Rapid Evolution Research Articles

Sub-6 GHz beamforming with low-cost software-defined radio: Design, testing, and performance evaluation

The rapid evolution of mobile communication systems has led to the introduction of novel technologies and techniques that address existing challenges, such as interference caused by multiple users and transmission through the millimetre-wave band. In this paper, we present a novel approach that utilises a low-cost software defined radio (SDR) unit to enable transmitting and receiving beamforming in the sub-6 GHz band. We provide a detailed account of the underlying concepts and background of beamforming and SDR, outline the design of multiple components, and report on the comprehensive testing and verification of a real-life prototype using over-the-air (OTA) methods. Our results demonstrate that the proposed approach achieves outstanding performance, with measurements of bit error rate (BER) that outperform existing solutions. In summary, this work contributes to the ongoing efforts to enhance mobile communication systems by providing an innovative, high-performance solution that is cost-effective and reliable using SDR technology in the sub-6 GHz band.

Next-Generation Sequencing and Emerging Technologies.

Genetic sequencing technologies are evolving at a rapid pace with major implications for research and clinical practice. In this review, the authors provide an updated overview of next-generation sequencing (NGS) and emerging methodologies. NGS has tremendously improved sequencing output while being more time and cost-efficient in comparison to Sanger sequencing. The authors describe short-read sequencing approaches, such as sequencing by synthesis, ion semiconductor sequencing, and nanoball sequencing. Third-generation long-read sequencing now promises to overcome many of the limitations of short-read sequencing, such as the ability to reliably resolve repeat sequences and large genomic rearrangements. By combining complementary methods with massively parallel DNA sequencing, a greater insight into the biological context of disease mechanisms is now possible. Emerging methodologies, such as advances in nanopore technology, in situ nucleic acid sequencing, and microscopy-based sequencing, will continue the rapid evolution of this area. These new technologies hold many potential applications for hematological disorders, with the promise of precision and personalized medical care in the future.

Improving routine use of clinical pathway decision support through integration of an EHR with a clinical library resource designed to provide evidence-based guidance within oncology workflows

BackgroundThe rapid evolution, complexity, and specialization of oncology treatment makes it challenging for physicians to provide care based on the latest and best evidence. We hypothesized that physicians would use evidence-based trusted care pathways if they were easy to use and integrated into clinical workflow at the point of care.MethodsWithin a large integrated care delivery system, we assembled clinical experts to define and update drug treatment pathways, encoded them as flowcharts in an online library integrated with the electronic medical record, communicated expectations that clinicians would use these pathways for every eligible patient, and combined data from multiple sources to understand usage over time.ResultsWe were able to achieve > 75% utilization of eligible protocols ordered through these pathways within two years, with > 90% of individual oncologists having consulted the pathway at least once, despite no requirements or external incentives associated with pathway usage. Feedback from users contributed to improvements and updates to the guidance.ConclusionsBy making our clinical decision support easily accessible and actionable, we find that we have made considerable progress toward our goal of having physicians consult the latest evidence in their treatment decisions.

Pathway to Approval of Innovative Radiopharmaceuticals in China

Pathway to Approval of Innovative Radiopharmaceuticals in China

The accurate diagnosis for COVID-19 variants using nearly initial-rough sets

The rapid evolution of rough-set theory has prompted the need for enhanced methodologies in medical diagnostics, particularly regarding COVID-19 variant detection. This study introduces refined mathematical techniques based on topological structures (called nearly initial-rough sets) derived directly from initial-rough sets. Four categories of rough-set methodologies are presented, demonstrating heightened accuracy through comprehensive comparisons against existing methods. By leveraging these techniques, a rule-based classification system for COVID-19 variants is established, achieving 100 % accuracy measures through rigorous testing against real-world and computer-generated data. The implications of these advancements in medical diagnosis hold promise for future research, offering accessible and precise tools for variant identification and prediction. Using a medical application as a case study, we demonstrate superiority through comparative analyses, aligning mathematical results with medical data and showcasing the potential for broader applications beyond experts in topology. Furthermore, the study outlines an algorithm simplifying implementation, particularly in MATLAB, and suggests future explorations in medical, economic, and diverse theoretical frameworks to enhance applicability.

Finding the Flow: Defining Roles and Responsibilities in the Adaptive Radiation Therapy Process

Finding the Flow: Defining Roles and Responsibilities in the Adaptive Radiation Therapy Process

Clinical evaluation of a deep learning CBCT auto-segmentation software for prostate adaptive radiation therapy

PurposeAim of the present study is to characterize a deep learning-based auto-segmentation software (DL) for prostate cone beam computed tomography (CBCT) images and to evaluate its applicability in clinical adaptive radiation therapy routine. Materials and methodsTen patients, who received exclusive radiation therapy with definitive intent on the prostate gland and seminal vesicles, were selected. Femoral heads, bladder, rectum, prostate, and seminal vesicles were retrospectively contoured by four different expert radiation oncologists on patients CBCT, acquired during treatment. Consensus contours (CC) were generated starting from these data and compared with those created by DL with different algorithms, trained on CBCT (DL-CBCT) or computed tomography (DL-CT). Dice similarity coefficient (DSC), centre of mass (COM) shift and volume relative variation (VRV) were chosen as comparison metrics. Since no tolerance limit can be defined, results were also compared with the inter-operator variability (IOV), using the same metrics. ResultsThe best agreement between DL and CC was observed for femoral heads (DSC of 0.96 for both DL-CBCT and DL-CT). Performance worsened for low-contrast soft tissue organs: the worst results were found for seminal vesicles (DSC of 0.70 and 0.59 for DL-CBCT and DL-CT, respectively). The analysis shows that it is appropriate to use algorithms trained on the specific imaging modality. Furthermore, the statistical analysis showed that, for almost all considered structures, there is no significant difference between DL-CBCT and human operator in terms of IOV. ConclusionsThe accuracy of DL-CBCT is in accordance with CC; its use in clinical practice is justified by the comparison with the inter-operator variability.

Niche Theory as an Underutilized Resource for the Study of Adaptive Radiations.

Biologists are often stuck between two opposing questions: Why are there so many species and why are there not more? Although these questions apply to the maintenance of existing species, they equally apply to the formation of new ones. The more species specialize in terms of their niches, the more opportunities arise for new species to form and coexist in communities. What sets an upper limit to specialization, thus setting an upper limit to speciation? We propose that MacArthur's theories of species packing and resource minimization may hold answers. Specifically, resources and individuals are finite-as species become increasingly specialized, each individual has fewer resources it can access. Species can only be as specialized as is possible in a given resource environment while still meeting basic resource requirements. We propose that the upper limit to specialization lies below the threshold that causes populations to be so small that stochastic extinctions take over, and that this limit is likely rarely approached due to the sequential timing by which new lineages arrive.

THEORETICAL AND PRACTICAL RESEARCH ON CHINA'S RESPONSE TO POPULATION AGING IN THE NEW ERA

Population aging is an important trend of social development. At present, China is in the largest aging process in history, catching up with or even surpassing the aging speed of Western countries in the rapid evolution, with many pressures and challenges under the complex internal and external environment. China has put forward a series of new theories, new ideas and new strategies on the work of aging, forming a theoretical system with rich connotation, complete system and strict logic, which guides the practice of the Chinese government to deal with population aging. This paper deeply analyzes the relevant theoretical basis, analyzes the current situation, characteristics and causes of China's aging, and summarizes the practice of China's innovative response to aging since 2010, in order to provide valuable experience for the rest of the world to deal with aging.

AI Enabled Chatbot

Abstract: As technology continues its rapid evolution, the integration of Artificial Intelligence (AI) into chatbot systems emerges as a pivotal frontier in reshaping human-computer interaction. This research paper embarks on a comprehensive exploration of the intricate synergy between AI technologies and chatbot development, with a primary focus on elevating user interactions to unprecedented levels of sophistication and intuitiveness. By delving into a nuanced understanding of methodologies, technologies, and challenges, this study aims to provide a holistic perspective on the present state and future potential of AIenabled chatbots. The journey begins with a retrospective analysis of the evolution of chatbots, tracing their trajectory from rulebased systems to the forefront of AI-driven conversational agents. The advent of AI, particularly Natural Language Processing (NLP) and machine learning algorithms, has ushered in a new era, enabling chatbots to not only comprehend but also respond intelligently, mirroring the complexities of human language.

Navigating the digital transformation journey: strategies for startup growth and innovation in the digital era

This study delves into the strategies for startup growth and innovation within the digital transformation journey, highlighting the imperative for startups to adapt and thrive in the digital era. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, industry reports, and case studies from 2013 to 2023 to unearth pivotal digital transformation strategies and their impact on startup success. The methodology ensures a comprehensive exploration of the digital landscape, focusing on the integration of digital technologies, the cultivation of a digital-first culture, and the navigation of regulatory landscapes. Key findings reveal that successful digital transformation for startups hinges on a multifaceted approach encompassing agile methodologies, digital literacy, customer-centric innovation, and proactive regulatory engagement. The study identifies emerging technologies, such as AI and blockchain, as critical enablers of innovation and operational efficiency, while emphasizing the significance of digital ecosystems and platform-based models for future growth. Finally, the research offers actionable recommendations for startups, including fostering innovation cultures, enhancing digital skills, engaging in ecosystem partnerships, and adopting customer-centric strategies. It also outlines future research directions, suggesting a focus on the impact of emerging technologies, digital transformation outside the tech sector, and longitudinal studies on startup success. This study contributes to the understanding of digital transformation strategies, providing a roadmap for startups aiming to navigate the complexities of the digital age and underscores the ongoing need for adaptive strategies in the face of rapid technological evolution.

Shop-EZ

Abstract: The rapid evolution of technology has transformed the landscape of commerce, ushering in an era dominated by electronic transactions and online marketplaces. This research paper aims to provide a comprehensive analysis of the emerging trends and challenges in the field of e-commerce. The study encompasses a broad spectrum of topics, including technological advancements, consumer behavior, regulatory frameworks, and the impact of global events on ecommerce ecosystems. The first section of the paper explores the latest technological trends shaping the e-commerce industry, such as artificial intelligence, blockchain, and augmented reality. Special attention is given to their influence on enhancing user experience, personalization, and security. The paper also delves into the integration of these technologies and their potential to revolutionize supply chain management and logistics in e-commerce operations.

Insights into chloroplast genome evolution in Rutaceae through population genomics

Chloroplast genomes, pivotal for understanding plant evolution, remain unexplored in Rutaceae, a family with key perennial crops like citrus. Leveraging next-generation sequencing data from 509 Rutaceae accessions across 15 species, we conducted a de novo assembly of 343 chloroplast genomes, unveiling a chloroplast variation map highlighting the heterogeneous evolution rates across genome regions. Notably, differences in chloroplast genome size primarily originate from large single-copy and small single-copy regions. Structural variants predominantly occurred in the single-copy region, with two insertions located at the single-copy and inverted repeat region boundary. Phylogenetic analysis, principal component analysis, and population genetic statistics confirmed the cohesive clustering of different Citrus species, reflecting evolutionary dynamics in Citrus diversification. Furthermore, a close chloroplast genetic affinity was revealed among Atalantia (previously regarded as primitive citrus), Clausena, and Murraya. Zanthoxylum formed a distinct group with heightened genetic diversity. Through expanding our analysis to include 34 published chloroplast genomes, we explored chloroplast gene selection, revealing divergent evolutionary trends in photosynthetic pathways. While Photosystem I and Photosystem II exhibited robust negative selection, indicating stability, the Nicotinamide adenine dinucleotide (NADH) dehydrogenase pathway demonstrated rapid evolution, which was indicative of environmental adaptation. Finally, we discussed the effects of gene length and GC content on chloroplast gene evolution. In conclusion, our study reveals the genetic characterization of chloroplast genomes during Rutaceae diversification, providing insights into the evolutionary history of this family.

A Study on Emergence of Quick Commerce

A Study on Emergence of Quick Commerce

Lifelong Learning Opportunities for Landscape Architecture Bachelor's Programs: Assessments on Landscape Design Component

Lifelong Learning Opportunities for Landscape Architecture Bachelor's Programs: Assessments on Landscape Design Component

Enhanced Road Safety through Integrated Speed Breaker, Pothole, and Lane Detection System

Abstract: Amidst the rapid evolution of autonomous vehicle technologies, ensuring road safety remains a paramount challenge. Effective detection of lanes and potential hazards, including speed breakers and potholes, is critical for safe autonomous driving. In this study, we introduce an innovative Lane, Speed Breaker, and Pothole Detection System (LSPDS) utilizing YOLOv4 Tiny, a state-of-the-art object detection algorithm and computer vision techniques. Our system integrates computer vision and machine learning techniques for analysis of road conditions. By employing camera sensors, we capture the road scene and apply image processing algorithms to identify lanes, speed breakers, and potholes. Moreover, the system incorporates Firebase for user authentication and SMS services for real-time alerts. YOLOv4 Tiny is employed for accurate detection and classification of these features within the captured images, thereby enhancing the perception capabilities of autonomous vehicles.

Enhancing automotive interior automation through face analysis techniques

In contemporary society, the progress and widespread adoption of automotive automation, particularly in autonomous driving technology, have been remarkable. The rapid evolution of this technology has equipped numerous vehicles with high-stability autonomous capabilities, significantly enhancing convenience for users. However, as autonomous driving continues its developmental trajectory, it is crucial to give equal attention to the advancement of interior automation technologies within vehicles. Exploring avenues that make these automated technologies smarter, safer, and more conducive to delivering heightened convenience and support to users is imperative. This realm presents a domain ripe with potential for innovation and substantial advancements that cater to the evolving needs and expectations of modern transportation.

Optimizing Treatment Quality in Head and Neck Cancer Therapy: Impact of Adaptive Intensity-Modulated Radiotherapy on Plan Quality and Tumor Volume Changes

Objective: This research analyses whether volumetric changes and PTV shifting affect adaptive intensity-modulated radiotherapy (IMRT) plan quality and head and neck cancer therapy. Methods: The research was carried out at the Sohag Cancer Centre in Egypt, including a sample of 36 individuals diagnosed with head and neck squamous cell carcinoma (HNSCC). Patients were chosen on the basis of the following criteria: locally progressed and node-positive malignancy, oropharyngeal cancer, oral cavity cancer, and unknown primary. The research used dynamic intensity-modulated radiation therapy (D-IMRT) to establish treatment plans and Siemens SOMATOM DEFINITION CT scanners to acquire 3D anatomical images. A range of 1.64 to 2.12 Gy was observed for the median dosage per fraction. Using the conformance index (CI) and the homogeneity index, the quality of the plan was assessed (HI). The threshold for statistical significance was set at 0.05. Results: The study analysed patients with unilateral and bilateral HNSCC tumors, with a majority having bilateral tumors. The findings demonstrated that the tumor PTV was significantly reduced as a consequence of the suggested adaptive radiation techniques. Both the homogeneity index and the conformity index were used to evaluate the plan's quality (HI). The maximal homogeneity index (HI) was reached after 7 and 14 fractions, respectively. The positive correlation between PTV reduction and CI after 7 fractions, while a negative correlation found after fractions 14 and 21. It was also shown that HI is positively correlated with PTV changes after 7 and 14. Conclusion: The use of adaptive radiotherapy in intensity-modulated radiation therapy (IMRT) treatment planning improves plan quality and decreases error rates due to tumor margin movement. Following radiation fractions, PTV shifting affects the homogeneity index (HI).

Ecological diversification preceded geographical expansion during the evolutionary radiation of Cataglyphis desert ants

Biological diversity often arises as organisms adapt to new ecological conditions (i.e., ecological opportunities) or colonize suitable areas (i.e., spatial opportunities). Cases of geographical expansion followed by local ecological divergence are well described; they result in clades comprising ecologically heterogeneous subclades. Here, we show that the desert ant genus Cataglyphis likely originated in open grassland habitats in the Middle East ∼18 million years ago and became a taxon of diverse species specializing in prey of different masses. The genus then colonized the Mediterranean Basin around 9 million years ago. The result was the rapid accumulation of species, and the appearance of local assemblages containing species from different lineages that still displayed ancestral foraging specialties. These findings highlight that, in Cataglyphis, ecological diversification preceded geographical expansion, resulting in a clade composed of ecologically homogeneous subclades.

A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized controller of software-defined networking provides a comprehensive network view, simplifying traffic analysis and offering direct programmability features. When combined with deep learning techniques, these characteristics enable the incorporation of intelligence into networks, leading to optimization and improved network management and maintenance. Therefore, this research aims to develop a model for traffic classification by application types and network attacks using deep learning techniques to enhance the quality of service and security in software-defined networking. The SEMMA method is employed to deploy the model, and the classifiers are trained with four algorithms, namely LSTM, BiLSTM, GRU, and BiGRU, using selected features from two public datasets. These results underscore the remarkable effectiveness of the GRU model in traffic classification. Hence, the outcomes achieved in this research surpass state-of-the-art methods and showcase the effectiveness of a deep learning model within a traffic classification in an SDN environment.