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The global mHealth app market is rapidly expanding, especially since the COVID-19 pandemic. However, many of these mHealth apps have serious issues, as reported in their user reviews. Better understanding their key user concerns would help app developers improve their apps' quality and uptake. While app reviews have been used to study user feedback in many prior studies, many are limited in scope, size and/or analysis. In this paper, we introduce a very large-scale study and analysis of mHealth app reviews. We extracted and translated over 5 million user reviews for 278 mHealth apps. These reviews were then classified into 14 different aspects/categories of issues reported. Several mHealth app subcategories were examined to reveal differences in significant areas of user concerns, and to investigate the impact of different aspects of mhealth apps on their ratings. Based on our findings, women's health apps had the highest satisfaction ratings. Fitness activity tracking apps received the lowest and most unfavourable ratings from users. Over half of users who reported troubles leading them to uninstall mHealth apps gave a 1-star rating. Half of users gave the account and logging aspect only one star due to faults and issues encountered while registering or logging in. Over a third of users who expressed privacy concerns gave the app a 1-star rating. However, only 6% of users gave apps a one-star rating due to UI/UX concerns. 20% of users reported issues with handling of user requests and internationalisation concerns. We validated our findings by manually analysing a sample of 1,000 user reviews from each investigated aspect/category. We developed a list of recommendations for mHealth apps developers based on our user review analysis.

The rapid expansion of urban areas and industrial units has put much strain on natural environments and biodiversity. Quantifying the impact of human pressures on avian biodiversity is vital for the identification, preservation, and restoration of important areas. Here, data collected in 11 coastal Mediterranean oases were used to assess the impact of urban and industrial landscapes and habitat structure on the richness of breeding birds. Results of generalized linear mixed models analyses showed a quadratic effect of distance to the industrial complex on breeding bird richness, being optimal (6.41 ± 0.89) at 24 km. The results also showed a negative effect of the cover of urban areas. Our analysis also emphasized the importance of southern oases for breeding bird richness mostly because of their remoteness from the industrial complex and their significant coverage of fruit trees and natural ground cover. Variation partitioning analysis revealed that the shared fraction of industrial landscape, oasis habitat structure, and space was relevant in explaining the richness of breeding birds. It is highly recommended to (i) uninstall the Gabès industrial complex from this Mediterranean area, (ii) enhance the habitat quality in southern oases by planting other fruit trees, such as pomegranate and olive, and (iii) pursue scientific research in these Mediterranean coastal oases, as they offer a good opportunity for assessment and improvement of knowledge on both the impact of industrialization on quality of habitats and the richness of bird species.

Herein we present the VMD Store, an open-source VMD plugin that simplifies the way that users browse, discover, install, update, and uninstall extensions for the Visual Molecular Dynamics (VMD) software. The VMD Store obtains data about all the indexed VMD extensions hosted on GitHub and presents a one-click mechanism to install and configure VMD extensions. This plugin arises in an attempt to aggregate all VMD extensions into a single platform. The VMD Store is available, free of charge, for Windows, macOS, and Linux at https://biosim.pt/software/ and requires VMD 1.9.3 (or later).

Chang-hua Christian Hospital needs to uninstall the 60Co unit. The mode of this 60Co teletherapy unit is SHIMADZU RTGS-10. The original lead head was taken as the source container of this 60Co unit. The source head was dismantled and put into the prepared wooden box, after the source was sealed. This study describes the planning and dismantling of the retirement and transport of the 60Co unit, and personal doses measured during the procedure. This work estimates the doses of radiation received by exposed workers during the dismantling of the machine. The workers received doses of approximately 53 microSv. This study shows that the original lead head can be used as the source container of this 60Co unit. The 60Co machine was smoothly dismantled and transported by conscientious and careful workers, using planned and controlled radiation protection, following the ALARA (as low as reasonably achievable) rule.

Audio/video-mediated communication between patients and clinicians using videoconferencing over telecommunication networks is a key component of providing teletreatments in rehabilitation. The objectives of this study were to (1) document the conditions of use, performance, and reliability of videoconferencing-based communication in the context of in-home teletreatment (TELE) following total knee arthroplasty (TKA) and (2) assess from the perspective of the providers, the quality attributes of the technology used and its impact on clinical objectives. Descriptive embedded study in a randomized controlled trial using a sample of 97 post-TKA patients, who received a total of 1,431 TELE sessions. Technical support use, service delivery reliability, performance, and use of network connection were assessed using self-report data from a costing grid and automated logs captured from videoconferencing systems. Physical therapists assessed the quality and impact of video-mediated communications after each TELE session on seven attributes. Installation of a new Internet connection was required in 75% of the participants and average technician's time to install test and uninstall technology (including travel time) was 308.4 min. The reliability of service delivery was 96.5% of planned sessions with 21% of TELE session requiring a reconnection during the session. Remote technical support was solicited in 43% of the sessions (interventions were less than 3-min duration). Perceived technological impacts on video-mediated communications were minimal with quality of the overall technical environment evaluated as good or acceptable in 96% of the sessions and clinical objectives reached almost completely or completely in 99% of the sessions. In-home rehabilitation teletreatments can be delivered reliably but requires access to technical support for the initial setup and maintenance. Optimization of the processes of reliably connecting patients to the Internet, getting the telerehabilitation platform in the patient's home, installing, configuring, and testing will be needed to generalize this approach of service delivery.

The current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in a multi-access edge computing environment is proposed (DCO-PSOMO). According to the CPU utilization and the memory utilization rate of the mobile terminal, this method can dynamically obtain the overload time by using a strong, locally weighted regression method. After detecting the overload time, the probability of successful downloading is predicted by the mobile user's dwell time and edge computing communication range, and the offloading is either conducted immediately or delayed. A computation offloading model was established via the use of the response time and energy consumption of the mobile terminal. Additionally, the optimal computing offloading algorithm was designed via the use of a particle swarm with a mutation operator. Finally, the DCO-PSOMO algorithm was compared with the JOCAP, ECOMC and ESRLR algorithms, and the experimental results demonstrated that the DCO-PSOMO offloading method can effectively reduce the offloading cost and terminal energy consumption, and improves the success probability of offloading and the user's QoS.

We report a rare case of a da Vinci robotic arm failure during a laparoscopic robot-assisted radical prostatectomy. The articulation joint of an Endowrist needle driver was broken and positioned at such an angle that made it impossible to remove through the trocar. In addition, it was later discovered that a small piece of the instrument was detached and remained inside the abdomen of the patient without even having been identified on subsequent radiological evaluation. In order to remove the broken instrument, we had to uninstall it from the robot arm and a bigger incision had to be made in the abdominal wall of the patient. The operation was completed without any other incidents. Testing the broken instrument for integrity is recommended to avoid this rare complication.

Human-robot collaboration (HRC) in structured assembly requires reliable state estimation and adaptive task planning under noisy perception and human interventions. To address these challenges, we introduce a design-grounded human-aware planning framework for human-robot collaborative structured assembly. The framework comprises two coupled modules. Module I, Perception-to-Symbolic State (PSS), employs vision-language models (VLMs) based agents to align RGB-D observations with design specifications and domain knowledge, synthesizing verifiable symbolic assembly states. It outputs validated installed and uninstalled component sets for online state tracking. Module II, Human-Aware Planning and Replanning (HPR), performs task-level multi-robot assignment and updates the plan only when the observed state deviates from the expected execution outcome. It applies a minimal-change replanning rule to selectively revise task assignments and preserve plan stability even under human interventions. We validate the framework on a 27-component timber-frame assembly. The PSS module achieves 97% state synthesis accuracy, and the HPR module maintains feasible task progression across diverse HRC scen

Legacy systems concentrate business rules, architectural decisions, and operational exceptions that often remain implicit in code, data, configuration, and maintenance practices. At the same time, language-model-based coding agents depend on reliable context, correctness criteria, and behavioral contracts to modify real systems with lower risk. This paper presents Reversa, a reverse documentation engineering framework for converting legacy software into traceable operational specifications for AI agents. Reversa organizes this process as a multi-agent pipeline: specialized agents map the project surface, analyze modules, extract implicit rules, synthesize architecture, write unit-level specifications, and review generated claims. The proposal emphasizes three mechanisms: traceability between code and specification, explicit confidence marking, and preservation of gaps for human validation. The framework is distributed as a Node.js CLI, installs skills across multiple agent engines, and uses a SHA-256 manifest to preserve modified files during update or uninstall operations. In addition to the architectural description, we report an exploratory case study on migrating an ATM from CO

Personalized computer-use agents are rapidly moving from expert communities into mainstream use. Unlike conventional chatbots, these systems can install skills, invoke tools, access private resources, and modify local environments on users' behalf. Yet users often do not know what authority they have delegated, what the agent actually did during task execution, or whether the system has been safely removed afterward. We investigate this gap as a combined problem of risk understanding and post-hoc auditability, using OpenClaw as a motivating case. We first build a multi-source corpus of the OpenClaw ecosystem, including incidents, advisories, malicious-skill reports, news coverage, tutorials, and social-media narratives. We then conduct an interview study to examine how users and practitioners understand skills, autonomy, privilege, persistence, and uninstallation. Our findings suggest that participants often recognized these systems as risky in the abstract, but lacked concrete mental models of what skills can do, what resources agents can access, and what changes may remain after execution or removal. Motivated by these findings, we propose AgentTrace, a traceability framework and

Large multimodal model powered GUI agents are emerging as high-privilege operators on mobile platforms, entrusted to perceive screen content and inject inputs across application boundaries. While these agents aim to automate complex tasks, we demonstrate that their design introduces a fundamental conflict with Android's strict application sandboxing. We present a novel cross-application Action Rebinding attack, which allows a malicious application with zero dangerous permissions to hijack the agent's execution and perform privileged operations on behalf of the attacker. Our attack exploits the inevitable observation-action gap inherent in the agent's reasoning pipeline. A malicious app can render a benign ``contextual carrier'' to elicit a planned action, and then swap the foreground to a sensitive target application during the reasoning latency. The agent, unaware of the transition, unwittingly executes the action in the privileged context. We further advance this attack by weaponizing the agent's own task-recovery logic to create programmable, multi-step exploit loops , and introducing an Intent Alignment Strategy (IAS) that manipulates the agent's reasoning to rationalize the hi

Machine learning (ML) models are increasingly integrated into modern mobile apps to enable personalized and intelligent services. These models typically rely on rich input features derived from historical user behaviors to capture user intents. However, as ML-driven services become more prevalent, recording necessary user behavior data imposes substantial storage cost on mobile apps, leading to lower system responsiveness and more app uninstalls. To address this storage bottleneck, we present AdaLog, a lightweight and adaptive system designed to improve the storage efficiency of user behavior log in ML-embedded mobile apps, without compromising model inference accuracy or latency. We identify two key inefficiencies in current industrial practices of user behavior log: (i) redundant logging of overlapping behavior data across different features and models, and (ii) sparse storage caused by storing behaviors with heterogeneous attribute descriptions in a single log file. To solve these issues, AdaLog first formulates the elimination of feature-level redundant data as a maximum weighted matching problem in hypergraphs, and proposes a hierarchical algorithm for efficient on-device depl

Reusing third-party software packages is a common practice in software development. As the scale and complexity of open-source software (OSS) projects continue to grow (e.g., Linux distributions), the number of reused third-party packages has significantly increased. Therefore, maintaining effective package management is critical for developing and evolving OSS projects. To achieve this, a package-to-group mechanism (P2G) is employed to enable unified installation, uninstallation, and updates of multiple packages at once. To better understand this mechanism, this paper takes Linux distributions as a case study and presents an empirical study focusing on its application trends, evolutionary patterns, group quality, and developer tendencies. By analyzing 11,746 groups and 193,548 packages from 89 versions of 5 popular Linux distributions and conducting questionnaire surveys with Linux practitioners and researchers, we derive several key insights. Our findings show that P2G is increasingly being adopted, particularly in popular Linux distributions. P2G follows six evolutionary patterns (\eg splitting and merging groups). Interestingly, packages no longer managed through P2G are more l

In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage. User behaviors on mobile app usage, including retention, installation, and uninstallation, can be a good indicator for both long-term and short-term interests of users. For example, if a user installs Snapseed recently, she might have a growing interest in photographing. Such information is valuable for numerous downstream applications, including advertising, recommendations, etc. Traditionally, user modeling from mobile app usage heavily relies on handcrafted feature engineering, which requires onerous human work for different downstream applications, and could be sub-optimal without domain experts. However, automatic user modeling based on mobile app usage faces unique challenges, including (1) retention, installation, and uninstallation are heterogeneous but need to be modeled collectively, (2) user behaviors are distributed unevenly over time, and (3) many long-tailed apps suffer from serious sparsity. In this paper, we present a tailored AutoEncoder-coupled Transformer Network (AETN), by which we overcome these challenges and achieve the goals of reducing manual efforts and boo

Information and communication technologies are moving towards a new stage where applications will be dynamically deployed, uninstalled, updated and (re)configured. Several approaches have been followed with the goal of creating a fully automated and context-aware deployment system. Ideally, this system should be capable of handling the dynamics of this new situation, without losing sight of other factors, such as performance, security, availability or scalability. We will take some of the technologies that follow the principles of Service Oriented Architectures, SOA, as a paradigm of dynamic environments. SOA promote the breaking down of applications into sets of loosely coupled elements, called services. Services can be dynamically bound, deployed, reconfigured, uninstalled and updated. First of all, we will try to offer a broad view on the specific deployment issues that arise in these environments. Later on, we will present our approach to the problem. One of the essential points that has to be tackled to develop an automated deployment engine will be to have enough information to carry out tasks without human intervention. In the article we will focus on the format and contents

Cloud computing has been regarded as the technology enabler for the Internet of Things (IoT). To ensure the most effective collection of IoT-based evidence, it is vital for forensic practitioners to possess a contemporary understanding of the artefacts from different cloud services. In this paper, we seek to determine the data remnants from the use of BitTorrent Sync version 2.0. Findings from our research using mobile and computer devices running Windows 8.1, Mac OS X Mavericks 10.9.5, Ubuntu 14.04.1 LTS, iOS 7.1.2, and Android KitKat 4.4.4 suggested that artefacts relating to the installation, uninstallation, log-in, log-off, and file synchronisation could be recovered, which are potential sources of IoT forensics. We also present a forensically sound investigation methodology for BitTorrent Sync.

The prevalence of smart mobile devices has promoted the popularity of mobile applications (a.k.a. apps). Supporting mobility has become a promising trend in software engineering research. This article presents an empirical study of behavioral service profiles collected from millions of users whose devices are deployed with Wandoujia, a leading Android app store service in China. The dataset of Wandoujia service profiles consists of two kinds of user behavioral data from using 0.28 million free Android apps, including (1) app management activities (i.e., downloading, updating, and uninstalling apps) from over 17 million unique users and (2) app network usage from over 6 million unique users. We explore multiple aspects of such behavioral data and present patterns of app usage. Based on the findings as well as derived knowledge, we also suggest some new open opportunities and challenges that can be explored by the research community, including app development, deployment, delivery, revenue, etc.

Many people around the world are worried about using or even downloading COVID-19 contact tracing mobile apps. The main reported concerns are centered around privacy and ethical issues. At the same time, people are voluntarily using Social Media apps at a significantly higher rate during the pandemic without similar privacy concerns compared with COVID-19 apps. To better understand these seemingly anomalous behaviours, we analysed the privacy policies, terms & conditions and data use agreements of the most commonly used COVID-19, Social Media & Productivity apps. We also developed a tool to extract and analyse nearly 2 million user reviews for these apps. Our results show that Social Media & Productivity apps actually have substantially higher privacy and ethical issues compared with the majority of COVID-19 apps. Surprisingly, lots of people indicated in their user reviews that they feel more secure as their privacy are better handled in COVID-19 apps than in Social Media apps. On the other hand, most of the COVID-19 apps are less accessible and stable compared to most Social Media apps, which negatively impacted their store ratings and led users to uninstall COVID-19

How can we track synchronized behavior in a stream of time-stamped tuples, such as mobile devices installing and uninstalling applications in the lockstep, to boost their ranks in the app store? We model such tuples as entries in a streaming tensor, which augments attribute sizes in its modes over time. Synchronized behavior tends to form dense blocks (i.e. subtensors) in such a tensor, signaling anomalous behavior, or interesting communities. However, existing dense block detection methods are either based on a static tensor, or lack an efficient algorithm in a streaming setting. Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step. AugSplicing is based on a splicing condition that guides the algorithm (Section 4). Compared to the state-of-the-art methods, our method is (1) effective to detect fraudulent behavior in installing data of real-world apps and find a synchronized group of students with interesting features in campus Wi-Fi data; (2) robust with splicing theory for dens