Search

Gratitude in the workplace can enhance work achievements and promote individual well‐being in organizational contexts. Although the Gratitude at Work Scale (GAWS) was developed in the USA, it has not been corroborated in the Persian culture. The purpose of this study was to validate the Persian version of the Gratitude at Work Scale (GAWS). Scale validation procedures included linguistic validation and face and content validity assessment by experts and analysis. There was a correlation of approximately 0.592 between two subscales of GAWS-MW and GAWS-SWE, indicating a moderate level of association. Scale validity analysis demonstrated a positive correlation between GAWS and measures gratitude (GQ6), gratitude toward God, subjective happiness and satisfaction with life scales. The Gratitude at Work Scale exhibited good psychometric properties in assessing the sense of gratitude at work in our population. This may contribute to improving overall individual and organizational performance. [ABSTRACT FROM AUTHOR]

This study examined the effectiveness of a spiritual/religious intervention on religious coping and eudaimonic psychological well-being in breast cancer survivors. A quasi-experimental design with pre- and post-tests and a control group was used to study 60 Iranian breast cancer patients. The 14-item Brief RCOPE and the Ryff's 6-Dimensional Psychological Well-Being Scales were administered at baseline and follow-up. An analysis of covariance revealed that spiritual/religious intervention was effective in increasing positive religious coping and reducing negative religious coping but not in increasing psychological well-being. Implications of the findings for the healthcare of women with breast cancer are discussed. [ABSTRACT FROM AUTHOR]

Flavonoids are known as valuable natural compounds and Chamomile is a rich source of flavonoids. The extraction efficiency of flavonoids is affected by various conditions. So it appears necessary to develop a practical method for the extraction of these compounds. In this study, we attempted to extract the phenolic compounds from chamomile using the surface capacity of titanium oxide nanoparticles (TiO2). UV and FT‐IR spectroscopy was used to determine the accuracy of the binding. The HPLC‐PDA technique was used for the quantification of phenolic compounds and the recovery test result was 84 %. Determining the antioxidant, phenolic, and flavonoid contents suggested that the developed method is successful for the extraction of phenolic compounds. The influential extraction parameters after optimization consisted of the pH of 7, extraction temperature of 25 °C, extraction time of 15 min, and TiO2 nanoparticle concentration of 4 mM. The method seems acceptable for industrial applications. [ABSTRACT FROM AUTHOR]

Anthropogenic activities and natural disturbances cause changes in natural ecosystems, leading to altered Plant Ecological Units (PEUs). Despite a long history of land use and land cover change detection, the creation of change detection maps of PEUs remains problematic, especially in arid and semiarid landscape. This study aimed to determine and describe the changes in PEUs patterns in the past and present, and also predict and monitor future PEUs dynamics using the multi-layer perceptron-Markov chain (MLP-MC) model in a semiarid landscape in Central Zagros, Iran. Analysis of PEUs classification maps formed the basis for the identification of the main drivers in PEUs changes. First, an optimal time-series dataset of Landsat images were selected to derive PEUs classification maps in three periods, each separated by 16 years. Then, PEUs multi-temporal maps classified for period 1 (years 1986–1988) period 2 (years 2002–2004), and period 3 (years 2018–2020) were employed to analyze and predict PEUs dynamics. The dominant transitions were identified, and the transition potential was determined by developing twelve sub-models in the final change prediction process. Transitions were modeled using a Multi-Layer Perceptron (MLP) algorithm. To predict the PEU map for period 3, two PEUs classification maps of period 1 and period 2 were used using the MLP-MC method. The classified map and the predicted map of period 3 were used to evaluate and validate the predicted results. Finally, based on the results, transitions of future PEUs were predicted for the year 2036. The MLP-MC model proved to be a powerful model that can predict future PEUs dynamics that are the result of current human and managerial activities. The findings of this study demonstrate that the impact of anthropogenic processes and management activities will become visible in the natural environment and ecosystem in less than a decade. [ABSTRACT FROM AUTHOR]

A common feature of collapse models and an expected signature of the quantization of gravity at energies well below the Planck scale is the deviation from ordinary quantum-mechanical behavior. Here, we analyze the general consequences of such modifications from the point of view of quantum information theory and we anticipate applications to different quantum systems. We show that quantum systems undergo corrections to the quantum speed limit which, in turn, imply the modification of the Heisenberg limit for parameter estimation. Our results hold for a wide class of scenarios beyond ordinary quantum mechanics. For some nonlocal models inspired by quantum gravity, the bounds are found to oscillate in time, an effect that could be tested in future high-precision quantum experiments. [ABSTRACT FROM AUTHOR]

The electric dipole moments of leptons (l-EDMs) could arise from the electrodynamics part of standard model extension (SME). In this work, we investigate the cross section of electron-positron collision in the Lorentz violation (LV) background via the l-EDMs. We find that the Lorentz violation parameter magnitudes constraint using LEP data to the level of | d LV e | < 3.6 × 10 - 17 , | d LV μ | < 9.5 × 10 - 10 , and | d LV τ | < 1.5 × 10 - 6 for electron, muon, and tau, respectively. Moreover, we show the time-location dependence of muon-EDMs and tau-EDMs in the LV background. Therefore, upper bounds for combinations of LV parameters are obtained by current and future terrestrial experiments. [ABSTRACT FROM AUTHOR]

Prevalent cohort sampling is commonly used to study the natural history of a disease when the disease is rare or it usually takes a long time to observe the failure event. It is known, however, that the collected sample in this situation is not representative of the target population which in turn leads to biased sample risk sets. In addition, when survival times are subject to censoring, the censoring mechanism is informative. In this paper, I propose a pseudo-partial likelihood estimation method for estimating parameters in the Cox proportional hazards model with right-censored and biased sampling data by adjusting sample risk sets. I study the asymptotic properties of the resulting estimator and conduct a simulation study to illustrate the finite sample performance of the proposed method. I also use the proposed method to analyze a set of HIV/AIDS data. [ABSTRACT FROM AUTHOR]

This study was conducted to determine the effect of group logotherapy on self-esteem, social skills, and anxiety among adolescent girls in Tehran, Iran. The research was performed in a semi-experimental method with two groups (15 girls per group): one experimental group with logotherapy and one control group with treatment as usual. The Coopersmith Self-Esteem Inventory, the Teenage Inventory of Social Skills, and the Beck Anxiety Inventory have been applied to both groups in the pre and post-intervention phases. Data were analyzed using Analysis Of Variance. Our results showed that group logotherapy increased self-esteem, but not the social skills and anxiety of these young girls. This study suggests that group logotherapy may be useful for boosting some aspects of positive functioning of adolescent girls like self-esteem but it may not be useful in improving their social skills or reducing their negative feelings in response to anxiety. Implications of results have been discussed. [ABSTRACT FROM AUTHOR]

Quercetin, a flavonoid found in fruits and vegetables, has been a part of human diets for centuries. Its numerous health benefits, including antioxidant, antimicrobial, anti-inflammatory, antiviral, and anticancer properties, have been extensively studied. Its strong antioxidant properties enable it to scavenge free radicals, reduce oxidative stress, and protect against cellular damage. Quercetin's anti-inflammatory properties involve inhibiting the production of inflammatory cytokines and enzymes, making it a potential therapeutic agent for various inflammatory conditions. It also exhibits anticancer effects by inhibiting cancer cell proliferation and inducing apoptosis. Finally, quercetin has cardiovascular benefits such as lowering blood pressure, reducing cholesterol levels, and improving endothelial function, making it a promising candidate for preventing and treating cardiovascular diseases. This review provides an overview of the chemical structure, biological activities, and bioavailability of quercetin, as well as the different delivery systems available for quercetin. Incorporating quercetin-rich foods into the diet or taking quercetin supplements may be beneficial for maintaining good health and preventing chronic diseases. As research progresses, the future perspectives of quercetin appear promising, with potential applications in nutraceuticals, pharmaceuticals, and functional foods to promote overall well-being and disease prevention. However, further studies are needed to elucidate its mechanisms of action, optimize its bioavailability, and assess its long-term safety for widespread utilization. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive community‐level model is developed advancing the use of agent‐based modeling for the purpose of community resilience planning. Agents to simulate businesses, healthcare system, and people are introduced, which are utilized along with a set of developed agents for the education system, utilities, and households to create an agent‐based model of the Centerville testbed subjected to tornado hazards. Resilience measures are also introduced to quantify the resilience of the community and its systems and make it possible to assess various strategies related to pre‐disaster preparedness for informed decision‐making. The results show the capability of the proposed computational model to capture the complex behaviors and dynamics of interdependent systems within a community and to evaluate the impact of disruptions on the community as well as a series of resilience measures (mitigation strategies) for the purposes of community planning. Therefore, the proposed model and its findings could provide a new tool for interested community stakeholders and decision‐makers assessing mitigation strategies to enhance the recovery time and resilience of the community. [ABSTRACT FROM AUTHOR]

Breast cancer may be associated with religious/spiritual problems for women. This multimethod qualitative study aims to show the components of religious/spiritual intervention strategies in breast cancer cases in Iran. The religious/spiritual intervention strategies were arranged according to the patient's religious/spiritual problems based on the proposed tree diagram. This working model includes answering questions regarding the religious/spiritual cause(s) of the disease; distinguishing between divine justice and getting sick; describing the patient's attitude toward God and its effect on viewing the disease; explaining the patient's feelings and emotional relationship with God; identifying weakened religious/spiritual beliefs and replacing them with accepted beliefs; and using daily practices to communicate with God. Implications of the findings and usefulness of this model are discussed. Spiritual care providers can use this model to provide religious/spiritual care services to Iranian Muslim women with breast cancer. [ABSTRACT FROM AUTHOR]

The emergence of the generalized uncertainty principle and the existence of a non-zero minimal length are intertwined. On the other hand, the Heisenberg uncertainty principle forms the core of the EPR paradox. Subsequently, here, the implications of resorting to the generalized uncertainty principle (or equally, the minimal length) instead of the Heisenberg uncertainty principle on the quantum non-locality are investigated by focusing on the Franson experiment in which energy-time entanglement is the backbone of understanding and explaining the results. The survey also unveils the power of such an experiment in verifying the generalized uncertainty principle. [ABSTRACT FROM AUTHOR]

In this work, adsorbents based on hydrochars and steam activated hydrochars developed from flax shives were used to adsorb carbamazepine (CBZ) from simulated contaminated water. The adsorption kinetics was investigated at various temperatures (293–313 K) and times (2–96 h). Four different kinetic models were used to analyze the experimental CBZ adsorption kinetic data. Among the tested various models, the pseudo-second-order kinetic model provided the best simulation to the kinetic data of both adsorbents with R2 of being 0.98 and above, and activation energy of 31.8 and 16.45 kJ/mol, respectively. The determined adsorption rate constant of the steam activated hydrochars ((5.88–9.09) × 10−5) was much higher than that of the hydrochars ((2.81–6.44) × 10−6). Further, X-ray photoelectron spectra and Near-Edge X-Ray Absorption Fine Structure (NEXAFS) Spectroscopy analysis were done before and after loading with CBZ. The results show π-π electron donor-acceptor interaction played an important role on the adsorption mechanism of CBZ on the above-mentioned adsorbents. Hydrophobic interaction also contributed while the roles of hydrogen bonding and electrostatic attraction between CBZ and adsorbents with opposite charges is insignificant. Pore filling may contribute. Desorption of CBZ from CBZ-loaded adsorbents was investigated with various organic solvents, and water at different pH (2, 6 and 10), among which ethanol was the most effective solvent to desorb CBZ. Overall, the results demonstrated that flax shives are promising feedstocks to be made into adsorbents for treatment of wastewater contaminated by antibiotics and additional pharmaceuticals. [Display omitted] • Flax shive adsorbents were used to adsorb carbamazepine (CBZ). • The adsorption kinetics was investigated at various temperatures and times. • The mechanisms of CBZ adsorption were elucidated. • Desorption of CBZ from CBZ-loaded adsorbents was investigated with various organic solvents and water at different pH. [ABSTRACT FROM AUTHOR]

The worldwide challenge of eliminating pharmaceutical contaminants requires immediate attention. Developing bio-based catalysts that are eco-friendly, reusable, and high-performance, employing starch (ST) and montmorillonite (MMT) as support, holds tremendous promise as a novel biocatalyst for pharmaceutical waste removal. In this study, a montmorillonite/α-Fe 2 O 3 /starch (MMT/α-Fe 2 O 3 /ST) bio-nanocomposite photocatalyst was successfully synthesized and used for acetaminophen (ACT) degradation under UVA-LED irradiation. The influence of operational factors, such as catalyst, ACT concentrations, and solution pH, on photocatalytic activity was examined in detail; catalyst: 0.75 g/L, pH: 7.1, leading to total ACT (10 mg/L) removal in ∼80 min. MMT/α-Fe 2 O 3 /ST showed excellent durability due to negligible Fe leaching. After four successive degradation cycles, ACT and TOC elimination efficiencies remained over 91 and 42.7 %. Compared to other anions studied, carbonate ions suppressed the most ACT degradation. Based on the radical scavenger experiments, hydroxyl and superoxide radicals and holes were involved in the MMT/α-Fe 2 O 3 /ST system. LC-MS results were used to propose ACT degradation pathways. This work illuminated the significance of biocatalysts in removing emerging pollutants from wastewater. • The MMT/α-Fe 2 O 3 /ST bio-nanocomposite degraded ACT under UVA-LED irradiation extremely effectively. • Proposed a degradation mechanism based on the identified intermediates. • Carbonate and nitrite ions inhibited ACT degradation, while chloride ions had no effect. • After four degradation cycles, ACT and TOC elimination efficiencies remained over 91 and 42.7 %. [ABSTRACT FROM AUTHOR]

There is a pressing need for sustainable sources of proteins to address the escalating food demands of the expanding global population, without damaging the environment. Lentil proteins offer a more sustainable alternative to animal-derived proteins (such as those from meat, fish, eggs, or milk). They are abundant, affordable, protein rich, nutritious, and functional, which makes them highly appealing as ingredients in the food, personal care, cosmetics, pharmaceutical and other industries. In this article, the chemical composition, nutritional value, and techno-functional properties of lentil proteins are reviewed. Then, recent advances on the extraction, purification, and modification of lentil proteins are summarized. Hurdles to the widespread utilization of lentil proteins in the food industry are highlighted, along with potential strategies to surmount these challenges. Finally, the potential applications of lentil protein in foods and beverages are discussed. The intention of this article is to offer an up-to-date overview of research on lentil proteins, addressing gaps in the knowledge related to their potential nutritional benefits and functional advantages for application within the food industry. This includes exploring the utilization of lentil proteins as nanocarriers for bioactive compounds, emulsifiers, edible inks for 3D food printing, meat analogs, and components of biodegradable packaging. [Display omitted] • Lentil is a green and promising source of alternative protein. • Lentil protein has potential applications for foods and beverages. • Several strategies can be used to enhance their functional properties. • Lentil protein is a great candidate as a nanocarrier and emulsifier. [ABSTRACT FROM AUTHOR]

Many consumers are replacing protein-rich animal-derived foods like meat, seafood, egg, and dairy products with plant-based alternatives in their diet for animal welfare, environmental, and human health reasons. High-intensity ultrasound processing is an energy-efficient technology that can be used to modify food products physically and chemically. It can be used to enhance the extraction, functionality, and sensory attributes of plant proteins, as well as to improve their digestibility. The use of high-intensity ultrasound in the processing of plant-derived proteins is reviewed, with an emphasis on its impact on their digestibility. The underlying mechanisms of action of sonication are highlighted, and its ability to enhance the digestibility of proteins from various plant sources is discussed, including cereals, pseudocereals, legumes, oilseeds, and green leaves. Challenges associated with the utilization of ultrasonic processing of plant proteins are also reviewed. The high-intensity fluctuating pressure waves generated during ultrasonication can increase protein digestibility through various mechanisms, including increasing its release from plant structures, disrupting protein aggregates, and altering protein structure. In general, ultrasonication can modify the primary, secondary, tertiary, and quaternary structures of proteins. This transformation can enhance the digestibility of plant proteins by exposing active hydrolytic sites, thus facilitating their breakdown by digestive enzymes. Moreover, the disruption of anti-nutritional compounds during ultrasonication can result in enhanced protein digestibility. These high-intensity ultrasound treatments can be used to improve the digestibility of plant proteins, thereby improving their nutritional profile. [Display omitted] • Ultrasound processing can be used to enhance the digestibility of plant proteins. • Disruption of anti-nutritional compounds can result in improved protein digestibility. • Nutritional quality of plant proteins enhanced by ultrasound treatment. • Ultrasonication improved the protein digestibility by structural transformation. [ABSTRACT FROM AUTHOR]

Pharmaceutical pollution has emerged as a growing concern. This research reported the pharmaceutical adsorption from water using adsorbents derived from agricultural byproduct oat hulls, representing natural cellulosic material. Carbamazepine commonly used to treat seizure was used as the model pharmaceutical pollutant. The adsorption kinetics was investigated in aid of mathematical models. The oat hull-derived steam activated hydrochars (SAHC) demonstrated notably higher adsorption rate constants ranging from 0.032 to 0.055 g/(mg·h), in contrast to their respective hydrochars (HC) without steam activation, which ranged from 0.002 to 0.014 g/(mg·h). The carbamazepine adsorption was primarily driven by the π-π electron donor-acceptor interaction, with hydrophobic interaction being a secondary factor. Hydrogen bonding and electrostatic attraction were found to be insignificant, and pore filling could also involve in the overall adsorption process. The desorption aimed to explore the process of removing carbamazepine from the loaded adsorbents by utilizing organic solvents and water under varying pH conditions (2, 6, and 10). Higher desorption efficiency, ranging from 50% to 53%, was achieved in the presence of ethanol. As per the findings of the study, oat hulls showed the potential to be utilized as a feasible feedstock for developing effective adsorbents to treat wastewater contaminated with antibiotics and pharmaceuticals similar to CBZ. [Display omitted] • The oat hull-derived steam activated hydrochars adsorbed CBZ faster than the hydrochars. • The CBZ adsorption was analyzed in aid of various adsorption kinetic models. • CBZ adsorption mechanism was elucidated, revealing multiple interactions. • Ethanol demonstrated efficient desorption of carbamazepine from the adsorbents. [ABSTRACT FROM AUTHOR]

Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible. To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO's software framework developed in Matlab which implements most of the state-of-the-art methods in the machine learning community. To demonstrate its utility, we chose a heterogeneous case study scene, a landscape in Southwest Iran to map PTs. In this area, four main PTs were identified, consisting of shrub land, grass land, semi-shrub land, and shrub land–grass land vegetation. Having developed 21 MLCAs using the same training and validation, datasets led to varying accuracy results. Gaussian process classifier (GPC) was validated as the top-performing classifier, with an overall accuracy (OA) of 90%. GPC follows a Laplace approximation to the Gaussian likelihood under the supervised classification framework, emerging as a very competitive alternative to common MLCAs. Random forests resulted in the second-best performance with an OA of 86%. Two other types of ensemble-learning algorithms, i.e., tree-ensemble learning (bagging) and decision tree (with error-correcting output codes), yielded an OA of 83% and 82%, respectively. Following, thirteen classifiers reported OA between 70% and 80%, and the remaining four classifiers reported an OA below 70%. We conclude that GPC substantially outperformed all classifiers, and thus, provides enormous potential for the classification of a diversity of land-cover types. In addition, its probabilistic formulation provides valuable band ranking information, as well as associated predictive variance at a pixel level. Nevertheless, as these are supervised (data-driven) classifiers, performances depend on the entered training data, meaning that an assessment of all MLCAs is crucial for any application. Our analysis demonstrated the efficacy of ARTMO's MLCA toolbox for an automated evaluation of the classifiers and subsequent thematic mapping. [ABSTRACT FROM AUTHOR]

Objective: Due to the Covid-19 pandemic crisis, which causes stress and threatens health, especially in vulnerable groups including pregnant women, the present study was conducted to determine the effect of individual counseling on the pregnant women's stress of Covid-19. Materials and methods: In this randomized controlled clinical trial, 66 pregnant women in 24 to 28 weeks were randomly divided into two groups of intervention (33 participants) and control (33 participants). At the beginning of the study, both groups completed the questionnaires of demographic information and pandemic related pregnancy test. Then, the intervention was performed in 3 sessions with the interval of one week, in the form of individual counseling by BELIFE method for the experimental group. The control group received only the routine services of the centers. The post-test was performed two weeks after the last consultation session for both groups. Data were analyzed using Stata-13 software. Significance level was considered 0.05. Results: The experimental and control groups were almost homogeneous in terms of quantitative and qualitative demographic variables. Total mean and standard deviation of stress score, before and after the intervention in the experimental group, were 40.27 ± 12.65 and 41.71 ± 1.74, respectively. These numbers in the control group were 33.84 ± 13.08 and 43.84 ± 1.69. Comparing the two groups in terms of stress score which was done after the intervention showed that although the stress score in the experimental group was lower than that in the control group, this difference was not statistically significant (p = 0.39). Conclusion: The results of this study show that although individual counseling for pregnant women was able to reduce the mean scores of stress of Covid-19 in the experimental group, this difference was not statistically significant. Therefore, although the BELIFE individual counseling method for pregnant women, who naturally suffer from pregnancy stress, is an acceptable way to reduce their stress, it is recommended to plan and implement early and more effective interventions for these women because the course of stress is severe in them and has an upward trend during Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

A common issue in longitudinal studies is that subjects' visits are irregular and may depend on observed outcome values which is known as longitudinal data with informative observation times (follow‐up). Semiparametric regression modeling for this type of data has received much attention as it provides more flexibility in studying the association between regression factors and a longitudinal outcome. An important problem here is how to select relevant variables and estimate their coefficients in semiparametric regression models when the number of covariates at baseline is large. The current penalization procedures in semiparametric regression models for longitudinal data do not account for informative observation times. We propose a variable selection procedure that is suitable for the estimation methods based on pseudo‐score functions. We investigate the asymptotic properties of penalized estimators and conduct simulation studies to illustrate the theoretical results. We also use the procedure for variable selection in semiparametric regression models for the STAR*D dataset from a multistage randomized clinical trial for treating major depressive disorder. [ABSTRACT FROM AUTHOR]

Detecting communities in complex networks is of paramount importance, and its wide range of real-life applications in various areas has caused a lot of attention to be paid to it, and many efforts have been made to have efficient and accurate algorithms for this purpose. In this paper, we proposed a non-cooperative game theoretic-based algorithm that is able to detect overlapping communities. In this algorithm, nodes are regarded as players, and communities are assumed to be groups of players with similar strategies. Our two-phase algorithm detects communities and the overlapping nodes in separate phases that, while increasing the accuracy, especially in detecting overlapping nodes, brings about higher algorithm speed. Moreover, there is no need for setting parameters regarding the size or number of communities, and the absence of any stochastic process caused this algorithm to be stable. By appropriately adjusting stop criteria, our algorithm can be categorized among those with linear time complexity, making it highly scalable for large networks. Experiments on synthetic and real-world networks demonstrate our algorithm's good performance compared to similar algorithms in terms of detected overlapping nodes, detected communities size distribution, modularity, and normalized mutual information. [ABSTRACT FROM AUTHOR]

Pollution of water sources by pharmaceuticals has attracted attention. In this work, carbamazepine (CBZ), as a model pharmaceutical pollutant, was used to investigate its characteristics of adsorption from an aqueous solution. Flax shives and oat hulls, representing cellulosic materials of agricultural by-products, were characterized and used to develop hydrochars and steam activated hydrochars adsorbents. Hydrothermal treatment and steam activation significantly enhanced the CBZ adsorption capacity. The isotherms of CBZ adsorption by the steam activated hydrochars were experimentally investigated at various temperatures (20–40 °C) and satisfactorily simulated by the Sips model. The steam activated hydrochars of flax shives and oat hulls had higher maximum CBZ adsorption capacities of 293.5 and 162.6 mg/g than the respective hydrochars (75.3 and 81.8 mg/g) and raw materials (16.5 and 15.0 mg/g). Based on the isotherm modeling results, the site energy distribution of CBZ adsorption was further determined. The weighted means of site energy obtained at 40 °C (13.3–15.0 kJ/mol) were higher than those at 30 °C (11.2–13.4 kJ/mol) and 20 °C (7.7–8.0 kJ/mol), indicating higher adsorption affinity, thus more favorable adsorption. Furthermore, the thermodynamic parameters of CBZ adsorption were determined. The results indicated that the CBZ adsorption on hydrochars and steam activated hydrochars were endothermic and spontaneous. [ABSTRACT FROM AUTHOR]

If trace amounts of antibiotics remain in the environment, they can lead to microbial pathogens becoming resistant to antibiotics and putting ecosystem health at risk. For instance, ciprofloxacin (CIP) can be found in surface and ground waters, suggesting that conventional water treatment technologies are ineffective at removing it. Now, a rGO/g-C 3 N 4 /SiO 2 nanocomposite was synthesized in this study to activate peroxydisulfate (PDS) under UVA-LED irradiation. UVA-LED/rGO-g–C 3 N 4 –SiO 2 /PDS system performance was evaluated using Ciprofloxacin as an antibiotic. Particularly, rGO/g-C 3 N 4 /SiO 2 showed superior catalytic activity for PDS activation to remove CIP. Operational variables, reactive species determination, and mechanisms were investigated. 0.85 mM PDS and 0.3 g/L rGO/g-C 3 N 4 /SiO 2 eliminated 99.63% of CIP in 35 min and mineralized 59.78% in 100 min at pH = 6.18. By scavenging free radicals, bicarbonate ions inhibit CIP degradation. According to the trapping experiments, superoxide (O 2 •−) was the main active species rather than sulfate (SO 4 •−) and hydroxyl radicals (•OH). RGO/g-C 3 N 4 /SiO 2 showed an excellent recyclable capability of up to six cycles. The UVA-LED/rGO-g–C 3 N 4 –SiO 2 /PDS system was also tested under real conditions. The system efficiency was reasonable. By calculating the synergistic factor (SF), this work highlights the benefit of combining composite, UVA-LED, and PDS. UVA-LED/rGO-g–C 3 N 4 –SiO 2 /PDS had also been predicted to be an eco-friendly process based on the results of the ECOSAR program. Consequently, this study provides a novel and durable nanocomposite with supreme thermal stability that effectively mitigates environmental contamination by eliminating antibiotics from wastewater. [Display omitted] • The constituents exhibited a powerful synergistic influence. • RGO/g-C 3 N 4 /SiO 2 demonstrated remarkable recyclability for a maximum of six cycles. • The degradation of CIP by various anions was evaluated and 59.78% were mineralized. • Toxicity estimation of the intermediate recognized was achieved by ECOSAR. [ABSTRACT FROM AUTHOR]

During the Late Jurassic-Early Cretaceous, a significant gas-bearing siliciclastic succession was deposited in the Kopeh Dagh fold-thrust belt in northeastern Iran. Study of the regional evolution of depositional system and geodynamic setting of the red beds continental strata can help in a better understanding of the development of an intracontinental extension in this part of the Eurasia Plate. However, this evolution is driven by faulting events that are not fully understood. Facies analysis in wells and outcrops of the Upper Jurassic-Lower Cretaceous Mozduran and Shurijeh formations in the Saleh Abad area, the easternmost part of the Kopeh Dagh Basin, led to the identification of two fluvial braided river systems in the Shurijeh Formation. Provenance and sedimentological studies indicate that siliciclastic sediments were supplied from the south into the north during the Late Jurassic-Early Cretaceous in the Kopeh Dagh Basin. The higher compositional and textural maturity in the upper part of the Shurijeh Formation relative to the lower part indicate more recycling and more extensive transport of the sediments. Two stages of normal faulting can be identified based on the tectonostratigraphic evolution in this interval: The first phase was initiated with polymictic conglomerates during the Late Jurassic in the lower part of the Shurijeh Formation, while the basin-controlling fault was a buried structure. The second phase started with an influx of quartz conglomerates when the basin-controlling fault reached the surface in the middle part of the Shurijeh Formation. This study integrates new field observations and interpretations to enhance understanding of a complex history of multiphase deformation controlled facies patterns, structural geometries of the Late Jurassic-Early Cretaceous. Renewed Cretaceous extension generated accommodation space for the transgressive Shurijeh Formation. • We present the first precise Tectonostratigraphy of the Upper Jurassic-Lower Cretaceous siliciclastics in the Eastern Kopeh Dagh fold and thrust belt, Iran. • New regional stratigraphical and depositional models with detailed tectonic data are presented. • We show the importance of tectonic inheritance and inversion processes in the belt. • We discuss the large-scale geodynamic evolution of NE Iran based on our new data. [ABSTRACT FROM AUTHOR]

The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point clouds. This paper presents a comparative study on the effects of the polarization design of a radar antenna on further improving the probability of detecting persistent scatterers. For this purpose, we introduce an extension of the existing scattering property-based generalized likelihood ratio test (GLRT) with realistic dependence on the transmitted/received polarizations. The test is based upon polarization basis optimization by synthesizing all possible polarimetric responses of a given scatterer from its measurements on a linear orthonormal basis. Experiments on both simulated and real data show, by means of objective metrics (probability of detection, false alarm rate, and signal-to-noise ratio), that polarization waveform optimization can provide a significant performance gain in the detection of multiple scatterers compared to the existing full-polarization-based detection method. In particular, the increased density of detected PSs at the studied test sites demonstrates the main contribution of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a possibilistic bi-objective bi-level programming model is proposed to optimize the management of scarce drugs supply and rationing under difficult situations such as COVID-19 outbreak. Multiple followers and single leader exist in the proposed Stackelberg model in which the followers are divided into two groups, manufacturers and suppliers. The first group has priority over the latter. The competition between these two groups is addressed using a lexicographical procedure. Moreover, the interaction between the individuals in each group is incorporated through a normalized Nash equilibrium. To avoid the discriminative allocations, the disability-adjusted life years (DALY) index is employed to improve the rationing process. Due to the significance of uncertainty in such a problem, a novel risk-based possibilistic expected value approach is devised to control the mean and risk values of the objective functions. Finally, to validate the proposed solution method, the model is solved by applying Karush–Kuhn–Tucker (KKT) conditions and using the data extracted from a real case study. The results reveal the applicability and usefulness of the proposed possibilistic model compared to the conventional possibilistic approach [ABSTRACT FROM AUTHOR]

Material transfer during scratching is a direct result of adhesion and abrasive processes. In this work, molecular dynamics simulations are performed to quantify the individual effect of adhesive strength and abrasion depth on material transfer during nanoscratching of Tungsten. It is found that while the influence of adhesion and abrasion is tightly coupled at small scratching depths, the coupling decreases by increasing the scratching depth. Furthermore, it is shown that there exists a critical adhesive strength as a function of scratching depth at which the material removal mechanism transitions from the atom-by-atom to the wear fragment removal. This study confirms that the contribution of the atom-by-atom removal mechanism is suppressed when ploughing dominates the process of material removal. [ABSTRACT FROM AUTHOR]

Interacting strategies in evolutionary games is studied analytically in a well-mixed population using a Markov chain method. By establishing a correspondence between an evolutionary game and Markov chain dynamics, we show that results obtained from the fundamental matrix method in Markov chain dynamics are equivalent to corresponding ones in the evolutionary game. In the conventional fundamental matrix method, quantities like fixation probability and fixation time are calculable. Using a theorem in the fundamental matrix method, conditional fixation time in the absorbing Markov chain is calculable. Also, in the ergodic Markov chain, the stationary probability distribution that describes the Markov chain's stationary state is calculable analytically. Finally, the Rock, scissor, paper evolutionary game are evaluated as an example, and the results of the analytical method and simulations are compared. Using this analytical method saves time and computational facility compared to prevalent simulation methods. [ABSTRACT FROM AUTHOR]

Polymers are known as thermally insulated materials with reported effective thermal conductivity (Keff) in the range of 0.1 to 0.5 Wm−1 K−1. However, increasing demand for smaller and more powerful electronics has created the need for thermally conductive polymers for use in heat exchangers and electronic packaging applications. Given this background, much research has been done over the past two decades to increase the Keff of polymers. Based on the strategy involved, those works can be divided into two main categories: (i) increasing the Keff of the neat polymer by aligning its chains orientation; and (ii) increasing polymer Keff by fabrication of polymeric composites with thermally conductive filler networks. Among these two strategies, the former is limited to nanoscale laboratory research and is difficult to scale up for mass production. Therefore, this work is mainly focused on the latter category, thermally conductive polymeric composites, which has a higher potential for large‐scale production. This work aims to summarize, evaluate, and highlight the successful strategies of the recent efforts in enhancing the thermal conductivity of polymer composites. The major achievements, future challenges, and the outlooks of high thermally conductive polymeric composites are presented by analyzing the results of about 300 works. [ABSTRACT FROM AUTHOR]

The Bell's inequality is a strong criterion to distinguish classical and quantum mechanical aspects of reality. Its violation is the net effect of the existence of non-locality in systems, an advantage for quantum mechanics over classical physics. The quantum mechanical world is under the control of the Heisenberg uncertainty principle that is generalized by quantum gravity scenarios, called generalized uncertainty principle (GUP). Here, the effects of GUP on the square of Bell operators of qubits and qutrits are studied. The achievements claim that the violation quality of the square of Bell inequalities may be a tool to get a better understanding of the quantum features of gravity. In this regard, it is obtained that the current accuracy of the Stern–Gerlach experiments implies upper bounds on the values of the GUP parameters. [ABSTRACT FROM AUTHOR]

Background: Even within the most populous countries in the Middle East, such as Iran, autoimmune encephalitis cases have been rarely reported. Objective: We aimed to describe the demographic, clinical, and paraclinical characteristics of Iranian patients with autoimmune encephalitis positive for anti-neuronal autoantibodies. Methods: This cross-sectional study included all patients diagnosed with autoimmune encephalitis and referred to our hospital, in Isfahan, Iran, from March 2016 to May 2020. Patients' demographic, clinical, laboratory, radiological, and electroencephalographic features were obtained from their medical records. Results: We identified a total of 39 (21 females, 53.8%) patients with autoimmune encephalitis (mean age = 34.9 ± 12.8 years). The most commonly detected antibody was anti-NMDAR (n = 26, 66.7%), followed by anti-GABABR (n = 8, 20.5%), anti-Zic4 (n = 4, 10.3%), and anti-GAD65 (n = 1, 2.6%) antibodies, in descending order of frequency. Two anti-NMDAR-positive patients had a history of systemic lupus erythematosus (SLE), and four had a prior history of herpes simplex encephalitis. Clinical presentations in patients positive for anti-Zic4 antibodies included cognitive decline (n = 4, 100%), seizures (n = 3, 75%), parkinsonism (n = 1, 25%), and stiff-person syndrome (n = 1, 25%). Conclusion: This was the first case series of Iranian patients with autoimmune encephalitis with some interesting observations, including SLE-associated anti-NMDAR encephalitis, as well as an unusual concurrence of anti-Zic4 antibody positivity and cognitive problems, seizures, parkinsonism, and stiff-person syndrome. [ABSTRACT FROM AUTHOR]

The low thermal conductivity of phase change materials has resulted in prolonged melting and freezing processes (charge and discharge) in these materials. This problem has limited the application of these materials in the field of thermal energy storage. In the present study, the effects of adding Fe3O4 magnetic nanoparticles at various concentrations as well as applying the magnetic field on the melting process of paraffin as phase change material have been experimentally studied. Thereupon, a cubic chamber in which the left wall applied a constant heat flux was used. At the optimum concentration of nanoparticles (1 mass%), the constant magnetic field with the intensities of 0.01 T and 0.02 T was applied and compared with the case of without field. It was inferred that using Fe3O4 magnetic nanoparticles, due to the increment of thermal conductivity, leads to a decrease in the temperature gradient in the horizontal direction and thus a decrease in melting time. Moreover, applying the magnetic field, due to the formation of high conductive clusters of nanoparticles, reduced the melting time and improved the heat transfer in paraffin. By using an optimum concentration of nanoparticles (1 mass%), in the absence and presence of the magnetic field, melting time is reduced by 8% and 12%, approximately. [ABSTRACT FROM AUTHOR]

Background: A great number of sedatives and anaesthetics have been used to perform surgeries or routine ophthalmologic examinations in animals and sometimes the combination of these medicines has more suitable effects than each one alone. Objectives: This paper aims to explore the main effects of Medetomidine + Acepromazine, Dexmedetomidine + Acepromazine on intraocular pressure, tear secretion and pupil diameter. Methods: To accomplish the aforementioned aim, 32 adult dogs (aged one‐to‐three‐years‐old) were clinically examined. Dogs were divided into four groups consisting of group DA, Dexmedetomidine (5 µg/kg) + Acepromazine (0.05 mg/kg); Group D, Dexmedetomidine (5 µg/kg); Group M, Medetomidine (10 µg/kg); Group MA, Medetomidine (10 µg/kg) + Acepromazine (0.05 mg/kg). The ocular factors including tear production, pupil diameter and intraocular pressure of both right and left eyes were first measured and then recorded in each dog at time T0 (−15 min). Afterwards, the drugs were administered intramuscularly, based on which the ocular factors were re‐measured at T1 (+5 min), T2 (+15 min) and T3 (+20 min). All four groups showed a reduction in intraocular pressure, which was significant in DA, D and M groups. Results: Furthermore, there was a fluctuation in the amount of tear secretion in DA and D groups (increase and then decrease), as well as a significant reduction in M and MA groups. Decreasing in pupil diameter also occurred in all four groups, but the reduction was significant only in DA and MA groups. Conclusion: According to the results obtained, as the changes caused by the systemic administration of the above drug compounds did not exceed the physiological range, it can be concluded that these combinations could be utilized as suitable sedatives or pre‐anaesthetic compounds in the eye surgeries. [ABSTRACT FROM AUTHOR]

Business recovery after a disaster plays an important role in the socioeconomic recovery of a community. This study focuses on the development of a probabilistic modelling approach for quantifying and predicting business recovery through Bayesian linear regression. The proposed modelling approach consists of three steps including data collection, development of model forms, and model selection through rigorous evaluation and elimination steps. Four attributes, namely business cease operation days, revenue recovery, customer retention, and employee retention, which describe the post-disaster recovery state of a business, are considered. One of the main contributions of this study is incorporating the interplay between household and businesses in a community in developing predictive business recovery models. Towards that direction, different methods to account for the effect of household recovery into the customer retention rate of a business are investigated and proposed. As an application, the proposed modelling approach is applied on the results of a longitudinal field study at the community of Lumberton, NC, which was heavily impacted by the 2016 Hurricane Matthew, focusing on business recovery. The predictive models proposed in this study may be further applicable in risk-based resilience assessment of communities following disastrous events. [ABSTRACT FROM AUTHOR]

[Display omitted] • Emerging technologies for protein extraction from amaranth are summarized. • Amaranth proteins could be used for formulating nanoparticle-based delivery systems. • The anti-nutritional factors of amaranth proteins are also reviewed. • Amaranth proteins provide great potential for improving the stability of bioactives. Amaranth proteins can be produced more sustainably than animal proteins, and they have amino acid compositions that are nutritionally balanced, which makes them attractive candidates for various applications in the food and pharmaceutical industries. This article provides an overview of the composition and techno-functional properties of amaranth protein, including its solubility, emulsification, gelation, foaming, and binding properties. These properties play an important role in the use of amaranth proteins for formulating nanoparticle-based delivery systems with good functional attributes. Amaranth proteins have structural and physicochemical properties suitable for fabricating protein-based nanoparticles. These nanoparticles can be used to encapsulate and control the release of bioactive compounds. However, challenges associated with the presence of anti-nutritional factors in amaranth proteins need to be addressed. These antinutrients negatively affect the bioavailability and digestibility of proteins and bioactive compounds. Hence, strategies to mitigate these challenges are discussed, including processing technologies and genetic engineering methods. [ABSTRACT FROM AUTHOR]

This study presents an approach to enhancing and expanding the structural performance of composite materials by tailoring their geometry. We explored the potential of continuous carbon fibre composite additive printing to create complaint frames based on hexagonal cell design with the aim to better understand and control their mechanical performance. Our investigation examines the failure behaviour of these frames under remote tensile loading. By experimenting with various geometries and aspect ratios at the frame sites, we gained insight into different failure loads and modes. To predict these results, we developed a computational model based on multiscale homogenisation and the Hashin damage criterion, which showed a high degree of precision compared to our experimental results. The findings validate the effectiveness of our computational model, but also highlight the practical applications of additive manufacturing of composites. This research aims to contribute to the advancement of structural design and material optimisation by engineering of composite materials for specific applications, emphasising the integration of their intrinsic strength and lightweight properties with material efficiency and compliance achieved through geometric design considerations. [Display omitted] • Systematic investigation of mechanics and failure of 3D printed continuous fibre composite hexagonal frames. • Experimental and numerical analysis of pre and post failure behaviour. • Multiscale homogenisation of additive manufactured continuous fibre composite frame. • Application of Hashin failure criteria to 3D printed composites. • Reliable prediction of failure loads and failure modes in compliant structure. [ABSTRACT FROM AUTHOR]

There is a pressing need for affordable, abundant, and sustainable sources of proteins to address the rising nutrient demands of a growing global population. The food and agriculture sectors produce significant quantities of waste and by-products during the growing, harvesting, storing, transporting, and processing of raw materials. These waste and by-products can sometimes be converted into valuable protein-rich ingredients with excellent functional and nutritional attributes, thereby contributing to a more circular economy. This review critically assesses the potential for agro-industrial wastes and by-products to contribute to global protein requirements. Initially, we discuss the origins and molecular characteristics of plant proteins derived from agro-industrial waste and by-products. We then discuss the techno-functional attributes, extraction methods, and modification techniques that are applied to these plant proteins. Finally, challenges linked to the safety, allergenicity, anti-nutritional factors, digestibility, and sensory attributes of plant proteins derived from these sources are highlighted. The utilization of agro-industrial by-products and wastes as an economical, abundant, and sustainable protein source could contribute towards achieving the Sustainable Development Agenda's 2030 goal of a "zero hunger world", as well as mitigating fluctuations in food availability and prices, which have detrimental impacts on global food security and nutrition. [ABSTRACT FROM AUTHOR]

This paper presents the use of modern survey techniques, particularly light detection and ranging (LiDAR) scanning, to collect time-sensitive information before and after shake table experiments. Two full-scale, three-story residential buildings were tested simultaneously on the largest shake table in the world. The focus of this study is on the use of LiDAR to document observations during these tests. The challenges experienced during this study prompted the development of a formalized survey procedure using LiDAR scanning techniques, which can be used by other researchers when planning to collect such time-sensitive data from similar experimental programs. In this paper, damage assessment through visual inspection, which is commonly performed during full-scale tests, is compared to postexperiment assessments using postprocessed LiDAR-derived point clouds. Various examples of damage to structural and nonstructural components, including columns, bracing, partition walls, and façades, are illustrated through postshaking visual inspections as well as LiDAR-derived point clouds. The feasibility of making accurate measurements using LiDAR point clouds, and automatically detecting damage using the point-to-point cloud comparison, is presented. Finally, the relationship between observations through traditional instruments (e.g., accelerometers and laser meters) and LiDAR is discussed. In one example, the measurements from eight laser meters around the buildings are used to validate the measurements obtained using LiDAR point clouds. It is concluded that observations through LiDAR are complementary to those from traditional instruments, while permanent/residual displacements after the tests can be measured from both traditional and modern instruments. [ABSTRACT FROM AUTHOR]

In this paper, we construct dark halo models which are merely supported by geometry of space time. We concentrate on the inhomogeneous space time around some spiral galaxies whose have dark matter. After characterizing the dark halo models, the possibility of the energy density of such a field which could justify the rotation curves of stars in the dark halo is explored. [ABSTRACT FROM AUTHOR]

In this study, the effect of surface activation of multi-walled carbon nanotubes (MWCNTs) by KOH along with loading of cobalt and lithium nanoparticles on the surface of MWCNTs are investigated. In the first step, surface activation parameters, i.e. MWCNT/KOH weight ratio, activation temperature, and activation time are optimized to give the highest hydrogen uptake. According to obtained results, the optimum synthesis conditions are MWCNT/KOH weight ratio of 1:5, 800 °C, and 1 h of activation duration. Afterward, cobalt and lithium metal nanoparticles are doped discretely on the surface of activated nanotubes. It is demonstrated that amounts of loaded cobalt and lithium metals are 5.5 and 1.9% wt, respectively. In addition, it is revealed that the amount of hydrogen storage capacity for cobalt-loaded and lithium-loaded MWCNTs are 1.06% wt. and 1.33% wt., respectively (at 278 K) which are higher than the capacity of pristine and activated MWCNT samples. • KOH activation has positive effect on porous structure and H 2 uptake of MWCNTs. • Optimization of MWCNT activation condition results in enhancement of H 2 uptake. • Cobalt and lithium doping could improve H 2 uptake through spillover mechanism. • Decomposition of H 2 molecules to atoms causes better migration to adsorbent cavities. • Lithium doping revealed the ability of this metal to enhance H 2 uptake of MWCNTs. [ABSTRACT FROM AUTHOR]

There is an urgent need for affordable, abundant, nutritionally balanced, and sustainable sources of proteins to meet the food demands of the growing global population. The utilization of sunflower meal/cake proteins (SMPs) in the food industry is currently limited due to their high levels of antinutritional factors, especially phenolics, as well as their limited functional properties. This review critically evaluates the potential of SMPs to meet global dietary protein requirements. Initially, the chemical composition and nutritional value of sunflower meal/cake are discussed. Then, the techno-functional attributes of SMPs are highlighted. Finally, challenges to using SMPs in the food industry are highlighted, as well as potential strategies to overcome them. The utilization of sunflower seed meal has the potential to alleviate malnutrition in severely affected countries. However, antinutritional factors must be removed from sunflower meal to improve its nutritional quality. This may be achieved using various processing technologies, including ultrasound, membrane, solvent, and heat treatments. The current review highlights the possible contribution of SMPs to address three of the United Nations' sustainable development goals: Goal 2 'Zero hunger'; Goal 3 'Good health and well-being'; and Goal 12 'Sustainable Food Production and Consumption'. [Display omitted] • Meeting food protein demands using sunflower oil industry by-products. • Sunflower waste is a sustainable source of functional food protein. • The application of sunflower meal/cake proteins in food is limited. • Sunflower meal/cake has the potential to alleviate malnutrition. [ABSTRACT FROM AUTHOR]

[Display omitted] • Electrospinning is a powerful tool for fabricating plant protein nanofibers. • Utilization of electrospun plant protein nanofibers in the food industry is discussed. • Plant proteins are blended with synthetic polymers to enhance their spinnability. • Zein is the most common plant protein for creating electrospun nanofibers. Electrospinning is a relatively simple technology capable to produce nano- and micron-scale fibers with different properties depending on the electrospinning conditions. This review critically investigates the fabrication of electrospun plant protein nanofibers (EPPNFs) that can be used in food and food packaging applications. Recent progress in the development and optimization of electrospinning techniques for production of EPPNFs is discussed. Finally, current challenges to the implementation of EPPNFs in food and food packaging applications are highlighted, including potential safety and scalability issues. The production of plant protein nanofibers and microfibers is likely to increase in the future as many industries wish to replace synthetic materials with more sustainable, renewable, and environmentally friendly biopolymers. It is therefore likely that EPPNFs will find increasing applications in various fields including active food packaging and drug delivery. [ABSTRACT FROM AUTHOR]

We develop a new approach to formulation and solution of mathematical models of one-dimensional advective–dispersive transport that specifies the hydrodynamic dispersion coefficient as a function of time since the solute has entered the flow field, termed 'age.' This approach addresses Taylor's concern about the use of time-dependent dispersion coefficients to model pre-asymptotic dispersion by replacing time-dependence with age-dependence, where age of solute is exposure-time to the flow. We show closed form solutions obtained for transport on the − ∞ < x < ∞ and a numerical solution for transport on the 0 < x < ∞ domain. We demonstrate how this works by application to an in-silico experiment recently published in a study addressing the same issue in a different manner. Our simple and intuitive approach matches the simulated pre-asymptotic data without additional terms or parameter fitting. The same principle applies to pre-asymptotic dispersion in other important upscaled one-dimensional transports e.g., in river corridor or groundwater flows. [Display omitted] Distribution of solute concentration over space, time and age resulting from two sequential pulses at x = 0 and pre-asymptotic advective–dispersive transport. • Age, not time or space, is used to model pre-asymptotic dispersion. • This addresses Taylor's critique about upscaled models of pre-asymptotic dispersion. • We obtain new closed-form solutions for pre-asymptotic to asymptotic conditions. [ABSTRACT FROM AUTHOR]

Three-body wear is a complex phenomenon occurring when abrasive particles present between two material surfaces. By developing a new experimental setup, we systematically study wear at the contact between stainless steel and three different rubbers (TPU, NBR and LSR) in a slurry environment, i.e. water containing sand particles. Results showed that wear on both metal and rubber pads increases exponentially with the sliding speed, while a weak correlation was observed with the applied normal load for the given range of load. Wear analysis showed that the stainless steel experienced the most wear when in contact with NBR and the least when in contact with LSR. The TPU rubber pads experienced the most wear, whereas the LSR pads barely exhibit any wear when in contact with the stainless steel. Furthermore, frictional measurements indicated an inverse relationship between the system coefficient of friction and the amount of wear observed on the rubber elements. These observations are discussed in terms of the probability of the sand particles being clung to the rubber pads, after which they abrade the steel surface. This probability is justified as a function of the rubber material properties and the system friction coefficient. The findings in this study suggest new directions for minimizing three-body wear at the contact between metals and rubbers. • In slurry environment, Steel surfaces wear most when in contact with rubber. • Sand particles cling to rubber, abrading steel surface via two-body wear. • Steel surface encounters the most and least wear when in contact with NBR and LSR. • LSR and TPU show the minimum and maximum wear when in contact with steel. [ABSTRACT FROM AUTHOR]

The global demand for plant-based proteins is rising due to environmental, sustainability, animal welfare, and human health concerns associated with animal proteins produced by the livestock industry. The most common method for isolating proteins from plant materials is alkaline extraction, however, this method is often not efficient and may damage some proteins. Several innovative processing technologies have been explored to enhance the efficiency of protein extraction, including sonication, microwaving, enzymes, and high-pressure homogenization. These methods typically promote cell wall disruption due to the generation of intense mechanical forces or through physicochemical mechanisms, which enhances extraction. In this review, the potential utilization of enhanced alkaline extraction methods for isolating plant-based proteins and their impact on functional modification of protein alone or in combination with other innovative technologies are evaluated. The research findings clearly indicate that a combination of novel methods is more effective for the recovery and functional modification of plant proteins. The use of this cell disruption approach is more efficient for protein extraction due to its less time and energy consumption Therefore, exploring the combination of alkaline treatments with other innovative technologies could be a promising area of investigation to address the major challenges faced by the food processing industry in terms of maximizing the value of their protein resources. [Display omitted] • Alkaline extraction is the most common method to isolate proteins from plants. • The combination of alkaline extraction with emerging technologies was evaluated. • Alkaline extraction efficiency improved by combining with innovative techniques. • Functional properties enhanced by ultrasound- and enzymatic-assisted extractions. [ABSTRACT FROM AUTHOR]

The system of P 2 O 5 –TiO2–CaO was investigated as a proper host for rare-earth ions. Glasses comprising P 2 O 5 , TiO 2 , and CaO were doped with neodymium (0.5, 1, and 1.5 mol%) through melting method at 1350–1400 °C. UV–Vis spectrum showed a rise in the absorbance by increasing the dopant content. The absorbance at the wavelength of 583 nm was 2.4 times higher than the glass containing 0.5 mol% Nd2O3 (G-Nd 0.5). Optical findings also indicated a decline in the emission intensity at all wavelengths. FT-IR and Raman analysis revealed that Nd entered the structure of the phosphate glass as a modifier. The direct Optical band gap energy showed a decline from 3.38 eV in G-Nd0 to 3.33 eV for G-Nd1.5. Nd3+ incorporation led to an increase in the Urbach energy as the highest and lowest Urbach energies were recorded in G-Nd1 (0.017 eV) and G-Nd0 (0.058). • The system of P 2 O 5 –TiO2–CaO was investigated as a proper host for Nd3+ ions. • UV–Vis spectrum showed a rise in the absorbance by increasing the dopant content. • The direct Optical band gap energy showed a decline from 3.38 eV in G-Nd0 to 3.33 eV for G-Nd1.5. • Nd3+ incorporation led to an increase in the Urbach energy as the highest was recorded in G-Nd0 (0.058). [ABSTRACT FROM AUTHOR]

In this paper, learning social networks from incomplete relationship data is proposed. Link prediction is addressed as a semi-supervised learning problem where the task is to predict a larger part of networks using available knowledge of smaller parts. By this assumption, social network extraction is translated into a classification problem. While in real case scenarios majority of links are unknown, we hypothesis self-training as the most common semi-supervised learning method can provide an effective approach for learning from unlabelled data. We proposed an interpolative self-training technique that leverages node information to generate a set of examples in learning phase along with their connections as their associated labels. The approach generates data by interpolation of documents assigned to a pair of nodes. Documents as the implicit content shared between the individual nodes provide a scope for the estimation of their similarities. Then generated training data are employed in a link prediction model with two different scenarios. The first scenario interprets the link prediction as a conventional classification problem in which we have examples from both positive (link) and negative (no-link) classes. However, the second scenario addresses more realistic case where only some positive examples (links or connections) are known. Social networks are usually very sparse structures. The sparsity of social networks implies that in the classification framework of link prediction, we deal with an imbalance class distribution in which among all possible links there are a few connections (positive class) vs. many disconnections (negative class). In order to deal with class skew and enhance the performance of the classifier, a data selection method based on node similarity was proposed. To evaluate the merit of the proposed methods, a set of experiments were conducted on co-authorship networks of 18 different domains. The result implies the feasibility of achieving significantly high performance for most of the networks using the proposed self training approach. [ABSTRACT FROM AUTHOR]

Block caving is a complex and large-scale mining method. The application of block caving is for low-grade, caveable and massive ore-bodies. Among the underground mining methods available, caving methods are favoured because of their low cost and high level of production. Generating a production schedule that will provide optimal operating strategies without geotechnical constraints is not practical in block caving. Establishing relationships among draw columns to consider depletion rates of other draw columns is complex but essential to provide a reasonable solution for real block-caving mines. This paper presents a mixed-integer linear programming (MILP) model to optimise the extraction sequence of drawpoints over multiple time horizons of block-cave mines with respect to the draw control systems. Four resolutions are formulated in this paper to guarantee practical solutions with respect to draw control managing in mined areas according to the draw rate and conditional draw rate constraints. Application and comparison of the four resolutions for production scheduling based on the draw control systems are presented using 325 drawpoints over 15 periods. The performance was analysed based on maximising the net present value (NPV) at a discount rate of 12%. The maximum obtained NPV from of the proposed MILP models is $451.07M. [ABSTRACT FROM AUTHOR]

The process of material removal during adhesive sliding contact (i.e. adhesive wear) remains one of the least understood areas in the field of tribology. The main reason for this limited understanding is that, direct observation and modeling of third body formation during adhesive contact present notorious challenges [1]. Recent coarse-grained 2D atomistic simulations [2] revealed the existence of a critical junction size, with larger asperity junctions forming third-body wear particles and smaller junctions smoothing plastically. In this study, we examine the critical junction size model in a three-dimensional configuration. We present a 3D coarse-grained model potential and large scale 3D simulations of third-body particle formation in an idealized single asperity contact. Our simulations validate the prediction of the critical junction size model [2] for the formation of third-body wear particles. Directions for future studies on computer modeling of wear processes are discussed. • Using a 3D coarse-grained model potential, we simulate the formulation process of a third body wear particle in an idealized asperity contact. • The junction size model, that is recently formulated based on 2D simulations (Aghababaei et. al., 2016 Nature Comm.), has been validated in an idealized 3D asperities contact. • The longstanding origins of third body formation during adhesive sliding and the absence of third body formation in the previous wear simulations are discussed. • Challenges and directions for future studies on computer modeling of wear processes are discussed. [ABSTRACT FROM AUTHOR]