Location Of Resources Research Articles

Improved Phishing Attack Detection with Machine Learning: A Comprehensive Evaluation of Classifiers and Features

In phishing attack detection, machine learning-based approaches are more effective than simple blacklisting strategies, as they can adapt to new types of attacks and do not require manual updates. However, for these approaches, the choice of features and classifiers directly influences detection performance. Therefore, in this work, the contributions of various features and classifiers to detecting phishing attacks were thoroughly analyzed to find the best classifier and feature set in terms of different performance metrics including accuracy, precision, recall, F1-score, and classification time. For this purpose, a brand-new phishing dataset was prepared and made publicly available. Using an exhaustive strategy, every combination of the feature groups was fed into various classifiers to detect phishing websites. Two existing benchmark datasets were also used in addition to ours for further analysis. The experimental results revealed that the features based on the uniform resource locator (URL) and hypertext transfer protocol (HTTP), rather than all features, offered the best performance. Also, the decision tree classifier surpassed the others, achieving an F1-score of 0.99 and being one of the fastest classifiers overall.

Application of GIS for analysis of environmental aspects of mineral deposits

The aim of the article was to improve the method of visualization of the map of minerals with layers of occurrence of rocks using 3D modeling using a combined combination of google earth pro and the Surfer program. Methodology used: 3D modeling of mineral deposits. The results of the study were obtained, a visualization of the map of minerals using the geoinformation model of the deposit was created. With the help of a combination of Google Earth Pro and the Surfer program, the zone of the territorial location of minerals and layers of rock occurrence were obtained, which will optimize the mining process to achieve maximum profit with minimal damage to the environment, namely, to prevent unwanted land disturbance. Originality. The integration of Google Earth Pro with the Surfer application has expanded the possibilities of visualization and analysis of mineral deposits. The combined use of these tools made it possible not only to obtain a detailed map of the location of resources, but also to determine volumes and identify the most valuable layers for extraction. The improved method has a significant positive impact on the environment, as it avoids unnecessary disturbance of landscapes. Practical value. The application of the approach is the integration of geographic information systems to create a three-dimensional geological model of the deposit and a technique for visualizing the map of minerals. This makes it possible to more effectively solve the issues of mining planning and maximize the benefits for the enterprise while minimizing the man-made load and disturbance of landscapes. This approach has a significant positive impact on the environment, as it avoids unnecessary disturbance of landscapes and reduces negative environmental impacts. Keywords: minerals, geographic information systems, 3D modeling, visualization, maps, coordinates, ecology, landscape disturbance.

Urban insights into mental health: A study of depression and anxiety factors among urban adults of Bangladesh in the COVID-19 pandemic

ABSTRACT This study investigates the prevalence of depression and anxiety symptoms among urban adults in Bangladesh and explores the associated socio-demographic factors. An online cross-sectional survey was conducted from March to May 2021 among the adults living in Dhaka City, Bangladesh, using a standard questionnaire designed using Google Forms and sent via a unique uniform resource locator (URL). The Depression Anxiety Stress Scale 21 (DASS-21) was employed to assess symptoms of mental health issues. The survey included responses from 993 participants. The results indicate that about 40% of the urban adults had moderate-severe anxiety symptoms, while about 46% had depression symptoms. Urban adults’ level of education, gender, occupation, place of birth, and asset quintile were found to have a significant correlation with increased anxiety and depression. The findings stressed the need for developing effective mental health services and educational programs for urban people to meet their mental health needs and prevention mechanisms. There is an urgent need for interventions that specifically target mental health issues.

Geological Trap Controlling the Residence Time of Groundwater in Assessment of Exploitation Zone for Its Sustainable Resources Case Study: The Slope of Karang Mount, Banten Province, Indonesia

The southern slope of Mount Karang is covered by complex volcanic deposits with complicated texture and structure. The study on zone or location of water resources which would be exploited required a comprehensive hydrogeological approach. Through detailed geological mapping, spring sampling, and well drilling were carried out. Representative spring water samples were taken to be analyzed in the laboratory, and to obtain the data of physical groundwater, chemical groundwater, stable isotopes 18O (oxygen-18), and deuterium contents, as well as the age of the groundwater. In general, the groundwater facies of the studied area showed Ca, Na, KHCO3 with several sites indicating changes to CaHCO3 during the dry and rainy seasons. The synthesis results of the stable isotope 18O (oxygen-18) and deuterium contents, verified by the physical and chemical groundwater controlled by geological setting in the groundwater subbasins, show the anomaly of residence time as trapped by the normal fault in the middle of the studied area. The existing normal fault might control this anomaly of residence time of groundwater surrounding site JH1, JH9, and JH20. However, the distribution of three different water source zones occurred. All group of groundwater indicated a complex flowing with geological setting controlling the physical, chemical content, and the age of the groundwater. At last, sites JH4, JH5, and JH9 show that the zones are proper to be developed as sustainability groundwater resources. Keywords: groundwater facies, stable isotope, groundwater flow, sustainable water

Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho

The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry.The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks.Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.

YouTube™ as a source of parents' information for craniosynostosis.

This study aimed to evaluate the content, reliability and quality levels of YouTube™ videos regarding craniosynostosis for parents' information. A keyword search for 'craniosynostosis' was conducted on YouTube, and the uniform resource locators of the first 160 videos were initially recorded and evaluated. Ninety-four videos that met the inclusion criteria were analysed. Each video received a score ranging from 0 to 9 points, classifying them as low-content (0-3), moderate-content (4-6) and high-content (7-9). The reliability scores adapted from DISCERN and Global Quality Scale (GQS) scores were recorded. The median content score for the videos was 4. According to the content scores, 34% of included videos (n = 32) were classified as low-content, 60.6% (n = 57) as moderate-content, and 5.3% (n = 5) as high-content. The median reliability score for the videos was 3, and the median GQS score was 3. The reliability and quality levels of videos classified as high-content and moderate-content were significantly superior to low-content videos (P < .05). Laypersons were identified as the most frequent source of information in the videos. However, most of the videos lacked information about syndromic/non-syndromic forms and specific complications. The importance of early referral/timing of surgery and minimally invasive surgical treatment techniques was mentioned in all high-content videos. The results of the present study suggest that YouTube™ in its current format, is not a fully trustable source for parents seeking information on craniosynostosis. Craniofacial units must increase the content, quality and reliability level of videos on craniosynostosis.

How to Analyse Emojis, gif s, Embedded Images, Videos, and url s: A Bakhtinian Methodological Approach

Abstract This article offers a means of analysing social networking, visual dialogues of emojis, gif s (images in the Graphics Interchange Format), embedded images, videos, and url s (Uniform Resource Locators). Doing so addresses these often overlooked and undervalued forms of visual communication, suggesting a unique means of gaining insights into their use within online interactions. Utilising a Bakhtinian methodology, the author extracts excerpts from her research, situated within Facebook, to demonstrate a Bakhtinian genre analysis, a framework that the author contends is adaptable to multiple social networking spaces. Highlighting emojis, gif s, embedded images, videos, and url s as integral components of online communication, an emphasis is placed on how the text dances with the visual, presenting a nuanced framework for such an analysis. Consequently, an argument is developed for the significance of visual dialogues in contemporary online spaces, and the need for researchers to better understand these dynamic forms of communication, offered through Bakhtinian dialogism.

Determination of server location in emergency care systems: an index proposal using data envelopment analysis and the hypercube queuing model

The objective of this article is to propose a new composite index (CI) that helps to determine the most effective location of servers in an Emergency Care System (ECS), using Benefit of the Doubt (BoD)/Data Envelopment Analysis (DEA) and the Hypercube queuing model. The CI proposed was developed in four stages: (1) definition of a number of possible ECS configurations through the application of mathematical partitions and permutations; (2) application of the hypercube queuing model to determine performance parameters for each ECS configuration; (3) application of DEA/BoD to build the CI and generate performance rankings, and (4) evaluation of the rankings obtained to define the best configuration for the ECS analyzed. Data from two real cases from Brazil were used to assess the CI proposal. The results obtained confirm that: (a) the hypercube model could, relatively quickly, determine the configuration parameters generated; (b) the application of an appropriate DEA/BoD model enabled the different configurations to be ranked with good discrimination; (c) a pattern in the relationship between ambulance concentration and configuration effectiveness could be identified; and (d) the CI proposed would benefit ECS managers who are making resource location decisions.

A Smishing Detection Method Based on SMS Contents Analysis and URL Inspection Using Google Engine and VirusTotal

Smishing is the delivery of phishing content to mobile users via a short message service (SMS). SMS allows cybercriminals to reach out to mobile end users in a new way, attempting to deliver phishing messages, mobile malware, and online scams that appear to be from a trusted brand. This paper proposes a new method for detecting smishing by combining two detection methods. The first method is uniform resource locators (URL) analysis, which employs a novel combination of the Google engine and VirusTotal. The second method involves examining SMS content to extract efficient features and classify messages as ham or smishing based on keywords contained within them using four well-known classifiers: support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The best results of the proposed method were 98.5%, 96.9%, 93.1%, and 95.05% in terms of accuracy, precision, detection rate, and F1-score, respectively. Furthermore, the evaluation results of the proposed method outperformed the state-of-the-art and showed that the proposed method is effective in detecting smishing messages.

Towards AI-augmented radiology education: a web-based application for perception training in chest X-ray nodule detection.

Artificial intelligence (AI)-based applications for augmenting radiological education are underexplored. Prior studies have demonstrated the effectiveness of simulation in radiological perception training. This study aimed to develop and make available a pure web-based application called Perception Trainer for perception training in lung nodule detection in chest X-rays. Based on open-access data, we trained a deep-learning model for lung segmentation in chest X-rays. Subsequently, an algorithm for artificial lung nodule generation was implemented and combined with the segmentation model to allow on-the-fly procedural insertion of lung nodules in chest X-rays. This functionality was integrated into an existing zero-footprint web-based DICOM viewer, and a dynamic HTML page was created to specify case generation parameters. The result is an easily accessible platform-agnostic web application available at: https://castlemountain.dk/mulrecon/perceptionTrainer.html.The application allows the user to specify the characteristics of lung nodules to be inserted into chest X-rays, and it produces automated feedback regarding nodule detection performance. Generated cases can be shared through a uniform resource locator. We anticipate that the description and availability of our developed solution with open-sourced codes may help facilitate radiological education and stimulate the development of similar AI-augmented educational tools. A web-based application applying AI-based techniques for radiological perception training was developed. The application demonstrates a novel approach for on-the-fly generation of cases in chest X-ray lung nodule detection employing deep-learning-based segmentation and lung nodule simulation.

BERT-Based Approaches to Identifying Malicious URLs.

Malicious uniform resource locators (URLs) are prevalent in cyberattacks, particularly in phishing attempts aimed at stealing sensitive information or distributing malware. Therefore, it is of paramount importance to accurately detect malicious URLs. Prior research has explored the use of deep-learning models to identify malicious URLs, using the segmentation of URL strings into character-level or word-level tokens, and embedding and employing trained models to differentiate between URLs. In this study, a bidirectional encoder representation from a transformers-based (BERT) model was devised to tokenize URL strings, employing its self-attention mechanism to enhance the understanding of correlations among tokens. Subsequently, a classifier was employed to determine whether a given URL was malicious. In evaluating the proposed methods, three different types of public datasets were utilized: a dataset consisting solely of URL strings from Kaggle, a dataset containing only URL features from GitHub, and a dataset including both types of data from the University of New Brunswick, namely, ISCX 2016. The proposed system achieved accuracy rates of 98.78%, 96.71%, and 99.98% on the three datasets, respectively. Additionally, experiments were conducted on two datasets from different domains-the Internet of Things (IoT) and Domain Name System over HTTPS (DoH)-to demonstrate the versatility of the proposed model.

Exploring the spatiotemporal evolution of bioenergy with carbon capture and storage and decarbonization of oil refineries with a national energy system model of Colombia

Bioenergy combined with carbon capture and storage (BECCS) has a high mitigation potential of greenhouse gases in the energy system. However, the feasibility of its deployment depends on co–location of suitable storage basins and biomass resources with low-carbon stocks. Moreover, national transition analyses towards low–carbon energy systems have often given little attention to the mitigation potential of existing oil refineries, which are major components of current energy systems. We parametrized and incorporated these knowledge gaps into an energy system optimization model and used it analyze mitigation pathways towards carbon neutrality of the Colombian energy system by midcentury.Our results show that modern bioenergy could contribute 0.8–0.9 EJ/y (48–51 %) to the final energy consumption by 2050 at a system cost of 29–35 B$/y. BECCS value chains could deliver a mitigation potential of 37–41 % of the cumulative avoided emissions between 2030 and 2050. Low–carbon retrofitting of existing oil refineries could contribute up to 19 % of the total biofuel production and 10 % of the total CO2 capture by 2050. The Andes and Caribbean could be promising regions for BECCS because of their high potential for biomass supply and carbon sinks. In contrast, Orinoquía has a high potential for bioenergy and more uncertainty of CCS, depending on the access to nearby carbon sinks.This framework could be used to harmonize between the visions of the energy and agricultural sectors, national government and the oil sector, and national and regional governments, towards integrated planning for low-carbon development.

Medium-term trends in economic and technological development of metals industry regions

Since metals industry regions are dependent on the location of production resources and infrastructure, as well as have highly capital-intensive production, they usually feature a slowly changing industrial and technological structure of production. In line with the hypothesis of the study, the technological structure of the manufacturing industry determines the differences in their economic development. The study aims to explore the medium-term trends and patterns in economic and technological development of the metals industry regions. The methodological basis of the research is the structural dynamic approach to studying regional economic development. The methods include multivariate data analysis and clustering algorithms, time series estimation and correlation analysis. The study uses the data on large and medium-sized enterprises by types of economic activities for 2006–2021 taken from an information and analytics system FIRA PRO. According to the findings, the Chelyabinsk, Sverdlovsk, Lipetsk, Vologda and Tula oblasts are metals industry regions. The identified medium-term trends in their economic development show different dynamics of input and output indicators of production both by all types of economic activities and in metals industry in particular. The study proves that the dynamics of metals industry regions’ development largely depends on technological characteristics of the manufacturing industry. The results of the research contribute to the understanding of the adaptation mechanisms of regions’ industry to the changing conditions of external environment.

SPASE metadata as a building block of a heliophysics science-enabling framework

Heliophysics and space weather research encompass the effects of solar output on practically the entire Solar System and are fundamentally cross-disciplinary. Cross-domain science investigations, such as in Sun-heliosphere interactions, solar wind-magnetosphere interactions, or magnetosphere-ionosphere coupling, often require the use of data, models, and other digital resources pertaining to different heliophysical domains: the Sun, the solar wind, the magnetosphere, the ionosphere, the thermosphere and the mesosphere. Due to differences in measurement platforms, techniques and instruments, heliophysics data obtained from different domains are diverse and complex, making the resource landscape difficult for untrained users to navigate. Without proper and adequate guidance from domain experts, it is often difficult for early-career scientists and non-domain experts to discover useful datasets and to know from where and how to obtain and understand the data they need to support their research. This paper describes the roles of metadata in providing the identification, location, access protocol, and detailed content description of a digital resource. More specifically, we point out that metadata written according to the Space Physics Archive Search and Extract (SPASE) metadata model are fully compatible with the FAIR principles so that digital resources described using the SPASE model can be uniformly Findable, Accessible, Interoperable, and Reusable. SPASE metadata can thus be the key element, the lingua franca so to speak, that enables unfettered information flow between data systems and services throughout the heliophysics data environment and lowers the understandability barrier of the resources to ensure their independent usability. After describing various components of the heliophysics data environment, their metadata requirements for effective operations, and some essential features of the SPASE metadata model, we then illustrate how metadata in SPASE can enable or facilitate the performance of different science tasks. The current status and future outlook of SPASE are also presented.

Adult neurogenesis does not explain the extensive post-eclosion growth of Heliconius mushroom bodies.

Among butterflies, Heliconius have a unique behavioural profile, being the sole genus to actively feed on pollen. Heliconius learn the location of pollen resources, and have enhanced visual memories and expanded mushroom bodies, an insect learning and memory centre, relative to related genera. These structures also show extensive post-eclosion growth and developmental sensitivity to environmental conditions. However, whether this reflects plasticity in neurite growth, or an extension of neurogenesis into the adult stage, is unknown. Adult neurogenesis has been described in some Lepidoptera, and could provide one route to the increased neuron number observed in Heliconius. Here, we compare volumetric changes in the mushroom bodies of freshly eclosed and aged Heliconius erato and Dryas iulia, and estimate the number of intrinsic mushroom body neurons using a new and validated automated method to count nuclei. Despite extensive volumetric variation associated with age, our data show that neuron number is remarkably constant in both species, suggesting a lack of adult neurogenesis in the mushroom bodies. We support this conclusion with assays of mitotic cells, which reveal very low levels of post-eclosion cell division. Our analyses provide an insight into the evolution of neural plasticity, and can serve as a basis for continued exploration of the potential mechanisms behind brain development and maturation.

Enhanced website phishing detection based on the cyber kill chain and cloud computing

&lt;span&gt;Crimeware-as-a-service (CaaS) presents a growing cybersecurity threat by facilitating the acquisition of tools for website phishing attacks. Detecting these attacks requires effective techniques to obtain accurate results in real time. Cloud machine learning (CML) emerged as a promising solution with the powerful tools of amazon web services (AWS). This study proposes a novel approach combining cyber kill chain concept with AWS technologies to enhance website phishing detection, using AWS SageMaker to preprocess an 11,430 uniform resource locators (URL) dataset and train 3 algorithms, which are: decision tree (DT), random forest (RF), and support vector machine (SVM), evaluate their performance through batch transform, and deploy them as separate endpoints. Prediction functions are then conducted on each endpoint and compared to batch transform results. Our findings demonstrate that the combination of the cyber kill chain concept and AWS CML significantly enhances website phishing detection by achieving results of 97% for RF in 0.48 seconds, 94% for SVM in 0.94 seconds, and 93% for DT in 0.52 seconds. By leveraging CML algorithms and breaking down attacks into stages, our approach identifies and disrupts attacks earlier, preventing damage. This research highlights the value of our approach in improving cybersecurity and protecting against website phishing attacks.&lt;/span&gt;

Leveraging GIS and ChatGPT for Social Goods and Higher Education

GIS (Geographic Information System) is a powerful tool for managing, analyzing, and presenting spatial data. It has a wide range of applications for social goods, from mapping public services (data) to analyzing the spatial distribution of information resources and use. On the other hand, ChatGPT is an AI-based chatbot that engages in natural language conversations with users. It can be trained to answer questions, provide assistance, and facilitate learning in social services and higher education. Our research combines the two technologies to find innovative and efficient ways of delivering social services and improved LIS education. For example, GIS can map out the locations of library resources, patron demographics, local history, and support other services (i.e., crisis responses). ChatGPT can provide personalized assistance and information to users based on their location and preferences. We intend to use GIS + ChatGPT to create more accessible services/APPs for the underrepresented population in community or higher education. Utilizing GIS and ChatGPT (and other generative AI services) will create more accessible and convenient ways for users to access public data, especially for the seniors and new immigrants in the Great Chicago Area. We will also explore new initiatives to improve the librarian’s professional training in “Content Engineering” and “Prompt Engineering” in the era of AI. We will also focus on the ethical implications of these technologies and ensure that they are used to respect user privacy and promote equitable access to quality information services and social justice.

Choosing wave energy devices for community-led marine energy development

As we pursue more justice and community driven renewable energy projects, more decision making is put in the hands of community leaders. Appropriate tools and resources are necessary for communities to determine what wave energy developer might fit their community needs and wave resource. While there are methods and tools for communities, researchers and wave energy developers to determine the wave resource or potential locations for wave energy development, we are lacking a tool to help communities take the next steps in pursuing wave energy.&#x0D; A community driven design process was undertaken in Sitka, AK, in conjunction with evaluating the local area for wave energy resource. The community had interest in wave energy development, a decent wave resource area accessible, and existing maritime expertise that could aid in operations and maintenance. Further, as a remote island, the community viewed alternative energy development positively as a way to achieve more energy independence. Still, in this community driven process, we identified a gap that while the community may be interested in a wave energy project, there was little data on which wave energy devices would be appropriate for the community and few tools to help them choose.&#x0D; This presentation will highlight questions that the participants from Sitka, AK still have after engaging in a community centered design process. We compare the requirements that the community prioritizes with the tools that researchers and developers have to evaluate those requirements, allowing assessment of whether these tools are useable and adequate for community use. We also show an analysis of a selection of current wave energy developers with some of the community focused factors and proposals for tools that may be useful in future community energy development. This research ultimately highlights what factors must be presented to a community for them to choose to pursue installation of a device.

Applying High-Resolution Satellite and UAS Imagery for Detecting Coldwater Inputs in Temperate Streams of the Iowa Driftless Region

Coldwater streams are crucial habitats for many biota including Salmonidae and Cottidae species that are unable to tolerate warmer water temperatures. Accurate classification of coldwater streams is essential for their conservation, restoration, and management, especially in light of increasing human disturbance and climate change. Coldwater streams receive cooler groundwater inputs and, as a result, typically remain ice-free during the winter. Based on this empirical thermal evidence, we examined the potential of very high-resolution (VHR) satellite and uncrewed aerial system (UAS) imagery to (i) detect coldwater streams using semi-automatic classification versus visual interpretation approaches, (ii) examine the physical factors that contribute to inaccuracies in detecting coldwater habitats, and (iii) use the results to identify inaccuracies in existing thermal stream classification datasets and recommend coverage updates. Due to complex site conditions, semi-automated classification was time consuming and produced low mapping accuracy, while visual interpretation produced better results. VHR imagery detected only the highest quality coldwater streams while lower quality streams that still met the thermal and biological criteria to be classified as coldwater remained undetected. Complex stream and site variables (narrow stream width, canopy cover, terrain shadow, stream covered by ice and drifting snow), image quality (spatial resolution, solar elevation angle), and environmental conditions (ambient temperature prior to image acquisition) make coldwater detection challenging; however, UAS imagery is uniquely suited for mapping very narrow streams and can bridge the gap between field data and satellite imagery. Field-collected water temperatures and stream habitat and fish community inventories may be necessary to overcome these challenges and allow validation of remote sensing results. We detected &gt;30 km of coldwater streams that are currently misclassified as warmwater. Overall, visual interpretation of VHR imagery it is a relatively quick and inexpensive approach to detect the location and extent of coldwater stream resources and could be used to develop field monitoring programs to confirm location and extent of coldwater aquatic resources.

The Role of the Neonatologist in Fetuses Diagnosed with Congenital Heart Disease.

Prenatal diagnosis of congenital heart disease (CHD) can decrease preoperative morbidity and mortality. Delivery room planning can improve cardiac hemodynamics and time to critical catheter and surgical interventions. Care algorithms have defined lesion-specific level-of-care assignments and delivery room action plans that can facilitate team-based approaches to safe deliveries. Neonatologists play critical roles in the care of fetuses diagnosed with CHD, from the time of diagnosis through the postnatal intensive care unit (ICU) stays. Prenatally, neonatologists are members of the multidisciplinary counseling teams, with expertise to counsel expectant parents about what to expect during the ICU stay, which is especially valuable in CHD associated with extracardiac or genetic anomalies. Neonatologists' role in delivery planning includes identification of the optimal delivery location and allocation of appropriate personnel and resources. After delivery, postnatal care considerations include hemodynamic stability, optimization of end-organ function, genetics consultation, developmentally appropriate care practices to encourage caregiver bonding, and optimization of care to improve neurodevelopmental outcomes of neonates with CHD.