Scientific Developments and New Technological Trajectories in Sensor Research

Advancements in biosensors for cancer detection: revolutionizing diagnostics

Cancer stands as the reigning champion of life-threatening diseases, casting a shadow with the highest global mortality rate. Unleashing the power of early cancer treatment is a vital weapon in the battle for efficient and positive outcomes. Yet, conventional screening procedures wield limitations of exorbitant costs, time-consuming endeavors, and impracticality for repeated testing. Enter bio-marker-based cancer diagnostics, which emerge as a formidable force in the realm of early detection, disease progression assessment, and ultimate cancer therapy. These remarkable devices boast a reputation for their exceptional sensitivity, streamlined setup requirements, and lightning fast response times. In this study, we embark on a captivating exploration of the most recent advancements and enhancements in the field of electrochemical marvels, targeting the detection of numerous cancer biomarkers. With each breakthrough, we inch closer to a future where cancer's grip on humanity weakens, guided by the promise of personalized treatment and improved patient outcomes. Together, we unravel the mysteries that cancer conceals and illuminate a path toward triumph against this daunting adversary. This study celebrates the relentless pursuit of progress, where electrochemical innovations take center stage in the quest for a world free from the clutches of carcinoma.

Wearable biosensors for human health: A bibliometric analysis from 2007 to 2022

Objective

This study aimed to determine the status of scientific production on biosensor usage for human health monitoring.

Methods

We used bibliometrics based on the data and metadata retrieved from the Web of Science between 2007 and 2022. Articles unrelated to health and medicine were excluded. The databases were processed using the VOSviewer software and auxiliary spreadsheets. Data extraction yielded 275 articles published in 161 journals, mainly concentrated on 13 journals and 881 keywords plus.

Results

The keywords plus of high occurrences were estimated at 27, with seven to 30 occurrences. From the 1595 identified authors, 125 were consistently connected in the coauthorship network in the total set and were grouped into nine clusters. Using Lotka's law, we identified 24 prolific authors, and Hirsch index analysis revealed that 45 articles were cited more than 45 times. Crosses were identified between 17 articles in the Hirsch index and 17 prolific authors, highlighting the presence of a large set of prolific authors from various interconnected clusters, a triad, and a solitary prolific author.

Conclusion

An exponential trend was observed in biosensor research for health monitoring, identifying areas of innovation, collaboration, and technological challenges that can guide future research on this topic.

A review of piezoelectric-electromagnetic hybrid energy harvesters for different applications

Social progress is inseparable from the utilization of energy, signals of extreme consumption of fossil energy and energy crisis appear frequently around the world. Human beings are paying more and more attention to new technologies and the sustainable development of energy collection and conversion. The emergence of piezoelectric, electromagnetic, electrostatic, and triboelectric mechanisms provides a variety of effective methods for new environmental energy collection and conversion technologies. Among them, the piezoelectric-electromagnetic hybrid energy harvester (P-EHEH) has been widely studied due to its high output power, simple structure, and easy miniaturization. Continuous progress has been made in the research of P-EHEH through theoretical exploration, structural optimization, and performance improvement. This Review focuses on the review of P-EHEH at the application level. A detailed introduction summarizes the research status of P-EHEH applied to human body devices, monitoring sensors, and power supply devices, as well as the development status of back-end electronic modules and interface circuits. The future challenges and development prospects of P-EHEH are anticipated.

Nobel laureates in Physics, Chemistry and Medicine: relation between research funding and citations.. [DOI: 10.21203/rs.3.rs-2907940/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

One of the vital problems in scientometrics is to explore the factors that affect the growth of citations in publications and in general the diffusion of knowledge in science and society. The goal of this study is to analyze the relation between funded and unfunded papers and citations of Nobel Laureates in physics, chemistry and medicine over 2019-2020 period and the same relation in these research fields as a whole to clarify the scientific development. Original results here reveal that in chemistry and medicine, funded papers of Nobel Laureates have higher citations than unfunded papers, vice versa in physics that has high citations in unfunded papers. Instead, when overall research fields of physics, chemistry and medicine are analyzed, funded papers have a higher level of citations than unfunded, with a higher scaling factor in chemistry and medicine. General properties of this study are that: a) funded articles receive more citations than unfunded papers in research fields of physics, chemistry and medicine, generating a high Matthew effect given by a higher accumulation and growth of citations with the growth of papers, b) funding increases the citations of articles in fields oriented to applied research (such as, chemistry and medicine) more than fields oriented to basic research (physics). Overall, then, results here can explain some characteristics of scientific dynamics, showing the critical role of funding to foster citations and diffusion of knowledge, also having potential commercial implications in applied research. Results here can be provide useful information to understand drivers of the scientific development in basic and applied research fields to better allocate financial resources in research fields directed to support a positive scientific and societal impact.

New trajectories of technologies for the removal of pollutants and emerging contaminants in the environment

Modern society has increasingly a diffusion of pollutants and emerging contaminants (e.g., different types of chemicals and endocrine disruptors in pharmaceuticals, pesticides, household cleaning, and personal care products, etc.) that have detrimental effects on the environment (atmosphere, hydrosphere, biosphere and anthroposphere) and also generate diseases and disorders on the people health. Environmental science requires efforts in the detection and elimination of manifold pollutants and emerging pollutants with appropriate product and process technologies. This study aims to analyze different paths of treatment technologies to investigate their evolution and predict new directions of promising technological trajectories to support the removal of contaminants directed to reach, whenever possible, sustainable development objectives. The work is mainly devoted to wastewater treatment technologies. A proposed model analyzes the evolution of patents (proxy of innovation and new technology) on publications (proxy of science and knowledge advances) to quantify the relative growth rate of new trajectories of technologies to remove pollutants and emerging contaminants. Results reveal that new directions of treatment technologies having an accelerated rate of growth are (in decreasing order): biochar and reverse osmosis in physical-based technologies, coagulation, and disinfection water treatments in chemical-based technologies and anaerobic processes in biological-based technologies. Other main technologies, such as carbon nanotubes and advanced oxidation processes, seem to be in the initial phase of development and need learning by using processes and further science and technology advances to be implemented as effective treatments and cost-effective. The results here are in accord with global water and wastewater equipment treatment market revenues by technology, showing a similar trend. These findings bring us to the main information to extend the knowledge about new directions of technologies for the treatment and/or elimination of pollutants and microorganisms that can support decisions of policymakers towards goals of sustainable development by reducing environmental degradation and people health disorders.

Mapping the Landscape of Indian Genomics Research: A Scientometric Analysis

This Scientometric study aimed to provide state-of-the-art information on research growth and trends, areas of potential growth and development in genomics in India, and identify the key players (organizations or institutions, and funding agencies). It was found that the number of publications and citations related to genomics research has been steadily increasing over the years, indicating a growing interest and investment in the field as the Indian Council of Agricultural Research was the leading contributor to the field. Among the 159 contributing countries from 2012 to 2021, India contributed 4.46 percent of publications. The Department of Biotechnology (Ministry of Science and Technology, India) provided the most funds for genomics research. In the last decade, research was primarily focused on "Genetic Diversity", "Polymorphism", "Comparative Genomics", "Phylogeny", " Random amplification of polymorphic DNA (RAPD)", "Single-nucleotide polymorphism (SNP)", "Polymerase chain reaction (PCR)", "Gene Expression", etc. The study's findings may shed light on the strengths and weaknesses of the country's research infrastructure, as well as the effectiveness of government policies and funding mechanisms.

Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency

Scholars and experts argue that future pandemics and/or epidemics are inevitable events, and the problem is not whether they will occur, but when a new health emergency will emerge. In this uncertain scenario, one of the most important questions is an accurate prevention, preparedness and prediction for the next pandemic. The main goal of this study is twofold: first, the clarification of sources and factors that may trigger pandemic threats; second, the examination of prediction models of on-going pandemics, showing pros and cons. Results, based on in-depth systematic review, show the vital role of environmental factors in the spread of Coronavirus Disease 2019 (COVID-19), and many limitations of the epidemiologic models of prediction because of the complex interactions between the new viral agent SARS-CoV-2, environment and society that have generated variants and sub-variants with rapid transmission. The insights here are, whenever possible, to clarify these aspects associated with public health in order to provide lessons learned of health policy that may reduce risks of emergence and diffusion of new pandemics having negative societal impact.

Evolution of Sensor Research for Clarifying the Dynamics and Properties of Future Directions

The principal goal of this study is to analyze the evolution of sensor research and technologies from 1990 to 2020 to clarify outlook and future directions. This paper applies network analysis to a large dataset of publications concerning sensor research covering a 30-year period. Results show that the evolution of sensors is based on growing scientific interactions within networks, between different research fields that generate co-evolutionary pathways directed to develop general-purpose and/or specialized technologies, such as wireless sensors, biosensors, fiber-optic, and optical sensors, having manifold applications in industries. These results show new directions of sensor research that can drive R&D investments toward promising technological trajectories of sensors, exhibiting a high potential of growth to support scientific, technological, industrial, and socioeconomic development.

Identification of Urban Functional Zones Based on the Spatial Specificity of Online Car-Hailing Traffic Cycle

The study of urban functional zoning is not only important for analyzing urban spatial structure but also for optimizing urban management and promoting scientific urban planning. Different areas undertaking different urban functions correspond to different traffic patterns and specific cycles. Here, a method named Urban Functional Zoning based on the Spatial Specificity (UFZ-SS) is proposed. The core of this method is to obtain urban spatial zoning through the specific cycles of traffic flows. First, UFZ-SS uses the Ensemble Empirical Modal Decomposition (EEMD) method to extract the specific periodic signal characteristics of traffic flows. Second, UFZ-SS calculates the contribution of online car-hailing traffic of different cycles in each zone. Then, the Gaussian Mixture Model (GMM) is utilized to classify all spatial zones into different spatial partitions based on the contribution of each periodic signal. Finally, this study validates UFZ-SS with the online car-hailing traffic volume in northeast Chengdu, China. The results show that the periodic characteristics of traffic can be effectively extracted and analyzed by the EEMD method, and highly distinct and accurate urban spatial partitioning results can be derived by spatial clustering based on the measures of specific cycles. Moreover, with the assistance of Point of Interest (POI) data, we verify the functional zones and structural patterns, which further demonstrates the validity and rationality of urban functional zones identified by UFZ-SS. This study provides a new potential perspective for the identification of urban functional zones, which may lead to a better understanding of the urban spatial structure and even urban planning.

Multi-Intensity Optimization-Based CT and Cone Beam CT Image Registration

Cancer is a highly lethal disease that is mainly treated by image-guided radiotherapy. Because the low dose of cone beam CT is less harmful to patients, cone beam CT images are often used for target delineation in image-guided radiotherapy of various cancers, especially in breast and lung cancer. However, breathing and heartbeat can cause position errors in images taken during different periods, and the low dose of cone beam CT also results in insufficient imaging clarity, rendering existing registration methods unable to meet the CT and cone beam CT registration tasks. In this paper, we propose a novel multi-intensity optimization-based CT and cone beam CT registration method. First, we use a multi-weighted mean curvature filtering algorithm to preserve the multi-intensity details of the input image pairs. Then, the strong edge retention results are registered using and intensity-based method to obtain the multi-intensity registration results. Next, a novel evaluation method called intersection mutual information is proposed to evaluate the registration accuracy of the different multi-intensity registration results. Finally, we determine the optimal registration transformation by intersection mutual information and apply it to the input image pairs to obtain the final registration results. The experimental results demonstrate the excellent performance of the proposed method, meeting the requirements of image-guided radiotherapy.

Scientific laws of research funding to support citations and diffusion of knowledge in life science

One of the main problems in scientometrics is to explore the factors that affect the growth of citations in publications to identify best practices of research policy to increase the diffusion of scientific research and knowledge in science and society. The principal purpose of this study is to analyze how research funding affects the citation-based performance of scientific output in vital research fields of life science, which is a critical province (area of knowledge) in science to improve the wellbeing of people. This study uses data from the Scopus database in 2015 (to assess the impact on citations in 2021, after more than 5 years) concerning different disciplines of life science, given by “agricultural and biological sciences”, “biochemistry, genetics, and molecular biology”, “Immunology and microbiology”, “neuroscience” and “pharmacology, toxicology and pharmaceutics”. Results demonstrate that although journals publish un-funded articles more than funded publications in all disciplines of life science, the fraction of total citations in funded papers is higher than the share in the total number of publications. In short, funded documents receive more citations than un-funded papers in all research fields of life science under study. Findings also support that citations of total (funded + un-funded), funded, and un-funded published papers have a power-law distribution in all five research fields of life science. Original results here reveal a general property in scientific development: funded research has a higher scaling potential than un-funded publications. Critical implications of research policy, systematized in a decision-making matrix, suggest that R&D investments in “Neuroscience” can generate a positive impact of scientific results in science and society-in terms of citations-higher than other research fields in medicine. Overall, then, results here can explain some characteristics driving scientific change and help policymakers and scholars to allocate resources towards research fields that facilitate the development and diffusion of scientific research and knowledge in life science for positive societal impact.

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.