Search Results (526)

The growing production of plastic waste and its recycling, from a circular economy perspective, faces challenges in finding solutions that are easy to implement, cheap in labor and energy during recycling, and locally implementable to avoid transportation. This work developed and validated a methodology to address these challenges. Designed for small-scale use at home or in schools following a Do It Yourself (DIY) approach, it transforms water bottles into plastic strips, which, after passing through an extruder nozzle, become filaments with a diameter of 1.75 mm. These can replace commercially available thermoplastic filaments. Specimens produced by additive manufacturing with recycled PET (rPET) and commercial PETG showed similar mechanical properties and can serve as alternatives to commercial PETG. PETG shows higher strength (30 MPa) compared to rPET (24 MPa), a slightly higher Young’s modulus of 1.44 GPa versus 1.43 GPa, and greater strain at failure with 0.03 mm/mm against 0.02 mm/mm, making it stiffer and more ductile. This simple and widely applicable local solution may absorb a considerable amount of bottle waste, offering an economical, sustainable alternative to commercial filaments. Full article

Achieving environmental sustainability has become an urgent priority in the era of rapid digital economic expansion, which presents both opportunities and challenges for environmental sustainable development. This study investigates the impact of digital economy (DIE) on environmental sustainability (ENS) through the dual dimensions of digital industrialization (DII) and industrial digitalization (IND), employing the wavelet-based quantile-on-quantile regression method to capture both quantile dependencies and temporal variations. The results reveal that DIE positively impacts ENS in the long term, while its short-term effects are mixed, with positive effects at lower and higher quantiles but negative impacts at mid-range quantiles of [0.35–0.45] and [0.65–0.7]. Specifically, DII exerts a predominantly negative short-term effect on ENS due to the environmental costs of digital infrastructure expansion, but turns positive in the long term as digital industrialization matures, especially in [0.85–0.95]. IND, conversely, exerts a consistently positive impact on ENS in both short- and long-term scenarios, highlighting its role in enhancing industrial efficiency and reducing emissions. These results emphasize the need for targeted policies, including prioritizing industrial digitalization, developing green infrastructure, and adopting phased digital development strategies to maximize environmental benefits. Full article

This study explores the integration of gamification into social and environmental systems to enhance urban biodiversity and foster the co-creation of ecosystems. It focuses on two key contributions: the development of tangible ecosystemic interventions, such as habitat extensions and edible landscapes, and the gamification of these interventions to engage communities. The interventions were codesigned using systems-oriented design methods, including gigamapping and prototyping, to produce scalable DIY solutions that empower communities to replicate these practices on their own. Additionally, urban games were created to incentivize participation by rewarding individuals for their contributions to biodiversity restoration. Full article

Recent advances in signal processing and AI-based inference enable the exploitation of wireless communication signals to collect information on devices, people, actions, and the environment in general, i.e., to perform Integrated Sensing And Communication (ISAC). This possibility offers exciting opportunities for Internet of Things (IoT) systems, but it also introduces unprecedented threats to the security and privacy of data, devices, and systems. In fact, ISAC operates in the wireless PHY and Medium Access Control (MAC) layers, where it is impossible to protect information with standard encryption techniques or with any other purely digital methodologies. The goals of this paper are threefold. First, it analyzes the threats to security and privacy posed by ISAC and how they intertwine in the wireless PHY layer within the framework of IoT and distributed pervasive communication systems in general. Secondly, it presents and discusses possible countermeasures to protect users’ security and privacy. Thirdly, it introduces an architectural proposal, discussing the available choices and tradeoffs to implement such countermeasures, as well as solutions and protocols to preserve the potential benefits of ISAC while ensuring data protection and users’ privacy. The outcome and contribution of the paper is a systematic argumentation on wireless PHY-layer privacy and security threats and their relation with ISAC, framing the boundaries that research and innovation in this area should respect to avoid jeopardizing people’s rights. Full article

Background: Diet and inflammation are both associated with hypertension. We aimed to investigate the relationship between the dietary inflammation index (DII), dietary patterns, and the risk of hypertension among Xinjiang residents. Methods: A total of 930 residents aged 20–80 from Shihezi and Tumushuk were selected as participants using a stratified whole cluster random sampling method. General demographic information, dietary data, and physical examination results were collected from the participants and DII scores were calculated. Restricted cubic spline was used to analyze the dose–response relationship between the DII and the risk of hypertension. LASSO regression was used to screen dietary factors associated with hypertension. Factor analysis was used to extract dietary patterns. Finally, logistic regression modeling was used to analyze the association between the DII, dietary patterns, and the risk of hypertension. Results: The DII was linearly and positively associated with the risk of developing hypertension. Logistic regression analysis showed that the prevalence of hypertension was 2.23 (95% CI: 1.53, 3.23) and 3.29 (95% CI: 2.26, 4.79) in the T2 and T3 groups, respectively, compared with the T1 group. Riboflavin and folate were associated with the risk of hypertension. In the vegetable–egg dietary pattern, the risk of hypertension was reduced by 33%, 39%, and 37% in groups Q2, Q3, and Q4, respectively, compared with group Q1 (Q2: OR = 0.67, 95% CI: 0.45, 0.99; Q3: OR = 0.61, 95% CI: 0.41, 0.92; Q4: OR = 0.63, 95% CI: 0.42, 0.96). Conclusions: The higher the DII score, the higher the risk of hypertension among residents of Xinjiang. In addition, vegetable–egg dietary patterns can reduce the risk of hypertension. Therefore, local residents should be scientifically instructed to increase their intake of vegetables and eggs. Full article

This article aims to highlight the importance of including quantitative measurements when conducting flow visualization tests, such as those performed in towing tanks, within fluid mechanics analysis. It investigates the possibility of measuring velocity fields with an economically accessible technique compared to other techniques that require large financial investments, such as traditional PIV. The development of a MATLAB R2024b code based on image recognition and the use of 3D-printed tracer particles is proposed. Code workflow and how to make a correct selection of the processing parameters and its activity are explained and demonstrated on artificial images, generated by a computer, as well as real images, obtained in a 2D-test in the tank, achieving an accuracy, in absolute values, of 95%. However, the proposed velocimetry system currently has one important limitation, the impossibility of distinguishing between particles in different planes, which limits the study to two-dimensional tests. Then, the opportunity to include this technique in the study of more complex tests requires further investigation. Full article

Mycotoxins are among the most concerning natural toxic food contaminants. Over the years, significant efforts have been made to characterize the risk associated with their exposure. However, assessing their toxicity has so far been elusive due to the lack of adequate models that closely mimic the physiological conditions of human cells in vivo. Here, we present the SpheroFlow Device (SFD), an efficient microfluidic platform designed, manufactured, and validated to evaluate mycotoxin-induced cytotoxicity under dynamic and continuous exposure in 3D culture settings. In the present study, we integrated human neuroblastoma SH-SY5Y spheroids into the SFD to assess the acute toxicity induced by the mycotoxins ochratoxin A (OTA) and patulin (PAT). The developed system enabled qualitative and quantitative measurements of concentration–response relationships and provided accurate control over the culture microenvironment. Our findings show that by enhancing 3D culture model by applying dynamic flow, SH-SY5Y spheroids exhibited different sensitivities to OTA and PAT compared to conventional static SH-SY5Y spheroids, confirming the critical role of culture models in mycotoxin toxicity assessment. This is the first study assessing the neurotoxicity of OTA and PAT on 3D neuroblastoma spheroids considering the contribution of fluid flow. Full article

Weather and soil water dictate farm operations such as irrigation scheduling. Low-cost and open-source agricultural monitoring stations are an emerging alternative to commercially available monitoring stations because they are often built from components using open-source, do-it-yourself (DIY) platforms and technologies. For irrigation management in an experimental vineyard located in Quiroga (Lugo, Spain), we faced the challenge of installing a low-cost environmental and soil parameter monitoring station composed of several nodes measuring air temperature and relative humidity, soil temperature, soil matric potential, and soil water content. Commercial solutions were either too expensive or did not meet our needs. This challenge led us to design the low-cost sensor system that fulfilled our requirements. This node is based on the ESP32 chip, and communication between the nodes and the gateway is carried out by LoRa technology. The gateway is also based on the ESP32 chip. The gateway uploads the data to an FTP server using a Wi-Fi connection with a 4G router while simultaneously storing the data on a memory card. The programming of the code for the nodes and the gateway is performed using the Arduino IDE. The equipment developed is proven to be effective and for managing vineyard irrigation based on the built-in sensors, with replicable results. It is, however, essential to calibrate the capacitive sensors for measuring soil water content in each soil type in order to enhance their ability to produce reliable results. In addition, the limits marking the beginning and end of irrigation tasks must be adjusted to local conditions and according to the producer’s specific vineyard objectives. Full article

In the rapidly evolving field of remote sensing, Deep Learning (DL) techniques have become pivotal in interpreting and processing complex datasets. However, the increasing reliance on these algorithms necessitates a robust ethical framework to evaluate their trustworthiness. This paper introduces a comprehensive ethical framework designed to assess and quantify the trustworthiness of DL techniques in the context of remote sensing. We first define trustworthiness in DL as a multidimensional construct encompassing accuracy, reliability, transparency and explainability, fairness, and accountability. Our framework then operationalizes these dimensions through a set of quantifiable metrics, allowing for the systematic evaluation of DL models. To illustrate the applicability of our framework, we selected an existing case study in remote sensing, wherein we apply our ethical assessment to a DL model used for classification. Our results demonstrate the model’s performance across different trustworthiness metrics, highlighting areas for ethical improvement. This paper not only contributes a novel framework for ethical analysis in the field of DL, but also provides a practical tool for developers and practitioners in remote sensing to ensure the responsible deployment of DL technologies. Through a dual approach that combines top-down international standards with bottom-up, context-specific considerations, our framework serves as a practical tool for ensuring responsible AI applications in remote sensing. Its application through a case study highlights its potential to influence policy-making and guide ethical AI development in this domain. Full article

We hypothesized that consumption of a diet containing the functional ingredient from black sticky rice, which is rich in anthocyanin, over a five-day period would improve anxiety, depression, and stress perception in adult volunteers based on the benefits of this compound. In this study, a total of 46 male and female adult volunteers with mild and moderate stress level were assigned to groups consuming a breakfast meal containing an anthocyanin-enriched functional ingredient at doses of 2 and 4 g per day for 5 days. The volunteers consumed three meals with a low DII but high DAQ-S, and the total calories consumed during the study period was 2000 kcal/day. Mental well-being, including depression, anxiety, and stress, together with AChE, MAO, Nrf2, 8OHdG, MDA, and the density of Lactobacillus and Bifidobacterium spp., were assessed at baseline and at the end of the study. Safety parameters were also examined. A diet containing both doses of the anthocyanin-enriched functional ingredient with a low DII but high DAQ-S was found to improve anxiety, depression, and stress, with changes in 8-OHdG and IL-6 levels. No other changes and toxicity-related parameters were observed. Our results show that the novel functional ingredient can improve anxiety, depression, and stress perception partly by decreasing oxidative stress and inflammation; however, randomized, double-blind, placebo-controlled studies with a larger sample size should be performed to confirm this benefit. Full article

Background: Pancreatic cancer (PC), a highly malignant cancer with a poor diagnosis, may be influenced by diet-related inflammation. This study examined the association between dietary inflammatory index (DII) scores and the incidence and prognosis of PC in Korea. Methods: A total of 55 patients with PC were matched with 280 healthy controls (HCs) by age and sex. We also analyzed the combined effects of DII scores and fasting blood glucose (FBG) levels or smoking status on the risk of PC and performed a survival analysis using the Cox proportional hazards method. Results: The DII scores were higher in the patients with PC than those in HCs (odds ratio [OR] = 3.36, confidence interval [CI] = 1.16–9.73, p = 0.03), and the effect was larger in women (OR = 6.13, CI = 1.11–33.82, p = 0.04). A high DII score was jointly associated with FBG ≥ 126 mg/dL in raising PC risk [OR = 32.5, relative excess risk due to interaction/synergy (RERI/S) index = 24.2/4.34, p-interaction = 0.04], indicating a multiplicative interaction. A high DII score combined with ex/current smoker status increased PC risk through an additive interaction (RERI/S = 1.01/1.54, p-interaction = 0.76). However, DII scores did not influence disease-free survival. Conclusions: The consumption of an anti-inflammatory diet, coupled with maintaining normal FBG levels and abstaining from smoking, may help reduce the risk of PC by mitigating pancreatic inflammation. Full article

Yield estimation is a key point theme for precision agriculture, especially for small fruits and in-field scenarios. This paper focuses on the metrological validation of a novel deep-learning model that robustly estimates both the number and the radii of grape berries in vineyards using color images, allowing the computation of the visible (and total) volume of grape clusters, which is necessary to reach the ultimate goal of estimating yield production. The proposed algorithm is validated by analyzing its performance on a custom dataset. The number of berries, their mean radius, and the grape cluster volume are converted to millimeters and compared to reference values obtained through manual measurements. The validation experiment also analyzes the uncertainties of the parameters. Results show that the algorithm can reliably estimate the number ($\mathrm{MPE}=-5\%$, $\sigma =6\%$) and the radius of the visible portion of the grape clusters ($\mathrm{MPE}=0.8\%$, $\sigma =7\%$). Instead, the volume estimated in ${\mathrm{px}}^3$ results in a $\mathrm{MPE}=0.4\%$ with $\sigma =21\%$, thus the corresponding volume in ${\mathrm{mm}}^3$ is affected by high uncertainty. This analysis highlighted that half of the total uncertainty on the volume is due to the camera–object distance d and parameter R used to take into account the proportion of visible grapes with respect to the total grapes in the grape cluster. This issue is mostly due to the absence of a reliable depth measure between the camera and the grapes, which could be overcome by using depth sensors in combination with color images. Despite being preliminary, the results prove that the model and the metrological analysis are a remarkable advancement toward a reliable approach for directly estimating yield from 2D pictures in the field. Full article

Background/Objectives: Dietary factors may significantly influence pain management in cancer survivors. However, a substantial gap exists regarding the relationship between nutrition and chronic pain in this population. This study examined differences in diet quality and dietary intake between breast cancer survivors (BCS) experiencing chronic pain and healthy controls (HC). It also aimed to understand the associations between dietary elements and pain-related outcomes within the BCS group. Methods: A case-control study was conducted with 12 BCS experiencing chronic pain and 12 HC (ages 18–65). Data collection included body composition, experimental pain assessments, pain-related questionnaires, and a 3-day food diary to calculate diet quality using the Healthy Eating Index-2015 (HEI-2015) and Dietary Inflammatory Index (DII). Statistical analyses evaluated group differences and associations between dietary factors and pain within the BCS group. Results: There were no significant differences in HEI-2015 scores between BCS and HC, but BCS had a significantly lower DII score (p = 0.041), indicating a more anti-inflammatory diet. BCS also showed higher intake of omega-3, vitamins B6, B12, A, D, and magnesium (p < 0.05). While total diet quality scores did not correlate with pain outcomes, several HEI-2015 and DII components, such as dairy, sodium, protein, vitamin C, and vitamin D, showed moderate positive or negative correlations with pain measures. Conclusions: Despite no overall differences in diet quality, BCS with chronic pain consumed more anti-inflammatory nutrients than HC. Complex correlations between specific dietary components and pain outcomes emphasise the need for further research to explore these links for chronic pain management in BCS. Full article

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery. Full article

Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO₂ emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching $85\%$ compared to $80.88\%$ for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of $9.5$ s compared to $2.100$ s for the other model. Full article