Road Map Research Articles

CHALLENGES OF GREEN SCHOOL QUALITY ASSESSMENT

The Green School Quality Standard offers criteria for integrating holistic institutional and sustainable development education, that demonstrate commitment to green education, integrate environmental principles and climate action into the delivery of school educational services. The ultimate goal of the standard is to provide a complete educational roadmap, that ensures continuity and interdependence between educational stages, from early years to higher education. In article recommendations are made for improving the implementation of the audit for quality of green education, discussing also the road map for completing audit processing of the results from activity of the green school.

Imaging in sensorineural and conductive hearing loss-an educational review.

Hearing loss is the most common sensory impairment globally and can affect all ages. It can be classified into two categories, conductive and sensorineural, though both conditions may coexist. Various causes may be responsible for hearing loss including congenital, infectious, inflammatory and neoplastic. Imaging will help detect or exclude such causes and in combination with the medical history and clinical findings, enable the necessary treatment to be initiated. Imaging also provides an accurate pre-operative anatomical road map for the surgeons to ensure the best possible surgical outcomes. The aim of this educational review is to present a range of common and rare diseases causing hearing loss and provide a brief explanation of the best imaging modalities for each. It also discusses briefly some important anatomical variants which the radiologists should highlight in their report to help prevent inadvertent post-operative complications.

Enhancing monitoring to promote early detection and eradication of invasive species

Ecological niche models are often used to predict the distribution of invasive species before or after they have been detected in new regions. Such models should also be used to guide surveys to promote the early detection and eradication of invasive species. Here we propose a practical framework that seamlessly uses ecological niche models to develop sampling routes that promote detection of invasive species. Our framework uses habitat suitability predictions and occurrence data on incursion populations to generate potential survey sites, which are then prioritized for sampling based on their size and suitability. The generated survey route is then displayed on an open street map platform. Our framework was developed into the ‘enmRoute' R package and a user‐friendly website to facilitate its application, and we validated our framework with a case study. We show that integrating ecological niche models with human transport routes promotes identification of survey sites that are predicted to collect more individuals and have a greater potential for species detection than traditional sampling approaches. Our framework may help industries, invasion biologists, and regulators develop economical and efficient survey programs for invasive pest monitoring that make eradication programs more attainable.

Towards a ‘Newer Normal’? A Bibliometric Analysis Examining Organisational Culture in the Post-COVID-19 Higher Education Landscape in the United Kingdom

Concern continues to grow over the changing nature of work that initially formed a ‘new normal’ within higher education institutions resulting from the COVID-19 pandemic. This situation is now being further impacted by subsequent economic and political challenges in the sector, which in turn shifts the workplace towards an even ‘newer normal’. The purpose of this article is to: (1) methodically and logically review the extant literature and research linking the influence of the COVID-19 pandemic to the organisational culture of higher education institutions in the United Kingdom; and (2) based on a bibliometric analysis, to offer a road map for further empirical research in the future. Researchers have found that changing workplace norms, such as remote and hybrid working and other forms of flexibility in approaches to learning and the availability of delivery modes, have become increasingly common, which has changed the nature of working practices and underpinning aspects of organisational culture. Therefore, it now appears opportune to update existing knowledge on organisational culture theory within the United Kingdom higher education context to support policy development and enhance workplace practices in the post-COVID-19 period. From a theoretical perspective, this article contributes to organisational culture literature by assimilating a dataset of nascent studies generated through keyword search on Clarivate Analytics Web of Science that have examined the impact of COVID-19 on organisational culture in United Kingdom higher education institutions. This emergent dataset was analysed using VOS Viewer, with the results of subsequent quantitative bibliometric analysis identifying the main existing research fronts as well as potential research directions for the empirical development of organisational culture. Based on this bibliometric analysis, the main suggested future research agendas that need to be addressed linked to organisational culture in the UK higher education sector in the post-COVID period are: (a) to further examine changing cultural norms and expectations; (b) hybrid approaches to working; (c) academic identity; and (d) organisational culture and the changing psychological contract. Such research is important as the UK Higher Education sector now progresses towards an even ‘newer normal’.

A road map to Hepatitis C Elimination: Yesterday, Today, and Tomorrow

A road map to Hepatitis C Elimination: Yesterday, Today, and Tomorrow

Editorial Comment: A Road Map for Implementing Transarterial Embolization in the Management of Chronic Joint Pain.

Editorial Comment: A Road Map for Implementing Transarterial Embolization in the Management of Chronic Joint Pain.

The Role of Artificial Intelligence in the Education of Students with Special Needs

Artificial intelligence (AI) based education represents a significant transformation in the field of education of our age. Artificial intelligence (AI) technology has great potential to enrich the learning experience of special needs students, provide support to teachers, and reduce inequalities in education. Artificial intelligence (AI) technologies can be used to more effectively understand the learning needs of special needs students and provide customized learning experiences for them. Expert systems, adaptive tutorial systems, dialogue-based systems, learning analytics and educational data mining are widely used AI systems developed to increase productivity in educational environments, facilitate achievement of learning goals, provide instant feedback and enrich student interaction. Assessing the learning inclinations, strengths, and weaknesses of students with special needs enables AI to provide tailored learning content and resources that cater to their individual requirements effectively. In this article, tried to present a road map to teachers and researchers by examining the role of AI-based technologies in the education of special needs students, the AI-based technologies that can be used, and the issues to be considered when choosing these technologies.

Offset curves: An application in road simulation.

Routable Digital Maps represent a recent advancement driven by the growth of Intelligent Transportation System applications. Efforts have been made to develop and utilize maps within computer systems that can autonomously identify optimal routes. Typically, online map visualizations lack essential information such as traffic patterns, construction zones, or lane counts, which are crucial for designing accurate and realistic routes. This research introduces a route planning method that aggregates and reuses various data points to establish a robust framework for developing new routing plans. The primary goal is to analyze the collected data and create a framework for estimating a geometrically smooth centerline of a road map route. This method involves generating a series of parallel curves, known as offset curves, to facilitate the creation of new routes and alternative lanes. The following examples illustrate some empirical results obtained from this approach.

Mapping the Past Two Decades of Nutrition and Food Security in Iran: A Co-Word Network Analysis

Background: Food security in communities can prevent health complications, so investigators have made efforts to find its related factors through various fields. This study aims to draw a road map for nutrition and food security research in Iran. Methods: Web of Science, PubMed, Scopus, Embase, and Islamic World Citation Center were searched for eligible publications in nutrition and food security related to Iran for the two decades from 2001 to 2020. Content analysis was done by a co-word network technique using VOSviewer software. Results: Finally, 28,995 scientific publications among 50,444 search results were eligible to include in this study. The research map was drawn using 403,262 keywords obtained from the title and abstract of the papers. A 23.53% growth rate of publications was seen. Iranian articles were mainly published in scientific journals under 10 subject categories. The highly repeated keywords of "treatment", "plant", "age", "risk", and "consumption" were in publications. Moreover, the articles were categorized into thematic clusters of "environmental and climate change", "health ", "food industry and food safety", and "agriculture and water resources management" which were related to nutrition and food security. An increasing trend was observed in the number of publications during the past two decades in Iran. Conclusion: The relation of clinical nutrition, malnutrition, diet, and in recent years, food production and climate change with food security have been extensively studied by Iranian researchers. However, they have neglected studies on public health and policy in food and nutrition security, which reveals their dominant clinical or agricultural approach.

“Deciphering The Enigma Of Nasal & Paranasal Anatomy Through Computed Tomography (CT) Imaging”

Introduction: the paranasal sinuses are mucosa-lined structures physically contiguous with the nasal cavity that aid in insulation, reducing cranial weight, heating & humidifying the air, imparting resonance to the voice, providing airway defense and simply to replace functionless bone. Sinusitis is rarely life threatening, but the complex anatomy of the facial planes, the associated venous and lymphatic spread and the close location to the central nervous system can lead on to serious complications. It is, therefore, important to understand the normal anatomy of the paranasal sinuses in order to understand the pathogenesis of sinus disease. Hence, the aim of the present study was deciphering the role of ct as the investigation of choice for the preoperative evaluation of the nose & paranasal sinuses and for delineation of obstructive inflammatory sinus disease to aid in the diagnosis and management of recurrent & chronic disease and to define the bony anatomy before surgery. Materials and methods: prospective cross-sectional study was conducted on 100 asymptomatic patients who were getting investigated by computed tomography for non-sinus indications in whom the paranasal sinus region could be included in the field of study referred to department of radio-diagnosis and imaging from indoor and outdoor departments of jan sewa hospital of dr. S. S. Tantia medical college, sri ganganagar. Results: the majority of patients were in the age group of 21-40 years (42.3%) with 70% being male. Patients in the age group equaling or more than 61 years & with diabetes had the most number of incidental findings (68.2 %) agger nasi was the commonest anatomical paranasal sinus variant (82.9 %) followed by concha bullosa (39.3 %), deviation of the nasal septum (35.4 %), onodi cells ( 12.2 %) & haller cells ( 11.8 %). Incidental paranasal sinus abnormality in the form of mucosal thickening, sinus secretions or polyps were detected in 51.9 % patients, mucosal thickening being most common (40.1 %) followed by secretions (31.9 %) and mucosal polyps ( 3.0 %). Incidental paranasal abnormalities were most commonly noted in the ethmoid sinuses (36.7 %) followed by the maxillary sinuses, sphenoid sinuses and then frontal sinuses in descending order of involvement. Conclusion: computed tomography (ct) provides a “road map” for diagnosis and management of recurrent & chronic paranasal sinus disease and to delineate the bony anatomy before sinus surgery and to get an insight into anatomical variants apropos of their clinical relevance & impact on sinus disease.

“Deciphering The Enigma Of Nasal & Paranasal Anatomy Through Computed Tomography (CT) Imaging”

Introduction: the paranasal sinuses are mucosa-lined structures physically contiguous with the nasal cavity that aid in insulation, reducing cranial weight, heating & humidifying the air, imparting resonance to the voice, providing airway defense and simply to replace functionless bone. Sinusitis is rarely life threatening, but the complex anatomy of the facial planes, the associated venous and lymphatic spread and the close location to the central nervous system can lead on to serious complications. It is, therefore, important to understand the normal anatomy of the paranasal sinuses in order to understand the pathogenesis of sinus disease. Hence, the aim of the present study was deciphering the role of ct as the investigation of choice for the preoperative evaluation of the nose & paranasal sinuses and for delineation of obstructive inflammatory sinus disease to aid in the diagnosis and management of recurrent & chronic disease and to define the bony anatomy before surgery. Materials and methods: prospective cross-sectional study was conducted on 100 asymptomatic patients who were getting investigated by computed tomography for non-sinus indications in whom the paranasal sinus region could be included in the field of study referred to department of radio-diagnosis and imaging from indoor and outdoor departments of jan sewa hospital of dr. S. S. Tantia medical college, sri ganganagar. Results: the majority of patients were in the age group of 21-40 years (42.3%) with 70% being male. Patients in the age group equaling or more than 61 years & with diabetes had the most number of incidental findings (68.2 %) agger nasi was the commonest anatomical paranasal sinus variant (82.9 %) followed by concha bullosa (39.3 %), deviation of the nasal septum (35.4 %), onodi cells ( 12.2 %) & haller cells ( 11.8 %). Incidental paranasal sinus abnormality in the form of mucosal thickening, sinus secretions or polyps were detected in 51.9 % patients, mucosal thickening being most common (40.1 %) followed by secretions (31.9 %) and mucosal polyps ( 3.0 %). Incidental paranasal abnormalities were most commonly noted in the ethmoid sinuses (36.7 %) followed by the maxillary sinuses, sphenoid sinuses and then frontal sinuses in descending order of involvement. Conclusion: computed tomography (ct) provides a “road map” for diagnosis and management of recurrent & chronic paranasal sinus disease and to delineate the bony anatomy before sinus surgery and to get an insight into anatomical variants apropos of their clinical relevance & impact on sinus disease.

Green Energy Transition: Evaluating Carbon-To-Liquid Technologies for 2050 Climate Goals and Energy Security

China's coal-to-liquid (CTL) sectors must quickly adjust to a low-carbon economy to meet the energy demand while mitigating environmental and energy security challenges. Similarly, China's climate goals for 2050 are gravely jeopardized if the industry's enormous ecological impact is not addressed. This research develops a conceptual framework for environment-security-economic evaluation of the high-carbon energy sector. Moreover, it constructs a system dynamics material model to predict the upgrade plan's industrial capacity and carbon emission scale. Finally, it calculates the dynamic optimal deployment scale at various epochs based on the sequential decomposition goal of "carbon peak" and "carbon neutrality." These measures are needed to anticipate the development of China's CTL industry. The road map predicts that by 2025, China's CTL industry will be able to meet at least 5% of the country's total oil demand. The adoption of scale A process will improve the economic and environmental performance of the CTL industry due to the cost savings and efficiency gains at a larger scale. In addition, the industry's overall energy efficiency will rise in 2023 and exceed 50% after 2035. Findings indicate that the CTL sector will approach a carbon peak in 2029 and neutrality and transition to hydrogen generation by 2050.

Multi-body dynamic assessment of vessel-floater accessibility for floating wind

Abstract The floating wind industry faces several challenges in the road map to full maturity of commercial-scale floating projects. Accessing the floating offshore wind turbines poses larger risks due to the added degrees of freedom of the platform, with respect to bottom-fixed technology. When a vessel approaches, the platform’s motion leads to the introduction of multi-body dynamic effects. This work deals with the modeling of the vessel-platform dynamics for the VolturnUS-S 15MW platform and IEA15MW Reference Wind Turbine and a generic Crew Transfer Vessel (CTV) by means of performing a fully coupled assessment. The hydrodynamic properties are computed by solving the multi-body radiation-difraction problem. The inertial and hydrodynamic properties are included in an OrcaFlex model to perform a time-domain analysis under several sea states. A P-controller represents the helmsman pushing the vessel against the access point. The contact forces are modeled using a linear fender system to represent slip-and-stick events during the CTV’s push-on maneuver. The resulting relative motions are analyzed, and industry-typical limits are considered to quantify accessibility levels. The results show that peak wave period and wave heading play a major role in the accessibility scores.

Policy Forum: GAAR Revisited—A Road Map for Continued Analytical Rigour

Policy Forum: GAAR Revisited—A Road Map for Continued Analytical Rigour

Change starts at home: summary of the 2023 inaugural Small Animal Antimicrobial Stewardship Workshop for US Veterinary Schools.

Small animal antimicrobial stewardship (AS) is emerging as a priority area in the global battle against antimicrobial resistance. Veterinary practices have limited support for implementation of AS programs, and even within veterinary schools, efforts are largely siloed and often limited in scope. Increased collaboration is needed to support and unify AS and infection prevention and control efforts, and to identify priorities for continued research. To address this, a 2-day in-person small animal AS workshop was held in August 2023. Delegates representing 23 US veterinary schools and 4 corporations/government agencies gathered for a series of lightning talks and focused group discussions in 3 domains: implementation of clinical AS programs, research opportunities and needs, and education. The workshop's goal was to identify and propose solutions for AS challenges. Meeting discussion identified a lack of resources and training as the greatest barriers to hospital AS program advancement and suggested creating standards for AS programs and a road map to support program development. Assessing antimicrobial treatment effects and performing studies to establish necessary treatment durations were considered the highest research priorities. Integrated educational practices were recommended to support unified messaging of AS concepts between preclinical and clinical training. The development of strategies to implement these suggestions was delegated to working groups with a goal to continue meeting biennially as a large group. Sharing news of these efforts is considered integral to heightening awareness and promoting implementation of AS practices moving forward in academic, specialty, and primary care settings.

ENVIRONMENTAL RISK ASSESSMENT OF TRANSPORTATION INFRASTRUCTURE DEVELOPMENT USING GIS IN LAGOS STATE

Transportation infrastructure development poses significant environmental risks, including habitat disruption, air and water pollution, and climate change. This study develops a GIS-based framework for ecological risk assessment of transportation infrastructure development. This research identifies high-risk areas and prioritizes mitigation measures by integrating spatial analysis, multi-criteria decision analysis, and expert consultations. The GIS-based approach evaluates environmental sensitivity, proximity to ecosystems, and potential hazards. Results provide valuable insights for sustainable transportation planning, policy-making, and ecological management. This study explored its application in solving Environmental Risk Assessment of Transportation Infrastructure Development GIS application in environmental Risk Assessment monitoring has mainly been divided into three aspects: water, soil, and atmosphere. Based on ArcGIS 10.1 software and ArcGIS 9.3.1 version, GIS has been applied in Transportation system monitoring and soil heavy metal concentration monitoring, respectively. A major function of planners is to promote the best use of a community's land and resources for different construction projects; especially critical are infrastructure projects on which economic development relies. Transportation projects typically involve both environmental and economic issues facing a community as it grows and changes. Considerations of sustainability, as well as the widespread use of collaborative planning, design, and construction, require tools that facilitate long-term impact analysis and easy communication among built-environment professionals. Environmental impact assessment (EIA) is instrumental in studying transportation project impacts, but it is a time-consuming process because of the large number of dependent and independent variables involved. A vulnerability grade map and road distribution map were produced, providing an overall vulnerability score for each of the three planning alternatives considered. A road alignment was indicated as the preferred corridor, which was further recommended for a project development and environmental (PD&E) study.

Fictional Verisimilities and the Didactic in Adaobi Tricia Nwaubani’s I do not come to you by Chance.

The 21st century Nigerian space has incontrovertibly incentivised the fictional writer with abounding dieges that predominantly narrativized the social realism of the author’s society. It is from this mimetic assemblage that Adaobi Tricia Nwaubani’s I Do Not Come to You by Chance (2009) stems from. This paper uses the Postcolonial Theory as an investigative and analytical tool to explore the significance of fictional verisimilitude in communicating the didactic in the Nigerian socio-political and economic situation. Meanwhile, this qualitative research is hinged on the novelist’s representational exactitude of the social realism of the Nigerian space while foregrounding the impact of the family as a social agency on the thematic preoccupation of the e-fraud or hustle narrative. Nwaubani’s novel is insightful and even prophetically rendered to capture the socio-economic fragmentation that has become emblematic of contemporary Nigerian space. This paper further probes the aggressive Nigerian existentialist consciousness ultimately rooted in the e-fraud narrative. This paper implicates the apparatuses of power for their insensibilities to the plight of the Nigerian youth inadvertently and seemingly affecting the demonising image of Nigeria/ns in the global marketplace. Pertinently, Nwaubani strongly provokes a didactic evocation as she foreshadows the place of the family as a retooling moral compass. This paper, therefore foregrounds that, the text ultimately acts as an ethical road map for the re-evaluation and re-charting of the recent moral decadence bedevilling the Nigerian space, especially in the 21st century city space.

Ontology and Bruxism: Do We Have Enough Information?

The idea of classifying and defining bruxism according to ontological principles may be interesting, but currently we just do not have enough information to label in a black or white manner the many facets of bruxism. In an era in which general knowledge on bruxism by the dental communities is surely in need of improvement, efforts to clarify the road map tracked by the current panelists who drafted the definition should be appraised carefully. The recent introduction of a standardized multidimensional evaluation system (i.e., Standardized Tool for the Assessment of Bruxism [STAB]) and a screening instrument for bruxism (i.e., BruxScreen) should be viewed as the starting points to enter a new era in the discipline of bruxism, in which non-hierarchical and non-preconceived approaches are used to collect data. Artificial intelligence strategies to mine data gathered with the above instruments might help building predictive models along the etiology-status-consequences trajectory, as recently suggested in a model for awake bruxism metrics. Until then, proposals to adopt ontological principles to classify bruxism will be merely based on speculations rather than on facts.

Urban Land Use Classification Model Fusing Multimodal Deep Features

Urban land use classification plays a significant role in urban studies and provides key guidance for urban development. However, existing methods predominantly rely on either raster structure deep features through convolutional neural networks (CNNs) or topological structure deep features through graph neural networks (GNNs), making it challenging to comprehensively capture the rich semantic information in remote sensing images. To address this limitation, we propose a novel urban land use classification model by integrating both raster and topological structure deep features to enhance the accuracy and robustness of the classification model. First, we divide the urban area into block units based on road network data and further subdivide these units using the fractal network evolution algorithm (FNEA). Next, the K-nearest neighbors (KNN) graph construction method with adaptive fusion coefficients is employed to generate both global and local graphs of the blocks and sub-units. The spectral features and subgraph features are then constructed, and a graph convolutional network (GCN) is utilized to extract the node relational features from both the global and local graphs, forming the topological structure deep features while aggregating local features into global ones. Subsequently, VGG-16 (Visual Geometry Group 16) is used to extract the image convolutional features of the block units, obtaining the raster structure deep features. Finally, the transformer is used to fuse both topological and raster structure deep features, and land use classification is completed using the softmax function. Experiments were conducted using high-resolution Google images and Open Street Map (OSM) data, with study areas on the third ring road of Shenyang and the fourth ring road of Chengdu. The results demonstrate that the proposed method improves the overall accuracy and Kappa coefficient by 9.32% and 0.17, respectively, compared to single deep learning models. Incorporating subgraph structure features further enhances the overall accuracy and Kappa by 1.13% and 0.1. The adaptive KNN graph construction method achieves accuracy comparable to that of the empirical threshold method. This study enables accurate large-scale urban land use classification with reduced manual intervention, improving urban planning efficiency. The experimental results verify the effectiveness of the proposed method, particularly in terms of classification accuracy and feature representation completeness.

Book Review: Review of The Grant Writing Guide: A Road Map for Scholars by Lai, Betty S.

Book Review: Review of <i>The Grant Writing Guide: A Road Map for Scholars</i> by Lai, Betty S.