Search Results (2,443)

Potassium ferrate(VI) (K₂FeO₄) as a particularly strong oxidant represents an effective and environmentally friendly waste water treatment material. When produced by anodic oxidation in highly alkaline aqueous solution, the K₂FeO₄ product is separated and sealed in inert plastic bags with the retention of some liquid phase with high pH. This method proved to be excellent for long-term storage at moderately low temperature (5 °C) for industrial applications. It is still imperative to check the ferrate(VI) content of the product whenever it is to be used. Fe-57 Mössbauer spectroscopy is an excellent tool for checking the ratio of ferrate(VI) to the degradation product iron(III) in a sample. For this purpose, normally the spectral areas of the corresponding subspectra are considered; however, this approximation neglects the possible differences in the corresponding Mössbauer–Lamb factors. In this work, we have successfully determined the Mössbauer–Lamb factors for the ferrate(VI) and for the most common iron(III) degradation products observed. We have found superparamagnetic behavior and low-temperature phase transformation for another iron(III) degradation product that made the determination of the Mössbauer–Lamb factors impossible in that case. The identities of a total of three different iron(III) degradation products have been confirmed. Full article

With nearly 40% of the total plastics produced being used for packaging, up to five trillion plastic bags are consumed in the world annually. The inadequate disposal of plastic waste and its persistence has become a serious challenge/risk to the environment, health, and well-being of living creatures, including humans. The natural degradation of plastics is extremely slow; large pieces of plastic may break down into microplastics (MPs) (1 μm–5 mm) or nanoplastics (NPs) (<1000 nm) after protracted physical, chemical, and/or biological degradations. A brief overview of the transport of micro- and nanoplastics in the aquatic, terrestrial, and atmospheric environments is presented. Details are provided on the exposure routes for these waste materials and their entry into humans and other biota through ingestion, inhalation, and dermal contact. The greatest concern is the cumulative impact of the heterogeneous secondary MPs and NPs on planetary and human health. Inhaled MPs and NPs have been shown to affect the upper respiratory tract, lower respiratory tract, and alveoli; prolonged exposure can lead to chronic inflammatory changes and systemic disease. These can also lead to autoimmune diseases and other chronic health conditions, including atherosclerosis and malignancy. Sustainable mitigation strategies to reduce the impact of MPs/NPs include source reduction, material substitution, filtration and purification, transformation of plastic waste into value-added materials, technological innovations, etc. Multidisciplinary collaborations across the fields of medicine, public health, environmental science, economics, and policy are required to help limit the detrimental effects of widespread MPs and NPs in the environment. Full article

This paper examines how smart cities can address land subsidence and liquefaction in the context of rapid urbanization in Japan. Since the 1960s, liquefaction has been an important topic in geotechnical engineering, and extensive efforts have been made to evaluate soil resistance to liquefaction. Currently, there is a lack of machine learning applications in smart cities that specifically target geological hazards. This study aims to develop a high-performance prediction model for estimating the depth of the bearing layer, thereby improving the accuracy of geotechnical investigations. The model was developed using actual survey data from 433 points in Setagaya-ku, Tokyo, by applying two machine learning techniques: artificial neural networks (ANNs) and bagging. The results indicate that machine learning offers significant advantages in predicting the depth of the bearing layer. Furthermore, the prediction performance of ensemble learning improved by about 20% compared to ANNs. Both interdisciplinary approaches contribute to risk prediction and mitigation, thereby promoting sustainable urban development and underscoring the potential of future smart cities. Full article

In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices. Full article

Products made from petroleum-derived plastic materials are linked to many environmental problems, such as greenhouse gas emissions and plastic pollution. It is desirable to manufacture products from environmentally friendly materials instead of petroleum-based plastic materials. Products made from biomass–fungi composite materials are biodegradable and can be utilized for packaging, construction, and furniture. In biomass–fungi composite materials, biomass particles (derived from agricultural wastes) serve as the substrate, and the fungal hyphae network binds the biomass particles together. There are many reported studies on the 3D printing of biomass–fungi composite materials. However, there are no reported studies on the biodegradation of 3D-printed samples from biomass–fungi composite materials. In this study, two types of biomass materials were used to prepare printable mixture hemp hurd and beechwood sawdust. The fungi strain used was Trametes versicolor. Extrusion based 3D printing was used to print samples. 3D-printed samples were left for five days to allow fungi to grow. The samples were then dried in an oven for 4 h at 120 °C to kill all the fungi in the samples. The samples were buried in the soil using a mesh bag and kept in an environmental chamber at 25 °C with a relative humidity of 48%. The weight of these samples was measured every week over a period of three months. During the testing period, the hemp hurd test samples lost about 33% of their original weight, whereas the beechwood sawdust samples lost about 30% of their original weight. The SEM (scanning electron microscope) micrographs showed the presence of zygospores in the test samples, providing evidence of biodegradation of the test samples in the soils. Additionally, the difference in peak intensity between the control samples and test samples (for both hemp hurd and beechwood sawdust) showed additional evidence of biodegradation of the test samples in the soils. Full article

The successful translation of biofortified orange maize (BOM) to a staple household food is dependent on farmers’ ability to cultivate and subsequently utilize it. In this study, we assessed farmers’ allocation of their land to the cultivation of BOM and conventional white maize (CWM) varieties in districts where the AFIKEPO Nutrition Program is implemented in Malawi. The results showed that farmers were skeptical of allocating more land to the cultivation of BOM. CWM was allocated significantly more land (1.75 ± 0.51 acres) than BOM (1.12 ± 0.32 acres) (p < 0.001) in all districts. More farmers (87.9%) allocated less than 1 acre to BOM cultivation. The cost of seeds did not explain the preference for CWM, as the price of seeds in Malawi Kwacha was similar for both maize types (p = 0.742). Consequently, the average number of bags (50 kg maize grains/bag) harvested was significantly lower (mean: 6.48 ± 8.27 bags; median: 4 bags) for BOM than for CWM (mean: 23.11 ± 20.54 bags; median: 17 bags). Interestingly, BOM was found to be more resistant to weevils during post-harvest storage, suggesting the potential for improved food and nutrition security for households. The knowledge of high grain resistance to weevils did not influence farmers to cultivate more BOM. This has the potential to negatively affect maize biofortification as an effective strategy to alleviate vitamin A deficiency (VAD) in developing countries. Farmers should be sensitized to identify the benefits of BOM so that they are willing to purchase seeds and grow BOM on a larger scale. Coupled with its higher resistance to storage weevils, superior taste, and nutrient content, the continued cultivation and consumption of BOM has the potential to contribute to the achievement of both food and nutrition security within communities. Full article

Time series forecasting plays a critical role in business planning by offering insights for a competitive advantage. This study compared three forecasting methods: the Holt–Winters, Bagging Holt–Winters, and Box–Jenkins methods. Ten datasets exhibiting linear and non-linear trends and clear and ambiguous seasonal patterns were selected for analysis. The Holt–Winters method was tested using seven initial configurations, while the Bagging Holt–Winters and Box–Jenkins methods were also evaluated. The model performance was assessed using the Root-Mean-Square Error (RMSE) to identify the most effective model, with the Mean Absolute Percentage Error (MAPE) used to gauge the accuracy. Findings indicate that the Bagging Holt–Winters method consistently outperformed the other methods across all the datasets. It effectively handles linear and non-linear trends and clear and ambiguous seasonal patterns. Moreover, the seventh initial configurationdelivered the most accurate forecasts for the Holt–Winters method and is recommended as the optimal starting point. Full article

Background/Objectives: Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying rates at which the brain ages among individuals. Methods: In this paper, we conducted both an exposome-wide association study (XWAS) and a genome-wide association study (GWAS) on white matter brain aging in the UK Biobank, revealing the multifactorial nature of brain aging. We applied a machine learning algorithm and leveraged fractional anisotropy tract measurements from diffusion tensor imaging data to predict the white matter brain age gap (BAG) and treated it as the marker of brain aging. For XWAS, we included 107 variables encompassing five major categories of modifiable exposures that potentially impact brain aging and performed both univariate and multivariate analysis to select the final set of nongenetic risk factors. Results: We found current tobacco smoking, dietary habits including oily fish, beef, lamb, cereal, and coffee intake, length of mobile phone use, use of UV protection, and frequency of solarium/sunlamp use were associated with the BAG. In genetic analysis, we identified several SNPs on chromosome 3 mapped to genes IP6K1, GMNC, OSTN, and SLC25A20 significantly associated with the BAG, showing the high heritability and polygenic architecture of human brain aging. Conclusions: The critical nongenetic and genetic risk factors identified in our study provide insights into the causal relationship between white matter brain aging and neurodegenerative diseases. Full article

Electroencephalography (EEG) helps to assess the electrical activities of the brain so that the neuronal activities of the brain are captured effectively. EEG is used to analyze many neurological disorders, as it serves as a low-cost equipment. To diagnose and treat every neurological disorder, lengthy EEG signals are needed, and different machine learning and deep learning techniques have been developed so that the EEG signals could be classified automatically. In this work, five ensemble models are proposed for EEG signal classification, and the main neurological disorder analyzed in this paper is epilepsy. The first proposed ensemble technique utilizes an equidistant assessment and ranking determination mode with the proposed Enhance the Sum of Connection and Distance (ESCD)-based feature selection technique for the classification of EEG signals; the second proposed ensemble technique utilizes the concept of Infinite Independent Component Analysis (I-ICA) and multiple classifiers with majority voting concept; the third proposed ensemble technique utilizes the concept of Genetic Algorithm (GA)-based feature selection technique and bagging Support Vector Machine (SVM)-based classification model. The fourth proposed ensemble technique utilizes the concept of Hilbert Huang Transform (HHT) and multiple classifiers with GA-based multiparameter optimization, and the fifth proposed ensemble technique utilizes the concept of Factor analysis with Ensemble layer K nearest neighbor (KNN) classifier. The best results are obtained when the Ensemble hybrid model using the equidistant assessment and ranking determination method with the proposed ESCD-based feature selection technique and Support Vector Machine (SVM) classifier is utilized, achieving a classification accuracy of 89.98%. Full article

New epidemics encourage the development of new mathematical models of the spread and forecasting of infectious diseases. Statistical epidemiology data are characterized by incomplete and inexact time series, which leads to an unstable and non-unique forecasting of infectious diseases. In this paper, a model of a conditional generative adversarial neural network (CGAN) for modeling and forecasting COVID-19 in St. Petersburg is constructed. It takes 20 processed historical statistics as a condition and is based on the solution of the minimax problem. The CGAN builds a short-term forecast of the number of newly diagnosed COVID-19 cases in the region for 5 days ahead. The CGAN approach allows modeling the distribution of statistical data, which allows obtaining the required amount of training data from the resulting distribution. When comparing the forecasting results with the classical differential SEIR-HCD model and a recurrent neural network with the same input parameters, it was shown that the forecast errors of all three models are in the same range. It is shown that the prediction error of the bagging model based on three models is lower than the results of each model separately. Full article

In this study, a critical evaluation and the benefits of using a waste and a virgin polymer in an asphalt mixture are presented. The present paper is the result of a three-year research effort to find a suitable recyclate compatible with asphalt binder and setting reaction conditions in the preparation of asphalt mixtures with the mentioned recyclate. This suitable candidate was recycled low-density polyethylene (LDPE), which was produced by recycling old, worn-out bags and films. An amount of 6% of LDPE by the weight of the binder content was suggested as the best amount of the modifier. Physical tests, including penetration, softening point, and kinematic viscosity have been carried out to prove the effectiveness of the modification on the binder properties. The effectiveness of the blending process and the appropriate concentration of additives led to a homogeneous polymer-modified bitumen without any imperfections in the structure. After successful preparation under laboratory conditions, this paper describes the preparation of asphalt mixtures directly in an asphalt-mixing plant and the subsequent implementation of a verification section. The overall composition of prepared polymer-modified asphalt mixtures has been studied. An important result of this study is the preparation of the asphalt mixture with waste LDPE that meets all the technical requirements. Moreover, it has been proven that this type of waste PE is fully applicable in asphalt-mixing plants in Slovakia, with zero or minimal financial burden on construction companies to complete the construction of their production facilities. Using such a technology, we can reduce the amount of waste plastics that otherwise end up in landfill. Full article

Buccal fat pads are biconvex adipose tissue bags that are uniquely found on both sides of the human face along the anterior border of the masseter muscles. Buccal fat pads are important determinants of facial appearance, facilitating gliding movements of facial masticatory and mimetic muscles. Buccal fad pad flaps are used for the repair of oral defects and as a rich and easily accessible source of mesenchymal stem cells. Here, we introduce the buccal fat pad anatomy and morphology and report its functions and applications for oral reconstructive surgery and for harvesting mesenchymal stem cells for clinical use. Future frontiers of buccal fat pad research are discussed. It is concluded that many biological and molecular aspects still need to be elucidated for the optimal application of buccal fat pad tissue in regenerative medicine. Full article

Sleep position recognition plays a crucial role in enhancing individual sleep quality and addressing sleep-related disorders. However, the conventional non-invasive technology for recognizing sleep positions tends to be limited in its widespread application due to high production and computing costs. To address this issue, an enhanced stacking model is proposed based on a specific air bag mattress. Firstly, the hyperparameters of the candidate base model are optimized using the Bayesian optimization algorithm. Subsequently, the entropy weight method is employed to select extreme gradient boosting (XGBoost), support vector machine (SVM), and deep neural decision tree (DNDT) as the first layer of the enhanced stacking model, with logistic regression serving as the meta-learner in the second layer. Comparative analysis with existing machine learning techniques demonstrates that the proposed enhanced stacking model achieves higher classification accuracy and applicability. Full article

Atlantic White Cedar (Chamaecyparis thyoides) (AWC) anchors a globally threatened ecosystem that is being impacted by climate change, as these trees are vulnerable to hurricane events, sea-level rises, and increasing salinity at the forest–marsh ecotone. In this study, we determined the current amount and distribution of AWC in an area that is experiencing sea-level rises that are higher than the global average rate. We used a combination of a field investigation and aerial photo interpretation to identify known locations of AWC, then integrated Sentinel-1 and 2A satellite data with abiotic variables into a species distribution model. We developed a spectral signature of AWC to aid in our understanding of phenology differences from nearby species groups. The selected model had an out-of-bag error of 7.2%, and 8 of the 11 variables retained in the final model were derived from remotely sensed data, highlighting the importance of including temporal data to exploit divergent phenology. Model predictions were strong in live AWC stands and, accurately, did not predict live AWC in stands that experienced high levels of mortality after Hurricane Sandy. The model presented in this study provides high utility for AWC management and tracking mortality dynamics within stands after disturbances such as hurricanes. Full article

(1) Background: Cultivating microgreens is emerging as an excellent market opportunity. Their easy, short, and sustainable production methods are the main reasons they are approved by growers. However, a feature that still prevents its further spread is the microbiological risk and their rapid senescence. The present study was conducted to evaluate the post-harvest storage and shelf life of arugula microgreens in different packaging through microbiological, physico-chemical, and sensory parameters; (2) Methods: Plants were stored at 5 °C in open air, vacuum sealed, and under modified atmosphere bags and tested at 0, 3, 5, 7, and 10 days; (3) Results: Microgreens stored in all packaging were safe for consumption within ten days. Regarding physical and chemical parameters, open packaging proved to be promising, with less weight loss and slower chlorophyll degradation. The sensory analysis demonstrated that the microgreens stored in the vacuum-sealed packaging showed a decrease in quality from the fifth day onwards for all attributes. However, the MAP presented good scores with a better visual quality, similar to the fresh microgreens. Full article