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1 month ago | 
nature.com | Ida-Johanne Jensen |Jørgen Lerfall |Rikke Harveland Ølberg |Lene Waldenstrøm |Victoria Aranda
Beef jerky is widely consumed for its convenience, sensory appeal, and shelf stability. Reducing sugar content is a public health priority but presents technological and sensory challenges. Drying time also plays a crucial role due to its economic and sustainability impacts. This study evaluated the effects of different drying times (2, 3, 4, and 6 h) and sugar levels (0.5%, 1%, and 1.5%) on beef jerky’s sensory and physicochemical properties. Drying time significantly influenced product quality, affecting sensory attributes. Results showed that reducing added sugar did not compromise the sensory properties preferred by consumers. Moreover, optimizing both drying time and sugar content can improve production efficiency while maintaining consumer acceptance. These findings highlight the potential for healthier beef jerky with enhanced sustainability.
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1 month ago | 
nature.com | Jung Hun Kang |Jun Lee |Dong-gi Lee |Jae Choi |Jun Baek Park |Yeasol Kwon | +2 more
We aimed to evaluate the relationship between serum testosterone levels and kidney stone prevalence in men. We examined cross-sectional data from 3234 men who participated in a health examination (2010–2020). A full metabolic work-up, including serum testosterone levels, was performed. Combined ultrasonography with KUB radiography was used for stone detection. The participants’ median age and testosterone concentration were 53.0 years and 4.7 ng/mL, respectively. A total of 178 men had kidney stones. A cutoff value for determining the presence of kidney stones was a testosterone concentration <3.33 ng/mL ng/mL. After adjusting for confounders, only age and a testosterone concentration <3.33 ng/mL were significantly related to the presence of kidney stones. However, body mass index, blood pressure, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, creatinine, blood urea nitrogen, HbA1c, uric acid, hs-CRP, calcium, aspartate transaminase, alanine aminotransferase, and albumin were not significantly and independently related to kidney stones. The odds ratios (95% confidence intervals) for kidney stones according to age and testosterone concentration <3.33 ng/mL were 1.029 (1.010–1.04) and 1.655 (1.071–2.556), respectively. Our study revealed that the prevalence of kidney stones significantly and independently increased when the serum testosterone was less than 3.33 ng/mL in men.
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1 month ago | 
nature.com | Wenqi Zhao |Wen Zheng |Zimin Wang |Xiaoli Pan |Gaoting Yang |Zongyi Zhao | +2 more
Alismatis Rhizoma (AR) is widely utilized as a natural medicine across many Asian countries. However, in China, due to its complex origins, AR quality varies, which can affect clinical efficacy. Therefore, there is a need for a method that is both fast and objective to determine the source of AR. In this study, a total of 400 samples of two species and four geographic origins from AR were imaged and processed. From these images, 17 features were extracted, including three shape (S), two color (C), and 12 texture features (T), resulting in a total of 6800 data points. Four commonly used classification models Random Forest (RF), Extreme Learning Machine (ELM), Back Propagation (BP) neural network, and Support Vector Machines (SVM) were tested to find the optimal combination of AR fusion features and classification models. The S + T-RF combinations achieved the best results, with 99.17% accuracy in two species identification and 96.67% accuracy in four geographic origin identification on test sets. These results suggest that image processing combined with the RF model can quickly and effectively identify the complex origins of AR and can provide a reference for the origins identification of other natural medicines.
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1 month ago | 
nature.com | Lixin Xiong |Shengqi Liu |Ban Liu |Victoria Aranda
Under the “dual carbon” goals, heavily polluting enterprises face dual pressures to reduce both pollution and carbon emissions, necessitating the urgent exploration of effective pathways for coordinated emission reductions. This study investigates the potential of digital transformation in enterprises to achieve synergistic emission reductions. First, the entropy method is employed to measure enterprise digitalization and pollutant levels, and the spatial–temporal evolution characteristics of regional coordinated emission reductions are analyzed. Subsequently, using panel data from heavily polluting enterprises in the Yangtze River Economic Belt, the study examines the impact of digital transformation on pollution and carbon reduction, its underlying mechanisms, and the moderating effects of environmental policies on these relationships. Robustness tests confirm the synergy between carbon and pollution emissions. The findings reveal that digital transformation contributes to the synergistic reduction of carbon and pollutant emissions in enterprises, primarily through two pathways: the coordinated integration of internal innovation resources and the collaborative engagement in external innovation networks. Furthermore, air pollution control policies and low-carbon city initiatives significantly enhance the synergistic emission reduction effects of digitalization. Interestingly, heavily polluting enterprises located in the downstream regions of the Yangtze River, those with smaller operational scales, or those facing strong financing constraints, demonstrate more pronounced synergistic emission reduction effects through digital transformation. Based on these conclusions, we recommend that governments focus on strengthening either “pollution reduction” or “carbon reduction” policies, as either alone can yield dual emission reduction benefits. Additionally, tailoring regional emission reduction policies to local conditions can maximize economic and environmental benefits.
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1 month ago | 
nature.com | Haytham F. Isleem |Victoria Aranda |Khalil Ivan |Wang Zonghui |Kalugina Olga Veniaminovna
The application of intelligent technology to enhance decision-making, optimize processes, and boost project economics and sustainability has the potential to significantly revolutionize the construction industry. However, there are several barriers to its use in small-scale construction projects in China. This study aims to identify these challenges and provide solutions. Using a mixed-methods approach that incorporates quantitative analysis, structural equation modeling, and a comprehensive literature review, the study highlights key problems. These include specialized challenges, difficulty with data integration, financial and cultural constraints, privacy and ethical issues, limited data accessibility, and problems with scalability and connection. The findings demonstrate how important it is to get rid of these barriers to fully utilize intelligent computing in the construction sector. There are recommendations and practical strategies provided to help industry participants get over these challenges. Although the study’s geographical emphasis and cross-sectional approach are limitations, they also offer opportunities for further investigation. This study contributes significantly to the growing body of knowledge on intelligent computing in small-scale construction projects and offers practical guidance on how businesses might leverage their transformative potential.
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1 month ago | 
nature.com | Shanshan Guo |Yun Chen |Yuanyuan Dang |Victoria Aranda
In public health emergencies, there is a critical need for accurate informational and emotional support to counteract misinformation and trauma. Online Health Communities (OHCs) serve as essential resources for real-time health counseling and support. This study investigates how OHCs facilitate the acquisition of informational and emotional support, crucial for guiding informed protective decisions. By integrating the Protective Action Decision Model (PADM) with social support theory, the research examines the impact of disaster-related information on patients’ decision-making within OHCs, aiming to optimize these platforms for public health response and preparedness. The study utilizes a dataset comprising 602 doctor-patient consultation dialogues from a Chinese OHC. Through text and sentiment analysis, the study quantifies the volume of information and sentiment, which serve as indicators of the level of informational and emotional support sought by patients. Environmental and social cues related to emergency situations are measured using disaster early forecast information and the volume of social media discussions on the emergency. Multiple linear regression models are employed to analyze the impact of these cues on patients’ behaviors, specifically their informational-seeking and emotional-seeking actions. It indicates that social cues have an impact on patients’ seeking informational support, while only in the high-uncertainty public health emergency, environmental cues are positively correlated with patients’ seeking both emotional and informational support. Additionally, stakeholder actions in the context of OHCs positively moderate the influence of environmental and social cues on individual protective actions to some extent. This study advances the understanding of OHCs by applying and empirically testing the PADM in a digital health context. It also explores the varying impacts of different types of public health emergencies on patient behavior within OHCs. The findings can guide healthcare providers and OHC administrators in enhancing support mechanisms, particularly during public health emergencies.
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2 months ago | 
nature.com | Ahmad Almadhor |Shtwai Alsubai |Gabriel Avelino Sampedro |Abdullah Al Hejaili |Monika Davidekova |Victoria Aranda | +1 more
Electric Vehicle Charging Station (EVCS) security is a growing concern in today’s connected world due to the growing complexity and frequency of cyber threats. Traditional Intrusion Detection Systems (IDS) for EV chargers struggle to detect novel or unexpected attacks due to their usage of predetermined signatures and limited detection capabilities. Existing EV charging station security systems are unable to identify many known and undiscovered threats since they primarily rely on feature selection and categorization accuracy. It is common for these systems to be constructed using conventional machine learning algorithms. So many common signs of attacks are ignored. This paper proposes a Transfer learning (TL) framework for cyber-physical attack detection in EVCS in order to overcome these difficulties and improve both accuracy and scalability. The weights preserved from the Deep Neural Network (DNN) model after implementing data normalization and min-max scaling techniques utilized for training are used to initialize a new model termed Transfer Learning. The study also provides a comparison with different DL models such as Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Long Short-Term Memory-Recurrent Neural Networks (LSTM-RNN), and Gated Recurrent Unit (GRU). The CICEVSE2024 (EVSE-A and EVSE-B) datasets are used to assess the framework, where one dataset is used to train and store weights, and the second is used to evaluate the learned patterns using transfer learning. Several evaluation matrices are used to evaluate the suggested model. The experimental results demonstrate that the TL model attained 93% accuracy. Consequently, the pre-train TL model provides a high degree of symmetry between EVCS security and the detection of malicious attacks.
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2 months ago | 
nature.com | Aditya R. Pote |Kamal Kumar |Dhruv Kumar |Vikash Kumar |Jagat Sesh Challa |Piyush Jhamnani | +3 more
This study evaluates the effectiveness of three leading generative AI tools-ChatGPT, Gemini, and Copilot-in undergraduate mechanical engineering education using a mixed-methods approach. The performance of these tools was assessed on 800 questions spanning seven core subjects, covering multiple-choice, numerical, and theory-based formats. While all three AI tools demonstrated strong performance in theory-based questions, they struggled with numerical problem-solving, particularly in areas requiring deep conceptual understanding and complex calculations. Among them, Copilot achieved the highest accuracy (60.38%), followed by Gemini (57.13%) and ChatGPT (46.63%). To complement these findings, a survey of 172 students and interviews with 20 participants provided insights into user experiences, challenges, and perceptions of AI in academic settings. Thematic analysis revealed concerns regarding AI’s reliability in numerical tasks and its potential impact on students’ problem-solving abilities. Based on these results, this study offers strategic recommendations for integrating AI into mechanical engineering curricula, ensuring its responsible use to enhance learning without fostering dependency. Additionally, we propose instructional strategies to help educators adapt assessment methods in the era of AI-assisted learning. These findings contribute to the broader discussion on AI’s role in engineering education and its implications for future learning methodologies.
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Mar 5, 2025 | 
nature.com | Bo Wang |Qikai Li |Shibin Niu |Victoria Aranda |Xiangtian Nie
Based on the identification and analysis of safety risks in TBM tunnel construction, we construct a comprehensive safety risk dynamic evaluation index system tailored to this context. To account for the dynamic nature of safety risks during TBM tunnel construction, variable weight theory is incorporated, allowing for a more accurate reflection of the time-varying importance of risk indicators. Building on the theoretical framework of attribute interval recognition theory, we establish a TBM tunnel construction safety risk evaluation model grounded in attribute interval theory. This model effectively addresses the various uncertainties inherent in the risk evaluation process for TBM tunnel construction. It determines the evaluation level and attribute intervals for each individual indicator, and subsequently calculates the interval values for both the individual indicators and the composite attribute. Finally, a case study demonstrates the model’s effectiveness and practical applicability.
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Feb 19, 2025 | 
nature.com | Eunjung Shin |Victoria Aranda |Ae-Ran Koh
With the global economic downturn and environmental crisis caused by the COVID-19 pandemic, sustainability is emerging as an important issue for fashion companies and consumers. This study uses big data analysis to examine changes in consumer perceptions of sustainable fashion consumption and ethical fashion consumption pre- and post-COVID-19. By distinguishing between these two concepts, this research contributes to resolving theoretical ambiguities that often lead to mixed consumer behaviors. We used Textom 6.0 to collect data from Korea’s most used TOP 3 websites: Naver, Daum, and Google, using the keywords “sustainable fashion consumption” and “ethical fashion consumption.” Through frequency analysis, we found sustainable fashion consumption developing from the trend of pursuing “eco-friendly” as a practical direction for consumption value, and ethical fashion consumption was expanding the scope of “vegan” from fashion to overall life. Further, we used LDA topic modeling, we found that post-pandemic, there has been a significant shift towards eco-friendly and vegan products,” Ethical fashion consumption confirmed consumers’ perceptions of the keyword “animal.” This study provides foundational data on the distinction between sustainable and ethical fashion and offers actionable insights for brands aiming to align with post-pandemic consumer values.
