Natural Input Research Articles

Towards an integrated pelagic and benthic analysis of long-term coastal eutrophication (Guanabara Bay, Brazil)

Coastal ecosystems are important destinations for natural and anthropogenic inputs, areas experiencing eutrophication from untreated sewage globally. However, the mechanistic link of how coastal waters and sediments can jointly respond to nutrient enrichment, especially nitrogen (N) and phosphorus (P), remains relatively unknown. Here, we assessed the interdecadal variability in N and P contents, comparing four surface water sampling sites (∼40-year monthly concentrations) and three 210Pb dated sediment cores (∼70-years) in an urban bay that has received increasingly high untreated sewage discharges over the past century (Guanabara Bay, Brazil). Major eutrophication was found, with differences between waters and sediments along sampling sites. The water concentrations of total phosphorus (TP) and dissolved inorganic nitrogen (DIN) yielded on average ∼10–45 % and ∼40–190 % higher in 2005–2017 compared to 1980–2005 period in the sampling site most influenced by the denser urban than the conserved area, respectively. Furthermore, 6-year average water concentrations indicated both recent initial decreases in the most urban area, potentially associated with new sanitation facilities, and high increases in those relatively more conserved areas. Conversely, TP and TN sediment burial rates were less sensitive to recent changes as compared to the water concentrations, yielding ∼11 and 31 % between 1980–2005 and 2005–2017, respectively, of which also provided baselines data for earlier periods without direct pelagic measurements (i.e., 1950–1980). Thus, these findings offer new insights into substantial temporal and intra-ecosystem spatial variability of coastal eutrophication, indicating that an integrated analysis between water monitoring and sediment burial is needed to improve the diagnosis and prediction capabilities necessary for effective long-term mitigation of coastal aquatic eutrophication worldwide.

Effect of extraction solution on functional properties of extracted protein from Bombay locust (Patanga succincta Johannson, 1763)

Alternative sources of protein are gaining popularity since they require less natural resource input, but still provide a significant amount of protein as compared to traditional protein sources. Insect protein is one of the alternative protein types, and due to its nutritional benefits, research interest in Bombay locust (Patanga succincta) (BL) protein has grown. In the present work, we aimed to determine the protein content, yield, molecular weight profile, and functional properties such as protein solubility, emulsion, and foam properties of BL protein extracted using distilled water, salt (NaCl), or alkaline (NaOH) solution, at the concentrations of 0.5, 1.0, or 1.5%. The highest protein extraction yield was alkaline soluble protein (22 - 28%), which was followed by water (16%) and salt (11 - 13%) soluble proteins. The protein powder prepared by 0.5% alkaline extraction had the highest foam capacity (33.33%) and foam stability (12.50%) (p < 0.05), but the water soluble protein powder had the highest emulsion activity index (118.3 m2/g) and emulsion stability index (52.45 min) (p < 0.05). These results indicated that the type and concentration of solution could have an impact on the protein extraction yield, molecular weight profile, and functional characteristics.

Human-machine Translation Model Evaluation Based on Artificial Intelligence Translation

As artificial intelligence (AI) translation technology advances, big data, cloud computing, and emerging technologies have enhanced the progress of the data industry over the past several decades. Human-machine translation becomes a new interactive mode between humans and machines and plays an essential role in transmitting information. Nevertheless, several translation models have their drawbacks and limitations, such as error rates and inaccuracy, and they are not able to adapt to the various demands of different groups. Taking the AI-based translation model as the research object, this study conducted an analysis of attention mechanisms and relevant technical means, examined the setbacks of conventional translation models, and proposed an AI-based translation model that produced a clear and high quality translation and presented a reference to further perfect AI-based translation models. The values of the manual and automated evaluation have demonstrated that the human-machine translation model improved the mismatchings between texts and contexts and enhanced the accurate and efficient intelligent recognition and expressions. It is set to a score of 1-10 for evaluation comparison with 30 language users as participants, and the achieved 6 points or above is considered effective. The research results suggested that the language fluency score rose from 4.9667 for conventional Statistical Machine Translation to 6.6333 for the AI-based translation model. As a result, the human-machine translation model improved the efficiency, speed, precision, and accuracy of language input to a certain degree, strengthened the correlation between semantic characteristics and intelligent recognition, and pushed the advancement of intelligent recognition. It can provide accurate and high-quality translation for language users and achieve an understanding of natural language input and output and automatic processing.

Spatial Variation, Ecological Risk, and Point Sources of Environmental Trace Metals in Lacustrine Ecosystems: An Assessment of Natural and Urban Inputs

ABSTRACT To gain insights into the spatial distribution, ecological risk and origins of heavy metals in surface sediments of lacustrine ecosystems in Cameroon, the content of 7 trace metals (Fe, Cr, Zn, Cu, Ni, Pb and Cd) in sediments from 60 sampling stations in 5 lacustrine environments of different formation contexts. The selected metals were investigated based on inductively coupled plasma mass spectrometry. The results revealed that the average concentrations of Cr, Cu and Cd were higher than the geochemical Threshold values. On the basis of the spatial variation, the studied metals vary quantitatively in the following order: Fe > Cr > Zn > Cu > Ni > Pb > Cd. The calculated enrichment factor (EF) and geo-accumulation index (Igeo) showed that, sediments from large cities and industrial sites had moderate enrichment (3 < EF ≤ 5) and were moderately to severely contaminated (2 < Igeo < 3) while those from low population and rural areas had minor enrichment (1 < EF ≤ 3) and were moderately contaminated (1 < Igeo < 2). With regards to cumulative pollution, the pollution load index (PLI) and the sediment pollution index (SPI) coupled with the potential ecological risk factor (Er) and the potential ecological risk index (RI), the sediments of the lakes are polluted (SPI and PLI > 1) to some extent but with a low ecological risk (Er < 40 and RI < 150). The results of Pearson correlation, principal component analysis (PCA), and cluster analysis (CA) identified three main sources: Ni would have a natural source; Pb from anthropogenic sources, and the other metals (Fe, Cr, Zn, Cu, and Cd) could have a mixed source (anthropogenic and natural). These findings serve as a scientific foundation for the advancement and use of these lake environments, which are the most attractive sites.

Accumulation, ecological health risks, and source identification of potentially toxic elements in river sediments of the Qinghai-Tibet Plateau, China

The Qarhan Salt Lake is the largest brine lake on the Qinghai-Tibet Plateau (QTP), which contains some potentially toxic elements (PTEs) due to the increase of human activities. This study endeavor encompassed the collection of 62 sediment samples from eight rivers that recharge the Qarhan Salt Lake, and the measurement of seven PTEs' concentrations (As, Hg, Pb, Cd, Cu, Ni, and Zn). Results indicated that the concentrations of most PTEs were lower than their local soil background values, except for Hg and Cd, which surpassed the background contents by 6.8 and 1.1 times, respectively. The Igeo and PLI outcomes demonstrated a relatively low level of Hg contamination. Hg was the most hazardous elements with the mean ecological risk factor of 273.88. The Hazard Index (HI) values for PTEs were computed at 0.20 and 0.46 for adults and children, respectively, indicating that neither group was at a non-carcinogenic risk. Through the utilization of source identification methods, this study identified three predominant sources responsible for PTEs in the area: natural geogenic sources, anthropogenic sources and atmospheric deposition inputs. The findings of this study provide a robust scientific foundation for the management of the ecological environment security of rivers in the QTP.

Lake Avernus Has Turned Red: Bioindicator Monitoring Unveils the Secrets of "Gates of Hades".

Lake Avernus is a volcanic lake located in southern Italy. Since ancient times, it has inspired numerous myths and legends due to the occurrence of singular phenomena, such as coloring events. Only recently has an explanation been found for them, i.e., the recurring color change over time is due to the alternation of cyanobacterial blooms that are a consequence of natural nutrient inputs as well as pollution resulting from human activities. This current report specifically describes the red coloring event that occurred on Lake Avernus in March 2022, the springtime season in this region of Italy. Our innovative multidisciplinary approach, the 'Fast Detection Strategy' (FDS), was devised to monitor cyanobacterial blooms and their toxins. It integrates remote sensing data from satellites and drones, on-site sampling, and analytical/bioinformatics analyses into a cohesive information flow. Thanks to FDS, we determined that the red color was attributable to a bloom of Planktothrix rubescens, a toxin-producing cyanobacterium. Here, we report the detection and identification of 14 anabenopeptins from this P. rubescens strain, seven of which are known and seven are newly reported herein. Moreover, we explored the mechanisms and causes behind this cyclic phenomenon, confirming cyanobacteria's role as reliable indicators of environmental changes. This investigation further validates FDS's effectiveness in detecting and characterizing cyanobacterial blooms and their associated toxins, expanding its potential applications.

Health risk assessment and quality reference values of potentially toxic elements in soils of the Southwestern Amazonas State – Brazil

The geochemical processes involved in the Amazon soils formation are not totally understood because its geological and pedological features were formed during different geological events and different times. The Southwestern region of Amazonas State is an example of a region where the soils were developed from rocks formed by sedimentary processes and, nowadays, the soils continue receiving seasonal inputs from the Andes sediments. Data on geochemical patterns of this region are scarce, and there is no information about the possible effects of the natural or unnatural enrichment of potentially toxic elements (PTEs). Thus, this study represents the first research into the geochemical patterns of PTEs in soils of Southwestern Amazonas State. Further, we carried out a human health risk assessment and a proposal for Quality Reference Values (QRV) for thirteen PTEs (As, Ba, Cd, Co, Cr, Cu, Mn, Ni, Pb, Ti, V, Zn and Zr). Our findings showed that the PTEs contents in soil samples collected in the lowland areas are strongly influenced by sediments inputs, while samples localized in the highland areas are weakly influenced by sediments inputs and present depletion of PTEs. Barium, Cr, Cu, Ni and Pb were the PTEs most influenced by sediment's deposition. The soil contamination assessment showed the existence of enrichment of the potentially toxic elements in soil samples, which may be a consequence of the natural inputs of the Andean sediments. Furthermore, Co and Cr exceed the safety zone for carcinogenic risk assessment, which indicates the need to monitor these elements and their possible effects on the health of the population in this region.

Potentially toxic elements in rainwater during extreme rainfall period in the megacity Beijing: Variations, sources, and reuse potential

Air pollution has become a worldwide problem, which resulted in various environmental threats to the human beings and surface ecosystem. It is thus significant to clarify the sink/source process of atmospheric contaminants, such as the potentially toxic elements (PTEs). The rainfall process is a vital sink for atmospheric PTEs, which can effectively reduce their content in atmosphere. However, the understanding blind spot of variations, sources, and reuse potential of rainwater PTEs in megacities (hotspots area of atmospheric PTEs deposition) still existed, particularly in extreme rainfall period. For this, the daily-scale rainwater samples were collected in Beijing City during extreme rainfall period, and the selected PTEs were measured and further analyzed. The results indicated a volume-weighted mean PTEs concentration (in μg/L) order of Zn (10.86) > Mn (3.21) > Fe (0.73) > Cu (0.69) > Ni (0.35) > V (0.22) > Cr (0.09) ≈ Pb (0.09) > Cd (0.04). Zn was the most abundant PTEs, which was 2∼3 magnitude orders greater than the low abundant PTEs, such as Cr, Pb, and Cd. The sources of PTEs and the dissolved processes of atmospheric components (e.g., particulate matter) were the predominant influencing factors of rainwater PTEs variations, and the rainfall amount was also the potential (not the critical) influencing factor, while the influence of rainwater pH is negligible. According to the enrichment factor and multivariate statistical analysis, the Cd, Zn, and Cu were primarily attributed to anthropogenic sources, which were typically derived from fossil fuel burning, vehicle emission, and domestic waste combustion. The crust sources mainly contributed the Fe, Cr, and V, while the other PTEs (Mn, Ni, and Pb) were derived from a combination of human and natural inputs. Reuse potential assessment revealed that the harvested rainwater was suitable for irrigation of urban green space and vegetation (low soil hazard), and may potentially be utilized for drinking purpose under extreme conditions (after addressing the problem of ammonia nitrogen). This work identified the PTEs characteristics in urban wet deposition, which will help knowledge of PTEs biogeochemical cycle and sustainable use of rainwater resources under climate and environmental change.

GraphPrior: Mutation-based Test Input Prioritization for Graph Neural Networks

Graph Neural Networks (GNNs) have achieved promising performance in a variety of practical applications. Similar to traditional DNNs, GNNs could exhibit incorrect behavior that may lead to severe consequences, and thus testing is necessary and crucial. However, labeling all the test inputs for GNNs can be costly and time-consuming, especially when dealing with large and complex graphs, which seriously affects the efficiency of GNN testing. Existing studies have focused on test prioritization for DNNs, which aims to identify and prioritize fault-revealing tests (i.e., test inputs that are more likely to be misclassified) to detect system bugs earlier in a limited time. Although some DNN prioritization approaches have been demonstrated effective, there is a significant problem when applying them to GNNs: They do not take into account the connections (edges) between GNN test inputs (nodes), which play a significant role in GNN inference. In general, DNN test inputs are independent of each other, while GNN test inputs are usually represented as a graph with complex relationships between each test. In this article, we propose GraphPrior ( GNN -oriented Test Prior itization), a set of approaches to prioritize test inputs specifically for GNNs via mutation analysis. Inspired by mutation testing in traditional software engineering, in which test suites are evaluated based on the mutants they kill, GraphPrior generates mutated models for GNNs and regards test inputs that kill many mutated models as more likely to be misclassified. Then, GraphPrior leverages the mutation results in two ways, killing-based and feature-based methods. When scoring a test input, the killing-based method considers each mutated model equally important, while feature-based methods learn different importance for each mutated model through ranking models. Finally, GraphPrior ranks all the test inputs based on their scores. We conducted an extensive study based on 604 subjects to evaluate GraphPrior on both natural and adversarial test inputs. The results demonstrate that KMGP, the killing-based GraphPrior approach, outperforms the compared approaches in a majority of cases, with an average improvement of 4.76% ~49.60% in terms of APFD. Furthermore, the feature-based GraphPrior approach, RFGP, performs the best among all the GraphPrior approaches. On adversarial test inputs, RFGP outperforms the compared approaches across different adversarial attacks, with the average improvement of 2.95% ~46.69%.

Artificial Intelligence-Based Chatbots to Combat COVID-19 Pandemic: A Scoping Review

Context: Artificial intelligence (AI) Chatbots are computer programs that simulate human conversation and use artificial intelligence, including machine learning and natural language processing, to interact with users via natural language. With the outbreak of the COVID-19 pandemic, the use of digital health technologies such as chatbots has accelerated. Objectives: This study aims to investigate the application of AI chatbots in combating the COVID-19 pandemic and explore their features. Methods: We reviewed the literature on health chatbots during the COVID-19 pandemic. PubMed, Scopus, Web of Science, and Google Scholar were searched using relevant keywords such as “chatbot”, “conversational agent,” and “artificial intelligence”. To select the relevant articles, we conducted title, abstract, and full-text screening based on inclusion and exclusion criteria. Chatbots, their applications, and design features were extracted from the selected articles. Results: Out of 673 articles initially identified, 17 articles were eligible for inclusion. We categorized the selected AI chatbots based on their roles, applications, and design characteristics. Around 70% of chatbots were designed to play a preventive role. Our review identified 8 key applications of the AI chatbots during the COVID-19 pandemic, which include (1) information dissemination and education, (2) self-assessment and screening, (3) connecting to health centers, (4) combating misinformation and fake news, (5) patient tracking and service delivery (6) mental health (7) monitoring exposure (8) vaccine information and scheduling. AI chatbots were deployed on various platforms, including mobile apps, web, and social media. Mobile-based chatbots were the most frequent. All chatbots used Natural Language Understanding (NLU) methods to understand natural language input and act on the user’s request. More than 50% of AI chatbots used NLU platforms, including Google Dialogflow, Rasa framework, and IBM Watson. Conclusions: AI chatbots can play an effective role in combating the COVID-19 pandemic. Increasing people’s awareness, optimizing the use of health resources, and reducing unnecessary encounters are some of the advantages of using AI chatbots during the COVID-19 outbreak. Using NLU platforms can be a suitable solution for developing AI chatbots in the healthcare domain. With advancements in the field of artificial intelligence, it seems that AI chatbots will have a promising future in healthcare, particularly in public health, chronic disease management, and mental health.

Deep learning in digital health with chatgpt: a study on efficient code generation

Abstract Background/Introduction Artificial Intelligence (AI) plays a significant role in the healthcare industry by providing innovative solutions to many health-related problems. Deep learning (DL) is a critical component in the development of AI-powered solutions; however, extensive knowledge is required to implement DL processes such as data preparation, model design, model training, and model evaluation. This has limited the widespread adoption of AI in the medical field, particularly in scenarios where non-technical professionals possess limited coding proficiency. Purpose This study evaluates the feasibility of using ChatGPT, a large language model developed by OpenAI, as an assistive tool for non-technical professionals to develop DL applications. The objective is to reduce the time and effort required in source code creation, while simplifying the overall process of application development. Methods ChatGPT was utilised to generate relevant source code for the creation of a DL application for echocardiogram view classification. Natural language input prompts were fed through the ChatGPT interface to produce modular scripts that fulfilled the purposes of video frame extraction, image pre-processing, dataset splitting, model training, and model evaluation. Input prompts were specified as a series of logical steps which explained the desired functionality, along with the necessary software libraries to be integrated. Results Experiments on code generation involved iteratively modifying input prompts through the rewording of sentences, reordering of steps, and usage of different libraries, to obtain working source code from ChatGPT responses. Results demonstrated that ChatGPT can generate fully usable programming scripts from natural language inputs, without the need for any modifications, to accomplish the various tasks involved in the DL process flow. Experiments were also conducted on locally sourced dobutamine stress echocardiogram videos, to verify script functionality and output reliability. The scripts successfully extracted, pre-processed, and split the video frames into training, validation, and testing sets. These datasets were then employed in the training and evaluation of a convolutional neural network which achieved an accuracy of above 80% when classifying seven different echocardiogram views. Conclusion ChatGPT has the potential to significantly reduce the time and effort required in developing DL applications. The successful implementation of the programming scripts suggest that ChatGPT can function as an assisting tool for non-technical professionals. Its ability to generate operational source code and streamline the development process could facilitate greater adoption of AI in healthcare and other industries. Future works shall explore the usage of ChatGPT in generating solutions to more heart-related use cases.Code generation and DL process flow

Modern Technology and Scope of Precision Agriculture (PA)

Traditional agriculture practices are time consuming, less productive and uneconomic. Due to uncontrolled use of resources and inputs frequently occurred by farmers caused environmental degradation, land loss and economic loss of farmers. Smart farming known as Precision agriculture which uses the modern technology and tools such as Global position system, Geographic information system, remote sensing and information and communication in integrations provides detail information about crop growth, soil conditions, nutrient level, irrigation systems as data sets which helps to take decisions in right time so that it increases the crop productivity. The use of tools like GPS, GIS and Remote sensing optimize the use of natural resources and inputs for a given crop production and quality. Agriculture becomes more productive and consistent by digital technology and effective use of resources and time. The main gist of this study is to present the scope and implications of the precision agriculture (PA) in future.

MASSAI: Multi-agent system for simulating sustainable agricultural intensification of smallholder farms in Africa

The research and development needed to achieve sustainability of African smallholder agricultural and natural systems has led to a wide array of theoretical frameworks for conceptualising socioecological processes and functions. However, there are few analytical tools for spatio-temporal empirical approaches to implement use cases, which is a prerequisite to understand the performance of smallholder farms in the real world. This study builds a multi-agent system (MAS) to operationalise the Sustainable Agricultural Intensification (SAI) theoretical framework (MASSAI). This is an essential tool for spatio-temporal simulation of farm productivity to evaluate sustainability trends into the future at fine scale of a managed plot. MASSAI evaluates dynamic nutrient transfer using smallholder nutrient monitoring functions which have been calibrated with parameters from Malawi and the region. It integrates two modules: the Environmental (EM) and Behavioural (BM) ones.•The EM assess dynamic natural nutrient inputs (sedimentation and atmospheric deposition) and outputs (leaching, erosion and gaseous loses) as a product of bioclimatic factors and land use activities.•An integrated BM assess the impact of farmer decisions which influence farm-level inputs (fertilizer, manure, biological N fixation) and outputs (crop yields and associated grain).•A use case of input subsidies, common in Africa, markedly influence fertilizer access and the impact of different policy scenarios on decision-making, crop productivity, and nutrient balance are simulated. This is of use for empirical analysis smallholder's sustainability trajectories given the pro-poor development policy support.

Environmental performance of nonthermal plasma dry and conventional steam reforming of methane for hydrogen production: Application of life cycle assessment methodology

The environmental performance of nonthermal plasma assisted dry reforming of methane (NTP-DRM) for hydrogen production from methane rich natural gas is investigated and compared with the current state of the art steam reforming technology through a cradle-to-gate life cycle assessment (LCA). The NTP-DRM technologies studied in this analysis include dielectric barrier, microwave, and pulsed plasma discharges. Due to the level of technology the study is considered as the prospective LCA of selected types of NTP-DRMs for hydrogen production. Of these, the NTP-DRM using microwave and pulsed plasma presented better environmental performance. The impact categories include midpoints levels applying TRACI 2.1 and AWARE for the water scarcity, using ecoinvent 3.8 and US NETL databases. The scenario analysis regarding carbon dioxide, electricity, and natural gas sources, revealed the highly dependency of NTP-DRMs technologies environmental performance to the energy efficiency of the processes. In terms of steam reforming of methane (SRM) despite the main contribution of steam use in the technology environmental performance, the uncertainty analysis showed higher sensitivity to natural gas input. Investigation showed that using renewable energy sources can have significant improvement regarding NTP-DRMs environmental performance and specifically global warming potential. Accordingly, at their current reactant conversion and product selectivity reaching the energy use of eV per molecule of reactant equal or less than 2, can be a proper target for the studied best case NTP-DRM to make their environmental behavior competitive with the state-of-the-art SRM technologies.

AdvCheck: Characterizing adversarial examples via local gradient checking

Deep neural networks (DNNs) are vulnerable to adversarial examples, which may lead to catastrophe in security-critical domains. Numerous detection methods are proposed to characterize the feature uniqueness of adversarial examples, or to distinguish DNN's behavior activated by the adversarial examples. Detections based on features may be compromised when faced with stronger attacks. Besides, they require a large amount of specific adversarial examples. Another mainstream, model-based detections, which characterize input properties by model behaviors, suffer from heavy computation cost. To address the issues, we introduce the concept of local gradient, and reveal that adversarial examples have a quite larger bound of local gradient than the benign ones. Inspired by the observation, we leverage local gradient for detecting adversarial examples, and propose a general framework AdvCheck. Specifically, by calculating the local gradient from a few benign examples and noise-added misclassified examples to train a detector, adversarial examples and even misclassified natural inputs can be precisely distinguished from benign ones. Through extensive experiments, we have validated the AdvCheck's superior performance to the state-of-the-art (SOTA) baselines, with detection rate (∼×1.2) on general adversarial attacks and (∼×1.4) on misclassified natural inputs on average, with average 1/200 time cost. We also provide interpretable results for successful detection.

Artificial Intelligence in Applied Medicine

Artificial intelligence (AI) uses advanced language models that employ deep learning techniques to generate responses that imitate human-generated ones when given natural language input. Healthcare staff has concerns regarding the use of artificial intelligence in applied medicine and the response provided by this system may need investigation by expert researchers [1]. The growth of AI in healthcare promises improved diagnostics, personalized treatments, and cost reduction, with potential benefits including equalized access and reduced mortality. However, ethical concerns, regulatory hurdles, and challenges in integrating AI into healthcare must be addressed for its successful revolution in the industry [2]. The utilization of AI in the realm of practical medicine prompts various concerns about bias, transparency, and ethics. AI algorithms may exhibit bias due to biased training data or decision-making processes, resulting in discrepancies in healthcare outcomes. The lack of transparency is a barrier with AI systems, since they often function as opaque entities, rendering it arduous to comprehend the decision-making process. Ethical concerns arise when using AI in the field of applied medicine. [2,3]. Artificial intelligence techniques possess the capability to be used in applied medicine. It is imperative to carry out further well-planned clinical trials before implementing these emerging techniques in a clinical setting.

Coupled patterns of natural and anthropogenic resources in typical ecosystems in coastal areas of China

The coastal area of Yancheng, China, is one of the hotspots for ecological research. Under the coupling of human and natural ecosystems, the region has gradually evolved into a coexistence of aquatic, agricultural and mudflat ecosystems. What are the patterns of natural and artificial resource inputs and patterns of change in ecosystems? How can ecological flows be analyzed at a uniform scale? Here, we selected six typical local ecosystems, namely, rice‒wheat for enterprises (RWE), rice‒wheat for smallholder households (RWS), chrysanthemum‒wheat (CW), fish polyculture (FP), juvenile crab farming (JF) and clam polyculture (CP), and analyzed their energy flow flux and sustainability based on emergy theory. The results showed that anthropogenic resource inputs were higher than natural resource inputs in all ecosystems, and the inputs of aquatic ecosystems were greater than those of agroecosystems. The greatest total input was 2.0 E+17 seJ/ha/yr for FP, and the lowest was 1.9 E+16 seJ/ha/yr for RWE. The proportions of renewable and artificial inputs for RWE, RWS, CW, FP, JF and CP were 32.8% vs. 96.1%, 40.3% vs. 96.5%, 34.7% vs. 97.0%, 32.6% vs. 99.4%, 55.1% vs. 98.5%, and 62.5% vs. 98.6%, respectively. The highest input to agroecosystems was nitrogen fertilizer, while in JF and CP, it was water, and feed (63.3%) accounted for the highest percentage of input in FP. JF and CP had lower environmental loads and higher sustainability than other ecosystems, but this still represents a high input compared to agroecosystems. Human-led resource coupling profoundly affects ecosystem sustainability, and various thresholds of energy use and ecological sustainability need to be studied in depth. Continuous exploration of methods and mechanisms for the maintenance and evolution of ecosystems with low total inputs and low inputs of non-renewable resources can contribute to high-quality sustainable development of an area or region.

Toward non-human-centered design: designing an academic article with ChatGPT

Non-human-centered design tools, such as ChatGPT, have shown potential as effective aids in academic article design. This study conducts a comparative evaluation of ChatGPT-3.5 and ChatGPT-4, examining their capabilities and limitations in supporting the academic article design process. The study aims to demonstrate the utility of ChatGPT as a writing tool and investigate its applicability and efficacy in the context of academic paper design. The author interacted with both versions of ChatGPT, providing prompts and analyzing the generated responses. In addition, a different expert academic was consulted to assess the appropriateness of the ChatGPT responses. The findings suggest that ChatGPT, despite its limitations, could serve as a useful tool for academic writing, particularly in the design of academic articles. Despite the limitations of both GPT-3.5 and GPT-4, GPT-3.5 offers a broader perspective, whereas GPT-4 provides a more in-depth and detailed approach to the design of articles. ChatGPT exhibits capabilities in aiding the design process, generating ideas aligned with the overall purpose and focus of the paper, producing consistent and contextually relevant responses to various natural language inputs, partially assisting in literature reviews, supporting paper design in terms of both content and format, and providing reasonable editing and proofreading for articles. However, limitations were identified, including reduced critical thinking, potential for plagiarism, risk of misinformation, lack of originality and innovation, and limited access to literature.

Pre-scaling up of ground nut varieties in selected districts of West and Kellem Wollega zones, Ethiopia

The study was conducted in Dale sadi, Dale wabera and Sayo districts of Kellem wollega Zone and Gimbi districts of west Wollega zone for two consecutive years of production(2016/17 and 2017/18 G.C ) on farmers’ field. The objective of the study was to pre-scale up and popularize the best-performing and preferred groundnut variety in the study area. The study addressed a total of 90 farmers in the two years of the project span. From a total of 90 farmers, male represents 73 and female 17. During farmers’ selection process, both female and male farmers were incorporated so as to avoid gender bias. Training was given to the selected farmers, development agents and district experts about improved groundnut technologies production and management. Full packages of the technologies were provided. Variety Manipinter was planted on plot area of 0.25ha on respective farmers’ field in the study area. The recommended rate of NPS 100kg/ha and Row planting method with the spacing of 60cm between rows and 10cm between plants were used respectively. To enhance farmer’s knowledge on Groundnut training was given for target community on agronomic practice of Groundnut. During training a total of 117 target communities were participated on training out of which 84 were male and 33 were male. An average production of 14.935 qt/ha and a total of 336 quintal of groundnut yield was harvested during project life span. Finally, farmers feedback concerning technology was collected and incorporated, accordingly the farmers feel as the Manipinter have relatively high yield, good seed quality (Grain size, color), disease tolerant than previously used varieties. Generally, since it is impossible to address all farmers only by the effort of the research center, district agriculture and natural resource bureau in collaboration with zonal agricultural and natural resource office and input supplying NGO’s should hold the turn to scale up the technology in wider scale. Res. Agric. Livest. Fish. Vol. 10, No. 2, August 2023: 147-153

Metal bioavailability, bioaccumulation, and toxicity assessment through sediment and edible biota from intertidal regions of the Aghanashini Estuary, India

The sediment cores and edible biota from the intertidal regions of the Aghanashini Estuary were studied for the assessment of metal toxicity. The estuarine sediments received natural input of metals through the weathering of Dharwar and peninsular gneisses, and laterites. The sediments were enriched in Fe, Mn, Zn, Cu, Co and Ni than the upper crustal value. Also, the concentration of Fe, Zn, Cu, Co and Ni was more in the Aghanashini Estuary than other estuaries around the world. The Enrichment Factor (EF) revealed enrichment of Zn, Cu, Co and Ni in sediments, while the Geo-accumulation Index (Igeo) exhibited unpolluted-moderately-strongly polluted class of Fe, Zn, Cu, Co and Ni. The enrichment and pollution of metals in sediments was due to anthropogenic sources (domestic sewage, aquaculture and agricultural discharge) in the estuary. The presence of metals at a high concentration in the residual fraction and at a significant proportion in the bioavailable fractions construed both natural and anthropogenic sources of metal, and their bioavailability in the estuary. The physico-chemical factors (ionic composition, H+ ions, redox potential, and microbial activity) regulated the adsorption and desorption of metals in sediments. The Screening Quick Reference Table (SQUIRT) revealed level of bioavailable Mn and Co higher than the Apparent Effects Threshold (AET) and thus, indicated adverse toxic effects on biota. The Risk Assessment Code (RAC) indicated medium-high-very high risk of Mn, Zn, Co, Cu and Ni to biota. Also, the concentration of Fe, Mn, Zn, Cu and Ni in biota was in excess of permissible limit which pointed to their toxicity to biota and their consumers. The Target Hazard Quotient (THQ) value > 1, and Hazard Index (HI) value > 10 revealed risk of metal toxicity to humans.