Maps of forest stand condition—the current phase of the forest-forming process—will be useful for foresters in their forest management in addition to the forest planning and cartographic materials. The mapping methodology was applied in the test area of the Bolshemurtinsky forest district of the Krasnoyarsk region, which is typical for the southern taiga forests of East Siberia. Source data for mapping was obtained on the basis of descriptions of the forest subcompartments on the GIS attribute table of the forest district. Forest stand confinement to the terrain relief indicators was identified on the basis of the SRTM 55-01 digital terrain model data. Spatial analysis has been performed using the ArcGIS Spatial Analyst module. Mapping capability has been shown not only for the year of forest inventory but also for the earlier period of time. To determine the predominant species and the age of the 100-year-old forest stand, a scheme was proposed in which the conceivable options are typified depending on the succession trend, the forest stand age prior to disturbance, and the period of reforestation. Map fragments of the test area as of 2006—the year of forest inventory—and as of 1906—the year of the intensive colonization beginning in southern Siberia—are demonstrated. Maps of forest condition in the test area represent successions that are typical in the southern taiga forests of Siberia: post-harvest, pyrogenic, and biogenic. The methodology of forest condition mapping is universal.

Over the course of many years, the Mekong Delta region has experienced relatively low and inconsistent levels of business attraction and low quality of the enterprise environment compared to other regions in Vietnam. To delve into whether this discrepancy reflects a negative perception of the business environment in the area, this study employs a dataset comprising the aggregate Provincial Competitiveness Index (PCI) and nine of its component scores, alongside other significant control variables, to analyze business attraction trends spanning from 2010 to 2020. It based on the modeling analysis for the panel data that includes Pool-OLS, FEM and REM models. The findings indicate that PCI serves as an important indicator influencing the quality of the business environment and plays a role in determining the location preferences of businesses. It is observed that public investment has exerted an impact on enticing new businesses to the region throughout this period. These research outcomes carry several policy implications, suggesting that public policy interventions can positively shape the business environment, consequently bolstering the appeal of business investments in the region.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.

This research presents an in-depth examination of the emotional effects of synchronous hybrid education on undergraduate university students at a pioneering private institution in educational innovation. The study had encompassed all courses that were delivered in a synchronous hybrid format, covering 16 courses and involving 241 students. Each student had been observed and recorded on two separate class sessions, with each recording lasting approximately 30 min. This comprehensive data collection had resulted in 409 recordings, each approximately 30 min in duration, translating to nearly an hour of observation per student across the classes, totaling close to 205 h of recordings. These recordings were subsequently processed using neuroscience software tools for advanced statistical analysis, effectively serving as a comprehensive survey of courses within this modality. The primary focus of the research was on the emotions experienced during both face-to-face and online classes and their subsequent influence on student behavior and well-being. The findings reveal higher emotional time ratios for positive emotions such as joy and surprise in face-to-face students. Notably, both groups exhibited comparable ratios for negative emotions like anger and sadness. The research underscores the emotional advantages of face-to-face interactions, which elicit stronger emotions, in contrast to online students who often feel detached and isolated.

Protein- and peptide-based medications are recognized for their effectiveness and lower toxicity compared to chemical-based drugs, making them promising therapeutic agents. However, their application has been limited by numerous delivery challenges. Polymeric nanostructures have emerged as effective tools for protein delivery due to their versatility and customizability. Polymers’ inherent adaptability makes them ideal for meeting the specific demands of protein-delivery systems. Various strategies have been employed, such as enzyme inhibitors, absorption enhancers, mucoadhesive polymers, and chemical modifications of proteins or peptides. This study explores the hurdles associated with protein and peptide transport, the use of polymeric nanocarriers (both natural and synthetic) to overcome these challenges, and the techniques for fabricating and characterizing nanoparticles.

Fungi can be used to remove or degrade polluting compounds through a mycoremediation process. Sometimes even more efficiently than prokaryotes, they can therefore be used to combat pollution from non-biodegradable polymers. Cellulose acetate is a commonly used material in the manufacture of cigarette butts, so when discarded, it generates pollution. The fungus Pleurotus ostreatus has the ability to degrade cellulose acetate through the enzymes it secretes. The enzyme hydrolyzes the acetyl group of cellulose acetate, while cellulolytic enzymes degrade the cellulose backbone into sugars, polysaccharides, or cellobiose. In addition to cellulose acetate, this fungus is capable of degrading other conventionally non-biodegradable polymers, so it has the potential to be used to reduce pollution. Large-scale cultivation of the fungus has proven to be more economically viable than conventional methods for treating non-biodegradable polymers, which is an additional advantage.