Fundamental Approach Research Articles

ARTIFICIAL INTELLIGENCE BASED RECOMMENDER SYSTEMS

Recommender systems anticipate customers' present preferences for certain items based on their past behavior, offering individualized service support to users. The evolution of recommender systems has naturally included artificial intelligence (AI), notably computational intelligence and machine learning techniques and algorithms to enhance prediction precision and resolve issues with cold start and data sparsity. This paper methodically examines the fundamental approaches and current practices in recommender systems.

Investigation of IEEE 802.16e LDPC Code Application in PM-DQPSK System

With the development of the Internet and information technology, optical fiber communication systems need to meet people’s information demand for large capacity and high speed. High-order phase modulation and channel multiplexing can improve the capacity and data rate of optical fiber communication systems, but they also bring the problem of bit error. To improve the transmission quality and reliability of optical fiber communication systems, forward error correction (FEC) coding techniques are commonly used, which serve as the fundamental approach to enhance the quality and reliability of fiber optic communication systems, ensuring that the received data remain accurate and reliable. The FEC in optical fiber communication systems is divided into three generations. The first generation FEC is mainly hard decision codewords, represented as RS code. The second generation FEC is mainly cascaded code, which stands for interleaved cascaded code. The third generation of FEC mainly refers to soft decision codes, which are represented as low-density parity-check (LDPC) codes. As a kind of FEC, LDPC codes stand out as pivotal contributors in the field of optical communication and have gained remarkable attention due to exceptional error correction performance and low decoding complexity. Based on IEEE802.16e standard, LDPC code with specific code length and rate is compiled and simulated in MATLAB and VPItransmissionMaker 10.1 and successfully incorporated into polarization multiplexed differential quadrature phase shift keying (PM-DQPSK) coherent optical transmission system. The simulation results indicate that the bit error rate (BER) can be reduced to 10−3 when the optical signal-to-noise ratio (OSNR) reaches 14.2 dB, and the BER experiences a reduction by nearly three orders of magnitude when the OSNR is 17.2 dB. These findings underscore the efficacy of LDPC codes in significantly improving the performance of optical communication systems, particularly in scenarios demanding robust error correction capabilities. This study provides valuable, significant results regarding the potential of LDPC codes for enhancing the reliability of optical transmission in real-world applications.

Structure and mechanism of directional heat induced asphalt pavement with thermal aggregation effect

Efficiently and actively dissipating accumulated heat within asphalt pavement is crucial for reducing pavement distress and improving subgrade stability. This paper proposed a novel inverted “T” shaped heat induced structure (IT-HIS) that facilitates the rapid transfer of heat from the pavement to the subgrade and shoulders. The IT-HIS consists of two components: a vertical gradient thermal conductivity structure utilizing steel slag, and a horizontal oriented carbon fiber heat induced structure. The heat transfer behavior of the IT-HIS was analyzed using a finite element heat transfer model created in ABAQUS, and the results were validated through indoor irradiation experiments. The findings demonstrated that the IT-HIS enhanced heat flow and promoted secondary aggregation in the vertical direction, while facilitated lateral heat dissipation. Compared to the ordinary group (OG), the heat flux of IT-HIS at depths of 5 cm and 6.5 cm increased by 19.8 % and 64.6 %, respectively. The indoor irradiation experiment results indicated that during the heat absorption period, the IT-HIS reduced temperatures at depths of 0 cm, 2 cm, and 5 cm by 1.5 ℃, 1.7 ℃, and 1.1 ℃, respectively. During the heat dissipation period, the IT-HIS reduced temperatures at depths of 0 cm, 5 cm, and 10 cm by 0.9 ℃, 0.7 ℃, and 1.8 ℃, respectively. The IT-HIS enhanced the cooling effect during daytime and inhibited heat upward during nighttime based on the gradient thermal conductivity structure. The proposal and validation of this structure provide a fundamental approach to forming large-scale high-speed microchannel heat dissipation within asphalt pavements, and is expected to provide experimental and theoretical foundations for the development of directional active cooling technologies.

Fuzzy Soft Set Theory Applications in Medical Diagnosis: A Comprehensive Review and the Roadmap for Future Studies

This paper provides a literature review addressing the use of soft sets in medical diagnosis. Distinguishing itself from the existing literature, the study offers a comprehensive analysis of how fuzzy soft sets can be integrated into diagnostic processes, highlighting a novel fusion of fuzzy and soft sets in medical applications. Any soft set on a countably infinite universe can be regarded as a fuzzy set, recognizing the limitations of traditional diagnostic tools in dealing with vague and incomplete information, our research aims to utilize the flexibility and comprehensiveness of fuzzy soft sets to enhance decision-making accuracy in medical scenarios. The primary objective of this research is to present a thorough and critical analysis of fuzzy soft set theory in medical diagnosis, aiming to establish it as a fundamental approach in the field. By combining all of these results, we can generate a comprehensive picture of the connections between the many theories that account for fuzziness and imprecision, which helps to fill in the blanks left by recent surveys. The review will focus on identifying the main research trends in this field: the primary research topics that soft set theory addresses in medical applications, the community is currently facing and the major theoretical concepts used to study these topics. The primary objective of this review is to assist in the identifying of emerging research directions. Some of these trends are promising and will help shape a new role for soft set theory in the area of medical applications. The fusion of fuzzy and soft sets in medical applications represents a crucial and necessary stage in the research and diagnosis procedures. Key results include the study on development of algorithms and models that outperform traditional methods in accuracy and reliability.

Manipulating 2D Membrane Interlayer Channels with Accelerated Mass-Transfer Behavior to Boost Solar Desalination.

The scarcity of fresh water necessitates sustainable and efficient water desalination strategies. Solar-driven steam generation (SSG), which employs solar energy for water evaporation, has emerged as a promising approach. Graphene oxide (GO)-based membranes possess advantages like capillary action and Marangoni effect, but their stacking defects and dead zones of flexible flakes hinders efficient water transportation, thus the evaporation rate lag behind unobstructed-porous 3D evaporators. Therefore, fundamental mass-transfer approach for optimizing SSG evaporators offers new horizons. Herein, a universal multi-force-fields-based method is presented to regularize membrane channels, which can mechanically eliminate inherent interlayer stackings and defects. Both characterization and simulation demonstrate the effectiveness of this approach across different scales and explain the intrinsic mechanism of mass-transfer enhancement. When combined with a structurally optimized substrate, the 4Laponite@GO-1 achieves evaporation rate of 2.782kg m-2 h-1 with 94.48% evaporation efficiency, which is comparable with most 3D evaporators. Moreover, the optimized membrane exhibits excellent cycling stability (10 days) and tolerance to extreme conditions (pH 1-14, salinity 1%-15%), verifies the robust structural stability of regularized channels. This optimization strategy provides simple but efficient way to enhance the SSG performance of GO-based membranes, facilitating their extensive application in sustainable water purification technologies.

Personalized analysis of human cancer multi-omics for precision oncology

Multi-omics technologies, encompassing genomics, proteomics, and transcriptomics, provide profound insights into cancer biology. A fundamental computational approach for analyzing multi-omics data is differential analysis, which identifies molecular distinctions between cancerous and normal tissues. Traditional methods, however, often fail to address the distinct heterogeneity of individual tumors, thereby neglecting crucial patient-specific molecular traits. This shortcoming underscores the necessity for tailored differential analysis algorithms, which focus on particular patient variations. Such approaches offer a more nuanced understanding of cancer biology and are instrumental in pinpointing personalized therapeutic strategies. In this review, we summarize the principles of current individualized techniques. We also review their efficacy in analyzing cancer multi-omics data and discuss their potential applications in clinical practice.

Neanderthal resilience and adaptability: insights from the Abric Pizarro faunal assemblage during the MIS 4

The examination of faunal assemblages through zooarchaeological analyses constitutes a fundamental approach for gaining insight into the intricate behaviours of Neanderthals. Previous investigations have primarily focused on periods of relative environmental stability, and this has provided a wealth of relevant archaeological data. However, our understanding of Neanderthal resilience during the MIS 4, a period presumably characterised by harsh environmental conditions, remains limited. This study presents the first comprehensive analysis of the faunal assemblages from Levels M and P at Abric Pizarro. The geographic location of Abric Pizarro in the southeast Pre-Pyrenees, combined with chronometric dating, offers a unique opportunity to explore Neanderthal behaviours during a poorly known chronological period. The detailed zooarchaeological analysis comprised taxonomic identification, taphonomic analysis and age-at-death profiling to explore the adaptability and flexibility in the Neanderthal diet. The findings indicate that Neanderthal groups incorporated a diverse range of protein resources from small herbivores (e.g., caprids) to very large herbivores (e.g., Bos/Bison). These results not only demonstrate an adaptability to changing environments in an area traditionally deemed unsuitable for long-term occupation, but also contributes significantly to our understanding of the complex behaviours exhibited by Neanderthals.

Fractional Scalar Field Cosmology

Considering the Friedmann–Lemaître–Robertson–Walker (FLRW) metric and the Einstein scalar field system as an underlying gravitational model to construct fractional cosmological models has interesting implications in both classical and quantum regimes. Regarding the former, we just review the most fundamental approach to establishing an extended cosmological model. We demonstrate that employing new methodologies allows us to obtain exact solutions. Despite the corresponding standard models, we cannot use any arbitrary scalar potentials; instead, it is determined from solving three independent fractional field equations. This article concludes with an overview of a fractional quantum/semi-classical model that provides an inflationary scenario.

New evidence of interdependence in forex markets: A connection of connection analysis

Uncovering the complex connections among components is a fundamental approach to understand underlying mechanisms and then guide decision-making in forex markets. Previous works mainly focused on the connections between currency themselves, this work extends this issue by proposing a novel framework to investigate the connections between currency connections. Selecting ten major traded currencies as samples, we examine the correlations and causations among currency connections under short-, medium- and long-term scales. The main results are: (1) the changes in currency correlations are not only correlated but also causally linked with other currency correlations; (2) there exists stronger causal interactions of currency connections in long-term scales compared with short and median scales; (3) In short-term scale, the correlation between EUR and GBP is key currency connection, while the connection between CAD and CNY is the key currency connection in long-term scale. In summary, revealing the connections of connections in forex markets substantially extends existing works, which provides new useful information to market participants.

Skeptisisme Profesional Auditor dari Sudut Pandang Filsafat Jawa “Ojo Dumeh, Eling lan Waspodo”

This study aims to explore and analyze auditors' professional skepticism through the lens of Javanese philosophy, specifically the concepts of "Ojo Dumeh, Eling, and Waspodo." Professional skepticism, as the critical and cautious attitude of auditors in performing their duties, is linked to the values and teachings in Javanese philosophy to understand how these principles can enrich and deepen audit practice. The study's findings suggest a striking alignment between the ethos of audit professional skepticism and the Javanese philosophical concept of Ojo Dumeh, Eling lan Waspodo. In essence, audit professional skepticism embodies a mindset of critical inquiry and careful evaluation, mirroring Ojo Dumeh's call to avoid easy acceptance and instead verify information rigorously. The concept of Eling, emphasizing mindfulness and awareness, resonates with auditors' need for meticulous attention to detail. Likewise, Waspodo's emphasis on caution and prudence echoes the fundamental approach of auditors to exercise skepticism and maintain a cautious stance in their assessments. This correspondence underscores the universal value of critical thinking, careful scrutiny, and mindful deliberation across cultural and professional domains.

Anion‐Responsive Colorimetric and Fluorometric Red‐Shift in Triarylborane Derivatives: Dual Role of Phenazaborine as Lewis Acid and Electron Donor

AbstractPhotophysical modulation of triarylboranes (TABs) through Lewis acid‐base interactions is a fundamental approach for sensing anions. Yet, design principles for anion‐responsive TABs displaying significant red‐shift in absorption and photoluminescence (PL) have remained elusive. Herein, a new strategy for modulating the photophysical properties of TABs in a red‐shift mode has been presented, by using a nitrogen‐bridged triarylborane (1,4‐phenazaborine: PAzB) with a contradictory dual role as a Lewis acid and an electron donor. Following the strategy, PAzB derivatives connected with an electron‐deficient azaaromatic have been developed, and these compounds display a distinct red‐shift in their absorption and PL in response to an anion. Spectroscopic analyses and quantum chemical calculations have revealed the formation of a tetracoordinate borate upon the addition of fluoride, narrowing the HOMO–LUMO gap and enhancing the charge‐transfer character in the excited state. This approach has also been demonstrated in modulating the photophysical properties of solid‐state films.

Anion-Responsive Colorimetric and Fluorometric Red-Shift in Triarylborane Derivatives: Dual Role of Phenazaborine as Lewis Acid and Electron Donor.

Photophysical modulation of triarylboranes (TABs) through Lewis acid-base interactions is a fundamental approach for sensing anions. Yet, design principles for anion-responsive TABs displaying significant red-shift in absorption and photoluminescence (PL) have remained elusive. Herein, a new strategy for modulating the photophysical properties of TABs in a red-shift mode has been presented, by using a nitrogen-bridged triarylborane (1,4-phenazaborine: PAzB) with a contradictory dual role as a Lewis acid and an electron donor. Following the strategy, PAzB derivatives connected with an electron-deficient azaaromatic have been developed, and these compounds display a distinct red-shift in their absorption and PL in response to an anion. Spectroscopic analyses and quantum chemical calculations have revealed the formation of a tetracoordinate borate upon the addition of fluoride, narrowing the HOMO-LUMO gap and enhancing the charge-transfer character in the excited state. This approach has also been demonstrated in modulating the photophysical properties of solid-state films.

Detect Gear Fault in Wind Turbine Gearbox based on Time Synchornous Averaging Without Phase Signal.

Wind energy is increasingly recognized as a worldwide sustainable and environmentally friendly power source. Nevertheless, an important barrier to further investment in wind energy is the large rate of failure that occurs to turbines. The gearbox, a crucial component, has a major effect on the performance of wind turbines. It consists of complex planetary and cylindrical gear systems, making it prone to failure and causing major defects in wind turbines. Therefore, there is an urgent need to minimize downtime and improve productivity in wind turbine gearbox operations. Recent decades have witnessed growing interest in fault diagnosis of gearboxes due to their widespread use and industry significance. The time synchronous averaging (TSA) method is widely used as a fundamental approach to detect faults in wind turbine gearboxes from mechanical vibration signals. Usually, applying this method requires a device to measure the vibration phase. Nevertheless, there are certain circumstances where installing a phase-measuring device might pose challenges. For instance, if the gearbox operates, it cannot be paused for installation. Additionally, if the gearboxes are enclosed, it becomes difficult to insert the device. The present paper presents an innovative technical approach to improve the time synchornous averaging method without requiring information about phase vibration. It also evaluates the effectiveness of the proposed method using feature values. An experimental test rig was set up to evaluate the algorithm's effectiveness, simulating different gear faults and load conditions.

A dynamic phase separation model for glass transition behavior in water-triggered shape memory polymer towards programmable recovery onset

Water-triggered shape memory polymers (SMPs) have been extensively studied for biomedical applications due to their advantages of non-thermal actuation capability. However, few studies have been carried out to explore the working principle of shape recovery onset, which is essentially determined by the complex reactions between polymer macromolecules and water molecules. In this study, we developed a phase separation model to describe the dynamic glass transition in water-triggered SMPs. Based on the phase transition theory, dense and dilute phase separations of polymer macromolecules can be achieved when the dynamic diffusions of water molecules in the SMPs undergo dehydration and absorption processes, respectively. Then, the dynamic glass transition is resulted from the dehydration and absorption of water molecules, leading to the dense and dilute phases in the SMPs. Therefore, a free-energy equation has been developed to characterize the recovery onset, in which the mixing free energy and elastic free energy are originated from the Flory–Huggins solution theory and phase separation model, respectively. Moreover, the glass transition and its connection to shape recovery behaviors, i.e. recovery ratio, relaxation time and dynamic mechanical modulus, have also been investigated, according to the Fick’s diffusion law. Meanwhile, onset of programmable recovery has been explained by the dynamic phase separation, based on the transpiration theory and permeability model. Finally, the proposed model is verified using the experimental results reported in the literature. This study is expected to provide a fundamental approach to formulate the constitutive relationship between the dynamic phase separation and programmable recovery onset in the water-triggered SMPs.

Vegetation pattern and topography determine erosion characteristics in a semi‐arid sandstone hillslope‐gully system

AbstractThe hillslope‐gully system serves as the primary contributor to both runoff and sediment yield. The WEPP (Water Erosion Prediction Project) model is often applied to investigate erosion characteristics at hillslope scale, demonstrating a high level of accuracy in simulating water erosion. In this study, according to in situ field monitoring (2014–2020) at a Pisha sandstone hillslope on the Loess Plateau, China, a total of 50 rainfall events’ data were used as climatic data to calibrate the soil parameters, and 11 different vegetation patterns and four slope gradients of hillslope‐gully systems were installed as inputs for the management and slope data, respectively. In systems A, B, C and D, the hillslope gradients were defined as 5°, 8°, 10° and 12° and the gully gradients as 15°, 20°, 25° and 30°, respectively. The results showed that the steeper the slope, the more severe the erosion. However, there was a critical value for the effect of slope on runoff. When the slope exceeded 8° and the gully exceeded 20°, the runoff no longer increased further and even decreased. The reduction in runoff in hillslope‐gully systems was in the following order (in mm): system D (3.4 ± 0.14) > system C (3.4 ± 0.14) > system B (3.39 ± 0.14) > system A (3.12 ± 0.13). Increasing vegetation cover could reduce erosion. Differences in runoff between vegetation patterns were not significant (p > 0.05) and ranged from 8% to 26%. However, there were significant differences in the sediment yield reduction benefits of different vegetation patterns (p < 0.05), ranging from 17% to 66%. It was observed that vegetation located in the lower slope produced a more pronounced effect in mitigating sediment when the degree of cover was the same. We conclude that implementing watershed management strategies based on the vegetation and topographic attributes of hillslope‐gully systems within the Loess Plateau, especially on Pisha sandstone hillslopes, serves as the fundamental approach to achieving sustainable watershed management.

Assessment of generic competences for foreign-language professional communication through applying the ADDIE model

Introduction. The paper aims to assess generic competencies for foreign-language professional communication formed through applying the ADDIE model. The goal of the study was to identify the way generic competencies are developed throughout the ESL course for veterinary students based on the authors’ implementation of the ADDIE model. Materials and Methods. The research was based on theoretical review of literature, comparative analysis of sources, terminological analysis, and investigation of teaching practices. The ADDIE method of instructional modeling was used as a fundamental research approach taken to designing a teaching aid and developing a training course, respectively. While testing, a sample of 59 first-year veterinary students and 5 lecturers of the Foreign Language Department at Omsk State Agrarian University were involved. In order to collect and study the research data, the authors applied the following evaluation methods: observation, survey and testing of students. The academic outcomes were evaluated by examining dynamics of foreign language communicative competence by expert teachers. The Wilcoxon T-test and Fisher test were used as mathematical statistics methods. Results. The authors applied the ADDIE instructional model to design a professional communication course in English for veterinary students based on competency-based approach. The materials were delivered during the 2022-23 academic year. The learning outcomes demonstrated by participants in the control and experimental groups proved the effectiveness of the author's teaching procedure in the formation of generic competencies for foreign language professional communication, the validity of which was confirmed using mathematical statistics methods. Conclusions. The proposed technology for designing a foreign language course based on the ADDIE model has demonstrated its effectiveness in the development of generic competencies for foreign-language professional communication, which was confirmed by more pronounced positive dynamics in the experimental group.

Harnessing ChatGPT for Effective Assessment and Feedback in Education

Incorporating artificial intelligence (AI) into educational settings has been thrust forward using information and communication technology to improve the teaching and learning processes. In November of 2022, a significant advancement in this field occurred with the launch of ChatGPT, a chatbot powered by artificial intelligence. This cutting-edge chatbot can create logical and instructive replies, much like those that human users generate. Because of its versatility, ChatGPT has emerged as a potential supporter of open education. It offers helpful assistance, coaching, and feedback to learners who are self-directed, which in turn increases the learners' levels of motivation and engagement. This article aims to conduct an in-depth comparison and analysis of the elements above, focusing on the possible uses of ChatGPT inside educational settings. The particular goals include lobbying for adopting ChatGPT in academic settings and providing educators with various strategies and approaches to use ChatGPT in a manner that is both deliberate and successful in pedagogical or research endeavours. One of the primary objectives of this study is to encourage a well-informed discussion on the use of ChatGPT in educational settings. When it comes to incorporating ChatGPT services into educational environments, it outlines the fundamental techniques and approaches that should be used simultaneously.

Discovery of a hyperalkaline liquid condensed phase: significance toward applications in carbon dioxide sequestration.

Bicarbonate ion-containing solutions such as seawater, natural brines, bovine serum and other mineralizing fluids have been found to contain hyperalkaline droplets of a separate, liquid condensed phase (LCP), that have higher concentrations of bicarbonate ion (HCO3 -) relative to the bulk solution in which they reside. The existence and unique composition of the LCP droplets have been characterized by nanoparticle tracking analysis, nuclear magnetic resonance spectroscopy, fourier transform infrared spectroscopy, dissolved inorganic carbon analysis and refractive index measurements. Carbon dioxide can be brought into solution through an aqueous reaction to form LCP droplets that can then be separated by established industrial membrane processes as a means of concentrating HCO3 -. Reaction of calcium with the LCP droplets results in calcium carbonate precipitation and mineral formation. The LCP phenomenon may bear on native mineralization reactions and has the potential to change fundamental approaches to carbon capture, sequestration and utilization.

Crowdsourcing and Public Participation to Advance Global Environmental Protection

The principles of international environmental law provide a robust framework for analyzing the legal dynamics of environmental hazards. Central to this framework is the principle of participation, which is pivotal in enhancing environmental protection and mitigating the progression of environmental hazards. By leveraging methods like crowdsourcing, this principle can foster a more engaged public involvement in safeguarding the biosphere. This research explores participatory democracy, access to information, public decision-making, and environmental litigation as fundamental approaches to achieving the objectives of international environmental legislation. It aims to illuminate the two-way interaction between these elements and assess the feasibility of actualizing the participation principle through crowdsourcing. The research examines the effectiveness of crowdsourcing as a tool for implementing this principle in supporting and protecting the environment. By reviewing and analyzing relevant sources, the article posits that increasing stakeholder participation, including civil groups and individuals, can enhance the effectiveness of environmental legislation and advance social and environmental justice. It advocates for the creation of platforms that facilitate information sharing and stakeholder engagement, along with mechanisms to ensure accountability and balance power, as crucial strategies for effective environmental governance.

A Comprehensive Discourse on Shallow Learning and its Applications

Shallow learning, a fundamental approach in machine learning, encompasses a variety of algorithms and techniques aimed at learning patterns and making predictions from labelled data. Unlike deep learning, which involves complex architectures with multiple layers of abstraction, shallow learning focuses on simpler models with limited complexity. This abstract explores the essence of shallow learning, its algorithms, applications, and challenges. Shallow learning algorithms include classic methods such as decision trees, support vector machines, k-nearest neighbours, and logistic regression, among others. These algorithms are typically trained using supervised learning techniques, where the model learns from input-output pairs to make predictions on new, unseen data. Shallow learning models excel in tasks such as classification and regression, where the goal is to assign labels or predict continuous values to input data. Applications of shallow learning span across various domains, including healthcare, finance, marketing, and cyber security. In healthcare, shallow learning models are used for disease diagnosis and prognosis prediction based on patient data. In finance, these models aid in fraud detection, credit scoring, and stock market prediction. Marketing applications involve customer segmentation and churn prediction, while in cyber security; shallow learning is utilized for malware detection and network intrusion detection.