Measuring attitudes toward work of males and females from a student population and from a bank population

An experiment was designed in order to measure the attitude toward work of 50 males and 50 females in a student sample and of 39 male managers and 39 female managers in a bank sample. The attitude scale, presented to the Ss, was designed by the author and contained 50 statements. Each of 44 statements pertained either to a Motivation factor, e.g. achievement, responsibility, etc. or to a Hygiene factor, e.g. salary, status, etc. with the remaining six used as Fillers. All statistical tests were performed at the .01 level of significance. An analysis of variance revealed a significant three factor interaction . Analysis of simple effects revealed: (1) Motivator scores were significantly higher than Hygiene scores for males in each sample; (2) Motivator scores were significantly higher than Hygiene scores for females in each sample; and (3) Motivator scores for female managers were significantly higher than Motivator scores for female students. There was no significant difference in Motivator scores or in Hygiene scores between sexes

Alien Registration- Foley, Henry J. (Lewiston, Androscoggin County)

https://digitalmaine.com/alien_docs/29949/thumbnail.jp

Gastrointestinal side effects and adequacy of enteral intake in hematopoietic stem cell transplant patients

BACKGROUND: Patients undergoing hematopoietic stem cell transplant (HSCT) can experience gastrointestinal (GI) side effects as a complication of the treatment. Limited research exists describing how the duration and severity of GI side effects influence the consumption of adequate calorie intake in this population. The purpose of this study was to assess differences in GI side effects between patients who consumed adequate calories compared with those who did not. METHODS: The MD Anderson Symptom Inventory-Gastrointestinal (MDASI-GI) tool was used to record daily GI side effects of 72 HSCT patients. Daily calorie intake was determined via calorie counts. Data were collected from day of transplant until engraftment. RESULTS: Median percentage of caloric needs consumed for all patients was 49.2% (interquartile range, 35.1-66.6). Calorie intake decreased from baseline to transplant day 8 as severity of GI symptoms increased. An inverse relationship between percentage of caloric needs met and MDASI-GI component score, MDASI-GI symptom score, and lack of appetite score was observed. The only significant difference in MDASI-GI symptom scores between those who consumed adequate calories and those who consumed inadequate calories was for diarrhea; subjects who consumed >60% of caloric needs had significantly lower median diarrhea scores. CONCLUSION: Most patients consumed <60% of their caloric needs from time of transplant to time of engraftment. More research is needed to provide insight into strategies to increase intake and to describe the implications of prolonged inadequate intake in HSCT patients

Developing and piloting a resource for training assessors in use of the Mini-CEX (mini clinical evaluation exercise).

The assessment of undergraduate medical students in the clinical setting has become a key priority for medical educators. Facilitating the successful translation of undergraduate theoretical knowledge into safe and appropriate postgraduate clinical practice represents a challenge in medical education [1]. Poor clinical performance of newly qualified doctors has been highlighted as a major issue relating to patient safety [2]. Performance based assessment in the undergraduate setting may assist in addressing this issue by assessing ‘doing’ rather than ‘knowing’. The mini clinical evaluation exercise (Mini-CEX) is a formative assessment used to assess the performance of medical students in a clinical context. It incorporates assessment by, and feedback from, an assessor, based on the direct observation of a student–patient consultation [3]. Conducted in a series of stages, the Mini-CEX allows focused assessment of key competencies (see Box 1) [3]

Clinicopathologic Features and Magnetic Resonance Imaging Findings in 24 Cats With Histopathologically Confirmed Neurologic Feline Infectious Peritonitis

Background: Feline infectious peritonitis (FIP) is the most common infectious central nervous system (CNS) disease in the cat and is invariably fatal. Improved means of antemortem diagnosis is required to facilitate clinical decision making. Information regarding the magnetic resonance imaging (MRI) findings of neurologic FIP currently is limited, resulting in the need for better descriptions to optimize its use as a diagnostic tool. Objective: To describe the clinicopathologic features and MRI findings in cases of confirmed neurologic FIP. Animals: Twenty-four client-owned cats with histopathologic confirmation of neurologic FIP. Methods: Archived records from 5 institutions were retrospectively reviewed to identify cases with confirmed neurologic FIP that had undergone antemortem MRI of the CNS. Signalment, clinicopathologic, MRI, and histopathologic findings were evaluated. Results: Three distinct clinical syndromes were identified: T3-L3 myelopathy (3), central vestibular syndrome (7), and multifocal CNS disease (14). Magnetic resonance imaging abnormalities were detected in all cases, including meningeal contrast enhancement (22), ependymal contrast enhancement (20), ventriculomegaly (20), syringomyelia (17), and foramen magnum herniation (14). Cerebrospinal fluid was analysed in 11 cases; all demonstrated a marked increase in total protein concentration and total nucleated cell count. All 24 cats were euthanized with a median survival time of 14 days (range, 2115) from onset of clinical signs. Histopathologic analysis identified perivascular pyogranulomatous infiltrates, lymphoplasmacytic infiltrates, or both affecting the leptomeninges (16), choroid plexuses (16), and periventricular parenchyma (13). Conclusions and Clinical Importance: Magnetic resonance imaging is a sensitive means of detecting neurologic FIP, particularly in combination with a compatible signalment, clinical presentation, and CSF analysis

Design of a high activity and selectivity alcohol catalyst

As specified in our original DOE grant proposal, the objective of this research is to design a new alumina-supported bimetallic catalyst for the selective hydrogenation of carbon monoxide to produce methanol and higher alcohols. A key feature of our research program is our intention to rationally design this catalyst based upon fundamental information about the structure, composition and reactivity of preliminary catalysts synthesized throughout the course of this work. During our first year, we have put in place many of the tools needed to synthesize and characterize our catalyst samples. Experimentation has focused on both the synthesis of catalysts designed for high oxygenate activity and the suppression of the secondary dehydration of methanol to dimethyl ether on both native gamma-alumina and a bimetallic catalyst supported on gamma-alumina. 2 refs., 1 tab

Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks Against Two Objective Functions Using a Novel Dataset

One of the important features of Routing Protocol for Low-Power and Lossy Networks (RPL) is Objective Function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the other hand, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyber-attacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine-learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models