Labeling Scheme Research Articles

SLAP: Segmented Reuse-Time-Label Based Admission Policy for Content Delivery Network Caching

‘‘Learned” admission policies have shown promise in improving Content Delivery Network (CDN) cache performance and lowering operational costs. Unfortunately, existing learned policies are optimized with a few fixed cache sizes while in reality, cache sizes often vary over time in an unpredictable manner. As a result, existing solutions cannot provide consistent benefits in production settings. We present SLAP , a learned CDN cache admission approach based on segmented object reuse time prediction. SLAP predicts an object’s reuse time range using the Long-Short-Term-Memory model and admits objects that will be reused (before eviction) given the current cache size. SLAP decouples model training from cache size, allowing it to adapt to arbitrary sizes. The key to our solution is a novel segmented labeling scheme that makes SLAP without requiring precise prediction on object reuse time. To further make SLAP a practical and efficient solution, we propose aggressive reusing of computation and training on sampled traces to optimize model training, and a specialized predictor architecture that overlaps prediction computation with miss object fetching to optimize model inference. Our experiments using production CDN traces show that SLAP achieves significantly lower write traffic (38%-59%), longer SSDs lifetime (104%-178%), a consistently higher hit rate (3.2%-11.7%), and requires no effort to adapt to changing cache sizes, outperforming existing policies.

Automating avalanche detection in ground-based photographs with deep learning

Automated avalanche detection has previously relied on satellite imagery, which is typically unsuitable for real-time monitoring due to long revisit times. To address this, we propose automating avalanche detection using photographs taken from the ground. This paper introduces image classification and avalanche segmentation tasks on a publicly-released dataset of 4090 photographs annotated by experts. Using the ResNet and YOLO architectures, we achieve avalanche detection F1 scores of 94.4% per image and 65.4% per avalanche region, demonstrating the potential that this method offers for avalanche monitoring.In contrast with existing approaches, we label images by avalanche type into four distinct categories: glide, loose-snow, slab, and no avalanche. This labelling scheme provides more detail on avalanche events than binary labels and is shown to improve model F1 scores. Moreover, our models do not require a digital elevation model, simplifying application to new areas. Trained models can be used for real-time avalanche monitoring and to gather temporally continuous data for the improvement of existing avalanche forecasting models.The code and dataset are available at github.com/j-f-ox/avalanche-detection.

Machine learning approach for predicting state transitions via shank acceleration data during freezing of gait in Parkinson’s disease

Rehabilitation using external cues has shown positive impact on Freezing-of-Gait (FoG) severity in Parkinson's disease (PD) patients. Research studies on FoG detection and prediction are widely accessible, however little is known about transitions from “FoG” to “normal-gait” phases and vice-versa. Especially, for the patient to recover completely from the freeze event as well as to avoid undesirable distractions due to long-term cue persistence, post-FoG periods must be optimally chosen for managing automatic cue deactivation. Therefore, our study attempts to device a machine-learning based approach for labelling of accelerometer data with state-transitions from “normal-gait” to “FoG” (i.e., FoG-prediction) as well as from “FoG” to “normal-gait” (i.e., unfreeze-prediction). Our prediction approach distinguishes itself by innovating in sensor utilization, feature-extraction, and latency reduction. We introduce a novel four-class labeling scheme based on state transitions, diverging from fixed-time periods utilized in prior studies. Model training involves regression and classification with a streamlined set of 23 hybrid features, minimizing latency with reduced window segments and operating on 4-second and 3-second pre- and post-FoG time-frames, respectively, exhibiting minimal 0.5-second latency. Our approach simply required one shank sensor providing minimum-instrumentation for the user's comfort. Four-class (No-FoG, pre-FoG, FoG, and post-FoG) data have been fed to train random-forest classifier. Our FoG prediction model achieved exceptional metrics, particularly for the “post-FoG” class, with 99.0 % accuracy, 97.4 % precision, 99.1 % sensitivity, and 98.1 % f1-score, along with 99.0 % cohen’s-kappa, 98.3 % jaccard-index, and 99.0 % Matthews-correlation-coefficient. Therefore, an intelligent-strategy for timely activation and deactivation of cues is the major contribution of our work.

Conformation of Pyroglutamated Amyloid β (3-40) and (11-40) Fibrils - Extended or Hairpin?

Amyloid β (Aβ) is a hallmark protein of Alzheimer's disease. One physiologically important Aβ variant is formed by initial N-terminal truncation at a glutamic acid position (either E3 or E11), which is subsequently cyclized to a pyroglutamate (either pE3 or pE11). Both forms have been found in high concentrations in the core of amyloid plaques and are likely of high importance in the pathology of Alzheimer's disease. However, the molecular structure of the fibrils of these variants is not entirely clear. Solid-state NMR spectroscopy studies have reported a molecular contact between Gly25 and Ile31, which would disagree with the conventional hairpin model of wildtype (WT-)Aβ1-40 fibrils, most often described in the literature. We investigated the conformation of the monomeric unit of pE3-Aβ3-40 and pE11-Aβ11-40 (and for comparison also wildtype (WT)-Aβ1-40) fibrils to find out whether the hairpin or a newly suggested extended structure dominates the structure of the Aβ monomers in these fibrils. To this end, solid-state NMR spectroscopy was applied probing the inter-residual contacts between Phe19/Leu34, Ala21/Leu34, and especially Gly25/Ile31 using suitable isotopic labeling schemes. In the second part, the flexible turn of the Aβ40 peptides was replaced by a (3-(3-aminomethyl)phenylazo)phenylacetic acid (AMPP)-based photoswitch, which can predefine the peptide conformation to either an extended (trans) or hairpin (cis) conformation. This enables simultaneous spectroscopic assessment of the conformation of the AMPP-photoswitch, allowing in situ structural investigations during fibrillation in contrast to structural techniques such as NMR spectroscopy or cryo-EM, which can only be applied to stable conformers. Both methods confirm an extended structure for the peptidic monomers in fibrils of all investigated Aβ variants. Especially the Gly25/Ile31 contact is a decisive indicator for the extended structure along with the characteristic absorption spectra of trans-AMPP-Aβ.

DNA combing versus DNA spreading and the separation of sister chromatids.

DNA combing and DNA spreading are two central approaches for studying DNA replication fork dynamics genome-wide at single-molecule resolution by distributing labeled genomic DNA on coverslips or slides for immunodetection. Perturbations in DNA replication fork dynamics can differentially affect either leading or lagging strand synthesis, for example, in instances where replication is blocked by a lesion or obstacle on only one of the two strands. Thus, we sought to investigate whether the DNA combing and/or spreading approaches are suitable for resolving adjacent sister chromatids during DNA replication, thereby enabling the detection of DNA replication dynamics within individual nascent strands. To this end, we developed a thymidine labeling scheme that discriminates between these two possibilities. Our data suggests that DNA combing resolves sister chromatids, allowing the detection of strand-specific alterations, whereas DNA spreading typically does not. These findings have important implications when interpreting DNA replication dynamics from data obtained by these two commonly used techniques.

STANet: Spatio-Temporal Adaptive Network and Clinical Prior Embedding Learning for 3D+T CMR Segmentation.

The segmentation of cardiac structure in magnetic resonance images (CMR) is paramount in diagnosing and managing cardiovascular illnesses, given its 3D+Time (3D+T) sequence. The existing deep learning methods are constrained in their ability to 3D+T CMR segmentation, due to: (1) Limited motion perception. The complexity of heart beating renders the motion perception in 3D+T CMR, including the long-range and cross-slice motions. The existing methods' local perception and slice-fixed perception directly limit the performance of 3D+T CMR perception. (2) Lack of labels. Due to the expensive labeling cost of the 3D+T CMR sequence, the labels of 3D+T CMR only contain the end-diastolic and end-systolic frames. The incomplete labeling scheme causes inefficient supervision. Hence, we propose a novel spatio-temporal adaptation network with clinical prior embedding learning (STANet) to ensure efficient spatio-temporal perception and optimization on 3D+T CMR segmentation. (1) A spatio-temporal adaptive convolution (STAC) treats the 3D+T CMR sequence as a whole for perception. The long-distance motion correlation is embedded into the structural perception by learnable weight regularization to balance long-range motion perception. The structural similarity is measured by cross-attention to adaptively correlate the cross-slice motion. (2) A clinical prior embedding learning strategy (CPE) is proposed to optimize the partially labeled 3D+T CMR segmentation dynamically by embedding clinical priors into optimization. STANet achieves outstanding performance with Dice of 0.917 and 0.94 on two public datasets (ACDC and STACOM), which indicates STANet has the potential to be incorporated into computer-aided diagnosis tools for clinical application.

Transnational governance standards in ensuring sustainable development and operation of hydropower projects in transboundary basins

The notion of “transnational” law encompasses a sophisticated corpus of globally applicable rules, standards and safeguards, often informal in origin, which are widely accepted around the world and supported across a range of business sectors and civil society constituencies. The recent proliferation of such “transnational” law extends to the hydropower sector, where normative standards and safeguards increasingly apply to promote and certify sustainable practices in the development and operation of hydropower projects. Such projects are associated with significant environmental and social impacts and the projected increase in hydropower investments, mainly in emerging economies, will require effective supplemental regulatory tools. Many such projects will be located in, or impact upon, international rivers, thereby adding a further layer of complexity regarding their effective regulation and increased the risk of inter-State disputes over the equitable use of shared waters. Such complexity and risk will be exacerbated by increased uncertainty due to climate change. Thus, the existence of widely accepted transnational standards, which are relevant and applicable to hydropower development globally, can play a key role in building trust between watercourse States and in promoting greater transboundary water cooperation. This article explores the potential role of transnational standards in improved regulation of environmental and social impacts associated with hydropower projects located on international watercourses and the extent to which such standards cohere with established and emerging requirements of international water law. It thus examines the synergies arising between these informal and formal regulatory frameworks with a view to developing a better understanding of their interaction in practice. In so doing, it focuses on two key categories of applicable transnational regime: a sustainability certification and labeling scheme dedicated to hydropower projects and operated by the Hydropower Sustainability Council; and MDB environmental and social safeguard standards imposed upon major water-related projects, including hydropower projects, particularly those adopted by the World Bank and the European Bank for Reconstruction and Development (EBRD).

Effects of environmental and nutritional labels on the dietary choices of consumers: Evidence from China

Dietary change has great potential to deliver food-related environmental benefits and human health advantages. Labeling has been proven effective in dietary behavior change as a “low-hanging fruit” among nudging methods, yet the experimental analysis in low- and middle-income countries (LMICs) is lacking. In this research, a real-world intervention experiment is designed to explore the effects of color-based carbon footprint and calorie content labeling schemes on the sales of dumpling in two Chinese university canteens. The sample for the control phrase and label phrase respectively consisted of 8755 and 9492 records. Overall, fewer consumers choose to dine (−15.25%) when the label interventions were in place to release from ethical dilemma. Detailed analyses show that environmental labeling results in a significant reduction in purchases of dumplings with high carbon footprints, cutting down 20.40 (15.25% of average daily sales, the same as other percentage data) sale units (‘Liang’ in Chinese, one sale unit empirically corresponding to 6 dumplings) and leading to ∼26.90% of total GHG mitigation, while calorie labeling raises the purchase of dumplings with both low and high energy density, raising 59.88 (7.56%) and 48.66 (6.14%) sale units respectively at the day level. Labeling induces effective response demographically heterogeneous, with men more sensitive to calorie labels within the U-shape trend while women more impacted by environmental labels. Also, postgraduates show greater perceptiveness to the intervention compared to undergraduates. Our evaluation of labeling effects suggests a well-designed and population-tailored label highlighting negative information such as immense environmental burdens would offer a promising and feasible chance to navigate consumers' behavior. Our findings provide implications for effective intervention strategy design in China, indicating extensive inspiration for a potential larger-scale dietary structure evolution and promising behavior intervention.

Effect of binary labeling schemes on PAS with BICM-ID system performance over the AWGN and optical fiber channels

Effect of binary labeling schemes on PAS with BICM-ID system performance over the AWGN and optical fiber channels

Benchmarking online food delivery applications against menu labelling laws: a cross-sectional observational analysis.

It is unknown how well menu labelling schemes that enforce the display of kilojoule (kJ) labelling at point-of-sale have been implemented on online food delivery (OFD) services in Australia. This study aimed to examine the prevalence of kJ labelling on the online menus of large food outlets with more than twenty locations in the state or fifty locations nationally. A secondary aim was to evaluate the nutritional quality of menu items on OFD from mid-sized outlets that have fewer locations than what is specified in the current scheme. Cross-sectional analysis. Prevalence of kJ labelling by large food outlets on OFD from August to September 2022 was examined. Proportion of discretionary ('junk food') items on menus from mid-sized outlets was assessed. Forty-three unique large food outlets on company (e.g. MyMacca's) and third party OFD (Uber Eats, Menulog, Deliveroo) within Sydney, Australia. Ninety-two mid-sized food outlets were analysed. N/A. On company OFD apps, 35 % (7/23) had complete kJ labelling for each menu item. In comparison, only 4·8 % (2/42), 5·3 % (2/38) and 3·6 % (1/28) of large outlets on Uber Eats, Menulog and Deliveroo had complete kJ labelling at all locations, respectively. Over three-quarters, 76·3 % (345/452) of menu items from mid-sized outlets were classified as discretionary. Kilojoule labelling was absent or incomplete on a high proportion of online menus. Mid-sized outlets have abundant discretionary choices and yet escape criteria for mandatory menu labelling laws. Our findings show the need to further monitor the implementation of nutrition policies on OFD.

Single-Molecule FRET-Based Multiplexed Detection.

Single-molecule multiplexed detection is a high-promise toolkit for the expanding field ofbiosensing and molecular diagnostics. Among many single-molecule techniques available today for biomarker sensing including fluorescence, force, electrochemical, spectroscopic, barcoding, and other techniques, fluorescence-based approaches are arguably the most widely used methods due to their high sensitivity, selectivity, and readily available fluorophore-labeling schemes for a wide variety of biomolecules. However, multiplexed imaging using fluorescence techniques has proven to be challenging due to the sophisticated labeling schemes often requiring multiple FRET (fluorescence resonance energy transfer) pairs and/or excitation sources, which lead to overlapping signals and complicate data analysis. Here, we describe a single-molecule FRET method that enables multiplexed analysis while still using only one FRET pair, and thus the described approach is a significant step forward from conventional FRET methods.

A blockchain-based framework for carbon management towards construction material and product certification

As one of the biggest resource consumers and carbon emitters, the construction industry plays a crucial role in global carbon reduction. Carbon certification or labelling schemes are efficient ways to assess and report the carbon footprints of construction materials and products (CMPs), providing the foundation for carbon management at the CMP level. However, existing carbon management for CMP certification relies heavily on traditional centralized data management tools, which suffer from data non-transparency and manipulation problems, making carbon footprints unreliable and hard to track. Therefore, benefiting from the immutability and traceability of blockchain, this paper introduces a distributed solution for transparent and secure carbon management towards CMP certification. Contributions lie in three aspects: (1) a Green Product Chain (GPChain) framework is proposed based on blockchain. This framework provides a distributed and transparent carbon data-sharing environment, enabling reliability and traceability during certification. (2) A blockchain data model is developed considering the privacy of carbon data for CMP certification. Besides, a privacy-preserving carbon data-sharing strategy is designed for pre-processing different sensitive data before being transferred to the blockchain. (3) GPChain smart contracts are developed to granularly control user interactions with blockchain and automatically generate distributed carbon footprint records. Results show that (1) GPChain’s feasibility of supporting carbon management for GMP certification and (2) the acceptable performance of GPChain smart contracts during operations.

A NEW EFFECTIVE LABELLING SCHEME For EFFICIENT QUERYING And UPDATING XML DOCUMENTS

Nowadays Extensible Mark-up Language (XML) is a dominant technology for formatting and exchanging data across the Internet world. Updating and retrieving a massive amount of XML data is an interesting and active research area. In addition, indexing XML data is a significant task to improve the efficiency of XML queries. Labelling nodes is the used technique for indexing XML data efficiently. There are many labelling schemes that have been proposed. However, these schemes have many limitations and shortcomings. Therefore, this paper aims to propose a new XML labelling scheme that addresses the issue of efficiency of XML query performance. Thus, this paper developed a new XML labelling scheme. Consequently, four experiments were designed in order to evaluate this. The results of these experiments suggest that the proposed scheme achieved the target results and showed an improvement in the performance and the efficiency of labelling XML documents.

Targeted Preparation and NMR Spectroscopic Characterization of Lys11-Linked Ubiquitin Trimers.

Ubiquitylation refers to the attachment of mono- or poly-ubiquitin molecules to a substrate protein. To shield ubiquitin chains against potential hydrolysis, a facile, click-chemistry based approach was recently established for the generation of site-specifically conjugated ubiquitin dimers relying on triazole-linkage. Here, the preparation of such ubiquitin chains was advanced by the generation of homotypic Lys11-linked ubiquitin trimers considering an isotopic labeling scheme in a moiety-wise manner. The structural and dynamical impact on the ubiquitin unit at proximal, central, or distal position that is potentially invoked by the respective other two moieties was systematically probed by heteronuclear high-resolution NMR spectroscopic approaches. As a result, conjugating a third ubiquitin moiety to the proximal or distal site of a ubiquitin dimer does not alter structural and dynamical characteristics as it has been seen for ubiquitin dimers. This observation suggests that recognition of a homotypically assembled ubiquitin chain by a potential substrate is primarily done by screening the length of a ubiquitin chain rather than relying on subtle changes in structure or dynamic properties of single ubiquitin moieties composing the chain.

Specificity Testing for NGT PCR-Based Detection Methods in the Context of the EU GMO Regulations.

The term new genomic techniques (NGTs) is an umbrella term used to describe a variety of techniques that can alter the genetic material of an organism and that have emerged or have been developed since 2001, when the existing genetically modified organism (GMO) legislation was adopted. The analytical framework used to detect GMOs in Europe is an established single harmonized procedure that is mandatory for the authorization of GM food and feed, thus generating a reliable, transparent, and effective labeling scheme for GMO products. However, NGT products can challenge the implementation and enforcement of the current regulatory system in the EU, relating in particular to the detection of NGT products that contain no foreign genetic material. Consequently, the current detection methods might fail to meet the minimum performance requirements. Although existing detection methods may be able to detect and quantify even small alterations in the genome, this does not necessarily confirm the distinction between products resulting from NGTs subject to the GMO legislation and other products. Therefore, this study provides a stepwise approach for the in silico prediction of PCR systems' specificity by testing a bioinformatics pipeline for amplicon and primer set searches in current genomic databases. In addition, it also empirically tested the PCR system evaluated during the in silico analysis. Two mutant genotypes produced by CRISPR-Cas9 in Arabidopsis thaliana were used as a case study. Overall, our results demonstrate that the single PCR system developed for identifying a nucleotide insertion in the grf1-3 genotype has multiple matches in the databases, which do not enable the discrimination of this mutated event. Empirical assays further support this demonstration. In contrast, the second mutated genotype, grf8-61, which contains a -3 bp deletion, did not yield any matches in the sequence variant database. However, the primer sequences were not efficient during the empirical assay. Our approach represents a first step in decision making for analytical methods for NGT detection, identification, and quantification in light of the European labeling regulations.

Chinese Adults’ Willingness to Pay for Mandatory Nutrients Reporting on Nutrition Facts Table

The Chinese food industry has opposed the mandatory inclusion of increased nutrients in the Nutrition Facts Table (NFT), thus impeding its improvement. This poses a challenge to the endeavors aiming to assist consumers in cultivating healthy dietary habits that incorporate reduced saturated fatty acids and added sugars while ensuring the adequate intake of essential micronutrients. This study conducted a choice experiment to investigate Chinese consumers’ preference for updated labeling schemes among 630 adults that were randomly selected from Central, North, East, South, Northwest, Southwest, and Northeast China. It revealed that respondents were willing to pay the highest premium for the most mandatory nutrients (22.575% of the food price per unit). Respondents preferred the NFT with the most mandatory nutrients if they met the following population characteristics: female; non-overweight or obese; without a college degree; possessed an annual household disposable income between 50,000 and 99,999 CNY; from North China; lived in rural areas and often cooked for family; cared about food nutrition. Two combinations of NFT information received the highest preference: (1) the NFT detailing the most mandatory nutrients and their content values and nutrient reference values (NRV%); (2) the NFT containing the most nutrients and the nutrients in 100 g/mL or a serving. The first and second combinations attracted a premium of 14.884% and 31.833% of the food price per unit, respectively.

Front-of-Pack Nutrition Labelling: are Health Policy Decisions Reconcilable with the WTO Disciplines?

Over the past decade, States have increasingly used various forms of front-of-pack nutrition labelling to prevent the proliferation of obesity and related non-communicable chronic diseases. Some forms of front-of-pack nutrition labelling highlight increased amounts of certain nutrients in foods and are mandatory for compliance. The growing popularity of such measures raises serious concerns amongst the Members of the World Trade Organization, since, to date, no position has been formed on the conformity of such labelling with the core disciplines of the WTO. This article attempts to reach that understanding and to suggest how national legislators may adjust their labelling initiatives so as to bring them in line with the WTO Agreement on Technical Barriers to Trade (TBT Agreement). Section 1 the paper analyses Art. 2.1 of the TBT Agreement (principle of non-discrimination) and concludes that the legislator should take into account all dietary options in order to avoid discriminati on against certain types of foods. Unpackaged goods should also be covered by regulatory interventions, since by nutritional composition they are similar to pre-packaged foods. Section 2 looks at Art. 2.2 of the TBT Agreement (principle of necessity) and summarises that the adopting Member has to be cautious when setting the goals of the labelling measure, since demonstration of its necessity would further require to separately establish what contribution to any of the stated objectives the measure makes. Moreover, the WTO Member must determine how labelling schemes discourage consumption of various nutrients. Otherwise, there may be a problem in demonstrating the expected regulatory results. The anticipated contribution of front-of-pack labelling could be enhanced by creating a supporting environment of regulatory interventions. Section 3 focuses on Arts. 2.4 and 2.5 of the TBT Agreement (harmonisation principle) and concludes that current international standards recognize the relevance and authority of regional studies, including those examining dietary patterns of locals. Nonetheless, this does not exempt the WTO Members from the need to provide a scientific justification for how the measure relates to the consumption of particular foods and nutrients.

Deep fusible skinning of animation sequences

Animation compression is a key process in replicating and streaming animated 3D models. Linear Blend Skinning (LBS) facilitates the compression of an animated sequence while maintaining the capability of real-time streaming by deriving vertex to proxy bone assignments and per frame bone transformations. We introduce a innovative deep learning approach that learns how to assign vertices to proxy bones with persistent labeling. This is accomplished by learning how to correlate vertex trajectories to bones of fully rigged animated 3D models. Our method uses these pretrained networks on dynamic characteristics (vertex trajectories) of an unseen animation sequence (a sequence of meshes without skeleton or rigging information) to derive an LBS scheme that outperforms most previous competent approaches by offering better approximation of the original animation sequence with fewer bones, therefore offering better compression and smaller bandwidth requirements for streaming. This is substantiated by a thorough comparative performance evaluation using several error metrics, and compression/bandwidth measurements. In this paper, we have also introduced a persistent bone labeling scheme that (i) improves the efficiency of our method in terms of lower error values and better visual outcome and (ii) facilitates the fusion of two (or more) LBS schemes by an innovative algorithm that combines two arbitrary LBS schemes. To demonstrate the usefulness and potential of this fusion process, we have combined the outcome of our deep skinning method with that of Rignet—which is a state-of-the-art method that performs rigging on static meshes—with impressive results.

Event Detection with Word-Word Relation Classification

Event detection (ED) is a critical task in information extraction, aiming to identify triggers and types. Current research has increasingly focused on fine-grained types, where a single sentence may contain multiple trigger words and event types. Previous models only considered sentence-level features, neglecting word-word features and word positional information in the text. We propose a novel labeling scheme that treats event detection as a word-word relation recognition task. In this approach, we first identify relationships between word pairs and then utilize these relationships to perform trigger and event-type detection. By adopting this method, we can efficiently and concurrently identify triggers and types in a sentence. Leveraging word pair relationships effectively addresses scenarios in which multiple trigger words appear in a single sentence. The results demonstrate that our approach outperforms several baseline models.

Assessing the potential of GHG emissions for the textile sector: A baseline study

The carbon footprint (CFP) is a measure of greenhouse gases (GHGs) emitted throughout the lifecycle of a product or activity, while the energy footprint (EFP) and water footprint (WFP) measure energy and water consumption, respectively. These footprints are essential for managing emissions and consumption and promoting low-carbon consumption. A carbon labeling scheme could help consumers make informed choices. Asia is a major textile producer and consumer, so studying textiles' carbon, energy, and water footprints is essential for managing domestic emissions, energy and water consumption, and international trade negotiations. This paper presents a method and framework for assessing CFP, EFP, and WFP at the product level and calculates the footprints for textile products. The results show that the total CFP of all textile products produced is 42,624.12 MT CO2e, with indirect emissions contributing significantly more than direct emissions. The total EFP is 248.38 PJ, with electricity consumption being the main contributor, while the total WFP is 80.71 billion liters. The spinning stage of production has the highest CFP and EFP, and energy consumption is the main contributor to all footprints. These results can help compare different products and reduce the footprints of the textile sector.