Parallel Data Research Articles

Impacts of Big Data Analytics for Agriculture tools and techniques

The present paper deals with “Impacts of Big Data Analytics for Agriculture tools and techniques.” A random sample 30 Various software platforms are developed to give information to farmers about new tools and techniques related to agriculture: (MySmartFarm, Awhere weather, Phenone, Farmlog, Datafloq, Farmeron) has been used by secondary source from Agriculture depart in the state of Bihar. The traditional tools and techniques (Big data) analysis massive amount of data. To store and analyze this type of data parallel computing and analyze paradigm is required. Big data analytic is used to weather changes and its impacts of agriculture in Bihar. From the big data analytic Agriculture framework is developed that identify disease based on symptoms similarity and recommend a solution based on high similarity and achieve their tools has been used. Then cleansing of data is done that is important information is extracted from unstructured redundant data and were normalization is done that is features are extracted from cleaned data. The data was used to analyze the agricultural tools and technique. It finds out disease name based on weather changes and its impacts of agriculture has been taken on past data. Result has been shows that data analytic in weather changes past 2018-2023 years that was.

Detector-embedded reconstruction of complex primitives using FPGAs

The slowdown of Moore’s law and the growing requirements of future HEP experiments with ever-increasing data rates pose important computational challenges for data reconstruction and trigger systems, encouraging the exploration of new computing methodologies. In this work we discuss a FPGA-based tracking system, relying on a massively parallel pattern recognition approach, inspired by the processing of visual images by the natural brain (“retina architecture”). This method allows a large efficiency of utilisation of the hardware, low power consumption and very low latencies. Based on this approach, a device has been designed within the LHCb Upgrade-II project, with the goal of performing track reconstruction in the forward acceptance region in real-time during the upcoming Run 4 of the LHC. This innovative device will perform track reconstruction before the event-building, in a short enough time to provide pre-reconstructed tracks (“primitives”) transparently to the processor farm, as if they had been generated directly by the detector. This allows significant savings in higher-level computing resources, enabling handling higher luminosities than otherwise possible. The feasibility of the project is backed up by the results of tests performed on a realistic hardware prototype, that has been opportunistically processing actual LHCb data in parallel with the regular DAQ in the LHC Run 3.

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

Discovering vaccines: the trial tale.

This paper reviews the importance of vaccine trials to public health. Vaccines are developed to build immunity to disease, which have helped to completely eliminate smallpox, and reduced incidence of polio and measles. A variety of breakthroughs made many years ago, such as the smallpox vaccine by Edward Jenner and the discoveries by Louis Pasteur, also set the stage for modern international immunization programs. A few events, such as the licensing of the polio vaccine and the passage of the Vaccination Assistance Act helped to improve the study of vaccines. In particular, vaccine trials may be RCTs, cluster trials, or cohort studies. The sample sizes will depend on the objectives, which would include the primary and secondary endpoints. The population under study and the geographical location also affect the trial design. Preclinical evaluation is usually the starting point of vaccine trials, where the safety and efficacy are researched on animal models or cell cultures. Animal models are selected based on their similarity to the target disease. Safety is checked in Phase I, efficacy in Phase II, and both in Phase III. Phase IV is a post-marketing surveillance of the vaccine's safety in real life. Regulatory bodies play a very vital role in ensuring that vaccines adhere to a very high standard of safety and efficacy, such as the FDA, as required. Ethical considerations, such as informed consent and the rights of participants, are innate and are implemented and enforced through laws, regulations, and ethical committees. Vaccine studies vary from the drug studies as it is focused on preventing illness in healthy patients as opposed to cure of diseases in drug trials. The dramatic development in vaccine research was driven by recent pandemics, with parallel processing and data collection in real time. Clinical trials of the vaccine are a foundation stone of public health in the reduction of sickness, offering immunity to diseases, and continuing the fight against infectious diseases. The present review is aimed at describing vaccine trials and their important aspects.

Clinical and biobehavioral phenotypic assessments and data harmonization for the RE-JOIN research consortium: Recommendations for common data element selection

BackgroundThe Restoring Joint Health and Function to Reduce Pain (RE-JOIN) Consortium is part of the Helping to End Addiction Long-term® (HEAL) Initiative. HEAL is an ambitious, NIH-wide initiative to speed scientific solutions to stem the national opioid public health crisis. The RE-JOIN consortium’s over-arching goal is to define how chronic joint pain-mediating neurons innervate different articular and peri-articular tissues, with a focus on the knee and temporomandibular joints (TMJ) across species employing the latest neuroscience approaches. The aim of this manuscript is to elucidate the human data gathered by the RE-JOIN consortium, as well as to expound upon its underlying rationale and the methodologies and protocols for harmonization and standardization that have been instituted by the RE-JOIN Consortium. MethodsThe consortium-wide human models working subgroup established the RE-JOIN minimal harmonized data elements that will be collected across all human studies and set the stage to develop parallel pre-clinical data collection standards. Data harmonization considerations included requirements from the HEAL program and recommendations from the consortium’s researchers and experts on informatics, knowledge management, and data curation. ResultsMultidisciplinary experts − including preclinical and clinical researchers, with both clinician-scientists- developed the RE-JOIN’s Minimal Human Data Standard with required domains and outcome measures to be collected across projects and institutions. The RE-JOIN minimal data standard will include HEAL Common Data Elements (CDEs) (e.g., standardized demographics, general pain, psychosocial and functional measures), and RE-JOIN common data elements (R-CDE) (i.e., both general and joint-specific standardized and clinically important self-reported pain and function measures, as well as pressure pain thresholds part of quantitative sensory testing). In addition, discretionary, site-specific measures will be collected by individual institutions (e.g., expanded quantitative sensory testing and gait biomechanical assessments), specific to the knee or TMJ. Research teams will submit datasets of standardized metadata to the RE-JOIN Data Coordinating Center (DCG) via a secure cloud-based central data repository and computing infrastructure for researchers to share and conduct analyses on data collected by or acquired for RE-JOIN. RE-JOIN datasets will have protected health information (PHI) removed and be publicly available on the SPARC portal and accessible through the HEAL Data Ecosystem. ConclusionData Harmonization efforts provide the multidisciplinary consortium with an opportunity to effectively collaborate across decentralized research teams, and data standardization sets the framework for efficient future analyses of RE-JOIN data collected by the consortium. The harmonized phenotypic information obtained will significantly enhance our understanding of the neurobiology of the pain-pathology relationships in humans, providing valuable insights for comparison with pre-clinical models.

GPU accelerated Staggered Update Procedure (SUP)

The advancement in programmable capability of graphics hardware has paved new opportunities in the domain of high performance computing (HPC). The computational fluid dynamics (CFD) community, being a significant user of HPC, has started exploiting the inherent data parallelism in the numerical solvers to be able to make efficient use of these many-core, high throughput accelerator based processors. In the present work, we examine the process of accelerating our CPU based Staggered Update Procedure (SUP) solver, i.e., a higher order accurate cell-centred finite volume solver by off-loading the computationally most expensive region of the code pertaining to the explicit residual computation. We have adopted OpenACC, a directive based programming model to expose parallelism in the code. The framework evolved for GPU porting in the context of SUP is also of value to those intending to port their CFD solvers based on classical finite volume methodology. The performance analysis is conducted using scalar convection–diffusion equations in both two- and three-dimensions. The findings demonstrate a speedup factor of 9 (in case of 2D) and 28 (in case of 3D) when considering the explicit residual alone, achieved with a single NVIDIA Tesla V100 GPU card. In addition, we could establish superior algorithmic scalability by the way of recovering near perfect serial performance, on the heterogeneous CPU+GPU architecture. Further, overall code acceleration can be achieved by porting other parts of the solver on GPU.

A Single-Pixel Event Photoactive Device for Real-Time, In-Sensor Spatiotemporal Optical Information Processing.

The increasing demand for energy-efficient, sophisticated optical sensing technologies in various applications, from machine vision to optical communication, highlights the necessity for innovations in spatiotemporal information sensing and processing at a nearly single-pixel scale. Traditional methods, including multi-pixel photodetector arrays and event-based camera systems, often fail to provide rapid, real-time detection and processing of dynamic events within the sensor. This shortcoming is particularly notable in handling high-dimensional spatiotemporal data, where the dependency on sequential data input and external processing tools leads to latency, reduced throughput, and heightened energy consumption, thereby impeding real-time parallel data processing capabilities. Here, a carrier-selective, single-pixel, position-sensitive planar photoactive device that integrates spatiotemporal event sensing with inherent short-term memory capabilities is introduced. The proof-of-concept single-pixel event photoactive device enables in-sensor spatiotemporal parallel optical information processing, efficiently managing multibit (>4 bit) data simultaneously and facilitating ultrafast (≈0.4µs) recognition of input patterns with low energy consumption (25 femtojoules). Additionally, by adjusting the operating speed from continuous to pulsed light illumination, the sensor array can detect trajectories and absolute position of events, offering in-sensor optical flow detection. This single-pixel event photodetector marks significant advancement toward developing compact, energy-efficient, ultrafast sensors suitable for a wide range of in sensor-based photonic applications.

A Review of Artificial Intelligence and Machine Learning in Product Life Cycle Management.

The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning and artificial intelligence (AI) marking significant milestones in this journey. Machine Learning (ML), a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture for manufacturing and process operations. This paper highlights the pivotal role of artificial intelligence in converting process data into actionable knowledge to support critical functions throughout the whole process life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

Machine Learning Analysis Using the Black Oil Model and Parallel Algorithms in Oil Recovery Forecasting

The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhance traditional reservoir simulation frameworks using black oil models. This research involves four main steps: collecting a synthetic dataset, preprocessing it, modeling and predicting the oil recovery factors with various machine learning techniques, and evaluating the model’s performance. The analysis was carried out on a synthetic dataset containing parameters such as porosity, pressure, and the viscosity of oil and gas. By utilizing parallel computing, particularly GPUs, this study demonstrates significant improvements in processing efficiency and prediction accuracy. While maintaining the value of the R2 metric in the range of 0.97, using data parallelism sped up the learning process by, at best, 10.54 times. Neural network training was accelerated almost 8 times when running on a GPU. These findings underscore the potential of parallel machine learning algorithms to revolutionize the decision-making processes in reservoir management, offering faster and more precise predictive tools. This work not only contributes to computational sciences and reservoir engineering but also opens new avenues for the integration of advanced machine learning and parallel computing methods in optimizing oil recovery.

Research on image compression technology based on improved SPIHT compression algorithm for power grid data

Abstract At present, the amount of information on power grid operation and maintenance monitoring image data is increasing, and the requirements for data compression are higher and higher. Based on the improved SPIHT image compression algorithm, this study presents the research of power grid data compression. First, the basic theory of image compression, and the principle of one-dimensional wavelet transform and two-dimensional wavelet transform are introduced. The development process, characteristics, and advantages of image coding are discussed. Then, the shortcomings of the SPIHT algorithm are analyzed, and the SPIHT coding is improved by parallel computation. The parallel wavelet transform algorithm based on the block idea and the parallel SPIHT coding algorithm based on the code tree are proposed in the data parallelism of the compression algorithm. At the same time, the data dependence between tasks in the process of SPIHT image compression coding is analyzed, and the task parallelism in the compression algorithm is realized by using the relative independence of tasks in different threshold coding. Finally, the application and simulation analysis of power grid data based on the SPIHT compression algorithm, the construction of power grid data model simulation, and the composition of two-dimensional power grid data images are carried out. Secondly, the obtained 2D power grid data image is compressed by the SPIHT algorithm and improved SPIHT algorithm, respectively, and the compression effect of the two algorithms on the power grid data image is compared. When the bit rate is 0.5, the compression effect of the improved SPIHT algorithm is 13.6506. When the bit rate is 1, the compression effect of the improved SPIHT algorithm is 18.9287. The results show that the improved SPIHT algorithm can compress the grid data to obtain better grid image quality.

Pulse program for improving learning accuracy and reducing programming energy consumption of ferroelectric synaptic transistor

Neuromorphic computing is a next‐generation computing technology featured by parallel data processing and adaptive learning. Two significant factors that improve learning accuracy are the ‘dynamic range’ and ‘linearity’ of the weight update. In a ferroelectric synaptic transistor, the weight update can be modulated by adjusting the applied voltage. The voltage pulse train should be carefully optimized to improve the learning accuracy and reduce programming energy consumption. In this study, we investigated the learning accuracy of neuromorphic computing based on the characteristics of synaptic devices and the program energy consumption according to pulse programs. We demonstrated changes in the analog conductance characteristics of ferroelectric thin‐film transistors by varying the pulse program for synaptic plasticity, discussed the characteristics for improving learning accuracy, and compared the programming energy consumption according to the pulse programs. We proposed a logarithmic‐incremental‐step pulse program that reduces programming energy consumption and improves learning accuracy.

Diabetes Prediction and Recommendation Model Using Machine Learning Techniques and MapReduce

Objectives: To deliver patient centric healthcare for diabetic patients using a fast and efficient diabetic prediction and recommendation model which will not only help in early diagnoses of disease but also recommend appropriate medicine for controlling it at stage 1. Methods: The Support Vector Machine Classifier is further enhanced with Particle Swarm Optimization (PSO) and used for the prediction of diabetes. Collaborative Filtering is used for drug recommendation, which produces a suitable list of medications that correspond to the diagnoses of diabetes patients. Improved Density-Based Spatial Clustering of Applications with Noise (I-DBSCAN) is proposed to cluster EHR data to get labels based on the symptoms of patients and map reduction is utilized to process the clustered data in parallel for quick recommendations. Findings: The accuracy of the SVM with the PSO model is 99.20%. The performance of I-DBSCAN is also compared with K-Means and regular DBSCAN using the Silhouette Score, Davies Bouldin Score, and the Calinski Harabasz Score. Also, I-DBSCAN was found to give a more accurate score. Novelty: The extensive volume of diabetes-related information stored in electronic health records (EHRs) through continuous monitoring devices poses a growing difficulty for healthcare professionals to effectively navigate and deliver patient-centered care. Machine Learning techniques like classification and recommendations can be utilized to facilitate early disease diagnosis and recommend appropriate medications. Keywords: Electronic health records (EHRs), Collaborative Filtering (CF), Recommendations, Improved Density Based Spatial Clustering of Applications with Noise (IDBSCAN), SVM classifier

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

Big Data Analytics Model with Deep Learning Architecture to Evaluate Live Dance Ecology Through the Internet

Dance ecology, a burgeoning field at the intersection of dance, technology, and environmental studies, relies on real-time data analysis for understanding and optimizing dance performances. This paper proposed a novel Parallel Edge Big Data Analytics (PEBDA) framework, designed to efficiently process and analyze dance movement data in real time. The proposed PEBDA model uses parallel processing in the edge computing model for the analysis of the live dance ecology. Through the parallel processing of the edge model in the network big data analytics is implemented for the estimation of the multiple nodes in the network. The PEBDA model estimates the nodes across multiple environments for the examination of the ecology in the live dance. Finally, through parallel processing classification is performed with the deep learning model for the classification of live dance ecology in the computing platform. The proposed PEBDA framework, assesses classification accuracy, precision, recall, and F1-score. The simulation analysis expressed that Node 8 consistently outperforms others, achieving exceptional accuracy and precision levels above 0.97. These findings highlight the potential of edge computing in revolutionizing dance ecology analysis, enabling enhanced real-time monitoring, decision-making, and optimization of dance performances.

High-quality implementation for a continuous-in-time financial API in C#

In recent years, there has been a rising interest in potentially complex software and financial industries with applications in many engineering fields. With this rise comes a host of developing a usable and consistent Application Programming Interface (API). Prioritize designing and building the software ensures to enrich the platform and emphasize inventorying APIs. In this paper, we proposed a high-quality API to implement the continuous-in-time financial model. The existing discrete framework cannot be evaluated at any time period, involving drawbacks in operating the data structures. Then, the continuous framework is implemented based on the measure theory paradigm. Our proposal uses mathematical modeling, which consists of some objects as measures and fields. It is suitable to develop this API in C# to provide the requirement quality in programming language professionally. This also integrates demands, codes, and verification in the system development life cycle. The advantages are aimed at increasing the structuring and readability. The presented work provides an overview of the design, implementation, testing, and delivery aspects of the API, highlighting the importance of architecture, testing, and numerical choices. The article gives an overview of the API by describing the implementation concerning the data structures and algorithms. These algorithms are based on using the Task Parallel Library (TPL) that makes the API easier and more fruitful for data parallel to benefit from the advantages provided by the .NET Framework.

ProteinFlow: An advanced framework for feature engineering in protein data analysis.

In the burgeoning field of proteins, the effective analysis of intricate protein data remains a formidable challenge, necessitating advanced computational tools for data processing, feature extraction, and interpretation. This study introduces ProteinFlow, an innovative framework designed to revolutionize feature engineering in protein data analysis. ProteinFlow stands out by offering enhanced efficiency in data collection and preprocessing, along with advanced capabilities in feature extraction, directly addressing the complexities inherent in multidimensional protein data sets. Through a comparative analysis, ProteinFlow demonstrated a significant improvement over traditional methods, notably reducing data preprocessing time and expanding the scope of biologically significant features identified. The framework's parallel data processing strategy and advanced algorithms ensure not only rapid data handling but also the extraction of comprehensive, meaningful insights from protein sequences, structures, and interactions. Furthermore, ProteinFlow exhibits remarkable scalability, adeptly managing large-scale data sets without compromising performance, a crucial attribute in the era of big data.

Enhancing Human Activity Recognition through Integrated Multimodal Analysis: A Focus on RGB Imaging, Skeletal Tracking, and Pose Estimation.

Human activity recognition (HAR) is pivotal in advancing applications ranging from healthcare monitoring to interactive gaming. Traditional HAR systems, primarily relying on single data sources, face limitations in capturing the full spectrum of human activities. This study introduces a comprehensive approach to HAR by integrating two critical modalities: RGB imaging and advanced pose estimation features. Our methodology leverages the strengths of each modality to overcome the drawbacks of unimodal systems, providing a richer and more accurate representation of activities. We propose a two-stream network that processes skeletal and RGB data in parallel, enhanced by pose estimation techniques for refined feature extraction. The integration of these modalities is facilitated through advanced fusion algorithms, significantly improving recognition accuracy. Extensive experiments conducted on the UTD multimodal human action dataset (UTD MHAD) demonstrate that the proposed approach exceeds the performance of existing state-of-the-art algorithms, yielding improved outcomes. This study not only sets a new benchmark for HAR systems but also highlights the importance of feature engineering in capturing the complexity of human movements and the integration of optimal features. Our findings pave the way for more sophisticated, reliable, and applicable HAR systems in real-world scenarios.

Remaining useful life prediction of milling cutters based on long-term data sequence and parallel fully convolutional feature learning

Remaining useful life prediction of milling cutters based on long-term data sequence and parallel fully convolutional feature learning

Validation of an IPE checklist developed to guide the design and evaluation of interprofessional learning activities

Despite Interprofessional Education (IPE) being integrated in most healthcare curricula with proven outcomes, barriers to implementation remain, including inconsistency in the quality of IPE activities and lack of high-quality instruments to evaluate the merit of planned and existing IPE activities. To mitigate these barriers, an interprofessional, multi-institutional team gathered data on the utility of an IPE checklist designed to assist educators evaluate the quality, value, and relevance of interprofessional resources and educational materials. A convergent, mixed method design was used to collect and analyze quantitative and qualitative data in parallel. Findings demonstrate the IPE checklist has real-world application and substantive evidence for evaluating IPE activities regardless of experience and expertise and the practical issues encountered. In conclusion, the IPE checklist can contribute to the evaluation of the quality of IPE activities and enhance the education of trainees who will ultimately deliver care to patients and populations.

O-117 Advances in PGT diagnostics - technologies beyond copy number

Abstract Genetic abnormalities are well recognised as the main factor contributing to the high rate of infertility, pregnancy loss and congenital abnormalities in humans. A prime example of this can be seen from recent studies showing that embryos identified as uniformly aneuploid following PGT-A have little to no reproductive potential. However, embryos shown to have no gross chromosomal abnormalities after PGT are not a guarantee of healthy pregnancy and many other undetected genetic issues are likely to influence embryo competence. Developments in both sample preparation and methods for DNA analysis are driving ever-increasing data yield from PGT platforms, permitting not only DNA quantitation, but also allowing qualitative evaluation of genotyping data in parallel. This opens the door to the possibility of overcoming some of the previous limitations of PGT, leading to a more comprehensive assessment of the genetic health and reproductive potential of embryos. How to use this increasing amount of information, and if it can tangibly improve test accuracy and clinical outcomes, remains open to debate. There is, for instance, no consensus on how to interrogate genotyping data for better distinction of clinically relevant aneuploidy as well as genome wide (ploidy) abnormalities and exogenous DNA contamination, which could impact the correct reporting of PGT results. There is also no consensus on the potential benefit of genotyping data to be used for the accurate distinction of meiotic recombination by linkage analysis, nor for fingerprinting of embryos to identify rare, but catastrophic, lab error events as a routine part of treatment. Finally, there is no clear path on how to utilise these improvements in embryo genotyping, to detect sub-chromosomal de novo genetic abnormalities that could impact embryo viability beyond the scope of current testing for chromosomal abnormalities alone. The aim of this presentation is therefore to focus on the current state of the ART and address the current and potential future uses of genotyping in PGT. Topics covered will include identification of biopsy sample contamination to determine the truth from the noise, through to how we can rescue embryos by utilising PGT as a molecular fertilisation check and how fingerprinting of embryo can be an invaluable tool in the clinic. The origin of aneuploidy will be explored and how genotyping is reshaping our understanding of aneuploidy landscape of the preimplantation embryo. From a more basic science perspective, the potential of genotyping to identify meiotic recombination will be discussed and how this fundamental element of human reproduction could be an important biomarker for reproductive competence. Lastly future perspectives on how the evolution of PGT technologies can bring us closer to the accurate detection of de novo genomic changes will be explored and how this is taking PGT-A beyond just aneuploidy detection. It is no secret that PGT for embryo selection continues to be a controversial area of reproductive medicine, and the field of PGT-A especially continues to show disquieting inconsistency in data analysis and reporting. This new era of genotyping brings with it information that can be used as a standardization tool helping to focus and facilitate improvements in the field of PGT for better and more consistent clinical reporting and outcomes.