Abstract The current situation of China's coal chemical industry faces many problems: 1) This industry causes environmental pollution; 2) employs inadequate environmental management system, wastewater pollution control standards, and energy consumption standards; 3) lacks environmental standards applicable to the coal chemical industry; 4) suffers from poor implementation of technical standards for coal use; 5) and lacks top-level design of a high salinity wastewater standard. A modern coal chemical industry demonstration area should be established to promote industrial agglomeration and development. Therefore, several suggestions are put forward to promote the modernization of the coal chemical industry: 1) The responsibilities, supervision tasks, scope, and implementation rules of local environmental law enforcement and supervision agencies should be improved and clarified at all levels; consequently, law enforcement and supervision work would be backed up by relevant laws. 2) To decrease the treatment cost of highly saline wastewater, a corresponding subsidy scheme should be formulated. 3) Research on the top-level design of the standard system for saline wastewater should be accelerated to standardize the treatment of saline wastewater. 4) Coal chemical enterprises should integrate environmental management into their daily production management system, constantly improve their management level, and reduce pollution generation and emission. 5) Furthermore, it is necessary to consider the recycling of wastes and both the separation and recovery of valuable resources as part of the treatment of wastewater from the coal chemical industry. 6) Moreover, economic policies can be used because economic leverage may be more effective than administrative orders or even regulations. 7) Finally, cooperation should be increased to promote the “green development, circular development, and low-carbon development” of the modern coal chemical industry.
Abstract The circular economy model has recently gained a lot of attention worldwide from scientists, business people and authorities. The importance of the transition towards a more circular economy has also been noticed in the European Union. The new regulations provide the enabling framework for the circular economy to flourish. At the same time, although there is no standardized approach to creating a circular economy, while defining appropriate policies, care must be taken that they are suitable for particular industries. The limits of the present linear economy model (take-make-waste) are extremely apparent when examining the textile and clothing industry. The transition to a circular economy requires significant changes in both production and consumption models. This article uses a literature review and industry examples to identify and evaluate challenges faced by the clothing and textile industry in adapting to the circular economy model.
This paper presents a method for text simplification based on two neural architectures: a neural machine translation (NMT) model and a fine-tuned large language model (LLaMA). Given the scarcity of existing resources for Estonian, a new dataset was created by combining manually translated corpora with GPT-4.0-generated simplifications. OpenNMT was selected as a representative NMT-based system, while LLaMA was fine-tuned on the constructed dataset. Evaluation shows LLaMA outperforms OpenNMT in grammaticality, readability, and meaning preservation. These results underscore the effectiveness of large language models for text simplification in low-resource language settings. The complete dataset, fine-tuning scripts, and evaluation pipeline are provided in a publicly accessible supplementary package to support reproducibility and adaptation to other languages.
Text simplification (TS) refers to the process of reducing the complexity of a text while retaining its original meaning and key information. Existing work only shows that large language models (LLMs) have outperformed supervised non-LLM-based methods on sentence simplification. This study offers the first comprehensive analysis of LLM performance across four TS tasks: lexical, syntactic, sentence, and document simplification. We compare lightweight, closed-source and open-source LLMs against traditional non-LLM methods using automatic metrics and human evaluations. Our experiments reveal that LLMs not only outperform non-LLM approaches in all four tasks but also often generate outputs that exceed the quality of existing human-annotated references. Finally, we present some future directions of TS in the era of LLMs.
Text simplification is essential for making complex content accessible to diverse audiences who face comprehension challenges. Yet, the limited availability of simplified materials creates significant barriers to personal and professional growth and hinders social inclusion. Although researchers have explored various methods for automatic text simplification, none fully leverage large language models (LLMs) to offer tailored customization for different target groups and varying levels of simplicity. Moreover, despite its proven benefits for both consumers and organizations, the well-established practice of plain language remains underutilized. In this paper, we https://simplifymytext.org, the first system designed to produce plain language content from multiple input formats, including typed text and file uploads, with flexible customization options for diverse audiences. We employ GPT-4 and Llama-3 and evaluate outputs across multiple metrics. Overall, our work contributes to research on automatic text simplification and highlights the importance of tailored communication in promoting inclusivity.
Maia Shoham, Natalie M. Baker, Meagan E. Peterson
et al.
BACKGROUND Climate change is a significant public health threat. Health care comprises 10% of greenhouse gas emissions in the United States, where surgery is especially resource intensive. We did a systematic review to assess and summarize the published evidence of the environmental impact of surgery. METHODS We searched Medline, Embase, Web of Science, and GreenFILE databases for publications that report any environmental impact measure by all surgical subspecialties, including anesthesia. Inclusion criteria were published in English, original research, and passed peer review. Because data were heterogeneous and the aim was broad, we conducted a qualitative summary of data. Where possible, we compare impact measures. RESULTS In the study, 167 articles were identified by our search strategy and reviewed, of which 55 studies met criteria. Eight were about anesthesia, 27 about operating room waste, and 6 were life cycle assessments. Other topics include carbon footprint and greenhouse gas emissions. Nine papers fell into 2 or more categories. Overall, the operating room is a significant source of emissions and waste. Using anesthetic gases with low global warming potential reduces operating room emissions without compromising patient safety. Operating room waste is often disposed of improperly, often due to convenience or knowledge gaps. There are environmental benefits to replacing disposable materials with reusable equivalents, and to proper recycling. Surgeons can help implement these changes at their institution. CONCLUSION Although there is a clear need to lower the carbon footprint of surgery, the quality of research with which to inform protocol changes is deficient overall. Our attempt to quantify surgery's carbon footprint yielded heterogeneous data and few standardized, actionable recommendations. However, this data serves as a starting point for important future initiatives to decrease the environmental impact of surgery.
Adrian Langley, Matthew Lonergan, Tao Huang
et al.
Improving the automatic and timely recognition of construction and demolition waste composition is crucial for enhancing business returns, economic outcomes and sustainability. While deep learning models show promise in recognizing and classifying homogenous materials, the current literature lacks research assessing their performance for mixed, contaminated material in commercial material recycling facility settings. Despite the increasing numbers of deep learning models and datasets generated in this area, the sub-domain of deep learning analysis of construction and demolition waste piles remains underexplored. To address this gap, recent deep learning algorithms and techniques were explored. This review examines the progression in datasets, sensors and the evolution from object detection towards real-time segmentation models. It also synthesizes research from the past five years on deep learning for construction and demolition waste management, highlighting recent advancements while acknowledging limitations that hinder widespread commercial adoption. The analysis underscores the critical requirement for diverse and high-fidelity datasets, advanced sensor technologies, and robust algorithmic frameworks to facilitate the effective integration of deep learning methodologies into construction and demolition waste management systems. This integration is envisioned to contribute significantly towards the advancement of a more sustainable and circular economic model.
Lars Klöser, Mika Beele, Jan-Niklas Schagen
et al.
This study pioneers the use of synthetically generated data for training generative models in document-level text simplification of German texts. We demonstrate the effectiveness of our approach with real-world online texts. Addressing the challenge of data scarcity in language simplification, we crawled professionally simplified German texts and synthesized a corpus using GPT-4. We finetune Large Language Models with up to 13 billion parameters on this data and evaluate their performance. This paper employs various methodologies for evaluation and demonstrates the limitations of currently used rule-based metrics. Both automatic and manual evaluations reveal that our models can significantly simplify real-world online texts, indicating the potential of synthetic data in improving text simplification.
Niful Islam, Md. Mehedi Hasan Jony, Emam Hasan
et al.
Improper disposal of e-waste poses global environmental and health risks, raising serious concerns. The accurate classification of e-waste images is critical for efficient management and recycling. In this paper, we have presented a comprehensive dataset comprised of eight different classes of images of electronic devices named the E-Waste Vision Dataset. We have also presented EWasteNet, a novel two-stream approach for precise e-waste image classification based on a data-efficient image transformer (DeiT). The first stream of EWasteNet passes through a sobel operator that detects the edges while the second stream is directed through an Atrous Spatial Pyramid Pooling and attention block where multi-scale contextual information is captured. We train both of the streams simultaneously and their features are merged at the decision level. The DeiT is used as the backbone of both streams. Extensive analysis of the e-waste dataset indicates the usefulness of our method, providing 96% accuracy in e-waste classification. The proposed approach demonstrates significant usefulness in addressing the global concern of e-waste management. It facilitates efficient waste management and recycling by accurately classifying e-waste images, reducing health and safety hazards associated with improper disposal.
The Stirling engine, which possesses external combustion characteristics, a simple structure, and high theoretical thermal efficiency, has excellent potential for utilizing finite waste heat and cold resources. However, practical applications of this technology suffered from thermal inefficiency due to the discontinuity and instability of waste resources. Despite advances in energy storage technology, temperature variations in the heat-exchanging fluids at the hot and cold ends of the Stirling engine remained significant obstacles. In this work, convective meta-thermal concentration (CMTC) was introduced between the heating (cooling) fluids and the hot (cold) end of the Stirling engine, employing alternating isotropic materials with high and low thermal conductivities. It was demonstrated that CMTC effectively enhanced the temperature difference between the hot and cold ends, leading to a remarkable improvement in Stirling engine efficiency. Particularly, when the Stirling engine efficiency tended to zero due to the limited availability of waste heat and cold resources, CMTC overcame this limitation, surpassing existing optimization technology. Further analysis under various operating conditions showed that CMTC achieved a significant thermal efficiency improvement of up to 1460%. This work expanded the application of thermal metamaterials to heat engine systems, offering an exciting avenue for sustainable energy utilization.
In this paper, we report herein, the conversion of waste prescription and non-prescription pharmaceuticals into carbonaceous materials. The hydrothermal carbonization (HTC) of the pharmaceuticals was carried out at temperatures of 180, 230 and 275 0C in closed reactors for 6, 12 and 24 hours, respectively. The main products from the carbonization process were in the solids, liquids and gas phases. The resulting hydrochars were shown to be very functionalized with a high degree of aromaticity and high carbon content (between 55% to 65%). The adsorptive capacity of the hydrochars to remove Pb2+ ions from an aqueous system was evaluated and compared to that of analytical reagent activated carbon (AR-AC) through batch adsorptive tests. The effect of contact time on batch adsorption experiments with an initial Pb2+ concentration of 50 mg/L was also evaluated. The results indicated that PH24_230 has a better adsorption capacity when compared to AR-AC; achieving over 97% removal of Pb2+ after 60 minutes. The batch adsorption studies were best described by the pseudo-second order kinetic model with coefficient of regression (R2) values above 0.99. Also, slow pyrolysis experiments were carried out to evaluate the difference in solid yields, char heating value and surface area. Pyrochar yields were slightly higher, while heating values were one order of magnitude lower when compared to hydrochars. The pyrolysis conducted at 700°C led to the pyrochar with the highest value of the surface area (63.15 m2/g). The study shows that valuable products can be generated successfully from the hydrothermal carbonization of waste pharmaceuticals. KEYWORDS: waste pharmaceuticals; hydrothermal carbonization; hydrochars, batch adsorption, adsorption capacity
Neural sentence simplification method based on sequence-to-sequence framework has become the mainstream method for sentence simplification (SS) task. Unfortunately, these methods are currently limited by the scarcity of parallel SS corpus. In this paper, we focus on how to reduce the dependence on parallel corpus by leveraging a careful initialization for neural SS methods from paraphrase corpus. Our work is motivated by the following two findings: (1) Paraphrase corpus includes a large proportion of sentence pairs belonging to SS corpus. (2) We can construct large-scale pseudo parallel SS data by keeping these sentence pairs with a higher complexity difference. Therefore, we propose two strategies to initialize neural SS methods using paraphrase corpus. We train three different neural SS methods with our initialization, which can obtain substantial improvements on the available WikiLarge data compared with themselves without initialization.
Platform ecosystems have captured a variety of markets, enabling coordination, transactions, and value co-creation between independent actors. A focal platform constitutes the central nexus of e-commerce ecosystems and fosters the interaction among ecosystem participants through their boundary resources. Standardizing these interfaces simplifies ecosystem entry for developers and increases the number of participants propelling the network effects, and thus the overall value of the ecosystem. Currently, there is a lack of prescriptive design knowledge guiding platform owners in designing successful e-commerce ecosystems. Addressing this issue, we followed a dual approach, reporting on a systematic literature review in which we identified design requirements and complemented these with a multiple-case study on selected e-commerce ecosystems. Aggregating the requirements resulted in six meta-requirements and 19 design principles that foster the standardization of focal e-commerce platforms. Our design principles simplify the development of complements and enable multi-homing for developers due to possible standardization across ecosystems.
Nuclear physics experiments are always in need of more and more advanced detection systems. During the last years relevant technological developments have come out with many improvements in terms of performance and compactness of detector materials, transducers, electronics, computing and data transmission. In light of these achievements some applications previously prohibitive, mainly because of size and cost, have become feasible. A few applications of nuclear detection techniques are discussed, starting from the neighboring field of particle beam diagnostics, moving to the medical diagnostics and ending up into the radioactive waste handling. New radiation sensors are shown and explained, as exploited in the DMNR project for the radioactive waste online monitoring which merged into the MICADO Euratom project.
The Grothendieck ring of Chow motives admits two natural opposite $λ$-ring structures, one of which is a special structure allowing the definition of Adams operations on the ring. In this work I present algorithms which allow an effective simplification of expressions that involve both $λ$-ring structures, as well as Adams operations. In particular, these algorithms allow the symbolic simplification of algebraic expressions in the sub-$λ$-ring of motives generated by a finite set of curves into polynomial expressions in a small set of motivic generators. As a consequence, the explicit computation of motives of some moduli spaces is performed, allowing the computational verification of some conjectural formulas for these spaces.
The goal of text simplification (TS) is to transform difficult text into a version that is easier to understand and more broadly accessible to a wide variety of readers. In some domains, such as healthcare, fully automated approaches cannot be used since information must be accurately preserved. Instead, semi-automated approaches can be used that assist a human writer in simplifying text faster and at a higher quality. In this paper, we examine the application of autocomplete to text simplification in the medical domain. We introduce a new parallel medical data set consisting of aligned English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained neural language models (PNLMs) on this dataset. We compare four PNLMs(BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of the sentence to be simplified can be incorporated to achieve better results (6.17% absolute improvement over the best individual model). We also introduce an ensemble model that combines the four PNLMs and outperforms the best individual model by 2.1%, resulting in an overall word prediction accuracy of 64.52%.
For both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given sentences. By substituting complex words with simple alternatives, lexical simplification (LS) is a recognized method to reduce such lexical diversity, and therefore to improve the understandability of sentences. In this paper, we leverage LS and propose a novel approach which can effectively improve the performance of PrLMs in text classification. A rule-based simplification process is applied to a given sentence. PrLMs are encouraged to predict the real label of the given sentence with auxiliary inputs from the simplified version. Using strong PrLMs (BERT and ELECTRA) as baselines, our approach can still further improve the performance in various text classification tasks.
Roberto Interdonato, Matteo Magnani, Diego Perna
et al.
Multilayer networks have been widely used to represent and analyze systems of interconnected entities where both the entities and their connections can be of different types. However, real multilayer networks can be difficult to analyze because of irrelevant information, such as layers not related to the objective of the analysis, because of their size, or because traditional methods defined to analyze simple networks do not have a straightforward extension able to handle multiple layers. Therefore, a number of methods have been devised in the literature to simplify multilayer networks with the objective of improving our ability to analyze them. In this article we provide a unified and practical taxonomy of existing simplification approaches, and we identify categories of multilayer network simplification methods that are still underdeveloped, as well as emerging trends.