Search Results (13)

Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random communication system (ML-RCS). It comprises a pretrained decision tree (DT)-based receiver that extracts binary information from the transmitted random noise carrier signals. The ML-RCS employs skewed alpha-stable ($\alpha$-stable) noise as a random carrier to encode the incoming binary bits securely. The DT model is pretrained on an extensively developed dataset encompassing all the selected parameter combinations to generate and detect the $\alpha$-stable noise signals. The legitimate receiver leverages the pretrained DT and a predetermined key, specifically the pulse length of a single binary information bit, to securely decode the hidden binary bits. The performance evaluations included the single-bit transmission, confusion matrices, and a bit error rate (BER) analysis via Monte Carlo simulations. The fact that the BER reached 10⁻³ confirms the ability of the proposed system to establish successful secure communication between a transmitter and legitimate receiver. Additionally, the ML-RCS provides an increased data rate compared to previous random communication systems. From the perspective of security, the confusion matrices and computed false negative rate of 50.2% demonstrate the failure of an eavesdropper to decode the binary bits without access to the predetermined key and the private dataset. These findings highlight the potential ability of unconventional ML-RCSs to promote the development of secure next-generation communication devices with built-in PLSs. Full article

Underwater sensor networks (UWSNs) play a vital role in marine exploration and environmental monitoring. However, due to the characteristics of underwater acoustic channels such as high delay, low bandwidth, and energy limitation, the design of an underwater media access control (MAC) protocol has brought great challenges, and existing MAC protocol designs rarely consider the influence of channel interference factors in networking. Therefore, this paper proposes a collision avoidance MAC protocol for clustering underwater sensor networks. The protocol first classifies users by combining the channel characteristics of underwater nodes and the distance measurement between nodes. Then, based on the clustering network, according to the channel correlation distance measurement between nodes and the communication range of the cluster head (CH), the transmit power in clusters is controlled to reduce the lifetime of the network based on the cumulative reduction in node power consumption. Finally, the cluster structure in each cluster is used to schedule the transmission of member nodes in the cluster, and at the same time, the energy consumption of nodes is reduced while multi-node collision-free transmission is realized. The simulation results show that the throughput of the proposed adaptive power control clustering MAC protocol (APCC-MAC) is 26.5% and 19.5% higher than that of packet-level slot scheduling (PLSS) algorithm and Cluster-Based Spatial–Temporal Scheduling (CSS) algorithm, respectively, providing better communication performance and stability for clustered underwater acoustic networks. Full article

This study accurately inverts key growth parameters of rice, including Leaf Area Index (LAI), chlorophyll content (SPAD) value, and height, by integrating multisource remote sensing data (including MODIS and ERA5 imagery) and deep learning models. Dehui City in Jilin Province, China, was selected as the case study area, where multidimensional data including vegetation indices, ecological function parameters, and environmental variables were collected, covering seven key growth stages of rice. Data analysis and parameter prediction were conducted using a variety of machine learning and deep learning models including Partial Least Squares (PLSs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory Networks (LSTM), among which the LSTM model demonstrated superior performance, particularly at multiple critical time points. The results show that the LSTM performed best in inverting the three parameters, with the LAI inversion accuracy on 21 August reaching a coefficient of determination (R²) of 0.72, root mean square error (RMSE) of 0.34, and mean absolute error (MAE) of 0.27. The SPAD inversion accuracy on the same date achieved an R² of 0.69, RMSE of 1.45, and MAE of 1.16. The height inversion accuracy on 25 July reached an R² of 0.74, RMSE of 2.30, and MAE of 2.08. This study not only verifies the effectiveness of combining multisource data and advanced algorithms but also provides a scientific basis for the precision management and decision-making of rice cultivation. Full article

Triclabendazole (TCB) is a well-established anthelmintic effective in treating fascioliasis, a neglected tropical disease. This study employs quality by design (QbD) to investigate the impact of TCB polymorphism and pharmacotechnical variables, from the development of immediate-release tablets to process optimization and green analysis. Critical process parameters (CPPs) and critical material attributes (CMAs), characterized by type of polymorph, composition of excipients (talc, lactose, cornstarch, and magnesium stearate), and compression force, were screened using a Plackett–Burman design (n = 24), identifying polymorphic purity and cornstarch as a CPP. To establish a mathematical model linking CPP to dissolution behaviour, a multiple linear regression (MLR) was applied to the training design (central composite design, n = 18). Simultaneously, a near-infrared spectroscopy coupled to partial least squares (NIR-PLSs) method was developed to analyze CPPs. An independent set of samples was prepared and analyzed using the NIR-PLSs model, and their dissolution profiles were also obtained. The PLSs model successfully predicted the CPPs in the new samples, yielding almost quantitative results (100 ± 3%), and MLR dissolution predictions mirrored the actual dissolution profiles (f2 = 85). In conclusion, the developed model could serve as a comprehensive tool for the development and control of pharmaceutical formulations, starting from the polymorphic composition and extending to achieve targeted dissolution outcomes. Full article

Social participation among older adults is a critical aspect that facilitates the improvement of their overall well-being. A critical factor influencing the social participation of older adults to achieve optimal aging is perceived accessibility to living infrastructure. The study aims to provide a comprehensive analysis of how perceived accessibility to transportation systems, public service facilities, and digital services influences life satisfaction and happiness among older adults. Survey data were collected from 200 households in South Korea and the research paper utilized the Partial Least Squares (PLSs) bootstrapping methodology with 5000 subsample iterations for analysis. The study shows that perceived accessibility to transportation systems, public service facilities, and digital services significantly influenced satisfaction among older adults. Satisfaction, in turn, had a positive effect on happiness. The implications for theory and practical implications were provided for officials and social service professionals concerning the geriatric population. Full article

Numerous studies have reported phenological changes and their driving mechanisms in spring flowering plants. However, there is little research on the shifts of winter flowering phenology and its response to forcing and chilling requirements. Based on the China Phenological Observation Network (CPON) ground observation data from nine sites over the past 20 years, we explored the spatial and temporal variation patterns of flowering plants and their response to chilling and forcing in wintersweet (Chimonanthus praecox L.), a common winter flowering plant species in temperate and subtropical zones of China. We used three chilling models (chilling hour, Utah, and dynamic models) and the growing degree hours (GDHs) model to calculate each site’s daily chilling and forcing. Using the partial least squares (PLSs) regression approach, we established the relationship between the first flowering date (FFD) and pre-season chilling and forcing in wintersweet, based on which we identified chilling and forcing periods and calculated chilling and forcing requirements. This study found that the FFD of wintersweet in China showed an overall advancement trend during the last 20 years. Still, there were temporal and spatial differences in the FFD of wintersweet among different sites. The PLS results showed that wintersweet also had periods of chilling and forcing, both of which co-regulated wintersweet flowering. We found the forcing and chilling requirements of wintersweet varied significantly from site to site. The higher the latitude is, the more chilling requirements are needed. The chilling requirements for wintersweet were about 6.9–34.9 Chill Portions (CPs) and 1.4–21.6 CP in the temperate and subtropical zones, respectively, with corresponding forcing requirements of 3.2–1922.9 GDH and 965.3–8482.6 GDH, respectively. In addition, we found that the temperature requirements of wintersweet were correlated by a negative exponential relationship, suggesting that chilling and forcing requirements have an antagonistic effect on initiating flowering phenology. Our results could help us understand how flowering dates of winter flowering plants respond to climate change. Full article

This manuscript describes molybdenum recovery from copper Pregnant Leaching Solutions (PLSs) in the copper oxide mining industry with high contents of chloride ions. This product was recovered from the copper leaching pond solutions of the Chilean National Copper Mining Corporation (CODELCO) using the ion exchange process. This process recovered molybdenum from initial Mo concentrations of 50 mg/L using two different anion−exchange resins. The first experiments, with 1 g/L Cl⁻, achieved recovery yields of 90% and molybdenum concentrates as CaMoO₄. However, the process was permanently halted because of the early saturation of the ion exchange resins given by high chloride concentrations (10 g/L Cl⁻) of the original copper PLS and the significant reagent consumption given by the low adsorption capacity. Static studies were developed to determine the adsorption isotherms, followed by continuous molybdenum recovery tests. The Langmuir adsorption parameters were determined as a function of the chloride concentration, giving absorption capacities from 180 to 250 mg Mo/gram of resin and recoveries from 63% to 90%. The breakthrough values for the DOWEX and Lewatit resins for chloride contents of 10 g/L were 180 and 245 BV, respectively, while for 1 g/L, these values were 620 and 890 BV. This allowed obtaining elution solutions of 890 mg Mo/L for the DOWEX resin and 1000 mg Mo/L for the Lewatit resin. Full article

Near-infrared spectroscopy (NIRS) is widely used in fruit and vegetable quality evaluations, usually after harvesting. In particular, the moisture content is a key parameter for determining product quality; processing phase, e.g., drying process; and economical value. NIRS methods are well-established for laboratory practices where the specimens are properly prepared and measurement conditions are well controlled. On the other hand, it is known that in-field NIRS measurements present several difficulties, as many influencing variables, such as mechanical vibrations, electrical and optical disturbances, and dust or dirt in general, can affect the spectral measurement. In this paper, we propose the design and present the prototype of a NIRS-based measuring system for the rapid determination of the moisture content of bales. The new system uses of a halogen lamp illumination unit to recover water absorption spectral data in the range of 900–1700 nm. The compact stainless steel body makes the instrument portable and easy to transport for rapid in-field MC measurements. The prototype system was characterized and its performance extensively evaluated in a laboratory environment. Finally, a preliminary test was carried out, where the moisture contents of 12 freshly harvested crops samples were measured using the partial least squares (PLSs) regression method. The obtained results show that our prototype system can estimate the alfalfa moisture content information with a coefficient of determination $R^2$ of 0.985 and a root mean square relative error of estimation of 7.1%. Full article

In Chile, the hydrometallurgical plants are operating below their capacity due to a depletion of copper oxide ores. To obtain suitable pregnant leach solutions (PLSs) for hydrometallurgical plants, leaching solutions combining iodine-based oxidants and hydrogen peroxide in a chloride–acid medium, at room temperature and pressure were studied. Factorial experiments were conducted to evaluate the effects of the different leaching solution reagents (KI, NaIO₃, NaCl, H₂O₂, and H₂SO₄). The results showed that the most influential variable is the H₂O₂ concentration; increasing the PLS concentration from 3 g/L to 15 g/L increased the copper extraction percentage by ~25%. In decreasing order of importance, the factorial experimental results showed that the H₂O₂, H₂SO₄, NaCl, NaIO₃, and KI concentrations affect the copper extraction percentage. The highest copper extraction percentage (i.e., 60.6%) was obtained using a leaching solution containing the highest reagent concentrations. At these conditions, the copper concentration in the PLS was 16.9 g/L. An economic evaluation of the laboratory-scale leaching experiments showed an increase in the unit cost (USD/t Cu) for experiments involving leaching solutions without H₂O₂ because of poor copper concentration in the PLS. As the concentrations of the reagents NaIO₃ and KI, increase, the unit cost increases, because the reagents are relatively expensive and have a limited effect on the copper extraction percentage. Full article

The General Allotment Act of 1887, also known as the Dawes Act, established the legal basis for the United States government to break up remaining tribally-owned reservation lands in the U.S. by allotting individual parcels to tribal members and selling the remaining “surplus.” This research explores the processes involved in mapping these historical allotments and describes a method to automatically generate spatial data of allotments. A custom geographic information systems (GIS) tool was created that takes tabular based allotment land descriptions and digital Public Land Survey (PLSS) databases to automatically generate spatial and attribute data of those land parcels. The Standing Rock Sioux Tribe of North and South Dakota was used as the initial study area to test the mapping technique, which resulted in successfully auto-mapping over 99.1% of allotted lands on the reservation, including the smallest aliquot parcels. This GIS technique can be used to map any tribal lands or reservation with allotment data available, and currently it can be used to map over 120 individual reservations using publicly available data from the Bureau of Land Management (BLM). Full article

Cochrane produces independent research to improve healthcare decisions. It translates its research summaries into different languages to enable wider access, relying largely on volunteers. Machine translation (MT) could facilitate efficiency in Cochrane’s low-resource environment. We compared three off-the-shelf machine translation engines (MTEs)—DeepL, Google Translate and Microsoft Translator—for Russian translations of Cochrane plain language summaries (PLSs) by assessing the quantitative human post-editing effort within an established translation workflow and quality assurance process. 30 PLSs each were pre-translated with one of the three MTEs. Ten volunteer translators post-edited nine randomly assigned PLSs each—three per MTE—in their usual translation system, Memsource. Two editors performed a second editing step. Memsource’s Machine Translation Quality Estimation (MTQE) feature provided an artificial intelligence (AI)-powered estimate of how much editing would be required for each PLS, and the analysis feature calculated the amount of human editing after each editing step. Google Translate performed the best with highest average quality estimates for its initial MT output, and the lowest amount of human post-editing. DeepL performed slightly worse, and Microsoft Translator worst. Future developments in MT research and the associated industry may change our results. Full article

The dopamine D3 receptor is an important CNS target for the treatment of a variety of neurological diseases. Selective dopamine D3 receptor antagonists modulate the improvement of psychostimulant addiction and relapse. In this study, five and six featured pharmacophore models of D3R antagonists were generated and evaluated with the post-hoc score combining two survival scores of active and inactive. Among the Top 10 models, APRRR215 and AHPRRR104 were chosen based on the coefficient of determination (APRRR215: R²_training = 0.80; AHPRRR104: R²_training = 0.82) and predictability (APRRR215: Q²_test = 0.73, R²_predictive = 0.82; AHPRRR104: Q²_test = 0.86, R²_predictive = 0.74) of their 3D-quantitative structure–activity relationship models. Pharmacophore-based virtual screening of a large compound library from eMolecules (>3 million compounds) using two optimal models expedited the search process by a 100-fold speed increase compared to the docking-based screening (HTVS scoring function in Glide) and identified a series of hit compounds having promising novel scaffolds. After the screening, docking scores, as an adjuvant predictor, were added to two fitness scores (from the pharmacophore models) and predicted Ki (from PLSs of the QSAR models) to improve accuracy. Final selection of the most promising hit compounds were also evaluated for CNS-like properties as well as expected D3R antagonism. Full article

Red seaweeds synthesize a great variety of sulfated galactans. Sulfated polysaccharides (PLSs) from seaweed are comprised of substances with pharmaceutical and biomedical potential. The aim of the present study was to evaluate the protective effect of the PLS fraction extracted from the seaweed Gracilaria birdiae in rats with naproxen-induced gastrointestinal damage. Male Wistar rats were pretreated with 0.5% carboxymethylcellulose (control group—vehicle) or PLS (10, 30, and 90 mg/kg, p.o.) twice daily (at 09:00 and 21:00) for 2 days. After 1 h, naproxen (80 mg/kg, p.o.) was administered. The rats were killed on day two, 4 h after naproxen treatment. The stomachs were promptly excised, opened along the greater curvature, and measured using digital calipers. Furthermore, the guts of the animals were removed, and a 5-cm portion of the small intestine (jejunum and ileum) was used for the evaluation of macroscopic scores. Samples of the stomach and the small intestine were used for histological evaluation, morphometric analysis and in assays for glutathione (GSH) levels, malonyldialdehyde (MDA) concentration, and myeloperoxidase (MPO) activity. PLS treatment reduced the macroscopic and microscopic naproxen-induced gastrointestinal damage in a dose-dependent manner. Our results suggest that the PLS fraction has a protective effect against gastrointestinal damage through mechanisms that involve the inhibition of inflammatory cell infiltration and lipid peroxidation. Full article