Fraunhofer USA Center Mid-Atlantic CMA

Peer recovery specialists (PRS) are increasingly recognized as key members of the substance use disorder (SUD) treatment workforce. Recent efforts have focused on expanding PRS roles to include the delivery of behavioral evidence-based interventions (EBIs), such as motivational interviewing, cognitive-behavioral therapy techniques, and brief interventions. This scoping review aims to identify the determinants that influence the implementation of PRS-delivered behavioral EBIs and the strategies used to optimize their delivery within diverse SUD treatment contexts. A systematic search was conducted in APA PsycINFO, Web of Science, Scopus, PubMed, and Google Scholar, following PRISMA-ScR guidelines. Studies were included if they examined PRS delivering behavioral EBIs for SUD and reported on at least one implementation outcome as defined by Proctor et al. (2011). Data extraction and thematic synthesis were conducted using a hybrid deductive-inductive coding framework. Twelve studies met inclusion criteria. The most commonly studied interventions included behavioral activation, motivational interviewing, and Screening, Brief Intervention, and Referral to Treatment (SBIRT). PRS-delivered behavioral EBIs demonstrated high acceptability, appropriateness, and feasibility, with strong participant engagement and satisfaction. Facilitators of implementation included the integration of PRS within existing service structures, the adaptability of interventions, and the unique relatability of PRS. Barriers included PRS role ambiguity, gaps in training, and systemic challenges such as lack of funding and limited access to adjunctive support services. Implementation outcomes such as adoption, sustainability, and cost were infrequently assessed, highlighting gaps in the current literature. The findings suggest that PRS-delivered behavioral EBIs hold promise in expanding access to evidence-based care for individuals with SUD. However, structured training, supervision, and organizational support are critical for successful implementation. Future research should prioritize evaluating long-term sustainability, supervision, and strategies to enhance the integration of PRS within healthcare systems. Incorporating methods to address systemic barriers faced by service recipients will be essential for maximizing the impact of PRS-delivered interventions in SUD treatment.

Although medications exist to effectively treat opioid use disorder (OUD), treatment retention is a pressing challenge. Peer recovery specialists (PRSs) may play an important role in OUD treatment retention, yet few evidence-based interventions to support OUD retention have been developed specifically for PRS delivery. Behavioral activation is a brief, reinforcement-based intervention with empirical support for improving depression and substance use outcomes, delivered typically by specialist mental health providers. Informed by key stakeholder feedback, our team adapted a behavioral activation and problem-solving intervention for PRS delivery ("Peer Activate") to improve methadone treatment retention. Building on a successful open-label pilot trial demonstrating initial feasibility, acceptability, and preliminary effectiveness of Peer Activate, the current type 1 hybrid randomized controlled trial evaluates the effectiveness of Peer Activate compared to treatment as usual on six-month methadone retention (primary) and longer-term implementation outcomes. The trial is being conducted at a large methadone treatment program in Baltimore City, Maryland. We are enrolling 200 patients who recently initiated methadone treatment or are experiencing challenges with methadone adherence in a randomized 1:1 ratio to receive Peer Activate plus treatment as usual (PA + TAU) or TAU only. Additionally, we are recruiting 12 stakeholders to provide feedback on implementation and sustainability. Peer Activate consists of four core intervention sessions delivered by a PRS with relevant lived experience and training in the intervention. Sessions focus on problem-solving barriers to retention and behavioral activation-increasing value-driven, substance-free activities-and continued skill practice and relapse prevention. Assessments are administered at baseline, post-treatment (approximately 3 months), and 6 months. The primary effectiveness outcome is methadone retention over 6 months, measured using chart review. Implementation outcomes are defined based on Proctor's model, including feasibility, acceptability, and fidelity of the intervention. This trial will provide insight as to whether a PRS-delivered intervention may be effective and feasible for improving methadone treatment retention and other behavioral health outcomes. If findings are promising, Peer Activate may provide a platform on which to incorporate an evidence-based behavioral activation approach into PRS training nationally. NCT05299515.

We propose extension of the concept of software correctness, and the associated task of software testing, to include interrogation and diagnosis, whereby actionable knowledge about the limitations and performance of the software is gleaned. The new paradigm is especially relevant for AI-based systems such as classifiers and large language models (LLMs) that involve interactions among complex software, underlying training/reference data and the analysis/input data. In this context, the need for both interrogation-to identify problematic outputs when there may be no measure of correctness-and diagnosisto determine where the problem lies-is evident. To make our rhetoric concrete, we present two case studies. Classifier boundaries enable characterization of robustness of classifier output and fragility of inputs. Cliques in metagenomic assembly provide novel insight into the software as well as the potential for performance improvement.

Tor onion services can be accessed and hosted anonymously on the Tor network. We analyze the protocols, software types, popularity and uptime of these services by collecting a large amount of .onion addresses. Websites are crawled and clustered based on their respective language. In order to also determine the amount of unique websites a de-duplication approach is implemented. To achieve this, we introduce a modular system for the real-time detection and analysis of onion services. Address resolution of onion services is realized via descriptors that are published to and requested from servers on the Tor network that volunteer for this task. We place a set of 20 volunteer servers on the Tor network in order to collect .onion addresses. The analysis of the collected data and its comparison to previous research provides new insights into the current state of Tor onion services and their development. The service scans show a vast variety of protocols with a significant increase in the popularity of anonymous mail servers and Bitcoin clients since 2013. The popularity analysis shows that the majority of Tor client requests is performed only for a small subset of addresses. The overall data reveals further that a large amount of permanent services provide no actual content for Tor users. A significant part consists instead of bots, services offered via multiple domains, or duplicated websites for phishing attacks. The total amount of onion services is thus significantly smaller than current statistics suggest.

Amazon's Mechanical Turk is a crowd sourcing technology that enables requesters to create tasks to be completed by human agents in exchange for compensation. Researchers in computer science have successfully used this service to quickly reach large numbers of subjects for a relatively low cost. However, the Mechanical Turk's model and policies introduce several experimental limitations and threats that must be controlled. In this short paper, we describe limitations imposed using Amazon's Mechanical Turk during an experiment on cyber-attack investigation techniques. While the experiment was successful, we were forced to change our experimental design and had to recover from some costly mistakes. The goal of this short paper is to identify these limitations and pitfalls and provide eight considerations for experimental design so that other researchers can maximize the benefits of using the Mechanical Turk as a research platform.

Background: Automatic static analysis (ASA) tools examine source code to discover issues, i.e. code patterns that are symptoms of bad programming practices and that can lead to defective behavior. Studies in the literature have shown that these tools find defects earlier than other verification activities, but they produce a substantial number of false positive warnings. For this reason, an alternative approach is to use the set of ASA issues to identify defect prone files and components rather than focusing on the individual issues. Aim: We conducted an exploratory study to investigate whether ASA issues can be used as early indicators of faulty files and components and, for the first time, whether they point to a decay of specific software quality attributes, such as maintainability or functionality. Our aim is to understand the critical parameters and feasibility of such an approach to feed into future research on more specific quality and defect prediction models. Method: We analyzed an industrial C# web application using the Resharper ASA tool and explored if significant correlations exist in such a data set. Results: We found promising results when predicting defect-prone files. A set of specific Resharper categories are better indicators of faulty files than common software metrics or the collection of issues of all issue categories, and these categories correlate to different software quality attributes. Conclusions: Our advice for future research is to perform analysis on file rather component level and to evaluate the generalizability of categories. We also recommend using larger datasets as we learned that data sparseness can lead to challenges in the proposed analysis process.

Knowledge patterns are one way to formalize and describe lessons learned and best practices (i.e., proven experiences) about structuring knowledge, the design of KM systems, or the development of underlying ontologies. Such patterns capture aspects that positively or negatively influence the KM activities. In the later case, where negative influences are described, such patterns are denoted as anti-patterns. Knowledge patterns and anti patterns support practitioners and researchers in their knowledge management (KM) activities and can help in developing KM systems as well as improving the quality of the systems themselves and that of the knowledge within (i.e., the quality of the knowledge). Thereby, patterns in KM represent a way of structuring knowledge as well as a form of language that helps knowledge engineers to communicate about knowledge and KM systems.

Business value analysis (BVA) quantifies the factors that provide value and cost to an organization. It aims at capturing value, controlling risks, and capitalizing on opportunities. GQM+Strategies is an approach designed to aid in the definition and alignment of business goals, strategies, and an integrated measurement program at all levels in the organization. In this paper we describe how to perform business value analysis (BVA) using the GQM+Strategies approach. The integration of these two approaches provides a coupling of cost-benefit and risk analysis (value goals) with operationally measurable business goals and supports the evaluation of business goal success and the effectiveness of the chosen strategies. An application of the combined approach is provided to illustrate the feasibility of the proposed method. It deals with the business goal of modernizing the product for the evolving market.

Fraunhofer SAVE (software architecture visualization and evaluation) is a tool for analyzing and optimizing the architecture of implemented software systems. SAVE is a joint development between Fraunhofer IESE (Institute for Experimental Software Engineering IESE in Kaiserslautern, Germany) and Fraunhofer Center Maryland (Center for Experimental Software Engineering in College Park, Maryland, USA). In this work we describe the capabilities of the tool to assure compliance of existing systems with their architecture. In particular, we show how compliance checking features of SAVE work to assure compliance with structural and behavioral architectural views, to assure compliance among variants in a product line context, and to incorporate constructive compliance checking to enable built-in compliance during development and evolution.