The Effect Of Quality Product, Brand Image, And Price Towards A Purchase Decision Of J.Co Donuts & Coffee In Tangerang

The development of the business world today is very fast, making business people have to compete with each other to attract consumers. One promising business is a business in the food sector such as donuts. Donuts are a type of food that is popular in Indonesian society. At present there are many donut outlets that stand in the center of the crowd. One of them is J.CO Donuts & Coffee in Tangerang which is the donut and coffee shop with the most consumers. With friendly prices and the brand image owned by J.CO Donuts & Coffee, consumers do not hesitate to buy their products.This study aims to determine the quality of the product, brand image and price of J.CO Donuts & Coffee purchasing decisions. The data used are primary data derived from questionnaires distributed online. Sampling using accidental sampling method with a sample size of 100 people. With the analysis technique used to analyze the influence of the variables in this study is to use the validity test, reliability test, classical assumption test, multiple regression analysis, and hypothesis testing using SPSS version 20. The result of this study is that there is a partial influence between variables of product quality, brand image and price on purchasing decisions. And the variables of product quality, brand image and price simultaneously influence purchasing decisions.

An autonomous drone for search and rescue in forests using airborne optical sectioning

Autonomous drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found, in total, 38 of 42 hidden persons. For experiments with predefined flight paths, the average precision was 86%, and we found 30 of 34 cases. For adaptive sampling experiments (where potential findings are double-checked on the basis of initial classification confidences), all eight hidden persons were found, leading to an average precision of 100%, whereas classification confidence was increased on average by 15%. Thermal image processing, classification, and dynamic flight path adaptation are computed on-board in real time and while flying. We show that deep learning–based person classification is unaffected by sparse and error-prone sampling within straight flight path segments. This finding allows search missions to be substantially shortened and reduces the image complexity to 1/10th when compared with previous approaches. The goal of our adaptive online sampling technique is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, because it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.

Online Sampling of Temporal Networks

Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real world. However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks. In this work, we propose a general framework for temporal network sampling with unbiased estimation. We develop online, single-pass sampling algorithms, and unbiased estimators for temporal network sampling. The proposed algorithms enable fast, accurate, and memory-efficient statistical estimation of temporal network patterns and properties. In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally weighted. In contrast to the prior notion of a △ t -temporal motif, the proposed formulation and algorithms for counting temporally weighted motifs are useful for forecasting tasks in networks such as predicting future links, or a future time-series variable of nodes and links. Finally, extensive experiments on a variety of temporal networks from different domains demonstrate the effectiveness of the proposed algorithms. A detailed ablation study is provided to understand the impact of the various components of the proposed framework.

Impacts of Zero Punishment on Student’s Behavior and Classroom Learning at Government Primary Schools

The aim of this study is to identify the impacts of zero punishment on student’s behaviour and classroom learning at government primary schools. This study was conducted at city Faisalabad of Pakistan. The target population was primary school teachers in the city. The sample of 106 was selected by using online sampling calculator www.surveysystem.com with confidence level 95% and interval level 8. By applying the simple random sampling technique the data were collected from the respondents through a well-developed questionnaire. The Statistical Package for Social Sciences (SPSS) was used to analyses the collected data and results were discussed for the findings. The study findings reveal that zero punishment have good positive impacts on student’s behaviour and classroom learning. The result is also observed that physical punishment has negative effects on the students like as they may stop from school or may fall in depression, fear and hatred. Zero punishment treats to encourage to learning and behave polite each other in the school. So teachers should ask pupils questions with politeness in the classroom to improve student learning and should be cooperative for student. Students should be motivated to participate in classroom activities with caringly. Teachers were expected to be lenient and not intimidate students into corporal punishment.

Mobile robotic systems in patient-facing functions: national acceptability survey, single site feasibility study and cost-effectiveness analysis

Objective: To understand the acceptability of patient-facing mobile robotic systems on a national scale, conduct a pilot feasibility study to deploy and measure satisfaction associated with clinical evaluation using a mobile telehealth robot in the emergency department (ED) and to build a decision analytic model to gauge the potential of a robotic system to prevent COVID-19 infections and conserve personal protective equipment in the ED. Design: Mixed study comprising an online sampling-based survey, single-site observational clinical trial and development of a decision analytic model. Setting: A quaternary care, urban, academic, emergency department in Boston, Massachusetts, USA. Participants: For the acceptability survey, we recruited N=1000 individuals living in the United States participating in an online sampling from the survey provider YouGov. In the ED study, we enrolled 40 individuals over 18 years old presenting to the ED for evaluation. Interventions: In the pilot ED study, consenting participants were exposed to a mobile robotic system facilitated triage interview controlled by an emergency medicine clinician. Afterwards, participants completed a survey to measure their satisfaction with the robotic system. Main outcome measures: Acceptability of mobile robot facilitated tasks in healthcare (national survey), satisfaction with interaction of a robotic system (ED study), number of potential SARS-CoV-2 infections avoided and cost savings (US dollars) per year per ED (decision analytic model). Results: In the national survey, participants rated the use of robotics for a variety of patient-facing healthcare functions useful or very useful. The perceived usefulness increased when asked to consider these functions in the context of the COVID-19 pandemic. In the ED, 40 patients completed study procedures; 92.5% (N=37) reported satisfaction with the robotic system. Most participants (82.5%, N=33) reported their experience being evaluated by a robotic system was as good as an in-person encounter. Our decision analytic model estimated that robotic evaluations could prevent 2.68 infections per ED yearly and save $1 million annually per ED by decreasing PPE and additional staffing in a triage space. Conclusions: Robotic systems were broadly acceptable across the US and their acceptance increased in the setting of COVID-19. Mobile robotic-enabled teleheath facilitated contactless evaluation of ED patients and was highly acceptable and equivalent to an in-person history. Robotic platforms may prevent healthcare-associated COVID-19 transmission to healthcare workers and have a significant cost savings if widely implemented among healthcare systems.

Sparse Tree Search Optimality Guarantees in POMDPs with Continuous Observation Spaces

Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve. Recent online sampling-based algorithms that use observation likelihood weighting have shown unprecedented effectiveness in domains with continuous observation spaces. However there has been no formal theoretical justification for this technique. This work offers such a justification, proving that a simplified algorithm, partially observable weighted sparse sampling (POWSS), will estimate Q-values accurately with high probability and can be made to perform arbitrarily near the optimal solution by increasing computational power.