Search results for: test techniques

Authors: Ling Ai, Ziyu Zhao, Zeyu Zhou, Xiaochen Yang, Heng Zhai, Stuart Holmes

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Optimizing the catalyst layer structure is crucial for enhancing the performance of proton exchange membrane fuel cells (PEMFCs) with low Platinum (Pt) loading. Current works focused on the utilization, durability, and site activity of Pt particles on support, and performance enhancement has been achieved by loading Pt onto porous support with different morphology, such as graphene, carbon fiber, and carbon black. Some schemes have also incorporated cost considerations to achieve lower Pt loading. However, the design of the catalyst layer (CL) structure in the membrane electrode assembly (MEA) must consider the interactions between the layers. Addressing the crucial aspects of water management, low contact resistance, and the establishment of effective three-phase boundary for MEA, multi-walled carbon nanotubes (MWCNTs) are promising CL support due to their intrinsically high hydrophobicity, high axial electrical conductivity, and potential for ordered alignment. However, the drawbacks of MWCNTs, such as strong agglomeration, wall surface chemical inertness, and unopened ends, are unfavorable for Pt nanoparticle loading, which is detrimental to MEA processing and leads to inhomogeneous CL surfaces. This further deteriorates the utilization of Pt and increases the contact resistance. Robust chemical oxidation or nitrogen doping can introduce polar functional groups onto the surface of MWCNTs, facilitating the creation of open tube ends and inducing defects in tube walls. This improves dispersibility and load capacity but reduces length and conductivity. Consequently, a trade-off exists between maintaining the intrinsic properties and the degree of functionalization of MWCNTs. In this work, MWCNTs were modified based on the operational requirements of the MEA from the viewpoint of interlayer interactions, including the search for the optimal degree of oxidation, N-doping, and micro-arrangement. MWCNT were functionalized by oxidizing, N-doping, as well as micro-alignment to achieve lower contact resistance between CL and proton exchange membrane (PEM), better hydrophobicity, and enhanced performance. Furthermore, this work expects to construct a more continuously distributed three-phase boundary by aligning MWCNT to form a locally ordered structure, which is essential for the efficient utilization of Pt active sites. Different from other chemical oxidation schemes that used HNO3:H2SO4 (1:3) mixed acid to strongly oxidize MWCNT, this scheme adopted pure HNO3 to partially oxidize MWCNT at a lower reflux temperature (80 ℃) and a shorter treatment time (0 to 10 h) to preserve the morphology and intrinsic conductivity of MWCNT. The maximum power density of 979.81 mw cm-2 was achieved by Pt loading on 6h MWCNT oxidation time (Pt-MWCNT6h). This represented a 59.53% improvement over the commercial Pt/C catalyst of 614.17 (mw cm-2). In addition, due to the stronger electrical conductivity, the charge transfer resistance of Pt-MWCNT6h in the electrochemical impedance spectroscopy (EIS) test was 0.09 Ohm cm-2, which was 48.86% lower than that of Pt/C. This study will discuss the developed catalysts and their efficacy in a working fuel cell system. This research will validate the impact of low-functionalization modification of MWCNTs on the performance of PEMFC, which simplifies the preparation challenges of CL and contributing for the widespread commercial application of PEMFCs on a larger scale.

Keywords: carbon nanotubes, electrocatalyst, membrane electrode assembly, proton exchange membrane fuel cell

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Authors: M. C. O. Ezeibe, F. I. O. Ezeibe

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Reasons viral diseases (including AI, HIV/AIDS, and COVID-19), tumors (including Cancers and Prostrate enlargement), and antimicrobial-resistant infections (AMR) are difficult to cure are features of the pathogens which normal cells do not have or need (biomedical markers) have not been identified; medicines that can counter the markers have not been invented; strategies and mechanisms for their treatments have not been developed. When cells become abnormal, they acquire negative electrical charges, and viruses are either positively charged or negatively charged, while normal cells remain neutral (without electrical charges). So, opposite charges' electrostatic attraction is a treatment mechanism for viral diseases and tumors. Medicines that have positive electrical charges would mop abnormal (infected and tumor) cells and DNA viruses (negatively charged), while negatively charged medicines would mop RNA viruses (positively charged). Molecules of Aluminum-magnesium silicate [AMS: Al₂Mg₃ (SiO₄)₃], an approved medicine and pharmaceutical stabilizing agent, consist of nanoparticles which have both positive electrically charged ends and negative electrically charged ends. The very small size (0.96 nm) of the nanoparticles allows them to reach all cells in every organ. By stabilizing antimicrobials, AMS reduces the rate at which the body metabolizes them so that they remain at high concentrations for extended periods. When drugs remain at high concentrations for longer periods, their efficacies improve. Again, nanoparticles enhance the delivery of medicines to effect targets. Both remaining at high concentrations for longer periods and better delivery to effect targets improve efficacy and make lower doses achieve desired effects so that side effects of medicines are reduced to allow the immunity of patients to be enhanced. Silicates also enhance the immune responses of treated patients. Improving antimicrobial efficacies and enhancing patients` immunity terminate infections so that none remains that could develop resistance. Some countries do not have natural deposits of AMS, but they may have Aluminum silicate (AS: Al₄ (SiO₄)₃) and Magnesium silicate (MS: Mg₂SiO₄), which are also approved medicines. So, AS and MS were used to formulate an AMS-brand, named Medicinal synthetic AMS {Al₄ (SiO₄)₃ + 3Mg₂SiO₄ → 2Al₂Mg₃ (SiO₄)₃}. To overcome the challenge of AMS, AS, and MS being un-absorbable, Dextrose monohydrate is incorporated in MSAMS-formulations for the simple sugar to convey the electrically charged nanoparticles into blood circulation by the principle of active transport so that MSAMS-antimicrobial formulations function systemically. In vitro, MSAMS reduced (P≤0.05) titers of viruses, including Avian influenza virus and HIV. When used to treat virus-infected animals, it cured Newcastle disease and Infectious bursa disease of chickens, Parvovirus disease of dogs, and Peste des petits ruminants disease of sheep and goats. A number of HIV/AIDS patients treated with it have been reported to become HIV-negative (antibody and antigen). COVID-19 patients are also reported to recover and test virus negative when treated with MSAMS. PSA titers of prostate cancer/enlargement patients normalize (≤4) following treatment with MSAMS. MSAMS has also potentiated ampicillin trihydrate, sulfadimidin, cotrimoxazole, piparazine citrate and chloroquine phosphate to achieve ≥ 95 % infection-load reductions (AMR-prevention). At 75 % of doses of ampicillin, cotrimoxazole, and streptomycin, supporting MSAMS-formulations' treatments with antioxidants led to the termination of even already resistant infections.

Keywords: electrical charges, viruses, abnormal cells, aluminum-magnesium silicate

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Authors: Justin Woulfe

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Siloed logistics and supply chain management systems throughout the Department of Defense (DOD) has led to disparate approaches to modeling and simulation (M&S), a lack of understanding of how one system impacts the whole, and issues with “optimal” solutions that are good for one organization but have dramatic negative impacts on another. Many different systems have evolved to try to understand and account for uncertainty and try to reduce the consequences of the unknown. As the DoD undertakes expansive digital transformation initiatives, there is an opportunity to fuse and leverage traditionally disparate data into a centrally hosted source of truth. With a streamlined process incorporating machine learning (ML) and artificial intelligence (AI), advanced M&S will enable informed decisions guiding program success via optimized operational readiness and improved mission success. One of the current challenges is to leverage the terabytes of data generated by monitored systems to provide actionable information for all levels of users. The implementation of a cloud-based application analyzing data transactions, learning and predicting future states from current and past states in real-time, and communicating those anticipated states is an appropriate solution for the purposes of reduced latency and improved confidence in decisions. Decisions made from an ML and AI application combined with advanced optimization algorithms will improve the mission success and performance of systems, which will improve the overall cost and effectiveness of any program. The Systecon team constructs and employs model-based simulations, cutting across traditional silos of data, aggregating maintenance, and supply data, incorporating sensor information, and applying optimization and simulation methods to an as-maintained digital twin with the ability to aggregate results across a system’s lifecycle and across logical and operational groupings of systems. This coupling of data throughout the enterprise enables tactical, operational, and strategic decision support, detachable and deployable logistics services, and configuration-based automated distribution of digital technical and product data to enhance supply and logistics operations. As a complete solution, this approach significantly reduces program risk by allowing flexible configuration of data, data relationships, business process workflows, and early test and evaluation, especially budget trade-off analyses. A true capability to tie resources (dollars) to weapon system readiness in alignment with the real-world scenarios a warfighter may experience has been an objective yet to be realized to date. By developing and solidifying an organic capability to directly relate dollars to readiness and to inform the digital twin, the decision-maker is now empowered through valuable insight and traceability. This type of educated decision-making provides an advantage over the adversaries who struggle with maintaining system readiness at an affordable cost. The M&S capability developed allows program managers to independently evaluate system design and support decisions by quantifying their impact on operational availability and operations and support cost resulting in the ability to simultaneously optimize readiness and cost. This will allow the stakeholders to make data-driven decisions when trading cost and readiness throughout the life of the program. Finally, sponsors are available to validate product deliverables with efficiency and much higher accuracy than in previous years.

Keywords: artificial intelligence, digital transformation, machine learning, predictive analytics

Procedia PDF Downloads 152

Authors: Mariza Kaskara, Stella Girtsou, Maria Prodromou, Alexia Tsouni, Christodoulos Mettas, Stavroula Alatza, Kyriaki Fotiou, Marios Tzouvaras, Charalampos Kontoes, Diofantos Hadjimitsis

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Natural hazard assessment and monitoring are crucial in managing the risks associated with fires, floods, and geohazards, particularly in regions prone to these natural disasters, such as Greece and Cyprus. Recent advancements in technology, developed by the BEYOND Center of Excellence of the National Observatory of Athens, have been successfully applied in Greece and are now set to be transferred to Cyprus. The implementation of these advanced technologies in Greece has significantly improved the country's ability to respond to these natural hazards. For wildfire risk assessment, a scalar wildfire occurrence risk index is created based on the predictions of machine learning models. Predicting fire danger is crucial for the sustainable management of forest fires as it provides essential information for designing effective prevention measures and facilitating response planning for potential fire incidents. A reliable forecast of fire danger is a key component of integrated forest fire management and is heavily influenced by various factors that affect fire ignition and spread. The fire risk model is validated by the sensitivity and specificity metric. For flood risk assessment, a multi-faceted approach is employed, including the application of remote sensing techniques, the collection and processing of data from the most recent population and building census, technical studies and field visits, as well as hydrological and hydraulic simulations. All input data are used to create precise flood hazard maps according to various flooding scenarios, detailed flood vulnerability and flood exposure maps, which will finally produce the flood risk map. Critical points are identified, and mitigation measures are proposed for the worst-case scenario, namely, refuge areas are defined, and escape routes are designed. Flood risk maps can assist in raising awareness and save lives. Validation is carried out through historical flood events using remote sensing data and records from the civil protection authorities. For geohazards monitoring (e.g., landslides, subsidence), Synthetic Aperture Radar (SAR) and optical satellite imagery are combined with geomorphological and meteorological data and other landslide/ground deformation contributing factors. To monitor critical infrastructures, including dams, advanced InSAR methodologies are used for identifying surface movements through time. Monitoring these hazards provides valuable information for understanding processes and could lead to early warning systems to protect people and infrastructure. Validation is carried out through both geotechnical expert evaluations and visual inspections. The success of these systems in Greece has paved the way for their transfer to Cyprus to enhance Cyprus's capabilities in natural hazard assessment and monitoring. This transfer is being made through capacity building activities, fostering continuous collaboration between Greek and Cypriot experts. Apart from the knowledge transfer, small demonstration actions are implemented to showcase the effectiveness of these technologies in real-world scenarios. In conclusion, the transfer of advanced natural hazard assessment technologies from Greece to Cyprus represents a significant step forward in enhancing the region's resilience to disasters. EXCELSIOR project funds knowledge exchange, demonstration actions and capacity-building activities and is committed to empower Cyprus with the tools and expertise to effectively manage and mitigate the risks associated with these natural hazards. Acknowledgement:Authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project.

Keywords: earth observation, monitoring, natural hazards, remote sensing

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Roven Tumaneng, Angelica Kristina Monzon, Ralph Sedricke Lapuz, Jose Don De Alban, Jennica Paula Masigan, Joanne Rae Pales, Laila Monera Pornel, Dennis Tablazon, Rizza Karen Veridiano, Jackie Lou Wenceslao, Edmund Leo Rico, Neil Aldrin Mallari

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Most protected area management plans in the Philippines, particularly the smaller and more remote islands suffer from insufficient baseline data, which should provide the bases for formulating measureable conservation targets and appropriate management interventions for these protected areas. Attempts to synthesize available data particularly on cultural and socio-economic characteristic of local peoples within and outside protected areas also suffer from the lack of comprehensive and detailed inventories, which should be considered in designing adaptive management interventions to be used for those protected areas. Mt Guiting-guiting Natural Park (MGGNP) located in Sibuyan Island is one of the poorly studied protected areas in the Philippines. In this study, we determined the highly biologically important areas of the protected area using Maximum Entropy approach (MaxEnt) from environmental predictors (i.e., topographic, bioclimatic,land cover, and soil image layers) derived from global remotely sensed data and point occurrence data of species of birds and trees recorded during field surveys on the island. A total of 23 trigger species of birds and trees was modeled and stacked to generate species richness maps for biological high conservation value areas (HCVAs). Forest habitat change was delineated using dual-polarised L-band ALOS-PALSAR mosaic data at 25 meter spatial resolution, taken at two acquisition years 2007 and 2009 to provide information on forest cover ad habitat change in the island between year 2007 and 2009. Determining the livelihood guilds were also conducted using the data gathered from171 household interviews, from which demographic and livelihood variables were extracted (i.e., age, gender, number of household members, educational attainment, years of residency, distance from forest edge, main occupation, alternative sources of food and resources during scarcity months, and sources of these alternative resources).Using Principal Component Analysis (PCA) and Kruskal-Wallis test, the diversity and patterns of forest resource use by people in the island were determined with particular focus on the economic activities that directly and indirectly affect the population of key species as well as to identify levels of forest resource use by people in different areas of the park.Results showed that there are gaps in the area occupied by the natural park, as evidenced by the mismatch of the proposed HCVAs and the existing perimeters of the park. We found out that subsistence forest gathering was the possible main driver for forest degradation out of the eight livelihood guilds that were identified in the park. Determining the high conservation areas and identifyingthe anthropogenic factors that influence the species richness and abundance of key species in the different management zone of MGGNP would provide guidance for the design of a protected area management plan and future monitoring programs. However, through intensive communication and consultation with government stakeholders and local communities our results led to setting conservation targets in local development plans and serve as a basis for the reposition of the boundaries and reconfiguration of the management zones of MGGNP.

Keywords: conservation gaps, livelihood guilds, MaxEnt, protected area

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Authors: F. Varga, Z. Liber, J. Jakše, A. Turudić, Z. Šatović, I. Radosavljević, N. Jeran, M. Grdiša

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Dalmatian pyrethrum (Tanacetum cinerariifolium (Trevir.) Sch. Bip.; Asteraceae), a source of the commercially dominant plant insecticide pyrethrin, is a species endemic to the eastern Adriatic. Genetic diversity of T. cinerariifolium was previously studied using amplified fragment length polymorphism (AFLP) markers. However, microsatellite markers (single sequence repeats - SSRs) are more informative because they are codominant, highly polymorphic, locus-specific, and more reproducible, and thus are most often used to assess the genetic diversity of plant species. Dalmatian pyrethrum is an outcrossing diploid (2n = 18) whose large genome size and high repeatability have prevented the success of the traditional approach to SSR markers development. The advent of next-generation sequencing combined with the specifically developed method recently enabled the development of, to the author's best knowledge, the first set of SSRs for genomic characterization of Dalmatian pyrethrum, which is essential from the perspective of plant genetic resources conservation. To evaluate the effectiveness of the developed SSR markers in genetic differentiation of Dalmatian pyrethrum populations, a preliminary genetic diversity study was conducted on 30 individuals from three geographically distinct natural populations in Croatia (northern Adriatic island of Mali Lošinj, southern Adriatic island of Čiovo, and Mount Biokovo) based on 12 SSR loci. Analysis of molecular variance (AMOVA) by randomization test with 10,000 permutations was performed in Arlequin 3.5. The average number of alleles per locus, observed and expected heterozygosity, probability of deviations from Hardy-Weinberg equilibrium, and inbreeding coefficient was calculated using GENEPOP 4.4. Genetic distance based on the proportion of common alleles (DPSA) was calculated using MICROSAT. Cluster analysis using the neighbor-joining method with 1,000 bootstraps was performed with PHYLIP to generate a dendrogram. The results of the AMOVA analysis showed that the total SSR diversity was 23% within and 77% between the three populations. A slight deviation from Hardy-Weinberg equilibrium was observed in the Mali Lošinj population. Allele richness ranged from 2.92 to 3.92, with the highest number of private alleles observed in the Mali Lošinj population (17). The average observed DPSA between 30 individuals was 0.557. The highest DPSA (0.875) was observed between several pairs of Dalmatian pyrethrum individuals from the Mali Lošinj and Mt. Biokovo populations, and the lowest between two individuals from the Čiovo population. Neighbor-joining trees, based on DPSA, grouped individuals into clusters according to their population affiliation. The separation of Mt. Biokovo clade was supported (bootstrap value 58%), which is consistent with the previous study on AFLP markers, where isolated populations from Mt. Biokovo differed from the rest of the populations. The developed SSR markers are an effective tool for assessing the genetic diversity and structure of natural Dalmatian pyrethrum populations. These preliminary results are encouraging for a future comprehensive study with a larger sample size across the species' range. Combined with the biochemical data, these highly informative markers could help identify potential genotypes of interest for future development of breeding lines and cultivars that are both resistant to environmental stress and high in pyrethrins. Acknowledgment: This work has been supported by the Croatian Science Foundation under the project ‘Genetic background of Dalmatian pyrethrum (Tanacetum cinerariifolium /Trevir./ Sch. Bip.) insecticidal potential’- (PyrDiv) (IP-06-2016-9034) and by project KK.01.1.1.01.0005, Biodiversity and Molecular Plant Breeding, at the Centre of Excellence for Biodiversity and Molecular Plant Breeding (CoE CroP-BioDiv), Zagreb, Croatia.

Keywords: Asteraceae, genetic diversity, genomic SSRs, NGS, pyrethrum, Tanacetum cinerariifolium

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Authors: Nanine Fouche

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The Cape Flats is a low-lying sand-covered expanse of approximately 460 square kilometres, situated to the southeast of the central business district of Cape Town in the Western Cape of South Africa. The aeolian sands masking this area are often loose and compressible in the upper 1m to 1.5m of the surface, and there is a general exceedance of the maximum allowable settlement in these sands. The settlement of shallow foundations on Cape Flats sands is commonly predicted using the results of in-situ tests such as the SPT or DPSH due to the difficulty of retrieving undisturbed samples for laboratory testing. Varying degrees of accuracy and reliability are associated with these methods. More recently, shear wave velocity (Vs) profiles obtained from seismic testing, such as continuous surface wave tests (CSW), are being used for settlement prediction. Such predictions have the advantage of considering non-linear stress-strain behaviour of soil and the degradation of stiffness with increasing strain. CSW tests are rarely executed in the Cape Flats, whereas SPT’s are commonly performed. For this reason, and to facilitate better settlement predictions in Cape Flats sand, equations representing shear wave velocity (Vs) as a function of SPT blow count (N60) and vertical effective stress (v’) were generated by statistical regression of site investigation data. To reveal the most appropriate method of overburden correction, analyses were performed with a separate overburden term (Pa/σ’v) as well as using stress corrected shear wave velocity and SPT blow counts (correcting Vs. and N60 to Vs1and (N1)60respectively). Shear wave velocity profiles and SPT blow count data from three sites masked by Cape Flats sands were utilised to generate 80 Vs-SPT N data pairs for analysis. Investigated terrains included sites in the suburbs of Athlone, Muizenburg, and Atlantis, all underlain by windblown deposits comprising fine and medium sand with varying fines contents. Elastic settlement analysis was also undertaken for the Cape Flats sands, using a non-linear stepwise method based on small-strain stiffness estimates, which was obtained from the best Vs-N60 model and compared to settlement estimates using the general elastic solution with stiffness profiles determined using Stroud’s (1989) and Webb’s (1969) SPT N60-E transformation models. Stroud’s method considers strain level indirectly whereasWebb’smethod does not take account of the variation in elastic modulus with strain. The expression of Vs. in terms of N60 and Pa/σv’ derived from the Atlantis data set revealed the best fit with R2 = 0.83 and a standard error of 83.5m/s. Less accurate Vs-SPT N relations associated with the combined data set is presumably the result of inversion routines used in the analysis of the CSW results showcasing significant variation in relative density and stiffness with depth. The regression analyses revealed that the inclusion of a separate overburden term in the regression of Vs and N60, produces improved fits, as opposed to the stress corrected equations in which the R2 of the regression is notably lower. It is the correction of Vs and N60 to Vs1 and (N1)60 with empirical constants ‘n’ and ‘m’ prior to regression, that introduces bias with respect to overburden pressure. When comparing settlement prediction methods, both Stroud’s method (considering strain level indirectly) and the small strain stiffness method predict higher stiffnesses for medium dense and dense profiles than Webb’s method, which takes no account of strain level in the determination of soil stiffness. Webb’s method appears to be suitable for loose sands only. The Versak software appears to underestimate differences in settlement between square and strip footings of similar width. In conclusion, settlement analysis using small-strain stiffness data from the proposed Vs-N60 model for Cape Flats sands provides a way to take account of the non-linear stress-strain behaviour of the sands when calculating settlement.

Keywords: sands, settlement prediction, continuous surface wave test, small-strain stiffness, shear wave velocity, penetration resistance

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Authors: Maria João Ferreira, Natalia Sierra-Garcia, Javier Cremades, Carla António, Ana M. Rodrigues, Helena Silva, Ângela Cunha

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Salicornia ramosissima, a halophyte that grows naturally in coastal areas of the northern hemisphere, is often considered the most promising halophyte candidate for extensive crop cultivation and saline agriculture practices. The expanding interest in this plant surpasses its use as gourmet food and includes their potential application as a source of bioactive compounds for the pharmaceutical industry. Despite growing well in saline soils, sustainable and ecologically friendly techniques to enhance crop production and the nutritional value of this plant are still needed. The root microbiome of S. ramosissima proved to be a source of taxonomically diverse plant growth-promoting bacteria (PGPB). Halotolerant strains of Bacillus, Salinicola, Pseudomonas, and Brevibacterium, among other genera, exhibit a broad spectrum of plant-growth promotion traits [e.g., 3-indole acetic acid (IAA), 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase, siderophores, phosphate solubilization, Nitrogen fixation] and express a wide range of extracellular enzyme activities. In this work, three plant growth-promoting bacteria strains (Brevibacterium casei EB3, Pseudomonas oryzihabitans RL18, and Bacillus aryabhattai SP20) isolated from the rhizosphere and the endosphere of S. ramosissima roots from different saltmarshes along the Portuguese coast were inoculated in S. ramosissima seeds. Plants germinated from inoculated seeds were grown for three months in pots filled with a mixture of perlite and estuarine sediment (1:1) in greenhouse conditions and later transferred to a growth chamber, where they were maintained two months with controlled photoperiod, temperature, and humidity. Pots were placed on trays containing the irrigation solution (Hoagland’s solution 20% added with 10‰ marine salt). Before reaching the flowering stage, plants were collected, and the fresh and dry weight of aerial parts was determined. Non-inoculated seeds were used as a negative control. Selected dried stems from the most promising treatments were later analyzed by GC-TOF-MS for primary metabolite composition. The efficiency of inoculation and persistence of the inoculum was assessed by Next Generation Sequencing. Inoculations with single strain EB3 and co-inoculations with EB3+RL18 and EB3+RL18+SP20 (All treatment) resulted in significantly higher biomass production (fresh and dry weight) compared to non-inoculated plants. Considering fresh weight alone, inoculation with isolates SP20 and RL18 also caused a significant positive effect. Combined inoculation with the consortia SP20+EB3 or SP20+RL18 did not significantly improve biomass production. The analysis of the profile of primary metabolites will provide clues on the mechanisms by which the growth-enhancement effect of the inoculants operates in the plants. These results sustain promising prospects for the use of rhizospheric and endophytic PGPB as biofertilizers, reducing environmental impacts and operational costs of agrochemicals and contributing to the sustainability and cost-effectiveness of saline agriculture. Acknowledgments: This work was supported by project Rhizomis PTDC/BIA-MIC/29736/2017 financed by Fundação para a Ciência e Tecnologia (FCT) through the Regional Operational Program of the Center (02/SAICT/2017) with FEDER funds (European Regional Development Fund, FNR, and OE) and by FCT through CESAM (UIDP/50017/2020 + UIDB/50017/2020), LAQV-REQUIMTE (UIDB/50006/2020). We also acknowledge FCT/FSE for the financial support to Maria João Ferreira through a PhD grant (PD/BD/150363/2019). We are grateful to Horta dos Peixinhos for their help and support during sampling and seed collection. We also thank Glória Pinto for her collaboration providing us the use of the growth chambers during the final months of the experiment and Enrique Mateos-Naranjo and Jennifer Mesa-Marín of the Departamento de Biología Vegetal y Ecología, the University of Sevilla for their advice regarding the growth of salicornia plants in greenhouse conditions.

Keywords: halophytes, PGPB, rhizosphere engineering, biofertilizers, primary metabolite profiling, plant inoculation, Salicornia ramosissima

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Authors: Nazym Zhumabayeva, Aleksandr Mezin, Alexandra Knysheva

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Women's underrepresentation in STEM is critical, worsened by ineffective engagement in educational practices. UNICEF Kazakhstan and GameLab KBTU's collaborative initiatives aim to enhance female STEM participation by fostering an inclusive environment. Learning from LEVEL UP's 2023 program, which featured a hackathon, the 2024 strategy pivots towards non-competitive gamification. Although the data from last year's project showed higher than average student engagement, observations and in-depth interviews with participants showed that the format was stressful for the girls, making them focus on points rather than on other values. This study presents a gamified educational system, DPG Lab, aimed at incentivizing young women's participation in STEM through the development of digital public goods (DPGs). By prioritizing collaborative gamification elements, the project seeks to create an inclusive learning environment that increases engagement and interest in STEM among young women. The DPG Lab aims to find a solution to minimize competition and support collaboration. The project is designed to motivate female participants towards the development of digital solutions through an introduction to the concept of DPGs. It consists of a short online course, a simulation videogame, and a real-time online quest with an offline finale at the KBTU campus. The online course offers short video lectures on open-source development and DPG standards. The game facilitates the practical application of theoretical knowledge, enriching the learning experience. Learners can also participate in a quest that encourages participants to develop DPG ideas in teams by choosing missions throughout the quest path. At the offline quest finale, the participants will meet in person to exchange experiences and accomplishments without engaging in comparative assessments: the quest ensures that each team’s trajectory is distinct by design. This marks a shift from competitive hackathons to a collaborative format, recognizing the unique contributions and achievements of each participant. The pilot batch of students is scheduled to commence in April 2024, with the finale anticipated in June. It is projected that this group will comprise 50 female high-school students from various regions across Kazakhstan. Expected outcomes include increased engagement and interest in STEM fields among young female participants, positive emotional and psychological impact through an emphasis on collaborative learning environments, and improved understanding and skills in DPG development. GameLab KBTU intends to undertake a hypothesis evaluation, employing a methodology similar to that utilized in the preceding LEVEL UP project. This approach will encompass the compilation of quantitative metrics (conversion funnels, test results, and surveys) and qualitative data from in-depth interviews and observational studies. For comparative analysis, a select group of participants from the previous year's project will be recruited to engage in the DPG Lab. By developing and implementing a gamified framework that emphasizes inclusion, engagement, and collaboration, the study seeks to provide practical knowledge about effective gamification strategies for promoting gender diversity in STEM. The expected outcomes of this initiative can contribute to the broader discussion on gamification in education and gender equality in STEM by offering a replicable and scalable model for similar interventions around the world.

Keywords: collaborative learning, competitive learning, digital public goods, educational gamification, emerging regions, STEM, underprivileged groups

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Authors: Yimer Mohammed Assen, Abiyot Legesse Kura, Engida Esyas Dube, Asebe Regassa Debelo, Girma Kelboro Mensuro, Lete Bekele Gure

Abstract:

Climate change has posed severe threats to many cultural landscapes of UNESCO world heritage sites recently. The UNESCO State of Conservation (SOC) reports categorized flooding, temperature increment, and drought as threats to cultural landscapes. This study aimed to examine variations and trends of rainfall and temperature extreme events and their threats to the UNESCO-designated Konso Cultural Landscape in southern Ethiopia. The study used dense merged satellite-gauge station rainfall data (1981-2020) with spatial resolution of 4km by 4km and observed maximum and minimum temperature data (1987-2020). Qualitative data were also gathered from cultural leaders, local administrators, and religious leaders using structured interview checklists. The spatial patterns, coefficient of variation, standardized anomalies, trends, and magnitude of change of rainfall and temperature extreme events both at annual and seasonal levels were computed using the Mann-Kendall trend test and Sen’s slope estimator under the CDT package. The standard precipitation index (SPI) was also used to calculate drought severity, frequency, and trend maps. The data gathered from key informant interviews and focus group discussions were coded and analyzed thematically to complement statistical findings. Thematic areas that explain the impacts of extreme events on the cultural landscape were chosen for coding. The thematic analysis was conducted using Nvivo software. The findings revealed that rainfall was highly variable and unpredictable, resulting in extreme drought and flood. There were significant (P<0.05) increasing trends of heavy rainfall (R10mm and R20mm) and the total amount of rain on wet days (PRCPTOT), which might have resulted in flooding. The study also confirmed that absolute temperature extreme indices (TXx, TXn, and TNx) and the percentile-based temperature extreme indices (TX90p, TN90p, TX10p, and TN10P) showed significant (P<0.05) increasing trends which are signals for warming of the study area. The results revealed that the frequency as well as the severity of drought at 3-months (katana/hageya seasons) was more pronounced than the 12-months (annual) time scale. The highest number of droughts in 100 years is projected at a 3-months timescale across the study area. The findings also showed that frequent drought has led to loss of grasses which are used for making traditional individual houses and multipurpose communal houses (pafta), food insecurity, migration, loss of biodiversity, and commodification of stones from terrace. On the other hand, the increasing trends of rainfall extreme indices resulted in destruction of terraces, soil erosion, loss of life and damage of properties. The study shows that a persistent decline in farmland productivity, due to erratic and extreme rainfall and frequent drought occurrences, forced the local people to participate in non-farm activities and retreat from daily preservation and management of their landscape. Overall, the increasing rainfall and temperature extremes coupled with prevalence of drought are thought to have an impact on the sustainability of cultural landscape through disrupting the ecosystem services and livelihood of the community. Therefore, more localized adaptation and mitigation strategies to the changing climate are needed to maintain the sustainability of Konso cultural landscapes as a global cultural treasure and to strengthen the resilience of smallholder farmers.

Keywords: adaptation, cultural landscape, drought, extremes indices

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Authors: Swaleh Aboud, Mishal Gudka, David Obura

Abstract:

Background: Coral reefs are becoming increasingly vulnerable due to several threats ranging from climate change to overfishing. This has resulted in increased management and conservation efforts to protect reefs from degradation and facilitate recovery. Recruitmentof new individuals are isimportant in the recovery process and critical for the persistence of coral reef ecosystems. Local coral community structure can be influenced by successful recruit settlement, survival, and growth Understanding coral recruitment patterns can help quantify reef resilience and connectivity, establish baselines and track changes and evaluate the effectiveness of reef restoration and conservation efforts. This study will examine the abundance and spatial pattern of coral recruits and how this relates to adult community structure, including the distribution of thermal resistance and sensitive genera and their distribution in different management regimes. Methods: Coral recruit and demography surveys were conducted from 2020 to 2022, covering 35 sites in 19coral reef locations along the Kenyan coast. These included marine parks, reserves, community conservation areas (CMAs), and open access areas from the north (Marereni) to the south (Kisite) coast of Kenya and across different reef habitats. The data was collected through the underwater visual census (UVC) technique. We counted adult corals (>10 cm diameter)of23 selected genera using belt transects (25 by 1 m) and sampling of 1 m2 quadrat (at an interval of 5m) for all coloniesless than 10 cm diameter. The benthic cover was collected using photo quadrats. The surveys were only done during the northeast monsoon season. The data wereanalyzed using the R program to see the distribution patterns and the Kruskal Wallis test to see whether there was a significant difference. Spearman correlation was also applied to assess the relationship between the distribution of coral genera in recruits and adults. Results: A total of 44 different coral genera were recorded for recruits, ranging from 3at Marereni to 30at Watamu Marine Reserve. Recruit densities ranged from 1.2±1.5recruit m-2 (mean±SD) at Likoni to 10.3± 8.4 recruit m-2 at Kisite Marine Park. The overall densityof recruitssignificantly differed between reef locations, with Kisite Marine Park and Reserve and Likonihaving significantly large differences from all the other locations, while Vuma, Watamu, Malindi, and Kilifi had significantly lower differences from all the other locations. The recruit generadensity along the Kenya coastwas divided into two clusters, one of which only included sites inKisite Marine Park. Adult colonies were dominated by Porites massive, Acropora, Platygyra, and Favites, whereas recruits were dominated by Porites branching, Porites massive, Galaxea, and Acropora. However, correlation analysis revealed a statistically significant positive correlation (r=0.81, p<0.05) between recruit and adult coral densities across the 23 coral genera. Marereni, which had the lowest densityof recruits, has only thermallyresistant coral genera, while Kisite Marine Park, with the highest recruit densities, has over 90% thermal sensitive coral genera. A weak positive correlation was found between recruit density and coralline algae, dead standing corals, and turf algae, whereas a weak negative correlation was found between recruit density and bare substrate and macroalgae. Between management regimes, marine reserves were found to have more recruits than no-take zones (marine parks and CMAs) and open access areas, although the difference was not significant. Conclusion: There was a statistically significant difference in the density of recruits between different reef locations along the Kenyan coast. Although the dominating genera of adults and recruits were different, there was a strong positive correlation between their coral communities, which could indicate self-recruitment processes or consistent distance seedings (of the same recruit genera). Sites such as Kisite Marine Park, with high recruit densities but dominated by thermally sensitive genera, will, on the other hand, be adversely affected by future thermal stress. This could imply that reducing the threats to coral reefs such as overfishingcould allow for their natural regeneration and recovery.

Keywords: coral recruits, coral adult size-class, cora demography, resilience

Procedia PDF Downloads 111