Assessment Methods Research Articles

Validation of upper extremity kinematics using Markerless motion capture

Movement research has typically been performed using three-dimensional (3D) marker-based motion capture, which is considered the “gold-standard” for biomechanical assessment. However, limitations exist due to the lack of portability, extensive preparation for data collection, marker placement training, error due to marker movement, and possible skin irritation due to marker adhesives. There is inherent error due to motion artifact stemming from skin movement and differences in marker placement between testers. Markerless motion capture systems are emerging as a new method of kinematic assessment. These methods require little preparation and there is no need to alter participant clothing. Markerless motion capture has also been validated for the lower extremity in healthy older adults during gait. However, it has not been validated for other populations or for the assessment of upper extremity (UE) motion. Therefore, the purpose of this study was to examine differences in calculated UE kinematics between marker-based and a markerless motion capture system. Participants attended two data collection sessions. Marker-based and markerless motion capture data was collected simultaneously while participants completed the Box and Blocks test (BBT). Kinematic and spatiotemporal data from both systems was exported using identical time series to ensure the same conditions for comparisons. Intraclass Correlation Coefficients (ICCs) were calculated to determine between session reliability for both systems on range of motion and peak joint angular data to ensure movement variability was not affecting measurement consistency. ICCs and Bland Altman statistics were also calculated between the systems. Root mean square deviation (RMSD) values were determined between demeaned UE joint angles for the two systems to examine movement pattern differences. The resulting between-session ICCs for each system showed that the markerless system shared similar reliability during this task as the marker-based system, further supporting the effect of variability on between-session reliability. Between-system ICCs resulted in good (0.7<ICC<0.9) to excellent (ICC>0.9) agreement. Bland Altman results confirmed the existence of measurement bias between the systems. RMSD values for all UE joint angles were found to be less than 6°. Overall, the results from this study support the use of markerless motion capture in clinical settings to examine upper extremity biomechanics in children.

Integrated nonurban-urban flood management using multi-objective optimization of LIDs and detention dams based on game theory approach

Flood events in urban and non-urban areas have long-term devastating impacts on communities resulting in casualties and economic damages. Different interconnected concepts of flood management lead decision-makers to broaden their focus from traditional points of view to transcend the disciplinary boundaries of hydrological modeling. So, evaluating interconnected aspects of flood control requires configuring an Integrated Flood Management (IFM) system. In this research, a novel comprehensive framework is proposed to control floods in urban and non-urban areas using Low-Impact Development (LID) practices and detention dams, simultaneously. In the first step, urban and non-urban areas of the Darakeh catchment in Tehran, Iran, as the study area, are modeled using Storm Water Management Model (SWMM). Next, by coupling the SWMM with Pareto Envelope-based Selection Algorithm II (PESA-II), optimal management scenarios are generated considering four hydrological-economic objectives. Each management scenario specifies the optimal sites and dimensions of detention dams and the optimal location and area of LIDs in non-urban and urban areas, respectively. Indeed, the innovative IFM framework provides a multi-dimensional trade-off between upstream and downstream flood protection. Then, interactions among the main stakeholders with conflicting interests are incorporated into utility functions. Ultimately, using COmplex PRoportional ASsessment (COPRAS) and Nash bargaining methods the most capable management scenarios are selected based on stakeholders' preferences. Results showed that the IFM framework can be significantly effective in flood severity attenuation. Based on the findings, proposed method warrants peak flow reduction up to 60.04 and 53.50 percent at the inlet and outlet of the urban area. Besides, the Nash bargaining approach tends to select a scenario with a higher peak flow reduction compared to the COPRAS method. Also, results signify that each stakeholder's relative importance and preferences can remarkably impact the decision-making process. In addition to resolving conflicts arising from implementing flood control measures, this framework can incorporate divergent aspects of flood management into a reliable system.

Environmental Performance of a Polyamide-Based Thermoplastic Compound with Brominated Flame Retardants

Thermoplastic compounds are present in various industrial, domestic and consumer applications thanks to their strength, lightness, flexibility and processing properties. However, thermoplastics are often derived from polymers formed from non-renewable sources, such as petroleum. In addition, the production processes have a high consumption of energy. For this reason, assessing the environmental impacts caused by the production process is of primary importance. This research evaluates the environmental performance of a polyamide-based thermoplastic compound with brominated flame retardants. The objective is to understand which phase of the production process has the most significant impacts. The assessment of environmental impacts was carried out through the Life Cycle Assessment methodology using SimaPro v9.5, Ecoinvent v3.9 database and ReCiPe 2016 v1.07 impacts assessment method. The primary data were generated and collected in a factory in northern Italy, and the Functional Unit considered is 1 kg of final product. The results show that the upstream phase accounts for more than 90% of the environmental profile, especially in human non-carcinogenic toxicity, with more than 56% of the total impacts and fine particulate matter formation with 18% shares. At the climate change level, the product delivers a total impact of 6.17 kg CO2 eq per kg of the final product. This study indicates that most of the impacts are caused by the production of raw materials. For this reason, it is necessary to introduce more sustainable and environmentally friendly raw materials.

Rapid and Precise Diagnosis of Retroperitoneal Liposarcoma with Deep-Learned Label-Free Molecular Microscopy.

The retroperitoneal liposarcoma (RLPS) is a rare malignancy whose only curative therapy is surgical resection. However, well-differentiated liposarcomas (WDLPSs), one of its most common types, can hardly be distinguished from normal fat during operation without an effective margin assessment method, jeopardizing the prognosis severely with a high recurrence risk. Here, we combined dual label-free nonlinear optical modalities, stimulated Raman scattering (SRS) microscopy and second harmonic generation (SHG) microscopy, to image two predominant tissue biomolecules, lipids and collagen fibers, in 35 RLPSs and 34 normal fat samples collected from 35 patients. The produced dual-modal tissue images were used for RLPS diagnosis based on deep learning. Dramatically decreasing lipids and increasing collagen fibers during tumor progression were reflected. A ResNeXt101-based model achieved 94.7% overall accuracy and 0.987 mean area under the ROC curve (AUC) in differentiating among normal fat, WDLPSs, and dedifferentiated liposarcomas (DDLPSs). In particular, WDLPSs were detected with 94.1% precision and 84.6% sensitivity superior to existing methods. The ablation experiment showed that such performance was attributed to both SRS and SHG microscopies, which increased the sensitivity of recognizing WDLPS by 16.0 and 3.6%, respectively. Furthermore, we utilized this model on RLPS margins to identify the tumor infiltration. Our method holds great potential for accurate intraoperative liposarcoma detection.

Transcending Linguistic Boundaries: A Technical Exploration of The Evolution and Future Trajectory of Corpus-Based Machine Translation

This review paper presents a comprehensive examination of the evolution, current state, and future perspectives of machine translation (MT). The field of MT, encompassing the automatic translation of text from one language to another, has undergone significant transformations from rule-based methods to statistical models, and more recently, to neural network-based approaches. By synthesizing current research and technological progress, this paper aims to provide a detailed understanding of the dynamic landscape of machine translation and its future directions. In today's globalized world, language barriers remain a significant obstacle to accessing information. Relying solely on human translators often fails to meet the growing demand for translation services, which is why Machine Translation (MT) tools are becoming increasingly popular. These tools have the potential to address this issue effectively. As a result, research in MT is expanding rapidly, and new paradigms are continually being developed. This paper presents a systematic literature review to identify the most widely used MT systems, their architectures, the quality assessment methods used to evaluate them, and which systems deliver the best results.

Comparison and Influence of a Multicomponent Educational Program on the Patient-Reported Outcomes of a Group of Patients With Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic disease that affects different areas of the patient's body. Patient education and health literacy is essential for them to participate actively in follow-up. The aim of this study was to assess differences between clinimetric measurements done by a medical team and patient-reported outcome measures (PROMs) in RA and understand the impact of patient education strategies in order to identify differences between RA assessment methods. This is a longitudinal cohort study. It included adult patients with RA and access to digital tools. These were divided into 3 groups by type of education. Group 1 included patients who participated in a multicomponent RA educational program. Group 2 did not have this multicomponent RA education. Group 3 did not receive any education. The 3 groups performed PROMs. Disease activity scales, functional class, and quality of life were measured. Univariate and bivariate analysis (χ2 and Wilcoxon for paired data) were done. Twenty-eight patients were included in group 1, 26 in group 2, and 37 in group 3. All were women. In group 1, there were no significant differences in clinimetrics between the medical team and patient's PROMs except for fatigue. In group 2 and group 3, significant differences were found. The RAPID3 and PAS variables did not show significant differences when analyzed by intervention subgroups. This study shows no differences between clinimetrics/PROMs for patients with a high-level education on RA and physicians. On the other hand, when patient did not have any RA education, the clinimetric results differed from physician measurement.

Research on the Quantitative Assessment Method of HVDC Transmission Line Failure Risk during Wildfire Disaster

It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters.

An integrated cost based approach for warehouse performance evaluation: A new multiphase model

Warehouses represent key links in domestic and international commodity flows. The increasing shortage of workers and increasing costs on the one hand, and the increasing number and stricter demands of users on the other hand lead warehouse managers to realize their operations as efficiently as possible. A proposed model has an objective of enabling companies to monitor warehouse performance in an authoritative, reliable, and simple way and define appropriate corrective measures accordingly. The proposed empirical research consists of three stages, where in the first stage a combination of Principal Component Analysis-Data Envelopment Analysis methods was applied in order to determine efficient warehouses based on 90 decision making units. In the second phase, a completely new method called Interval Fuzzy Rough Pivot Pair-wise Relative Criteria Importance Assessment method used for determining criteria weights was developed and applied, which is one of the most important novelties of this study. In the last phase, the Interval Fuzzy Rough Measurement of Alternatives and Ranking according to the Compromise Solution method was applied to rank the alternatives. Twelve criteria were observed to evaluate 21 alternatives. Based on the results, it was concluded that salary stood out as the most important criterion, while amortization stood out as the least significant criterion. On the other hand, alternatives A9 and A10 stood out as the best-ranked alternatives while A1, A2, and A3 stood out as the least efficient ones. The paper provides clear scientific contributions that are reflected in the reduction of the gap that was observed after reviewing the literature where there is a lack of papers dealing with this task. Also, the combination of methods applied in the paper has not been used so far, so it can be said that this paper represents an excellent basis for further research. The model has practical contributions as it allows decision-makers to make quality decisions regarding the operation of their warehouses in different time periods or observation periods, as well as it represents a decision support tool that can be used for better warehouse management.

How to assess a university campus planning — a case study based on students’ spatio-temporal behavior

ABSTRACT The planning of university campus directly impacts students’ quality of study and living, and there is a considerable amount of research on campus planning assessment, most of which uses post-occupancy evaluation methods. This paper tries to propose a new comprehensive assessment method for campus planning, which collects spatio-temporal behavioral data of students in their daily life through GPS software, then assesses the actual effect of campus planning comprehensively by analyzing the spatio-temporal behavioral characteristics of students. This paper also verifies the reliability of the student behavior characteristics analysis through questionnaire and explores the possible biases. The results indicate that, in addition to significant advantages in reliability, accuracy, and comprehensive judgment, the proposed method can visually reflect specific issues existing within tangible campus spaces and their impact on student behavior. Therefore, it can provide practical and reliable evidence for the practice of campus planning renewal. This method used in this paper is also applicable to other fixed group living spaces within a certain scale, like communities and neighborhoods.

Landslide Susceptibility Analysis and Hazard Zonation in Nuwakot District, Nepal

Landslide mostly occurred in highly rugged topography, erosional steeply sloping land form and fragile geological structural in mountainous region. Landslide susceptibility describe the likelihood relation of occurring landslide in an area controlled with their local terrain condition. Landslide susceptibility analysis (LSA) work is used to determine the spatial distribution of landslides prone area and applicable to predict the future landslides occurrences, which is vital for averting and mitigating regional landslide disasters. LSA. In this study, LSA is carried with the bivariate analysis incorporating twelve causative factors of landslide. The frequency ratio (FR) technique were used for computing the relative frequency (RF) as priority rank and entropy index (E) as weight of causative factor. Result of LSA shows that the probability of occurring landslides is 80 percent, with 27 percent of that risk being high risk, 38 percent being medium risk, and 15 percent being low risk. The prediction accuracy of the model is 87.89 percent, showing reliable and satisfactorily validation rates with good accuracy. So, the present study demonstrates that the quantitative assessment methods explored may have a promising potential for landslide assessment and prediction in the high hill and Himalaya regions.

Lymph node metastasis in cutaneous squamous cell carcinoma of the head and neck

BackgroundThe study aimed to assess the impact of parotid lymph nodes (LNs) on the prognosis of patients with cutaneous squamous cell carcinomas of the head and neck (HNcSCC), and to develop an alternative LN assessment method to enhance locoregional control (LRC) and overall survival (OS) stratification.MethodsWe retrospectively enrolled patients with surgically treated HNcSCC. Primary outcome variables were LRC and OS. The influence of parotid LNs and different LN assessment methods on prognosis was analyzed using Cox models, and comparisons were made using the C-index, Akaike Information Criterion, and Bayesian Information Criterion.ResultsA total of 126 patients were included. Both intraparotid and periparotid LN statuses significantly linked with prognosis. The presence of extranodal extension (ENE) in cervical LNs, rather than parotid LNs, was predictive of decreased LRC and OS. In the Cox analysis, only N3 of the AJCC N classification, when compared to N0, showed reduced LRC and OS. In comparison to N0P1, only N0P3/N1P1 and N2P2/N2P3 of the O’Brien staging system tended to predict poorer LRC, with no subgroup emerging as an independent predictor for OS. The proposed LN assessment method, based on the number of metastatic LNs and ENE status in cervical LNs, demonstrated superior performance in terms of C-index, Akaike Information Criterion, and Bayesian Information Criterion compared to other systems.ConclusionParotid LNs were significant determinants of prognosis in metastatic HNcSCC. The novel LN assessment method proposed (1–2 vs. 3–4 vs. 5 + or ENE) displayed similar survival stratification to the AJCC N and O’Brien staging systems.

Relation Between the Perceived Parenting Style and Grit of Adolescents Aged 15-20

The paper presents an analysis of the relation between the perceived parental style and the grit of adolescents. The theoretical background includes the ideas about the interconnection between the parenting and the grit. Research data were collected from 738 adolescents aged 15-20 in the Slovak Republic attending general secondary schools and specialized secondary schools (ISCED 3). Their average age was 17.19 year (SD = 1.36). We used two assessment methods: The Grit Scale and Parenting Style Inventory II. The linear regression modelling showed that the most significant predictors of the grit, passion and perseverance are male sex responsiveness and autonomy granting of the parents. The results are important in the psychological and educational praxis in the relation to the future carrier selections and personal beliefs about ability to reach the self-defined goals.

MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.

The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurate and automated methods for quantitative assessment of breast volume can optimize breast reconstruction surgery and assist physicians in clinical decision making. The aim of this study was to develop an artificial intelligence model for automated segmentation of the breast and measurement of volume. A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step. The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists. The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Offistretch: camera-based real-time feedback for daily stretching exercises

AbstractIn this paper, we present OffiStretch, a camera-based system for optimal stretching guidance at home or in the workplace. It consists of a vision-based method for real-time assessment of the user’s body pose to provide visual feedback as interactive guidance during stretching exercises. Our method compares the users’ actual pose with a pre-trained target pose to assess the quality of stretching for a number of different exercises. We utilize angular and spatial pose features to perform this comparison for each individual exercise. The result of this pose assessment is presented to the user as real-time visual feedback on an "augmented mirror" display. As our method relies simply on a single RGB camera, it can be easily utilized in everyday training scenarios. We validate our method in a user study, comparing users’ performance and motivation in stretching when receiving audio-visual guidance on a TV screen both with and without our live feedback. While participants performed equally well in both conditions, feedback boosted their motivation to perform the exercises, highlighting its potential for increasing users’ well-being. Moreover, our results suggest that participants preferred stretching exercises with our live feedback over the condition without the feedback. Finally, an expert evaluation with professional physiotherapists reveals that further work must target improvements of the feedback to ensure correct guidance during stretching.

An investigation into the impact of vocabulary retrieval practice as a method of formative assessment in a Latin AS-level unseen translation context

Abstract Unseen translation forms a central part of assessment and teaching both in the Latin GCSE and A Level. Developing skills for the unseen translation presents several challenges for the classroom teacher; unseens can be introduced using a scaffolded approach, yet pupils must learn to develop independence for the examination. Unseens can often take an entire lesson, or more to translate, precluding the opportunity for meaningful, immediate feedback. Furthermore, classes of mixed- ability students often suffer from staggered completion rates and unequal attention being divided among students. Within the curriculum, unseen classes can additionally suffer from feeling severed from the specification as passages contain unfamiliar material, the content is discrete from the set texts, and lack of an overarching framework for approaching unseens can make them feel irrelevant to pupils. Therefore, striking a balance between productive support for in-class unseen translation practice and nurturing pupil confidence requires a clear strategy. This article investigates the effect of two methods of formative assessment used in preparation for an unseen translation lesson with a year 12 class. Reflecting on the results of the investigation, this article discusses the opportunities different tasks may afford the Latin teacher for developing a vocabulary curriculum that supports long-term retention of vocabulary, increases the efficiency of unseen classes, and allows the unseen passage to be received as part of a wider framework of learning.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center.

PURPOSEOF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen itsfoundation.

Joint operational situation assessment and firepower allocation method based on enhanced potential energy model

The assessment of battlefield situations and the allocation of firepower are pivotal prerequisites for conducting joint operational planning. In response to the challenges posed by multi-service joint operations, this paper presents a methodology based on enhanced operational potential energy model for the assessment of battlefield situations and the generation of optimal firepower allocation plan. We focuses on the three-fold factors influencing operational effectiveness: spatial, informational, and environmental factors, and completes the enhanced operational potential energy model by integrating the assessment of joint operational situations and the generation of optimal firepower allocation plans. Finally, the effectiveness of this methodology is demonstrated through operational simulations in a joint anti-ship combat scenario.

Safety Assessment Method of High-speed Rail Interval Structure Based on Weighted Bayesian Network

Safety Assessment Method of High-speed Rail Interval Structure Based on Weighted Bayesian Network

Enhanced Network Security Protection through Data Analysis and Machine Learning: An Application of GraphSAGE for Anomaly Detection and Operational Intelligence

With the Internet's rapid expansion, network security challenges have become increasingly complex and prominent. Traditional protection methods, largely dependent on predefined rules and patterns, demonstrate limited effectiveness against sophisticated and unknown network attacks, failing to harness the full potential of extensive network data. This study addresses the challenges faced by modern cybersecurity, particularly the limitations of traditional defense methods in countering unknown and complex attacks, by proposing a solution that integrates data analysis and machine learning technologies. The focus of this research is placed on network security anomaly detection as well as on intelligent network operations and maintenance exception management based on graph network algorithms, aiming to enhance security defense capabilities and operational efficiency. Specifically, the main contributions and innovations of this paper include: 1. Innovations in sampling, aggregation, and loss functions within the Graph Sample and Aggregation (GraphSAGE) model to improve the accuracy and robustness of the model for network anomaly detection; 2. The introduction of a novel network anomaly root cause analysis and localization model, which, combined with an optimized root cause likelihood assessment method and search scheme, significantly enhances the speed and accuracy of anomaly localization; 3. The design of an integrated decision support system that can automatically adjust protection strategies as network conditions change, achieving a high level of automation and intelligence in cybersecurity management. This work not only provides effective technical support for network security protection but also opens new avenues for future cybersecurity research.

Environmental Repercussions of Craft Beer Production in Northeast Brazil

Beer is the most widely consumed alcoholic beverage in the world, and the craft beer market has been continuously growing in recent years. The objective of this study is to detail the production of craft beer and quantify its environmental impacts. The microbrewery is located in João Pessoa, northeast Brazil, and produces 180,000 L/year. The life cycle assessment methodology is employed, and 16 environmental indicators have been selected. Two environmental impact assessment methods are used: IPCC 2021 GWP 100y and ILCD 2011 Midpoint. The results indicate that the best packaging options (lowest environmental impacts) are 10 L stainless-steel kegs and 330 mL aluminum cans. The primary hotspot is the distribution to the points of sale, which employs diesel vehicles. When electric vehicles substitute diesel ones, the environmental impacts are three times lower. The adoption of electric mobility and increasing the consumption of local products are two strategies that can be explored to further mitigate the environmental impacts associated with craft beer.