Field Of Science Research Articles

Time series (2003–15) analysis of selected physicochemical parameters in Indian Ocean: Cumulative impacts prediction on coral bleaching using machine learning

Coral bleaching is an important ecological threat worldwide, as the coral ecosystem supports a rich marine biodiversity to survive. Sea surface temperature was considered a major culprit; however, later it was observed that other water parameters like pH, tCO2, fCO2, salinity, dissolved oxygen, etc. also play a significant role in bleaching. In the present study, all these parameters of the Indian Ocean area for 15 years (2003–2017) were collected and analysed using machine learning language. The main aim is to see the cumulative impacts of various ocean parameters on coral bleaching. Introducing machine learning in environmental impact assessment studies is a new approach, and the prediction of coral bleaching using simulation of physico-chemical parameters interactions shows 70 % accuracy for the prediction of the future bleaching event. This study can be probably the first step in the application of the machine learning language for the prediction of coral bleaching in the field of marine science.

Artificial intelligence and design of experiments for resource adequacy assessment in power systems

Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity of energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current modeling approaches. With our systematic literature review, we want to close the gap between the three disciplines (1) resource adequacy assessments, (2) artificial intelligence (AI), and (3) design of experiments. For this, we conduct a large-scale systematic review of selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex probabilistic resource adequacy assessment models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not yet been sufficiently covered. Eventually, we define a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of resource adequacy accounting for modeling the complexity and uncertainties of future developments.

Extraction Optimization, Structure Analysis and Antioxidant Activity of Polysaccharide from Sanghuangporus baumii

Introduction: In this study, we optimized the extraction process, analyzed the structure and assessed the antioxidant activity of Sanghuangporus baumii polysaccharide (SBP). The present results provide important information for the Sanghuangporus baumii polysaccharides in potential natural antioxidant effects. Background: The extraction and structural analysis of polysaccharides from Sanghuangporus has gained significant attention in the fields of chemistry, medicine, and life sciences. There is great significance in maximizing the extraction of polysaccharides from Sanghuangporus and developing their potential products in a scientific and rational manner. Objective: The study was designed to establish an efficient and practical extraction process for SBP, and then investigated the structure and the antioxidant activity. Methods: The Response Surface Methodology (RSM) based on Box-Behnken design was used to explore the ultrasound-assisted extraction of SBP, and the structure of SBP was studied by ultraviolet spectroscopy, infrared spectroscopy and other instrumental analysis methods. The total antioxidant capacity of SBP was studied by the Ferric Reducing Ability of Plasma (FRAP) method, and the scavenging capacity of ABTS+·, DPPH· and OH· was carried out as the index to investigate its antioxidant activity in vitro. Results: The statistical analysis results showed that the optimal conditions for extracting SBP were an ultrasound time of 20.74 min, ultrasound power of 268.40 W and material-liquid ratio of 1:25.71. Under optimal conditions, the experimental yield of SBP was 3.36 ± 0.01%. The RSM optimization process was applied to the experiment of complex enzyme extraction of SBP, and the yield increased to 4.72 ± 0.03%. Structural analysis showed that SBP mainly consisted of glucose, a small amount of mannose and galactose, and the molecular weight distribution was uneven, mainly concentrating in the three parts of 24.5,6.4,2.5 kDa. Moreover, SBP exhibited dose-dependent and strong reducing power and radical scavenging activity. For DPPH·, ABTS+· and OH· radical scavenging assays, IC50 values were 1.505 ± 0.050 mg/ml, 0.065 ± 0.036 mg/ml and 0.442 ± 0.007 mg/ml, respectively. Conclusion: In the present study, a β-linked heteroglucan (SBP) was extracted using the optimized process combining enzymes from the fruiting bodies of Sanghuangporus baumii. SBP exhibited effective and dose-dependent antioxidant activities. Our findings were of great value in terms of developing polysaccharides with potential natural antioxidants.

Language learning strategies research in English as foreign language contexts: A systematic literature review

The specific processes, techniques, and actions that learners take to facilitate their language learning have been widely explored under the concept of language learning strategies (LLS); however, more exploration is needed about recent investigations in this area, as calls for new theorization of strategies research have emerged. This systematic literature review aimed at exploring the prevailing research methodologies and educational settings appertaining to LLS in English as foreign language (EFL) contexts. The study analyzed 42 articles published from 2017 to 2023 in journals in the field of social sciences in the Scopus and ERIC databases. The findings show how non-intervention quantitative approaches are predominant in LLS research, occasionally accompanied by qualitative data collection methods. Accordingly, most research has favored descriptive and correlational designs, identifying the relationships between the use of strategies and variables such as language proficiency, demographic aspects, motivation, and self-regulation. Grounded on the revision of existing evidence, this article advises future strategy-based research to focus on primary and secondary levels of education, strategy instruction, cultural aspects, and qualitative research designs.

Machine learning based tool for the efficient estimation of geometric features of aggregated aerosol particles

While significant progress has been made in developing models for the formation and transport of aerosol aggregates, there is still a need for a simple, versatile tool capable of estimating intrinsic properties of aggregated particles. Scalar friction factor is an important parameter used extensively in the field of aerosol science. The scalar friction factor for non-spherical particles can be computed with the information on two geometric parameters, hydrodynamic radius (Rh) and orientationally averaged projected area (PA), depending on the momentum transfer regime. Although the existing methods for the estimation of these descriptors are efficient, many applications involve frequent estimation of these geometric descriptors, which can be time-consuming. We propose a Machine Learning (ML) based tool that can predict these descriptors using Fractal Dimension, pre-exponential factor, number of monomers and anisotropy factors as the input. An extensive database comprising fractal parameters, anisotropy factors, Rh, and PA is developed for testing and training the ML models. Five ML methods were assessed, with random forest (RF) identified as the most effective. The RF model demonstrated high accuracy in the testing phase, with R-squared value of 0.9875 for Rh and 0.9979 for PA, and average errors of 3.17% and 1.21% for Rh and PA, respectively. The predicted Rh and PA values were then used to estimate other relevant 3-dimensional properties such as mobility diameter, shape factor, and aerodynamic diameter, with the results indicating high accuracy of the prediction tool. Python-based tool offers ease of use, and can be easily integrated with other numerical codes.

Physicochemical Properties and Functionalities of Food Emulsifiers

Purpose: This study sought to analyze the physiochemical properties and functionalities of food emulsifiers. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the physiochemical properties and functionalities of food emulsifiers. Preliminary empirical review revealed that the importance of considering emulsifier properties in food product development. The findings underscored the significance of optimizing emulsifier selection and formulation to meet consumer demands for healthier, more sustainable, and better-performing food products. Continued research in this area promises to drive further innovation and advancements in food technology. Unique Contribution to Theory, Practice and Policy: The Interfacial Tension theory, HLB (Hydrophilic- Lipophilic Balance) theory and Emulsion Phase Inversion theory may be used to anchor future studies on the physiochemical properties and functionalities of food emulsifiers. The study made significant contributions to theoretical understanding, practical application, and policy considerations in the field of food science and technology. It advanced theoretical knowledge by exploring molecular mechanisms and integrating principles from colloid and interface science. In practice, the study recommended systematic approaches for emulsifier selection and process optimization to enhance product quality and consistency. From a policy perspective, it emphasized the importance of regulatory guidelines and transparency in emulsifier usage. Additionally, the study advocated for sustainability, innovation, and consumer-centric approaches to promote environmental responsibility, drive technological advancements, and prioritize consumer well-being in emulsifier research and application.

Synthesis, characterization, DFT calculations, and anticancer properties of VI-period and f-block elements-substituted zinc oxide nanopowders

Nanotechnology provides promising ways in which a wide range of new materials can be manufactured which appear to have enormous potential in the field of biomedicine and life sciences. ZnO can be applied in many physical and optical processes and used as a good anti-cancer agent. ZnO NPs are recognized as SAFE or GRAS, non-toxic and biocompatible particles. In this study, wurtzite ZnO samples co-doped by rare earth elements (Tb, Er) were prepared via sol-gel auto-combustion route and their crystallographic structure as well as their morphologies were examined using XRD and TEM techniques. The analysis disclosed that all synthesized products crystallized in a hexagonal structure and the crystallites/particles size diminished with the increase of co-doping content. The electronic structure and optical properties of the different products were assessed using DFT calculations. Doping Tb and Er rare earth elements into ZnO has a significant effect on its optoelectronic properties. The computational study showed that the co-doping of Tb and Er on pure ZnO decreased the band gap and increased the refractive index. The extinction spectrum of pristine ZnO showed a peak in the ultraviolet region at 5 eV, while those of the co-doped ZnO samples displayed stronger peaks in the infrared region (near 0.4 eV). The anticancer properties of Tb/Er co-doped ZnO nanoparticles were examined on cervical cancer (HeLa cells) and colon cancer (HCT-116) by using an in vitro MTT assay. The cell viability assay findings indicated a reduction in HeLa and HCT-116 cells after 48 hours of treatment of the synthesized samples. The treatment with co-doped ZnO nanoparticles produced chromatin condensation and formation of apoptotic bodies in HCT-166 cells, which indicates that the prepared co-doped ZnO nanoparticles induced programmed cell death in HCT-116 cells. According to these results, it can be concluded that the prepared Tb/Er co-doped ZnO nanoparticles have promising applications in developing new optoelectronic devices and also in treating cervical and colon cancers.

Different Disciplines Have Different Perspectives, So What Are the Implications for the Multidisciplinary Field of Alcohol Public Health Science? A Reply to "All Boundaries Have Two Sides" by Bray and Kenkel (2024).

Different Disciplines Have Different Perspectives, So What Are the Implications for the Multidisciplinary Field of Alcohol Public Health Science? A Reply to "All Boundaries Have Two Sides" by Bray and Kenkel (2024).

Edwards Curve Digital Signature Algorithm for Video Integrity Verification on Blockchain framework

Within the field of forensic science, video integrity is an essential component that ensures the validity and dependability of visual evidence that is essential for court cases. Maintaining the integrity of justice in a time when digital modification tools are easily accessible requires making sure that video recordings are unaltered. Video footage is carefully inspected by forensic analysts who use advanced tools to look for indications of manipulation or change. This paper presents a novel, lightweight approach for verifying the integrity of video data. The proposed method utilizes the blockchain and the Edwards Curve Digital Signature Algorithm, coupled with the BLAKE2b hash function. Signatures are generated for video clips with a predetermined size called video segments and stored in blocks chronologically. The signature of the previous block is stored in the current block to add another layer of security. At the time of validation, these signatures are verified. Experimental results show that the proposed method outperforms state-of-the-art methods in both speed and security. Our method is capable of identifying any type of forgery on any video file, by anyone, at any given moment, with insignificant additional storage requirements. Furthermore, our security analysis shows that our method is resilient against various types of attacks, including collision attacks, key substitution attacks, side-channel attacks, and chosen message attacks. The proposed lightweight video integrity verification method is better suited for use in resource constrained devices.

Food Texture Perception and Its Influence on Consumer Preferences

Abstract Purpose: The general purpose of this study was to evaluate food texture perception and its influence on consumer preferences. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to food texture perception and its influence on consumer preferences. Preliminary empirical review revealed that that food texture plays a pivotal role in shaping consumer preferences and purchase decisions across various product categories. Through a review of empirical studies, it was found that texture preferences are influenced by sensory experiences, cultural norms, and individual differences. Cross-cultural variability in texture preferences was noted among Western, Asian, and Middle Eastern populations, highlighting the importance of considering cultural factors in product development. Furthermore, texture was found to elicit emotional responses during eating, with certain textures evoking positive emotions while others elicited negative ones. The practical implications of texture research were underscored, emphasizing its relevance for food manufacturers, marketers, and policymakers in developing products that align with consumer expectations and promoting healthier food choices. Unique Contribution to Theory, Practice and Policy: The Sensory-Specific Satiety theory, Elaboration Likelihood Model (ELM) and Schema theory may be used in anchoring future studies on food texture perception and its influence on consumer preferences. The study provided recommendations that contributed to theory, practice, and policy in the field of food science and consumer behavior. The study suggested further exploration of texture perception mechanisms, tailored product development strategies based on sensory evaluations, and sensory marketing approaches emphasizing texture cues. Additionally, it recommended integrating sensory considerations into food regulations, promoting consumer education on texture-related labeling, and investing in sensory research and innovation. These recommendations aimed to enhance understanding of texture perception, improve product development and marketing strategies, and inform policy initiatives to meet consumer preferences effectively.

Design of RIXS Beamline at Shenzhen Innovation Light Facility

An optical design of the RIXS beamline for the Shenzhen Innovation Light Facility (SILF) was reported. The beamline will cover an energy range from 200 to 1500 eV, with three endstations: a Q-RIXS for momentum-dependent high-resolution resonant inelastic x-ray scattering, an e-RIXS for high-throughput resonant inelastic x-ray scattering and an APXPS. All endstations share the same undulator, front end optics and the main branch of beamline until the deflecting mirrors after the monochromator. The Q-RIXS and e-RIXS will be located in two separate branches, while the APXPS endstation is mounted on a free port located right between the exit slit and the refocusing optics for the e-RIXS endstation. This design allows for the mounting of different techniques and the preparation of sample environments without disrupting the operation of the other branch, thus maximizing the utilization of beamtime. The combined energy resolution is expected to be better than 50 meV for Q-RIXS and better than 100 meV for e-RIXS. Once available, it will provide in-demand, versatile and high-quality soft x-ray spectroscopy techniques to users in fields of quantum, energy and surface science.

TREASUREHUNT: Transients and Variability Discovered with HST in the JWST North Ecliptic Pole Time-domain Field

The James Webb Space Telescope (JWST) North Ecliptic Pole (NEP) Time-domain Field (TDF) is a >14′ diameter field optimized for multiwavelength time-domain science with JWST. It has been observed across the electromagnetic spectrum both from the ground and from space, including with the Hubble Space Telescope (HST). As part of HST observations over three cycles (the “TREASUREHUNT” program), deep images were obtained with the Wide Field Camera on the Advanced Camera for Surveys in F435W and F606W that cover almost the entire JWST NEP TDF. Many of the individual pointings of these programs partially overlap, allowing an initial assessment of the potential of this field for time-domain science with HST and JWST. The cumulative area of overlapping pointings is ∼88 arcmin2, with time intervals between individual epochs that range between 1 day and 4+ yr. To a depth of m AB ≃ 29.5 mag (F606W), we present the discovery of 12 transients and 190 variable candidates. For the variable candidates, we demonstrate that Gaussian statistics are applicable and estimate that ∼80 are false positives. The majority of the transients will be supernovae, although at least two are likely quasars. Most variable candidates are active galactic nuclei (AGNs), where we find 0.42% of the general z ≲ 6 field galaxy population to vary at the ∼3σ level. Based on a 5 yr time frame, this translates into a random supernova areal density of up to ∼0.07 transients arcmin−2 (∼245 deg−2) per epoch and a variable AGN areal density of ∼1.25 variables arcmin−2 (∼4500 deg−2) to these depths.

Inference of Dynamic Spatial Panel Data Model and Its Application in Carbon Emission Analysis

The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized Method of Moments (GMM) and Quasi-Maximum Likelihood (QML). In this paper, we employ the method of Empirical Likelihood (EL) for statistical inference of the dynamic spatial panel data model and obtain confidence regions for the parameters. Through numerical simulations, we present the performance of the confidence regions obtained using the Empirical Likelihood (EL) and the Asymptotically Normal (NA) methods under finite samples and compare the two approaches. Finally, we analyze carbon using the suggested model and techniques.

Biological invasions are a population-level rather than a species-level phenomenon.

Biological invasions pose a rapidly expanding threat to the persistence, functioning and service provisioning of ecosystems globally, and to socio-economic interests. The stages of successful invasions are driven by the same mechanism that underlies adaptive changes across species in general-via natural selection on intraspecific variation in traits that influence survival and reproductive performance (i.e., fitness). Surprisingly, however, the rapid progress in the field of invasion science has resulted in a predominance of species-level approaches (such as deny lists), often irrespective of natural selection theory, local adaptation and other population-level processes that govern successful invasions. To address these issues, we analyse non-native species dynamics at the population level by employing a database of European freshwater macroinvertebrate time series, to investigate spreading speed, abundance dynamics and impact assessments among populations. Our findings reveal substantial variability in spreading speed and abundance trends within and between macroinvertebrate species across biogeographic regions, indicating that levels of invasiveness and impact differ markedly. Discrepancies and inconsistencies among species-level risk screenings and real population-level data were also identified, highlighting the inherent challenges in accurately assessing population-level effects through species-level assessments. In recognition of the importance of population-level assessments, we urge a shift in invasive species management frameworks, which should account for the dynamics of different populations and their environmental context. Adopting an adaptive, region-specific and population-focused approach is imperative, considering the diverse ecological contexts and varying degrees of susceptibility. Such an approach could improve and refine risk assessments while promoting mechanistic understandings of risks and impacts, thereby enabling the development of more effective conservation and management strategies.

The influence of Mn-doping types on electronic structure and dilute magnetic property of Ca2Ge

The dilute magnetic semiconductor (DMS) materials have important application prospects in the field of electronic science and technology. Ca2Ge is a new environmentally friendly semiconductor material. The structure characteristics indicate that the dilute magnetic properties can be achieved by doping, in the theory. There are CI, CII, CIII, OI, OII and OIII six doped types by Mn doping. For the CI, CII and OIII, Ca2Ge is a semiconductor with a band gap of 0.21 eV, 0.21 eV and 0.065 eV, respectively; For the OII, Ca2Ge turns into a semi-metallic; For the CIII and OI, Ca2Ge turns into a metal. Ca2Ge trans into ferromagnetism with Mn doped, the molecular magnetic moments of CI, CII, CIII, OI, OII and OIII are 5 μB, 5 μB, 1 μB, 5 μB, 5 μB and 7 μB, respectively. The results of the projected density of states (PDOS) indicated that the magnetism mainly comes from the Mn 3d orbital spin, the orbital population shows that the spin of Mn atom affects the spin of Ca atom and the spin of Ge atom, the magnetic moment that is excited by Mn atom doping.

Tilapia head gelatins to stabilize fish oil emulsions and the effect of extraction methods

The preparation and use of gelatins from fish by-products have attracted much attention in the field of food science. Herein, four types of tilapia head gelatins were extracted and characterized: hot water-pretreated gelatin (HWG), acetic acid-pretreated gelatin (AAG), sodium hydroxide-pretreated gelatin (SHG), and pepsin enzyme-pretreated gelatin (PEG). The gel strength values followed the order: PEG (74 ± 1 Bloom) > AAG (66 ± 1) > HWG (59 ± 1) > SHG (34 ± 1). The foaming properties, fish oil emulsion viscosity, emulsion activity, and emulsion stabilization ability followed this order: PEG > HWG ≥ AAG > SHG. The effect mechanisms of extraction methods and gelatin concentrations on the emulsion stability involved the interfacial tension, emulsion viscosity, and fat-binding capacity. This work provided important knowledge for analyzing the relations between the structure and function of gelatin. It also provided a high-value application method of fish wastes.

Dual-learning Multi-hop Nonnegative Matrix Factorization for community detection

As an important branch of network science, community detection has garnered significant attention. Among various community detection methods, nonnegative matrix factorization (NMF)-based community detection approaches have become a popular research topic. However, most NMF-based methods overlook the network’s multi-hop information, let alone the community detection results specific to each hop of the network. In this paper, we propose Dual-learning Multi-hop NMF (DL-MHNMF), a method that considers not only the multi-hop connectivity between two nodes but also factors in the shared results across multiple hops and the impact of differences in the specific results at each hop on the shared outcomes. An efficient iterative optimization algorithm with guaranteed theoretical convergence is proposed for solving DL-MHNMF. Methodologically, by iteratively removing the specific results during the optimization process of DL-MHNMF, we achieve enhanced detection accuracy, which is also verified by subsequent experiments. Specifically, we compare fourteen algorithms on eleven publicly available datasets, and experimental results show that our algorithm outperforms most state-of-the-art methods.

Feasibility Analysis of a CubeSat Mission for Space Rider Observation and Docking

AbstractIn the last few years, the number of orbiting satellites has increased exponentially, in particular due to the development of the New Space Economy. Even if this phenomenon makes the space more accessible, bringing a great contribution to the scientific, economic and technological fields, on the other hand it contributes to the overpopulation of the space background. Therefore, it is necessary to develop new techniques to manage the space environment, such as in orbit servicing, which is a procedure that aims to refuel and repair satellites to extend their operational life. A first step to reach this goal is to inspect closely the object of interest to study its features. In this framework, the Space Rider Observer Cube (SROC) mission is being developed. SROC is a payload that will be deployed by Space Rider (SR), an uncrewed and reusable robotic spacecraft designed by ESA (European Space Agency). SROC is a 12U CubeSat, whose goal is to carry out inspection manoeuvres around SR, then re-enter on board using a safe docking system to come back to Earth. The feasibility of a mission similar to SROC has been simulated during a university class, starting from the definition of the system requirements with particular focus on the analysis of the payloads and subsystems, to ensure the achievement of the mission goals. In particular, the CubeSat is equipped with an imaging payload to capture high resolution images of Space Rider surface and a docking mechanism. Then, the design of the orbit and the simulation of the effects of the space environment on the CubeSat have been studied using GMAT, SYSTEMA, MATLAB and other numerical tools. The results of the study are useful for future missions, aiming to inspect orbiting objects, such as operative satellites for in orbit servicing, space debris and dead satellites to study their geometries and plan their removal.

Employing fruit juices to hydrolyze edible bird's nest and enhance the antioxidant, anti-tyrosinase, and wound-healing activities of the hydrolysates

Enzymatic hydrolysis of edible bird's nest (EBN) has attracted great interest in both scientific and commercial fields due to the enhancement of solubility and nutraceutical values. The present study attempted to investigate the hydrolysis of EBN with papaya (Carica papaya L.), pineapple (Ananas comosus (L.) Merr.), and cantaloupe (Cucumis melo L.) juices as well as two commercial enzymes papain and bromelain. Our analysis revealed that EBN hydrolysis with pineapple juice and bromelain produced a degree of hydrolysis (DH) value of approximately 27 % while it was about 25 % for the hydrolysis with cantaloupe juice and 22 % for the hydrolysis with papaya juice and papain after 4 h of treatment. When EBN was digested by fruit juices and enzymes, the protein solubility and free sialic acid content were increased and the highest values were achieved for EBN hydrolysis with pineapple juice and bromelain (estimately 11 mg/mL of soluble protein and 18 g/kg of free sialic acid). The ABTS•+-scavenging, •OH-scavenging, and anti-tyrosinase capacities were higher in the EBN hydrolysates by papaya juice (IC50 of 0.034, 0.108, and 0.419 mg/mL, respectively), pineapple juice (IC50 of 0.025, 0.045, and 0.190 mg/mL, respectively), and cantaloupe juice (IC50 of 0.031 mg/mL, 0.056, and 0.339 mg/mL, respectively) than in the hydrolysates by unhydrolyzed EBN (IC50 of 0.094, 0.366, and 1.611 mg/mL, respectively). An improvement in ABTS•+-scavenging, •OH-scavenging, and anti-tyrosinase abilities was also observed for the hydrolysates by papain (IC50 of 0.041, 0.129, and 0.417 mg/mL, respectively) and bromelain (IC50 of 0.025, 0.069, and 0.336 mg/mL, respectively) but in a lesser extent as compared to the hydrolysates by respective papaya and pineapple juices. Noticeably, the EBN hydrolysates by fruit juices remarkably enhanced the wound closure in human fibroblasts by about 1.4–1.8 times after 24 h of treatment whereas this property was insignificant in the hydrolysates by enzymes. As papaya, pineapple, and cantaloupe juices are easily obtainable and have pleasant flavors, our results provide a possible method to hydrolyze EBN and apply the resultant hydrolysates in functional food products.

Overstaying our welcome: On the rise of women's seniority in the academy

AbstractIssues surrounding gender equality are—and should be—front and centre in the water resources community, and other science, technology, engineering and mathematics (STEM) fields. Very necessarily, the focus tends to be on recruitment, offering support for students and early career academics. The leaky pipeline concept used to describe the incremental loss of women from STEM fields with career duration describes a disproportionate loss of senior women, creating a parallel problem where highly qualified, top tier academics are lost from the system after significant financial and personnel investment by institutions. Ultimately, the leaky pipeline undermines the extensive investment of the hydrology and other STEM communities in equity, diversity, inclusion and accessibility (EDIA) recruitment and retention programmes by cutting short career ambitions and the trajectories of diverse top performing individuals, resulting in no net benefit of EDIA policy investments and a lack of diversity with seniority. Addressing this critical gender gap requires the attention and support of the hydrology community of practice with specific focus on generating opportunities for advancement, confronting systemic and structural biases and improving education around allyship. Institutions and professional organizations need to consciously grow diversity in leadership and recognize and outwardly manage the perception of academic excellence around slow research and education that attracts increased diversity. Supporting allyship, reducing competitiveness among community members and reinforcing collaboration will not only attract, but retain, a higher proportion of diversity in the hydrology community, academia and STEM professions in general. It is time for the water resources (and other STEM) communities to demand broader accountability and recognition of the barriers to women, implement and reward more diverse definitions of research excellence, and offer allyship training to the community of practice at large.