its state relative to changes in all its neighboring cells, and by progression, in relation to all the cells of entire space-time lattice. Alternatively, the “real” universe may itself be built on a space cell lattice, an irregular foam of space cells, in which each cell may be conceived as a multidimensional cell of distortable space, the shape of which fully describes (a) the four basic forces (gravity, electromagnetic, strong, weak) observed at that cell of space, and (b) the probability (or weight distribution) of any quantum states overlapping the cell and its neighbors. At an appropriate scale, it would appear that this conceptual model would resolve apparent conflicts between general relativity and quantum physics. It would also provide a new interpretation of Planck’s constant as description of the number of space cell events associated with any set of observable events. If formulae operating at a lattice cell level can be improve our ability to understand and model larger scale phenomena, this would be strong evidence in favor of the theory that mathematics is not just a human invention but rather an inherent feature of space-time itself.

In this paper, a horizontally moving suspended mass pendulum base is designed and controlled using robust control theory. H infinity optimal loop shaping with first and second order desired loop shaping function controllers are used to improve the performance of the system using Matlab/Simulink Toolbox. Comparison of the H infinity optimal loop shaping with first and second order desired loop shaping function controllers for the proposed system have been done to track the desired angular position of the pendulum using step and sine wave input signals and a promising result has been obtained succesfully.

Brazil became a major player in the global beef market, after the market liberalization in the 1990’s, and experiencing a substantial development. Nonetheless, the environmental impact of beef production associated with land use and greenhouse gas emissions has been under discussion. The necessity of increased adoption of sustainable technologies, however, may preclude some farmers to keep up to date with technological advances. This article describes the major historical changes in beef farming and how they have been shifting production systems toward a sustainable intensification of grass-fed beef, with likely impacts for farmers. We combined an extensive literature review, public data and our own insights, as senior researchers, to achieve that. The trajectory shown here evidenced the substantial technological development underway in Brazilian beef farming, with strong support of public policies for decarbonizing agriculture. Nonetheless, the pace of this sustainable transition may affect small to medium farmers unable to cope. Our recommendations, therefore, involve a broad program of technical assistance and training on sustainable technologies and other supportive practices, including financial and digital literacy. Additionally, we suggest an alternative credit system, with progressive borrowing allowances to ensure financial resources to support a sustainable and inclusive transition of Brazilian beef farming.