Innocent Preferences in Hume\u27s Morality

Hume believes it is common and natural for people to have preferences for character traits similar to their own, but he remains silent on how to separate the innocent preferences from the blameworthy ones. This paper looks to Hume\u27s morality to answer this question, ultimately arguing for two jointly sufficient criteria: 1) a preference is innocent so long as it doesn’t prevent one from adopting the general point of view and 2) a preference is innocent so long as it is not met with disapproval from a spectator viewing it from the general point of view. I argue that these criteria leave most preferences unscathed, and this result highlights a distinctive pluralism in Hume. I consider the ramifications for this pluralism and argue that it gives Hume’s morality an appeal over more rigid moral theories. I conclude by considering the challenge of factionalism that arises from my interpretation

A GIS inventory of trails in John Forrest National Park Western Australia

Protected areas are facing serious threat due to direct and indirect impacts of human activities. Official and vistor created trails monitored and investigated for management and protection purpose. Geospatial tools are highly implacable in impact assessment and inventory research. GIS inventory was conducted to understand the spatial distribution of formal and informal trails in the study area. We estimated that the linear distribution of trails is 151km of which 52% are informal trails. We have explored that the minimum width along user created trails is 0.6 meters, and the maximum width is 3 meters with a mean value of 2.3m. The inventory estimated the trail footprint covers an area of 69ha in the national park (out of 2676). User activity is an important aspect of trail inventory by analysing trails attributes (width, slope, TTF) off track activities have been identified as well. Flatter and wider trails are an indictaion of ORV and management vehicles driving on trails. Hotspots such as vandalism, TTFs, and informal boundary intersections were mapped as well, clearing vegetation on an area of 138ha in the study area. We observed that approximately 197ha of vegetation been lost due to planned, unplanned trails and hotspots. Trail slope is significant for impact assessment can be used to estimate erosion potential and hazard assessment. Slope distribution models (with 0 to 25 interval) were developed for formal, informal trails in the national park. Steeper slope, lower width is an indication of bike riding and walking on user created trails. We verified trail parameters and user related issues (Trail technical features, width, length, activity, vandalism, informal boundary intersects, ORV, bike riding on both planned and unplanned tracks) while ground-truthing survey (appendix figure 2). Comparing our inventory results with investigations conducted around the world accuracy were found around 75% to 78% (varying with a spatial and spectral resolution of available data). We concluded that GIS and remote sensing is capable of conducting inventory, efficiency can be improved by using higher spatial resolution data and then integrating inventory with field survey. We recommend that park management should involve users (bike riders) in data collection process as to educate them and understand their behaviour

Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images

Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer. We proposed novel features that are suitable for depth data. These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements. Lastly, advanced hidden Markov model is used to recognize different activities. In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition. Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate. Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs) to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition

A Study of the Detection of Defects in Ceramic Insulators Based on Radio Frequency Signatures.

The presence of defects in outdoor insulators ultimately results in the initiation of partial discharge (PD) activity. Because insulation failure and the consequent breakdown of power equipment can occur due to the cumulative adverse effects of partial discharges, it is important to detect PD activity in its early stages. Current techniques used in PD off-line analyses are not suitable for detecting defective insulators in the field. The work presented in this thesis involved the investigation of a number of cases of insulator defects, with the goal of developing an online RF-based PD technique for monitoring ceramic disc insulators that exhibit a variety of defects. The first three classes examined were an intentionally cracked ceramic insulator disc; a disc with a hole through the cap, which creates internal discharges; and a completely broken insulator disc. The fourth class involved an external corona noise using a point-to-plane setup. The defective discs were considered individually and were also incorporated into strings of 2, 3, and 4 insulators as a means of capturing the radiated RF signatures under external high voltage AC power. The captured RF pulses were processed in order to extract statistical, spectral, and wavelet packet based features. Feature reduction and selection is carried out and classification results pertaining to each feature-set type were obtained. To classify the discharges arising from different types of defects, an artificial neural network (ANN) algorithm was applied to the extracted features, and recognition rates of more than 90% were reported for each class. In addition, the position of the defective insulator within the string was varied and high defect classification results exceeding 90% were reported regardless of the position

Antitumor, Analgesic, and Anti-inflammatory Activities of Synthesized Pyrazolines

Nitrogen heterocyclic compounds such as pyrazolines have been found to possess a broad spectrum of biological activities such as anticancer, antitubercular, anti-inflammatory, analgesic, and antidepressant activities. Pyrazoline derivatives IV, V (a–e) have been synthesized from the intermediate chalcones III (a–h) by cyclizing with phenyl hydrazine and hydrazine hydrate. The structures of these compounds were confirmed by IR, NMR, and mass spectroscopy. Biological studies of the synthesized compounds showed promising antitumor, analgesic, and anti-inflammatory activities. The compounds were tested for their in vitro antitumor activity against EAC tumor cell lines. Compounds IVa and IVb showed the highest cytotoxicity of 80% at a 200 μg mL concentration. Among the tested compounds, IVa and Vd seem to be more effective analgesic agents. Compounds IVc, IVd, and Ve are found to be the most effective anti-inflammatory agents. Thus the results show that synthesized compounds possess antitumor, analgesic, and anti-inflammatory activity. It was observed that the test compounds with electron withdrawing groups (halogens) on the aromatic ring favors antitumor, analgesic, and anti-inflammatory activity

IMPACT OF SCHOOL CLOSURE DUE TO COVID-19 ON DISABLED STUDENT'S WELLBEING IN LAHORE

Abstract COVID-19 can have a significant impact on pupils in general. Fiscal and physical health concerns, fast change, and increasing isolation all affect students and everyone. Students with health/disabilities face more risks in a variety of respects than non-disabled students. The present research was conducted to find out Impact of School Closure Due to COVID-19 on Disabled Students’ Psychological Wellbeing in Lahore. Quantitative research method was used as a method of inquiry. The target population was the disabled students enrolled in high level special education schools of Lahore. Participants was selected through the simple random sampling method. The available population size for this study is 873, comprising both male and female students. Out of this, 267 students were targeted sample. Data was collected through the adopted questionnaires. Already validated questionnaire of “WELL-BEING QUESTIONNAIRE FOR PISA” was used for the survey. The social survey through the questionnaire was used in the examination of the existing study. Quantitative data analysis was done through the SPSS. Linear regression analysis used to test the hypothesis. Questionnaire’s reliability was tested by the Cronbach’s alpha test that proved data excellence. There was significant and positive relationship between our independent variable “school closure” and dependent variable “well-being

Influence of body mass index and polycystic ovarian syndrome on ICSI/IVF treatment outcomes: A study conducted in Pakistani women

Background: Obesity may establish a crucial barrier for effective fertility treatment in polycystic ovary syndrome (PCOS) females.Objective: To compare results of intra-cytoplasmic sperm injection (ICSI) in females with and without polycystic ovarian syndrome and further appraise the effect of obesity in PCOS females.Materials and methods: A cross-sectional study from June 2015 to July 2016 included non-PCOS and PCOS (recognized by Rotterdam criteria) females who underwent ICSI. The PCOS were further stratified into non-obese and Obese according to the South Asian criteria for body mass index. Results were categorized on the basis of beta-human chorionic gonadotropin (β-hCG) and transvaginal scan into non-pregnant (β-hCG /ml), preclinical abortion (β-hCG \u3e25 mIU/ml with no fetal cardiac activity) and clinical pregnancy (β-hCG \u3e25 mIU/ml with fetal cardiac activity on transvaginal scan). In addition, reproductive outcomes; implantation rate, clinical pregnancy rate and miscarriage rate among obese and non-obese PCOS and non-PCOS patients were compared.Results: Our results revealed 38.5% clinical pregnancy rate in non-PCOs females, 23.8% in non-obese PCOS females whereas 26.4% in obese PCOS. Preclinical abortions were found to be highest (31.5%) in non-obese PCOS females and were the lowest (26.2%) in non-PCOS females. In non-PCOS group and non-obese PCOS females 35.4% and 44.6%, respectively, failed to become pregnant.Conclusion: The success after ICSI in terms of number of clinical pregnancies was more in non-PCOS patients as compared to PCOS. Increase in body mass index reflected a negative impact on the reproductive outcome in PCOS patients

Impact of Performance Appraisal on Employee Performance

This manuscript is an attempt of the researchers to highlight the significance of performance appraisal in the organizations and business world. Appraisal is one of the most significant and effective tool that can lead an organization to vanquish their ultimate goals by improving the efficiency and effectiveness of the employees. Performance appraisal helps managers to identify the gap between desired and actual performance and in case of deficiency, it can be removed by imparting required training. Fair performance evaluation and proper training motivates employees that results in improved performance and achieve organizational competitiveness. Keywords: Performance Appraisal, Employee Performanc