Targeted magnetic drug delivery (TMDD) is a promising approach relevant to multimodalcancer therapy. A majority of the TMDD models represent a mixture of blood and nanoparticlesas a one-phase solution. However, in many cases it is an oversimplistic assumption. The existingtwo-phase models are usually one-way coupled, i.e. the blood flow has an impact on the MN flow.However, the inverse impact of the MNs on the the blood is not included. To eliminate these draw-backs the model is governed by two–way coupled momentum and temperature equations for theblood flow and the MN. The numerical procedure invokes the stream function–vorticity formula-tion and an efficient numerical procedure. The model, validated by experimental results, has beenapplied to analyze the formation of vortices generated by a combination of the magnetic force (MF)and the drag force (DF). The model also simulates the zones of TMDD and the corresponding changesin the vorticity. Finally, the model includes the impact of the size and concentration of the MNs onthe temperature of the blood. These important scenarios cannot be analyzed by the earlier models.
The lateral stiffness and shear strength of the steel plate shear wall (SPSW) system are provided by the diagonal tension field generated in the infill plate. The SPSW is a relatively new structural system, which has been accepted by several regulations and codes. A new form of this system is the composite steel plate shear wall (CSPSW), which consists of a steel plate and fiber-reinforced polymer (FRP) layers at one or both sides of the infill plate. The advantages of this system are low weight, high energy absorption, and low space occupancy. In this paper, the retrofitting method using carbon fiber-reinforced polymer (CFRP) sheets on one and both sides of the steel infill plate is used. Several parameters, including infill plate thickness, number of surface coverage of CFRP sheet, and fiber orientation, are considered in determining the behavior of the system under cyclic loading. Nonlinear static analysis is used in the ABAQUS finite element (FE) program for 16 numerical models. After ensuring the proper performance of the simulated model with the reference test specimen, numerical models were developed. The results of numerical FE studies showed that using CFRP sheets compared to non-retrofitted models can increase the yield strength and lateral load-bearing capacity of the system. In some cases, energy absorption increased by 50%. Besides, adding more layers of CFRP sheets did not have much effect on lateral load-bearing capacity, whereas it was able to convert the failure mode from the diagonal buckling of the wall to CFRP layer rupture and, in some cases, to the separation of the CFRP sheet.
Gladiolus (Gladiolus grandiflorus L.) is a commercial ornamental plant cultivated for its inflorescences. Improving quality and vase life (VL) of gladiolus inflorescences is an important research topic for both the growers and the sellers. For this reason, we studied the effects of preharvest potassium (K⁺) foliar application on postharvest physiological and biochemical changes. Our hypothesis was that K⁺ foliar application could improve preharvest physicochemical responses such as photosynthesis and stomatal conductance and also improve postharvest quality by reducing oxidative damage in cells. In series of pot experiments we studied the effects of 0 %, 1 %, 2 % and 3 % K⁺ on net CO2 assimilation (As), stomatal conductance (gs), transpiration (E), water use efficiency (WUE), soluble sugars (SS), total soluble proteins (TSP), VL and antioxidant activity of gladiolus cv. Manhatten. The results showed that K⁺ application, especially on inflorescences treated with the highest K⁺ concentrations, positively affected As and WUE. This resulted in higher SS and TSP by 45 % and 93 %, respectively. Potassium supplementation improved VL and reduced postharvest oxidative stress by enhancing superoxide dismutase (SOD) and catalase (CAT) enzyme activities. The 3 % K⁺ treatments increased SOD by up to 107 % and CAT by up to 188 %, compared to the control inflorescences. K⁺ treatments at 3 % significantly reduced malondialdehyde (MDA) and hydrogen peroxide (H2O2) contents, by up to 42 % and 57 %, respectively indicating a strong reduction of oxidative stress. We suggest that, K⁺ supplementation strategies may improve postharvest quality of cut gladiolus inflorescences and extend VL by enhancing antioxidant activity and reduce oxidative stress.
Carbon fullerenes are well-known spherical clusters of carbon atoms. Due to their extraordinary chemical, physical and structural properties, these nanomaterials have already proved their significant potential for use in, almost, every technological field. Based on the success behind these hollow carbon molecules, a comprehensive theoretical attempt is made here to provide the fundamentals and describe the basic structural characteristics of a new family of fullerene-like nanomaterials called hereafter α-fullerynes. The proposed nanomaterials are derived from inserting acetylenic (Csingle bondCtriple bondCsingle bondC) and butatrienic (Cdouble bondCdouble bondCdouble bondC) bonds in place of single and double carbon bonds in standard fullerenes. To ensure the chemical stability of the arisen molecules, i.e. α-fullerynes, an appropriate combination of bond order is utilized. Each three-bond structure may be either of the acetylenic (Csingle bondCtriple bondCsingle bondC) or of the butatrienic (Cdouble bondCdouble bondCdouble bondC) type. To explore, develop, and optimize the geometry of these new spherical carbon molecules, the reactive force-field (ReaxFF) interatomic potential is adopted. In order to investigate the chemical stability of the developed α-fullerynes, their potential energies are computed and compared with those of the original generator fullerenes, while their structural and geometrical characteristics are discussed in detail. Then, molecular dynamics (MD) simulations are performed to study the structural properties, density, and stiffness constants of periodic crystal structures made by these nanomaterials called henceforward α-fullerytes.
A core research activity in many scientific domains concerns gathering data via questionnaire-based surveys. Meanwhile, annotation projects, that require input by field specialists, are invaluable in various research areas. Surveys and annotation projects share inherent similarities, since they both depend on participants who are prompted to answer a set of questions referring to particular artifacts (e.g., text segments). Both tasks are hindered by their dependence on volunteering, often requiring participants of a particular background, thus burdening research conductors to seek suitable ones. In this paper, we present SurvAnnT, a platform that facilitates the creation and management of surveys and annotation projects. SurvAnnT goes beyond existing tools offering customizable gamification aspects to motivate participation, as well as expert finding mechanisms to facilitate the identification of suitable participants.
One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by the recommender system. Collaborative filtering algorithms can be applied to both sparse and dense datasets, and each of these dataset categories involves different kinds of risks. As far as the dense collaborative filtering datasets are concerned, where the rating prediction coverage is, most of the time, very high, we usually face large rating prediction times, issues concerning the selection of a user’s near neighbours, etc. Although collaborative filtering algorithms usually achieve better results when applied to dense datasets, there is still room for improvement, since in many cases, the rating prediction error is relatively high, which leads to unsuccessful recommendations and hence to recommender system unreliability. In this work, we explore rating prediction accuracy features, although in a broader context, in dense collaborative filtering datasets. We conduct an extensive evaluation, using dense datasets, widely used in collaborative filtering research, in order to find the associations between these features and the rating prediction accuracy.
The transition to university is connected to potentially obesogenic dietary changes. Our aim was to assess the relation of Mediterranean diet adherence, and a posteriori dietary and meal patterns with adiposity in Greek students at the University of the Peloponnese. A total of 346 students (269 women) participated. Anthropometry was performed, and a food frequency questionnaire was administered. The MedDietScore was higher in women and was not linearly related to adiposity. Principal component analysis revealed six patterns: (1) legumes/vegetables/fruits/tea/dairy/whole grains, (2) juice/sodas/liquid calories, (3) olive oil/fats, (4) meat/poultry/fish, (5) alcohol/eggs/dairy and (6) fast foods/sweets. Patterns 4 and 6 were related to overweight/obesity probability (OR = 1.5, 95% CI: 0.995–2.538 and OR = 2.5, 95% CI: 1.07–6.06, respectively) and higher waist circumference (men). Men “early eaters” (breakfast/morning/afternoon snack) had a higher MedDietScore and lower overweight probability (OR = 0.47, 95% CI: 0.220–1.020). Poor meal and dietary patterns relate to overweight and central obesity, which is important for targeted health promotion programs.
As the volume and complexity of distributed online work increases, collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of online collaborations by grouping workers in a top-down fashion and according to a set of predefined decision criteria. This approach often means that workers have little say in the collaboration formation process. Depriving users of control over whom they will work with can stifle creativity and initiative-taking, increase psychological discomfort, and, overall, result in less-than-optimal collaboration results—especially when the task concerned is open-ended, creative, and complex. In this work, we propose an alternative model, called Self-Organizing Pairs (SOPs), which relies on the crowd of online workers themselves to organize into effective work dyads. Supported but not guided by an algorithm, SOPs are a new human-centered computational structure, which enables participants to control, correct, and guide the output of their collaboration as a collective. Experimental results, comparing SOPs to two benchmarks that do not allow user agency, and on an iterative task of fictional story writing, reveal that participants in the SOPs condition produce creative outcomes of higher quality, and report higher satisfaction with their collaboration. Finally, we find that similarly to machine learning-based self-organization, human SOPs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible collaborators.
Salt stress severely limits the productivity of crop plants worldwide and its detrimental effects are aggravated by climate change. Due to a significant world population growth, agriculture has expanded to marginal and salinized regions, which usually render low crop yield. In this context, finding methods and strategies to improve plant tolerance against salt stress is of utmost importance to fulfill food security challenges under the scenario of the ever-increasing human population. Plant priming, at different stages of plant development, such as seed or seedling, has gained significant attention for its marked implication in crop salt-stress management. It is a promising field relying on the applications of specific chemical agents which could effectively improve plant salt-stress tolerance. Currently, a variety of chemicals, both inorganic and organic, which can efficiently promote plant growth and crop yield are available in the market. This review summarizes our current knowledge of the promising roles of diverse molecules/compounds, such as hydrogen sulfide (H 2 S), molecular hydrogen, nitric oxide (NO), hydrogen peroxide (H 2 O 2), melatonin, chitosan, silicon, ascorbic acid (AsA), tocopherols, and trehalose (Tre) as potential primers that enhance the salinity tolerance of crop plants.
Digital marketing, especially search engine optimization (SEO), is an integral part of websites today. Airlines in the COVID-19 era have to use every possible means to survive despite the adverse conditions for both entrepreneurship and travel. Many of them have allocated resources and money to develop SEO strategies by applying SEO techniques to their websites to gain more visitors and bookings. Thus, this research is focused on analyzing airlines’ website presence as regards the implemented SEO techniques and their effect on airlines’ website traffic. In the first phase of the research, we gathered web data from 243 airline firms during a one-year observation period (December 2020–December 2021) using our own-developed tool. Furthermore, we proceeded to create an exploratory model using fuzzy cognitive mapping. From the technical SEO point of view and the descriptive analysis, we conclude that the traffic on airlines’ websites and, consequently, their sustainability are inseparably linked to the corresponding SEO techniques and technologies used.
The compressive capacity of the column is one of the key parameters in the design. The importance of such structural members and their performance under load conditions are very effective in the overall behavior of the structure, and its failure can lead to the collapse of the entire structure. Therefore, determining the capacity of columns is considered an important issue in structural problems. Thus, this article presents an applicable computational framework to predict the compression capacity of stirrups-confined concrete. A machine learning model based on neuro-fuzzy systems was considered to formulate the proposed model. For this purpose, some experimental datasets were gathered from the literature to tune the unknown parameters of the model and evaluate its accuracy. The target, the ratio of the ultimate axial capacity to bearing area, was predicted with consideration of the column properties, including the compressive strength of concrete, stirrups section area, dimension of the stirrups, and the column section. The results showed that the proposed framework could be used as an applicable technique to determine the compressive capacity of the stirrups-confined concrete columns.
The aim of the present research was to identify and analyse the biocontrol capacity of nine local Trichoderma spp. isolates against Lasiodiplodia theobromae. The isolates were identified as T. asperellum (3), T. harzianum (5) and T. koningiospsis (1). These fungi significantly slowed L. theobromae mycelial development in vitro, with maximum reductions of 75.4 and 64.1% reported with T. asperellum BRS-1 and T. harzianum BRS-7, respectively. The antagonistic fungi were able to inhibit the growth of L. theobromae through the production of mycotoxic compounds. After 5 days of incubation, the fungi produced a large number of conidia and targeted lytic enzymes in solid-state fermentation (SSF). The principal component analysis (PCA) clustered Trichoderma strains according to their phylogenetical relationships and demonstrated a tight association between the species sub-group and antagonistic features. These findings suggest that local soil-derived Trichoderma spp. have the potential to be efficient biological agents against L. theobromae.
In an attempt to open the black box of high-growth firms within turbulent economic environments, this paper explores the role of corporate strategy, employee human capital and R&D capabilities in achieving exceptional growth performance in a crisis-hit economy. Relative and absolute growth measures based on both employment and sales are computed utilizing survey data on 1500 firms in the midst of the Greek crisis. Our findings indicate that adopting a geographical diversification strategy significantly increases the likelihood of becoming a fast-growing firm, irrespective of the growth metric used. Entering in diverse product markets and taking advantage of R&D capabilities appear to additionally contribute to relative employment change in HGFs of smaller size. Based on the absolute employment growth indicator, we provide some evidence that HGFs of larger size are able to grow fast through product diversification, acquiring other firms or by investing on training low-skilled employees. Nevertheless, hiring already highly educated persons seems to matter only for sales HGFs, while research collaborations are found to negatively affect the probability of growing fast in terms of sales.
The convection-diffusion-reaction (CDR) equation has been extensively used to simulate a variety of physical phenomena. A robust numerical method for solving linear CDR problems is the Boundary Element Method (ΒΕΜ). However, the conventional BEM leads to dense coefficient matrices and as a result the memory requirements grow quadratically with respect to the number of degrees of freedom. In this work, a Local Domain BEM (LD-BEM) for solving the transient CDR equation with a constant velocity field is presented. The domain of interest is fragmented into small subdomains and the integral representation of the solution is considered separately for each of the subdomains. Eliminating the fluxes at all subdomain interfaces, the proposed LD-BEM leads to sparse linear system coefficient matrices and a reduced number of degrees of freedom. Eight numerical examples are solved to assess the efficiency and accuracy of the proposed method.
This research paper aims to develop a mathematical model for the interpretation of experimental data for propellant solids. A simple model is constructed by assuming a spherical unit cell. Equations are written with respect to the volume fraction of the filler particles as well as the fraction of the area that is unbonded. A sphere of a finite radius is assumed, containing a rubber matrix inside of which is a rigid filler particle of a smaller radius. A parameter is assigned for the porosity of the material while effective expressions for the shear and the bulk modulus and the strain energy function are written for the composite material. Based on the proposed strain energy function, the stress–strain relations are defined for the propellant solids. The model is based on four material parameters that were evaluated using Farris’s experimental data.
Introduction During the COVID-19 pandemic various degrees of lockdown were applied by countries around the world. It is considered that such measures have an adverse effect on mental health but the relationship of measure intensity with the mental health effect has not been thoroughly studied. Here we report data from the larger COMET-G study pertaining to this question. Material and Methods During the COVID-19 pandemic, data were gathered with an online questionnaire from 55,589 participants from 40 countries (64.85% females aged 35.80 ±13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Anxiety was measured with the STAI, depression with the CES-D and suicidality with the RASS. Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively. Statistical Analysis It included the calculation of Relative Risk (RR), Factorial ANOVA and Multiple backwards stepwise linear regression analysis Results Approximately two-thirds were currently living under significant restrictions due to lockdown. For both males and females the risk to develop clinical depression correlated significantly with each and every level of increasing lockdown degree (RR 1.72 and 1.90 respectively). The combined lockdown and psychiatric history increased RR to 6.88 The overall relationship of lockdown with severity of depression, though significant was small. Conclusions The current study is the first which reports an almost linear relationship between lockdown degree and effect in mental health. Our findings, support previous suggestions concerning the need for a proactive targeted intervention to protect mental health more specifically in vulnerable groups
Apart from their applications to almost all sectors of the human activity, fuzzy mathematics is also importantly developed on a theoretical basis providing useful links even to classical branches of pure mathematics, like Algebra, Analysis, Geometry, Topology, etc. The present paper comes across the steps that enabled the extension of the concept of topological space, the most general category of mathematical spaces, to fuzzy structures. Fuzzy and soft topological spaces are introduced in particular, the fundamental concepts of limits, continuity, compactness and Hausdorff space are defined on them and examples are provided illustrating them.
The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to various cyber threats. This paper explores the potential of using network profiling, machine learning, and game theory, to secure IoT against cyber-attacks. The proposed anomaly-based intrusion detection solution dynamically and actively profiles and monitors all networked devices for the detection of IoT device tampering attempts as well as suspicious network transactions. Any deviation from the defined profile is considered to be an attack and is subject to further analysis. Raw traffic is also passed on to the machine learning classifier for identification of potential attacks. To complement this solution, an intrusion response system is used to act upon the generated alerts and compute the mitigation actions at real-time. Performance assessment of the proposed methodology is conducted on the Cyber-Trust testbed using normal and malicious network traffic. The experimental results show that the proposed anomaly detection system delivers promising results with an overall accuracy of 98.35% and 0.98% of false-positive alarms, resulting in the mitigation of the majority of the executed attacks.
This paper analyses herding behaviour within bitcoin and foreign exchange majors before and during the Covid-19 pandemic. We utilise both static and time-varying parameter regression herding measures to assess herding intensity based on hourly and daily frequencies, covering the period from 1 March 2018 to 28 February 2022. Our hourly static and time-varying model results indicate the absence of herding (hence, the presence of anti-herding behaviour) within bitcoin and the foreign exchange majors before and during Covid-19. In daily herding analyses, however, while we do not find evidence of herding within bitcoin or the foreign exchange majors, we do observe strong time-varying herding within the foreign exchange majors after excluding bitcoin both before and during Covid-19, and during both up- and down-market days. We conclude that herding behaviour between foreign exchange majors tends to be time-varying and horizon-dependent. Our results could be useful for bitcoin and foreign exchange investors, traders, researchers and regulators, helping them to strengthen their understanding of herding behaviour before and during periods of market stress such as the period of Covid-19. 50 days' free access to the article is provided below: https://authors.elsevier.com/a/1fn8a3j1Yp-4KW
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