With the continuous development of technology, traditional marketing methods no longer meet the needs of the main forces of social consumption, and people urgently need more innovative and personalized marketing strategies. E-commerce companies must develop a comprehensive customer-oriented marketing strategy based on big data and multi-channel to achieve their long-term healthy development. This paper first investigated the impact of the digital economy on e-commerce enterprises, focused on the transformation of the digital economy on the marketing model, expounded the development analysis of e-commerce in the digital economy era, and described the development trend of e-commerce marketing in the digital economy era. Then, this paper expounded the current problems faced by e-commerce enterprises, and discussed the lack of integrity, homogeneity, large-scale marketing strategies, and the lack of analysis and application of big data. After that, this paper put forward the marketing strategy of e-commerce enterprises in the digital economy era, and studied it from three aspects, namely, building a reasonable product management structure, marketing strategy based on customized marketing content, and social media marketing strategy based on information sharing. Then this paper proposed to use genetic algorithm to strengthen the marketing strategy of e-commerce enterprises. Finally, based on experiments and surveys, this paper used genetic algorithms to strengthen the construction of e-commerce enterprise marketing strategy in the digital economy, and concluded that the new e-commerce enterprise marketing strategy was 21% more satisfactory than the traditional new e-commerce enterprise marketing strategy. Through comparison, it can see that the integrity of the marketing plan of the new e-commerce enterprise's marketing strategy was 0.33 higher than that of the traditional e-commerce enterprise's marketing strategy, and the integrity of the promotion strategy was 0.34 higher than that of the traditional e-commerce enterprise's marketing strategy. After using the new e-commerce enterprise marketing strategy, the improved management structure was 0.29 higher than that of the traditional monitoring system, and the high quality of products was 0.18 higher than that of the traditional system.
Diversity and homeostasis of gut bacterial composition is highly associated with the pathogenesis of insulin dysfunction and type 1 diabetes melittus (T1D), hence emerged in parallel with the activation of autoimmunity. We aimed to study the bioactive potential of essential oil from Zanthoxylum myriacanthum var. pubescens Huang (Maqian) through computational approaches. Twelve chemical constituents derived from Maqian essential oil were docked with selected proteins (i.e., 3pig, 1kho, 7dmq, 4m4d, 2z65, 4glp, and 3fxi) in which are involved in gut microbiota modulation in T1D. Subsequently, the prediction of bioavailability properties of the small molecules were evaluated. Among all chemical constituents, the post-docking interaction analysis demonstrated that α-phellandrene exhibits the strongest binding affinity and induces gut microbiota modulation with β-fructofuranosidase from Bifidobacterium longum. The current result revealed the potential of 3-Carene and α-Pinene in inducing specific changes in gut microbiota downregulating Clostridium perfringens and quenching Leptotrichia shahii respectively. β-Pinene possess exceptionally strong binding affinity that effectively disrupt the interaction between lipopolysaccharide and its cognate receptors, while α-Phellandrene was exhibited the uppermost binding affinity with TLR4/MD2 and could likely target TLR4 stimulating lipopolysaccharide. Our results are the first to report on the gut microbiota modulation effects of α-Phellandrene and β-Phellandrene via actions on LPS binding to CD14 and the TLR4 co-receptor signaling. In conclusion, our findings based on computational approaches, small molecules from Maqian present as promising agents which could regulate inflammatory response and modulate gut microbiota in type 1 diabetes mellitus.
Global organizations are still facing challenges in achieving sustainable performance despite the surge in digital technologies. It is imperative that firms invest in digital capabilities to secure sustainable market performance in the face of a barrage of novel inventions. Today, ensuring a resilient future demands business to focus on digital ambidexterity capabilities (i.e., exploitation and exploration), digitalized strategy adoption, and digital transformation. This study investigates the intricate dynamics between digital capabilities and digitalization strategies and their impact on sustainable business performance. The research employed a questionnaire-driven methodology to gather data from managerial personnel within industries. Results show that digital exploitation and exploration capabilities significantly enhance sustainable business performance. The research also establishes the beneficial effect of adopting a digitalization strategy on business performance and innovation. Market-driven business model innovation emerges as a critical factor, not only driving sustainable performance but also serving as a mediating link between various digital strategies and business success. Moreover, the study highlights the importance of digital leadership capabilities, further strengthening the relationship between innovative business models and sustainable performance. The findings underscore the synergistic effect of digital competencies and strategic digitalization in promoting sustainable and innovative practices in today's digital-driven business landscape.
Carbon nanotube (CNT) reinforcement can lead to a new way to enhance the properties of composites by transforming the reinforcement phases into nanoscale fillers. In this study, the buckling response of functionally graded CNT-reinforced composite (FG-CNTRC) sandwich beams was investigated experimentally and analytically. The top and bottom plates of the sandwich beams were composed of carbon fiber laminated composite layers and hard core. The hard core was made of a pultruded glass fiber-reinforced polymer (GFRP) profile. The layers of FG-CNTRC surfaces were reinforced with different proportions of CNT. The reference sample was made of only a pultruded GFRP profile. In the study, the reference sample and four samples with CNT were tested under compression. The largest buckling load difference between the reference sample and the sample with CNT was 37.7%. The difference between the analytical calculation results and experimental results was obtained with an approximation of 0.49%-4.92%. Finally, the buckling, debonding, interlaminar cracks, and fiber breakage were observed in the samples.
The hydrological regimes of watersheds might be drastically altered by climate change, a majority of Pakistan's watersheds are experiencing problems with water quality and quantity as a result precipitation changes and temperature, necessitating evaluation and alterations to management strategies. In this study, the regional water security in northern Pakistan is examined about anthropogenic climate change on runoff in the Kunhar River Basin (KRB), a typical river in northern Pakistan using Soil and Water Assessment tool (SWAT) and flow durarion curve (FDC). Nine general circulation models (GCMs) were successfully utilized following bias correction under two latest IPCC shared socioeconomic pathways (SSPs) emission scenarios. Correlation coefficients (R2), Nash-Sutcliffe efficiency coefficients (NSE), and the Percent Bias (PBIAS) are all above 0.75. The conclusions demonstrate that the SWAT model precisely simulates the runoff process in the KRB on monthly and daily timescales. For the two emission scenarios of SSP2-4.5 and SSP5-8.5, the mean annual precipitation is predicted to rise by 3.08 % and 5.86 %, respectively, compared to the 1980-2015 baseline. The forecasted rise in mean daily high temperatures is expected to range from 2.08 °C to 3.07 °C, while the anticipated increase in mean daily low temperatures is projected to fall within the range of 2.09 °C-3.39 °C, spanning the years 2020-2099. Under the two SSPs scenarios, annual runoff is estimated to increase by 5.47 % and 7.60 % due to climate change during the same period. Future socioeconomic growth will be supported by a sufficient water supply made possible by the rise in runoff. However, because of climate change, there is a greater possibility of flooding because of increases in both rainfall and runoff. As a result, flood control and development plans for KRB must consider the climate change's possible effects. There is a chance that the peak flow will move backwards relative to the baseline.
Traditional approaches to monitoring water quality in aquaculture tanks present numerous limitations, including the inability to provide real-time data, which can lead to improper feeding practices, reduced productivity, and potential environmental risks. To address these challenges, this study aimed to create an accurate water quality monitoring system for Asian seabass fish farming in aquaculture tanks. This was achieved by enhancing the accuracy of low-cost sensors using simple linear regression and validating the IoT system data with YSI Professional Pro. The system's development and validation were conducted over three months, employing professional devices for accuracy assessment. The accuracy of low-cost sensors was significantly improved through simple linear regression. The results demonstrated impressive accuracy levels ranging from 76% to 97%. The relative error values which range from 0.27% to 4% demonstrate a smaller range compared to the values obtained from the YSI probe during the validation process, signifying the enhanced accuracy and reliability of the IoT sensor by using simple linear regression. The system's enhanced accuracy facilitates convenient and reliable real-time water quality monitoring for aquafarmers. Real-time data visualization was achieved through a microcontroller, Thingspeak, Virtuino application, and ESP 8266 Wi-Fi module, providing comprehensive insights into water quality conditions. Overall, this adaptable tool holds promise for accurate water quality management in diverse aquatic farming practices, ultimately leading to improved yields and sustainability.
This mixed-method study explores the acceptance of ChatGPT as a tool for Metacognitive Self-Regulated Learning (MSRL) among academics. Despite the growing attention towards ChatGPT as a metacognitive learning tool, there is a need for a comprehensive understanding of the factors influencing its acceptance in academic settings. Engaging 300 preservice teachers through a ChatGPT-based scenario learning activity and utilizing convenience sampling, this study administered a questionnaire based on the proposed Technology Acceptance Model at UTM University's School of Education. Structural equation modelling was applied to analyze participants' perspectives on ChatGPT, considering factors like MSRL's impact on usage intention. Post-reflection sessions, semi-structured interviews, and record analysis were conducted to gather results. Findings indicate a high acceptance of ChatGPT, significantly influenced by personal competency, social influence, perceived AI usefulness, enjoyment, trust, AI intelligence, positive attitude, and metacognitive self-regulated learning. Interviews and record analysis suggest that academics view ChatGPT positively as an educational tool, seeing it as a solution to challenges in teaching and learning processes. The study highlights ChatGPT's potential to enhance MSRL and holds implications for teacher education and AI integration in educational settings.
Mycotoxins, harmful compounds produced by fungal pathogens, pose a severe threat to food safety and consumer health. Some commonly produced mycotoxins such as aflatoxins, ochratoxin A, fumonisins, trichothecenes, zearalenone, and patulin have serious health implications in humans and animals. Mycotoxin contamination is particularly concerning in regions heavily reliant on staple foods like grains, cereals, and nuts. Preventing mycotoxin contamination is crucial for a sustainable food supply. Chromatographic methods like thin layer chromatography (TLC), gas chromatography (GC), high-performance liquid chromatography (HPLC), and liquid chromatography coupled with a mass spectrometer (LC/MS), are commonly used to detect mycotoxins; however, there is a need for on-site, rapid, and cost-effective detection methods. Currently, enzyme-linked immunosorbent assays (ELISA), lateral flow assays (LFAs), and biosensors are becoming popular analytical tools for rapid detection. Meanwhile, preventing mycotoxin contamination is crucial for food safety and a sustainable food supply. Physical, chemical, and biological approaches have been used to inhibit fungal growth and mycotoxin production. However, new strains resistant to conventional methods have led to the exploration of novel strategies like cold atmospheric plasma (CAP) technology, polyphenols and flavonoids, magnetic materials and nanoparticles, and natural essential oils (NEOs). This paper reviews recent scientific research on mycotoxin toxicity, explores advancements in detecting mycotoxins in various foods, and evaluates the effectiveness of innovative mitigation strategies for controlling and detoxifying mycotoxins.
Alzheimer's disease (AD) is increasingly becoming a major public health concern in our society. While many studies have explored the use of natural polyketides, alkaloids, and other chemical components in AD treatment, there is an urgent need to clarify the concept of multi-target treatment for AD. This study focuses on using network pharmacology approach to elucidate how secondary metabolites from Dictyostelium discoideum affect AD through multi-target or indirect mechanisms. The secondary metabolites produced by D. discoideum during their development were obtained from literature sources and PubChem. Disease targets were selected using GeneCards, DisGeNET, and CTD databases, while compound-based targets were identified through Swiss target prediction and Venn diagrams were used to find intersections between these targets. A network depicting the interplay among disease, drugs, active ingredients, and key target proteins (PPI network) was formed utilizing the STRING (Protein-Protein Interaction Networks Functional Enrichment Analysis) database. To anticipate the function and mechanism of the screened compounds, GO and KEGG enrichment analyses were conducted and visually presented using graphs and bubble charts. After the screening phase, the top interacting targets in the PPI network and the compound with the most active target were chosen for subsequent molecular docking and molecular dynamic simulation studies. This study identified nearly 50 potential targeting genes for each of the screened compounds and revealed multiple signaling pathways. Among these pathways, the inflammatory pathway stood out. COX-2, a receptor associated with neuroinflammation, showed differential expression in various stages of AD, particularly in pyramidal neurons during the early stages of the disease. This increase in COX-2 expression is likely induce by higher levels of IL-1, which is associated with neuritic plaques and microglial cells in AD. Molecular docking investigations demonstrated a strong binding interaction between the terpene compound PQA-11 and the neuroinflammatory receptor COX2, with a substantial binding affinity of -8.4 kcal/mol. Subsequently, a thorough analysis of the docked complex (COX2-PQA11) through Molecular Dynamics Simulation showed lower RMSD, minimal RMSF fluctuations, and a reduced total energy of -291.35 kJ/mol compared to the standard drug. These findings suggest that the therapeutic effect of PQA-11 operates through the inflammatory pathway, laying the groundwork for further in-depth research into the role of secondary metabolites in AD treatment.
China's distinctive educational approach, particularly its emphasis on ideological and political education, has garnered considerable academic attention for its impact on shaping individual values, fostering citizenship, and maintaining social stability. Despite the Chinese government's prioritization of ideological and political education, academic research in this field appears constrained, with existing studies predominantly focusing on normative and descriptive aspects. Normative research delineates how ideological and political education should be executed, while descriptive research illustrates its practical implementation. The effectiveness of these approaches is significantly diminished if they are not adequately interconnected-when only the current reality is explained without providing tools for improvement or when prescribed steps for improvement lack a basis in specific contexts. This paper conducts a comprehensive review of research on ideological and political education using ATLAS. ti 9 for thematic analysis. The review aims to unveil the intricate landscape of current research in China and address key questions: What are the primary trends in the literature on ideological and political education between 2021 and July 2023? What challenges does ideological and political education face? Through a direct exploration of these issues, this paper seeks to optimize the ideological and political education system, elevate its adaptability and effectiveness, and open avenues for research, fostering a more dynamic, inclusive, and resilient development of ideological and political education.
Significant progress has been made over the years to improve the stability and efficiency of rapidly evolving tin-based perovskite solar cells (PSCs). One powerful approach to enhance the performance of these PSCs is through compositional engineering techniques, specifically by incorporating a mixed cation system at the A-site and B-site structure of the tin perovskite. These approaches will pave the way for unlocking the full potential of tin-based PSCs. Therefore, in this study, a theoretical investigation of mixed A-cations (FA, MA, EA, Cs) with a tin-germanium-based PSC was presented. The crystal structure distortion and optoelectronic properties were estimated. SCAPS 1-D simulations were employed to predict the photovoltaic performance of the optimized tin-germanium material using different electron transport layers (ETLs), hole transport layers (HTLs), active layer thicknesses, and cell temperatures. Our findings reveal that EA0.5Cs0.5Sn0.5Ge0.5I3 has a nearly cubic structure (t = 0.99) and a theoretical bandgap within the maximum Shockley-Queisser limit (1.34 eV). The overall cell performance is also improved by optimizing the perovskite layer thickness to 1200 nm, and it exhibits remarkable stability as the temperature increases. The short-circuit current density (Jsc) remains consistent around 33.7 mA/cm2, and the open-circuit voltage (Voc) is well-maintained above 1 V by utilizing FTO as the conductive layer, ZnO as the ETL, Cu2O as the HTL, and Au as the metal back contact. This configuration also achieves a high fill factor ranging from 87 % to 88 %, with the highest power conversion efficiency (PCE) of 31.49 % at 293 K. This research contributes to the advancement of tin-germanium perovskite materials for a wide range of optoelectronic applications.
The advancement of artificial intelligence (AI) and the ubiquity of social media have become transformative agents in contemporary educational ecosystems. The spotlight of this inquiry focuses on the nexus between AI and social media usage in relation to academic performance and mental well-being, and the role of smart learning in facilitating these relationships. Using partial least squares-structural equation modeling (PLS-SEM) on a sample of 401 Chinese university students. The study results reveal that both AI and social media have a positive impact on academic performance and mental well-being among university students. Furthermore, smart learning serves as a positive mediating variable, amplifying the beneficial effects of AI and social media on both academic performance and mental well-being. These revelations contribute to the discourse on technology-enhanced education, showing that embracing AI and social media can have a positive impact on student performance and well-being.
Tetraalkylammonium salt (TAS) is an organic salt widely employed as a precursor, additive or electrolyte in solar cell applications, such as perovskite or dye-sensitized solar cells. Notably, Perovskite solar cells (PSCs) have garnered acclaim for their exceptional efficiency. However, PSCs have been associated with environmental and health concerns due to the presence of lead (Pb) content, the use of hazardous solvents, and the incorporation of TAS in their fabrication processes, which significantly contributes to environmental and human health toxicity. As a response, there is a growing trend towards transitioning to safer and biobased materials in PSC fabrication to address these concerns. However, the potential health hazards associated with TAS necessitate a thorough evaluation, considering the widespread use of this substance. Nevertheless, the overexploitation of TAS could potentially increase the disposal of TAS in the ecosystem, thus, posing a major health risk and severe pollution. Therefore, this review article presents a comprehensive discussion on the in vitro and in vivo toxicity assays of TAS as a potential material in solar energy applications, including cytotoxicity, genotoxicity, in vivo dermal, and systemic toxicity. In addition, this review emphasizes the toxicity of TAS compounds, particularly the linear tetraalkyl chain structures, and summarizes essential findings from past studies as a point of reference for the development of non-toxic and environmentally friendly TAS derivatives in future studies. The effects of the TAS alkyl chain length, polar head and hydrophobicity, cation and anion, and other properties are also included in this review.
This study evaluated the effects of potato, wheat, rice, and corn starch on growth performance, blood parameters, digestive enzyme activity, antioxidative response, and gut microbiota of African catfish, Clarias gariepinus. A control diet (a commercial fish diet) and four different starch (potato, PO; wheat, WH; corn, CO; rice, RC) formulations were fed to African catfish with average weight of 10.5g (n = 30) for eight weeks. The experiment was conducted in triplicates. At the end of the feeding trial, the growth performance of African catfish fed with potato starch (PO) was significantly higher than other treatment groups. Furthermore, this group recorded significant and lowest feed conversion ratio (FCR) compared to other groups. Meanwhile, there were no significant differences in all tested hematological parameters and antioxidative response between the groups. Digestive enzyme activities in the fish intestines, including amylase, lipase, and protease, were significantly higher in African catfish fed with the PO diet. In addition, this group demonstrated substantially lower viscerosomatic index (VSI) and hepatosomatic index (HSI) than other groups, indicating that the fish has more meat on its body. The PO diet group also recorded significantly higher Akkermansia muciniphila, a good gut microbiota. Therefore, the PO diet potentially improves African catfish's growth performance and health status.
This research aimed to identify the factors that influence impulse buying behavior during livestreaming and advance the existing literature based on a proposed conceptual framework grounded in the stimulus-organism-response (S-O-R) model. We also tested the moderating effects of price perception and scarcity persuasion. An online self-administered questionnaire was used to collect data from 837 Chinese participants aged over 18 years. The data were analyzed using partial least squares structural equation modeling using Smart-PLS version 4.0. The findings showed that susceptibility to social influence, impulse buying tendency, cognitive reactions, affective reactions, and the urge to buy impulsively are statistically significant predictors of impulse buying during livestreaming, with price perception and scarcity persuasion as moderators. The study expands the S-O-R model for livestreaming impulse buying in e-commerce context, highlighting its multifaceted nature and revealing the mediating role of Urge to Buy Impulsively in translating cognitive and emotional factors into impulse buying behavior. These insights offer practical guidance for marketers to design tailored strategies that leverage psychological triggers and external cues to enhance consumer engagement and encourage desired behaviors, ultimately leading to more effective marketing campaigns and improved consumer experiences.
The Internet of Vehicles (IoV) emerges as a pivotal extension of the Internet of Things (IoT), specifically geared towards transforming the automotive landscape. In this evolving ecosystem, the demand for a seamless end-to-end system becomes paramount for enhancing operational efficiency and safety. Hence, this study introduces an innovative method for real-time driver identification by integrating cloud computing with deep learning. Utilizing the integrated capabilities of Google Cloud, Thingsboard, and Apache Kafka, the developed solution tailored for IoV technology is adept at managing real-time data collection, processing, prediction, and visualization, with resilience against sensor data anomalies. Also, this research suggests an appropriate method for driver identification by utilizing a combination of Convolutional Neural Networks (CNN) and multi-head self-attention in the proposed approach. The proposed model is validated on two datasets: Security and collected. Moreover, the results show that the proposed model surpassed the previous works by achieving an accuracy and F1 score of 99.95%. Even when challenged with data anomalies, this model maintains a high accuracy of 96.2%. By achieving accurate driver identification results, the proposed end-to-end IoV system can aid in optimizing fleet management, vehicle security, personalized driving experiences, insurance, and risk assessment. This emphasizes its potential for road safety and managing transportation more effectively.
Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020-2059) and the distant future (2060-2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.
This study analyses which aspects of sitting and working furniture ergonomics that may be influenced and how they are assessed. To gather information on the types and assessment techniques connected with influencing furniture ergonomics, a systematic review of the literature was conducted. The papers in the systematic review were published between 2012 and 2022. The articles applied the Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines to limit the 41 papers that were eventually included (N = 41) to those containing keywords like ergonomics, human factors, comfort, working furniture, Chair, assessment and evaluation. The research objective of this systematic review is to provide a comprehensive overview of sitting and working furniture and the main findings, obtaining common assessment techniques for this type of furniture and their suitability. According to the relevant studies, the publications were categorized by summarizing factors like region, gender, research methods, ergonomic assessment techniques and methods used, correlation between assessment techniques and methods, etc. Summaries of the data extracted from the included papers are provided and the applicability of some approaches are assessed. Only a small number of authors have evaluated the ergonomics of furniture used in homes. One of the research gaps is the paucity of research on gender segregation, secular trends, and cultural contexts. These studies heavily rely on quantitative research techniques, and the articles may lack credibility due to the homogeneity of the evaluation techniques. Finally, the authors offer some suggestions for the appropriate ergonomic analysis of furniture.
Herbal treatments have been utilized for millennia to cure a variety of ailments. There are over 20, 000 herbal remedies available to treat cancer and other disease in humans. In Ayurveda, traditional plants having revitalizing and nourishing characteristics are known as "Rasayanas." They have anti-inflammatory, anticancer, anti-microbicidal, antiviral, and immunomodulatory effects on the immune system. Immunomodulation is a mechanism through which the body stimulates, suppresses, or boosts the immune system to maintain homeostasis. Plant-derived immunomodulators are typically phytocompounds, including carbohydrates, phenolics, lipids, alkaloids, terpenoids, organosulfur, and nitrogen-containing chemicals. Immunomodulation activity of phytocompounds from traditional plants is primarily mediated through macrophage activation, phagocytosis stimulation, peritoneal macrophage stimulation, lymphoid cell stimulation, and suppression or enhancement of specific and non-specific cellular immune systems via numerous signalling pathways. Despite extensive research, the precise mechanism of immunomodulation of most traditional plants has not yet been fully elucidated, justifying the need for further experimentation. Therefore, this review describes the immunomodulatory agents from traditional plants such as Curcuma longa L., Panax ginseng C.A. Meyer, and Moringa oleifera Lam, further highlighting the common molecular targets and immunomodulatory mechanism involved in eradicating diseases.
The structural and electronic properties of ReS2 different forms - three-dimensional bulk and two-dimensional monolayer - were studied within density functional theory and pseudopotentials. A method for standardizing the description of bulk unit cells and "artificial" slab unit cells for DFT research has been proposed. The preference of this method for studying zone dispersion has been shown. The influence of the vacuum layer thickness on specified special high-symmetry points is discussed. Electron band dispersion in both classical 3D Brillouin zones and transition to 2D Brillouin zones in the proposed two-dimensional approach using the Niggli form of the unit cell was compared. The proposed two-dimensional approach is preferable for low-symmetry layered crystals such as ReS2. It was established that the bulk ReS2 is a direct gap semiconductor (band gap of 1.20 eV), with the direct transition lying in the X point of the first Brillouin zone, and it is in good agreement with published experimental data. The reduction in material dimension from bulk to monolayer was conducted with an increasing band gap up to 1.45 eV, with a moving direct transition towards the Brillouin zone center. The monolayer of ReS2 is a direct-gap semiconductor in a wide range of temperatures, excluding only a narrow range at low temperatures, where it comes as a quasi-direct gap semiconductor. The transition, situated directly in the Γ-point, lies 3.3 meV below the first direct transition located near this point. The electronic density of states of ReS2 in the bulk and monolayer cases of ReS2 were analyzed. The molecular orbitals were built for both types of ReS2 structures as well as the electron difference density maps. For all types of ReS2 structures, an analysis of populations according to Mulliken and Voronoi was carried out. All calculated data is discussed in the context of weak quantum confinement in the 2D case.