Technological Breakthroughs Research Articles

Navigating the Multimodal Landscape: A Review on Integration of Text and Image Data in Machine Learning Architectures

Images and text have become essential parts of the multimodal machine learning (MMML) framework in today’s world because data are always available, and technological breakthroughs bring disparate forms together, and while text adds semantic richness and narrative to images, images capture visual subtleties and emotions. Together, these two media improve knowledge beyond what would be possible with just one revolutionary application. This paper investigates feature extraction and advancement from text and image data using pre-trained models in MMML. It offers a thorough analysis of fusion architectures, outlining text and image data integration and evaluating their overall advantages and effects. Furthermore, it draws attention to the shortcomings and difficulties that MMML currently faces and guides areas that need more research and development. We have gathered 341 research articles from five digital library databases to accomplish this. Following a thorough assessment procedure, we have 88 research papers that enable us to evaluate MMML in detail. Our findings demonstrate that pre-trained models, such as BERT for text and ResNet for images, are predominantly employed for feature extraction due to their robust performance in diverse applications. Fusion techniques, ranging from simple concatenation to advanced attention mechanisms, are extensively adopted to enhance the representation of multimodal data. Despite these advancements, MMML models face significant challenges, including handling noisy data, optimizing dataset size, and ensuring robustness against adversarial attacks. Our findings highlight the necessity for further research to address these challenges, particularly in developing methods to improve the robustness of MMML models.

Advanced Preparation Methods for Ceramic Membrane Materials in Electrochemical Applications

The outstanding thermal, chemical, and mechanical properties of ceramic membranes have attracted increasing attention, offering advantages over polymer and metal counterparts. Exploring the specialized applications of ceramic membranes through various preparation methods poses a daunting challenge for contemporary researchers. Traditional preparation methods are essentially unable to meet the requirements of complex membrane structures. For instance, in ceramic fuel cell applications, cells composed of ceramic membrane materials exhibit high resistance and low conductivity, which seriously hinders the progress of new high-performance ceramic fuel cells. Therefore, it is necessary to improve preparation methods to improve the electrochemical performance of devices composed of ceramic membrane materials. In recent years, breakthroughs in various new processing technologies have propelled the performance of ceramic membrane devices. This paper will focus on the following aspects. Firstly, traditional preparation methods and advanced preparation methods of ceramic membrane materials will be discussed. Secondly, high-performance ceramic membrane materials prepared by different advanced preparation methods are introduced, and the electrochemical properties of the devices composed of ceramic membrane materials are elaborated in combination with different testing and characterization methods. Finally, the prospects and future direction of the preparation of ceramic membrane materials by advanced preparation methods are summarized.

Biomass Energy Gasification Technology and Application

Globally, the dual challenges of energy crisis and environmental pollution encourage people to seek sustainable energy solutions. Biomass energy, with its renewable and environmentally friendly characteristics, has gradually become an important choice to replace the traditional energy. Biomass gasification technology, as an advanced thermochemical process to convert biomass energy into combustible gas, not only improves the efficiency of energy utilization, but also shows great potential in the field of energy conversion and environmental protection due to its cleanliness and operability. In this paper, we deeply analyze the basic principles of biomass gasification technology, including the key steps such as pyrolysis, gasification and gas purification, and explore the application strategies of this technology in biomass energy conversion. In particular, the paper details the innovative practice of Shanghai Diyan Environmental Protection Technology Co., Ltd. in the field of biomass gasification, including its technological breakthrough in printing and dyeing sludge treatment, as well as the national utility model patent obtained. The successful cases of the company not only show the advantages of biomass gasification technology in practical industrial applications, but also provide valuable experience and inspiration for the same industry. Through these studies and practices, this paper aims to provide theoretical basis and technical support for the efficient and environmental protection utilization of biomass energy, in order to promote the further development and application of biomass energy gasification technology.

Electronic Skin: Opportunities and Challenges in Convergence with Machine Learning.

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin's functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological breakthroughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition.

Station-keeping HAPS mission through optimal sprint and drift trajectories

Due to the latest technological breakthroughs, High-Altitude Pseudo Satellites (HAPS) have recently become a feasible solution with great potential in the aerospace industry for Earth observation and communications, among other applications. Minimizing the energy consumption of these solar powered platforms is critical and, in the case of lighter than air vehicles, leads to smaller and more manageable platforms. When stratospheric airships perform a station-keeping mission, a certain displacement from the Earth surface reference point is usually admissible. This flexibility makes it possible to define an optimal control law for the airship that minimizes the energy required to fly in a 24-hour cycle, leading to a sprint and drift trajectory. This study analyzes the impact on the energy balance of the mission that stems from the changes in the allowed station-keeping radius. It also considers the effects of the daylight hours, the wind intensity, and the characteristics of the onboard energy system. The associated optimal control problems are rigorously solved numerically by means of a transcription method with regularization. The results define the optimal sprint and drift trajectories adapted to every scenario, providing the time evolution of the available power that controls the flight. The analysis indicates that following the optimal trajectory leads to weight savings in the energy system of about 5.4 kilograms per kilometer of the station-keeping radius. It entails that, for example, if a 20 kilometer radius is allowed, the energy required decreases more than 6% and the payload capacity increases about a 43% when compared to the fixed-point flight.

High-throughput discovery of MHC class I- and II-restricted T cell epitopes using synthetic cellular circuits.

Antigen discovery technologies have largely focused on major histocompatibility complex (MHC) class I-restricted human T cell receptors (TCRs), leaving methods for MHC class II-restricted and mouse TCR reactivities relatively undeveloped. Here we present TCR mapping of antigenic peptides (TCR-MAP), an antigen discovery method that uses a synthetic TCR-stimulated circuit in immortalized T cells to activate sortase-mediated tagging of engineered antigen-presenting cells (APCs) expressing processed peptides on MHCs. Live, tagged APCs can be directly purified for deconvolution by sequencing, enabling TCRs with unknown specificity to be queried against barcoded peptide libraries in a pooled screening context. TCR-MAP accurately captures self-reactivities or viral reactivities with high throughput and sensitivity for both MHC class I-restricted and class II-restricted TCRs. We elucidate problematic cross-reactivities of clinical TCRs targeting the cancer/testis melanoma-associated antigen A3 and discover targets of myocarditis-inciting autoreactive T cells in mice. TCR-MAP has the potential to accelerate T cell antigen discovery efforts in the context of cancer, infectious disease and autoimmunity.

Coal measure gas resources matter in China: Review, challenges, and perspective

Achieving the dual carbon goals of peaking by 2030 and neutrality by 2060 is significantly aided by the growth of coal measure gas research and development, especially for China to optimize its primary energy consumption. We critically review the distribution, geological characteristics, methods of liberation and then recovery by hydraulic fracturing of coal measure gas in China and present a roadmap to optimize this recovery. The gas-bearing system is the focus of this recovery, but this system is embedded within its sedimentary environment and modulated by tectonic and hydrogeological controls that affect gas exploration and recovery. However, to improve the development of coal measure gas in China, bottleneck problems remain to be solved, such as accurately predicting reservoir behavior in dessert regions, optimizing well patterns, and deploying optimal horizontal well trajectories. Additionally, the technology breakthroughs on deep co-production of coal measure gas, automatic fracturing and intelligent drainage are imminent. Basically, developing new techniques and conducting improved geological surveys are essential to ensure the sustainable supply of coal measure gas resource. Thus, this review presents a comprehensive introduction to coal measure gas resources in China, of utility to academic researchers and engineers in enhancing the understanding of the current situation and in projecting future development.

Increasing acceptance of AI-generated digital twins through clinical trial applications.

Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real-world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI-generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI-generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI-generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI-generated digital twins in clinical practice.

Melodia: A Python Library for Protein Structure Analysis.

Analysing protein structure similarities is an important step in protein engineering and drug discovery. Methodologies that are more advanced than simple RMSD are available but often require extensive mathematical or computational knowledge for implementation. Grouping and optimising such tools in an efficient open-source library increases accessibility and encourages the adoption of more advanced metrics. Melodia is a Python library with a complete set of components devised for describing, comparing and analysing the shape of protein structures using differential geometry of three-dimensional curves and knot theory. It can generate robust geometric descriptors for thousands of shapes in just a few minutes. Those descriptors are more sensitive to structural feature variation than RMSD deviation. Melodia also incorporates sequence structural annotation and three-dimensional visualisations. Melodia is an open-source Python library freely available on https://github.com/rwmontalvao/Melodia_py, along with interactive Jupyter Notebook tutorials. Supplementary data are available at Bioinformatics online.

Next-generation electronic devices: Innovations in integrated circuits, sensors, and computing architectures

This paper delves into the forefront of electronic engineering, showcasing the evolution of integrated circuits (ICs), sensor technology advancements, and the latest enhancements in microprocessors and microcontrollers. It underscores the significant strides made towards miniaturization, energy efficiency, and the integration of high-performance computing (HPC) within these devices. Through detailed examination, the paper highlights three pivotal areas: the revolutionary fabrication techniques enabling nanometer-scale ICs; the emergence of nano-sensing devices, wireless sensor networks (WSNs), and flexible sensors transforming the landscape of environmental monitoring and healthcare; and the development of energy-efficient architectures and security enhancements in microprocessors and microcontrollers. Employing quantitative analyses and mathematical models, the paper provides insights into the technological breakthroughs driving these advancements, including dynamic voltage and frequency scaling (DVFS), near-threshold computing (NTC), and the implementation of hardware-based security measures [1]. This comprehensive analysis not only illuminates the current state of electronic engineering but also outlines the potential future directions of the field, emphasizing the interdisciplinary approaches required to tackle the challenges of modern electronic device design and application.

Nonionizing diagnostic imaging modalities for visualizing health and pathology of periodontal and peri-implant tissues.

Radiographic examination has been an essential part of the diagnostic workflow in periodontology and implant dentistry. However, radiographic examination unavoidably involves ionizing radiation and its associated risks. Clinicians and researchers have invested considerable efforts in assessing the feasibility and capability of utilizing nonionizing imaging modalities to replace traditional radiographic imaging. Two such modalities have been extensively evaluated in clinical settings, namely, ultrasonography (USG) and magnetic resonance imaging (MRI). Another modality, optical coherence tomography (OCT), has been under investigation more recently. This review aims to provide an overview of the literature and summarize the usage of USG, MRI, and OCT in evaluating health and pathology of periodontal and peri-implant tissues. Clinical studies have shown that USG could accurately measure gingival height and crestal bone level, and classify furcation involvement. Due to physical constraints, USG may be more applicable to the buccal surfaces of the dentition even with an intra-oral probe. Clinical studies have also shown that MRI could visualize the degree of soft-tissue inflammation and osseous edema, the extent of bone loss at furcation involvement sites, and periodontal bone level. However, there was a lack of clinical studies on the evaluation of peri-implant tissues by MRI. Moreover, an MRI machine is very expensive, occupies much space, and requires more time than cone-beam computed tomography (CBCT) or intraoral radiographs to complete a scan. The feasibility of OCT to evaluate periodontal and peri-implant tissues remains to be elucidated, as there are only preclinical studies at the moment. A major shortcoming of OCT is that it may not reach the bottom of the periodontal pocket, particularly for inflammatory conditions, due to the absorption of near-infrared light by hemoglobin. Until future technological breakthroughs finally overcome the limitations of USG, MRI and OCT, the practical imaging modalities for routine diagnostics of periodontal and peri-implant tissues remain to be plain radiographs and CBCTs.

The Future of Environmental Engineering Technology: A Disruptive Innovation Perspective

Scientific and technological revolutions and industrial transformations have accelerated the rate of innovation in environmental engineering technologies. However, few researchers have evaluated the current status and future trends of technologies. This paper summarizes the current research status in eight major subfields of environmental engineering—water treatment, air pollution control, soil/solid waste management, environmental biotechnology, environmental engineering equipment, emerging contaminants, synergistic reduction of pollution and carbon emissions, and environmental risk and intelligent management—based on bibliometric analysis and future trends in greenization, low carbonization, and intelligentization. Disruptive technologies are further identified based on discontinuous transformation, and ten such technologies are proposed, covering general and specific fields, technical links, and value sources. Additionally, the background and key innovations in disruptive technologies are elucidated in detail. This study not only provides a scientific basis for strategic decision-making, planning, and implementation in the environmental engineering field but also offers methodological guidance for the research and determination of breakthrough technologies in other areas.

Accuracy Analysis of DeepL: Breakthroughs in Machine Translation Technology

This study examines the accuracy and technological innovations of DeepL, a prominent machine translation tool. Through a comprehensive literature review, we analyze DeepL's performance compared to other translation systems, exploring its advanced neural network architecture and training methods. Key findings indicate that DeepL consistently outperforms other tools in BLEU scores and human evaluations, particularly excelling in handling context, idiomatic expressions, and specialized terminology. The research highlights DeepL's use of the Transformer model, diverse training data, and techniques like transfer learning and data augmentation. Practical applications across academic, professional, and educational sectors are discussed, with special emphasis on how DeepL benefits students and teachers by facilitating multilingual learning, enhancing comprehension of foreign texts, and assisting in accurate translation of academic materials. User feedback underscores DeepL's accuracy and user-friendly features. While demonstrating significant advancements in machine translation technology, this study also acknowledges areas for potential improvement, contributing to the ongoing development of AI-driven language solutions.

Granular satellite data to assess the potential for nature based solutions at a national scale: a proof of concept with data from Rwanda and Lesotho

ABSTRACT Nature-based Solutions (NbS) form a substantial part of cost-efficient climate change mitigation options. However, public financial flows towards NbS have been limited. Among the factors impeding investments in NbS are challenges in reliable remote project identification and comparison. In this article, we demonstrate a technological solution to these challenges. Using cloud-based satellite data processing and the proliferation of open geospatial data, we developed a methodology to map NbS suitability based on a set of biophysical, pedological, hydrological and climatological criteria. To provide a proof of concept, we identify potential project areas for eight types of NbS in Rwanda and Lesotho. Building on these spatially-explicit NbS suitability data layers (accessible at https://williamouellette.users.earthengine.app/view/ncs-potential), we develop marginal abatement cost curves for projects in Rwanda and Lesotho. We thus provide a proof of concept for remote identification and project comparison. These technological developments can help investors, such as multilateral donors or governments, alleviate additionality concerns that are implied by local project selection. Key Policy Insights Investment in climate change mitigation through Nature-based Solutions (NbS) is limited, in spite of the large, cost-efficient potential for achieving national and global goals. Remote project identification and comparison can help alleviate some of the impediments to international investment in NbS. Such remote identification and comparison of NbS projects is now possible because of breakthroughs in cloud-based satellite data processing technologies and increased availability of geospatial data. We present a proof of concept, establishing marginal abatement cost curves for climate change mitigation through NbS in Rwanda and Lesotho. Coupled with the institutional improvements necessary to create a reliable intergovernmental carbon credit market, these technological developments have the potential to boost international investment in NbS.

Redefining Organizational Culture for the Digital Age: A Model Proposal for Digital Organizational Culture

As technology breakthroughs and digital innovations continue to transform business environments, traditional organizational cultures are frequently found insufficient in promoting the innovative thinking, agility, and resilience needed for long-term success. This article presents a comprehensive framework for rethinking organizational culture in the digital age. The model is designed to help organizations navigate the complexity and take advantage of the digitalized economy's opportunities. Based on Schein’s organizational culture model, this study thoroughly examines existing literature to identify the essential elements of a digital culture framework. The suggested model prioritizes incorporating digital tools and mindsets at all levels of the organization, creating an atmosphere that promotes experimentation, cooperation, and a proactive approach to survive in the digital era. Furthermore, the study explores the crucial elements in promoting and integrating digital values, the function of human resource practices in facilitating the growth of digital skills, and the influence of digital culture on achieving a competitive advantage in the digitalized economy. By highlighting the crucial interaction between technology, people, and organizational processes, this study seeks to assist organizations in redefining their culture to thrive in the digital era by offering practical insights and a structured framework.

Focusing on New Discoveries in Food Technology and Creating a New Future of Nutrition and Health: An Introduction to the 4th International Symposium of Food Science, Nutrition and Health in Dalian, China.

The 4th International Symposium on Food Science, Nutrition and Health (ISFSNH) was held at the Shangri-La Hotel in Dalian, China, on May 29-31, 2023. The symposium explored the connotations and needs of "The Great Food Perspective" under the theme "Focusing on new discoveries in food technology and creating a new future of nutrition and health" to better address the global emerging diverse food needs. The ISFSNH covered four areas: (1) food processing theory and technology, (2) food safety and quality control, (3) precision nutrition and health, and (4) creation of nutritious and healthy foods. More than 1000 scholars and entrepreneurs from more than 100 colleges and universities globally attended the conference. This special issue of the Journal of Agricultural and Food Chemistry highlights the important topics of the 4th ISFSNH and includes more than 20 papers.

Iso-propagation vortices: a breakthrough in optical communication technology

Iso-propagation vortices: a breakthrough in optical communication technology

The evolution of the ambidextrous innovation synergy strategy of new entrants from the perspective of key core technology monopoly

Ambidextrous innovation synergy is an effective way for new entrants and R&D entities to break the blockade of key core technologies. This paper constructs a tripartite evolutionary game model of new entrants, the R&D entity, and monopoly enterprises under the monopoly situation of key core technologies, discusses the dynamic equilibrium process of how new entrants cooperate with the R&D entity to carry out the ambidextrous innovation synergy strategy, and extends the model to the policy subsidy situations of different development stages of key core technology. The results show that the monopoly of key core technologies enhances the original innovation search ability of new entrants and promotes the evolution of enterprise imitation innovation to the exploratory innovation strategy. In the basic research stage of key core technology, the exploratory innovation strategy of new entrants is more sensitive to the cost of network embedding and the original innovation knowledge search. New entrants prefer the imitation innovation strategy, and policy subsidies have no significant effect on exploratory innovation. In the promotion stage of the key core technology market, fiscal and tax subsidies can more easily promote the evolution of new entrants from the imitative innovation strategy to the exploratory innovation strategy than R&D subsidies, and network embeddedness can induce enterprises to carry out exploratory innovation only when a certain threshold is reached. In addition, this paper discusses the influence mechanism of monopoly enterprises' suppression intensities and key core technology breakthrough probabilities on the evolution equilibrium of new entrants' ambidextrous innovation synergy strategies.

The challenges and breakthroughs in the development of diagnostic monoclonal antibodies

AbstractOver the past century, the field of antibody discovery has undergone significant evolution, excluding the current exploration stage of artificial intelligence‐based antibody generation and the often overlooked non‐animal sourced antibody discovery, which typically requires mature in vitro affinity and the selection of high‐quality antigen formulations. This journey has traversed various stages, from methods involving serum‐based antibody acquisition, the isolation of B cells capable of perpetual antibody production through hybridoma technology, to the in‐depth exploration of genetic material using the phage display system, and the current stage involving diverse single B cell screening techniques. Additionally, the emergence of machine learning has brought impressive scientific and technological breakthroughs across research domains, proving to be a powerful application in the field of antibody discovery. However, each technique comes with its limitations, such as variability and control challenges in serum‐based acquisition, lengthy and difficult hybridoma‐derived antibody development, potential limitations in sequence and epitope diversity due to immunization biases in phage display techniques, and costly single B cell screening. Protein mass spectrometry sequencing, with shorter acquisition time and lower costs, is seen as a shortcut by diagnostic companies, impacting traditional antibody development. In diagnostic antibody development, methodological differences in downstream assays and the impact of constant regions outside the Fv core are often neglected. This paper deeply analyzes challenges, proposing innovative strategies for the next generation of diagnostic antibody development. Aimed at moving closer to the gold standard of antibody discovery, these strategies enhance the competitiveness of diagnostic reagent products.

Digitalization and its Impact on the Development of Contemporary Marketing Strategies

"The marketing landscape is always changing in this day and age due to technological breakthroughs, so it is important to comprehend these changes for theoretical and practical applications. Examining the development of digital marketing is the goal of this study, which will concentrate on the crucial shift to analytics-driven decision-making, omnichannel consumer experiences, the influence of social media dynamics, and the incorporation of blockchain and artificial intelligence (AI) technology. The methodology, which makes use of an extensive analysis of recent literature and case studies, is based on a study of how these technologies may be used to improve marketing tactics and solve issues that they raise, like the digital divide and data protection. The findings reveal a significant shift towards more personalized, efficient, and ethically aware marketing practices. Notably, the adoption of AI and blockchain technologies demonstrates the potential for enhancing customer interaction and security, underscoring a move towards more targeted and meaningful consumer engagement. This transition not only highlights the importance of adapting to digital innovations but also stresses the need for ongoing research and ethical considerations in their application. The study contributes to the discourse by offering insights into navigating the digital marketing ecosystem, advocating for strategic innovation, and emphasizing ethical practices in leveraging digital tools. This synthesis serves to enlighten both academic and industry practitioners about the ongoing digital marketing revolution, underlining the importance of agility and ethical integrity in achieving marketing success in the digital age."