Basic Processes Research Articles

Influence of the Spectral Composition of Illuminating Light Sources on Biometric and Phytochemical Characteristics of Ocimum basilicum L.

Precise adaptation of the greenhouse lighting spectrum to basic photophysiological processes can effectively and directionally stimulate plant growth and development. The optimal spectrum depends on the plant species and the stage of development and could be assessed empirically. The aim of this study is to determine the LED illumination spectrum that provides a significant improvement in the growth rate and accumulation of biologically active compounds for basil plants (Ocimum basilicum L.) under hydroponic cultivation compared to more traditional lighting sources. The following light sources with various emission spectra were used: an LED lamp within a spectral range of 400–800 nm (B:G:R 15%:5%:80%); a high-pressure sodium lamp (HPS) (B:G:R 5%:45%:50%); a compact fluorescent lamp (B:G:R 20%:40%:40%); a grow LED strip (B:G:R 15%:40%:45%); a white LED lamp (B:G:R 30%:45%:25%); a customized LED lighting setup in color ratios 100%B, 75%B + 25%R, 50%B + 50%R, 25%B + 75%R, 100%R, and natural lighting. A photosynthetic photon flux density (PPFD) of 150 μmol∙m−2∙s−1 was provided with all the sources. It was demonstrated reliably that employing the LED strip as an illumination device gives a 112% increase in basil plant yield compared to the HPS; the transpiration coefficient for the LED strip is six times lower than for the HPS. The content of flavonoids in the basil aerial parts on the 30th, 50th, and 70th days of development is 3.2 times higher than for the HPS; the metabolite composition is also more uniform for LED strip lighting.

Potential Impact of Using ChatGPT-3.5 in the Theoretical and Practical Multi-Level Approach to Open-Source Remote Sensing Archaeology, Preliminary Considerations

This study aimed to evaluate the impact of using an AI model, specifically ChatGPT-3.5, in remote sensing (RS) applied to archaeological research. It assessed the model’s abilities in several aspects, in accordance with a multi-level analysis of its usefulness: providing answers to both general and specific questions related to archaeological research; identifying and referencing the sources of information it uses; recommending appropriate tools based on the user’s desired outcome; assisting users in performing basic functions and processes in RS for archaeology (RSA); assisting users in carrying out complex processes for advanced RSA; and integrating with the tools and libraries commonly used in RSA. ChatGPT-3.5 was selected due to its availability as a free resource. The research also aimed to analyse the user’s prior skills, competencies, and language proficiency required to effectively utilise the model for achieving their research goals. Additionally, the study involved generating JavaScript code for interacting with the free Google Earth Engine tool as part of its research objectives. Use of these free tools, it was possible to demonstrate the impact that ChatGPT-3.5 can have when embedded in an archaeological RS flowchart on different levels. In particular, it was shown to be useful both for the theoretical part and for the generation of simple and complex processes and elaborations.

Event Segmentation in the Audio Description of Films

To make the content of films available to a visually impaired audience, a sighted translator can provide audio description (AD), a verbal description of visual events. To achieve this goal, the audio describer needs to select what to describe, when to describe it, and how to describe it, as well as to express the information aurally. The efficacy of this communication is critically dependent upon basic cognitive processes of how the sighted audio describer perceives and segments the film’s unfolding chain of events and in what way the visually impaired end users conceive the structure, content, and segmentation of such events in relation to the produced AD. There is, however, virtually no research on this interplay in relation to AD. In this study, we scrutinize live AD of a film from two trained audio describers and examine how events are structured, segmented and construed in their AD. Results demonstrate that the event segmentation experienced from the film is indeed a fundamental part of how AD is structured and construed. It was found that AD at event boundaries was highly sensitive to different spatiotemporal circumstances and this relationship depends on semantic resources for expressing AD. Lay summary In our everyday lives, we naturally organize and remember our experiences as meaningful sequences of events. This happens the same way when we watch movies – we see them as a series of events happening in different places, times, and with changing character dynamics. Now, think about making this cinematic experience accessible to people with visual impairments. This is where a sighted translator, called an audio describer, becomes really important. To make movies understandable for a visually impaired audience, a sighted audio describer describes crucial visual events in the film. Their goal is to create a "narrative equivalence" between the original film and the audio-described version. Thus, the main challenges for audio describers are deciding what information to describe, when to do it, and how to articulate it effectively. In the current case study, we looked at two audio descriptions for the Swedish film "Skumtimmen" (English title: "Echoes from the Dead"). These descriptions were created by trained audio describers. We wanted to see how these narrators organized, broke down, and verbalized the course of events in their audio descriptions. To achieve this, we focused on two important parts of storytelling – changes in time and location – and paid special attention to how often and in what way audio describers verbalized these event boundaries. What we found is that both describers indeed verbalized most event boundaries. This shows that these boundaries are a big part of how audio descriptions are put together. We also learned that the descriptions at these event boundaries were very sensitive to when and where key events unfolded in the story. The way these boundaries were described depended on fundamental cognitive and linguistic resources to talk about space and time. Our findings help us understand how breaking down events in a film is a crucial part of shaping audio descriptions. Plus, our research highlights how important it is to study how events are broken down and verbalized, giving useful insights to improve audio descriptions and train future audio describers. The ultimate goal is to make the experience better for visually impaired viewers, contributing to a more inclusive and enjoyable audiovisual world.

Theoretical Study on the Mechanism of the Hydrogenation of Azo (N═N) Bonds to Amines Catalyzed by Manganese.

The mechanism of the transition metal manganese complex Mn(PhPNN)(CO)2Br (CA-4) that catalyzed the hydrogenation of the azo (N═N) bond to amines has been investigated using the PBE0 function. The results show that the whole reaction involves three basic processes: (1) the addition of H2 to CA gives IN2, which can hydrogenate the azo (N═N) bond at the later stage; (2) hydrogenation of azobenzene by IN2, which gives 1,2-diphenylhydrazine (PhNHNHPh); and (3) hydrogenation of 1,2-diphenylhydrazine by IN2, which affords aniline (PhNH2). The results suggest that the hydrogenation of CA and hydrogenation of azobenzene by IN2 to afford PhNHNHPh are easy to occur due to the low barriers, and the overall rate-determining step is the formation of IN11 and PhNH2 by breaking the N-N bond in the stage of hydrogenation of 1,2-diphenylhydrazine by IN2, with an energy barrier of 39.1 kcal/mol. The computed results are in good agreement with the experimental results. The mechanism of the azobenzene reaction catalyzed by manganese was analyzed by charge and orbital analysis in detail. The theoretical results provide a deeper understanding of the mechanism and fully explain the experimental facts.

Software Tools for Digitalizing the Basic Processes and Services in the Higher Schools of the Republic of Bulgaria

The article examines the issues of the application of modern software tools and technologies for digitization of the main processes and services in the higher education institutions of the Republic of Bulgaria. The topic is complex and debatable, both from the point of view of the applied methods and technologies, and in terms of reporting the effectiveness of these services. The acceleration of the digitization processes necessitates and requires the taking of specific measures to ensure the level of cyber security of the organization. One of the measures in this direction is for the developed web applications to be based on the latest technologies, as well as to carry out specific vulnerability tests before launching the services. As a result of the analyzed theoretical statements, as well as on the basis of the specific software implementation, some of the positive moments in expanding students’ access to modern digital services have been outlined.

Австрійська школа як напрям дослідження в економіці – правда та міфи

The article is concerned with studying of the Austrian school of economics, comparison of approaches of different representatives of the school to the analysis of basic economic phenomena and processes, determining differences between scientists' views and their research methods. The importance of the ideas of Austrian economists for modern economic theory in the context of interaction between the state and the market is emphasized. The purpose of the article is to study the theoretical concepts of the Austrian school in the context of the search for new theoretical approaches to solve modern economic problems. The methods of comparative studies and the problem-personified approach to the study of the history of economic ideas are used. A comparison of the main ideas of the representatives of the Austrian school is carried out in historical and logical sequence. The results of the study give a description of the main theses of Carl Menger, Friedrich von Hayek, Ludwig von Mises and their scientific followers. The nature of prices and subjective valuation as well as price stabilization policy are revealed in the paper according to Austrian tradition. Attention is paid to the subjective approach, namely subjective information and costs. It is emphasized that entrepreneurship is a major force and Austrian believe that an entrepreneur's profit comes from the assumption of simple identification of risk with error. Today, the Austrian approach is of particular relevance, according to which it is impossible to scientifically know what will happen tomorrow, since it depends mainly on knowledge and information that has not yet been created in the entrepreneurial process. The results of research are important for understanding the need to revival of interest in this strand of economics, which for decades had been treated marginally in textbooks of economic thought. Austrian theoretical approaches should be used to analyze the processes of modern economic development and solve complex multifaceted economic problems.

Perspectives of the use of macro- and micro elements in poultry feeding

Poultry breeding is one of the fastest growing segments of the agricultural sector in many countries of the world, including Ukraine. Currently, Ukraine ranks 8th in terms of production of eggs and broilers in the world. A sufficient amount of minerals and vitamins in the diet is the key to good production of poultry products. Feeding feed deficient in vitamins and minerals can cause numerous health problems in chickens, including death in some cases. The poultry farmer must monitor the health of the chickens every day. Therefore, the development of the practice of feeding a balanced diet with the necessary amount of minerals to prevent premature disease of poultry caused by nutrient deficiency is relevant today. In poultry farming, mineral substances are used as one of the ways to increase metabolism in birds. Mineral substances are part of the molecules of complex organic structures that participate in metabolic processes, including digestion, thereby improving the assimilation and use of feed nutrients. The lack of microelements in feed causes diseases of the joints of the limbs of poultry, a decrease in its productivity, and a deterioration in the quality of eggshells. Zinc, copper, manganese, iron, iodine, selenium, cobalt are important trace elements in the feed of broiler chickens. Therefore, the effectiveness of the use of trace elements is an actual topic of modern poultry feeding. Mineral substances are useful in poultry farming, as they provide prevention of various diseases. Organic minerals in small amounts can be added to the diet of birds, because minerals are better absorbed by the bird than mineral salts. Demand for alternative natural feed additives with mineral content is important, especially as an alternative to antibiotics. They can have a preventive effect against various diseases, participate in numerous physical and biochemical mechanisms and basic processes of growth and development. However, further large-scale research is needed for the further use of macro- and micronutrients in commercial poultry farming.

Atomic layer deposition of magnetic thin films: Basic processes, engineering efforts, and road forward

Atomic layer deposition (ALD) is known as a key enabler of the continuous advances in device engineering for microelectronics. For instance, the state-of-the-art transistor technology depends entirely on ALD-grown high-κ materials. Another application branch where ALD could potentially play a similar important role in future is the magnetic thin film devices. Spin-based devices based on high-quality magnetic thin films are anticipated to provide high-efficiency operations with low power consumption. The strict quality demands the magnetic thin films must fulfill in the next-generation applications form the strong bases for the efforts to implement ALD in this application area. In this first comprehensive review on the topic, our aim is to provide an insightful account of the ALD processes so far developed for magnetic materials and to highlight the application-relevant magnetic properties of the thus fabricated thin films. Moreover, we discuss the various innovative engineering efforts made toward different multi-layered and nanostructured composite materials and complex architectures uniquely enabled by the sophisticated self-terminated film-growth mechanism of ALD. The review is finished with a brief outlook toward the future prospects and challenges in the field.

Facial emotional recognition in patients with dementia, part of the ReDLat study

AbstractBackgroundThe social domain impairments associated with Alzheimer’s disease and frontotemporal lobar degeneration have been linked to basic neurocognitive processes such as visual emotion identification through facial expressions.MethodIn this study, we explore the recognition of seven basic emotions presented in the Mini‐SEA test in a sample of 84 Colombian individuals with dementia (32 with Alzheimer’s Disease and 30 with Frontotemporal Dementia) and compare it with 22 healthy participants from the RedLat study.ResultThe results of the behavioral performance show a lower performance in people with dementia compared to healthy subjects, however, no significant differences are reported between the patient group (AD and FTD).ConclusionThese findings may be associated with the clinical heterogeneity of the evaluated subjects.

ICT-based fluorescent probes for intracellular pH and biological species detection.

Fluorescent probes, typically based on the intramolecular charge transfer (ICT) mechanism, have received considerable research attention in cell detection due to their non-invasiveness, fast response, easy regulation, high sensitivity, and low damage tolerance for in vivo bio-samples. Generally, intracellular pH and biological species such as various gases, metal ions, and anions constitute the foundation of cells and participate in the basic physiological processes, whose abnormal level can lead to poisoning, cardiovascular disease, and cancer in living organisms. Therefore, monitoring of their quantity plays an essential role in understanding the status of organisms and preventing, diagnosing, and treating diseases. In the last decades, remarkable progress has been made in developing ICT probes for the detection of biological elements. In this review, we highlight the recent ICT probes focusing primarily on the detection of intracellular pH, various gases (H2S, CO, H2O2, and NO), metal ions (Cu2+, Hg2+, Pb2+, Zn2+, and Al3+), and anions (ClO-, CN-, SO3 2-, and F-). In addition, we discuss the issues and limitations of ICT-based fluorescent probes for in vivo detection and explore the clinical translational potential and challenges of these materials, providing valuable guidance and insights for the design of fluorescent materials.

Peculiarities of Respiration and Photosynthesis Processes of Plants Belonging to the Genera Catalpa L. and Paulownia L. Used in Landscaping of the City of Vanadzor

Key words: global climate change, biodiversity, greening, introduced species The species composition of biodiversity is negatively affected by global climate change. As a result of global warming, changes in the physiological processes of many plant species occur. Due to global warming, it has been recorded that many introduced plant species are well adaptable in the wild and suitable for cultivation. Presumably plants, belonging to the genera Catalpa L. and Paulownia L. are such species of plants. Given the projections of global warming, the selection of plant species for urban greening should prioritize not only their fast growth rate and adaptability but also an examination of their fundamental physiological processes, specifically focusing on photosynthesis and respiration, within the specific urban environment. Based on the research conducted in the city of Vanadzor in June and August 2023, it has been determined that plants from the genera Catalpa L. and Paulownia L. are among the introduced species utilized for the city's landscaping. Previously, plant species belonging to these genera were not used in landscaping. In all likelihood, the climatic conditions of Vanadzor were unfavorable for growing these plants. Due to the increase in average annual temperature, plants belonging to these two genera were discovered in the green spaces of the city of Vanadzor. In the trees belonging to the genus Catalpa L. in the city of Vanadzor, there was a recorded increase in photosynthetic activity by 1.025 times at the end of the growing season. This is significant because the utilization of this plant type for city greening can help reduce CO2 concentration in the atmosphere. On the other hand, the examination of the basic physiological processes in trees belonging to the genus Paulownia L. indicates a high rate of respiration intensity, resulting in the release of CO2 gas into the atmosphere in these plant species. These findings emphasize the need for a scientific approach to landscaping, taking into account the projected climate changes in the city of Vanadzor.

Explaining First Language Acquisition in Terms of Basic Behavioral Processes: Introduction to the Special Section.

This special section of Perspectives on Behavior Science follows up on a virtual panel discussion on the role of operant and Pavlovian processes in children's language learning. We present four articles, including two contributed by panelists, that illustrate the explanatory power of operant conditioning processes in the study of language.

Award for Distinguished Scientific Contributions: Jennifer A. Richeson.

The APA Award for Distinguished Scientific Contributions honors psychologists who have made distinguished theoretical or empirical contributions to basic research in psychology. "For illuminating with theoretical boldness and methodological rigor how sociocultural group memberships such as race and socioeconomic status shape how people reason about, respond to, and experience inequality and injustice. Jennifer A. Richeson has shown how threat impacts misperceptions of inequality and reactions to increasing racial diversity. Her steadfast commitment to being inclusive in the world and research is illustrated in her research on the dynamics of interracial interactions from the perspectives of members of both culturally devalued and traditionally dominant groups. Together, these contributions have transformed our understanding of how prejudice and stereotypes shape basic processes of mind and behavior." (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

The ultimate objective of modern engineering applications in mining and material science is to develop good quality novel materials with desirable qualities. Machine Learning (ML) is used in the mining industry to provide solutions to complex problems of the mining industry and improve the efficiency of the overall system. ML methods are increasingly being used by materials scientists to uncover hidden trends in data and generate predictions. Furthermore, data centric techniques can provide useful insights into the basic processes that influence material behaviour while simultaneously reducing human labour in large data processing. The ability of persons to find new materials and infer complex relationships is important for the development of new materials. Large amounts of machine-readable data must be available to use statistical methodologies to speed materials research. In mining engineering, ML can be used for analyzing geographical data, assessing the risk of rock fall, predicting equipment failures and impact of mining activities on the environment etc. Material science data may be used in a variety of ways, including property prediction, the search for new materials and discovering synthesis methods. Selecting proper machine learning techniques to provide solutions is very important and that is discussed here. The purposes of this paper are to provide a comprehensive list of different ML techniques which are applied for the mining and material science domain.

Catalytic oxidation of carbon monoxide over copper oxide with interfering gases involved for industrial buildings: An experimental and theoretical study

With the intention of thoroughly eliminating carbon monoxide (CO) from indoor environment, it is one of the most promising methods of converting it into carbon dioxide (CO2) via catalytic oxidation processes. Therefore, in this study, catalytic oxidation mechanisms of CO over copper oxide (CuO) with interfering gases involved are investigated via theoretical calculations and experimental verification, which mainly include basic catalytic oxidation processes over CuO, effects of different interfering gases and analyses of thermodynamic features. According to results, catalytic oxidation of CO over CuO can be performed in three different routes and rate-determining transition-state (TS) energy barriers vary a lot from route to route at high temperature owing to different thermal stability. In the meantime, SO2, H2O and NO are theoretically proved to affect catalytic oxidation of CO via competitive adsorption, high oxidizability and hydrogen bonds, which are accompanied with different sensitivities to changes of temperature or temperature power exponents. In experiments, it is found that impurity of lattice planes and small specific surface areas in CuO are key to restricting catalytic oxidation performances. What is more, some extra chemical reactions in experiments (e.g. SO2 + O2 + CuO → CuSO4, NO + O2 + CO → CO2 + NO) caused by interfering gases colossally affect catalytic oxidation performances of CuO. The whole study provides crucial information for indoor air purification in industrial buildings by using cheap adsorbents/catalysts.

Predicting real-time within-vehicle air pollution exposure with mass-balance and machine learning approaches using on-road and air quality data

Modelling the air pollutant concentrations within-vehicles is an essential step to estimate our daily exposure to air pollution. This is a challenging issue however, since the processes that affect the exposures within-vehicles change with different driving patterns and ventilation settings. This study introduces an innovative approach that combines mass-balance principles and machine learning techniques, leveraging ambient air quality, on-road and within-vehicle measurements of particulate matter (PM10, PM2.5, PM1), nitrogen dioxide (NO2), nitrogen oxides (NOx), aerosol lung surface deposited area (LSDA) and ultrafine particles (UFP) under different ventilation settings to estimate air pollution exposure levels within vehicles. The first model (MB) includes basic physical and chemical processes and follows a mass-balance approach to estimate the within-vehicle concentrations. The second model (ML) applies data driven machine learning algorithms to a training set of observations to predict unseen within-vehicle concentrations. By using a number generator, the whole observational dataset was divided to 80:20 and 80% was used to build and train the ML model, while 20% was used for validation. Both models demonstrated good predictions of observations apart from an underestimation in UFP and LSDA. The ML model showed better predictive power than the MB model and had skill in predicting the unseen within-vehicle exposures. The ML model predictions were as good as the MB model for most of the species and improved for NO2. The ML model demonstrated good index of agreement (IOA >0.69) and Pearson correlation coefficient (r > 0.80) for all the species. The inclusion of air quality data from nearby monitoring stations instead of on-road (sampled while driving), in the ML model showed promising and new capabilities to within-vehicle exposure predictions. In an era where air pollution is a growing concern, understanding and predicting within-vehicle air pollution exposure is of great importance for public health and environmental research. This research not only advances the field of exposure assessment but (at no extra cost) also demonstrates practical implications for real-time exposure mapping and health impact assessment of vehicle occupants with existing infrastructure.

Copper Coordination‐Based Nanomedicine for Tumor Theranostics

AbstractCopper (Cu) is an essential micronutrient for a variety of basic biological processes, including cell metabolism, proliferation, and angiogenesis. Recently, the concept of cuproptosis has been found to be related to the direct binding of Cu to the lipoylated components of the tricarboxylic acid cycle. This further demonstrates the important role of Cu and promotes Cu‐involved biomedical research. Currently, Cu‐related nanomedicines is mainly based on Cu coordination chemistry, and targeting Cu become a research hotspot for cancer therapy. On one hand, chelating excess Cu in vivo to inhibit angiogenesis, on the other hand, supplementing Cu and Cu‐coordinated complexes to produce oxidative stress and proteasome inhibition. In this review, the principles of Cu‐coordination chemistry and the biochemical functions of Cu ions are described, and a comprehensive review of Cu‐coordinated nanomedicine, including Cu chelation and Cu supplementation, delivery of Cu‐coordinated nanodrugs, and Cu‐coordinated nanocarriers is provided. Finally, the limitations are analyzed to point out the directions for improvement in future research. It is believed that the development of Cu‐coordinated nanomedicine will enter the fast track and show great promise in cancer theranostics.

Exploring How do Expectations Influence Learning

Memory is a core component of human cognitive functioning and is vital for learning, decision-making, and quality of life. This paper reviews the correlation between setting expectations and aiding memory, focusing on the effects of constructing, capturing, and adjusting expectations during memory on memory performance. First, the paper describes the basic principles and processes of memory and clarifies the role of setting expectations in memory. Then, the critical role of setting expectations in memory is demonstrated. Finally, the role of expectations on memory interference, memory extraction, and memory retention is discussed, as well as directions for future research.

The Taxonomy of Predict-Observe-Explain (POE) as a Teaching Strategy and Thinking Process of Chemistry Stakeholders

Predict-observe-explain (POE) is a strategy being used during scientific investigations and experiments. However, POE-related studies mainly focused on its validity and reliability as a teaching strategy in classrooms. Thus, this paper extends the context of POE strategy as science process skills, as cognitive thinking skills in performing experiments, and answering scientific inquiries among chemistry stakeholders: students, teachers, and chemists. The study sees POE as a way to develop critical thinking and builds the skills needed for further academic and professional endeavors in the different fields of science. Through a constructivist grounded theory methodology using focus group discussions for students and in-depth interviews with the teachers and chemists, the study: described how students, teachers, and chemists exemplified POE strategy in terms of its process, and identified mutual patterns of POE process from the chemistry stakeholders. The study revealed that: student POE taxonomy is focused on basic processes and structures of POE; teacher POE taxonomy is a guide in their lesson planning; and chemist POE taxonomy is based on local and international compliance during experimentation. Therefore, POE is a science process skill for chemistry stakeholders where the POE taxonomy used by the chemistry stakeholders are different based on experiences, needs, and context.

Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. Here, to observe the effect of these processes, we introduce the spatially embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a three-dimensional Euclidean space, where the communication of constituent neurons is constrained by a sparse connectome. We find that seRNNs converge on structural and functional features that are also commonly found in primate cerebral cortices. Specifically, they converge on solving inferences using modular small-world networks, in which functionally similar units spatially configure themselves to utilize an energetically efficient mixed-selective code. Because these features emerge in unison, seRNNs reveal how many common structural and functional brain motifs are strongly intertwined and can be attributed to basic biological optimization processes. seRNNs incorporate biophysical constraints within a fully artificial system and can serve as a bridge between structural and functional research communities to move neuroscientific understanding forwards.