Signal Appropriation of Explicit HIV Status Disclosure Fields in Sex-Social Apps used by Gay and Bisexual Men

HIV status disclosure fields in online sex-social applications ("apps") are designed to help increase awareness, reduce stigma, and promote sexual health. Public disclosure could also help those diagnosed relate to others with similar statuses to feel less isolated. However, in our interview study (n=28) with HIV positive and negative men who have sex with men (MSM), we found some users preferred to keep their status private, especially when disclosure could stigmatise and disadvantage them, or risk revealing their status to someone they knew offline in a different context. How do users manage these tensions between health, stigma, and privacy? We analysed our interview data using signalling theory as a conceptual framework and identify participants developing 'signal appropriation' strategies, helping them manage the disclosure of their HIV status. Additionally, we propose a set of design considerations that explore the use of signals in the design of sensitive disclosure fields

Evaluation of Elastomeric Impression in Fixed Partial Denture-A Retrospective Analysis of Patient Records

 The aim of this study is to evaluate defects in the impressions made for fixed partial denture prosthesis. For this, patient records of those who underwent replacement of missing teeth with fixed partial denture were collected. A total of 50 fixed partial denture impressions were evaluated. The data collection was done considering the following parameters - impression technique, number of prepared units, defect in the facial margin , defect or cut through finish lines, defect onto the axial wall, defect in material polymerisation, exposure of heavy body through wash material, retraction cord embedded in impression, air bubbles and voids, type of tray used, number of cords preferred. The collected data was tabulated in excel sheet. Statistical analysis was done in SPSS software. This study shows that 90% were double wash impressions, 10% were single wash impressions. About 36% showed defects on the facial margin, 62% showed defective cuts through the 360 degree flash, 38% defect on the axial wall, 38% showed air bubbles and voids. Majority of the impressions were made after packing   two cords. Within the limit of the present study it is concluded that double wash impression technique is the most preferred. The voids and bubbles were the majority of the defects that were present than any other defects

High Fidelity Data Collection: Managing the Collection Process Throughout the Deployment Lifecycle (SYS 19)

We aim to design a high fidelity collection process that primarily aims to design human interaction into the system to do two things: first, diagnose and/or fix problems that the system cannot address automatically, and second, gather extra sensory observations, such as physical samples, that provide contextual information useful for determining data integrity and easing data analysis. We believe that carefully integrating human input into the collection path will facilitate collection of datasets that are more useful than completely autonomously collected data

Experiences with the Extensible Sensing System ESS

The Extensible Sensing System (ESS) has been in use for several years in a variety of sensor network deployments. It is a key component of a collection of tools that together are a nearly complete, end-to-end, sensor-to-user facility for deploying and managing a sensor network. This paper provides the context and architectural overview of ESS, along with selected deployment details and a series of lessons learned. Lesson areas include connectivity, interactivity, energy vs. robustness, vertical integration, and real-time visibility. The current version of ESS reflects changes from these lessons; further, new tools are in development that complement ESS

Poor power quality is a major barrier to providing optimal care in special neonatal care units (SNCU) in Central India [version 1; peer review: 2 approved]

Background: Approximately 25% of all neonatal deaths worldwide occur in India. The Indian Government has established Special Neonatal Care Units (SNCUs) in district and sub-district level hospitals to reduce neonatal mortality, but mortality rates have stagnated. Reasons include lack of personnel and training and sub-optimal quality of care. The role of medical equipment is critical for the care of babies, but its role in improving neonatal outcomes has not been well studied.  Methods: In a qualitative study, we conducted seven focus group discussions with SNCU nurses and pediatric residents and thirty-five key informant interviews and with pediatricians, residents, nurses, annual equipment maintenance contractors, equipment manufacturers, and Ministry of Health personnel in Maharashtra between December 2019 and November 2020. The goal of the study was to understand challenges to SNCU care. In this paper, we focus on current gaps and future needs for SNCU equipment, quality of the power supply, and use of SNCU equipment. Results: Respondents described a range of issues but highlighted poor power quality as an important cause of equipment malfunction. Other concerns were lack of timely repair that resulted in needed equipment being unavailable for neonatal care. Participants recommended procuring uninterrupted power supply (UPS) to protect equipment, improving quality/durability of equipment to withstand constant use, ensuring regular proactive maintenance for SNCU equipment, and conducting local power audits to discern and address the causes of power fluctuations. Conclusions: Poor power quality and its negative impact on equipment function are major unaddressed concerns of those responsible for the care and safety of babies in SNCUs in Central India. Further research on the power supply and protection of neonatal equipment is needed to determine a cost-effective way to improve access to supportive care in SNCUs and desired improvements in neonatal mortality rates

Fixing Faults with Confidence

This paper presents Confidence, a tool for identifying and addressing faults in wireless sensing systems. Confidence pinpoints potential sensor and network faults in real time, allowing users to validate unexpected data and address any failures in the field. By introducing a well defined, low-dimension feature space, and functions to map sensor data into this space, we are able to achieve fault detection and diagnosis with relatively simple mechanisms such as outlier detection. Users can directly modify system outcomes by altering a classification label in instances when Confidence's automated algorithm draws the wrong inference. This label is applied to all similar points in the feature space, enabling Confidence to learn from user interaction in the field. This abstraction for incorporating user knowledge provides a lightweight and easy-to-understand interface for the user, while limiting user burden and reducing the required a priori environmental knowledge. Confidence has performed well on real-world deployments, including one deployment of 130 sensors, replayed datasets, and network simulations. Confidence accurately detects and diagnoses at least 90% of all data, and user interaction improves it's performance

Fixing Faults in Wireless Sensing Systems with Confidence

This paper presents Confidence, a tool for identifying and addressing faults in wireless sensing systems. Confidence pinpoints potential sensor and network faults in real time, allowing users to validate unexpected data and address any failures in the field. By introducing a well defined, low-dimension feature space, and functions to map sensor data into this space, we are able to achieve fault detection and diagnosis with relatively simple mechanisms such as outlier detection. Users can directly modify system outcomes by altering a classification label in instances when Confidence's automated algorithm draws the wrong inference. This label is applied to all similar points in the feature space, enabling Confidence to learn from user interaction in the field. This abstraction for incorporating user knowledge provides a lightweight and easy- to-understand interface for the user, while limiting user bur- den and reducing the required a priori environmental knowledge. Confidence has performed well on real-world deployments, including one deployment of 130 sensors, replayed datasets, and network simulations. Confidence accurately detects and diagnoses at least 90% of all data, and user interaction improves it's performance

Fixing Faults with Confidence

This paper presents Confidence, a tool for identifying and addressing faults in wireless sensing systems. Confidence pinpoints potential sensor and network faults in real time, allowing users to validate unexpected data and address any failures in the field. By introducing a well defined, low-dimension feature space, and functions to map sensor data into this space, we are able to achieve fault detection and diagnosis with relatively simple mechanisms such as outlier detection. Users can directly modify system outcomes by altering a classification label in instances when Confidence's automated algorithm draws the wrong inference. This label is applied to all similar points in the feature space, enabling Confidence to learn from user interaction in the field. This abstraction for incorporating user knowledge provides a lightweight and easy-to-understand interface for the user, while limiting user burden and reducing the required a priori environmental knowledge. Confidence has performed well on real-world deployments, including one deployment of 130 sensors, replayed datasets, and network simulations. Confidence accurately detects and diagnoses at least 90% of all data, and user interaction improves it's performance