Utilization of Family Labor in Broiler Production and Marketing in Ashley County Arkansas

This study is being conducted and analyzed to determine the extent that family labor may be utilized to supplement family income. The study endeavors to give a step by step process that may be followed in investigating the possibility and feasibility of such projects being used to supplement income by the utilization of family labor. To point out major problem areas in the production, management and marketing of broilers. To refer to sources of information and assistance available in the area of production, management and marketing. Finally to show where low income families have improved their standards of living by utilizing family labor effectively. This study is limited to low income families in Ashley County Arkansas, who are engaged in broiler production for the purpose of supplementing their income by utilizing family labor

Orange Alba: The Civil Religion of Loyalism in the Southwestern Lowlands of Scotland since 1798

This study introduces the idea that, taken together, the major institutional frameworks of the ultra-Protestant culture of loyalism in the southwestern lowlands of Scotland can be conceived as a civil religion. I argue that loyalist civil religion in lowland Scotland was comprised of a distinct set of institutions including the Orange Order, Glasgow Rangers Football Club, loyalist street gangs and paramilitaries and loyalist flute bands. The elements that informed each of these loyalist groups were not unrelated, but part of a multidimensional and interactive civil religious movement. Each institution appealed to a wide range of viewpoints within the loyalist community but they all rallied around the same general “cause” and participated in the same ritual gatherings. Loyalist civil religion in the urban lowlands was articulated through an understood system of rituals, folklore, symbols and moral values related to the Protestant Irish’s shared experience of historical conflict and victimization at the hands of Roman Catholics. Regular ritual commemorations of past events guided contemporary loyalist agendas and actions. Through the folk collage of symbols, songs and other folk displays at loyalist ritual events, the history and contemporary goals of loyalism were relayed to future generations of potential loyalists. The recurrent celebration of past military heroes, battles and blood sacrifices in the name of the loyalist cause helped to legitimize and sustain loyalist culture in Scotland, even after the civil religion of loyalism developed into a civil religion of a “Lost Cause.” This work argues that loyalist civil religion was not just a formation of an agreed-upon national creed, but functioned to unify a subgroup within a nation driven to articulate its identity in a way contrary to the national status quo. Loyalist civil religion forged not only a banner of collective allegiance, but also a charter for action. Loyalists not only believed they had the right to pursue their “way of life,” but they were united by the belief that they were engaged in a constant battle with the “shadowy” forces of Roman Catholicism whose collective was supposedly engaged in an ongoing quest to undermine the cherished British “civil and religious liberties” secured by William III in 1690 at the Battle of the Boyne

Performance of straightbred and crossbred swine

The objectives of this study were (1) to compare certain performance traits of hogs produced by two breeding systems, straightbreeding and crossbreeding, and (2) to compare the relative performance of straightbreds and of various crosses

Temporal and Intensity Relationships between Physical Activity and Drunkorexia Behaviors among First-Year College Students

The purpose of this research was to examine the temporal element of physical activity (PA) in relation to drunkorexia occurrences and how PA intensities predict severity of drunkorexia behaviors. No research, to our knowledge, has explored if students are proactively or reactively engaging in PA to reduce the effects of calories consumed from alcohol nor if there exists a dose relationship among PA intensities (sedentary, moderate, vigorous) and severity of drunkorexia behaviors. A convenience sample of first-year college students living in residence halls was selected to participate in the current study. Participants completed an online survey including The Drunkorexia Motives and Behaviors scales, The International Physical Activity Questionnaire – Short Form, and typical daily PA and alcohol participation. Inclusion criteria for statistical analysis included participants who had engaged in drunkorexia behaviors within the past four weeks prior to the commencement of the fall semester. Results indicate first-year college students who are pre-drinking exercisers to consume higher quantities of alcohol and engage in higher levels of moderate and vigorous PA. Additionally, vigorous PA significantly predicts severity of drunkorexia behaviors. This research helps to discern PA’s role within first-year college students who engage in drunkorexia behaviors

Factors Influencing the Development of a Successful Cybersecurity Culture

All organizations are susceptible to the risks of cyberattacks. Protecting an organization\u27s data and technology is crucial as such systems are frequent targets of attacks from those seeking to gain personal benefit or destroy the organization. Specifically, these external actors either hold an organization’s data for ransom or they disrupt the organization’s system by either shutting it down or changing the way the system runs. Literature has indicated that organizations can address and minimize threats and attacks is to develop a cybersecurity culture through implementation of intentional factors designed specifically to build such a culture. This paper examines the results of 276 cybersecurity leaders who participated in a survey to determine the number of cultural intentional factors implemented by the organization and number of cyberattacks experience during 2022. The results suggest that organizations can minimize cyberattacks by employing specific factors such as top management engagement, champions, communication, knowledge, security awareness, security training, motivation, and trust

Aseptic meningitis in a patient taking etanercept for rheumatoid arthritis: a case report

Background \ud We report a case of a 53 year old lady recently commenced on etanercept, an anti-TNF (tumour necrosis factor) therapy for rheumatoid arthritis presenting with \ud confusion, pyrexia and an erythematous rash. \ud \ud Case presentation \ud A lumbar puncture was highly suggestive of bacterial meningitis, but CSF cultures produced no growth, and polymerase chain reactions (PCR) for all previously reported bacterial, fungal and viral causes of meningitis were negative. \ud \ud Conclusions \ud This case report describes aseptic meningitis as a previously unreported complication of etanercept therapy, and serves as a reminder of the rare but potentially lifethreatening risk of serious infections in patients taking anti-TNF therapy for a variety of conditions

Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews

Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target this absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones, to be generated. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.Comment: Accepted for publication in the 25th International Conference on Natural Language & Information Systems (NLDB 2020), DFKI Saarbr\"ucken Germany, June 24-26 202

Data-gathering method for use in modeling energy research, development, and demonstration programs

The development and testing of a data-gathering method for use in a computer program designed to model energy research, development, and demonstration programs for decisionmakers are described. The data-gathering method consists of face-to-face interviews with the scientists working on the projects that will be modeled by the computer program. The basic information gained from an interview includes time estimates for reaching certain project goals and the probability of achieving those goals within the times estimated. The interview method is based on decision analysis techniques. The Magnetic Fusion Energy program of the US Department of Energy was selected as the test case for this project. The data gathering method was used at five fusion projects to determine whether it could meet its design criteria. Extensive statistical analysis was performed to learn how much the expert's answers agreed, what factors were likely to enter into their estimates, and how their estimates corresponded to the officially scheduled dates and to the dates that the project goals were actually achieved. The interview method was considered to have met its design criteria and to be a valid tool for planning

Automatic extraction of candidate nomenclature terms using the doublet method

BACKGROUND: New terminology continuously enters the biomedical literature. How can curators identify new terms that can be added to existing nomenclatures? The most direct method, and one that has served well, involves reading the current literature. The scholarly curator adds new terms as they are encountered. Present-day scholars are severely challenged by the enormous volume of biomedical literature. Curators of medical nomenclatures need computational assistance if they hope to keep their terminologies current. The purpose of this paper is to describe a method of rapidly extracting new, candidate terms from huge volumes of biomedical text. The resulting lists of terms can be quickly reviewed by curators and added to nomenclatures, if appropriate. The candidate term extractor uses a variation of the previously described doublet coding method. The algorithm, which operates on virtually any nomenclature, derives from the observation that most terms within a knowledge domain are composed entirely of word combinations found in other terms from the same knowledge domain. Terms can be expressed as sequences of overlapping word doublets that have more specific meaning than the individual words that compose the term. The algorithm parses through text, finding contiguous sequences of word doublets that are known to occur somewhere in the reference nomenclature. When a sequence of matching word doublets is encountered, it is compared with whole terms already included in the nomenclature. If the doublet sequence is not already in the nomenclature, it is extracted as a candidate new term. Candidate new terms can be reviewed by a curator to determine if they should be added to the nomenclature. An implementation of the algorithm is demonstrated, using a corpus of published abstracts obtained through the National Library of Medicine's PubMed query service and using "The developmental lineage classification and taxonomy of neoplasms" as a reference nomenclature. RESULTS: A 31+ Megabyte corpus of pathology journal abstracts was parsed using the doublet extraction method. This corpus consisted of 4,289 records, each containing an abstract title. The total number of words included in the abstract titles was 50,547. New candidate terms for the nomenclature were automatically extracted from the titles of abstracts in the corpus. Total execution time on a desktop computer with CPU speed of 2.79 GHz was 2 seconds. The resulting output consisted of 313 new candidate terms, each consisting of concatenated doublets found in the reference nomenclature. Human review of the 313 candidate terms yielded a list of 285 terms approved by a curator. A final automatic extraction of duplicate terms yielded a final list of 222 new terms (71% of the original 313 extracted candidate terms) that could be added to the reference nomenclature. CONCLUSION: The doublet method for automatically extracting candidate nomenclature terms can be used to quickly find new terms from vast amounts of text. The method can be immediately adapted for virtually any text and any nomenclature. An implementation of the algorithm, in the Perl programming language, is provided with this article

Aseptic meningitis in a patient taking etanercept for rheumatoid arthritis: a case report

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