Acquisition Efforts Research Articles

Show Her the Money

Female-founded businesses struggle to attract private investment but seem to attract more within the sport industry. The purpose of this exploratory study was to examine this discrepancy and determine what factors contribute to private funding of female-owned sport businesses. This study examined 440 female-founded sport-related businesses with a founding date between 2010-2020 from the Crunchbase database. There was $2.21 billion invested into 207 female-founded sport companies during the identified period. Using multiple logistic regression and signaling theory, results of the study suggest that female founders in sport should focus capital acquisition efforts on revenue-generating business models and successfully pitching investors, as this leads to greater funding. Social network analysis indicates that the investing environment for female sport businesses is loosely connected with a potential role for start-up accelerators. This study helps to further understand the investment environment surrounding women-owned businesses in sport.

Methodological aspects of correlative, multimodal, multiparametric breast cancer imaging: from data acquisition to image processing for AI-based radioproteomics in a preclinical setting

Preclinical high-field magnetic resonance imaging (MRI) systems offer a diverse array of MRI techniques, providing rich multiparametric MRI (mpMRI) platforms for studying numerous biological parameters. mpMRI platforms prove particularly indispensable when investigating tumors that exhibit profound intratumoral heterogeneity, such as breast cancer. A thoughtful comprehension of the origins of intratumoral heterogeneity is imperative for the judicious assessment of new targeted therapies and treatment interventions. Furthermore, when data from mpMRI are complemented with data from other in vivo imaging modalities, such as positron emission tomography (PET), and correlated with data from ex vivo modalities, such as matrix-assisted laser desorption imaging mass spectrometry (MALDI IMS), the in vivo parameters can be further elucidated at a molecular level and microscopic scale. Nevertheless, extracting meaningful scientific insights from such complex datasets necessitates the utilization of machine learning (ML) approaches to discern region-specific radiomic features. The development of correlative, multimodal imaging (CMI) workflows, such as one incorporating MRI, PET and MALDI IMS, is inherently challenging, given the many technological and methodological challenges related to multimodal data acquisition as well as the physiological limitations of the laboratory mice of the investigation. Standardization efforts in image acquisition and processing are required to increase the reproducibility and translatability of CMI data. To address the challenges of developing standardized CMI workflows and stimulate dialog regarding this area of need, we present a practical workflow to investigate tumor heterogeneity in breast cancer xenografts across various spatial scales. Our workflow entails simultaneous functional MRI and PET acquisitions in living mice, followed by correlation with post-imaging MALDI IMS and histologic data. Additionally, we propose data preprocessing steps for potential ML applications. We illustrate the feasibility of this workflow through two examples, showcasing its effectiveness in comparing in vivo and ex vivo images to evaluate tumor metabolism and hypoxia in mice with breast cancer xenografts.

Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI.

The superficial white matter (SWM) consists of numerous short-range association fibers connecting adjacent and nearby gyri and plays an important role in brain function, development, aging, and various neurological disorders. Diffusion MRI (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the SWM. However, detailed imaging of the small, highly-curved fibers of the SWM is a challenge for current clinical and research dMRI acquisitions. This work investigates the efficacy of mapping the SWM using in vivo ultra-high-resolution dMRI data. We compare the SWM mapping performance from two dMRI acquisitions: a high-resolution 0.76-mm isotropic acquisition using the generalized slice-dithered enhanced resolution (gSlider) protocol and a lower resolution 1.25-mm isotropic acquisition obtained from the Human Connectome Project Young Adult (HCP-YA) database. Our results demonstrate significant differences in the cortico-cortical anatomical connectivity that is depicted by these two acquisitions. We perform a detailed assessment of the anatomical plausibility of these results with respect to the nonhuman primate (macaque) tract-tracing literature. We find that the high-resolution gSlider dataset is more successful at depicting a large number of true positive anatomical connections in the SWM. An additional cortical coverage analysis demonstrates significantly higher cortical coverage in the gSlider dataset for SWM streamlines under 40 mm in length. Overall, we conclude that the spatial resolution of the dMRI data is one important factor that can significantly affect the mapping of SWM. Considering the relatively long acquisition time, the application of dMRI tractography for SWM mapping in future work should consider the balance of data acquisition efforts and the efficacy of SWM depiction.

Assessment of short-term supply disruption impacts for the Swiss mobility, energy and ICT sectors – Application of the SPOTTER approach

In this paper, supply disruption impacts along supply chains within the Swiss mobility, energy and ICT sectors are assessed in the short-term (i.e. the next 5 years) using the recently developed SPOTTER approach with the objective to identify hotspots and technologies associated with comparably low supply risks. SPOTTER has been chosen because, compared to other approaches, it is the only one that allows for an assessment along the full supply chain and it is integrated into the Life Cycle Sustainability Assessment framework. To decrease the respective data acquisition efforts for the here-performed supply risk analysis of entire sectors, in a first step, material/product flows that are most influential for an assessment with SPOTTER are identified and considered for the calculation of related impact scores. Our study analyzes critical material and product flows between trade partner countries along relevant supply chains. It thus identifies new hotspots and adds relevant country-specific information to current knowledge. In particular, the identified hotspots refer to volatile prices of solar panels, interrupted trade of oil and gas from Russia, Nigeria and Niger as well as disrupted imports of electronic devices from China. Suggestions for mitigation strategies comprise hedging and/or diversifying the supply chains. Other identified hotspots refer to the critical supply of cobalt and natural graphite along battery supply chains, which could for example be addressed by supporting and/or incentivizing the construction of a circular economy. A third category of identified hotspots indicates risks related to the trade of hafnium and nuclear power plant equipment that could be mitigated by shifting to less risky energy technologies. Our comparisons of overall impacts between relevant technologies suggest higher supply risks for conventional cars, solar panels and Chinese laptops compared to battery electric cars, wind turbines and German laptops. In these comparisons, more details regarding disruptions along full product and fuel supply chains are considered than in current studies.

Semantic Guided prototype learning for Cross-Domain Few-Shot hyperspectral image classification

Hyperspectral image(HSI) classification has found extensive application in the field of remote sensing. However, traditional HSI classification methods typically rely on a large number of labeled samples, incurring significant costs and requiring considerable time and effort for acquisition. To address this issue, this paper proposes a novel cross-domain few-shot learning method termed Semantic Guided Prototype Learning (SGPL), which aims to optimize prototype learning through the incorporation semantic information, thereby enhancing the classification accuracy of hyperspectral images in few-shot scenarios. The core idea of the SGPL method is to integrate semantic information into the prototype learning framework, thereby enhancing the expressive capacity of each category prototype. Specifically, HSI category labels are encoded via one-hot encoding, and the corresponding HSI features are concatenated with these encoded vectors to construct an augmented feature representation. These augmented feature vectors are subsequently fed into a prototype generator, which is designed to learn the generation of category prototypes from the concatenated features. Through the application of one-hot encoding and feature concatenation, SGPL effectively captures and leverages discriminative information between classes. Moreover, the proposed model exhibits superior performance on three different HSI datasets: Salinas, the University of Pavia(UP), and Indian Pines(IP) with overall classification accuracies of 91.27%, 85.84%, and 69.33%, respectively, outperforming other methods. The source code is available at https://github.com/AIYAU/SGPL.

Reprocessing and interpretation of legacy seismic data using machine learning from the Granada Basin, Spain

The Granada Basin (Spain) is a Neogene sedimentary depression with irregular geomorphology and deep depocenters. It is located in the most seismically hazardous part of the Iberian Peninsula with an historically experienced extremely destructive earthquakes, followed by periods of low to moderate seismicity. In 1980s the Chevron Oil Company collected a set of 30 deep seismic reflection sections in this Basin of which only the results on paper are kept. Due to the fact that many of these seismic profiles are currently located in urban areas and the economic cost of carrying out a similar exploration, it was decided to recover these old data and apply a post-stack treatment to improve their quality. The purpose of this study is to show the applied reprocessing flow and, with the new sections, to present a spatial model of the basin. The first stage of recovery and enhacement of seismic sections has consisted in three phases: first, high-resolution scanning of paper copies to TIFF images followed by the transformation of TIFF images to SEG-Y format; second, poststack processing workflow to increasing resolution and lateral coherence of these seismic lines; and third, it has been used a machine learning algorithm, among others, increasing the spatial resolution, signal-to-noise ratio, and coherence of the seismic signals. In addition, basement horizons, as well as three sedimentary sequences, were identified in all seismic sections and interpolated to create a three-dimensional basement model composed by normal faults, horst and grabens related to the seismotectonic behavior of the basin. As an overall assessment, this work is an example of the usefulness of ‘recycling’ legacy seismic data, which nowadays are usually in archived boxes, but at the time required a great economic and acquisition effort.

BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES.

Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.

Can Picture Dictionaries Unlock Vocabulary Mastery? Unveiling Insights Through Quasi-Experimental Research

The current study assessed the effectiveness of employing a Picture Dictionary to enhance the vocabulary acquisition of junior high school learners. A significant improvement in vocabulary comprehension was found to have been correlated with learners taking advantage of a visual dictionary compared to those who did not use such a resource. This idea points out that visual dictionaries could be a good option for students to enhance their vocabulary acquisition efforts. This research used a quasi-experimental design and the test as the research instrument to determine the students' vocabulary mastery. In this research, the test is the research instrument to determine the student's vocabulary mastery. This research has been conducted on seventh-grade students in the academic year 2022-2023 with a total sample of sixty respondents. A random sampling technique was employed to determine the group of respondents. The experimental and control groups' average scores differ, as determined by the data analysis. By comparison, the control group attained an average score of 61.7, but the group participating in the experiment attained a mean score 80. The average scores indicate that the group participating in the experiment achieved a mean score greater than the control class.

Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing

Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.

Trends in Knowledge Capturing and Its Success Factors: A Systematic Literature Review

Knowledge management (KM) is a systematic approach that facilitates the development, dissemination, and creation of value from information and knowledge within an organization. Knowledge management is deemed the most valuable asset for many organizations. However, the implementation of a knowledge capturing model is still a challenging task. In this research, a Systematic Literature Review (SLR) methodology is employed to identify and analyze diverse approaches to capturing and utilizing knowledge in various organizational domains. The search process is executed across five databases, namely, Scopus, Science Direct, Emerald Insight, Taylor &amp; Francis Online, and IEEE. The study findings reveal a total of 24 knowledge capture methods employed across diverse organizational contexts and knowledge types. Among these methods, the questionnaire was mentioned in 4 articles, an interview in 2 articles, and an expert interview in two articles. The success factors were classified into 20 types, including common knowledge, organizational capabilities, quality of knowledge, trust and acceptance, leadership capabilities, feedback and learning, performance measurement, reward and recognition, efficiency, knowledge management integration, organizational culture, knowledge managers tools and techniques, effectiveness, motivation, information technology, and knowledge management process, training, knowledge representation, and collaboration. Additionally, the identified trending methods and eight key success factors that influence implementation of various knowledge capture models add a new understanding of the critical elements for success in knowledge capture initiatives. This review, in accordance with PRISMA guidelines, provides a foundation for future research efforts in knowledge acquisition and systematic literature review.

Interpreting the differences in microbial carbon and nitrogen use efficiencies estimated by 18O labeling and ecoenzyme stoichiometry

Microbial carbon and nitrogen use efficiencies (CUE and NUE) are central to our understanding of soil C and N cycling. Although both the 18O-labeling approach and ecoenzyme stoichiometry model have been widely used to estimate microbial CUE and NUE, comparisons of the two methods are scarce. Here, we investigated soil microbial CUE and NUE of 11 locations along a forest transect in eastern China using both the 18O-approach and ecoenzyme model. We found that microbial CUE-18O and NUE-18O estimated by the 18O-approach were positively related to each other and both decreased with soil acidification and phosphorus acquisition as measured by enzyme activity. However, CUEC:N and NUEN:C, estimated by C:N stoichiometry using the enzyme model were negatively related to each other and neither showed any relationship to soil acidification or phosphorus acquisition. Finally, CUEC:P and NUEN:P estimated according to C:P stoichiometry were positively related to each other and both increased with soil acidification and phosphorus acquisition efforts. We suggest that a reason for these differences is that the 18O-approach reflects the intracellular biochemical transformation of C, N, and P while the enzyme models reflect stoichiometric imbalances between external substrates and microbial biomass, i.e., different aspects of microbial metabolism. Moreover, differences between the C:N and C:P-based enzyme models resulted from P-limitation to soil microorganisms in this system. For these reasons, microbial element use efficiencies need to be clearly defined, i.e., as derived from growth-based or soil enzyme-based measurements, and caution must be made when comparing results obtained from different methods, and under different conditions of resource availability.

The Arnold Arboretum’s Campaign for the Living Collections: A Case Study in Living Collection Development

In 2015, the Arnold Arboretum of Harvard University began an ambitious plant acquisition program: The Campaign for the Living Collections. Prior to the initiative’s launch, the Arboretum underwent several years of strategic planning to assess the values, strengths, and gaps within its renowned living collection of temperate woody plants and then set goals that would profoundly shape the collection and its research and conservation potential for decades if not for centuries. Core genera, conservation value, phylogenetic breadth, biogeography, and climate change responses were among the priority themes used to generate a targeted list of 395 desiderata to acquire from wild populations. In only a few years, the Campaign’s 26 formal expeditions and other acquisition efforts have yielded 631 accessions of 263 highest-priority desiderata, representing 66.6% of the overall goal. This venture represents one of the most transformative and deliberate collection development activities at the Arnold Arboretum and among botanical gardens in the current era. These successes are due to a combination of factors that include visionary yet realistic strategic planning and goal setting, adherence to high standards of documentation and reporting, and authentic relationship building among collaborators.

Stewarding our resources: Building a sustainable IPUMS archival document access system

IPUMS at the University of Minnesota has created the world’s largest accessible database of census and survey microdata. The IPUMS suite of products contains nine harmonized data products. The largest of these projects, IPUMS International (IPUMS-I) has supported the curation and preservation of ancillary materials received during data acquisition efforts. Archival staff have preserved thousands of unique pieces of census and survey documentation, creating bibliographic records using an extended Dublin Core profile that supports the use of controlled vocabularies to enhance findability for the project staff and outside users. The goal of this curation work was to create a findable, searchable, and downloadable document access system for our internal use and to support IPUMS researchers. This paper describes our experience constructing a tool that supports exploration and dissemination of these archived materials. During this development, we gained valuable insight about stewarding our resources that are applicable to research organizations responsible for curating, preserving, and disseminating archival materials.

Developing systems to encourage FAIR and secure research data

Welcome to first issue of IASSIST Quarterly for 2024; this is Volume 48 of the journal (IQ 48(1) 2024). The three papers in this issue represent different aspects of researchers’ use of data archives. Experts from three major data archives (ICPSR, IPUMS, and GESIS) share their experiences developing systems to make research resources in their archives more findable, accessible, and usable. Lafia, Million, and Hemphill in their article “Exploratory and directed search strategies at a social science data archive” investigate search strategies of users of a large social science data archive. In an effort to understand how users search for curated research data, the authors analyze data queries issued through the Inter-University Consortium for Political and Social Research (ICPSR) website’s search box. The study is meant to inform archives and repositories of their users’ experience and help develop systems that encourage dataset findability, accessibility and reuse. The authors identify two types of data searches, exploratory and directed searches. They find that while users that issued exploratory queries were able to navigate the ICPSR website successfully and refine their searches, they may benefit from more explicit support for query reformation. The authors suggest ways development of tools and training could improve users’ experience. In the article “Stewarding our resources: Building a sustainable IPUMS archival document access system” Daina Magnuson describes IPUMS experience building a web interface to support exploration and dissemination of archival materials associated with IPUMS International (IPUMS-I). Ancillary materials, including thousands of unique pieces of census and survey documentation were obtained by IPUMS-I during data acquisition efforts. This rich source material was curated and preserved by archival staff. Magnuson describes in details the development of a system that would make these resources findable, searchable and downloadable to internal users as well as IPUMS researchers. Strategies describe may inform other data archives considering developing similar tools. Deborah Wiltshire in her article “Developing canonical ‘Safe Researcher’ training materials for Trusted Research Environment” describes the development of training materials for researchers applying to access restricted data through Trusted Research Environment (TRE). The increased use of a virtual desktop environment, as opposed to physical safe rooms, offers researchers easier and more flexible access to restricted data; however, it offers fewer physical safeguards. Attending a mandatory ‘Safe Researcher’ training is one of the principles of the Five Safe Framework[i] developed by the UK’s Office for National Statistics to bridge this gap and assure the safe use of sensitive data. The process described in the article could be easily adapted by other TREs according to their needs and settings (e.g., in-person vs. online training). In the spirit of making information findable, accessible and reusable, we’re happy to share that the Open Journal System platform from the University of Alberta has enabled author linking to ORCID. This means that IQ authors are now able to connect their ORCID iDs to their IQ publications, so that their ORCID profiles will automatically update with those citations as their articles are published. For existing IQ account holders, log into your account at the upper right of the IQ page, and go to Profile &gt; Public. Then click “Create or Connect your ORCID iD” (just below the Homepage URL field). For details, see this video walkthrough of profile authentication. New IQ users will automatically be prompted to link (or create) their ORCID iDs during IQ account creation. Authors are also encouraged to note their ORCID iD link with their affiliation and contact information in an endnote when submitting a manuscript for publication. We hope to see many of you at the 49th Annual IASSIST Conference in Halifax, Nova Scotia, Canada, May 28-31, 2024! Ofira Schwartz and Michele Hayslett, March 2024

Examining online education at tribal community colleges

AbstractFor students at tribal colleges, online courses provide access to college coursework, particularly for non‐traditional students and students who live in rural communities. In addition, online courses provide unique opportunities for Indigenous students to connect with their tribal communities and to participate in cultural revitalization and language acquisition efforts being led by tribal colleges, regardless of their proximity to a tribal college. This article analyzes descriptive data of two‐year tribal colleges that provide a snapshot of their presence and spending to support online education. We offer policy recommendations to support greater online education opportunities for Indigenous students and to expand Indigenous cultural revitalization and language acquisition through online courses.

The Position of the Principle of Legality vs the Principle of Opportunity in the Accemination of the Prosecutor's Demands in the Replic Agenda (Valencya Case Study at the Karawang State Attorney)

Withdrawing criminal charges against Valencya during a trial's replication stage has generated debate, seemingly balancing the principles of legality and opportunity. This issue is significant because the Prosecutor in the Valencya Case has effectively assumed the role of Legal Advisor. This study presents a descriptive analysis of normative research within the criminal procedure system. By detailing the placement of legal charges in the Valencya case, a thorough analysis is conducted regarding the Public Prosecutor's authority in determining the legality and the discretion to discontinue prosecution. The Principle of Opportunity justifies discontinuation efforts in charge acquisition, even when clearance requirements are conducted in the replica agenda. While prioritizing legal interests to achieve justice for victims, the principle of legality can be waived, aligning with the concept of restorative justice in the Prosecutor's Office agenda.

@llegra: a chatbot for Vallader

AbstractExtinct and endangered languages have been preserved primarily through audio conservation and the collection and digitization of scripts and have been promoted through targeted language acquisition efforts. Another possibility would be to build conversational agents like chatbots or voice assistants that can master these languages. This would provide an artificial, active conversational partner which has knowledge of the vocabulary and grammar and allows one to learn with it in a different way. The chatbot, @llegra, with which one can communicate in the Rhaeto-Romanic idiom Vallader was developed in 2023 based on GPT-4. It can process and output text and has voice output. It was additionally equipped with a manually created knowledge base. After laying the conceptual groundwork, this paper presents the preparation and implementation of the project. In addition, it summarizes the tests that native speakers conducted with the chatbot. A critical discussion elaborates advantages and disadvantages. @llegra could be a new tool for teaching and learning Vallader in a memorable and entertaining way through dialog. It not only masters the idiom, but also has extensive knowledge about the Lower Engadine, that is, the area where Vallader is spoken. In conclusion, it is argued that conversational agents are an innovative approach to promoting and preserving languages.

Climate and human stressors on global penguin hotspots: Current assessments for future conservation.

As charismatic and iconic species, penguins can act as "ambassadors" or flagship species to promote the conservation of marine habitats in the Southern Hemisphere. Unfortunately, there is a lack of reliable, comprehensive, and systematic analysis aimed at compiling spatially explicit assessments of the multiple impacts that the world's 18 species of penguin are facing. We provide such an assessment by combining the available penguin occurrence information from Global Biodiversity Information Facility (>800,000 occurrences) with three main stressors: climate-driven environmental changes at sea, industrial fisheries, and human disturbances on land. Our analyses provide a quantitative assessment of how these impacts are unevenly distributed spatially within species' distribution ranges. Consequently, contrasting pressures are expected among species, and populations within species. The areas coinciding with the greatest impacts for penguins are the coast of Perú, the Patagonian Shelf, the Benguela upwelling region, and the Australian and New Zealand coasts. When weighting these potential stressors with species-specific vulnerabilities, Humboldt (Spheniscus humboldti), African (Spheniscus demersus), and Chinstrap penguin (Pygoscelis antarcticus) emerge as the species under the most pressure. Our approach explicitly differentiates between climate and human stressors, since the more achievable management of local anthropogenic stressors (e.g., fisheries and land-based threats) may provide a suitable means for facilitating cumulative impacts on penguins, especially where they may remain resilient to global processes such as climate change. Moreover, our study highlights some poorly represented species such as the Northern Rockhopper (Eudyptes moseleyi), Snares (Eudyptes robustus), and Erect-crested penguin (Eudyptes sclateri) that need internationally coordinated efforts for data acquisition and data sharing to understand their spatial distribution properly.

Blended acquisition with temporally signatured/modulated and spatially dispersed source array: The first pilot survey onshore Abu Dhabi

AbstractRecently, we established a blended‐acquisition method: temporally signatured and/or modulated and spatially dispersed source array that jointly uses various signaturing and/or modulation in the time dimension and dispersed source array in the space dimension. We acquired and processed the first pilot programme with our method onshore Abu Dhabi. In this paper, we review the resulting acquisition‐productivity enhancement in the time dimension and discuss it in the space dimension as well. We then review the deblended‐data‐reconstruction processing, followed by imaging processing, and discuss their performance and resulting data quality. We last establish a relationship between the acquisition productivity and the processing performance. We found that this method significantly enhances the acquisition productivity compared to conventional blending methods. For the processing performance, the deblended data can successfully be reconstructed from the blended data; afterwards, the subsurface sections can naturally be imaged from the deblended data. Furthermore, this method owns a relationship: The deblended‐data‐reconstruction performance increases with the acquisition time (i.e. the acquisition effort that is inversely proportional to the acquisition productivity) up to a plateau; the imaging processing improves the data quality as the flow makes progress and eventually reaches a high data quality after poststack processing, regardless of the acquisition effort.

“A STUDY ON IMPACT OF INNOVATIVE STRATEGY TOWARDS CUSTOMERS ACQUISITION AT NRCONTAINERS PRIVATE LIMITED, DODDABALLAPUR”

The primary objective of this research is to evaluate how NRContainers has strategically leveraged innovation to enhance its customer acquisition efforts. Through an extensive analysis of the company's innovative initiatives, market trends, and customer feedback, this study seeks to uncover valuable insights into the relationship between innovation and customer acquisition. Preliminary findings suggest that NRContainers has successfully implemented innovative strategies, such as product diversification, technological advancements, and customer-centric approaches, which have positively influenced its customer acquisition efforts. Furthermore, the study identifies key challenges and opportunities in the company's pursuit of innovation-driven customer acquisition. As NRContainers continues its journey of adaptation and innovation in a competitive marketplace, the insights from this study will serve as a valuable guide for informed decision-making and strategic planning, ultimately shaping the company's future growth trajectory. KEYWARDS: Customer Acquisition, Innovative Strategy, NRContainers