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Over the last three decades, business-to-business (B2B) sales practices have undergone significant change. This study evaluates the current responsibilities, skills, and knowledge sets required in B2B sales professionals. It identifies roles, qualifications, competencies, and the B2B sales position taxonomy. To achieve these objectives, we conducted three studies. The first study included an analysis of 565 job descriptions to identify the current task and skill requirements of B2B salespeople. The second survey included 380 B2B salespeople and incentivized the development of a new up-to-date taxonomy for sales positions. Our third and final study consisted of 33 semi-structured interviews with B2B sales professionals, and was designed to predict the future skillsets required in B2B sales professionals. Our results discuss the conception and current status of the B2B sales profession, and primarily highlight the role of future B2B salespeople. Lastly, this study offers a set of B2B sales-related recommendations based on our results. [ABSTRACT FROM AUTHOR]

The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance. [ABSTRACT FROM AUTHOR]

Two organizations that focus on tech-based health innovation have partnered to improve the mental health of young adults. In this "tech for good" project, Hopelab and Grit Digital Health worked together to create an app using human-centered product design. With the core needs of users at the forefront, the team developed an app both with and for college students to combat loneliness, a prevalent issue that can lead to other problems such as depression, anxiety, and even dropping out of college. The team also applied systems thinking to address the root causes of this "wicked problem," resulting in their Nod app, which has improved outcomes for college students. This article is based on the author's award-winning case study. [ABSTRACT FROM AUTHOR]

Disruptions can adversely affect profitability, service level, and even the viability of supply networks (SNs). Natural disasters, the COVID-19 pandemic, and geopolitical tensions heightened disruption risks for SNs. We propose a methodological framework to guide the design of resilient SNs, with a focus on efficient resilience. In fact, despite an increasing economic and social interest in SN resilience and a large variety of qualitative resilience frameworks, there is a lack of model-based frameworks to guide decision makers in the complex task of designing resilient SNs. Differently to the existing literature, that mostly focuses on industry- or disruption-specific approaches, the framework of this paper is general, independent from sectors and causes. It consists of three steps to guide practitioners through model-based stress testing of their SNs: it integrates and organises methods from the literature, from the selection of data to be collected to the validation of the resilient and efficient SN. We illustrate the application of descriptive, predictive, and prescriptive analytics at each phase using real-life case studies data supplemented by literature references. Finally, we present an industrial case study with the step-by-step application of the framework highlighting its strengths and objectives. [ABSTRACT FROM AUTHOR]

A major problem in employee selection coalesces around convincing decision‐makers (e.g., hiring managers) to use analytically derived models. Existing recommendations in the literature largely focus on convincing executives to adopt analytical models and then exert their top‐down influence on lower‐level hiring decisions. In contrast to these solutions, we explore end user modifiability (i.e., allowing decision‐makers to modify a statistical model before use) as a bottom‐up approach for increasing hiring managers' implementation of analytical recommendations. From a utility standpoint, we consider how incorporating end user modifiability into hiring decisions will result in a less statistically valid, but potentially more valuable, organizational selection process. We explore these ideas in two studies. In Study 1, we experimentally test whether model modification increases decision‐maker reliance on a statistical model, as well as how much decision‐makers need to modify a model in order to use it. In Study 2, we examine the extent that modifiability introduces implicit biases that might adversely affect marginalized groups. Results suggest that modifiability can increase decision‐makers' perceived usefulness of a model and, importantly, that only a small amount of modifiability is needed to elicit this effect. Further, end user modifications were statistically insignificant predictors of hiring rates across race‐based subgroups, though supplementary analyses suggest important cautionary nuance. Given that analytical models are rarely perfectly or wholly implemented, end user modifiability may offer a viable solution for organizations seeking to increase the implementation of algorithmic guidance in selection decisions, even if it deviates modestly from a statistical optimality. [ABSTRACT FROM AUTHOR]

Not all relevant data come in structured formats. We created this teaching case to introduce students to an important data analysis technique that can be used on unstructured data: text analytics. This case focuses on why MD&A disclosures may provide auditors, regulators, and investors with decision-relevant information during the global pandemic. There are a wide variety of tools that can be used to introduce accounting students to text analytics. In the Teaching Notes, we provide instructions for Alteryx, a technology that offers a low-code solution to text analytics. Low-code solutions allow faculty and students to focus on critical thinking and applying text analytics to accounting. The learning objectives are (1) expand students' understanding of text analytics in an accounting setting, (2) develop students' skills necessary for performing text analytics, and (3) expand students' critical thinking skills as they apply text analytics in accounting or business contexts. Data Availability: Data are available from public sources cited in the text and materials that are available for download. [ABSTRACT FROM AUTHOR]

To understand audience-oriented journalistic role performance, one must understand how journalists conceptualize and cater to their audience. Giving the audience what it wants is a complex endeavor, with varying goals and hybridized end results, in newsrooms with fewer resources serving increasingly polarized audiences. Through a triangulation of data—content analysis at the subdimension level to examine the range and hybridity of audience-oriented journalistic product presenting the civic, service and infotainment roles; a survey to identify journalists' attitudes toward the use of audience data and social media in their work; and interviews with journalists that revealed how their journalistic practice and audience perceptions were impacted by quantitative (metrics and analytics) and qualitative data (comments/social media interactions)—this research fills a gap in understanding about the connection between journalists, their audiences, and audience data when it comes to journalistic role performance. Findings show that in Canada the infotainment role is a significant part of reporting, but entertaining often comes with a goal of educating, as does service journalism. There are no "bad" journalistic roles, but there are a lot of journalists trying to figure out which ones might best catch, engage, and retain an ever-shrinking news audience. [ABSTRACT FROM AUTHOR]

Effective comparative public policy research necessitates three key aspects. Firstly, it requires concepts that allow the differences in policies and policy developments across various domains to be discerned. Secondly, even with robust concepts established, they typically must be "put into practice". This means scholars need analytical tools that can either provide theoretical insights helpful in creating careful research designs or extract relevant information from laws, legislation, and other governmental sources and organize it meaningfully. Lastly, the objective of these endeavors is to produce statistics on policies or their effects that permit robust conclusions to be drawn about policy efficacy and impact. Building upon this "trinity" as the foundation of successful comparative policy research, the new section editors of the Journal of Comparative Policy Analysis (JCPA) are seeking contributions in the areas of policy concepts, analytics, and statistics. This introductory article further clarifies what these aspects entail and presents examples from existing research that may align with this section. [ABSTRACT FROM AUTHOR]

Recent ground-breaking research in the Internet of Things (IoT) and drone technology has brought about revolutionary advancements in automation, remote sensing, and remote operations. Drones have gained massive popularity in IoT applications, like remote monitoring and task automation. Consequently, an innovative business model called Drones-as-a-Service (DaaS), which aims at leasing out drones for commercial use, is gaining momentum in the market. This spike in demand for drones is credited to their ease of deployment, flexibility of operation, and risk-free functioning. To fully utilize the advantages that drones provide and further delve into the flourishing field of drone research, it is essential to acquaint ourselves with the scope of this field and understand the capabilities of a drone. The methodology employed in this survey paper encompassed a thorough literature review to establish a foundation, followed by the design and implementation of a comprehensive survey instrument. As a result, this survey provides a means for the reader to explore diverse drone research topics, ranging from basic drone types and deployment architectures to complex drone networking and communication systems. Intelligence-based enhancements for drone communications and Blockchain-based security solutions have also been discussed. We have conducted detailed comparisons between different classes and architectures of drones, drone communication protocols (WiFi, LTE, 5G, satellites), and drone-enabled 5G networks (Ad hoc networks and Software Defined Networks). Blockchain-based and Intelligence-assisted solutions for 5G-related drone network security concerns have also been discussed. We also highlight the utility of drones in present-day Smart City Infrastructure, ranging across multitudes of applications, as well as in the Precision Agriculture domain and their future scope. Finally, we outline significant security challenges and cyber-attacks faced by drones and their countermeasures, as proposed in the state-of-the-art literature. In conclusion, we end this survey by discussing the future scope and directions of drone research. [ABSTRACT FROM AUTHOR]

Background: Nuisance and false alarms distract clinicians from urgent alerts, raising patient safety risks. Local Problem: High alarm rates in a pediatric progressive care unit resulted in experiencing 180-250 alarms per day or 1 alarm every 3 to 4 minutes per clinician. Methods: Through Plan-Do-Study-Act cycles, environmental, policy, and technology changes were implemented to decrease the average alarms/day/bed and percentage of time in alarm. Interventions: Alarm settings tailored to patient needs using features embedded within the patient monitoring system were implemented and monitored with the assistance of alarm champions. Results: The average number of alarms/day/bed decreased from 177.69 to 96.94 over the course of 10 years, a 45.45% reduction. The percentage of time in alarm decreased from 7.52% to 2.83%, a 62.37% reduction. Conclusions: Arming clinicians with technology to analyze real-time clinical data made alarms meaningful and actionable, decreasing false alarms without compromising patient safety. [ABSTRACT FROM AUTHOR]

Copyright of Retos, Revista de Ciencias Administrativas y Económicas is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The adoption and use of organizational performance management techniques has evolved in the US federal government over the past three decades. This began with an emphasis on creating a supply of performance information largely for accountability and budgeting purposes. Its emphasis shifted to creating a demand for performance information to inform improvement strategies. Nevertheless, the resulting performance governance framework and its accompanying administrative routines have not been adopted consistently across agencies or at lower levels within agencies. However, recent movements to develop more sustainable uses of data and evidence may hold lessons for the future of the federal performance movement. [ABSTRACT FROM AUTHOR]

Recombinant adeno-associated virus (rAAV) is the most widely used viral vector for in vivo human gene therapy. To ensure safety and efficacy of gene therapy products, a comprehensive analytical profile of the rAAVs is needed, which provides crucial information for therapeutic development and manufacturing. Besides information on rAAV quantities and possible contaminating DNA and protein species, assessing rAAV quality is of utmost importance. In vitro biopotency and methods to determine the full/empty ratio of rAAV capsids are commonly applied, but methods to assess the integrity of the viral genome are still rarely used. Here we describe an orthogonal approach to characterize rAAV quality. Two biologically different rAAV9s from different stages of the bioprocess, generated each with two different transfection reagents, were investigated. In vitro biopotency tests in all cases demonstrated that rAAV9s generated with transfection reagent FectoVIR® possessed a higher biological activity. Mass-based analytical methods, such as sedimentation velocity analytical ultracentrifugation (AUC) and mass photometry, showed a high share of full capsids (>80 %) at late process stages but did not detect any differences in the rAAV9s from the different transfection reagents. Multiplex dPCR and Nanopore long-read sequencing both demonstrated that, also in late-stage process samples, sample heterogeneity was relatively high with a rather small share of full-length transgenes of ∼10–40 %. Intriguingly, both methods detected a higher share of complete transgenes in rAAV9 generated with transfection reagent FectoVIR® instead of Polyethylenimine (PEI), and thereby explain the differences already observed in the biopotency assays. This study therefore emphasizes the necessity to utilize multiple, orthogonal methods to gain a better understanding of recombinantly manufactured AAVs. • FectoVIR significantly increased rAAV9 potency compared to other reagents. • About 80 % of rAAV capsids are fully filled, with no difference due to reagent choice, as shown by AUC and mass photometry. • FectoVIR improves rAAV9 genome integrity, as shown by results from multiplex dPCR and Nanopore sequencing. • Preparation steps preceding Nanopore sequencing affect the distribution of fragments and the accuracy of titer measurements. • Employing a combination of mass- and sequence-based analytical methods is crucial for the thorough characterization of rAAV9s. [ABSTRACT FROM AUTHOR]

This paper focuses on the operation and results of the gas treatment pilot plant for the purification and conditioning of steel mill gases, a key element of the Carbon2Chem® Technical Center in Duisburg, Germany. Using highly sensitive analytics, it is demonstrated that steel mill gas impurities which are detrimental to downstream catalytic conversion are reduced to single‐digit ppb levels. Strategies for adapting the gas purification process to take account of the transformation of steelmaking towards direct reduction are also discussed. [ABSTRACT FROM AUTHOR]

In the past decade, new approaches to the discovery and development of vaccines have transformed the field. Advances during the COVID‐19 pandemic allowed the production of billions of vaccine doses per year using novel platforms such as messenger RNA and viral vectors. Improvements in the analytical toolbox, equipment, and bioprocess technology have made it possible to achieve both unprecedented speed in vaccine development and scale of vaccine manufacturing. Macromolecular structure‐function characterization technologies, combined with improved modeling and data analysis, enable quantitative evaluation of vaccine formulations at single‐particle resolution and guided design of vaccine drug substances and drug products. These advances play a major role in precise assessment of critical quality attributes of vaccines delivered by newer platforms. Innovations in label‐free and immunoassay technologies aid in the characterization of antigenic sites and the development of robust in vitro potency assays. These methods, along with molecular techniques such as next‐generation sequencing, will accelerate characterization and release of vaccines delivered by all platforms. Process analytical technologies for real‐time monitoring and optimization of process steps enable the implementation of quality‐by‐design principles and faster release of vaccine products. In the next decade, the field of vaccine discovery and development will continue to advance, bringing together new technologies, methods, and platforms to improve human health. [ABSTRACT FROM AUTHOR]

The article "The Future of Personalized Medicine and How the Healthcare Internet of Things is Reshaping Treatment Plans and Patient Experiences" offers a comprehensive exploration of the transformative landscape of healthcare. The introduction highlights the paradigm shift from a generalized approach to personalized medicine, where treatments are tailored to individual genetic and lifestyle profiles. Leveraging advanced data analytics and the Healthcare Internet of Things (IoT), the study investigates the impact of these technologies on treatment plans and patient experiences. Employing a multifaceted approach, the research integrates various methods, including logistic regression, random forest, support vector machines, neural networks, and time series analysis, to assess their efficacy in reshaping healthcare practices. Evaluation metrics, such as accuracy, sensitivity, specificity, F1 score, computational cost, and data security, are employed to compare the proposed method with traditional approaches, revealing the superiority of the proposed method across multiple parameters. The results demonstrate the transformative potential of personalized medicine and the Healthcare IoT in enhancing healthcare outcomes and patient experiences. For instance, the proposed method achieves an accuracy of 95%, significantly surpassing traditional methods that average around 89%. Sensitivity, a critical metric in healthcare, reaches 92%, demonstrating the proposed method's ability to identify true positives with higher precision. Additionally, the computational cost of the proposed method, at 0.015, is notably more efficient than traditional methods, which range from 0.020 to 0.022. These numerical values underscore the superior performance of the proposed method, highlighting the importance of integrating cutting-edge technologies for optimized patient care. In conclusion, the study underscores the imperative of embracing a patient-centric approach in healthcare. [ABSTRACT FROM AUTHOR]

The use of big data analytics (BDAs) allows for the collection, management, and analysis of large volumes of data, which helps in making real-time decisions. This study aims to assess how the application of BDA impacts the performance of healthcare supply chain management (HCSCMP). Through a systematic literature review, the research explores how BDA enhances efficiency within healthcare supply chains (HCSCs) and identifies the critical factors enabling successful BDA implementation. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs' efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges.

Background: Almost 80 years after implementation of the industrial ethanol fractionation process, based on the pioneering work of Edwin J. Cohn's research group, it has not lost its importance in providing life-saving biotherapies to patients. The focus has shifted from albumin, which was first used to treat intensive care patients, to immunoglobulin G preparations. Nowadays the latter enables effective, life-long treatment of patients suffering from immunodeficiencies. Ethanol concentration, pH, temperature, protein, and salt concentration are diligently varied during the Cohn fractionation process to finally yield fractions in which the main plasma proteins are enriched at adequate purity. Total protein concentration of the intermediates, probably not as important as ethanol levels and pH, has nevertheless an indisputable influence on the process' performance Objectives: Total protein measurement is therefore a valuable in process-control for versatile process performance monitoring. For this purpose, we investigated the application of analytical digital refractometry. Design: For our pilot study, we used six consecutive batches of nine relevant intermediates covering Takeda's whole industrial-scale Cohn Fractionation process. Methods: Total protein concentrations of the intermediates were measured with the method of Kjehldal to obtain reference data and digital analytical refractometry, using 10-kDa ultrafiltration to obtain a protein free permeate serving as a sample-specific blank. Results and Conclusions: Specific refractive index increment dn/dc values, allowing the calculation of the total protein concentrations by refractive index measurement, were obtained for the Cohn Fractionation intermediates investigated. Thus, analytical digital refractometry, a simple, fast, and robust technique, was shown to be fit for this purpose. [ABSTRACT FROM AUTHOR]

Player movement is a fundamental component of evaluating performance in most team sports. Movement can be evaluated across multiple scales, referring to the function of anatomical structures through various planes of motion or an individual regulating their field position based on the movement of opposition players. Developments in commercially available tracking systems have afforded end users the ability to investigate the spatiotemporal features of movement in fine detail. These advancements, in conjunction with overlaid contextual information, have provided insights into the strategies adopted by players in relation to their movement. Understanding movement beyond its semantic value allows practitioners to make informed decisions surrounding performance evaluation and training design. This investigation proposes a framework to guide the analysis of player movement within team sports environments. The framework describes how operational standards for assessing movement can be designed in reference to theory and a set training philosophy. Such practice allows for the spatial and temporal complexities within team sports to be described and could potentially lead to betterapplied outcomes through greater interdisciplinary collaboration and an improved holistic understanding of movement. To inform its development, this study evaluates the current research and identifies several open questions to guide future investigations. [ABSTRACT FROM AUTHOR]

This paper explores how the combination of artificial intelligence, simulation, and e-collaborative (AISEC) tools can support accessibility in analytics courses within higher education. In the era of online and blended learning, addressing the diverse needs of students with varying linguistic backgrounds and analytical proficiencies poses a significant challenge. This paper discusses how the combination of AISEC tools can contribute to mitigating barriers to accessibility for students undertaking analytics courses. Through a comprehensive review of existing literature and empirical insights from practical implementations, this paper shows the synergistic benefits of using AISEC tools for facilitating interactive engagement in analytics courses. Furthermore, the manuscript outlines practical strategies and best practices derived from real-world experiences carried out in different universities in Spain, Ireland, and Portugal. [ABSTRACT FROM AUTHOR]

Analyzing performance indicators in rugby is essential for improving training programs and team dynamics. A key direction is to compare these indicators between winning and losing teams in prestigious rugby tournaments to identify patterns and strategic insights that can enhance team performance. However, current research frequently examines isolated competitions and lacks thorough comparative analysis across different tournaments. This study aims to analyze and identify the differences between winning and losing men's rugby union teams across various competitions in 2023. This study analysed performance data from men's rugby competitions in 2023, encompassing matches from The Six Nations Championship, The Rugby Championship, and the Rugby World Cup. A total of 68 matches were examined, categorizing 47 matches from the Rugby World Cup and 21 from international competitions for comparative analysis. Data collection included 32 performance indicators for both winning and losing teams, analysed using statistical methods to identify significant differences. Comparative analysis of team performance indicators between the Rugby World Cup and international competitions, including The Six Nations Championship and The Rugby Championship, reveals significant differences. Winning teams in the Rugby World Cup scored significantly higher points on average (41.5 ± 13.2) compared to international competitions (32.8 ± 7.7), reflecting greater variability in performance at the World Cup. Conversely, losing teams in the Rugby World Cup averaged fewer points (13.2 ± 8.0) compared to international competitions (16.3 ± 6.9). These findings underscore distinct competitive levels across different rugby tournaments. The analysis revealed that in the World Cup, 18 indicators (56.3%) exhibit statistically significant differences between losing and winning teams, whereas the analysis of performances in international competitions identified only 4 (12.5%) statistically significant differences between losing and winning teams. [ABSTRACT FROM AUTHOR]

Accurate and timely reporting of adverse events (AEs) in clinical trials is crucial to ensuring data integrity and patient safety. However, AE under-reporting remains a challenge, often highlighted in Good Clinical Practice (GCP) audits and inspections. Traditional detection methods, such as on-site investigator audits via manual source data verification (SDV), have limitations. Addressing this, the open-source R package {simaerep} was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting at each clinical trial site. This package leverages patient-level AE and visit data for its analyses. To validate its efficacy, three member companies from the Inter coMPany quALity Analytics (IMPALA) consortium independently assessed the package. Results showed that {simaerep} consistently and effectively identified AE under-reporting across all three companies, particularly when there were significant differences in AE rates between compliant and non-compliant sites. Furthermore, {simaerep}'s detection rates surpassed heuristic methods, and it identified 50% of all detectable sites as early as 25% into the designated study duration. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Ablai Khan KazUIRandWL: Series 'Pedagogical Sciences' is the property of Kazakh Ablai Khan University of International Relations & World Languages and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The focus of this study was to assess the skill requirements for data analytics positions and to understand data analysis employment expectations for new graduates. Furthermore, this work seeks to highlight issues relevant to curriculum management in university degree programs. 786 job postings were analyzed for domain-related, soft skills, as well as degree requirements. Soft skills, often referred to as people skills, comprised the largest part of the results (11 of the top 21 skills). Results revealed the most frequent soft skills were related to communication and teams or teamwork. The most frequent domain skills were related to visualization, data cleaning, data extraction and programming. Implications for curriculum based on results are discussed, and suggestions for future research are provided. [ABSTRACT FROM AUTHOR]

The Titanic disaster is a topic that continues to fascinate. As the importance of analytics continues to grow in industry, data literacy skills have become increasingly important in business education. This project allows students to use the passenger data from the Titanic to build their data literacy skills using an engaging, experiential topic. The project requires students to extract, transform, describe, analyze, and draw conclusions regarding the factors that impacted survival on the Titanic. The project can be deployed using various application software and tools. We describe how the assignment can be completed using Excel, Tableau, and Python using the Pandas library. [ABSTRACT FROM AUTHOR]

In the digital era, multimodal behavioral research has emerged as a pivotal discipline, integrating diverse data sources to comprehensively understand human behavior. This paper defines and distinguishes data governance from mere data management within this context, highlighting its centrality in assuring data quality, ethical handling, and participant protection. Through a meticulous review of the literature and empirical experience, we identify key implementation strategies and elucidate the benefits and risks of data governance frameworks in multimodal research. A demonstrative case study illustrates the practical applications and challenges, revealing enhanced data reliability and research integrity as tangible outcomes. Our findings underscore the critical need for robust data governance, pointing to future advancements in the field, including the development of adaptive governance frameworks, innovative big data analytics solutions, and user-friendly tools. These enhancements are poised to amplify the utility of multimodal data, propelling behavioral science forward. [ABSTRACT FROM AUTHOR]

Aquaculture is a critical source of seafood production, addressing the global demand for fish products. Suggesting a Deep learning-based classification technique for fishes specifically Indian Major Carp (IMC) as Mrigala, Catla and Rohu is the major objective of this paper along with detecting the disease among them. This world inside hydrosphere has their own discrete living manner. Yet they are not untouched by diseases; fishes mostly affected when young carry pathogens which cause various infections naturally or due to environmental pollutants including chemical and hazardous waste. This paper proposed the classification and prediction of diseases of fishes in aquaculture using Deep Learning based customized Convolutional Neural Network with ResNet-50 model. The proposed model performance metric compared with recent state-of-art techniques. ResNet-50 classifies accurately the IMC and type of disease the fishes are suffering from. [ABSTRACT FROM AUTHOR]

Recent advancements in Enterprise Information Systems (EISs) have transitioned from primarily supporting operational and tactical processes to enabling strategic decision-making through integrated analytics. This systematic review critically examines global literature from 2010 to 2023, focusing on the factors influencing the adoption of analytical components in EISs and assessing their impact on strategic decision-making in organizations. Following the PRISMA 2020 guidelines, we meticulously selected and reviewed articles from the Scopus database, employing a robust taxonomy based on the technology–organization–environment (TOE) framework to categorize findings. Our methodology involved a thorough screening of 234 studies, leading to a final analysis of 45 peer-reviewed articles that met our stringent criteria. These studies collectively underscore a significant gap in organizational capabilities, notably in the business ecosystems surrounding EISs, which hampers the effective adoption and utilization of advanced analytics. The results highlight a distinct need for improved understanding and implementation strategies for integrated analytics within EISs to enhance strategic decision-making processes. This review identifies critical factors for integrating analytics into Enterprise Information Systems (EISs), emphasizing technological, organizational, and environmental dimensions. It highlights a significant gap in models guiding ERP systems with Business Intelligence (BI) capabilities and underscores the need for robust research to enhance strategic decision-making through analytics. [ABSTRACT FROM AUTHOR]

With the passage of IRDA Act 1999, Insurance Industry was opened to private companies and as of now we have 70+ Insurance Companies in India including Life, General and Reinsurance Branches. In order to encourage technological innovation IRDAI has constituted Sandbox committee which allows insuretech companies to innovate products and adopt Use and Tile instead of File and Use. Artificial Intelligence and Data Analytics started impacting businesses across spectrum positively. Insurance Industry is no exception. Role of Artificial Intelligence has only quadrupled in recent years. What started as a Sandbox moment approved by regulator has taken a giant leap with the advent of AI, Block chain and Analytics? This paper is to highlight key developments in Insurance Industry adopting Artificial Intelligence, Block chain and Analytics. [ABSTRACT FROM AUTHOR]

The formulation of analytical queries over Knowledge Graphs in RDF is a challenging task that presupposes familiarity with the syntax of the corresponding query languages and the contents of the graph. To alleviate this problem, we introduce a model for aiding users in formulating analytic queries over complex, i.e., not necessarily star schema-based, RDF Knowledge Graphs. To come up with an intuitive interface, we leverage the familiarity of users with Faceted Search systems. In particular, we start from a general model for Faceted Search over RDF data, and we extend it with actions that enable users to formulate analytic queries, too. Thus, the proposed model can be used not only for formulating analytic queries but also for exploratory purposes, i.e., for locating the desired resources in a Faceted Search manner. We describe the model from various perspectives, i.e., (1) we propose a generic user interface for intuitively analyzing RDF Knowledge Graphs, (2) we define formally the state space of the interaction model and the required algorithms for producing the user interface actions, (3) we present an implementation of the model that showcases its feasibility, and (4) we discuss the results of an evaluation with users that provides evidence for the acceptance of the method by users. Apart from being intuitive for end users, another distinctive characteristic of the proposed model is that it allows the gradual formulation of complex analytic queries (including nested ones). [ABSTRACT FROM AUTHOR]

Traffic flow on highways is a dynamic process in which characteristics of road segments vary in time and space. Traffic congestion is the adverse effect caused dby an increase in travel time which is unprecedented by time and space. Temporal and spatial information of traffic flow is an integral component in the assessment of highway traffic flow. The spatial–temporal traffic flow dependency on highways can be well assessed when temporal traffic information in preceding time instances is sequenced. Thus, the SCAE-LSTM network is proposed considering time and space. This study investigates the estimation of traffic flow on highways based on spatial and temporal traffic sequences. Sequencing highway traffic information has motivated the authors to propose the method. The performance of the method is experimented on State Highways SH 49 and SH 49-A of Chennai Metropolitan City, Tamil Nadu, India. The computational complexity of the method is analyzed empirically. The significant outcome of the proposed method is reported in the experimental study. The traffic flow estimated using the proposed method has shown reduced complexity compared to other baseline methods. Finally, research directions to work in future are presented towards the end. [ABSTRACT FROM AUTHOR]

This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pools booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn. [ABSTRACT FROM AUTHOR]

Professional athletes often change teams throughout the course of their careers. A common belief in sport is that players desire to have exceptionally better performances against teams that they have previously played for. The purpose of the current study was to investigate if there is support for the belief that athletes compete better against teams they have previously played for. We compared 229 major league baseball pitchers' career performance statistics to performance statistics against teams they have previously played for using paired sample t-tests. Our analysis of major league baseball pitchers indicated that they played significantly better against former teams as indicated by nine common pitcher performance metrics: Earned run average, opponents' batting average, walks plus hits per inning, opponents' on-base percentage, opponents' slugging average, opponents' on-base percentage plus slugging, strike-outs per nine innings pitched, walks per nine innings pitched, and hits per nine innings pitched. Evidence suggests that baseball pitchers perform better against teams they have previously played for compared against teams with no prior affiliation. Advanced sports analytics could help demonstrate psychological effects in sport. [ABSTRACT FROM AUTHOR]

This paper presents a review of the field of systems thinking and strategic management The evolution of the areas of interest between systems thinking and strategic management follows similar patterns with more prescriptive developments occurring within the 1960s until 1980s; then, an increasing focus on emergence and transformation emerged in later years. From the review, there seems to exist synergies between multiple strands in Systems Thinking and Strategic Management. Suggestions to facilitate the interaction between both fields are proposed, considering the type of processes to generate strategies and the perspectives employed to visualize the organizations as systems. Hopefully, the paper can contribute to embedding systemic approaches to the strategic management of organizations and society. [ABSTRACT FROM AUTHOR]

Using a labour process lens, this research focuses on the structured antagonism that characterises the employment relationship. This article seeks to further our understanding of how news organisations employ control strategies to extract the labour power of journalists and achieve organisational objectives, and we pay particular attention to the role of editors in this regard. We also explore the responses of journalists as workers to managerial control which can include accommodation, resistance, compliance, or consent. The findings are based on an empirical case study of a local newspaper incorporating interviews with editors and journalists. The case study reveals how journalists' work intensified with the turn to digital content, and because of reduced staffing since COVID-19, but editors ensured high levels of productivity through distribution of digital analytics and constant monitoring. [ABSTRACT FROM AUTHOR]

The exponential growth of digital data has created new challenges and opportunities to process, store, and analyze large datasets. Cloud computing has transformed the way businesses manage their IT infrastructure by providing a scalable and cost-effective platform for data storage and processing. Because of this, businesses now have access to new opportunities to exploit big data and thus acquire insights into the behavior of their customers, increase the efficiency of their operations, and drive innovation. Furthermore, big data analysis enables the exploration of links between a set of independent data, revealing many aspects that enable the prediction of correct decisions that can aid the achievement of desired goals. With this in mind, the purpose of this article is to investigate the ways in which big data analysis can be put to use in fields such as healthcare, presenting specific instances of effective use along the way. The purpose of this study is to provide a comprehensive overview of the opportunities and application areas offered by big data and cloud computing, as well as to highlight the need for businesses and organizations to adopt these technologies to maintain their competitive edge in the digital age. Furthermore, this paper emphasizes the importance of big data analysis in facilitating decision-making and goal achievement, and it encourages businesses and organizations to adopt these technologies to stay competitive in an increasingly data-driven world. It also discusses the ethical, legal, and security issues that arise when dealing with large amounts of data, as well as ways to address these challenges. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required — from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

In this paper, we propose and assess the accuracy of new fuel consumption estimation models for vehicle routing. Based on real-world data consisting of instantaneous fuel consumption, time-varying speeds observations, and high-frequency traffic, we propose effective methods to estimate fuel consumption. By carrying out nonlinear regression analysis using supervised learning methods, namely Neural Networks, Support Vector Machines, Conditional Inference Trees, and Gradient Boosting Machines, we develop new models that provide better prediction accuracy than classical models. We correctly estimate consumption for time-dependent point-to-point routing under realistic conditions. Our methods provide a more precise alternative to classical regression methods used in the literature, as they are developed for a specific situation. Extensive computational experiments under realistic conditions show the effectiveness of the proposed machine learning consumption models, clearly outperforming macroscopic and microscopic consumption models such as the Comprehensive Modal Emissions Model (CMEM) and the Methodology for Estimating air pollutant Emissions from Transport (MEET). Based on sensitivity analyses we show that MEET underestimates real-world consumption by 24.94% and CMEM leads to an overestimation of consumption by 7.57% with optimised parameters. Our best machine learning model (Gradient Boosting Machines) exhibited superior estimation accuracy with a gap of only 1.70%. [ABSTRACT FROM AUTHOR]