Search

Although studies dealing with adsorption/desorption (and/or retention/release) of pollutants present in environmental compartments is a classical field of research, recent papers are focusing on some weak points of investigations and publications within the area. In addition, an increasing number of works are being published related to new possibilities and alternatives in this kind of research works, many of them in relation to the use of artificial intelligence (AI). Considering the existence of eventual controversies, eventual mistakes, and the convenience of suggesting alternatives to go ahead in the future, in this work, after taking into account some relevant publications in the previous literature, a simple workflow is proposed as a kind of protocol to revise successive steps that could guide the direction to follow when programing research dealing with the retention/release of pollutants in soils and sorbent materials. [Display omitted] • Recent papers show weak points of publications on pollutants adsorption/desorption. • Increasing works on the use of artificial intelligence in this kind of research. • A protocol is proposed to guide programing research on pollutants retention/release. [ABSTRACT FROM AUTHOR]

Flood modelling and forecasting can enhance our understanding of flood mechanisms and facilitate effective management of flood risk. Conventional flood hazard and risk assessments usually consider one driver at a time, whether it is ocean, fluvial or pluvial, without considering the compound nature of flood events. In this paper, we developed a novel approach for modelling and forecasting compound coastal-fluvial floods using a two-step framework. In step one, a hydrodynamic model is used to simulate floodwater propagation; while in step two, machine learning (ML) models are used to generate flood forecasts. The architecture of hydrodynamic-ML forecasting system incorporates a hydrodynamic model covering a specific domain, with individual ML models trained for each pixel. In total 7 ML models including: Support Vector Regression (SVR), Support Vector Machine (SVM), Radial Basis Function (RBF), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree (DT), and Artificial Neural Network (ANN) were applied in this study. Forecasting compound floods is achieved using two sets of inputs: timeseries of river discharges in the upstream fluvial section and downstream ocean water levels in the coastal areas. The accuracy of the flood forecasting system is demonstrated for Cork City, Ireland; and modelling performance was evaluated using several statistical tools. Results show that the proposed models can provide reliable estimates of flood inundation and associated water depths. Overall, the RBF model exhibits the best performance. Despite the complexity of compound multi-driver floods, this study shows that the coupled hydrodynamic-ML approach can forecast coastal-fluvial flood with limited hydraulic and hydrological input data. This system overcomes the limitations of traditional hydrodynamic model-based systems where trade-offs between the always competing numerical model accuracy and computational time prohibit the model to be used for short-term flood forecasting. Once trained, the ML component of the coupled system can perform flood forecasting in near real-time, potentially integrating into a flood early warning system. Accurate flood forecasting has a wide range of positive societal impacts, including improved flood preparedness, increased confidence, better resource allocation, reduced flood damage, and potentially even flood prevention. • Novel technique to improve compound coastal-fluvial flood modelling. • Flood modelling technique based on seven Machine Learning methods. • Approach is successfully implemented, and results are evaluated over Cork, Ireland. • The results demonstrate minimal quantitative errors for the proposed models. [ABSTRACT FROM AUTHOR]

Indole-3-acetic acid (IAA) derived from Actinobacteria fermentations on agro-wastes constitutes a safer and low-cost alternative to synthetic IAA. This study aims to select a high IAA-producing Streptomyces -like strain isolated from Lake Oubeira sediments (El Kala, Algeria) for further investigations (i.e., 16S rRNA gene barcoding and process optimization). Subsequently, artificial intelligence-based approaches were employed to maximize IAA bioproduction on spent coffee grounds as high-value-added feedstock. The specificity was the novel application of the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Box (L-BFGS-B) optimization algorithm. The new strain AW08 was a significant producer of IAA (26.116 ± 0.61 μg/mL) and was identified as Streptomyces rutgersensis by 16S rRNA gene barcoding and phylogenetic inquiry. The empirical data involved the inoculation of AW08 in various cultural conditions according to a four-factor Box Behnken Design matrix (BBD) of Response surface methodology (RSM). The input parameters and regression equation extracted from the RSM-BBD were the basis for implementing and training the L-BFGS-B algorithm. Upon training the model, the optimal conditions suggested by the BBD and L-BFGS-B algorithm were, respectively, L-Trp (X1) = 0.58 %; 0.57 %; T° (X2) = 26.37 °C; 28.19 °C; pH (X3) = 7.75; 8.59; and carbon source (X4) = 30 %; 33.29 %, with the predicted response IAA (Y) = 152.8; 169.18 μg/mL). Our findings emphasize the potential of the multifunctional S. rutgersensis AW08, isolated and reported for the first time in Algeria, as a robust producer of IAA. Validation investigations using the bioprocess parameters provided by the L-BFGS-B and the BBD-RSM models demonstrate the effectiveness of AI-driven optimization in maximizing IAA output by 5.43-fold and 4.2-fold, respectively. This study constitutes the first paper reporting a novel interdisciplinary approach and providing insights into biotechnological advancements. These results support for the first time a reasonable approach for valorizing spent coffee grounds as feedstock for sustainable and economic IAA production from S. rutgersensis AW08. [Display omitted] • Multifunctional Streptomyces rutgersensis AW 08 represents a novel viable source for indole-3-acetic acid. • Spent coffee grounds can substitute laboratory-grade substrates for Streptomyces fermentation. • L-BFGS-B algorithm implementation with RSM-BBD achieves fast and effective bioprocess optimization. • Artificial intelligence-driven approaches increase indole-3-acetic acid yields up to fivefold. [ABSTRACT FROM AUTHOR]

In recent years there has been increased use of machine learning (ML) techniques within mathematics, including symbolic computation where it may be applied safely to optimise or select algorithms. This paper explores whether using explainable AI (XAI) techniques on such ML models can offer new insight for symbolic computation, inspiring new implementations within computer algebra systems that do not directly call upon AI tools. We present a case study on the use of ML to select the variable ordering for cylindrical algebraic decomposition. It has already been demonstrated that ML can make the choice well, but here we show how the SHAP tool for explainability can be used to inform new heuristics of a size and complexity similar to those human-designed heuristics currently commonly used in symbolic computation. [ABSTRACT FROM AUTHOR]

Encapsulated cell technology (ECT) is a targeted delivery method that uses the genetically engineered cells in semipermeable polymer capsules to deliver cytokines. Thus far, ECT has been extensively utilized in pharmacologic research, and shows enormous potentials in the treatment of posterior segment diseases. Due to the biological barriers within the eyeball, it is difficult to attain effective therapeutic concentration in the posterior segment through topical administration of drug molecules. Encouragingly, therapeutic cytokines provided by ECT can cross these biological barriers and achieve sustained release at the desired location. The encapsulation system uses permeable materials that allow growth factors and cytokines to diffuse efficiently into retinal tissue. Moreover, the ECT based treatment can be terminated timely when we need to retrieve the implant, which makes the therapy reversible and provides a safer alternative for intraocular gene therapy. Meanwhile, we also place special emphasis on optimizing encapsulation materials and enhancing preservation techniques to achieve the stable release of growth factors and cytokines in the eyeball. This technology holds great promise for the treatment of patients with dry AMD, RP, glaucoma and MacTel. These findings would enrich our understandings of ECT and promote its future applications in treatment of degenerative retinopathy. This review comprises articles evaluating the exactness of artificial intelligence-based formulas published from 2000 to March 2024. The papers were identified by a literature search of various databases (PubMed/MEDLINE, Google Scholar, Cochrane Library and Web of Science). [Display omitted] [ABSTRACT FROM AUTHOR]

Partially fake audio, a variant of deep fake that involves manipulating audio utterances through the incorporation of fake or externally-sourced bona fide audio clips, constitutes a growing threat as an audio forgery attack impacting both human and artificial intelligence applications. Researchers have recently developed valuable databases to aid in the development of effective countermeasures against such attacks. While existing countermeasures mainly focus on identifying partially fake audio at the level of entire utterances or segments, this paper introduces a paradigm shift by proposing frame-level systems. These systems are designed to detect manipulated utterances and pinpoint the specific regions within partially fake audio where the manipulation occurs. Our approach leverages acoustic features extracted from large-scale self-supervised pre-training models, delivering promising results evaluated on diverse, publicly accessible databases. Additionally, we study the integration of boundary and deepfake detection systems, exploring their potential synergies and shortcomings. Importantly, our techniques have yielded impressive results. We have achieved state-of-the-art performance on the test dataset of the Track 2 of ADD 2022 challenge with an equal error rate of 4.4%. Furthermore, our methods exhibit remarkable performance in locating manipulated regions in Track 2 of the ADD 2023 challenge, resulting in a final ADD score of 0.6713 and securing the top position. • Simultaneously detecting partially spoofed audio and locating the manipulated regions. • Exploring model integration techniques in identifying fake regions. • Investigating self-supervised pre-training models in partially spoofing detection. [ABSTRACT FROM AUTHOR]

Existing knowledge distillation methods use large amount of bilingual data and focus on mining the corresponding knowledge distribution between the source language and the target language. However, for some languages, bilingual data is not abundant. In this paper, to make better use of both monolingual and limited bilingual data, we propose a new knowledge distillation method called Dual Knowledge Distillation (DKD). For monolingual data, we use a self-distillation strategy which combines self-training and knowledge distillation for the encoder to extract more consistent monolingual representation. For bilingual data, on top of the k Nearest Neighbor Knowledge Distillation (kNN-KD) method, a similar self-distillation strategy is adopted as a consistency regularization method to force the decoder to produce consistent output. Experiments on standard datasets, multi-domain translation datasets, and low-resource datasets show that DKD achieves consistent improvements over state-of-the-art baselines including kNN-KD. • Monolingual data can better help low-resource machine translation. • Dual Knowledge Distillation can better use bilingual and monolingual data. • Self-distillation can extract more consistent monolingual representation. • Consistency regularization can force the decoder to generate consistent output. [ABSTRACT FROM AUTHOR]

Document-level Relation Extraction(DocRE) aims to extract relations between entities from documents. In contrast to sentence-level relation extraction, it requires extracting semantic relations from multiple sentences. It is necessary to further improve the performance of the above algorithm in order to extract document-level relation. Therefore, the DocRE algorithms have to deal with more complex entity structure relationships and the need to unite semantic relationships between different sentences when reasoning about relationships between entities. The proposed algorithms fail to infer relationships between entities when dealing with complex entity structure relationships. In this paper, we propose an entity mentions deep attention framework that efficiently infers entity relationships through entity structure and contextual information. Firstly, a structural dependency module of entities is designed to achieve interaction between different mentions of the entity. Secondly, a deep contextual attention component proposed to enrich the semantic information between entities by entity-related contexts. Finally, we use a distance mapping component to solve the problem of entity pairs that are far away from each other. According to our implementation results, our model outperforms the state-ofthe-art models on three public datasets DocRED, DGA, and CDR. • We created dependencies for entities and various mentions to enable structural reasoning. • The deep contextual attention module is designed to enhance context semantic information and improve model reasoning. • A location mapping module enhances entity representations for accurate capture of remote entity relationships. • The experimental results demonstrate the superiority of our EMDA model. [ABSTRACT FROM AUTHOR]

The last decade has witnessed enormous improvements in science and technology, stimulating the growing demand for economic and cultural exchanges in various countries. Building a neural machine translation (NMT) system has become an urgent trend, especially in the low-resource setting. However, recent work tends to study NMT systems for low-resource languages centered on English, while a few studies focus on low-resource NMT systems centered on other languages such as Chinese. To achieve this, the low-resource multilingual translation challenge of the 2021 iFLYTEK AI Developer Competition provides the Chinese-centric multilingual low-resource NMT tasks, where participants are required to build NMT systems based on the provided low-resource samples. In this paper, we present the competition-winning system that leverages monolingual word embeddings data enhancement, bilingual curriculum learning, and contrastive re-ranking. In addition, a new Incomplete-Trust (In-trust) loss function is proposed to replace the traditional cross-entropy loss when training. The extensive experimental results demonstrate that the implementation of these ideas leads to better performance than other state-of-the-art methods. All the experimental codes are released at: https://github.com/WENGSYX/Low-resource-text-translation. • Focusing on Chinese low-resource neural translation for further development. • Presenting new ideas for translation by enhancing pre-trained language models. • Self-developing optimization loss to further improve translation quality. • The proposed system outperforms the existing translation engine. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions. • This paper studies the effects of AI on China's GTFP from internal-structural perspective. • GTFP is decomposed into three internal factors by the global Malmquist index. • AI has positive effect on GTFP through different impacts on internal factors. • Improving resource allocation efficiency is the most critical channel of AI on GTFP. • AI plays a more significant role in promoting GTFP in less developed areas. [ABSTRACT FROM AUTHOR]

Several cities in the developing world, of which the capital city of India, New Delhi, is an example, often experience air quality in which pollutant levels go way above the levels considered hazardous for human health. To bring down the air quality to within permissible limits quickly, the measures typically taken involve shutting down certain high-polluting activities for some time to enable the air quality to recover temporarily. This paper presents a first-ever model based on artificial neural networks to forecast the extent of reduction in air quality parameters that can be achieved and the time period within which a change can be experienced when the source of the emissions is cut off temporarily. The model is based on the extensive data on the extent of reduction in air quality parameters that occurred during the lockdown that was imposed during the COVID-19 pandemic. The non-linear autoregressive exogenous network-based model chosen for the purpose employs the hour since stopping of emissions, relative humidity, wind speed, wind direction, and ambient temperature as input parameters to predict the rate of change of PM 2.5 with respect to the concentration at the start of the stopping of the emissions. Air quality data from a key monitoring station in New Delhi was used to develop the model. The model predicted the rate of drop in PM 2.5 with an R and MSE of 0.0044 and 0.9736, respectively, while training and 0.0095 and 0.9583 while testing. The model was then tested with data from 19 other stations in New Delhi, and accuracy of the model was found to be exceptionally accurate, with the correlation between the measured and the predicted PM 2.5 levels ranging from 0.74 to 0.94 and the MSE ranging from 0.0110 to 1.0746. Thus, the model can be employed to determine the number of hours of temporary stoppage of emissions required for the PM 2.5 concentration to reach safe levels. The methodology of development of the model can be extrapolated to construct models tailored for use in other parts of the world as well. • A novel ANN -based model is reported to predict rate of drop of PM 2.5 • Model has been trained on extensive dataset from the covid lockdown in Delhi. • Duration and efficacy of emergency measures to reduce emissions can be estimated. • The model uses easy-to-measure meteorological parameters as inputs. • Model has been validated for 19 stations of various land-use types in Delhi. [ABSTRACT FROM AUTHOR]

• An automated brain parcellation tool, 'Auto-Parc', has been developed to identify cortical regions and subcortical structures, as well as the locations of their supplying vascular structures. • The tool identifies 58 regions using a deep, 3D, sliding-window, U-Net architecture. • The use of a single network for the entire image allows for rapid re-training of the network as needed. • The tool is used as the first step of a fully-automated fMRI analysis protocol for assessment of mTBIs. Functional MRI has emerged as a powerful tool to assess the severity of Post-concussion syndrome (PCS) and to provide guidance for neuro-cognitive therapists during treatment. The next-generation functional neuro-cognitive imaging protocol (fNCI2) has been developed to provide this assessment. This paper covers the first step in the analysis process, the development of a rapidly re-trainable, machine-learning, brain parcellation tool. The use of a sufficiently deep U-Net architecture encompassing a small (39 × 39 × 39 voxel input, 27 × 27 × 27 voxel output) sliding window to sample the entirety of the 3D image allows for the prediction of the entire image using only a single trained network. A large number of training, validating, and testing windows are thus generated from the 101 manually-labeled Mindboggle images, and full-image prediction is provided via a voxel-vote method using overlapping windows. Our method produces parcellated images that are highly consistent with standard atlas-based methods in under 3 min on a modern GPU, and the single network architecture allows for rapid retraining (<36 hr) as needed. [ABSTRACT FROM AUTHOR]

• This study proposes a method of automatic generation of architectural space based on user activities in a metaverse and analyzes users' perceptions of such spaces. • In reference to the theory of environmental psychology, we constructed a theoretical framework for quantitative analysis of activities, based on which, we developed a metaverse space. • Architectural space images were produced based on grammatical rules that converted the analysis results into text as well as a deep-learning-based text-to-image generator. • Twenty participants, after participating in the metaverse experience and receiving the architectural space image, responded to two questionnaires on their perceptions and opinions. • We analyzed the users' opinions on the architectural space created using the data they provided. Recently, architectural offices and IT companies have undertaken projects to build and sell cities in metaverses. However, it is considered inefficient to undertake such construction individually, building-by-building, using 3D modeling techniques. Fortunately, the use of artificial intelligence (AI) offers a breakthrough solution. This paper proposes a method of automatic generation of architectural spaces that is based on user activities in a metaverse and that can analyze users' perceptions of such spaces. To this end, in the present study, by referencing the theory of environmental psychology, we constructed a theoretical framework that can quantitatively analyze activities, based on which, we developed a metaverse space. Architectural space images were produced based on grammatical rules, which converted the analysis results into text, as well as a deep-learning-based text-to-image generator. Twenty participants responded to two questionnaires on their perceptions and opinions after participating in the metaverse experience and after receiving the architectural space image. Consequently, we analyzed the users' opinions on the architectural space created using the data they provided. Further, we reviewed the validity of the architectural design of the virtual space created using the proposed approach and explored the possibilities of its future utilization. [ABSTRACT FROM AUTHOR]