HR Process Automation: A Bibliometric Analysis

Automation is interpreted as the replacement of manual operations by electronics and computer-controlled systems. Human resource management is an indispensable part of every firm be it the space of retail, healthcare, education or any other sector. Activities such as hiring new workers, training, or making sure that local labour laws are obeyed with HR processes and are a crucial part of every organisation. HR has typically been believed of as an extremely manual department procedure. Employees are accustomed to doing this manually and getting the job done themselves. But everything around the HR processes are changing rapidly. HR Automation is a tool for increasing the efficiency of an employment organisation by freeing employees from tedious repetitive tasks and allowing them to focus on more complex assignments such as decision-making and strategy creation. Automation is interpreted as the replacement of manual operations by electronics and computer-controlled systems. By automating regular and routine HR tasks, organisations may lead to significant savings and resources they expend on manual HR processing and preparation. The HR space is being invaded by automation, and any automation that can be implemented will be implemented very quickly. This article is written with the help of Scopus, Web of Science, Google Scholar and Crossref databases. This article will be useful for upcoming researchers, students and managers in the field of HRM across the world

Predictive analytics and data management in beef cattle production medicine

Doctor of PhilosophyDepartment of Diagnostic Medicine/PathobiologyRobert L. LarsonBradley J. WhiteUtilization of data analytics allows for rapid and real-time decision making in the food animal production industry. The objective of my research was to implement and utilize different data analytic strategies in multiple sectors of the beef cattle industry in order to determine management, health, and performance strategies. A retrospective analysis using reproductive and genomic records demonstrated that a bull will sire a larger number of calves in a multiple sire-pasture compared to other bulls in the same pasture. A further study was performed to determine if behavior differences existed among bulls in a multiple-sire pasture, and the ability of accelerometers to predict breeding behaviors. Machine learning techniques used classifiers on accelerometer data to predict behavior events lying, standing, walking, and mounting. The classifiers were able to accurately predict lying and standing, but walking and mounting resulted in a lower predictable accuracy due to the extremely low prevalence of these behaviors. Finally, a new form of meta-analysis to the veterinary literature, a mixed treatment comparison, was able to accurately identify differences in metaphylactic antimicrobials on outcomes of bovine respiratory disease morbidity, mortality, and retreatment morbidity. The meta-analysis was not successful in determining the effects of metaphylactic antimicrobials on performance outcomes

A livestock information system roadmap for Ethiopia

Agriculture is one of the pillars of the Ethiopian economy and the overall economic growth of the country is highly dependent on the success of the agricultural sector. Livestock is an integral part of the agricultural sector and the contribution of live animals and their products to the agricultural economy is immense. Livestock production plays a substantial role in Ethiopia through the provision of food, income, employment and many other contributions. “A Livestock Information System Roadmap for Ethiopia” is a guidance document that sets out a path for the development of a livestock information system for Ethiopia. It encompasses discrete steps covering system development, resource and capability requirements, and governance implementation that can be followed to produce a bespoke information system. This system is a key component of the Ethiopian digital strategy and will accelerate progress on the delivery of Ethiopia’s strategic plan for agriculture (Ten-Year Strategic Development Plan, 2021). The Livestock Information System Roadmap is the outcome of a collaboration between Livestock Improvement Corporation (LIC)(NZ), Ministry of Agriculture (MoA), Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), and the Bill & Melinda Gates Foundation (BMGF)

ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and Challenges of Confned and Extensive Precision Livestock Production

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confned operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative fve-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This fve-step process acts as a guide to realize anticipated benefts from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confned and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confned operations will beneft from required advances in precision technology and PSMs, ultimately strengthening the benefts from precision technology to achieve short- and long-term goals

Opportunities for utilising DNA-based technologies to enhance traceability and profitability in the Australian Red Meat Industry

The potential role of genomics to further improve traceability levels in the red meat industry was investigated, as DNA-based technologies offer unambiguous identification with a range of auxiliary benefits. Modelling indicated that genotyping the entire national beef herd for traceability is unlikely to compensate for the additional costs involved. However, potential additional benefits (faster genetic gain and better management decisions) make several implementation models appear to be highly attractive. Genotyping the national sheep flock is impractical, but the cost of using DNA judiciously to augment NLIS could be quite cost effective.Perceptions, concerns and experiences around traceability, DNA technologies, costs and willingness to pay were explored through industry consultations. Application of genomic technology for traceability alone was not generally seen as attractive, but the ancillary benefits that accompany lifetime traceability would make the value proposition more attractive.There are instances of DNA already being used to augment traceability and there is a strong argument for taking a proactive approach to coordinating its wider use. The longer the delay, the greater the costs and difficulties arising from fragmentation. A program of work with a series of stop/go decision points is proposed, to be coordinated and overseen by an appropriately-qualified steering group

Assessment of Manure Treatment Technology Adoption and Feedlot Runoff Monitoring Opportunities

Livestock products contain valuable nutrients that enhance human health, and their production generates positive local and national economic impacts. The livestock sector also deals with large quantities of environmentally important nutrients in manure. The purpose of this thesis is to further the aims of researchers and educators working at the intersection of livestock production and the environment to identify and promote best practices for livestock production and manure management that are economically and environmentally sustainable. The second chapter explored options to evaluate nutrient flows in feedlot runoff at the pen scale. Pen scale monitoring is an important consideration for research into management effects on nutrient flows. Thus, two runoff monitoring systems were designed for the “700 alley” section of the feedlot at the Eastern Nebraska Research Extension and Education Center. The first design utilizes an edge-of-field runoff monitoring system that utilizes flumes, automatic sensors, and automatic samplers to monitor runoff flow and composition, with an estimated cost of $13,100 per pen. The second option involves a runoff collection concrete tank with five different tank sizes, with larger tanks being able to hold runoff from a greater share of runoff events. To automate the tank systems, an Arduino-controlled automatic valve connected to an Internet of Things platform via Wi-Fi was designed and a prototype was tested. The estimated cost of the automatic tanks systems is between$2,200 and $3,300, depending on tank size. In the third chapter, surveys and focus groups were conducted to understand manure treatment technology usage and the decision-making process behind the adoption of those technologies among swine and dairy producers in the Midwest. The surveyed dairy and swine producers indicated that they most valued technology with low management and maintenance demand, adaptability to specific farm conditions, and high performance capacity. The primary desired outcomes of treatment systems currently in use on the surveyed farms were compatibility with the preferred land application system, retention of storage or treatment capacity, and attainment of regulatory requirements. For future technology adoption, technologies that allow nutrient exports and reduce workload were favored in addition to the current operational outcomes. Initial cost, operational cost, and return on investment are the main barriers to upgrading the manure management system in swine and dairy farms; and management demand is an important barrier in swine operations. Changes in regulations and fluctuating availability of manure hauling services are the important trends affecting farm manure management. For future extension programming, university lead farmer discussion groups in manure management are recommended as producers value and trust information coming from their peers. Advisor: Amy M. Schmidt and Richard Stowel

Adsorbents for the sequestration of the Pimelea toxin, simplexin

Pimelea poisoning affects cattle grazing arid rangelands of Australia, has no known remedy and significant outbreaks can cost the industry $50 million per annum. Poisoning is attributable to consumption of native Pimelea plants containing the toxin simplexin. Charcoal, bentonite and other adsorbents are currently used by the livestock industry to mitigate the effects of mycotoxins. The efficacy of such adsorbents to mitigate Pimelea poisoning warrants investigation. Through a series of in vitro experiments, different adsorbents were evaluated for their effectiveness to bind simplexin using a simple single concentration, dispersive adsorbent rapid screening method. Initial experiments were conducted in a rumen fluid based medium, with increasing quantities of each adsorbent: sodium bentonite (Trufeed®, Sibelco Australia), biochar (Nutralick®Australia) and Elitox® (Impextraco, Belgium). Data showed the unbound concentration of simplexin decreased with increasing quantities of each adsorbent tested. Sodium bentonite performed best, removing ~95% simplexin at 12 mg/mL. A second experiment using a single amount of adsorbent included two additional adsorbents: calcium bentonite (Bentonite Resources, Australia) and a synthetic adsorbent (Waters, USA). The concentration of simplexin remaining in the solution after 1 h, the amount able to be desorbed off the adsorbent-toxin matrix with replacement fresh fluid, and the amount remaining bound to the adsorbent were measured. All samples containing an adsorbent were statistically different compared to the blank (p < 0.05), indicating some binding activity. Future work will explore the binding mechanisms and behaviour of the toxin-adsorbent complex in the lower gastrointestinal tract

Applying machine learning algorithms to medical knowledge

Dissertação de mestrado integrado em Engenharia InformáticaAchieving great and undeniable success in a great variety of industries and businesses has made the term Big Data very popular among the scientific community. Big Data (BD) refers to the ever fast-growing research area in Computer Science (CS) that comprises many work areas across the world. The healthcare sector is widely known to be highly proficient in the production of big quantities of data. It can go from health information, such as the patient’s blood pressure and cholesterol levels, to more private and sensitive data, such as the medical procedures history or the report of ongoing diseases. The application of sophisticated techniques enables a profound and rigorous analysis of data, something a human cannot do in real-time. However, a machine is capable of rapidly collect, group, storage and examine vast amounts of data and extract unknown and possi bly interesting knowledge from it. The algorithms used can discover hidden relationships between attributes that prove to be very useful for a corporation’s work. Buried structures within the produced data can also be detected by these techniques. Machine Learning (ML) methods can be adjusted and modelled to different input representations - this adaptability is one of the factors that contributes to its blooming prosperity. The main goal is to make predictions on data, by building utterly efficient models that can accurately take in the data and thus predict a certain outcome. This is especially important to the healthcare industry since it can considerably improve the lives of many patients. Everything from detecting a type of disease, predicting the chance of morbidity after a hospital stay, to aid in the decision making of treatment strategies are vital to patients as well as to clinicians. Any improvement over established methods that have been previously studied, tested and published are an asset that will improve the patient’s satisfaction about the healthcare performance in medical institutions. This can be achieved by refining those algorithms or implementing new approaches that will make better predictions on the given data. The main objective of this dissertation is to propose ML approaches having acknowledged and evaluated the existent methods used in clinical data. In order to fulfill this goal, an analysis of the state of the art of medical knowledge repositories and scientific papers published related to the selected keywords selected was performed. In this line of work, it is crucial to understand, compare and discuss the results obtained to those previously published. Thus, one of the goals is to suggest new ways of solving those problems and measuring them up against the existent ones.Obter um sucesso enorme e inegável numa grande variedade de indústrias e companhias, tomou o termo Big Data (BD) muito popular entre a comunidade científica. Big Data refere-se à área de investigação em Engenharia Informática que revela um crescimento rápido e está envolvida em várias áreas em todo o mundo. O setor da saúde é universalmente con-hecido por ser altamente frutífero na produção de grandes quantidades de dados. Podem variar desde dados de saúde, tais como, o valor da pressão sanguínea e nível de coles-terol do paciente, até dados mais confidenciais, como o histórico de cirurgias realizadas e doenças diagnosticadas. A aplicação de técnicas sofisticadas permite uma análise profunda e rigorosa dos dados -algo que um ser humano não consegue fazer em tempo real. No entanto, uma máquina não tem dificuldades em recolher, agrupar, armazenar e analisar rapidamente grandes quanti-dades de dados e extrair deles conhecimento que era desconhecido e, possivelmente, interessante. Os algoritmos usados podem ser usados para descobrir relações desconhecidas entre os vários atributos, que se podem revelar bastante úteis para o dia-a-dia de uma empresa. Estruturas e padrões escondidos nos dados podem ser também detetados através das mesmas técnicas. Os métodos de Machine Learning (ML) podem ser ajustados e modela-dos de forma a aceitar diferentes representações de dados de entrada - esta adaptabilidade é um dos fatores mais proeminentes que contribui para a sua prosperidade. O principal objetivo é fazer previsões sobre os dados, de modo a construir modelos totalmente eficientes que possam analisar os dados de forma precisa, e, assim, prever um determinado resultado. Isto é especialmente importante para o setor da saúde, uma vez que pode melhorar consideravelmente a vida de muitos pacientes. Tudo, desde a deteção de um certo tipo de doença, prever a probabilidade de morbilidade após um internamento até a auxiliar na tomada de decisão em relação a estratégias de tratamento, é vital para os pacientes, bem como para os médicos. Portanto, qualquer melhoria em relação a métodos já estabelecidos que foram previamente estudados, testados e publicados é uma mais-valia que melhorará a satisfação do paciente em relação à sua experiência com os serviços de saúde. Tal pode ser alcançado refinando esses algoritmos ou mesmo implementando novas abordagens que farão melhores previsões sobre os dados. O principal objetivo desta dissertação é propor abordagens de ML, fazendo um reconhecimento e avaliando os métodos existentes utilizados em dados médicos. Desta forma, foi posta em prática uma análise ao estado da arte de repositórios de conhecimento médico, bem como a artigos científicos relacionados com esses conjuntos de dados. Assim, é fundamental compreender, comparar e discutir os resultados obtidos com os publicados anteriormente. Portanto, um dos objetivos é sugerir novas formas de resolver os problemas, tecendo uma comparação com os existentes

Research and Creative Activity, July 1, 2020-June 30, 2021: Major Sponsored Programs and Faculty Accomplishments in Research and Creative Activity, University of Nebraska-Lincoln

Foreword by Bob Wilhelm, Vice Chancellor for Research and Economic Development, University of Nebraska-Lincoln: This booklet highlights successes in research, scholarship and creative activity by University of Nebraska–Lincoln faculty during the fiscal year running July 1, 2020, to June 30, 2021. It lists investigators, project titles and funding sources on major grants and sponsored awards received during the year; fellowships and other recognitions and honors bestowed on our faculty; books and chapters published by faculty; performances, exhibitions and other examples of creative activity; patents and licensing agreements issued; National Science Foundation I-CORPS teams; and peer-reviewed journal articles and conference presentations. In recognition of the important role faculty have in the undergraduate experience at Nebraska, this booklet notes the students and mentors participating in the Undergraduate Creative Activities and Research Experience (UCARE) and the First-Year Research Experience (FYRE) programs. While metrics cannot convey the full impact of our work, they are tangible measures of growth. A few achievements of note: • UNL achieved a record $320 million in total research expenditures in FY 2020, a 43• Our faculty earned 1,508 sponsored research awards in FY 2020. University-sponsored industry activity also spurred economic growth for Nebraska. • Nebraska Innovation Campus created 1,948 jobs statewide and had a total economic impact of$372 million. • Industry sponsorship supported $19.2 million in research expenditures. • NUtech Ventures brought in$6.48 million in licensing income. I applaud the Nebraska Research community for its determination and commitment during a challenging year. Your hard work has made it possible for our momentum to continue growing. Our university is poised for even greater success. The Grand Challenges initiative provides a framework for developing bold ideas to solve society’s greatest issues, which is how we will have the greatest impact as an institution. Please visit research.unl.edu/grandchallenges to learn more. We’re also renewing our campus commitment to a journey of anti-racism and racial equity, which is among the most important work we’ll do. I am pleased to present this record of accomplishments. Contents Awards of $5 Million or More Awards of$1 Million to $4,999,999 Awards of$250,000 to $999,999 50 Early Career Awards Arts and Humanities Awards of$250,000 or More Arts and Humanities Awards of $50,000 to$249,999 Arts and Humanities Awards of $5,000 to$49,999 Patents License Agreements National Science Foundation Innovation Corps Teams Creative Activity Books Recognitions and Honors Journal Articles 105 Conference Presentations UCARE and FYRE Projects Glossar