Search Results (33)

Background/Objectives: The glymphatic system plays a crucial role in clearing brain metabolic waste, and its dysfunction has been correlated to various neurological disorders. The Diffusion Tensor Imaging Analysis Along the Perivascular Space (DTI-ALPS) index has been proposed as a non-invasive marker of glymphatic function by measuring diffusivity along perivascular spaces; however, its sensitivity to sleep-related changes in glymphatic activity has not yet been validated. This study aimed to evaluate the feasibility of using the DTI-ALPS index as a quantitative marker of dynamic glymphatic activity during sleep. Methods: Diffusion tensor imaging (DTI) data were obtained from 12 healthy male participants (age = 24.44 ± 2.5 years; Pittsburgh Sleep Quality Index (PSQI) < 5), once while awake and 16 times during sleep, following 24 h sleep deprivation and administration of 10 mg zolpidem. Simultaneous MR-compatible electroencephalography was used to determine whether the subject was asleep or awake. DTI preprocessing included eddy current correction and tensor fitting. The DTI-ALPS index was calculated from nine regions of interest in projection and association areas aligned to standard space. The final analysis included nine participants (age = 24.56 ± 2.74 years; PSQI < 5) who maintained a continuous sleep state for 1 h without awakening. Results: Among nine ROI pairs, three showed significant increases in the DTI-ALPS index during sleep compared to wakefulness (Friedman test; p = 0.027, 0.029, 0.034). These ROIs showed changes at 14, 19, and 25 min after sleep induction, with FDR-corrected p-values of 0.024, 0.018, and 0.018, respectively. Conclusions: This study demonstrated a statistically significant increase in the DTI-ALPS index within 30 min after sleep induction through time-series DTI analysis during wakefulness and sleep, supporting its potential as a biomarker reflecting glymphatic activity. Full article

The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R² values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste. Full article

Cemented paste backfill (CPB) plays an important role in sustainable mining by providing structural support and reducing surface subsidence. While traditional destructive testing methods such as unconfined compressive strength (UCS) tests offer valuable understanding of material strength, they require a lot of resources, are time-consuming, and environmentally unfriendly. However, non-destructive testing (NDT) techniques such as ultrasonic pulse velocity (UPV), electrical resistivity (ER), and acoustic emission (AE) provide sustainable alternatives by preserving sample integrity, minimizing waste, and enabling real-time monitoring. This study systematically reviews and quantitatively compares the effectiveness of UPV, ER, and AE in predicting the strength of CPB. Meta-analysis of 30 peer-reviewed studies reveals that UPV and AE provide the most consistent and reliable correlations with UCS, with R² values of 0.895 and 0.896, respectively, while ER shows more variability due to its sensitivity to environmental factors. Additionally, a synthetic model combining UPV, AE and ER demonstrates improved accuracy in predicting strength. This hybrid approach enhances predictions of material performance while supporting sustainability in mining and construction. Our research advocates for better testing practices and presents a promising direction for future infrastructure projects, where real-time, non-invasive monitoring can enhance material performance evaluation and optimize resource use. Full article

Hyperspectral imaging (HSI) is an advanced imaging technique that captures detailed spectral information across multiple fields. This review explores its applications in counterfeit detection, remote sensing, agriculture, medical imaging, cancer detection, environmental monitoring, mining, mineralogy, and food processing, specifically highlighting significant achievements from the past five years, providing a timely update across several fields. It also presents a cross-disciplinary classification framework to systematically categorize applications in medical, agriculture, environment, and industry. In counterfeit detection, HSI identified fake currency with high accuracy in the 400–500 nm range and achieved a 99.03% F1-score for counterfeit alcohol detection. Remote sensing applications include hyperspectral satellites, which improve forest classification accuracy by 50%, and soil organic matter, with the prediction reaching R² = 0.6. In agriculture, the HSI-TransUNet model achieved 86.05% accuracy for crop classification, and disease detection reached 98.09% accuracy. Medical imaging benefits from HSI’s non-invasive diagnostics, distinguishing skin cancer with 87% sensitivity and 88% specificity. In cancer detection, colorectal cancer identification reached 86% sensitivity and 95% specificity. Environmental applications include PM2.5 pollution detection with 85.93% accuracy and marine plastic waste detection with 70–80% accuracy. In food processing, egg freshness prediction achieved R² = 91%, and pine nut classification reached 100% accuracy. Despite its advantages, HSI faces challenges like high costs and complex data processing. Advances in artificial intelligence and miniaturization are expected to improve accessibility and real-time applications. Future advancements are anticipated to concentrate on the integration of deep learning models for automated feature extraction and decision-making in hyperspectral imaging analysis. The development of lightweight, portable HSI devices will enable more on-site applications in agriculture, healthcare, and environmental monitoring. Moreover, real-time processing methods will enhance efficiency for field deployment. These improvements seek to enhance the accessibility, practicality, and efficacy of HSI in both industrial and clinical environments. Full article

Smart cities are urban areas that use advanced technologies to make urban living better through efficient resource management, sustainable development, and improved quality of life. Hyperspectral imaging (HSI) is a noninvasive and nondestructive imaging technique that is revolutionizing smart cities by offering improved real-time monitoring and analysis capabilities across multiple urban sectors. In contrast with conventional imaging technologies, HSI is capable of capturing data across a wider range of wavelengths, obtaining more detailed spectral information, and in turn, higher detection and classification accuracies. This review explores the diverse applications of HSI in smart cities, including air and water quality monitoring, effective waste management, urban planning, transportation, and energy management. This study also examines advancements in HSI sensor technologies, data-processing techniques, integration with Internet of things, and emerging trends, such as combining artificial intelligence and machine learning with HSI for various smart city applications, providing smart cities with real-time, data-driven insights that enhance public health and infrastructure. Although HSI may generate complex data and tends to cost much, its potential to transform cities into smarter and more sustainable environments is vast, as discussed in this review. Full article

Waste-to-energy technology has proven effective in reducing the mass and volume of waste, thereby minimizing contamination sources and residual fractions. However, high moisture content in waste significantly reduces the efficiency of energy recovery. Biodrying has shown great potential for moisture reduction through microbial activity, enhancing the efficiency of waste-to-energy processes. While the lack of proper real-time monitoring methods hinders the optimization of the biodrying processes. This study proposes an efficient method for monitoring the biodrying of municipal solid waste based on real-time electrical resistivity monitoring. During biodrying, resistivity was measured alongside key parameters like temperature, weight, gas emissions from the biodrying process, relative air humidity, moisture, and waste density. The results indicate a good correlation between bulk electrical resistivity (441–614 Ω·m) and temperature increases above ambient within the first 36 h (r² = 0.97–0.99). Statistical analyses also revealed the correlations of electrical resistivity with waste density (negative correlation, r² = 0.68–0.97) and gas emissions (moderate to strong, r² = 0.45–0.72) during different biodrying phases. These findings demonstrate the relationship between electrical resistivity and key biodrying parameters, which can be used for the development of biodrying and to enhance process control efficiency, thus enhancing sustainable waste management efficiency. Full article

Significance: We describe a novel, specimen-free diagnostic platform that can immediately detect both a metabolite (glucose) or an infection (COVID-19) by non-invasively using Raman spectroscopy and machine learning. Aim: Current diagnostic testing for infections and glucose monitoring requires specimens, disease-specific reagents and processing, and it increases environmental waste. We propose a new hardware–software paradigm by designing and constructing a finger-scanning hardware device to acquire Raman spectroscopy readouts which, by varying the machine learning algorithm to interpret the data, allows for diverse diagnoses. Approach: A total of 455 patients were enrolled prospectively in the COVID-19 study; 148 tested positive and 307 tested negative through nasal PCR testing conducted concurrently with testing using our viral detector. The tests were performed on both outpatients (N = 382) and inpatients (N = 73) at Holy Name Medical Center in Teaneck, NJ, between June 2021 and August 2022. Patients’ fingers were scanned using an 830 nm Raman System and then, using machine learning, processed to provide an immediate result. In a separate study between April 2023 and August 2023, measurements using the same device and scanning a finger were used to detect blood glucose levels. Using a Dexcom sensor and an Accu-Chek device as references, a cross-validation-based regression of 205 observations of blood glucose was performed with a machine learning algorithm. Results: In a five-fold cross-validation analysis (including asymptomatic patients), a machine learning classifier using the Raman spectra as input achieved a specificity for COVID-19 of 0.837 at a sensitivity of 0.80 and an area under receiver operating curve (AUROC) of 0.896. However, when the data were split by time, with training data consisting of observations before 1 July 2022 and test data consisting of observations after it, the model achieved an AUROC of 0.67, with 0.863 sensitivity at a specificity of 0.517. This decrease in AUROC may be due to substantial domain shift as the virus evolves. A similar five-fold cross-validation analysis of Raman glucose detection produces an area under precision–recall curve (AUPR) of 0.58. Conclusions: The combination of Raman spectroscopy, AI/ML, and our patient interface admitting only a patient’s finger and using no specimen offers unprecedented flexibility in introducing new diagnostic tests or adapting existing ones. As the ML algorithm can be iteratively re-trained with new data and the software deployed to field devices remotely, it promises to be a valuable tool for detecting rapidly emerging infectious outbreaks and disease-specific biomarkers, such as glucose. Full article

Asbestos fibers are well-known carcinogens, and their rapid detection is critical for ensuring safety, protecting public health, and promoting environmental sustainability. In this work, short-wave infrared (SWIR) spectroscopy, combined with machine learning (ML), was evaluated as an environmentally friendly analytical approach for simultaneously distinguishing the asbestos type, asbestos-containing materials in various forms, asbestos-contaminated/-uncontaminated soil, and asbestos-contaminated/-uncontaminated cement, simultaneously. This approach offers a noninvasive and efficient alternative to traditional laboratory methods, aligning with sustainable practices by reducing hazardous waste generation and enabling in situ testing. Different chemometrics techniques were applied to discriminate the material classes. In more detail, partial least squares discriminant analysis (PLS-DA), principal component analysis-based discriminant analysis (PCA-DA), principal component analysis-based K-nearest neighbors classification (PCA-KNN), classification and regression trees (CART), and error-correcting output-coding support vector machine (ECOC SVM) classifiers were tested. The tested classifiers showed different performances in discriminating between the analyzed samples. CART and ECOC SVM performed best (${Recall}_M$ and ${Accuracy}_M$ equal to 1.00), followed by PCA-KNN (${Recall}_M$ of 0.98–1.00 and ${Accuracy}_M$ equal to 1.00). Poorer performances were obtained by PLS-DA (${Recall}_M$ of 0.68–0.72 and ${Accuracy}_M$ equal to 0.95) and PCA-DA (${Recall}_M$ of 0.66–0.70 and ${Accuracy}_M$ equal to 0.95). This research aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being), by enhancing human health protection through advanced asbestos detection methods, and SDG 12 (Responsible Consumption and Production), by promoting sustainable, low-waste testing methodologies. Full article

Environmental pollution from plastic debris is raising concerns not only for the vulnerability of marine species to ingestion but also for potential human health hazards posed by small particles, known as microplastics. In this context, marine areas suffer from a lack of constant shoreline cleanups to remove accumulated debris, preventing their degradation and fragmentation. To establish optimal strategies for streamlining plastic recovery and recycling operations, it is important to have a system for recognizing plastic debris on the beach and, more specifically, for identifying the type of polymer and mapping (e.g., topologically assessing) the distribution of plastic debris on shoreline sands. This study aims to provide an operative tool finalized to perform an in situ detection, analysis, and characterization of plastic debris present in the coastal environment (i.e., beaches), adopting a near-infrared (NIR)-based hyperspectral imaging (HSI) approach. In more detail, the possibility of identifying and classifying polymers of plastic debris by NIR-HSI in three different areas along the Pontine coastline of the Lazio region (Latina, Italy) was investigated. The study focused on three distinct beaches (i.e., Foce Verde, Capo Portiere, and Sabaudia), each characterized by a different type of sand. For each location, the adopted approach allowed for the systematic classification of the various types of plastic waste found. Three Partial Least Squares Discriminant Analysis (PLS-DA) classification models were developed using a cascade detection strategy. The first model was designed to distinguish plastics from other materials in sand samples, the second to detect plastic particles in the sand, and the third to classify the type of polymer composing each identified plastic particle. Obtained results showed that, on the one hand, plastics were correctly detected from sand and other materials (i.e., sensitivity = 0.892–1.000 and specificity = 0.909–0.996), and on the other, the recognition of polymer type was satisfactory, according to the performance statistical parameters (i.e., sensitivity = 1.000 and specificity = 0.991–1.000). This research highlights the potential of the NIR-HSI approach as a reliable, non-invasive method for plastic debris monitoring and polymer classification. Its scalability and adaptability suggest possible future integration into mobile systems, enabling large-scale monitoring and efficient debris management. Full article

Properly managing aquatic organisms is crucial, including protecting endemic species and controlling invasive species. From a circular economy perspective, the sustainable use of aquatic species as a source of bioactive molecules is an area that is increasingly being explored. This includes the use of non-edible portions of seafood, which could pose considerable risks to the environment due to current methods of disposal. Therefore, it is of paramount importance to ensure that the exploitation of these resources does not result in the transfer of pollutants to the final product. This study analyzed two types of non-edible parts from the crayfish Procambarus clarkii: the abdominal portion of the exoskeleton (AbE) and the whole exoskeleton (WE), including the cephalothorax. These portions could potentially be utilized in the context of eradication activities regulated by local authorities. A screening analysis of four classes of pollutants, including pesticides, per- and polyfluoroalkyl substances (PFAS), phthalic acid esters (PAEs), and trace elements (TEs), was performed. The only analytes detected were TEs, and significant differences in the contamination profile were found between AbE and WE. Nevertheless, the levels recorded were comparable to or lower than those reported in the literature and below the maximum levels allowed in the current European legislation for food, suggesting that their potential use is legally permitted. In terms of scalability, the utilization of the entire non-edible P. clarkii portion would represent a sustainable solution for the reuse of waste products. Full article

Background: Amyotrophic lateral sclerosis (ALS) is a neuromuscular progressive disorder characterized by limb and bulbar muscle wasting and weakness. A total of 30% of patients present a bulbar onset, while 70% have a spinal outbreak. Respiratory involvement represents one of the worst prognostic factors, and its early identification is fundamental for the early starting of non-invasive ventilation and for the stratification of patients. Due to the lack of biomarkers of early respiratory impairment, we aimed to evaluate the role of chest dynamic MRI in ALS patients. Methods: We enrolled 15 ALS patients and 11 healthy controls. We assessed the revised ALS functional rating scale, spirometry, and chest dynamic MRI. Data were analyzed by using the Mann–Whitney U test and Cox regression analysis. Results: We observed a statistically significant difference in both respiratory parameters and pulmonary measurements at MRI between ALS patients and healthy controls. Moreover, we found a close relationship between pulmonary measurements at MRI and respiratory parameters, which was statistically significant after multivariate analysis. A sub-group analysis including ALS patients without respiratory symptoms and with normal spirometry values revealed the superiority of chest dynamic MRI measurements in detecting signs of early respiratory impairment. Conclusions: Our data suggest the usefulness of chest dynamic MRI, a fast and economically affordable examination, in the evaluation of early respiratory impairment in ALS patients. Full article

The efficient management of slurry, which is a by-product rich in nutrients derived from feces, urine, cleaning water, and animal waste that stands out for its high concentration of nutrients such as nitrogen, phosphorus, and potassium, is of vital importance, highlighting the importance of slurry management in storage ponds, which. The Murcia–Spain region has an important number of pig farms. Hence, infrastructures dedicated to managing by-products are necessary to prevent environmental pollution and eutrophication of groundwater. The aim of a recent study was to evaluate the relationship between electrical values and geochemical parameters of pig slurry stored in a pond using ERT and geochemical analysis. In addition, the study was designed to monitor the pond to determine the geochemical characteristics of the slurry and to assess the risk of lateral contamination. The study results indicate a noticeable decrease in electrical resistivity values at 0.4 and 1.6 m depth in surveys 1 and 2. The reduction ranges from 50 to 100 percent. This paper presents a new method for monitoring slurry ponds using electrical resistivity tomography. This non-invasive method provides detailed information on the distribution and characteristics of the fluids, as well as a clear picture of the electrical resistivity of the subsurface. Full article

The critical role of energy in contemporary life and the environmental challenges associated with its production imply the need for research and exploration of its novel resources. The present review paper emphasizes the continuous exploitation of non-renewable energy sources, suggesting the transition toward renewable energy sources, termed ‘green energy’, as a crucial step for sustainable development. The research methodology involves a comprehensive review of articles, statistical data analysis, and examination of databases. The main focus is biomass, a valuable resource for bioenergy and biopesticide production, highlighting not only its traditional diverse sources, such as agricultural waste and industrial residues, but also non-edible invasive alien plant species. This study explores the utilization of invasive alien species in circular economy practices, considering their role in bioenergy and biopesticide production. The potential conflict between bioproduct acquisition and food sector competition is discussed, along with the need for a shift in approaching non-edible biomass sources. The paper emphasizes the untapped potential of under-explored biomass resources and the necessity for policy alignment and public awareness. Species with a significant potential for these sustainable strategies include Acer negundo L., Ailanthus altisima (Mill.) Swingle., Amorpha fruticosa L., Elaengus angustifolia L., Falopia japonica (Houtt.) Ronse Decr., Hibiscus syriacus L., Koelreuteria paniculata Laxm., Paulownia tomentosa Siebold and Zucc., Partenocissus quenquefolia (L.) Planch., Rhus typhina L., Robinia pseudoacacia L. and Thuja orientalis L. In conclusion, the paper highlights the intertwined relationship between energy, environmental sustainability, and circular economy principles, providing insights into Serbia’s efforts and potential in adopting nature-based solutions for bioenergy and biopesticides acquisition. Full article

The development of plastic materials based on cassava reduces the dependence on non-biodegradable petroplastics, and enhances the sustainability of the cassava value chain. In this sense, cassava starch (CS) is used as a reinforcer of biocomposites that also contain brown seaweed Rugulopteryx okamurae (RO). RO is an invasive species whose accumulation poses a strong environmental burden in the strait of Gibraltar. Because it can be used as a biopolymer, its use in the plastics industry would promote a healthy ecosystem. Thus, RO/CS mixtures with different RO/CS ratios (from 100/0 to 30/70) were processed through injection moulding at 140 °C. The thermal properties of plastic samples have been analysed through calorimetric, thermogravimetric and rheological techniques. Moreover, the mechanical properties, hydrophilicity, and microstructure of samples have also been studied. Thus, biopolymer degradation of the composites seems to happen at 213–303 °C, as revealed by thermal gravimetric analysis (TGA) of the samples, whereas an exothermic peak observed in DSC at 350–500 °C would be related to the degradation of organic compounds in anaerobic conditions. Rheological tests evidenced a softening of the RO/CS biocomposites when CS content increased in the formulation, so that elastic moduli dropped from 23.72 MPa in the 70/30 to 5.69 MPa for 30/70. However, RO/CS biocomposites became more resistant and deformable as CS content increased: maximum stress and strain at break increased from 78.2 kPa and 0.14% (70/30 system) to 580 kPa and 25.2% (30/70), respectively. Finally, no important differences were observed in their water uptake capacities or microstructures when increasing CS ratio in the mixture. As cassava starch can be extracted from agro-industrial wastes (i.e., cassava peel and bagasse), its use in biocomposites could be of great use for a more sustainable approach for plastic materials. Full article

Plastic in agriculture is frequently used to protect crops and its use boosts output, enhances food quality, contributes to minimize water consumption, and reduces the environmental impacts of agricultural activities. On the other hand, end-of-life plastic management and disposal are the main issues related to their presence in this kind of environment, especially in respect of plastic degradation, if not properly handled (i.e., storage places directly in contact with the ground, exposure of stocks to meteoric agents for long periods, incorrect or incomplete removal). In this study, the possibility of using an in situ near infrared (NIR: 1000–1700 nm) hyperspectral imaging detection architecture for the recognition of various plastic wastes in agricultural soils in order to identify their presence and also assess their degradation from a recovery/recycling perspective was explored. In more detail, a Partial Least Squares—Discriminant Analysis (PLS-DA) classifier capable of identifying plastic waste from soil was developed, implemented, and set up. Results showed that hyperspectral imaging, in combination with chemometric approaches, allows the utilization of a rapid, non-destructive, and non-invasive analytical approach for characterizing the plastic waste produced in agriculture, as well as the potential assessment of their lifespan. Full article