This paper hopes to discuss and suggest a systematic, interdisciplinary model of integrating biodiversity education in STEM education, with the aim of increasing ecological literacy, encouraging interdisciplinary problem-solving, and encouraging positive attitudes towards conservation careers. It is also assessed how this integration affects student engagement and learning outcomes in the study. It used a mixed-methods design, which included baseline curriculum mapping, a teacher survey, and the introduction of a biodiversity-based integrated module of STEM. The course syllabi, learning objectives, and instructional materials were reviewed in Biology, Environmental Science, Data Science, and Engineering subjects, and the curriculum mapping. The intervention session consisted of problem-focused and evidence-based tasks, and the pre- and post-intervention tests consisted of ecological literacy, understanding of biodiversity, and STEM application skills. Student reflective journals and open-ended surveys based on qualitative data were used to collect the data. There was also no difference between the baseline curriculum mapping: 92.5% of the biology and 83.3% of the Environmental Science courses included the content on biodiversity, in contrast to 30% of Data Science and 18.2% of Engineering. The results of post-intervention assessments indicated a great enhancement of students in the following areas: Ecological literacy level increased to 72.3 ± 8.4 (Cohen's d = 1.76), Biodiversity concepts to 70.1 ± 7.1 (Cohen's d = 1.89), and STEM application skills to 74.4 ± 9.5 (Cohen's d = 1.65). Biodiversity in STEM education improves academic achievement and student learning. The paper has identified the necessity of Curricular reforms to overcome the low level of inclusion of biodiversity in Data Science and Engineering. This evidence indicates that interdisciplinary biodiversity education delivers cognitive and affective learning outcomes, making students ready to work in the conservation field and environmental research.

Environmental pollution is an important common health concern across the world with patterns of exposure being quite spatial and time varying. Traditional measures of pollution and epidemiological research tends to use sparse monitoring systems and single studies, which restrict their ability to resolve dynamic effects of exposure and health at large scale. To overcome these shortcomings, this research is one of the deep learning based multimodal frameworks that incorporate remote sensing information and biomedical health records in the overall spatio-temporal analysis of environmental pollution and health risk prediction. Pollution indicators of aerosol optical depth and trace gas concentrations collected by satellites are used together with anonymized biomedical information (incidence of diseases and hospital admission history) with a deep learning architecture, which is an attention competition. The suggested model will use convolutional neural networks to learn the spatial pollution features, and temporal modeling networks to learn the dynamics of evolving exposure factors, the fusion mechanism is based on the attention, the cross-modal relationships between the environment and health data will be learned. Real-world experimental analyses on experiment datasets show the proposed multimodal approach is always better than the traditional single-modal, and statistical analysis, with a higher rate of pollution estimation and better rate of health risk prediction. The maps of spatial heat analyses also demonstrate that the model is successful in determining regions of high risks and the most exposing time periods. The findings prove that the combination of heterogeneous environmental and biomedical data with the help of advanced deep learning procedures can be used to make more sensible and valuable assessments of environmental health. The article identifies the possibility of multimodal artificial intelligence systems to assist in active environmental health monitoring, early signs of danger, and evidence based approach to policymaking in the management of sustainable human public health.

The growth performance and feed utilization efficiency of tilapias mark aquaculture sustainability. The Oreochromis niloticus in this research was cultured under regulated aquaculture conditions, and its growth was modeled using the von Bertalanffy Growth Function (VBGF) to estimate feed conversion. The VBGF parameters were also adjusted to the actual value obtained in the 120-day rearing phase and the Weight and length of the fish were recorded at uniform intervals. The model was further scaled repeatedly to simulate empirical data with a tilapia growth curve that is a sigmoid, or S-shaped curve. Also, the dietary information regarding the intake was computed to obtain the value of Feed Conversion Ratio (FCR) which permitted the establishment of a correlation between growth and protein energy consumption. The findings of the research indicated that there is a high correlation between model predictions and growth, thereby demonstrating the effectiveness of VBGF in predicting the osmotic pressure of biomass and effective feeding plans. The model could be used to improve feed application for biomass production and profitability in aquaculture, which is generally constrained by resource limitations. As demonstrated in the model estimates, the model is quite precise in estimating the optimal harvest age and evaluating the impact of environmental/nutritional changes. In short, the model broadens the scope of application of VBGF by incorporating an effective, easy, and passive approach to simulating tilapia development, enabling more effective management of fish farming enterprises.

Marine ecosystems such as fishing grounds, biodiversity havens, carbon sinks, and coastal protectors have begun experiencing increased strains with the ever-mounting impacts of climate change. This work examines the problem using Earth and Planetary Science approaches, including satellite remote sensing, oceanographic modeling, and geochemical assessments of waters and sediments. We analyze changes such as the increase in biological productivity of living marine species, sea surface temperatures, ocean acidification, deoxygenation, and changes in marine currents. Through ecosystem service models, we also assess the sustainability of human populations, food security, and coastal resilience in regions impacted by climate change and the exacerbation of stressors on the oceans. Special attention is given to the widespread decline in coral reefs' health, disruptive global carbon cycles, and the many commercially valuable fish species' range shifts towards the poles. Areas of low adaptive capacity and high exposure are highlighted for urgent conservation intervention. This study demonstrates the importance of Earth and Planetary Science in temporal and spatial modeling of ecological change. Based on the results, policies and actions that aim to sustain the culture and supporting services marine ecosystems provide should focus more on climate change impacts. To put it in other words, this research aims to guide the management policies of marine resources, considering the marine ecosystems' services for the environment and human health.

Invasion of alien species poses an enormous threat to agriculture and biodiversity, and irreparably harms the existing balance of ecosystems. Predictive invasion modeling plays a crucial role in mitigating the impacts of such invasions. In this study, CLIMEX will be used in modeling the global distribution of species of concern based on climatic factors: temperature, moisture, and seasonal stress indices. The model classifies entire appropriate climatic zones into various categories based on the Dominant Climatic Index (EI) and estimates ecological balance levels. The models were tested across latitudes and environmental conditions, with assumed parameter values and limits set to biological boundaries. The findings indicate that the greatest vulnerability to the possibility of invasion is found in the temperate and subtropical regions, with the most impacted areas being in the mid-latitude regions, where an increment in the values of EI can be noticed. Results are delivered based on mathematical spatial suitability mapping and synthesized tables of ecological risk evaluation. Models of this kind can simplify the monitoring of invasive species using surveillance systems and help with early prevention, management, and tracking. This study can prove the relevance of CLIMEX in the ecological risk evaluation and management of invasive species in climate change conditions.

This study aimed to evaluate the effectiveness of edible films and coatings formulated using Chitosan and whey protein in improving the internal quality, shelf life, and microbial safety of chicken eggs during storage. New eggs were divided into chitosan-coated, whey protein coated, combined Chitosan whey protein, and uncoated control groups, immersed in their respective solutions, dried, and stored at room temperature for 28 days. Egg quality parameters, including Haugh unit, albumen height, and weight loss, were recorded weekly, and microbial contamination was measured using standard plate counts. Egg-coated showed considerably greater Haugh units, lesser weight loss, and lower microbial loads compared to uncoated ones. Chitosan and combined chitosan/ whey protein coatings were found to have the best preservative properties because they have better properties regarding the barrier and inherent antimicrobial activity, which maintains albumen quality and prolongs the shelf life. The findings verify the fact that biodegradable chitosan and whey protein films provide an eco-friendly alternative to conventional egg preservation. Simultaneously, progress in the research on animal vocalizations has improved conservation efforts by ensuring accurate biodiversity tracking and preventing ecological disturbances at an early stage. Sound-related ecological measurements, further enriched by the acoustic recognition systems (AI-based), enable automated detection of species, evaluation of the quality of habitat, and the detection of degradation of ecosystem soundscapes introduced by pollution. These are technologies that are commonly applied in wildlife conservation that demonstrate the wider scope of applying biosensing and machine learning techniques to the study of food preservation, offering new avenues of real-time tracking of food spoilage, microbial activities, and environmental changes.

There is an increase in the development of water hydraulic systems that are connected to environmental sustainability, which demands designs that balance engineering performance and environmental responsibility. This paper presents ECO HYDRA, an innovative, environmentally conscious working model that optimizes hydraulic system design through multi-objective and sustainability-based analysis. The model incorporates fluid dynamic simulation and a stepwise expansion of the NSGA II evolutionary framework, incorporating eco-penalty considerations for carbon emissions, material consumption, and water quality degradation. In contrast to traditional hydraulic design methods, ECO HYDRA reduces energy usage, leaks, and environmental harm simultaneously, while maximizing the incorporation of renewable sources and enhancing the system's resilience to climate change. Evaluation metrics include ecological indicators, such as the lifecycle CO2 footprint, water conservation efficiency, and renewable utilization, which provide practical input into sustainable development. Urban water distribution and irrigation case studies, along with reductions of up to 20% in pumping energy, 15% in water losses, and 12% in embodied CO2 emissions, relative to baseline designs. By connecting the optimization of engineering with ecological performance, ECO-HYDRA has also provided a scaling route for designing water hydraulic systems that actively contribute to environmental protection and resource efficiency in current ecological engineering.

Air pollution susceptibility maps are important for planning and dealing with natural disasters in Kirkuk province such as Kirkuk study area. The present study uses Analytic hierarchy process (AHP) models to analyse the susceptibility of Kirkuk and its surrounding area to air pollution. Three data sources are used to create the air pollution inventory map, such as previous studies, field investigation and monitoring image. The satellite image used in this investigation is a monitoring image, which was downloaded from the USGS website and consists of 11 bands with temporal and spatial resolutions of 16 days and 10 meters respectively. Twelve factors of air pollution conditioning were studied in this case study to build Analytical Hierarchy Process (AHP) models to produce an air pollution assessment map. These factors include: aspect, slope, land use, elevation, gemi; NDVI; ndwi; ndmi, lai, savi, fvc. According to the AUC area under the curve, the result reveals that the model's disturbance level scale with AUC value of 0.6 has the maximum prediction accuracy of 65.416 percent and the accuracy area according to the method is 31.276. The pollution susceptibility maps generated by AHP produce an accurate prediction of the susceptibility of Kirkuk part to air pollution, which can be used as a tool for land use planning and disaster mitigation.

In the aquatic environments, heavy metal pollution remains a major challenge across the world since the metals of cadmium, lead, chromium, and mercury are toxic, persistent, and bioaccumulative. Traditional remediation mechanisms such as chemical precipitation, membrane filtration and adsorption can be costly to operate, produce secondary pollutants and are not adaptable to changing environmental conditions. The paper describes a new, integrated approach that will involve bio-engineering of microbial systems alongside a network of IoT to monitor the environment in order to implement intelligent, effective and scalable heavy metal cleanup in water bodies. Modifications to biosorption, bioaccumulation, redox conversion, and metal precipitation of engineered microbial strains to increase metal-responsiveness: engineered strains of Pseudomonas putida, Shewanella oneidensis, Ralstonia metallidurans and metal-binding cyanobacteria were developed by incorporating metal-responsive genetic circuits, observation of metallothionein overexpression and optimization of electron transfer pathways. To make these engineered microbes immobile to guarantee stability and reusability and biocontainment, there was use of sophisticated encapsulation matrices. Refining on the biological system, a distributed IoT network had been implemented with electrochemical heavy metal sensors and environmental probes, to allow real-time and continuous monitoring of the combinations of metal concentrations, pH, temperature, and dissolved oxygen. Machine learning models have been used to identify data sent over low-power communication protocols to predict contamination variation and autonomously control microbial deployment in response to controlled biocapsule release mechanisms. The outcome of prototype simulations and controlled experiments in microcosm showed that the integrated system was able to yield much better efficiencies of metal removal improvements of 25 to 60 percent over the wild-type strains, which held up to reasonable functional stability when faced with variation of environmental conditions. The integrated biological and digital system provides a strong platform to the future, autonomous remediation systems that can intelligently react to contamination dynamics. The study provides a basis of scalable self-regulating environmental biotechnical systems that could be implemented in rivers, lakes, industrial effluents and other susceptible water environment to solve the age-old issues of heavy metal pollution.

Soil contamination by heavy metals endangers the environment, food security, and public health alike. Existing analytical approaches to estimate how metals bind to soil particles typically employ Langmuir and Freundlich isotherms; yet these models cannot adequately represent the intricate, nonlinear interactions among soil chemistries and the dissolved metals. Instead, we applied Artificial Neural Networks (ANNs) to cope with the inherent complexity. A series of feed-forward networks received, as input layers, key physicochemical variables soil pH, cation exchange capacity (CEC), organic matter percentage, clay fraction supplemented with initial concentrations of lead (Pb), cadmium (Cd), copper (Cu), and zinc (Zn). Hidden layers captured the multivariate nonlinear spectra, and the output layer quantified adsorbed mass for each species. The network training employed diverse datasets curated from laboratory batch experiments and reached R² coefficients surpassing 0.95 in nearly all validation folds. Imbalanced or distorted data cases, often compromising conventional models, did not derail the ANNs. The speed with which realistic field samples can be processed renders the approach practical for frequent, nationwide screening. By incorporating ANNs into geochemical diagnostics, the research provides authorities and industries with a low-cost, yet robust, forecasting protocol for assessing the binding potential of legacy or emerging contaminants, thus guiding targeted analytical follow-ups and optimizing decontamination design schemes.