Simple Summary Aquaculture is now well-established as a provider of protein for human consumption, and its contribution will be paramount to providing food for a nine billion population in 2050. Protein is usually the major constituent of fish feeds and the most expensive ingredient. For years, fishmeal was the preferential protein source in fish diets, but environmental and economic concerns led to the search for more sustainable proteins. Hence, research on alternative protein sources to fishmeal was fruitful, being firstly directed to terrestrial plant ingredients. Recently, research on novel ingredients, such as insect meals, macroalgae, microalgae, and yeasts, has proliferated. However, the impacts of protein and its constituents (amino acids) go beyond fish growth. Thus, this review will provide knowledge on the impacts of alternative/novel protein sources on fish stress and immune responses, disease resistance, and health. Although some negative impacts of alternative ingredients, for instance, on gut integrity and immune responses have been observed, research results also point to the potential beneficial effects of novel ingredients, such as insect meals, on fish health. This information is essential to the development of innovative diets that guarantee the production of healthy fish with high quality standards and optimised welfare conditions. Abstract Aquaculture has been challenged to find alternative ingredients to develop innovative feed formulations that foster a sustainable future growth. Given the most recent trends in fish feed formulation on the use of alternative protein sources to decrease the dependency of fishmeal, it is fundamental to evaluate the implications of this new paradigm for fish health and welfare. This work intends to comprehensively review the impacts of alternative and novel dietary protein sources on fish gut microbiota and health, stress and immune responses, disease resistance, and antioxidant capacity. The research results indicate that alternative protein sources, such as terrestrial plant proteins, rendered animal by-products, insect meals, micro- and macroalgae, and single cell proteins (e.g., yeasts), may negatively impact gut microbiota and health, thus affecting immune and stress responses. Nevertheless, some of the novel protein sources, such as insects and algae meals, have functional properties and may exert an immunostimulatory activity. Further research on the effects of novel protein sources, beyond growth, is clearly needed. The information gathered here is of utmost importance, in order to develop innovative diets that guarantee the production of healthy fish with high quality standards and optimised welfare conditions, thus contributing to a sustainable growth of the aquaculture industry.
Anibal Mantilla-Guerra, Christian Mejia-Escobar, Jorge Azorin-Lopez
et al.
The use of clean energy is a global trend, with solar photovoltaic plants serving as a cornerstone of this energy transition. To support this rapid growth, optimize energy utilization, and enable a wide range of applications and services, it is essential to have access to more sophisticated and detailed solar data. Specifically, existing datasets lack integration, contain significant gaps, and have limited geographic coverage. In contrast, this study proposes a reliable, standardized, and multidimensional dataset with a global scope. Through a reproducible methodology and automated processes, we have successfully collected, generated, and combined 27 attributes of geographic, topographic, logistical, climate, and power nature, which are critical for the study of photovoltaic plants worldwide. Based on descriptive statistical analysis of the 58,978 records comprising the compiled dataset, the raw data have been transformed into valuable information for the energy sector. This demonstrates the utility of this product as a source of knowledge discovery, publicly available to the academic and professional communities.
The rise of generative artificial intelligence (GAI) has led to alarming predictions about its environmental impact. However, these predictions often overlook the fact that the diffusion of innovation is accompanied by the evolution of products and the optimization of their performance, primarily for economic reasons. This can also reduce their environmental impact. By analyzing the GAI ecosystem using the classic A-U innovation diffusion model, we can forecast this industry's structure and how its environmental impact will evolve. While GAI will never be green, its impact may not be as problematic as is sometimes claimed. However, this depends on which business model becomes dominant.
Indra Agus Riyanto, Heru Hendrayana, Yuli Widyaningsih
et al.
Lake Toba is one of the prioritized conservation lakes in Indonesia, crucial for domestic needs, tourism, fisheries, agriculture, and power generation. However, Lake Toba’s water levels and discharge have declined in recent decades. This study aimed to enhance water recharge and the water level of Lake Toba through hydrological modeling using the SWAT analysis and regional and specific conservation measures. The SWAT analysis utilized input data, including climate, soil, geomorphology, land use, and hydrology, to generate both existing and post-conservation water balance models. Conservation methods were categorized into civil engineering and vegetative approaches. Vegetative conservation techniques included agroforestry and MPTS (Multi-Purpose Tree Species), while civil engineering methods encompassed terracing, trenching, and infiltration wells. Regional conservation focused on land use plant types, while specific conservation involved detailed classification of the Lake Toba watershed by slope class, plant type, and land use. The analysis revealed significant land use changes in the Lake Toba catchment area, with dry fields increasing from 72,961 ha to 125,000 ha, a decrease in water level from 905 to 903 m above sea level, and a reduction in discharge from 180 m³/s to 125 m³/s. SWAT analysis shows that the existing inflow to Lake Toba is 131 m³/s (with 78% accuracy), with potential increases to 250 m³/s due to regional conservation and 180 m³/s due to specific conservation. Conservation efforts have also improved discharge rates across 39 sub-watersheds of Lake Toba, both monthly and annually.
Akhilesh Chandrapati, Jay Prakash Singh, Yenda Damodhara Rao, Meenakshi Rana, Somnath K. Holkar and Seweta Srivastava
Grapevine (Vitis vinifera L.) is one of the major crops grown commercially throughout the world. In recent years, there have been major losses to grapevine production due to the challenges caused mainly due to fungal diseases like downy mildew, powdery mildew, grey mold, black rot, and anthracnose. In the last few decades, rampant chemical fertilization and bio-magnification of hazardous chemicals have posed a threat to human health and destroyed the health of the soil as well as crops. For effective management of these fungal diseases of grapes, nowadays, many researchers are conducting various studies on endophytes, which are proven to be better bio-control agents to suppress the growth and development of grapevine phytopathogens. Endophytes are eco-friendly, effective, and easy to apply at field levels, making endophyte-based formulations suppress the growth and development of grapevine pathogens without causing any detrimental effects to the beneficial micro-organisms present at the rhizospheric zone of soil and host plants as compared to the traditional fungicides usage. It also competes with these pathogens for nutrition, space, and colonization. It helps in the production of secondary metabolites with antifungal properties for preventing the growth of fungal pathogens that cause damage to the grapevine crop. It also induces a defense mechanism in grapevine crops against diseasecausing fungal phytopathogens. In this review article, biocontrol mechanisms of endophytes and their potential application in the management of grapevine fungal diseases have been discussed.
Environmental effects of industries and plants, Science (General)
Alexandros Maziotis, Ramón Sala-Garrido, Manuel Mocholi-Arce
et al.
Within the water-energy-carbon nexus, understanding the trade-offs among resource use, drinking water delivery, and greenhouse gas emissions is essential for ensuring the sustainable provision of drinking water. Addressing this challenge, this study estimates four performance indices—technical efficiency, ecological efficiency, process environmental quality efficiency, and overall efficiency —using a unified framework based on Data Envelopment Analysis. The analysis was conducted under two scenarios, reflecting water companies’ prioritization of production and environmental dimensions. The empirical application, performed on a sample of English and Welsh water companies, revealed that water companies focused on technical efficiency, achieving average scores of 0.648 and 0.798 under environmental and production prioritization scenarios, respectively. In contrast, process environmental quality and ecological efficiency metrics were considerably lower, with average values of 0.179 and 0.129 for the environmental scenario and 0.153 and 0.131 for the production scenario. The temporal evolution of these performance indices highlights a trade-off between technical efficiency and performance indices integrating carbon emissions under the environmental prioritization scenario.
Preserving isolated plant populations and defining their geographic range, especially for rare and threatened species, is vital in conservation ecology. The current study examined the ecological status and potential conservation trajectories of Juniperus oxycedrus subsp. macrocarpa (Sm.) Neilr in the only Libyan area with significant populations of this tree. The habitat was identified, and its climatic, edaphic, and vegetation characteristics were analyzed. The species' landscape distribution was studied, with regeneration and mortality percentage estimated, along with influencing factors. The results revealed that the soil in the area is predominantly sandy in texture and infertile. The accompanying vegetation cover is comprised of 112 species and dominated by dwarf plant species. Previous records of this tree species east of Derna City are no longer extant. The study documented 4135 scattered trees covering an area estimated at around 260 hectares, with another 24 individuals located along the road to Al-Qubah city. The average height of the trees was 2.33 m, while the average canopy area and volume per individual were 38.18 m2 and 174.30 m3, respectively. The percentage of regeneration was estimated at 16.42%, while the mortality was 3.07%. Despite their apparent resilience to poor soil properties, arid climate, and proximity to the sea, this tree is primarily threatened by unsustainable human activities, resulting in habitat alteration and landscape modification. Therefore, urgent and efficient management measures are required to preserve this taxon at the national level. This should involve designating the study area as a protected zone to ensure long-term ecological conservation and sustainable management
Irshad Ali Zardari, Surih Sibaghatullah Jagirani, Ningsheng Chen
et al.
Northern Pakistan is a rough, mountainous region with high gradients, disintegrated lithology, glaciers on the highest peaks, and a seismically active area. District Astor is among the most susceptible locations, with yearly landslides due to various causes. This research has developed a comprehensive landslide inventory and a susceptibility model for the chosen region. Frequency Ratio is the most generally utilized probabilistic method; a moderately Analytical Hierarchy Process (AHP). The Frequency Ratio (FR) model technique has been used to ascertain the connection between both variables that cause landslides and landslides that have been mapped. A persistent Scattered Interferometry Radar (InSAR) technique was employed to investigate deformation movement in the vulnerable zones of the extracted models, finding a high Line-of-Sight (LOS) displacement velocity in both models' extremely sensitive areas. The derived Landslide Susceptibility Index (LSI) models had a prediction accuracy of 84.4% and 78.0% for the FR and AHP methods, respectively, calculated by applying the Area Under Curve (AUC) derived from the Receiver Operating Characteristic (ROC) approach. Finally, five susceptibility classes were assigned to both Landslide hazard index maps. Because the research region is prone to landslides, these susceptible models will be useful in delineating hazardous zones for future landslide catastrophes and utilized in decision-makers' planning strategies for Development initiatives in the studied region.
Transport infrastructure assets are essential for society, but they also usually result in significant environmental effects. To assess and reduce these effects, various methods such as life cycle assessment (LCA) and material flow analysis (MFA) have been applied to transport infrastructure assets. This paper provides a comprehensive review of the current research on the application of LCA and MFA to transport infrastructure assets, focusing on energy and greenhouse gas emissions. Considering 640 publications, the paper identifies the aims and drivers of the studies, the scope and boundaries of transport infrastructure assets, tools and data sources used for LCA and MFA, and the key findings and limitations of previous studies and models. The paper also highlights the research gaps and future research directions, such as integrating LCA and MFA, expanding the scope of transport infrastructure assets considered, improving the data quality and availability, and addressing the uncertainties and trade-offs in the assessment. The paper aims to provide a critical overview of the state-of-the-art methods and knowledge on the environmental assessment of TIA, and to suggest ways to improve the performance of TIA in the future.
Youcef Fekir, Brahim Cherouati, Rachid Khalladi
et al.
Monitoring and mapping surface water dynamics is a key element in studying and modelling their roles in any hydrological system. However, the fast and accurate extraction of these surfaces is a major challenge due to the spatio-temporal variety of surface water bodies. Through its spatial and temporal capabilities and the synoptic view it offers, remote sensing becomes a very powerful tool in this kind of problem. In recent years, Sentinel 2 optical and Sentinel 1 radar data have shown their effectiveness in the study of natural resources and water surfaces in particular. In this context, this study has taken advantage of remote sensing data to detect and extract surface water bodies. The approach adopted in this work is to use indices derived from high-resolution satellite images for the period 2015-2020. For this, this study used a series of Sentinel 2 MSI (Multi-Spectral Imager) multi-spectral images and Sentinel 1 synthetic radar images. These images are acquired over the Merdja Sidi Abed dam located in western Algeria and allowed us to derive spectral indices by combining several bands such as: Normalised Difference Vegetation Index (NDVI), Modified Normalised Difference Water Index (MNDWI), and the radar backscatter coefficient (????0). The automatic extraction of the dam extent shows a significant degradation of the surface, which has fallen from 815 ha in 2015 to 28 ha in 2020. The signal sensitivity of radar sensors such as Sentinel 1 and the better spatial (10 m) and temporal (5 days) resolution of the Sentinel 2 MSI sensor are very practical ways to track water bodies over time.
The exponential growth of data is making query processing increasingly critical for modern computing infrastructure, yet the environmental impact of database operations remains poorly understood and largely overlooked. This paper presents ATLAS, a comprehensive methodology for measuring and quantifying the environmental footprint of analytical database systems, considering both operational impacts and manufacturing costs of hardware components. Through extensive empirical evaluation of four distinct database architectures (DuckDB, MonetDB, Hyper, and StarRocks), we uncover how fundamental architectural decisions affect environmental efficiency. Our findings reveal that environmental considerations in database operations are multifaceted, encompassing both immediate operational impacts and long-term sustainability implications. We demonstrate that architectural choices can significantly influence both power consumption and environmental sustainability, while deployment location emerges as a critical factor that can amplify or diminish these architectural advantages.
Guillaume Hummel, David Pflieger, Alexandre Berr
et al.
Transfer RNAs (tRNAs) are universal adaptors of the genetic code, yet their evolutionary dynamics across photosynthetic eukaryotes remain underexplored. Here, we present the largest comparative re-analysis integrating the PlantRNA database with published data to explore tRNA gene evolution. We find that tRNA gene repertoires have been deeply shaped by ecological transitions, genome architecture, and translational demands. Terrestrialization marks a major shift in tRNA evolution, characterized by the loss of selenoproteins and their dedicated selenocysteine tRNAs in land plants compared to algae. Patterns of intron prevalence, position, and structure diverged among lineages, with extensive intron loss occurring around the origin of land plants. Organellar genomes exhibit divergent trajectories: mitochondrial tRNA sets are highly labile due to recurrent gene losses, imports, and horizontal transfers, whereas plastid repertoires are comparatively stable with lineage-specific exceptions. In parallel, angiosperm nuclear tRNA genes exhibit reinforced cis-regulatory elements, consistent with increased and developmentally complex translational demands, and their copy number correlates tightly with codon usage and amino acid composition. Finally, conserved yet family-biased clustering of nuclear tRNA genes reveals contrasting organizational principles in plants versus metazoans. Together, these findings establish tRNA gene evolution as a major determinant of translational capacity and a key driver of photosynthetic diversification.
Nicolás Gaggion, Noelia A. Boccardo, Rodrigo Bonazzola
et al.
Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking six distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters, and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional Principal Component Analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.
Accurate indoor localization is crucial in industrial environments. Visible Light Communication (VLC) has emerged as a promising solution, offering high accuracy, energy efficiency, and minimal electromagnetic interference. However, VLC-based indoor localization faces challenges due to environmental variability, such as lighting fluctuations and obstacles. To address these challenges, we propose a Transfer Learning (TL)-based approach for VLC-based indoor localization. Using real-world data collected at a BOSCH factory, the TL framework integrates a deep neural network (DNN) to improve localization accuracy by 47\%, reduce energy consumption by 32\%, and decrease computational time by 40\% compared to the conventional models. The proposed solution is highly adaptable under varying environmental conditions and achieves similar accuracy with only 30\% of the dataset, making it a cost-efficient and scalable option for industrial applications in Industry 4.0.
Elizabet Matolisi, Nurhayati Damiri, Momon Sodik Imanudin
et al.
Pathogenic microorganisms present in river water pollution are a serious health risk. Escherichia coli is a pathogen commonly found in water. Escherichia coli is an indicator of water contaminated with feces. These pathogens have an impact on human health. Aur River is one of the tidal rivers in Palembang City. From the observations, many people dispose of domestic waste into river bodies, so the river is at risk of being polluted by pathogenic microorganisms. When it recedes, the water looks pitch black, and when it is high, many people bathe, brush their teeth, and wash their clothes in this river. Thus, this study aimed to monitor water quality, including total coliform, of the Aur River Palembang City. The results of this monitoring can be used as the basis for making a policy and strategy to control the quality of this river water. This study was carried out on the Aur River, which is 2.5 km long. Measurements were made at three stations, namely at the upstream, middle, and downstream. Based on measurements of the pH, BOD, COD, DO, and total coliform concentrations, it is concluded that the Aur River has been polluted because the BOD, COD, DO, and total coliform were above the quality standards. The pH is still at the quality standard.
J. B. M. Seeletse, N. A. Ndukwe and J. P. H. van Wyk
Sawdust, a product of the forest industry is mostly left untreated as solid waste. This phenomenon is well observed along the Lagos Lagoon in Nigeria where hundreds of trees are cut daily by sawmills to deliver wood for mainly the furniture industry. Different types of trees are utilized in this manner and the massive amounts of sawdust produced as a result of these activities are polluting the environment causing health risks for humans and animals. Cellulose, a glucose bio-polymer is a major structural component of sawdust and could be developed as a renewable energy resource should the cellulose be degraded into glucose, a fermentable sugar. This saccharification was done with Aspergillus niger cellulase and to make the cellulose more susceptible for cellulase action the sawdust was delignified with hydrogen peroxide. Both delignified and non-delignified sawdust were treated with the cellulase enzyme at incubation temperatures of 30°C, 40°C, 50°C, and 60°C. Delignification proved to be effective as an increased amount of sugar was released from all delignified sawdust materials relative to the non-delignified materials when saccharified with A. niger cellulase. Most of the materials were degraded at an incubation temperature of 40°C and 50°C and the highest percentage saccharification of 58% was obtained during the degradation of delignifed cellulose from the tree, Ricindendron heudelotti
Environmental effects of industries and plants, Science (General)
Earth observation data have shown promise in predicting species richness of vascular plants ($α$-diversity), but extending this approach to large spatial scales is challenging because geographically distant regions may exhibit different compositions of plant species ($β$-diversity), resulting in a location-dependent relationship between richness and spectral measurements. In order to handle such geolocation dependency, we propose \textit{Spatioformer}, where a novel geolocation encoder is coupled with the transformer model to encode geolocation context into remote sensing imagery. The Spatioformer model compares favourably to state-of-the-art models in richness predictions on a large-scale ground-truth richness dataset (HAVPlot) that consists of 68,170 in-situ richness samples covering diverse landscapes across Australia. The results demonstrate that geolocational information is advantageous in predicting species richness from satellite observations over large spatial scales. With Spatioformer, plant species richness maps over Australia are compiled from Landsat archive for the years from 2015 to 2023. The richness maps produced in this study reveal the spatiotemporal dynamics of plant species richness in Australia, providing supporting evidence to inform effective planning and policy development for plant diversity conservation. Regions of high richness prediction uncertainties are identified, highlighting the need for future in-situ surveys to be conducted in these areas to enhance the prediction accuracy.
Purpose: Recent geopolitical, economic, and social changes worldwide directly impact the operations of manufacturing enterprises. This paper aims to highlight the potential outcomes achieved through the implementation of modern technological solutions within the scope of Industry 4.0 in organizations operating in the manufacturing sector. The presented results are part of wider research (Michna et al., 2021). Design/methodology/approach: The research methods consist of a comprehensive literature review of the subject under study and the results of empirical research conducted in 2023 based on multiple case study in production companies in Poland. Findings: The study presents the effects achieved by companies after implementing Industry 4.0 solutions, such as: production cost reduction, increase in profits, productivity and production efficiency and improvements in working conditions. Research limitations/implications: The study was conducted in two selected manufacturing plants operating in the Polish market, with the limitation being the sample selection and the subjective assessment of the study participants. Practical implications: The research results provide an overview of potential and achieved effects and changes in the functioning of the enterprise following the implementation of Industry 4.0 technologies. An additional categorization into effects achieved in socio-economic, environmental areas, and overall corporate governance allows for situating the organization's performance in light of recent legislative changes related to the Fit for 55 packages (Fit for 55, 2021). Originality/value: The study can assist practitioners and specialists in Industry 4.0 in analyzing the potential effects of implementing new technological solutions and aid in planning future activities within the organization. Keywords: Industry 4.0, Results, Performance, Production. Category of the paper: Empirical research results.