IRIS Università degli Studi dell'Aquila
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Leveraging synthetic trace generation of modeling operations for intelligent modeling assistants using large language models
No full textContext: Due to the proliferation of generative AI models in different software engineering tasks, the research community has started to exploit those models, spanning from requirement specification to code development. Model-Driven Engineering (MDE) is a paradigm that leverages software models as primary artifacts to automate tasks. In this respect, modelers have started to investigate the interplay between traditional MDE practices and Large Language Models (LLMs) to push automation. Although powerful, LLMs exhibit limitations that undermine the quality of generated modeling artifacts, e.g., hallucination or incorrect formatting. Recording modeling operations relies on human-based activities to train modeling assistants, helping modelers in their daily tasks. Nevertheless, those techniques require a huge amount of training data that cannot be available due to several factors, e.g., security or privacy issues. Objective: In this paper, we propose an extension of a conceptual MDE framework, called MASTER-LLM, that combines different MDE tools and paradigms to support industrial and academic practitioners. Method: MASTER-LLM comprises a modeling environment that acts as the active context in which a dedicated component records modeling operations. Then, model completion is enabled by the modeling assistant trained on past operations. Different LLMs are used to generate a new dataset of modeling events to speed up recording and data collection. Results: To evaluate the feasibility of MASTER-LLM in practice, we experiment with two modeling environments, i.e., CAEX and HEPSYCODE, employed in industrial use cases within European projects. We investigate how the examined LLMs can generate realistic modeling operations in different domains. Conclusion: We show that synthetic traces can be effectively used when the application domain is less complex, while complex scenarios require human-based operations or a mixed approach according to data availability. However, generative AI models must be assessed using proper methodologies to avoid security issues in industrial domains
A Decades-Long Journey of Palmitoylethanolamide (PEA) for Chronic Neuropathic Pain Management: A Comprehensive Narrative Review
Get PDF: Palmitoylethanolamide (PEA) has been prescribed in neuropathic pain management for over 20 years. This study aims to summarize what has been published on the topic in the last 15 years and determine the appropriateness of the prescribing. It describes the pharmacological aspect of PEA, especially focusing on its pharmacodynamics and pharmacokinetics. Then, it deeply explores why PEA may be useful in the pharmacological management of both neuropathic and mixed pain. Finally, it examines some innovative patent, which aims to address obstacles encountered with conventional PEA formulations, for its pharmacodynamic characteristics. One of them (Equisetum-PEA) seems promising. It partially ameliorates the bioavailability and the targeted distribution. It seems to introduce novel advancements that can potentially enhance the therapeutic effectiveness of PEA in terms of its anti-inflammatory, antioxidant, and analgesic properties. The deep literature analysis aims to examine the potential advantages of PEA, in the context of several pathological conditions that may benefit from this molecule. It focuses on various published data regarding the clinical efficacy of PEA in managing neuropathic and mixed pain. Also, it tries to understand if it can modernize the field of therapy based on PEA, thus offering a better treatment option for individuals with chronic long-term inflammation, oxidative stress, and neuropathic or mixed pain with a neuropathic component. The study examines the possible impact of PEA on personalized medicine strategies and its potential for translation into clinical practice. It analyses the possibilities that PEA has in enhancing patient outcomes in a range of central nervous system and inflammatory conditions. A complete analysis of the therapeutic potentialities of this product was missing. This extensive narrative review makes a valuable contribution to the ongoing comprehension of PEA therapy. It establishes a foundation for further exploration in research and potential uses in clinical settings.Palmitoylethanolamide (PEA) has been prescribed in neuropathic pain management for over 20 years. This study aims to summarize what has been published on the topic in the last 15 years and determine the appropriateness of the prescribing. It describes the pharmacological aspect of PEA, especially focusing on its pharmacodynamics and pharmacokinetics. Then, it deeply explores why PEA may be useful in the pharmacological management of both neuropathic and mixed pain. Finally, it examines some innovative patent, which aims to address obstacles encountered with conventional PEA formulations, for its pharmacodynamic characteristics. One of them (Equisetum-PEA) seems promising. It partially ameliorates the bioavailability and the targeted distribution. It seems to introduce novel advancements that can potentially enhance the therapeutic effectiveness of PEA in terms of its anti-inflammatory, antioxidant, and analgesic properties. The deep literature analysis aims to examine the potential advantages of PEA, in the context of several pathological conditions that may benefit from this molecule. It focuses on various published data regarding the clinical efficacy of PEA in managing neuropathic and mixed pain. Also, it tries to understand if it can modernize the field of therapy based on PEA, thus offering a better treatment option for individuals with chronic long-term inflammation, oxidative stress, and neuropathic or mixed pain with a neuropathic component. The study examines the possible impact of PEA on personalized medicine strategies and its potential for translation into clinical practice. It analyses the possibilities that PEA has in enhancing patient outcomes in a range of central nervous system and inflammatory conditions. A complete analysis of the therapeutic potentialities of this product was missing. This extensive narrative review makes a valuable contribution to the ongoing comprehension of PEA therapy. It establishes a foundation for further exploration in research and potential uses in clinical settings
DeepMig: A transformer-based approach to support coupled library and code migrations
No full textContext: While working on software projects, developers often replace third-party libraries (TPLs) with different ones offering similar functionalities. However, choosing a suitable TPL to migrate to is a complex task. As TPLs provide developers with Application Programming Interfaces (APIs) to allow for the invocation of their functionalities after adopting a new TPL, projects need to be migrated by the methods containing the affected API calls. Altogether, the coupled migration of TPLs and code is a strenuous process, requiring massive development effort. Most of the existing approaches either deal with library or API call migration but usually fail to solve both problems coherently simultaneously. Objective: This paper presents DeepMig, a novel approach to the coupled migration of TPLs and API calls. We aim to support developers in managing their projects, at the library and API level, allowing them to increase their productivity. Methods: DeepMig is based on a transformer architecture, accepts a set of libraries to predict a new set of libraries. Then, it looks for the changed API calls and recommends a migration plan for the affected methods. We evaluate DeepMig using datasets of Java projects collected from the Maven Central Repository, ensuring an assessment based on real-world dependency configurations. Results: Our evaluation reveals promising outcomes: DeepMig recommends both libraries and code; by several projects, it retrieves a perfect match for the recommended items, obtaining an accuracy of 1.0. Moreover, being fed with proper training data, DeepMig provides comparable code migration steps of a static API migrator, a baseline for the code migration task. Conclusion: We conclude that DeepMig is capable of recommending both TPL and API migration, providing developers with a practical tool to migrate the entire project
Representativeness of the Natura 2000 network for preserving plant biodiversity in the European Union
Get PDFThe Natura 2000 (N2K) network of protected areas is one of the main tools for area-based conservation in the European Union (EU), yet its role in preserving plant biodiversity requires better understanding. We examined data kept in the European Vegetation Archive from over 1.2 million vegetation plots and obtained over 14.2 million plant species occurrences. To test the N2K network's representativeness of plant species gamma diversity, we compared the number and percentage of native and conservation priority species in- and outside the N2K network throughout the EU and for individual countries, biogeographical regions, and combinations thereof. We then determined whether N2K sites hosted more species than sites outside the network with the species–area relationship. Overall, almost 90% of the native vascular plant species occurred at least once in the N2K network. Yet, significant variation exists across countries and biogeographical regions—from 0% of species in the Boreal region of Lithuania, to 98% in the Alpine region of Croatia—indicating that local N2K sites are not equally representative of the regional gamma diversity. Nonetheless, the N2K network contains more species than land outside the network when area is taken into account. The planned expansion of the N2K network, as mandated by the European Biodiversity Strategy for 2030, should prioritize areas with currently underrepresented elements of the EU vascular flora
Long-term preservation of kidney function with SGLT-2 inhibitors versus comparator drugs in people with type 2 diabetes and chronic kidney disease
Get PDFAimsChronic kidney disease (CKD) is a prevalent and serious complication of type 2 diabetes (T2D). This study aims to evaluate kidney outcomes in a real-world cohort of patients with T2D and CKD who received SGLT2 inhibitors (SGLT2i) or other glucose-lowering medications (GLM).Materials and MethodsThis retrospective, multicentre study analysed data from patients aged 18-80 years with T2D and CKD, who initiated an SGLT2i or other GLM between 2015 and 2020. The primary outcome was the change in estimated glomerular filtration rate (eGFR) over time. Secondary outcomes included albuminuria changes and adverse kidney events. Propensity score matching was used to balance baseline characteristics between the two groups.ResultsAfter matching (n = 2020/group), patients (100% T2D with CKD) had a mean age of 63 years, BMI 32 kg/m2, HbA1c 8.2%. New-users of SGLT2i exhibited a slower decline in eGFR compared with new users of comparators (mean difference 1.43 mL/min/1.73 m2; p = 0.048). Albuminuria improved significantly more in the SGLT2i group, with a greater likelihood of category improvement (hazard ratio [HR] 1.17; p = 0.007). SGLT2i initiation was associated with a lower incidence of kidney outcomes, including a >= 40% eGFR reduction (HR 0.63; p = 0.004). When the comparison was restricted to SGLT2i versus GLP-1RA (n = 1266/group), the eGFR slope was significantly better with SGLT2i (mean difference 0.62 mL/min/1.73 m2/year; p = 0.046).ConclusionsIn this large, real-world cohort, initiation of SGLT2i was associated with a significantly slower decline in kidney function and improved albuminuria compared with other diabetes drugs, including GLP-1RA. These findings support SGLT2i as the most effective T2D treatment to slow CKD progression
AMBER: AI-Enabled Java Microbenchmark Harness
No full textJMH is the standard framework for developing and running Java microbenchmarks-lightweight performance tests used to evaluate the execution time of small Java code segments. A key challenge in designing JMH microbenchmarks is determining the appropriate number of warm-up iterations- repeated executions needed to bring microbenchmarks to a performance steady state. Too few warm-up iterations can compromise result quality, as performance measurements may not accurately reflect steady-state behavior. Conversely, too many warm-up iterations can unnecessarily increase testing time. Here, we present AMBER, an AI-enabled extension of JMH, which leverages Time Series Classification algorithms to predict the beginning of the steady-state phase at run-time and dynamically halt warm-up iterations accordingly. Empirical results show the potential of Amber in enhancing the cost-effectiveness of Java microbenchmarks. A demo video of Amber is available at https://www.youtube.com/watch?v=7zOngDQ1z_k
Thirty-Five Years of Non-Destructive Testing in Santa Maria Della Croce di Roio Church, L’Aquila, Italy (A.D. 1625): Assessing the Impact of Restoration and Seismic Events
Get PDFThis study presents the results of over thirty years of non-destructive testing (NDT) in a historic church, providing an unprecedented time analysis of the structural and material integrity of the building and its works of art. During this time, the church has undergone several restorations and two major seismic events. The diagnostics, which include a calibrated mix of established and advanced micro-destructive and non-destructive (NDT) techniques such as X-ray fluorescence, holographic interferometry, electronic speckle pattern interferometry (ESPI), infrared thermography, and IR reflectography, provide critical insights into the impact of the restoration interventions and the earthquakes on the church’s artistic heritage. The results indicate varying degrees of effectiveness of the restoration efforts, highlighting both areas of successful conservation and emerging vulnerabilities. This long-term study highlights the importance of continuous monitoring and its integration with NDT in identifying the effects of time and strong events occurring during the life of artworks that influence their state of conservation
EXPLORING THE INTERPLAY OF BODY COMPOSITION, PHYSICAL ACTIVITY, AND OPTIMISM ON SLEEP DURATION AND QUALITY IN POSTMENOPAUSAL WOMEN
No full textINTRODUCTION: Over 50% of postmenopausal women experience sleep disturbances, which negatively impact their
quality of life. Poor sleep reduces physical activity (PA) and worsens body composition (BC) changes due to estrogen de-
pletion. Maintaining a positive attitude can help manage menopause symptoms. This study analyses the relationship
between BC, physical activity PA, optimism and sleep in postmenopausal women.
METHODS: The study included 20 women (59.06 ±5.96 years) and half of the participants had been in late postmeno-
pause. Moderate-vigorous physical activity (MVPA) and sleep parameters (TST, total sleep time; SOL, sleep onset latency;
SE, sleep efficiency; SFI, sleep fragmentation index) were recorded using Actigraph GT3X+ accelerometers. The recom-
mended levels were as follows: MVPA= 150 min/week, TST= 7 hours, SE= 85% and SFI< 5 events/hour. Fat mass (FM, %),
trunk skeletal muscle mass (TSMM), and appendicular skeletal muscle mass were measured with InBody120 bioimped-
ance. The index of appendicular skeletal muscle mass (ASMMI) was calculated as ASMM divided by height squared.
Obesity was defined as FM = 35%, and muscle mass deficit was identified with ASMMI < 5.5 kg/m2. Dispositional opti-
mism was assessed using the LOT-R scale. Data were summarized using descriptive statistics and significance was ac-
cepted as p = 0.05. Partial correlation coefficient were used to assess associations between variables.
RESULTS: In the sample, 95% experienced natural menopause, 85% did not use hormone therapy, and 35% used sleep-
affecting medication. Nine participants were obese, and eight had low muscle condition. All women had SE = 85% and SFI
< 5 events per hour, but 25% had insufficient sleep duration. Average MVPA was 243.55 minutes per week (90% were
active) and LOT-R was 15.30 points. In early postmenopause, higher adiposity correlated with better SE (r=0.75, p=0.02),
and more optimism correlated with longer SOL (r=0.67, p=0.05). MVPA inversely related to SE (r=-0.76, p=0.02). Significant
associations (p=0.01), in the overall sample included SOL and LOT-R (r=0.54), SE and %FM (r=0.42), and SFI and TSMM
(r=0.56).
CONCLUSION: The results indicate that SE and SOL is influenced by adiposity levels and optimism in early postmenopausal
women. Better trunk muscle condition is linked to a higher SFI. More active women and those with longer estrogen deple-
tion show lower SE. A replication with a more representative sample is recommended
