The realm of education is witnessing a transformative integration with Artificial Intelligence (AI), poised to redefine the contours of pedagogical strategies. Central to this transformation is the emergence of personalized learning experiences, where AI endeavors to tailor educational content and interactions to resonate with individual learners' unique needs, preferences, and pace. This paper delves into the multifaceted dimensions of AI-driven personalized learning, from its potential to enhance e-learning modules, the advent of AI-powered virtual tutors, to the ethical challenges it surfaces. As the tapestry of education becomes more intertwined with digital innovations, understanding AI's role in individualizing learning becomes paramount.
Platelet-rich plasma (PRP) has gained widespread usage in the treatment of various chronic wounds due to its ease of preparation and high safety profile. But not every treatment can achieve satisfactory results. The quality of PRP is based on individual biological characteristics. Autologous PRP technology is greatly affected by the patient himself. Individual responses to PRP treatment are different and will produce different therapeutic effects. Especially in patients with uncontrollable factors such as aging, diabetes, and coronary heart disease, the use of antiplatelet drugs may potentially reduce the quality of autologous platelet-rich plasma (PRP). When treating related diseases, it is important to consider the impact of factors such as age, gender, and the specific disease. Currently, many researchers are focusing on the development of allogeneic PRP technology to avoid the inconvenience of collecting autologous blood and reduce potential negative effects that may exist in the patient's disease itself. In this review, we explore the factors generally recognized in current research which influence the efficacy of PRP.
The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) devices has led to the emergence of Edge AI, a transformative solution that enables data processing directly on the IoT devices or "at the edge" of the network. This paper explores the benefits of Edge AI, emphasizing reduced latency, bandwidth conservation, enhanced privacy, and faster decision-making. Despite its advantages, challenges like resource constraints on IoT devices persist. By examining the practical implications of Edge AI in sectors like healthcare and urban development, this study underscores the paradigm shift towards more efficient, secure, and responsive technological ecosystems.
In the evolving landscape of software development, Human-Centric Software Engineering (HCSE) is emerging as a pivotal paradigm, prioritizing human needs and experiences at the core of software engineering processes. This research delves into the fundamental principles of HCSE, its implications on software quality, and the enhanced user satisfaction it promises. Through comprehensive surveys, case study analyses, and user feedback sessions, this study reaffirms the increasing significance of HCSE in modern software development. Despite its evident benefits, the research also sheds light on the organizational barriers hindering its broad adoption. As software systems continue to intertwine with our daily lives, the research underscores the imperative shift from mere functionality to creating holistic, human-centered software experiences.
Blockchain technology, primarily acclaimed for its instrumental role in underpinning cryptocurrencies, has seen rising prominence in a multitude of applications outside the digital currency realm. Its decentralized infrastructure coupled with cryptographic integrity offers solutions to longstanding challenges across various industries, including supply chain, healthcare, and finance. This research endeavor delves into these multifarious applications, providing insights into the potential benefits and the existing impediments in the broader adoption of blockchain technology.
Artificial Intelligence (AI) systems, particularly deep learning models, have revolutionized numerous sectors with their unprecedented performance capabilities. However, the intricate structures of these models often result in a "black-box" characterization, making their decisions difficult to understand and trust. Explainable AI (XAI) emerges as a solution, aiming to unveil the inner workings of complex AI systems. This paper embarks on a comprehensive exploration of prominent XAI techniques, evaluating their effectiveness, comprehensibility, and robustness across diverse datasets. Our findings highlight that while certain techniques excel in offering transparent explanations, others provide a cohesive understanding across varied models. The study accentuates the importance of crafting AI systems that seamlessly marry performance with interpretability, fostering trust and facilitating broader AI adoption in decision-critical domains.
Given the pressure on land in the Central Region due to economic development and the search for opportunities, the best land for agriculture needs to be identified, so as to preserve it from urbanization, the expansion of mining areas, infrastructure and other occupations. It is in this context that the issue of mapping to identify agricultural zones and assess their accessibility was raised. This is an opportune moment to feed reflections on planning, agricultural production and natural resource management in the Center Region of Cameroon. GIS-based multi-criteria spatial analysis of data on land use, hydrographic network, slopes, soils and their suitability for cultivation provided precise, geolocalized information on land potentially suitable for agriculture in general. Following the analyses, we were able to establish and highlight the areas suitable for agriculture and accessible, which amounted to 8%, or 5662.42 km2 for high-potential and accessible areas. This was followed by 56%, or 37144.95 km2 for medium-potential and accessible zones, and finally 6%, or 4238.93 km2 for low-potential and accessible zones. Once the accessible areas had been removed, 5% or 3028.27 km2 of high-potential inaccessible areas remained, 19%, that is 14639.46 km2 of medium-potential inaccessible areas and 2% that is 967.12km2 of low-potential inaccessible areas.
NLP models have demonstrated susceptibility to adversarial attacks, thereby compromising their robustness. Even slight modifications to input text possess the capacity to deceive NLP models, leading to inaccurate text classifications. In the present investigation, we introduce Lexi-Guard: an innovative method for Adversarial Text Generation. This approach facilitates the rapid and efficient generation of adversarial texts when supplied with initial input text. To illustrate, when targeting a sentiment classification model, the utilization of product categories as attributes is employed, ensuring that the sentiment of reviews remains unaltered. Empirical assessments were conducted on real-world NLP datasets to showcase the efficacy of our technique in producing adversarial texts that are both more semantically meaningful and exhibit greater diversity, surpassing the capabilities of numerous existing adversarial text generation methodologies. Furthermore, we leverage the generated adversarial instances to enhance models through adversarial training, demonstrating the heightened resilience of our generated attacks against model retraining endeavors and diverse model architectures.
MIMO technology was proposed as early as 1908 to cope with wireless channel fading. In 1995, Bell Labs was the first to discover the great potential of MIMO system in channel capacity, and in 1996, Foshini of Bell Labs first proposed a space-time coding scheme, i.e., the diagonal-Bell Labs hierarchical space-time model, which can obtain very high spectrum utilization, but due to the complexity of its structure, it is difficult to be applied in practice, and is now rarely investigated. 1998, P.W. Wolniansky et al. gave a simple and practical space-time coding scheme on this basis, i.e., the vertical-Bell Labs layered space-time model. In 1998, P.W. Wolniansky et al. gave a simple and practical space-time coding scheme on this basis, i.e., Vertical Bell Labs Layered Space-Time (V-BLAST, Vertical Bell Labs Layered Space-Time) model, which can obtain very high spectrum utilisation and is easy to implement, and therefore has received wide attention once it was proposed. In this paper, we focus on airtime layered codes as well as the ZF detection algorithm and the MMSE detection algorithm in VBLAST systems and improve them to further enhance the performance of the two detection algorithms through sequential serial interference cancellation.
This research paper critically examines China’s role as the world’s largest emitter of carbon dioxide and its pivotal position in global climate change mitigation efforts. The analysis encompasses China’s environmental policies initiated since 1979, formally approved by the legislative body, the NPC, in 1989. Though significant economic developments were made since the country’s reform and opening in 1979, it is acknowledged that this progress has been accompanied by substantial environmental degradation. In response, the government amended environmental laws in 2014, reflecting a commitment to address these challenges. However, this paper contends that substantial efforts are still required to achieve meaningful environmental improvement. The research further delves into the anticipated impacts of Chinese policies on crucial aspects, namely Climate Change, Resource Management, and the well-being of Future Generations, providing comprehensive insights into the multifaceted implications of China’s environmental trajectory.
Computer vision, an interdisciplinary field bridging artificial intelligence and image processing, seeks to bestow machines with the capability to interpret and make decisions based on visual data. As the digital age propels forward, the ubiquity of visual content underscores the importance of efficient and effective automated interpretation. This paper delves deeply into the modern advancements and methodologies of computer vision, emphasizing its transformative role in various applications ranging from medical imaging to autonomous driving. With the increasing complexity of visual data, challenges arise pertaining to real-time processing, scalability, and the ethical implications of automated decision-making. Through an exhaustive literature review and novel experimentation, this research demystifies the multifaceted domain of computer vision, elucidating its potential and constraints. The study culminates in a visionary outlook, highlighting future avenues for research, including the fusion of augmented reality with computer vision, novel deep learning architectures, and ensuring ethical AI practices in visual interpretation.
In the modern technological tapestry, the security of database systems has burgeoned into a prominent concern for institutional frameworks. This urgency is invigorated by a dual confluence: the shifting industry paradigm which underscores the primacy of expansive data collections, coupled with the proliferation of legislative frameworks that zealously guard the sanctity of individual consumer data. The core aim of this discourse is to furnish a panoramic understanding of indispensable measures to bolster database security, with an amplified emphasis on countering SQL injection threats. The introductory segment delineates essential fortification strategies and succinctly touches upon optimal practices for shaping a database environment’s network topography and error mitigation methodologies. Subsequent to this panoramic insight, the discourse pivots to spotlight a diverse array of methodologies to discern and neutralize SQL injection forays.
Digital media technology is playing an increasingly important role in curriculum teaching, which provides new possibilities for the development and reform of education. This paper discusses the application of digital media technology in curriculum teaching, and points out its advantages in improving teaching quality, enhancing teacher-student interaction, promoting personalized learning, enhancing learning experience and improving teaching efficiency. However, the application of digital media technology also faces some challenges and problems, such as information overload, privacy protection, and high technical threshold, which need to be further studied and discussed. At the same time, the application of digital media technology needs to be closely combined with the teaching content of the course, and according to different course characteristics and student needs, the appropriate technology and way can be selected to achieve the best teaching effect. In the future, with the continuous development and progress of digital media technology, its application in curriculum teaching will be more extensive and in-depth. Digital media technology will be closely integrated with education, creating a more intelligent, personalized and efficient teaching environment for us, and promoting the development and progress of education. It can be seen that digital media technology has broad application prospects and potential in curriculum teaching, and it is worth our further exploration and research to make greater contributions to the development and reform of education.
HER2 protein overexpression is associated with the malignant degree and poor prognosis of breast cancer. HER2 levels are elevated in 20% of breast tumors. Several covalent tyrosine kinase inhibitors have been found to reduce tumor cell survival and proliferation in vitro and inhibit downstream HER2 signaling. In the field of protein structure prediction, AlphaFold2, which achieved excellent results in CASP14, can periodically predict protein structures with atomic precision in the absence of similar protein structures. In this study, AlphaFold2 was used to predict the monomeric structure of the HER2 protein. This predicted structure was compared to the conformation of HER2 in complex with a covalent inhibitor, allowing for an examination of the conformational changes induced by the inhibitor. By combining the conformational changes of HER2 protein with the docking results of Protein-Ligand Interaction Profiler, other potential binding sites were identified, which could further reveal the mechanism of drug discovery.
The ever-increasing complexity of cyber threats mandates advanced defense mechanisms. Intrusion Detection Systems (IDS) have emerged as fundamental tools in cybersecurity, incessantly monitoring networks for any suspicious activities. This paper offers an in-depth examination of IDS, tracing its evolution, methodologies, challenges, and future trajectories, substantiating the assertions with empirical studies and research.
Spectrum sensing technology is a crucial aspect of modern communication technology, serving as one of the essential techniques for efficiently utilizing scarce information resources in tight frequency bands. This paper first introduces three common logical circuit decision criteria in hard decisions and analyzes their decision rigor. Building upon hard decisions, the paper further introduces a method for multi-user spectrum sensing based on soft decisions. Then the paper simulates the false alarm probability and detection probability curves corresponding to the three criteria. The simulated results of multi-user collaborative sensing indicate that the simulation process significantly reduces false alarm probability and enhances detection probability. This approach effectively detects spectrum resources unoccupied during idle periods, leveraging the concept of time-division multiplexing and rationalizing the redistribution of information resources. The entire computation process relies on the calculation principles of power spectral density in communication theory, involving threshold decision detection for noise power and the sum of noise and signal power. It provides a secondary decision detection, reflecting the perceptual decision performance of logical detection methods with relative accuracy.
The intersection of artificial intelligence (AI) and software engineering marks a transformative phase in the technology industry. This paper delves into AI-driven software engineering, exploring its methodologies, implications, challenges, and benefits. Drawing from data sources such as GitHub and Bitbucket and insights from industry experts, the study offers a comprehensive view of the current landscape. While the results indicate a promising uptrend in the integration of AI techniques in software development, challenges like model interpretability, ethical concerns, and integration complexities emerge as significant. Nevertheless, the transformative potential of AI within software engineering is profound, ushering in new paradigms of efficiency, innovation, and user experience. The study concludes by emphasizing the need for further research, better tooling, ethical guidelines, and education to fully harness the potential of AI-driven software engineering.
With the rapid development of information technology, artificial intelligence technology is gradually being introduced in the field of education to achieve more efficient and personalized teaching. The educational concept of combining intelligence with numbers and cultivating abilities with all elements emerged in this context, aiming to promote the development and reform of education through the deep integration of artificial intelligence and education. The combination of digital and intelligent technologies emphasizes the dual role of digitalization and intelligence in the field of education, which promotes the quality and effectiveness of education through mutual promotion of digital means and intelligent technologies. Digitalization can provide education with massive data resources and efficient processing capabilities, while intelligence can achieve precise identification and satisfaction of students' personalized needs based on these data and resources. The joint cultivation of elements and abilities emphasizes the joint cultivation of literacy and abilities, that is, in the education process, not only should we focus on imparting knowledge, but also on cultivating students' literacy and abilities. Literacy includes information literacy, innovation literacy, humanistic literacy, etc., while abilities include learning ability and practical ability. Through the co cultivation of elements and abilities, students can better adapt to the development and changes of future society.
Linux is an open source Operating system that is for the most part freely available to the public. Due to its customizability and cost to performance benefits Linux has quickly been adopted by users and companies alike for use in applications such as servers and workstation. As the spread of Linux continues it is important for security specialists to understand the platform and the security issues that affect the platform as well. This paper seeks to first educate the users on what the Linux platforms is and what it offers to the user or company. And it then will expand upon some common or recent vulnerabilities that Linux faces due to the way it functions. After explaining some exploits, the paper will then seek to explain some hardening solutions that are available on the platform.
This study employs friction stir welding (FSW) technology to achieve the butt welding of 2mm thick 1060 aluminum and T2 copper. The research investigates the macroscopic formation, tensile properties, microhardness, and electrochemical corrosion behavior of the welded joints. The results indicate that the welded joints exhibit excellent formation, with a tensile strength reaching 84.76% of that of the 1060 aluminum material. Well-formed welded joints can be obtained by controlling the rotation speed and welding speed within a certain range. However, the rotation speed has a more significant impact on the microhardness in the weld zone. The corrosion potential of T2 copper is higher than that of 1060 aluminum, forming a macroscopic galvanic couple between the two materials. The corrosion potential of the welded joint falls between that of T2 copper and 1060 aluminum.