Technological Breakthroughs Research Articles

Integration and performance analysis of artificial intelligence and computer vision based on deep learning algorithms

This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vision provide strong support for training deep learning algorithms. The tight integration of these two fields has given rise to a new generation of advanced computer vision systems, significantly surpassing traditional methods in tasks such as machine vision image classification and object detection. In this paper, typical image classification cases are combined to analyze the superior performance of deep neural network models while also pointing out their limitations in generalization and interpretability, proposing directions for future improvements. Overall, the efficient integration and development trend of deep learning with massive visual data will continue to drive technological breakthroughs and application expansion in the field of computer vision, making it possible to build truly intelligent machine vision systems. This deepening fusion paradigm will powerfully promote unprecedented tasks and functions in computer vision, providing stronger development momentum for related disciplines and industries.

NEED FOR HUMAN RESOURCE SOFTWARE IN ORGANIZATIONS

Considering how quickly technology is changing in today's world, businesses must modify their HR procedures to satisfy the needs of the digital era. This paper examines how technology breakthroughs have affected the role that HR professionals play, highlighting new developments and the difficulties they provide. The report evaluates the competency levels of HR specialists in technology-based businesses using empirical research, highlighting the need for reliable HR software solutions for effectively tackling the challenges of contemporary labor management. . Keywords— Human Resource Management System, HR Software, Workforce Management, HR Challenges, Recruitment Software, Human Resource Technology.

INTEGRATING SOLAR POWER WITH EXISTING GRIDS: STRATEGIES, TECHNOLOGIES, AND CHALLENGES – A REVIEW

Integrating solar power into existing electrical grids is critical to realizing a sustainable and resilient global energy landscape. This review paper offers a thorough overview of solar power's significance and potential for transforming energy systems, emphasizing its benefits for grid stability, fossil fuel reduction, and environmental sustainability. It details essential strategies for successful solar integration, such as adopting both grid-tied and off-grid systems, advancements in energy storage to mitigate solar intermittency, and applying innovative grid technologies for enhanced energy management. Additionally, the paper navigates through the technological breakthroughs aiding solar power's incorporation into grids and confronts the challenges, including technical issues like grid scalability and economic barriers such as investment costs, regulatory complexities, and social-environmental impacts. Drawing on case studies from leading regions in solar integration, it distills lessons learned and identifies strategies and technologies that have surmounted these hurdles. The review also projects future trends and innovations in solar technology and grid management, underscoring the need for ongoing innovation and supportive policies to bolster solar power's role in energy systems. By collating insights into solar power integration's current state and prospects, this paper will guide policymakers, industry players, and researchers toward harnessing solar energy's full potential, highlighting its indispensable contribution to a sustainable energy future.

Sequencing the transcriptome of single cells: a path to discovering cellular biodiversity

Single-cell transcriptomics (scRNA-Seq) is a breakthrough technology that has opened the way to characterizing gene expression with unprecedented resolution. It has enabled the discovery of the cellular diversity of organisms and tracing their developmental processes. A range of technological solutions have been developed to allow analysis of tens of thousands to even a million cells in a single experiment, as well as an extensive set of tools for bioinformatics analysis of the generated data. The wealth of information provided by scRNA-Seq and the possibility of using this method to study cells, organoids, tissues and even entire organisms determine its wide range of applications. In this paper, we present the experimental and computational parts of the scRNA-Seq procedure, as well as the most important applications of this technology in biomedicine, developmental biology and plant biology.

Advanced Dressings for Chronic Wound Management.

Wound healing, particularly for chronic wounds, presents a considerable difficulty due to differences in biochemical and cellular processes that occur in different types of wounds. Recent technological breakthroughs have notably advanced the understanding of diagnostic and therapeutic approaches to wound healing. The evolution in wound care has seen a transition from traditional textile dressings to a variety of advanced alternatives, including self-healing hydrogels, hydrofibers, foams, hydrocolloids, environment responsive dressings, growth factor-based therapy, bioengineered skin substitutes, and stem cell and gene therapy. Technological advancements, such as 3D printing and electronic skin (e-skin) therapy, contribute to the customization of wound healing. Despite these advancements, effectively managing chronic wounds remains challenging. This necessitates the development of treatments that consider performance, risk-benefit balance, and cost-effectiveness. This review discusses innovative strategies for the healing of chronic wounds. Incorporating biomarkers into advanced dressings, coupled with corresponding biosensors and drug delivery formulations, enables the theranostic approach to the treatment of chronic wounds. Furthermore, integrating advanced dressings with power sources and user interfaces like near-field communication, radio frequency identification, and Bluetooth enhances real-time monitoring and on-demand drug delivery. It also provides a thorough evaluation of the advantages, patient compliance, costs, and durability of advanced dressings, emphasizing smart formulations and their preparation methods.

Haram Lost and Found

With its great religious significance, the Holy Mosque in Makkah welcomes millions of tourists every year. The problem of misplaced objects at this holy site poses a problem for both guests and guards. In order to tackle this issue and improve worshippers' and visitors' overall experience, this study project explores the ideation, creation, and testing of a state-of-the-art mobile application called the "Lost and Found Assistance" app. This software makes use of modern technologies to expedite the process of finding misplaced items inside the Holy Mosque. The 'Lost and Found Assistance' app's main objective is to offer a workable answer to anyone who have misplaced their belongings inside the enormous Holy Mosque. It presents an easy-to-use and effective platform that enables users to report misplaced things, facilitating their prompt recovery. Additionally, it enables users to look for misplaced items, providing comfort to individuals who could have momentarily misplaced priceless items while visiting. In order to provide insight on the Holy Mosque's hallowed legacy and importance in the Islamic world, this study initiative examines the historical significance of the building. It explores the issues surrounding misplaced objects at this hallowed location, highlighting the demand for an up-to-date, technologically advanced solution. In addition, the research highlights the revolutionary role that mobile applications play in a variety of contexts as it examines the current state of lost and found services. Through an analysis of current applications and technological breakthroughs, it pinpoints chances to modify these advances to meet the particular difficulties presented by the Holy Mosque. The entire development process of the 'Lost and Found Assistance' app is described, from the first conceptualization to the design and coding stages. The project emphasizes the value of intuitive design concepts and user-friendly interfaces in making sure that the software is useful and accessible to a variety of users. An analysis of the app's usefulness in helping users find misplaced goods within the Holy Mosque completes the research endeavor. It talks about user pleasure and feedback, highlighting how the software improves users' experiences. It also looks at how widely the app might be used and offers ideas for improvements down the road. The 'Lost and Found Assistance' app helps to maintain a calm and serene atmosphere inside the Holy Mosque by utilizing contemporary technologies. The ability of technology to solve long-standing issues and improve the pilgrimage and visitor experience for millions of worshippers and tourists who visit this holy site is demonstrated by this study effort.

BacScan: a novel genome-wide strategy for uncovering broadly immunogenic proteins in bacteria.

In response to the global threat posed by bacterial pathogens, which are the second leading cause of death worldwide, vaccine development is challenged by the diversity of bacterial serotypes and the lack of immunoprotection across serotypes. To address this, we introduce BacScan, a novel genome-wide technology for the rapid discovery of conserved highly immunogenic proteins (HIPs) across serotypes. Using bacterial-specific serum, BacScan combines phage display, immunoprecipitation, and next-generation sequencing to comprehensively identify all the HIPs in a single assay, thereby paving the way for the development of universally protective vaccines. Our validation of this technique with Streptococcus suis, a major pathogenic threat, led to the identification of 19 HIPs, eight of which conferred 20-100% protection against S. suis challenge in animal models. Remarkably, HIP 8455 induced complete immunity, making it an exemplary vaccine target. BacScan's adaptability to any bacterial pathogen positions it as a revolutionary tool that can expedite the development of vaccines with broad efficacy, thus playing a critical role in curbing bacterial transmission and slowing the march of antimicrobial resistance.

Are artificial intelligence and blockchain the key to unlocking the box of clean energy?

The fourth industrial revolution has brought about key technological breakthroughs represented by artificial intelligence (AI) and blockchain (BC), which provide an opportunity for the development of clean energy (CE). We employ a wavelet-based quantile-on-quantile approach to investigate the correlation among AI, BC and CE over different periods and time horizons. The results show that although the integration of AI and CE systems requires a lot of time, it can significantly boost CE development in the long run. However, BC cannot significantly promote CE development except for a few extreme periods. Our results are supported by the mechanism of interaction among AI, BC and CE development, which highlights their relationship. Therefore, the government should focus on promoting the development of new information technologies and their integration with the CE system, which is crucial for the implementation of energy transition strategies. In addition, low-carbon investors should focus on these technologies because of their relationship with CE.

Transformational Changes in the Creative Sector under the Influence of Generative AI Technologies

Each time period of economic development is characterized by one or another key, central technology that links the technological structure together. In the 19th century, this technology became the use of steam engines, from the beginning of the 20th century – electricity and internal combustion engines, in the 90s – computerization and the emergence of network technologies, then – cellular communications and mobile broadband Internet, then – the emergence of social networks and social media. Now, according to the consensus opinion of industry experts, the function of such technology is performed by generative artificial intelligence, which emerged just a few years ago, but has already significantly changed many sectors of the economy. The article discusses the main aspects associated with the emergence of breakthrough technologies of generative artificial intelligence. The key factors that influenced the emergence of technological capabilities were studied, such as the growth of the computing capabilities of processors, a consistent increase in funding for innovative projects, and an increase in publication activity in this direction. Particular attention is paid to the characteristics of the value chain of generative artificial intelligence technologies. It is shown that hardware requirements are becoming higher, a previously non-existent stage of creating basic models appears, as well as a layer of companies adapting basic models to the tasks of business practice by learning from industry data. We have also shown that generative artificial intelligence technologies will have the greatest impact on the creative sector of the economy, since they allow us to significantly speed up and optimize processes at all stages of creating the value of a product, reducing the need for manual labor, but not replacing it completely.

Visual Saliency Detection using Deep Learning

Salient object discovery models mimic the gesture of human beings and capture the most salient region/ object from the images or scenes. This field has numerous important operations in both computer vision and pattern recognition tasks. Despite hundreds of models proposed in this field, it still has a large room for exploration. This paper demonstrates a detailed overview of the recent progress of saliency discovery models in terms of heuristic- grounded ways and deep literacy- grounded ways. We've bandied and reviewed it’s co-related fields, similar as Eye obsession- vaticination, RGBD salient- object- discovery, co- saliency object discovery, and videotape- saliency- discovery models. Image saliency object discovery can fleetly prize useful information from image scenes and further assay it. At present, the traditional saliency target discovery technology still has the edge of outstanding target that can not be well saved. Convolutional neural network( CNN) can prize largely general deep features from the images and effectively express the essential point information of the images. This paper designs a model which applies CNN in deep saliency object discovery tasks. It can efficiently optimize the edges of focus objects and realize largely effective image saliency discovery through multilayer nonstop point birth, refinement of layered boundary, and original saliency point emulsion. The experimental result shows that the proposed system can achieve further robust saliency discovery to acclimate itself to complex background terrain.

High fidelity FEM based on deep learning for arbitrary composite material structure

Due to the outstanding performance, composite materials are widely used and analyzing their properties and designing them based on performance has become a crucial task in the field of many manufacturing industries. Composite materials possess complex multiscale structures, and traditional fine-scale finite element modeling and analysis may lead to severe computational resource challenges. To overcome this difficulty, breakthroughs in key technologies of multiscale accelerated analysis algorithms are required. In this study, an innovative approach based on artificial intelligence and multiscale finite element method is presented. This approach involves partitioning the entire composite material structure into coarse grids that resemble homogenous structures of similar size, providing results consistent to fine-grid finite element analysis. By utilizing CNN for image feature recognition and employing the CGAN adversarial method, coarse-grid equivalent stiffness matrices and multiscale shape functions from completely random microstructures of composite materials can be obtained. Consequently, this enables a rapid response process from microstructure to low-resolution grid to high-resolution physical field, with remarkably accurate physical field results. Moreover, compared to traditional fine-grid finite element methods, this approach significantly reduces memory usage and computation time. This method is applicable to composite materials with varying shaped inclusions, different component properties, and diverse geometric distributions, allowing these materials to perform high-fidelity finite element calculations on coarse grids and predict their mechanical behavior. Furthermore, this breakthrough opens avenues for accelerating the optimization design of composite materials with diverse mechanical functionalities, by employing a bottom-up approach.

A Review on Deep Learning Based Tomato Leaf Disease Detection for Smart Farming

The leaf diseases have come a serious concern for the agrarian assiduity, yet timely opinion and recognition are challenging in multitudinous regions of the globe owing to a deficit of automated crop complaint identification methods. However, food instability will rise, affecting the country’s income, if factory conditions aren't honored in a prompt way. Factory complaint identification is critical for successful crop forestallment and control of conditions, as well as ranch product operation and decision- timber. Factory complaint discovery technologies aid in finding infected shops in their early phases and also help the stoner in cost-effectively expanding factory complaint identification system to a variety of shops. This paper’s major donation is a piled ensemble fashion grounded on Machine literacy and Deep literacy ways. This exploration composition also elaborates on how factory complaint discovery frame will be realized using new segmentation and point birth strategies for rooting significant features for classification. Once the features are uprooted, they're transmitted to the pall platform to apply web enabled automated covering system. The proposed piled ensemble literacy is estimated by comparing different machine literacy and Deep literacy ways models exercising perfection, recall. When compared to traditional machine literacy and deep literacy ways approaches, the findings show that the proposed fashion achieves about 99% delicacy.

From Gene Editing to Biofilm Busting: CRISPR-CAS9 Against Antibiotic Resistance-A Review.

In recent decades, the development of novel antimicrobials has significantly slowed due to the emergence of antimicrobial resistance (AMR), intensifying the global struggle against infectious diseases. Microbial populations worldwide rapidly develop resistance due to the widespread use of antibiotics, primarily targeting drug-resistant germs. A prominent manifestation of this resistance is the formation of biofilms, where bacteria create protective layers using signaling pathways such as quorum sensing. In response to this challenge, the CRISPR-Cas9 method has emerged as a ground-breaking strategy to counter biofilms. Initially identified as the "adaptive immune system" of bacteria, CRISPR-Cas9 has evolved into a state-of-the-art genetic engineering tool. Its exceptional precision in altering specific genes across diverse microorganisms positions it as a promising alternative for addressing antibiotic resistance by selectively modifying genes in diverse microorganisms. This comprehensive review concentrates on the historical background, discovery, developmental stages, and distinct components of CRISPR Cas9 technology. Emphasizing its role as a widely used genome engineering tool, the review explores how CRISPR Cas9 can significantly contribute to the targeted disruption of genes responsible for biofilm formation, highlighting its pivotal role in reshaping strategies to combat antibiotic resistance and mitigate the challenges posed by biofilm-associated infectious diseases.

A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.

Abstract PO1-25-09: A new generation of breast cancer modeling has been achieved through somatic precision gene editing, offering high flexibility and efficiency

Abstract Breast cancer, along with many other cancers, is primarily caused by genetic mutations, typically involving the alteration of nucleotides (A, G, C, T) within genes (with a total of 12 possible mutations). Gene sequencing advancements have facilitated the identification of numerous gene mutations in tumor samples. However, comprehending the precise mechanisms through which these gene mutations contribute to tumor initiation, development, and their impact on tumor characteristics cannot be solely determined from these findings. Therefore, it is crucial to observe the influence of mutations on tumor initiation and development from the moment they occur. Since conducting such observations in human subjects is impractical, animal models with simulated gene mutations have become essential for studying tumor initiation and development. Mice, with a genome comprising approximately 2.7 billion nucleotides, are commonly used as animal models in cancer research due to their cost-effectiveness and high similarity to humans. About 90% of their genes share similar functions, particularly those associated with cancer development. However, achieving precise alterations to specific nucleotides among the 2.7 billion nucleotides within live mouse cells (with a total of 12 possible changes) has presented significant technical challenges. In this study, we present modifications to the CRISPR/Cas9 vector system, enabling homology-directed repair-mediated precise editing of any proto-oncogene in murine somatic mammary epithelial cells, thereby generating mammary tumor models with exceptional flexibility and efficiency. The resulting tumors exhibit significant advantages over traditional mouse models. This technological breakthrough bridges the gap between the potential of CRISPR technology and the accuracy of mouse models, facilitating the study of human tumor evolution and preclinical drug testing. Citation Format: Wen Bu, Yi li. A new generation of breast cancer modeling has been achieved through somatic precision gene editing, offering high flexibility and efficiency [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-25-09.

Free-Standing DNA Origami Superlattice to Facilitate Cryo-EM Visualization of Membrane Vesicles.

Technological breakthroughs in cryo-electron microscopy (cryo-EM) methods open new perspectives for highly detailed structural characterizations of extracellular vesicles (EVs) and synthetic liposome-protein assemblies. Structural characterizations of these vesicles in solution under a nearly native hydrated state are of great importance to decipher cell-to-cell communication and to improve EVs' application as markers in diagnosis and as drug carriers in disease therapy. However, difficulties in preparing holey carbon cryo-EM grids with low vesicle heterogeneities, at low concentration and with kinetic control of the chemical reactions or assembly processes, have limited cryo-EM use in the EV study. We report a straightforward membrane vesicle cryo-EM sample preparation method that assists in circumventing these limitations by using a free-standing DNA-affinity superlattice for covering holey carbon cryo-EM grids. Our approach uses DNA origami to self-assemble to a solution-stable and micrometer-sized ordered molecular template in which structure and functional properties can be rationally controlled. We engineered the template with cholesterol-binding sites to specifically trap membrane vesicles. The advantages of this DNA-cholesterol-affinity lattice (DCAL) include (1) local enrichment of artificial and biological vesicles at low concentration and (2) isolation of heterogeneous cell-derived membrane vesicles (exosomes) from a prepurified pellet of cell culture conditioned medium on the grid.

Malignant lymphomas - quo vadis? - What developments await us in diagnostics and therapy?

The diagnosis and treatment of malignant lymphoma is rapidly advancing, offering hope but also highlighting inherent limitations. Technological breakthroughs in sequencing technologies enable more precise subtyping and risk stratification. For example, in diffuse large B-cell lymphoma (DLBCL), exome sequencing revealed molecular subtypes. Understanding these subtypes sheds light on lymphomagenesis and prognosis, and may provide targets for tailored therapies. Additionally, tumor-derived cell-free DNA (ctDNA) detected in blood plasma allows for genotyping, risk stratification, and measurement of minimal residual disease (MRD). Current studies often examine drug effectiveness through "all-comer" approaches or in transcriptionally defined subtypes. Molecular agnostic studies increasingly focus on clinically defined high-risk patients (e.g., using the IPI) to better demonstrate the statistical significance of therapy effects. Improved patient selection can enhance the cost-effectiveness of modern, often expensive, therapies.

Plant-mediated green synthesis of cerium oxide nanoparticles: A critical perspective of some unclear issues

Nanotechnology is undoubtedly our century's leading scientific and technological breakthrough since it encounters applications in several fields. In parallel, modern societies are facing drawbacks related to environmental concerns. For this reason, several areas are attempting to make the production process eco-friendly. In this direction, the first work using plant extracts to obtain cerium oxide nanoparticles (CNPs) was published in 2011. After more than 10 years, many advances were achieved regarding this green synthesis (GS) route, using a variety of plant types. However, some critical issues remain unclear or with less experimental evidence, such as the role of plant extracts during the synthesis (reducing, oxidizing, chelating, stabilizers, or capping agents) and difficulties in obtaining CNPs with shapes different from spherical ones. Considering the broad possible applications of these nanoparticles in many fields and the importance of their properties, this review aims to critically discuss some uncovered/unsolved issues related to the role of phytocompounds during the CNPs green synthesis and the shape of the produced nanoparticles. Environmental and scale-up aspects are also briefly discussed. The main limitations found are the lack of agreement about the mechanisms of formation of CNPs by plant-mediated green synthesis and the difficulty in obtaining morphologies like nanocubes, nanotubes, and nanowires. Despite the challenges, the various suggestions and insights presented may help overcome these limitations and expand the potential of CNPs.

Biological and bio-inspired materials: Multi-scale modeling, artificial intelligence approaches, and experiments

Biological materials often possess remarkable properties and functionalities owing to their complex hierarchical and composite structures. Learning from nature can lead to revolutionary breakthroughs in materials science and innovative new technologies. This Special Issue titled “Biological and Bio-inspired Materials: Multi-scale Modeling and Artificial Intelligence Approaches” is a collection of research articles and comprehensive reviews utilizing multi-scale modeling, artificial intelligence approaches, and experiments to elucidate the characteristics of biological materials and design and optimize bio-inspired materials. The computational approaches of interest include but are not limited to molecular dynamics, lattice spring models, finite element analysis, genetic algorithms, neural networks, generative adversarial networks, and other modeling and artificial intelligence approaches for better understanding the structure-property relationships and underlying mechanisms of biological (natural) materials, and reproducing, designing, and optimizing bio-inspired materials. Novel experimental results, fabrication strategies, and applications of biological, bio-inspired and biomedical materials are also collected in this special issue.

Future avenues in Drosophila mushroom body research.

How does the brain translate sensory information into complex behaviors? With relatively small neuronal numbers, readable behavioral outputs, and an unparalleled genetic toolkit, the Drosophila mushroom body (MB) offers an excellent model to address this question in the context of associative learning and memory. Recent technological breakthroughs, such as the freshly completed full-brain connectome, multiomics approaches, CRISPR-mediated gene editing, and machine learning techniques, led to major advancements in our understanding of the MB circuit at the molecular, structural, physiological, and functional levels. Despite significant progress in individual MB areas, the field still faces the fundamental challenge of resolving how these different levels combine and interact to ultimately control the behavior of an individual fly. In this review, we discuss various aspects of MB research, with a focus on the current knowledge gaps, and an outlook on the future methodological developments required to reach an overall view of the neurobiological basis of learning and memory.