Limited Scalability Research Articles

Surfactant-Mediated Assembly of Precision-Size Liposomes.

Liposomes can greatly improve the pharmacokinetics of therapeutic agents due to their ability to encapsulate drugs and accumulate in target tissues. Considerable effort has been focused on methods to synthesize these nanocarriers in the past decades. However, most methods fail to controllably generate lipid vesicles at specific sizes and with low polydispersity, especially via scalable approaches suitable for clinical product manufacturing. Here, we report a surfactant-assisted liposome assembly method enabling the precise production of monodisperse liposomes with diameters ranging from 50 nm to 1 μm. To overcome scalability limitations, we used tangential flow filtration, a scalable size-based separation technique, to readily concentrate and purify the liposomal samples from more than 99.9% of detergent. Further, we propose two modes of liposome self-assembly following detergent dilution to explain the wide range of liposome size control, one in which phase separation into lipid-rich and detergent-rich phases drives the formation of large bilayer liposomes and a second where the rate of detergent monomer partitioning into solution controls bilayer leaflet imbalances that promote fusion into larger vesicles. We demonstrate the utility of controlled size assembly of liposomes by evaluating nanoparticle uptake in macrophages, where we observe a clear linear relationship between vesicle size and total nanoparticle uptake.

Recent advances in continuous flow synthesis of metal-organic frameworks and their composites.

Metal-organic frameworks (MOFs) and their composites have garnered significant attention in recent years due to their exceptional properties and diverse applications across various fields. The conventional batch synthesis methods for MOFs and their composites often suffer from challenges such as long reaction times, poor reproducibility, and limited scalability. Continuous flow synthesis has emerged as a promising alternative for overcoming these limitations. In this short review, we discuss the recent advancements, challenges, and future perspectives of continuous flow synthesis in the context of MOFs and their composites. The review delves into a brief overview of the fundamental principles of flow synthesis, highlighting its advantages over batch methods. Key benefits, including precise control over reaction parameters, improved scalability and efficiency, rapid optimization capabilities, enhanced reaction kinetics and mass transfer, and increased safety and environmental sustainability, are addressed. Additionally, the versatility and flexibility of flow synthesis techniques are discussed. The article then explores various flow synthesis methods applicable to MOF and MOF composite production. The techniques covered include continuous flow solvothermal synthesis, mechanochemical synthesis, microwave and ultrasound-assisted flow synthesis, microfluidic droplet synthesis, and aerosol synthesis. Notably, the combination of flow chemistry and aerosol synthesis with real-time characterization is also addressed. Furthermore, the impact of flow synthesis on the properties and performance of MOFs is explored. Finally, the review discusses current challenges and future perspectives in the field of continuous flow MOF synthesis, paving the way for further development and broader application of this promising technique.

Advances in Inorganic Foam Materials Fabricated Via Blowing Strategy: A Comprehensive Review.

Two-dimensional (2D) materials with excellent properties and widespread applications have been explosively investigated. However, their conventional synthetic methods exhibit concerns of limited scalability, complex purification process, and incompetence of prohibiting their restacking. The blowing strategy, characterized by gas-template, low-cost, and high-efficiency, presents a valuable avenue for the synthesis of 2D-based foam materials and thereby addresses these constraints. Whereas, its comprehensive introduction has been rarely outlined so far. This review commences with a synopsis of the blowing strategy, elucidating its development history, the statics and kinetics of the blowing process, and the choice of precursor and foaming agents. Thereafter, we dwell at length on across-the-board foams enabled by the blowing route, like BxCyNz foams, carbon foams, and diverse composite foams consisting of carbon and metal compounds. Following that, a wide-ranging evaluation of the functionality of the foam products in fields such as energy storage, electrocatalysis, adsorption, etc. is discussed, revealing their distinctive strength originated from the foam structure. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future research priorities in this rapidly developing method.

Digitalization for enhancing reading habits: the improved hybrid book recommendation system with genre-oriented profiles

PurposeReading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users discover new materials but also enhances their engagement and satisfaction with the library’s offerings, contributing to a holistic learning experience.Design/methodology/approachThis study presents a web-based solution, the Web-Based Hybrid Intelligent Book Recommender System (W_HybridBook), as a solution that addresses challenges like cold start issues and limited scalability by factoring in user preferences and item similarities in generating book recommendations. The paper improves the traditional hybrid system using Genre-Oriented Profiles (GOPs) instead of original rating profiles of users when determining similarities between individuals. Consumption-based genre profiles (W_HybridBook-CBP) are created by assessing whether an item has received any ratings in the dataset, and vote-based genre profiles (W_HybridBook-VBP) are generated by considering the genre categories based on the magnitude of the user’s rating.FindingsThe comparative results indicated that users are quite satisfied with the recommendations generated by W\\_HybridBook-VBP profiling, with an average rating of 4.0633 and a precision value of 0.7988. W\\_HybridBook-VBP is also the fastest way with respect to the algorithm and recommendation run time.Originality/valueThe proposed W\\_HybridBook has been then enhanced by adopting two user profiling strategies to boost the similarity calculation process in the recommendation generation phase. This system provides ranking-based recommendations by mainly integrating well-known collaborative and content-based filtering strategies. A dataset has been collected by considering the preferences of both users and academics at Izmir Bakircay University, which is one of the universities with the highest number of books per student. More importantly, this dataset has been released and become publicly available for future research in the recommender system field.

Self-Referenced Au Nanoparticles-Coated Glass Wafers for In Situ SERS Monitoring of Cell Secretion.

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for discrimination of bimolecules in complex systems. However, its practical applications face challenges such as complicated manufacturing procedures and limited scalability of SERS substrates, as well as poor reproducibility during detection which compromises the reliability of SERS-based analysis. In this study, we developed a convenient method for simultaneous fabrication of massive SERS substrates with an internal standard to eliminate the substrate-to-substrate differences. We first synthesized Au@CN@Au nanoparticles (NPs) which contain embedded internal standard molecules with a single characteristic peak in the Raman-silent region, and then deposited the NPs on 6 mm glass wafers in a 96-well plate simply by centrifugation for 3 min. The one-time obtained 96 SERS substrates have excellent intrasubstrate uniformity and intersubstrate repeatability for SERS detection by using the internal standard (relative standard deviation = 10.47%), and were able to detect both charged and neutral molecules (crystal violet and triphenylphosphine) at a concentration of 10-9 M. Importantly, cells can be directly cultured on glass wafers in the 96-well plate, enabling real time monitoring of the secretes and metabolism change in response to external stimulation. We found that the release of nucleic acids, amino acids and lipids by MDA-MB-231 cells significantly increased under hypoxic conditions. Overall, our approach enables fast and large-scale production of Au@CN@Au NPs-coated glass wafers as SERS substrates, which are homogeneous and highly sensitive for monitoring trace changes of biomolecules.

Chick Embryo Chorioallantoic Membrane as a Platform for Assessing the InVivo Efficacy of Chimeric Antigen Receptor T-cell Therapy in Solid Tumors.

The fertilized chicken egg chorioallantoic membrane (CAM), a highly vascularized membrane nourishing the developing embryo, also supports rapid growth of three-dimensional vascularized tumors from engrafted cells and tumor explants. Because murine xenograft models suffer limitations of time, cost, and scalability, we propose CAM tumors as a rapid, efficient screening tool for assessing anti-tumor efficacy of chimeric Ag receptor (CAR) T cells against solid tumors. We tested the efficacy of human epidermal growth factor receptor 2 (HER2)-specific CAR T cells against luminescent, HER2-expressing (FaDu, SCC-47) or HER2-negative (MDA-MB-468) CAM-engrafted tumors. Three days after tumor engraftment, HER2-specific CAR T cells were applied to tumors grown on the CAM. Four days post-CAR T cell treatment, HER2-expressing FaDu and SCC-47 tumors treated with CAR T showed reduced viable cancer cells as assessed by luciferase activity. This reduction in viable tumor cells was confirmed by histology, with lower Ki-67 staining observed in CAR T cell-treated tumors relative to T cell-treated controls. Persistence of CAR T in CAM and tumor tissue 4 days post-treatment was confirmed by CD3 staining. Altogether, our findings support further development of the chick CAM as an invivo system for rapid, scalable screening of CAR T cell efficacy against human solid tumors.

Mass cytometric single cell immune profiles of peripheral blood from acute myeloid leukemia patients in complete remission with measurable residual disease.

Measurable residual disease (MRD) is detected in approximately a quarter of AML chemotherapy responders, serving as a predictor for relapse and shorter survival. Immunological control of residual disease is suggested to prevent relapse, but the mechanisms involved are not fully understood. We present a peripheral blood single cell immune profiling by mass cytometry using a 42-antibody panel with particular emphasis on markers of cellular immune response. Six healthy donors were compared with four AML patients with MRD (MRD+) in first complete remission (CR1MRD+). Three of four patients demonstrated a favorable genetic risk profile, while the fourth patient had an unfavorable risk profile (complex karyotype, TP53-mutation) and a high level of MRD. Unsupervised clustering using self-organizing maps and dimensional reduction analysis was performed for visualization and analysis of immune cell subsets. CD57+ natural killer (NK)-cell subsets were found to be less abundant in patients than in healthy donors. Both T and NK cells demonstrated elevated expression of activity and maturation markers (CD44, granzyme B, and phosho-STAT5 Y694) in patients. Although mass cytometry remains an expensive method with limited scalability, our data suggest the utility for employing a 42-plex profiling for cellular immune surveillance in whole blood, and possibly as a biomarker platform in future clinical trials. The findings encourage further investigations of single cell immune profiling in CR1MRD+ AML-patients.

Comprehensive Characterization of Islet Remodeling in Development and in Diabetes Using Mass Cytometry.

The pancreatic islet is the functional and structural unit of the pancreatic endocrine portion. Islet remodeling occurs in both normal development and pathogenesis of type 1 (T1D) and type 2 diabetes (T2D). However, accurately quantifying changes in islet cellular makeup and hormone expressions poses significant challenges due to large intra- and inter-donor heterogeneity and the limited scalability of traditional methods such as immunostaining. The cytometry by time-of-flight (CyTOF) technology enables simultaneous quantification of more than 30 protein markers at single-cell resolution in a high-throughput fashion. Moreover, with distinct DNA and viability markers, single live cells can be explicitly selected in CyTOF. Here, leveraging the CyTOF data generated by the Human Pancreas Analysis Program, we characterized more than 12 million islet cells from 71 donors. Our data revealed continued age-related changes in islet endocrine cell compositions, but the maturity of endocrine cells is reached by 3 years of age. We also observed significant changes in beta cell numbers and key protein expressions, along with a significant increase in bihormonal cells in T1D donors. In contrast, T2D donors exhibited minimal islet remodeling events. Our data shine a light on the islet dynamics during development and diabetes pathogenesis and suggest divergent pathogenesis processes of T1D and T2D. Our comprehensive approach not only elucidates islet plasticity but also establishes a foundation for integrated CyTOF analysis in islet biology and beyond.

Prediction of Frequency-Dependent Optical Spectrum for Solid Materials: A Multioutput and Multifidelity Machine Learning Approach.

The frequency-dependent optical spectrum is pivotal for a broad range of applications from material characterization to optoelectronics and energy harvesting. Data-driven surrogate models, trained on density functional theory (DFT) data, have effectively alleviated the scalability limitations of DFT while preserving its chemical accuracy, expediting material discovery. However, prevailing machine learning (ML) efforts often focus on scalar properties such as the band gap, overlooking the complexities of optical spectra. In this work, we employ deep graph neural networks (GNNs) to predict the frequency-dependent complex-valued dielectric function across the infrared, visible, and ultraviolet spectra directly from the crystal structures. We explore multiple architectures for the spectral multioutput representation of the dielectric function and utilize various multifidelity learning strategies, such as transfer learning and fidelity embedding, to address the challenges associated with the scarcity of high-fidelity DFT data. Additionally, we model key solar cell absorption efficiency metrics, demonstrating that learning these parameters is enhanced when integrated through a learning bias within the learning of the frequency-dependent absorption coefficient. This study demonstrates that leveraging multioutput and multifidelity ML techniques enables accurate predictions of optical spectra from crystal structures, providing a versatile tool for rapidly screening materials for optoelectronics, optical sensing, and solar energy applications across an extensive frequency spectrum.

MP-GUARD: A novel multi-pronged intrusion detection and mitigation framework for scalable SD-IoT networks using cooperative monitoring, ensemble learning, and new P4-extracted feature set

The ever-increasing complexity of the Internet of Things (IoT) environment demands robust and adaptable intrusion detection frameworks, as existing approaches struggle with real-time traffic analysis, limited scalability, and static feature sets. This paper introduces MP-GUARD, a novel framework that leverages Software-Defined Networking (SDN), machine learning (ML), and a multi-controller architecture to address these challenges. MP-GUARD tackles multi-pronged intrusion attacks in IoT networks by offering real-time intrusion detection, collaborative traffic monitoring, and multi-layered attack mitigation. It achieves this through two core modules: P4-Assisted Cooperative Traffic Monitoring (CTM-P4) and Multi-Pronged Intrusion Detection and Mitigation (MPIDM). CTM-P4 facilitates real-time communication among multiple controllers, enabling dynamic feature extraction leveraging the interconnected state tables within P4-enabled switches. This module introduces a new 22-feature set (12 extracted and 10 computed) for comprehensive network analysis. MPIDM leverages the detailed network insights from CTM-P4 for attack identification and prevention. It introduces Stacked Ensemble Learning with Dynamic P4-Based Feature Selection (SELDP4-FS), achieving exceptional performance with 99.32 % accuracy, 99.24 % F1-score, and 0.49 % false positive rate. Additionally, MPIDM boasts efficient response and detection times of 16ms and 11ms, respectively. Beyond accuracy, MP-GUARD demonstrates significant advantages in terms of scalability and efficiency. The multi-controller architecture offers a 65 % reduction in overhead compared to single-controller setups. Furthermore, this work introduces the Mean Accuracy Steadiness Level (MASL) metric to assess model stability under varying traffic conditions. By combining P4-based feature extraction, dynamic feature selection, cooperative monitoring, ensemble learning, and a multi-controller architecture, MP-GUARD presents a significant contribution to IoT security, offering a scalable and adaptable solution for securing future SD-IoT deployments against evolving threats.

A Systematic Literature Review on The Strategic Shift to Cloud ERP: Leveraging Microservice Architecture and MSPs for Resilience and Agility

The COVID-19 pandemic has necessitated profound changes in the business and technology landscapes, compelling organizations to reassess their Enterprise Resource Planning (ERP) systems. Traditional ERP systems have demonstrated significant limitations in agility, scalability, and resilience, prompting a strategic shift towards cloud-based ERP solutions. This systematic literature review (SLR) aims to critically evaluate the transformation of ERP systems through the adoption of Microservice Architecture (MSA) and the integration of Managed Service Providers (MSPs), highlighting their role in enhancing system flexibility and operational continuity in a post-pandemic world. We conducted a systematic analysis of 124 scholarly articles published since 2010 to compare traditional ERP systems with MSA-based Cloud ERP solutions. Key insights reveal that MSA significantly improves system modularity and adaptability, addressing the shortcomings of monolithic architectures. Additionally, MSPs offer crucial support in managing the complexities of cloud transitions, ensuring security and efficiency. Our findings underscore the importance of a holistic approach to ERP modernization, integrating technological advancements with strategic business objectives. This study not only fills a critical gap in the literature but also provides actionable recommendations for practitioners and policymakers aiming to enhance ERP systems’ resilience and agility. Future research directions are proposed to further explore the synergistic potential of cloud ERP, MSA, and MSPs in fostering innovative and sustainable business practices.

High-density perfusion cultures of the marine bacterium Rhodovulum sulfidophilum for the biomanufacturing of oligonucleotides

Therapeutic oligonucleotides (ONs) are typically manufactured via solid-phase synthesis, characterized by limited scalability and huge environmental footprint, limiting their availability. Biomanufactured ONs have the potential to reduce the immunogenic side-effects, and to improve the sustainability of their chemical counterparts. Rhodovulum sulfidophilum was demonstrated a valuable host for the extracellular production of recombinant ONs. However, low viable cell densities and product titer were reported so far. In this work, perfusion cell cultures were established for the intensification of ON biomanufacturing. First, the perfusion conditions were simulated in 50 mL spin tubes, selected as a scale-down model of the process, with the aim of optimizing the medium composition and process parameters. This optimization stage led to an increase in the cell density by 44 % compared to the reference medium formulation. In addition, tests at increasing perfusion rates were conducted until achieving the maximum viable cell density (VCDmax), allowing the determination of the minimum cell-specific perfusion rate (CSPRmin) required to sustain the cell culture. Intriguingly, we discovered in this system also a maximum CSPR, above which growth inhibition starts. By leveraging this process optimization, we show for the first time the conduction of perfusion cultures of R. sulfidophilum in bench-scale bioreactors. This process development pipeline allowed stable cultures for more than 20 days and the continuous biomanufacturing of ONs, testifying the great potential of perfusion processes.

Smart reception: An artificial intelligence driven bangla language based receptionist system employing speech, speaker, and face recognition for automating reception services

In recent times, service robots (SR) have become widely accepted in a variety of fields as an alternative to traditional reception methods. Artificial Intelligence (AI) driven systems are seen as efficient labor alternatives, resulting in several SR models already being developed, primarily designed for English-speaking environments. Despite a large Bangla-speaking population, the exploration and implementation of Bangla language support within AI reception systems remain unexplored due to limited resources, posing significant challenges for deployment. This study presents a novel AI-enabled receptionist framework tailored to Bangla-speaking environments, addressing the limitation of Bangla language resources for developing automated reception systems. Leveraging advanced AI technologies including Face Recognition, Speaker Recognition, Automatic Speech Recognition (ASR), Text-to-Speech Synthesis, and Question Answering System, our integrated system demonstrates promise to automate Bangla reception systems. Our suite of models yields positive performance, with a face recognition accuracy of 99.38%, a speaker recognition system with 5.83 Equal Error Rate (ERR), ASR model achieving a Word Error Rate (WER) of 9.005%, TTS model scoring a Mean Opinion Score (MOS) of 4.10, and a question-answering system with a validation loss of 0.03%. Real-world evaluation among 1664 users in a university setting achieved over 75% user satisfaction, with 88% expressing interest in real-life implementation, showcasing usability across different domains. Limitations such as limited training data, scalability, and environmental sensitivity persist, underscoring the need for further development. Nevertheless, our framework demonstrates potential for real-life implementation, fostering human-robot interaction in Bangla-speaking contexts and paving the way for future innovations in AI-driven Bangla receptionist systems.

Scalable Image Clustering to screen for self-produced CSAM

The number of cases involving Child Sexual Abuse Material (CSAM) has increased dramatically in recent years, resulting in significant backlogs. To protect children in the suspect’s sphere of influence, immediate identification of self-produced CSAM among acquired CSAM is paramount. Currently, investigators often rely on an approach based on a simple metadata search. However, this approach faces scalability limitations for large cases and is ineffective against anti-forensic measures. Therefore, to address these problems, we bridge the gap between digital forensics and state-of-the-art data science clustering approaches. Our approach enables clustering of more than 130,000 images, which is eight times larger than previous achievements, using commodity hardware and within an hour with the ability to scale even further. In addition, we evaluate the effectiveness of our approach on seven publicly available forensic image databases, taking into account factors such as anti-forensic measures and social media post-processing. Our results show an excellent median clustering-precision (Homogeinity) of 0.92 on native images and a median clustering-recall (Completeness) of over 0.92 for each test set. Importantly, we provide full reproducibility using only publicly available algorithms, implementations, and image databases.

Improving Blockchain Consistency Bound by Assigning Weights to Random Blocks

Ironclad Method Boosts Blockchain Consistency and Speeds up Block Production Researchers have developed a novel method called Ironclad to address the scalability limitations of blockchains based on the Nakamoto consensus protocol. Although these blockchains have shown promise in various applications, the consensus properties of the protocol impose inherent scalability constraints. A key property, known as consistency, poses a challenge by presenting a trade-off between block production speed and the system’s security against adversarial attacks. To overcome this, the researchers propose the Ironclad method, which introduces a unique approach by assigning different weights to randomly selected blocks. By applying the Ironclad method to the original Nakamoto protocol and conducting rigorous analysis of its consensus properties, the researchers demonstrate a significant improvement in the consistency bound. This advancement allows for a much faster block production rate compared with the original protocol while maintaining the same level of security guarantees. The Ironclad method presents a promising solution to enhance blockchain scalability, overcoming the limitations of the Nakamoto consensus protocol. This breakthrough has the potential to unlock new possibilities for blockchain applications, paving the way for improved transaction speeds and increased efficiency in various industries.

Low-light image enhancement based on cell vibration energy model and lightness difference

Low-light image enhancement algorithms play a crucial role in revealing details obscured by darkness in images and substantially improving overall image quality. However, existing methods often suffer from issues like color or lightness distortion and possess limited scalability. In response to these challenges, we introduce a novel low-light image enhancement algorithm leveraging a cell vibration energy model and lightness difference. Initially, a new low-light image enhancement framework is proposed, building upon a comprehensive understanding and analysis of the cell vibration energy model and its statistical properties. Subsequently, to achieve pixel-level multi-lightness difference adjustment and exert control over the lightness level of each pixel independently, a lightness difference adjustment strategy is introduced utilizing Weibull distribution and linear mapping. Furthermore, to expand the adaptive range of the algorithm, we consider the disparities between HSV space and RGB space. Two enhanced image output modes are designed, accompanied by a thorough analysis and deduction of the relevant image layer mapping formulas. Finally, to enhance the reliability of experimental results, certain image faults in the SICE database are rectified using the feature matching method. Experimental results showcase the superiority of the proposed algorithm over twelve state-of-the-art algorithms. The resource code of this article will be released at https://github.com/leixiaozhou/CDEGmethod.

Efficient musculoskeletal annotation using free-form deformation

Traditionally, constructing training datasets for automatic muscle segmentation from medical images involved skilled operators, leading to high labor costs and limited scalability. To address this issue, we developed a tool that enables efficient annotation by non-experts and assessed its effectiveness for training an automatic segmentation network. Our system allows users to deform a template three-dimensional (3D) anatomical model to fit a target magnetic-resonance image using free-form deformation with independent control points for axial, sagittal, and coronal directions. This method simplifies the annotation process by allowing non-experts to intuitively adjust the model, enabling simultaneous annotation of all muscles in the template. We evaluated the quality of the tool-assisted segmentation performed by non-experts, which achieved a Dice coefficient greater than 0.75 compared to expert segmentation, without significant errors such as mislabeling adjacent muscles or omitting musculature. An automatic segmentation network trained with datasets created using this tool demonstrated performance comparable to or superior to that of networks trained with expert-generated datasets. This innovative tool significantly reduces the time and labor costs associated with dataset creation for automatic muscle segmentation, potentially revolutionizing medical image annotation and accelerating the development of deep learning-based segmentation networks in various clinical applications.

Hybrid ant colony-based inter-cluster routing protocol for FANET

This study addresses the challenges in large-scale unmanned aerial vehicle (UAV) clusters, specifically the scalability issues and limitations of using reactive routing protocols for inter-cluster routing. These traditional methods place an excessive burden on cluster heads and struggle to adapt to frequently changing topologies, leading to decreased network performance. To solve these problems, we propose an innovative inter-cluster routing protocol (ICRP), which is based on a hybrid ant colony algorithm. During the route establishment phase, ICRP uses this algorithm to identify the optimal relay node. This approach is inspired by the foraging behavior of Physarum polycephalum, combining factors such as the number of hops from the source node, the load condition of the node, and its weight in the pheromone calculation. In the route maintenance phase, ICRP uses a predictive repair and contraction mechanism to dynamically maintain routes, accommodating the high mobility of UAVs. Comparative simulations in OMNeT + + showed that this protocol surpasses ad-hoc on-demand distance vector (AODV), fuzzy-logic-assisted-AODV, and Enhanced-Ant-AODV routing protocols in packet delivery rate and end-to-end transmission delay. Furthermore, it showed superior adaptation to network environments with high-speed node mobility.

Advancements in object detection: From machine learning to deep learning paradigms

The evolution of object detection from traditional machine learning approaches to advanced deep learning techniques marks a significant milestone in the field of computer vision. Initially, object detection relied on algorithms such as Support Vector Machines (SVMs) and decision trees, leveraging handcrafted features like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) for classification and recognition tasks. However, these methods exhibited limitations in scalability and adaptability to complex environments. The breakthrough came with the adoption of Convolutional Neural Networks (CNNs), which transformed the landscape by automating feature extraction, thereby enhancing detection accuracy and efficiency. Subsequent innovations in network architectures, such as R-CNN, YOLO, and SSD, have continually refined object detection capabilities, optimizing both speed and precision. This paper examines the progression of object detection technologies, focusing on the impact of deep learning models and the optimization of network structures. It also delves into the quantitative analysis of model performance, highlighting the role of data augmentation and advanced training techniques in overcoming real-world detection challenges. Through this exploration, the paper aims to provide comprehensive insights into the current state and future directions of object detection techniques.

Toward Graph Self-Supervised Learning With Contrastive Adjusted Zooming.

Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an unsupervised manner, they suffer from certain limitations, such as the heavy reliance on monotone contrastiveness and limited scalability. To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised GRL algorithm via graph contrastive adjusted zooming, namely, G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme. Specifically, this mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales: micro (i.e., node level), meso (i.e., neighborhood level), and macro (i.e., subgraph level). First, we generate two augmented views of the input graph via two different graph augmentations. Then, we establish three different contrastiveness on the above three scales progressively, from node, neighboring, to subgraph level, where we maximize the agreement between graph representations across scales. While we can extract valuable clues from a given graph on the micro and macro perspectives, the neighboring-level contrastiveness offers G-Zoom the capability of a customizable option based on our adjusted zooming scheme to manually choose an optimal viewpoint that lies between the micro and macro perspectives to better understand the graph data. In addition, to make our model scalable to large graphs, we use a parallel graph diffusion approach to decouple model training from the graph size. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms the state-of-the-art methods consistently.