Continuous Evolution Research Articles

Nanoparticle-based electrochemical sensing

Electrochemical sensing has emerged as a potent technique for detecting and quantifying various analytes due to its inherent sensitivity, simplicity, and potential for miniaturization. Leveraging their distinctive physicochemical properties, nanoparticles have sparked a revolution in electrochemical sensing, enhancing sensing platforms’ sensitivity, selectivity, and stability. This paper presents the background and significance of nanoparticle-based electrochemical sensing, along with the diverse applications of nanoparticles in science and engineering. The fundamental principles of nanoparticles in electrochemical sensing are explored, highlighting their unique advantages in electrocatalysis, charge transfer facilitation, and signal amplification. The applications of various types of nanoparticles in electrochemical sensing are discussed, elucidating the role of nanomaterials in designing advanced conduction strategies such as impedance spectroscopy, cyclic voltammetry, and amperometry.Furthermore, this review delves into the applications of nanoparticle-based electrochemical sensors across domains, including gas detection, environmental monitoring, and the field of biosensors. The practicality of these sensors in real-world scenarios is showcased through case studies, emphasizing their rapid response, high specificity, and potential for multi-analyte detection. The challenges and prospects of this field are also examined. Addressing nanoparticle stability, reproducibility, and scalability issues is crucial for the successful commercial translation of these sensing platforms. In conclusion, this review underscores the transformative impact of nanoparticles on the landscape of electrochemical sensing. Their multifunctionality, coupled with evolving conduction methodologies, promises to develop ultra-sensitive and reliable sensing platforms that contribute to advancements across various application domains. With the continuous evolution of nanoparticle synthesis techniques, electrochemical sensing is poised to achieve new frontiers of sensitivity and specificity.

Highlights of Veterinary Entomology, 2022.

The field of veterinary entomology is dominated by research concerning insects and arthropods that negatively impact the health of domestic animals. A curated selection of peer-reviewed research which was highlighted at the 2022 Joint Meeting between the Entomological Society of America, the Entomological Society of Canada, and the Entomological Society of British Columbia, which prioritized exploring entomology through the lens of art, science, and culture are summarized. Articles are categorized into (i) biting and non-biting flies, (ii) ectoparasites, and (iii) review articles, with the overall goal of showcasing innovative methodologies, addressing overlooked questions in veterinary entomology, and highlighting comprehensive reviews. While not exhaustive, the selected studies represent a myriad of arthropods, methodologies, and perspectives, to inspire future scientists with diverse research avenues and emphasize the continual evolution and importance of entomological studies in today's world.

Integrating technology and special education: innovative approaches to teaching Autism

This research addressed the integration of technology in special education, focusing specifically on the teaching of students with autism. The main objective was to examine innovative technological approaches applied to special education and assess their effectiveness in the educational process of individuals with autism. Using a literature review methodology, several studies and articles discussing the use of technologies such as digital games, virtual reality, flipped classroom, and collaborative learning in the context of special education were analyzed. The results revealed the significant impact of these technologies on improving engagement and the development of social and cognitive skills in students with autism. However, challenges such as accessibility, costs, and the need for specific educator training were identified. Future perspectives indicate an optimistic scenario, with the continuous evolution of educational technologies promoting increasingly personalized and inclusive approaches. The study concluded by emphasizing the importance of technology as a valuable resource in the education of individuals with autism, despite the existing challenges.

Image Denoising Techniques Using Unsupervised Machine Learning and Deep Learning Algorithms: A Review

The continuous evolution of imaging technologies has accentuated the demand for robust and efficient image denoising techniques. Unsupervised machine learning algorithms have emerged as promising tools for addressing this challenge. This review scrutinizes the efficacy, versatility, and limitations of various unsupervised machine learning approaches in the area of image denoising. The paper commences with a clarification of the foundational concepts of image denoising and the pivotal role unsupervised machine learning plays in enhancing its efficacy. Traditional denoising methods, encompassing filters and transforms, are briefly outlined, highlighting their insufficiencies in handling complicated noise patterns prevalent in modern imaging systems. Subsequently, the review delves into an exploration of unsupervised machine learning techniques tailored for image denoising. This includes an in-depth analysis of methodologies such as clustering deep learning. Each technique is surveyed for its architectural variation, adaptability, and performance in denoising diverse image datasets. Additionally, the review encompasses an evaluation of prevalent metrics used for quantifying denoising performance, discussing their relevance and applicability across varying noise types and image characteristics. Furthermore, it delineates the challenges faced by unsupervised techniques in this domain and charts prospective avenues for future research, emphasizing the fusion of unsupervised methods with other learning paradigms for heightened denoising efficacy. This review merges empirical insights, critical analysis, and future perspectives, serving as a roadmap for researchers and practitioners navigating the landscape of image denoising through unsupervised machine learning methodologies.

CRISPR/Cas Technology: The Unique Synthetic Biology Genome-Editing Tool Shifting the Paradigm in Viral Diagnostics, Defense, and Therapeutics.

The emergence of the COVID-19 pandemic has starkly exposed our significantly limited ability to promptly identify and respond to emergent biological threats. Consequently, there is an urgent need to advance biotechnological methods for addressing both known and unforeseen biological hazards. Recently, the CRISPR/Cas system has revolutionized genetic engineering, enabling precise and efficient synthetic biology applications. Therefore, this review aims to provide a comprehensive introduction to the fundamental principles underlying the CRISPR/Cas system and assess the advantages and limitations of various CRISPR/Cas-based techniques applicable to the detection of, defense against, and treatment of viral infections. These techniques include viral diagnostics, the development of antiviral vaccines, B cell engineering for antibody production, viral activation/interference, and epigenetic modifications. Furthermore, this review delves into the challenges and bioethical considerations associated with use of the CRISPR/Cas system. With the continuous evolution of technology, the CRISPR/Cas system holds considerable promise for addressing both existing and unforeseen biological threats. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 26 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Exploration of the Lithium Storage Mechanism in Monoclinic Nb2O5 as a Function of the Degree of Lithiation

Insertion‐type Nb2O5 is a promising candidate for high‐power lithium‐ion anodes. Among the various polymorphs, monoclinic Nb2O5 (H‐Nb2O5) is considered as one of the most promising materials. Herein, the impact of decreasing the lower cutoff potential, i.e., increasing the amount of lithium that is inserted into the crystal structure, from the commonly used 1.0 V versus Li+/Li to 0.8 V and even 0.01 V is explored, yielding reversible specific capacities of 260, 280, and 400 mAh g−1, respectively, at a specific current of 0.05 A g−1. Remarkably, such increase in capacity does not come along with a deterioration of the cycling stability—at least initially. In fact, the comprehensive investigation of the reaction mechanism via operando/ex situ X‐ray diffraction, operando/ex situ X‐ray absorption spectroscopy, ex situ high‐resolution transmission electron microscopy, and operando isothermal microcalorimetry reveals that the extension of the voltage range does not affect the crystal structure during the first couple of cycles, but there is a continuous evolution upon long‐term cycling.

Leveraging of Gradient Boosting Algorithm in Misuse Intrusion Detection using KDD Cup 99 Dataset

This study addresses the persistent challenge of intrusion detection as a long-term cybersecurity issue. Investigating the efficacy of machine learning algorithms in anomaly and misuse detection. Research employs supervised learning for misuse detection and explain anomaly detection. Focused on adaptability and continual evolution the study explores the application of ensemble learning models AdaBoost, LightGBM, and XGBoost. Applying these algorithms in the context of intrusion detection. Utilizing the KDD Cup 99 dataset as a benchmark the paper assesses and compares the performance of these models. Besides, illuminating their effectiveness particularly in identifying smurf attacks within the cybersecurity landscape.

Addressing complexity in motor learning: Practical strategies for teachers and coaches

This research embarks on an exploration delving into the intricacies of motor skill acquisition and its versatile application across various contexts. Its core objective resides in fostering a robust discourse within this domain. Emphasizing the qualitative dimension of mastering motor skills underscores an urgent need for evidence-backed teaching methodologies that boast universal applicability. In the realms of physical education and sports, a profound comprehension of fundamental theoretical frameworks stands as a pivotal requirement to facilitate effective pedagogy. The continuous evolution of innovative teaching approaches within motor and sports education accentuates the necessity for a meticulous examination of the foundational theoretical underpinnings supporting these advancements. The principal aim of this investigation primarily revolves around distilling the fundamental principles embedded within the ecological-dynamic approach to learning. Furthermore, it endeavors to illuminate actionable directives tailored for instructional purposes. These directives aspire to cultivate adaptive learning environments conducive to the acquisition of motor and sports skills, prioritizing flexibility over rigidity and nurturing an environment that encourages perpetual learning experiences. To achieve this ambitious goal, an exhaustive scrutiny of recent literature has been undertaken, placing significant emphasis on notable contributions propelling the ongoing discourse forward. This comprehensive review endeavors to enrich the dialogue surrounding evidence-based teaching methodologies within the realm of motor skill acquisition and sports education.

CYBERSECURITY DYNAMICS IN NIGERIAN BANKING: TRENDS AND STRATEGIES REVIEW

This paper provides an in-depth review of the cybersecurity dynamics within the Nigerian banking sector, emphasizing recent trends and strategic approaches to address emerging challenges. As a review paper, it synthesizes existing literature, reports, and case studies to offer a comprehensive understanding of the current cybersecurity landscape in Nigerian banks. The focus is on identifying the predominant cyber threats, analyzing the sector's response strategies, and evaluating the effectiveness of these measures in the context of Nigeria's unique socio-economic and regulatory environment. Our analysis reveals a notable escalation in cyber threats, particularly phishing, ransomware, and insider attacks, which have been intensified by the rapid digital transformation in banking services. The review identifies key factors contributing to these challenges, such as the increasing sophistication of cybercriminals, the digital literacy gap among customers, and the evolving nature of cyber threats. It also examines the strategic responses of Nigerian banks, including the adoption of advanced security technologies, enhanced staff training, and collaboration with government and international cybersecurity bodies.The paper concludes that Nigerian banks have made significant strides in fortifying their cybersecurity defenses. However, it also highlights the need for more robust regulatory frameworks, increased customer awareness initiatives, and a shift towards more integrated and proactive cybersecurity strategies. The findings of this review underscore the critical need for continuous evolution and investment in cybersecurity measures to effectively counter the dynamic and complex nature of cyber threats in the Nigerian banking sector.
 Keywords: Cybersecurity, Cybersecurity Dynamics, Nigerian Banking Sector, Digital Transformation, Cyber Threats, Phishing Attacks, Ransomware, Insider Threats, Regulatory Framework, Central Bank of Nigeria (CBN), Compliance Challenges, Security Technologies, Cybersecurity Awareness, Artificial Intelligence (AI), Machine Learning (ML), Blockchain Technology.

Numerical Study of Consolidation of Slurry Tailings Considering Continuous Update of Material Properties

AbstractExplicitly modelling tailings consolidation behaviour contributes to improve integrated management approaches and accurately estimate the storage capacity of tailings storage facilities (TSFs) to better predict their static and dynamic stability. However, slurry tailings demonstrate a highly non-linear evolution of stiffness and hydraulic conductivity during consolidation, thus significantly complexifying the determination of their hydromechanical properties. In this study, an approach to update Mohr Coulomb parameters and simulate the continuous evolution of hydraulic conductivity and stiffness of tailings materials with the reduction of the void ratio was proposed and embedded in a finite difference code to more realistically simulate the evolution of material properties during sequential loadings. The model was validated using laboratory column tests and various predictive functions were tested to estimate hydraulic conductivity for field applications. Finally, the developed approach was applied to a simplified model of tailings impoundment to illustrate practical applications. Results from this study indicated that the approach developed was able to capture the non-linearity properties of tailings during consolidation, and that using continuously updated stiffness and hydraulic conductivity could induce significantly different magnitude and rate of consolidation than models with constant properties. Predictive models such as Kozeny–Carman and Kozeny–Carman Modified models also gave a satisfactory estimation of tailings behaviour, at least for preliminary studies. The simple modifications to the numerical codes proposed in this paper could therefore significantly improve the numerical simulation of tailings behaviour in the short term and contribute to a better planning of deposition plans.

The ClearPoint Array Frame: An MRI Compatible System that Supports Non-craniotomy, Multi-trajectory (NCMT) Stereotactic Procedures

With continued evolution in stereotactic techniques and an expanding armamentarium of surgical therapeutic options, non-craniotomy stereotactic procedures in neuro-oncology are becoming increasingly complex, often requiring multi-trajectory approaches. Here we demonstrate that the ClearPoint SmartFrame Array®, a second-generation MRI-compatible stereotactic frame, supports such non-craniotomy, multi-trajectory (NCMT) stereotactic procedures. We previously established a case report demonstrating the feasibility of NCMT through the ClearPoint SmartFrame Array®. Here we prospectively followed the next ten consecutive patients who underwent such multi-trajectory procedures to establish procedural safety and clinical utility. Ten patients underwent complex, multi-trajectory stereotactic procedures, including combinations of needle biopsy + cyst drainage and laser interstitial thermal therapy (LITT) targeting geographically distinct regions of neoplastic lesions under the same anesthetic event. The median maximal radial error of stereotaxis was 1.0 mm. In all cases, definitive diagnosis was achieved, and >90% of the intended targets were ablated. The average stereotaxis time for the multi-trajectory procedure was 119 + 22.2 minutes, comparing favorably to our previously published results of single-trajectory procedures (80 + 9.59 minutes, p=0.125). There were no procedural complications. Post-procedure, the neurologic condition of one patient improved, while the remaining nine patients remained stable. All patients were discharged home, with a median hospital stay of one day (range: 1-12 days). With a median follow-up of 376 days (range: 155-1438 days), there were no 30-day readmissions or wound complications. Geographically distinct regions of brain cancer can be safely and accurately accessed through the ClearPoint Array® frame in NCMT stereotactic procedures.

Landscape of T cell epitopes displays hot mutations of SARS-CoV-2 variant spikes evading cellular immunity.

The continuous evolution of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) has been accompanied by the emergence of viral mutations that pose a great challenge to existing vaccine strategies. It is not fully understood with regard to the role of mutations on the SARS-CoV-2 spike protein from emerging viral variants in T cell immunity. In the current study, recombinant eukaryotic plasmids were constructed as DNA vaccines to express the spike protein from multiple SARS-CoV-2 strains. These DNA vaccines were used to immunize BALB/c mice, and cross-T cell responses to the spike protein from these viral strains were quantitated using interferon-γ (IFN-γ) Elispot. Peptides covering the full-length spike protein from different viral strains were used to detect epitope-specific IFN-γ+ CD4+ and CD8+ T cell responses by fluorescence-activated cell sorting. SARS-CoV-2 Delta and Omicron BA.1 strains were found to have broad T cell cross-reactivity, followed by the Betastrain. The landscapes of T cell epitopes on the spike protein demonstrated that at least 30 mutations emerging from Alpha to Omicron BA.5 can mediate the escape of T cell immunity. Omicron and its sublineages have 19 out of these 30 mutations, most of which are new, and a few are inherited from ancient circulating variants of concerns. The cross-T cell immunity between SARS-CoV-2 prototype strain and Omicron strains can be attributed to the T cell epitopes located in the N-terminal domain(181-246 aa [amino acids], 271-318 aa) and C-terminal domain (1171-1273 aa) of the spike protein. These findings provide in vivo evidence for optimizing vaccine manufacturing and immunization strategies for current or future viral variants.

Monoclonal antibodies that are effective against all COVID-19 -related viruses

All licensed COVID-19 antibody treatments become ineffective over time as new mutants emerge. We isolated antibodies from an individual who recovered from SARS-1 and received the COVID vaccine, that work against all COVID-related viruses including the latest Omicron variants. These broad-targeting antibodies could make future-proof therapeutics to counter the continual evolution of coronaviruses.

ConTIG: Continuous representation learning on temporal interaction graphs

Representation learning on temporal interaction graphs (TIG) aims to model complex networks with the dynamic evolution of interactions on a wide range of web and social graph applications. However, most existing works on TIG either (a) rely on discretely updated node embeddings merely when an interaction occurs that fail to capture the continuous evolution of embedding trajectories of nodes, or (b) overlook the rich temporal patterns hidden in the ever-changing graph data that presumably lead to sub-optimal models. In this paper, we propose a two-module framework named ConTIG, a novel representation learning method on TIG that captures the continuous dynamic evolution of node embedding trajectories. With two essential modules, our model exploits three-fold factors in dynamic networks including latest interaction, neighbor features, and inherent characteristics. In the first update module, we employ a continuous inference block to learn the nodes’ state trajectories from time-adjacent interaction patterns using ordinary differential equations. In the second transform module, we introduce a self-attention mechanism to predict future node embeddings by aggregating historical temporal interaction information. Experiment results demonstrate the superiority of ConTIG on temporal link prediction, temporal node recommendation, and dynamic node classification tasks of four datasets compared with a range of state-of-the-art baselines, especially for long-interval interaction prediction.

Development and application of a second-generation multilingual tool for invasion risk screening of non-native terrestrial plants

Under the increasing threat to native ecosystems posed by non-native species invasions, there is an urgent need for decision support tools that can more effectively identify non-native species likely to become invasive. As part of the screening (first step) component in non-native species risk analysis, decision support tools have been developed for aquatic and terrestrial organisms. Amongst these tools is the Weed Risk Assessment (WRA) for screening non-native plants. The WRA has provided the foundations for developing the first-generation WRA-type Invasiveness Screening Kit (ISK) tools applicable to a range of aquatic species, and more recently for the second-generation ISK tools applicable to all aquatic organisms (including plants) and terrestrial animals. Given the most extensive usage of the latter toolkits, this study describes the development and application of the Terrestrial Plant Species Invasiveness Screening Kit (TPS-ISK). As a second-generation ISK tool, the TPS-ISK is a multilingual turnkey application that provides several advantages relative to the WRA: (i) compliance with the minimum standards against which a protocol should be evaluated for invasion process and management approaches; (ii) enhanced questionnaire comprehensiveness including a climate change component; (iii) provision of a level of confidence; (iv) error-free computation of risk scores; (v) multilingual support; (vi) possibility for across-study comparisons of screening outcomes; (vii) a powerful graphical user interface; (viii) seamless software deployment and accessibility with improved data exchange. The TPS-ISK successfully risk-ranked five representative sample species for the main taxonomic groups supported by the tool and ten angiosperms previously screened with the WRA for Turkey. The almost 20-year continuous development and evolution of the ISK tools, as opposed to the WRA, closely meet the increasing demand by scientists and decision-makers for a reliable, comprehensive, updatable and easily deployable decision support tool. For terrestrial plant screening, these requirements are therefore met by the newly developed TPS-ISK.

Comparison Study of Earth Observation Characteristics between Moon-Based Platform and L1 Point of Earth-Moon System

The unceasing quest for a profound comprehension of the Earth system propels the continuous evolution of novel methods for Earth observation. Of these, the Lagrange points situated in the cislunar space proffer noteworthy prospects for space-based Earth observation. Although extant research predominantly centers on Moon-based Earth observation and the L1 point within the Sun-Earth system, the realm of cislunar space remains relatively unexplored. This paper scrutinizes the overarching characteristics of the L1 point within the Earth-Moon system concerning Earth observation. A pivotal enhancement is introduced through the incorporation of the halo orbit. This research comprehensively analyzes the relative motion between the halo orbiter and the Earth, achieved via orbit determination within a rotating coordinate system, followed by a transformation into the Earth coordinate system. Subsequently, numerical simulations employing ephemeris data unveil the observing geometry and Earth observation characteristics, encompassing the distribution of nadir points, viewing angles, and the spatiotemporal ground coverage. As a point of reference, we also present a case study involving a Moon-based platform. Our findings reveal that the motion of the halo orbit, perpendicular to the lunar orbital plane, results in a broader range of nadir point latitudes, which can extend beyond 42°N/S, contingent upon the orbit’s size. Additionally, it manifests a more intricate latitude variation, characterized by the bimodal peaks of the proposed temporal complexity curve. The viewing angles and the spatiotemporal ground coverage closely resemble those of Moon-based platforms, with a marginal enhancement in coverage frequency for polar regions. Consequently, it can be deduced that the Earth observation characteristics of the L1 point within the Earth-Moon system bear a close resemblance to those of Moon-based platforms. Nevertheless, considering the distinct advantages of Moon-based platforms, the lunar surface remains the paramount choice, boasting the highest potential for Earth observation within cislunar space. In summation, this study demonstrates the Earth observation characteristics of the L1 point within the Earth-Moon system, emphasizing the distinctions between this and Moon-based platforms.

Joint optimization of sampling point and sensing threshold for spectrum sensing

AbstractWith the continuous evolution and in‐depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal‐to‐noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two‐dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q‐learning is considered in this paper to obtain the sub‐optimal solution due to the non‐convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme.

Accelerated evolution of chosen genes.

Orthogonal replication enables rapid continuous biomolecular evolution in Escherichia coli.

Digitization Transformation Empowers High-Quality Development of Small and Medium-Sized Enterprises in Jilin Province

In the rapidly evolving digital landscape, small and medium-sized enterprises (SMEs) in Jilin Province, China, face a critical juncture in their growth and sustainability. This paper delves into the current state of digital transformation among these SMEs, examining the intricate web of opportunities and challenges that define their journey towards a digital future. Through a comprehensive analysis, we explore the various facets of digital transformation, including the provincial government's support initiatives, the strategic approaches adopted by SMEs, and the inherent hurdles such as increased operational costs, leadership and cultural challenges, skill gaps, and technological adoption issues. The findings underscore the importance of a multi-pronged strategy involving government support, private sector collaboration, workforce development, and fostering a culture of innovation and adaptability. The paper highlights the need for Jilin's SMEs to not only embrace technological changes but also to rethink their operational models to stay competitive in a global digital economy. Looking ahead, the paper suggests that with continuous strategic efforts, policy support, and an adaptive mindset, SMEs in Jilin can successfully navigate their digital transformation journey, tapping into new opportunities for growth and innovation in an increasingly digital world. The future trajectory for these enterprises is one of continued evolution, where adaptability and strategic planning are key to harnessing the full potential of digital technologies. As digital transformation continues to reshape the global business landscape, SMEs in Jilin Province stand on the brink of significant opportunities, poised to redefine their role in the regional and global economy through innovative and sustainable digital strategies.

Establishing a synthetic orthogonal replication system enables accelerated evolution in E. coli.

The evolution of new function in living organisms is slow and fundamentally limited by their critical mutation rate. Here, we established a stable orthogonal replication system in Escherichia coli. The orthogonal replicon can carry diverse cargos of at least 16.5 kilobases and is not copied by host polymerases but is selectively copied by an orthogonal DNA polymerase (O-DNAP), which does not copy the genome. We designed mutant O-DNAPs that selectively increase the mutation rate of the orthogonal replicon by two to four orders of magnitude. We demonstrate the utility of our system for accelerated continuous evolution by evolving a 150-fold increase in resistance to tigecycline in 12 days. And, starting from a GFP variant, we evolved a 1000-fold increase in cellular fluorescence in 5 days.