Growth In Domains Research Articles

Did Crop Domestication Change the Fitness Landscape of Root Response to Soil Mechanical Impedance? An in-silico Analysis.

Root axes with greater penetration ability are often considered to be beneficial in hard soils. We hypothesized that maize root phenotypes with greater plasticity (meaning reduced elongation in response to mechanical impedance, i.e. a 'stop signal') have fitness advantages over phenotypes with reduced plasticity (i.e. unimpeded root elongation) in native (virgin, uncultivated) soils, by reallocating root foraging to softer, presumably wetter, soil domains, and that the value of the stop signal reduced with soil cultivation and crop domestication. We used OpenSimRoot to simulate native and cultivated soils and evaluated maize root phenotypes with varying axial and lateral root penetration ability in water, nitrogen, and impedance regimes associated with Neolithic agriculture. The stop signal was advantageous in native soils but was less beneficial in cultivated, irrigated soils. Reduced root foraging in hard, dry topsoil enabled root growth in deeper domains where water is available, resulting in an improved balance of resource expenditure and acquisition. The value of the stop signal declined during crop domestication with the advent of irrigation, which increased water availability in the topsoil. Soil cultivation reduced N availability, while irrigation increased N leaching, resulting in a shift in the fitness landscape, with greater lateral root length (i.e. reduced plasticity) being advantageous by colocalizing root foraging with N availability. The importance of the stop signal is evident in modern high-input systems in which drought is a limiting factor. Our results support the hypotheses that the reduction of lateral root growth by mechanical impedance is adaptive in native soil, but became less adaptive with soil cultivation and irrigation associated with Neolithic agriculture.

An insight on technical regulations for new activities in space

Space Traffic Management (STM) is becoming increasingly important as new space activities emerge, such as mega-constellations, on-orbit servicing etc. These developments, although promising great advances, nevertheless present complicated issues that demand stringent technical constraints. Effective STM necessitates extensive rules to ensure the safety, sustainability, and orderly execution of space operations. This research emphasizes the critical need for updated technical regulations that address the operational complexities involved with these new activities. It seeks to build a secure and collaborative space environment by establishing detailed legislative frameworks and emphasizing international cooperation, so ensuring the long-term profitability and success of space exploration and usage. Through meticulous analysis, the paper identifies regulatory gaps and proposes a robust framework capable of accommodating the evolving landscape of regulations. Drawing upon humanity's collective experience across different domains, the proposed framework aims to foster responsible growth in outer space while safeguarding its integrity for future generations. This paper examines the existing regulatory framework applicable to four distinct categories of New Technologies and Activities in space; identifies gaps where they exist; and suggests a capable and robust regulatory framework. This framework leverages humankind's experience in other domains to nurture and assist responsible growth in the outer space domain while ensuring proper stewardship of that domain, now and in the future. This paper is based on the report prepared by members of working group 4.2 of the Space Traffic Management Committee (TC 26).

Transfer learning through physics-informed neural networks for bubble growth in superheated liquid domains

In this paper, a physics-informed neural network (PINN) technique is developed to study the heat and mass transfer for the process of vapour bubble growth in a superheated liquid domain and tested using three working fluids including water, R-134a and FC-72. The work represents a novel step in the development of PINNs for phase change scenarios where surface tension effects dominate, and acts as a necessary validation stage before PINN techniques can be applied to complex boiling analysis. Initially, a forward analysis was performed using water and R-134a as working fluids. For each of these investigations, the PINN algorithm was trained on 50 % of the available CFD data. The proposed algorithm was able to accurately infer velocity fields, particularly in the near-interfacial region. The resultant circulatory flow was found to maintain the desired circular shape of the growing bubbles. As a result, when predicting the evolution of a water vapour bubble, the developed PINN algorithm produced a reduction in peak error by 0.87 % compared to CFD reference data, and 3.42 % reduction in peak error for prediction of the evolution of the R-134a vapour bubble. To test and optimise the transfer learning capabilities of the developed methodology, the evolution of an FC-72 vapour bubble in superheated FC-72 was predicted without supplying supporting observational data. For this scenario, the PINN algorithm produced a peak error within 1.3 % of the unobserved CFD reference data. The proposed approach confirms the robustness of PINN methodologies as a method of solving phase-change problems where surface tension plays a pivotal, promising to expedite parametric studies in practice. This study represents a pioneering effort in the development of PINNs for phase change by applying the current algorithm to investigate bubble growth within superheated liquid domains, serving as a basis for the application of PINNs for boiling problems and as a benchmark for inverse training strategy.

Observation of Polymerization‐Induced Phase Separation During Polymerization of Methyl Methacrylate in the Presence of Poly(Ethylene Glycol)

AbstractPolymerization‐induced phase separation takes place when a miscible component of the monomer becomes immiscible as the polymer is formed. One example is the polymerization of methyl methacrylate (MMA) in the presence of poly(ethylene glycol) (PEG). Depending on the initial conditions, microscopic structures, including porous structures, co‐continuous monolith structures, and particle aggregation structures, are obtained. In this study, the phase separation process is analyzed using an optical microscope and fluorescence microscope. The nucleation or spinodal decomposition in the initial stage, subsequent growth in the microscopic domains, and eventual fixation of the structure are observed. The observed images are analyzed using image analysis software. The particle number and the particle size as a function of reaction time are evaluated. Conversion and molecular weight distribution as a function of reaction time are also analyzed.

Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block

To improve the precision of defect categorization and localization in images, this paper proposes an approach for detecting surface defects in hot-rolled steel strips. The approach uses an improved YOLOv5 network model to overcome the issues of inadequate feature extraction capacity and suboptimal feature integration when identifying surface defects on steel strips. The proposed method achieves higher detection accuracy and localization precision, making it more competitive and applicable in real production. Firstly, the multi-scale feature fusion (MSF) strategy is utilized to fuse shallow and deep features effectively and enrich detailed information relevant to target defects. Secondly, the CSPLayer Res2Attention block (CRA block) residual module is introduced to reduce the loss of defect information during hierarchical transmission, thereby enhancing the extraction of fine-grained features and improving the perception of details and global features. Finally, the experimental results indicate that the mAP on the NEU-DET and GC10-DET datasets approaches 78.5% and 67.3%, respectively, which is 4.9% and 2.1% higher than that of the baseline. Meanwhile, it has higher precision and more precise localization capabilities than other methods. Furthermore, it also achieves 59.2% mAP on the APDDD dataset, indicating its potential for growth in further domains.

Self-Attention conditional generative adversarial network optimised with crayfish optimization algorithm for improving cyber security in cloud computing

The decentralized and distributed architecture of cloud computing promotes adoption and growth in various societal domains, including education, government, information technology, business, entertainment. Cloud computing (CC) makes a broad range of information technologies available. Security and privacy are key challenges in storing big data in the cloud. To overcome this challenge, a Self-Attention Conditional Generative Adversarial Network Optimised with Crayfish Optimization Algorithm for Improving Cyber Security in Cloud Computing (CybS-CC-SACGANCOA) is proposed in this paper to enhance the safety of CC environment. The input data is amassed from NSL-KDD database. After that, the data is fed to pre-processing segment. The segment of pre-processing eliminates Cloud data termination and missing value replacement using Reformed Phase Conserving Vibrant Range Compression filter. The results of pre-processing serves for feature selection. The optimal features are selected by means of Manta Ray Foraging Optimization Algorithm (MRFOA). Cloud data is categorized as normal and abnormal data, like Denial-of-Service (DoS), Probe, Remote to local attack (R2L), User to root attack (U2R) by the help of SACGAN. Crayfish Optimization Algorithm (COA) is proposed to optimize the SACGAN classifier that classifies anomaly data precisely. The proposed CSCC-SACGANCOA technique is activated in Python under some metrics. The proposed CSCC-SACGANCOA approach has attained higher detection accuracy, lower computation time, higher AUC, higher scalability and lower detection error rate compared to the existing methods.

Synthesis and Characterization of ZIF 67 Manganese Bimetal for Electrochemical Sensor Application

The field of sensor applications has witnessed substantial growth in diverse domains, including agriculture, food, oil, environment, and medicine. Among the varied methodologies employed, electrochemical methods stand out. The judicious selection of appropriate materials in this context significantly augments sensor efficiency. Zeolitic Imidazolate Framework 67 (ZIF 67), a highly porous material with an expansive surface area, offers facile synthesis, yet is impeded by limited conductivity. The introduction of additional metal derivatives has been reported to enhance its conductivity, with manganese, a transition metal, identified for its potential conductivity improvement and its influence on particle size. Thus, this paper aims to comprehensively explore the properties of manganese-modified ZIF 67, spanning structural, morphological, and electrochemical properties. Research findings indicate that manganese doping enhances crystallinity, as evident from X-ray diffraction analysis, while also impacting particle size (from x¯ 514.4 nm to 944 nm), as assessed through SEM measurements. Furthermore, electrochemical performance reveals heightened peak current during redox processes reaching ±64 µA, indicative of improved conductivity from the ZIF 67 (±13 µA) and the bare (±43 µA). In light of these outcomes, this material emerges as a promising candidate for electrochemical sensor development.

Implementing and evaluating a new Silent Rank Attack in RPL-Contiki based IoT networks

Abstract IoT networks are witnessing a rapid growth in various domains of our daily life, offering more attractive features in terms of measurement accuracy, easy implementation and affordable deployment costs. This outstanding boom is not undoubtedly far away from different challenging issues that impede the network efficiency and quality. The security concern remains one among the prominent issues that affect both the edge and the core IoT network where risks increase in conjunction with the network expansion. RPL is the well-known routing protocol for the edge part of the IoT network, intended to meet the requirements of the constrained IoT devices. Despite its various advantages, RPL remains suffering from various security attacks targeting the topology, the traffic, and the nodes resources. Our work presents a new silent decreased rank attack against RPL-Contiki, as well as a lightweight countermeasure. The obtained results on a random studied topology show that almost half the existing nodes in the topology were attracted by the planted malicious node, through its falsified low rank. Moreover, an increase of 12.5% in the control overhead and an increase of 15% in the total consumed energy are recorded compared to the attack-free topology. On the other hand, the attack did not heavily affect the PDR, but the latency showed an increase of 45% compared to the attack free case. This damaging effect makes this modified rank attack a serious threat to IoT RPL based networks.

Effects of Customized Employment on the Independence of Youth With Intellectual and Developmental Disabilities.

Participation in work during school is a known predictor of postsecondary employment for transition-age youth with intellectual and developmental disabilities (IDD). Recent research has demonstrated the benefits of work on skill development in major life domains. The purpose of this study was to examine the impact of customized employment on the support needs of youth with IDD with a significant disability compared to those receiving treatment as usual. Findings indicated significant increases in independence in three specific domains for the treatment group versus control on the Supports Intensity Scale-Adult Version, including Home Living, Employment, and Protection and Advocacy. Findings suggest a therapeutic effect of work activities on growth and development in important life domains. Implications for future research, policy, and practice are discussed.

Accelerated Computing A Biomedical Engineering and Medical Science Perspective

The rapid advancements in technological computing have brought about significant growth and transformation in various domains of engineering, particularly in the field of medical science and human health. With the progress in computer vision, image processing, machine learning, deep learning, and data science, there has been a revolutionary change in healthcare, addressing a wide range of medical conditions and human anatomy perspectives. The integration of these technologies has not only improved medication and disease control but has also provided solutions for complex tasks and issues related to human anatomy threats in the health sector. This research focuses on the impact of accelerated computing in biomedical engineering, providing insights into the modern utility of toolsets in Bioinformatics and mechanics with artificial intelligence within medical science and also diving into understanding the human anatomy. Additionally, it explores the concept of functional genomics and its potential to provide insights into future disease and health issues, paving the way for advancements in healthcare for the foreseeable future and beyond.

Interactive Effects of CEOs’ Firm-Specific Experience and Versatile Experiences on Pursuit of New Growth Opportunity

Prior research has opposing views about the merits of firm-specific managerial experience, and empirical findings are mixed. To address this problem, we ask when firm-specific managerial experience is helpful versus harmful in pursuit of new growth opportunities. We propose that when CEOs’ firm-specific experience is combined with versatile managerial experiences, its growth-constraining effect is alleviated due to increased adaptability of CEOs' human capital to support growth in new business domains. We find support for this argument in a sample of U.S. food firms and their pursuit of the organic food opportunity during 1997 to 2007. We also find that CEOs’ versatile experience by itself is not sufficient for the pursuit of new growth, but it is when the two types of experience are combined synergistically that firms can achieve higher rates of opportunity pursuit. In this vein, our research reconciles the alternative theoretical perspectives on firm-specific experience and invites further consideration of experience combinations where both positive and negative synergies drive the ultimate impact of key managerial experiences.

Integrating Sustainable Design into Advanced Electrochemical Manufacturing

Considering the green engineering principles developed by Anastas and Zimmerman or detailed from the Sandestin conference of 2004 as presented by Abraham and Nguyen there is a duty of care as environmental stewards of our technology to closely examine where we can reduce impact regarding nascent electrochemical technologies.Comparison of state-of-the-art electrochemical manufacturing technologies highlight the design options competing technologies offer when we consider the sourcing, manufacturing, emissions, and other ways of embodying the environmental footprint of our designed processes.Impact on natural systems, circular life-cycle thinking and creating engineering solutions beyond our current or dominant manufacturing technologies to improve, innovate and invent solutions for a transition to a clean energy future are highlighted. Principles of conservation and improvement of natural ecosystems are considered to inform novice scientists and engineers and lead to insight in opportunities for advanced system developments for mature industry representatives and experienced experts.Challenges in greening of existing systems offers opportunities for continued growth in the industrial domain. Triple bottom line examination of how our actions can improve manufacturing processes, as well as benefit globally relevant sustainable development goals. Reframing the basis for how we design electrochemical processes from a cost-efficiency relationship to include additional considerations in our practice using simple guidelines to address cell architectures, integration and interconnection of energy and material flows, and careful material selection are shown to have a win-win benefit when we examine intergenerational scope of our practice.

Elevating Mentorship Competency for Sustained Impact via the University of Zambia Mentor Training Program.

The University of Zambia (UNZA) Mentor Training Program is conducted annually to strengthen the mentorship capacity of postgraduate programs for the health professions. This intensive five-session course trains faculty members in the mentorship of students. Established by senior UNZA leaders and US-based collaborators, this program was designed to address gaps in mentorship identified at the institutional level. Faculty facilitators developed the course curriculum and used a train-the-trainer model to ensure program sustainability. Participants were faculty members who mentor PhD and Master of Medicine students. To assess the program's impact, mentors and their mentees completed questionnaires on the mentor's mentoring competencies at the end of the course and 1 year later. Competency scores were compared longitudinally to quantify potential changes in mentoring behaviors. Mentors and mentees alike noted mentor growth in all competency domains from postcourse to 1 year later, providing evidence of a trend toward improvement in mentorship and that the program may have sustainable and positive effects on mentoring behaviors over time. Salient areas of growth corresponded to emphasized topics and discussions, including addressing diversity, aligning expectations, assessing capacities, motivating mentees, and fostering independence. These findings suggest that mentors internalized this content and transferred it to behavior change. The behavior changes may reveal a larger change in the institutional environment around the mentoring of students. The UNZA Mentor Training Program appears to have sustained impact after a year and should bode well for future benefits to students, faculty, and the institution.

Nonexistence results for elliptic problems with supercritical growth in thin planar domains

Nonexistence results for elliptic problems with supercritical growth in thin planar domains

A scoping review of literature on the application of swarm intelligence in the object classification domain

This scoping review aims to explore the various swarm technologies and how they have been used in the object classification domain with the desire to motivate the design of a generic swarm intelligence ontology based on the components of various swarm technologies. We used the PRISMA-ScR as a guide to our scoping review protocol. We conducted a search across thirteen databases and a random search as well on the internet for articles. We performed screening of all the articles by title to remove duplicates, we further on did a screening by the year of publication to ensure that all articles to be considered were published between 2012 and 2022 and we then did abstract or text synthesis. Our search query retrieved 3224 potential articles from the thirteen databases and 10 articles from a random search on the internet making a total of 3234 articles identified. Deduplication and screening were done on the identified articles and 287 articles which satisfied our inclusion criteria remained. We grouped the articles into three categories namely year of publication, swarm technology and swarm application. The year of publication showed a linear trend line which is an indication of growth in the swarm intelligence domain. Of the six categories of aims we identified we voluntarily chose to ignore articles where the aim was not specified. We noticed that 64.9% of articles were aimed at either modifying or improving. The swarm technology category indicated that 58.54% of the included articles were based on the Particle Swarm Optimization either independently or as part of a hybrid algorithm. 83.97% of the articles used classification as their swarm application. Interesting to note was the appearance of feature selection and optimization in this category. This scoping review gave an overview of how swarm technologies have been used in the object classification domain. Further research can be done by bringing and using existing algorithms in the development of generic swarm intelligence inspired ontologies.

Mind the gap: The AURORAL ecosystem for the digital transformation of smart communities and rural areas

Rural areas play a crucial role in addressing challenges related to climate change, food provision, biomass, and energy. At the same time, digital solutions have proven essential in improving safety, quality of life, and resilience in daily life. However, the lower population density and the lack of digital infrastructure in such rural areas make it difficult to develop technology-driven private businesses and public services. This can negatively impact socio-economic indicators and hinder the development of new services to cover peoples’ needs. For this reason, in this document, we seek to provide a stronger focus on rural regions in digitalization efforts and create new opportunities for rural communities. For that, we analyze the barriers and needs of the rural environment and present AURORAL, a digital service platform designed to meet the needs and contexts of rural areas. This ecosystem, comprising sustainable and multi-interoperable apps and services, can help communities succeed in innovation and smart transformation, providing the necessary infrastructure to facilitate long-lasting social, environmental, and economic benefits by prioritizing openness, interoperability, and decentralization. On the principle that the full potential of these technologies can only be realized when they are integrated into societal and economic activity and organization, AURORAL aims to promote economic growth and digitalization in the rural domain and contribute to bridging the digital divide between rural and urban areas.

A Review of Digital Watermarking Approaches for Forensic Applications

Abstract: There has been remarkable growth in the digital domain. This has led to the production of a huge amount of digital data in the form of text messages, documents, audio/speech signals, digital images, videos, etc. In forensics, digital information (i.e., images of crime scenes, etc.) is very important to examine cases and collect the proof regarding the incident. This further leads to judge the victims and culprits by the court for appropriate actions. So, even a slight change (intentional/ unintentional) may lead to the wrong judgement. Several digital signal processing tools are easily available to process this type of digital data, which means data may get disrupted, altered, or deform in such a way that the authenticity gets compromised. In such cases, it can be difficult to recognize the source information as well as to check the originality/intactness of the digital data. In the recent past, several techniques have come into existence to protect digital data against security issues. Steganography, cryptography, and watermarking are examples of security techniques, which have been widely used for data security and forensic applications for a long time. Specifically, digital watermarking techniques provide many useful features to help users regarding data security and authentication without affecting other data characteristics. This work is an attempt to provide a review of recent digital image watermarking approaches for forensic applications.

Strongly nonlinear parabolic equations with natural growth in general domains

Strongly nonlinear parabolic equations with natural growth in general domains

MCSO-MW: An Energy Optimized Data Switching Scheme for Multi-hop Wireless Sensor Networks

Due to continuous growth in the field of wireless sensor networks (WSN), it holds enormous growth in the multidisciplinary domains to provide a promising solution. Especially, on the internet of things (IoT) based edge device network, WSN plays a significant area in the field of data collection and physical entity monitoring. The effectiveness of the routing scheme in WSN can be directly measured through the amount of data transmission, network topology, intermediate hop counts, and data transmission distance. Similarly, an ideal energy model can improve the entire system’s performance and sustainability over successive data transmission. In this paper, a multi-objective chicken swarm optimization (CSO) algorithm is adopted to optimize energy consumption over WSN and improve the system performance through an effective routing approach. Furthermore, the proposed approach is adopted to partition the entire sensor network into multiple clusters and select the partitioned head among the sensor nodes. The competence of the proposed approach is validated against other existing approaches.

Conferencing and Presentation Strategies for Young Academics in the Global Landscape

This article underscores the crucial role of nurturing early-career physicians' academic growth and well-being in the fast-paced medical domain. It delivers guidance and resources for budding academicians, emphasizing the importance of international conferences for knowledge exchange and reputation building. The author shares practical tips on effective communication and academic presentations, urging researchers to concentrate on personal interests and publish in respected journals.