Search Results (8,643)

The inclusion of the Internet of Things (IoT) in indoor agricultural systems has become a fundamental tool for improving cultivation systems by providing key information for decision-making in pursuit of better performance. This article presents the design and implementation of an IoT-based agricultural system installed in a plant growth chamber for hydroponic cultivation under controlled conditions. The growth chamber is equipped with sensors for air temperature, relative humidity (RH), carbon dioxide (CO₂) and photosynthetically active photon flux, as well as control mechanisms such as humidifiers, full-spectrum Light Emitting Diode (LED) lamps, mini split air conditioner, pumps, a Wi-Fi surveillance camera, remote monitoring via a web application and three Nutrient Film Technique (NFT) hydroponic systems with a capacity of ten plants each. An ATmega2560 microcontroller manages the smart system using the MODBUS RS-485 communication protocol. To validate the proper functionality of the proposed system, a case study was conducted using lettuce crops, in which the impact of different nutrient solution concentrations (50%, 75% and 100%) on the phenotypic development and nutritional content of the plants was evaluated. The results obtained from the cultivation experiment, analyzed through analysis of variance (ANOVA), show that the treatment with 75% nutrient concentration provides an appropriate balance between resource use and nutritional quality, without affecting the chlorophyll content. This system represents a scalable and replicable alternative for protected agriculture. Full article

Developing a rapidly gel-forming, in situ sprayable hydrogel with wound dressing functionality is essential for enhancing the wound healing process. In this study, a novel sprayable hydrogel-based wound dressing was developed by combining thermo- and pH- responsive polymers including Pluronic F127 (PF127) and N-succinyl chitosan (NSC). NSC was prepared by modifying chitosan with succinic anhydride, as confirmed by Fourier-transform infrared spectroscopy and nuclear magnetic resonance spectroscopy. The NSC synthesized using a succinic anhydride-to-chitosan molar ratio of 5:1 exhibited the highest degree of substitution, resulting in a water-soluble polymer effective over a broad pH range. The formulation process of the PF127:NSC sprayable hydrogel was optimized and evaluated based on its sol–gel phase transition behavior, clarity, gelation time, liquid and moisture management, stability, and cytotoxicity. These properties can be suitably tailored by adjusting the concentrations of PF127 and NSC. Moreover, the antioxidant capacity of the hydrogels was enhanced by incorporating Azadirachta indica (neem) extract, a bioactive compound, into the optimized sprayable hydrogel. Both neem release and antioxidant activity increased in a dose-dependent manner. Overall, the developed sprayable hydrogel exhibited favorable sprayability, appropriate gelation properties, controlled drug release, and antioxidant activity, underscoring its promising translational potential as a wound dressing. Full article

Focal chondral defects of the knee are a common cause of pain and functional limitation in active individuals and may predispose to early degenerative joint changes. Given the limited regenerative capacity of hyaline cartilage, biologically based surgical strategies have emerged to promote tissue repair and restore joint function. This narrative review critically examines current treatment approaches that rely on autologous cell sources and scaffold-supported regeneration. Particular emphasis is placed on techniques that stimulate endogenous repair or support chondrocyte-based tissue restoration through the use of autologous biomaterial constructs. The influence of lesion morphology, joint biomechanics, and patient-specific variables on treatment selection is discussed in detail, focusing on the differences between tibiofemoral and patellofemoral involvement. Biologically driven approaches have shown promising mid- to long-term outcomes in selected patients, and are increasingly favoured over traditional methods in specific clinical scenarios. However, the literature remains limited by heterogeneity in study design, follow-up duration, and outcome measures. This review aims to provide an evidence-based, morphology-informed framework to support the clinical decision-making process in the management of knee cartilage defects. Full article

White-tailed deer (Odocoileus virginianus) are a cervid species native to the Americas with ecological, social, and economic significance. Managers must consider several factors when working to maintain the health and sustainability of these wild herds, including reproduction, particularly pregnancy and recruitment rates. White-tailed deer have a variable reproductive capacity, with age, health, and habitat influencing this variability. However, it is unknown whether chronic wasting disease (CWD) impacts reproduction and, more specifically, if CWD infection alters a female deer’s probability of pregnancy. Our study addressed this question using data from 9783 female deer culled in northern Illinois between 2003 and 2023 as part of the Illinois Department of Natural Resources’ ongoing CWD management program. Multilevel Bayesian logistic regression was employed to quantify the relationship between pregnancy probability and covariates like maternal age, deer population density, and date of culling. Maternal infection with CWD was found to have no significant effect on pregnancy probability, raising concerns that the equal ability of infected and non-infected females to reproduce could make breeding, which inherently involves close physical contact, an important source of disease transmission between males and females and females and their fawns. The results also identified that female fawns (<1 year old) are sensitive to county-level deer land cover utility (LCU) and deer population density, and that there was no significant difference in how yearlings (1–2 years old) and adult (2+ years old) responded to these variables. Full article

The present study explores the application of RNNs for the prediction and propagation of flood waves along a section of the Bârsa River, Romania, as a fast alternative to classical hydraulic models, aiming to identify new ways to alert the population. Five neural architectures were analyzed as follows: S-RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU. The input data for the neural networks were derived from 2D hydraulic simulations conducted using HEC-RAS software, which provided the necessary training data for the models. It should be mentioned that the input data for the hydraulic model are synthetic hydrographs, derived from the statistical processing of recorded floods. Performance evaluation was based on standard metrics such as NSE, $R^2$ MSE, and RMSE. The results indicate that all studied networks performed well, with NSE and $R^2$ values close to 1, thus validating their capacity to reproduce complex hydrological dynamics. Overall, all models yielded satisfactory results, making them useful tools particularly the GRU and Bi-GRU architectures, which showed the most balanced behavior, delivering low errors and high stability in predicting peak discharge, water level, and flood wave volume. The GRU and Bi-GRU networks yielded the best performance, with RMSE values below 1.45, MAE under 0.3, and volume errors typically under 3%. On the other hand, LSTM architecture exhibited the most significant instability and errors, especially in estimating the flood wave volume, often having errors exceeding 9% in some sections. The study concludes by identifying several limitations, including the heavy reliance on synthetic data and its local applicability, while also proposing solutions for future analyses, such as the integration of real-world data and the expansion of the methodology to diverse river basins thus providing greater significance to RNN models. The final conclusions highlight that RNNs are powerful tools in flood risk management, contributing to the development of fast and efficient early warning systems for extreme hydrological and meteorological events. Full article

Background: Disruptions in freight transportation—such as service delays, infrastructure failures, and labor strikes—pose significant challenges to the reliability and efficiency of intermodal networks. To address these challenges, this study introduces Adaptive Intermodal Transportation (AIT), a resilient and flexible planning framework that enhances Synchromodal Freight Transport (SFT) by integrating real-time disruption management. Methods: Building on recent advances, we propose two novel strategies: (1) Reassign with Delay Buffer, which enables dynamic rerouting of shipments within a user-defined delay tolerance, and (2) (De)Consolidation, which allows splitting or merging of shipments across services depending on available capacity. These strategies are incorporated into a re-planning module that complements a baseline optimization model and a continuous disruption-monitoring system. Numerical experiments conducted on a Great Lakes-based case study evaluate the performance of the proposed strategies against a benchmark approach. Results: Results show that under moderate and high-disruption conditions, the proposed strategies reduce delay and disruption-incurred costs while increasing the percentage of matched shipments. The Reassign with Delay Buffer strategy offers controlled flexibility, while (De)Consolidation improves resource utilization in constrained environments. Conclusions: Overall, the AIT framework demonstrates strong potential for improving operational resilience in intermodal freight systems by enabling adaptive, disruption-aware planning decisions. Full article

Background/Objectives: Hemodialysis catheter-related bloodstream infection (HD-CRBSIs) is a main cause of morbidity in hemodialysis. New preventive strategies have emerged, such as using lock solutions with antiseptic or antibiotic capacity. In this study, the antimicrobial effect was analyzed in vitro and with a catheter model of lock solutions of gentamicin (LSG), gentamicin/heparin (LSG/H), and gentamicin/citrate (LSG/C) in clinical and ATCC strains of Pseudomonas aeruginosa and Staphylococcus aureus. Methods: The formation, minimum inhibitory concentration, and minimum inhibitory concentration of the biofilm and minimum biofilm eradication concentration of the lock solutions were determined. Additionally, colony-forming unit assays were performed to evaluate the antimicrobial efficacy of the lock solutions in a hemodialysis catheter inoculation model. Results: The minimum inhibitory concentration (MIC) of planktonic cells of both P. aeruginosa and S. aureus for LSG/H and LSG/C was 4 µg/mL. In the minimum biofilm inhibitory concentration (MBIC) tests, the LSG/H was less effective than LSG/C, requiring higher concentrations for inhibition, contrary to the minimum biofilm eradication concentration (MBEC), where LSG/H was more effective. All lock solutions eradicated P. aeruginosa biofilms in the HD catheter model under standard conditions. Nevertheless, under modified conditions, the lock solutions were not as effective versus ATCC and clinical strains of S. aureus. Conclusions: Our analysis shows that the lock solutions studied managed to eradicate intraluminal mature P. aeruginosa in non-tunneled HD catheters under standard conditions. Biofilm inhibition and eradication were observed at low gentamicin concentrations, which could optimize the gentamicin concentration in lock solutions used in HD catheters. Full article

Despite the significant advancements in treatment and prevention, the fight against cancer is ongoing worldwide. This study evaluated the pharmacological properties and anticancer activity of Afzelia quanzensis bark, traditionally used by the indigenous communities of KwaZulu Natal and Eastern Cape Provinces of South Africa to treat cancer and related illnesses. Phytochemical screening, high-performance liquid chromatography–diode array detection (HPLC-DAD), and Fourier-transform infrared spectroscopy (FTIR) analyses were carried out using established protocols. The antioxidant activity was assessed via the 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity and nitric oxide radicals. The anticancer activity was evaluated using the MTT assay (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide). Phytochemical analysis revealed the presence of saponins, flavonoids, terpenoids, alkaloids, steroids, cardiac glycosides, and phlobatannins. The HPLC-DAD analysis detected seven distinctive peaks in the aqueous extract and three distinctive peaks in the methanolic extract. The FTIR spectra of the aqueous extract displayed characteristic peaks corresponding to O-H, C=O, C=C, and =C–H functional groups. Among the tested extracts, the methanol extract exhibited the strongest antioxidant activity, followed by the ethanolic extract, in both DPPH and nitric oxide. The methanol extract showed a higher cell proliferation inhibition against the DU-145 cancer cell line with the percentage of inhibition of 37.8%, followed by the aqueous extract with 36.3%. In contrast, limited activity was observed against PC-3, SK-UT-1, and AGS cell lines. The results demonstrated notable dose-dependent antioxidant and antiproliferative activities supporting the ethnomedicinal use of Afzelia quanzensis bark in cancer management. These findings warrant further investigation into its bioactive constituents and mechanisms of action. Full article

Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article

Eutrophication has become the primary water quality issue for most of the freshwater and coastal marine ecosystems in the world. Caused by excessive nitrogen (N) and phosphorus (P) inputs, it has a significant impact on aquatic ecosystems, resulting in algal blooms, oxygen depletion, and biodiversity loss. Zeolites have been identified as effective adsorbents for removal of these pollutants, improving water quality and ecosystem health. Kinetic and isotherm adsorption experiments were conducted to examine the adsorption efficiency of four zeolites of various origins (Greek, Slovakian, Turkish, and Bulgarian) and a specific modification (ZeoPhos) to determine the most effective material for N and P removal. The aim of the study is to discover the best zeolite for chemical adsorption in eutrophic waters by comparing their adsorption capacities and pollutant removal efficiencies along with SEM, TEM, and X-RD spectrographs. Slovakian ZeoPhos has been identified as the best-performing material for long-term and efficient water treatment systems for eutrophication management. Full article

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting, version 2.1.4) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Background: Fixed-dose combinations have advanced in many therapeutic areas, including otorhinolaryngology, where hearing disorders are increasingly prevalent. Objectives: The present study focuses on developing and evaluating a new capsule combining nicergoline (NIC), piracetam (PIR), and hawthorn extract (HE) for the management of sensorineural hearing loss. Methods: The first phase methodology comprised preformulation studies (DSC, FTIR, and PXRD) to assess compatibility among active substances and excipients. Subsequently, four formulations were prepared and tested for flowability, dissolution behavior in acidic and neutral media, and stability under oxidative, thermal, and photolytic stress. Quantification of the active substances and flavonoids was performed using validated spectrophotometric and HPLC-UV methods. Results: Among the tested variants, the F1 formulation (4.5 mg NIC, 200 mg PIR, 50 mg HE, 2.5 mg magnesium stearate, 2.5 mg sodium starch glycolate, and 240.5 mg monohydrate lactose per capsule) displayed optimal technological properties, superior dissolution in acidic media, and was further selected for evaluation. The antioxidant activity of the formulation was confirmed through the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, Trolox Equivalent Antioxidant Capacity (TEAC), and iron chelation tests, and was primarily attributed to the flavonoid content of the HE. Acute toxicity tests in mice and rats indicated a high safety margin (LD₅₀ > 2500 mg/kg), while ototoxicity assessments showed no adverse effects on auditory function. Conclusions: The developed formulation displayed good stability, safety, and therapeutic potential, while the applied workflow could represent a model for the development of future fixed-dose combinations. Full article

The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target. Full article

Urban flooding has significantly impacted the livelihoods of households and communities worldwide. It highlights the urgency of focusing on both flood preparedness and adaptation strategies to understand the community’s perception and adaptive capacity. This study investigates the levels of risk perception, flood preparedness, and adaptive capacity, while also exploring the inter-relationships among these factors within the context of urban flooding in Malaysia. A quantitative approach was employed, involving a structured questionnaire administered to residents in flood-prone urban areas across Greater Kuala Lumpur, Malaysia. A total of 212 responses were analysed using descriptive statistics, categorical index classification, and Spearman correlation analysis. The findings indicate that residents generally reported high levels of risk perception and preparedness, although adaptive capacity exhibited greater variability, with a mean score of 3.97 (SD = 0.64). Positive associations were found among risk perception, flood preparedness, and adaptive capacity. This study contributes to the existing knowledge by providing evidence on community resilience and highlighting key factors that can guide flood management policies and encourage adaptive planning at the community level. Full article