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In vitro and in vivo human metabolism profiling of designer benzodiazepines using high-resolution mass spectrometry
No full textDesigner benzodiazepines continue to emerge on the recreational drug market, and are particularly implicated in poly-drug intoxications, posing challenges for clinical and forensic toxicology. It is therefore important to characterize biomarkers that are useful for tentative confirmation of designer benzodiazepine (mis)use. In vitro human metabolism studies were thus conducted for at least three hours and analysis was performed using ultrahigh performance liquid chromatography tandem high-resolution mass spectrometry to identify their metabolites and proscribed consumption markers. Where bio-sample of reported benzodiazepine intoxication was available, they were analyzed with the same method. The main phase I metabolism pathway involved hydroxylation, particularly at the 4 carbon position of the diazepine ring in fluclotizolam, flubrotizolam, and desalkylgidazepam, the latter yielding 3 hydroxy desalkylgidazepam as its main metabolite. Alternative hydroxylation sites and subsequent phase II glucuronidation were observed for fluetizolam and bretazenil, while gidazepam metabolism was characterized by transformations of its hydrazine chain. These findings underscore the structural dependence of benzodiazepine metabolism and highlight the complexity of identifying consumption markers for compounds undergoing extensive hepatic clearance. Human hepatocyte models successfully simulated both phase I and II metabolism, enabling detection of novel metabolites and pathways within three hours of incubation. Such data are critical for establishing reliable biomarkers of use, though comprehensive pharmacokinetic parameters, including renal and hepatic clearance and protein binding, remain limited. Importantly, active metabolites such as 3 hydroxy desalkylgidazepam, from our in silico molecular modelling, may contribute to prolonged pharmacodynamic effects, warranting further in vitro and in vivo investigations. High resolution mass spectrometry combined with hepatocyte models provides a robust approach for metabolite profiling and biomarker identification. These methodologies are essential for confirming designer benzodiazepine intoxication, and thus supporting harm reduction strategies, and informing public health interventions in response to evolving new psychoactive substances
Impact and Effect of Formative Feedback Improvement for Dormant Deficiencies in MCAD Education
Full text linkConsiderable improvement in student performance and in learning outcomes has been achieved with an educational intervention previously introduced to a CAD course for mechanical engineering. This intervention was based on the novel dormant deficiency concept and metric, and a software-based feedback agent, among other innovations. This step aimed to address the shortcomings of most software tools and educational interventions for automated grading and assessment of CAD models, such as those currently provided to students in CAD courses. Most approaches are not structured to assess CAD model quality with respect to robustness and alterability, due to their static and exclusive nature, which often leads them to discount CAD model regeneration processes and their impact after alteration. However, it also became evident that students still have difficulties handling and correcting shortcomings and errors in their CAD models regarding certain types of dormant deficiencies. This led to several spin-off projects. One such project aimed at extending and improving the quality, scope, and detail of the feedback generated by the software-based feedback agent. After successful prototyping and usability testing, this improved feedback intervention was provided to all CAD course students. The results and outcomes of that project, together with an empirical study, are reported in this paper
Ground-based sensor platforms for continuous monitoring of olive tree and fruit: a review
No full textPrecision agriculture (PA) is a farming management concept that emphasizes the application of information technology to collecting and using high-resolution data (ranging from seasonal periods up to minute intervals) for on-time agricultural practices with respect to soil, plant, and climate. Therefore, constant data collection (continuous monitoring) is beneficial in PA. Continuous monitoring could be performed on soil, plant, and environment, however, plants act as a connector between soil and environment, and its physiological response reflects an integrated effect. Continuous plant-based monitoring can be employed for olive tree and fruit management. Several sensor platforms have been used for monitoring olive orchards and among them, ground-based is the most suitable platform for continuous plant monitoring (up to minute intervals). Information such as, water stress, disease and pest status, fruit growth, fruit maturation, fruit size, and yield could be provided by continuous plant-based monitoring via ground-based sensor platforms. This methodological review addresses current applications and challenges of continuous plant-based monitoring of olive trees and fruit via ground-based platforms. Finally, we hypothesize possible future development for providing a wider range of agricultural services
Response of different olive cultivars to late frosts in the Marche region (Italy)
No full textIn Europe, the intensity and frequency of late-winter frosts and spring frosts are increasing as a result of climate change. In olive (Olea europaea L.), frost damage can affect different tissues, from the leaves to the trunk, including inflorescences. A partial loss of inflorescences may not change the season’s fruit set, but if too many flowers are lost it would lead to a corresponding loss of production. On the Adriatic coast of central Italy, during the night of April 11, 2022, a few hours below zero were recorded. The low temperatures damaged the inflorescences of olives with different intensities based on cultivar and area. To evaluate the severity of the damages, the number of living and dead inflorescences were collected in several cultivars among several orchards. Data show that each cultivar showed a different behaviour in different orchards. ‘Arbequina’ showed 6% of dead inflorescences in the orchard of Maiolati Spontini, and 95% in the orchard of Agugliano. In Maiolati Spontini, only ‘FS-17’ showed a great loss of inflorescences. In the orchard of Fermo, ‘Rosciola’ showed higher damages. This different behaviour could be explained by small differences in temperature and humidity that are site specific, and to the exact phenological stage of the flowers in that particular moment. These observations could be helpful in determining the most adaptable cultivars to the future climate of central Italy. This objective is possible only by considering a mixture of cultivars, due to the different responses of the cultivars to the different and unpredictable types of frosts each year (winter, late winter, spring)
Progettazione, ottimizzazione e analisi di sostenibilità del processo di Filament Winding di componenti assialsimmetrici in materiale composito
No full textIl presente lavoro di ricerca si inserisce nella crescente esigenza di sviluppare processi produttivi innovativi e sostenibili, in grado di coniugare prestazioni meccaniche elevate e, al contempo, costi e impatti ambientali contenuti. In questo contesto, l’attenzione è stata posta sul processo di Filament Windiing (FW), una tecnologia automatizzata per la realizzazione di componenti in materiale composito a matrice polimerica fibrorinforzata, ampiamente utilizzata nei settori aerospaziale, automobilistico, energetico, nautico e molti altri ancora.
L’obiettivo principale dello studio è stato quello di analizzare in maniera integrata aspetti tecnologici, meccanici, ambientali ed economici legati al processo di FW.
L’approccio metodologico utilizzato ha previsto tre fasi complementari e interconnesse: lo studio sperimentale su scala laboratoriale del processo di FW, volto a valutare l’effetto dei principali parametri di processo sulle proprietà meccaniche dei manufatti; la progettazione di componenti strutturali industriali con successiva analisi degli impatti ambientali ed economici mediante le metodologie standardizzate di Life Cycle Assessment (LCA) e Life Cycle Costing (LCC) e, infine, lo sviluppo di un modello matematico predittivo basato su una regressione multipla non lineare per la stima dei tempi di avvolgimento e del consumo energetico in funzione dei parametri di input, successivamente integrato in un modello per la valutazione della sostenibilità ambientale ed economica del processo.
L’attività sperimentale ha permesso di individuare le correlazioni più significative tra i parametri tecnologici e le proprietà meccaniche dei componenti in composito, evidenziando il ruolo determinante dell’angolo di avvolgimento. Le analisi LCA e LCC hanno evidenziato come la fase di produzione delle fibre di carbonio costituisca la principale fonte di impatto ambientale, mentre l’automazione del processo di FW consente di ridurre significativamente i consumi energetici e i costi di manodopera rispetto a processi tradizionali.
Il modello predittivo sviluppato ha fornito un’accurata stima dei tempi di produzione e del fabbisogno energetico, consentendo di integrare le valutazioni prestazionali, economiche e ambientali in un unico strumento di supporto decisionale. Tale approccio ha portato alla definizione di un modello integrato di sostenibilità, applicabile in ambito industriale per ottimizzare la progettazione dei componenti e la gestione dei parametri di processo nell’ottica di Design for Environment.
Pertanto, questo lavoro contribuisce ad ampliare le conoscenze scientifiche sul processo di FW, ancora poco trattato in letteratura dal punto di vista della sostenibilità, e offre un modello applicabile a livello industriale per il miglioramento del ciclo produttivo di componenti in composito, favorendo l’adozione di soluzioni a basso peso con impatti ambientali contenuti, pur trattandosi di componenti realizzati in materiale composito.The present work is in line with the growing need to develop innovative and sustainable manufacturing processes able to combine high mechanical performance with low costs and reduced environmental impacts. In this context, the attention has been focused on Filament Winding (FW) process which is an automated technology to produce fiber-reinforced polymer-matrix composite components widely used in aerospace, automotive, energy, marine and many other industrial sectors.
The main goal of this study is to analyse the technological, mechanical, environmental and economic aspects related to the FW process, by using an integrative approach.
The methodology adopted consisted of three complementary and interconnected phases: a laboratory-scale experimental investigation of the FW process, aimed to assess the effect of the main process parameters on the mechanical properties of the manufactured components; the design of industrial structural components followed by environmental and economic impact analyses using the standardized methodologies of Life Cycle Assessment (LCA) and Life Cycle Costing (LCC); and finally, the development of a predictive mathematical model based on a non-linear multiple regression to estimate winding times and energy consumption as a function of input parameters, subsequently integrated into a model for the evaluation of environmental and economic sustainability of the process.
The experimental study allowed to identify the most relevant correlations between technological parameters and the mechanical properties of the wound composite components, highlighting the crucial role of the winding angle. The LCA and LCC analyses showed that the main source of the environmental impacts is related to the production of the carbon fibers, while the automation of the FW process significantly reduces energy consumption and labour costs compared to traditional manufacturing techniques.
The predictive model developed provided an accurate evaluation of production times and energy demand, enabling performance, economic, and environmental assessments to be integrated into a single decision-support tool. This approach led to the definition of an integrated sustainability model, which can be useful for the industrial to optimize the component design and the process parameters management, according with Design for Environment principles.
Therefore, this work contributes to expanding the scientific knowledge about the FW process, which has not yet been widely discussed in literature from a sustainability perspective, and offers a model that can be applied by industries to improve the production cycle of composite components, promoting the adoption of lightweight solutions with limited environmental impacts, even though the components are realized by composite materials
Marine benthic foraminifera diversity in extreme environments: A case study from the Edisto Bay (Ross Sea, Antarctica)
Full text linkAntarctica and its coastal systems are highly sensitive to climate change, with rapidly evolving environmental conditions affecting benthic ecosystems. This study presents a detailed analysis of living benthic foraminiferal communities in Edisto Inlet (Ross Sea, Antarctica), a poorly explored coastal area. We investigated the spatial distribution of foraminiferal assemblages and their relationship with environmental variables, including sedimentation rate, redox potential, organic matter content, and bottom currents. By integrating foraminiferal, sedimentological, and oceanographic data, three distinct environmental zones were identified within the inlet, revealing pronounced ecological gradients: outer (station 180), middle (station 24), and inner (station 34). The outer zone exhibited high-energy conditions, well-oxygenated sediments, and diverse, abundant communities dominated by calcareous species Trifarina angulosa, monothalamous morphotypes Micrometula sp., and agglutinated taxa (Miliammina arenacea, Portatrochammina antarctica). The middle section, characterized by moderate sedimentation and suboxic conditions, supported lower diversity and abundance, with agglutinated species (Paratrochammina bartrami, Portatrochammina antarctica) and monothalamous taxa (Tinogullmia sp., Psammophaga magnetica) thriving in organic-rich sediments. The inner zone presented low-energy, highly hydrated sediments with strong microbial activity, a markedly reduced redox potential, and suboxic to anoxic conditions, where opportunistic calcareous (Globocassidulina biora, Globocassidulina subglobosa, Bolivinellina pseudopunctata) and monothalamous species (Psammosphaerid spp., Hippocrepinella hirudinea) persisted. Overall, our findings emphasize the strong link between benthic foraminiferal assemblages and local physico-chemical conditions, providing essential ecological baselines for Antarctic fjord systems. By demonstrating the responsiveness of living foraminifera to environmental variability, this study offers valuable insights into present ecosystem functioning and the implications of ongoing climate change for polar coastal environments
Large Language Models Powered Expert Systems for Decision Making in Facility Management
No full textLa crescente complessità dei processi decisionali nel Facility Management richiede sistemi intelligenti capaci di integrare l’esperienza umana con tecnologie adattive. I sistemi esperti tradizionali, pur affidabili ed esplicabili, restano statici e limitati, e faticano ad affrontare domini dinamici e ricchi di dati come la sicurezza antincendio, dove occorre conciliare informazioni non strutturate, vincoli normativi e di salvaguardia della vita. I Large Language Models (LLMs) offrono capacità rilevanti di estrazione e ragionamento, ma mancano della struttura e della trasparenza necessarie nei contesti ad alto rischio. Questa tesi propone un framework ibrido di knowledge engineering umano ed AI, che unisce il formalismo di CommonKADS al ragionamento adattativo degli LLM mediante la metodologia Chain-of-Agents (CoA).
Il framework CoA interpreta il knowledge engineering come un processo di progettazione continuo. Invece di costruire un sistema esperto statico, esso genera un sistema decisionale che si adatta ai casi specifici. Basandosi sulla Knowledge Level Theory di Allen Newell, si distingue un meta-livello (progettazione della conoscenza) e un livello applicativo (esecuzione), permettendo una co-evoluzione tra formulazione del problema e risoluzione. Al meta-livello, sotto la supervisione umana, gli LLM trasformano informazioni non strutturate (come narrazioni e planimetrie) in parametri strutturati e poi producono il workflow CoA. Gli esperti validano ogni output, garantendo tracciabilità e coerenza. Al livello applicativo, la catena validata di agenti LLM esegue i compiti di ragionamento autonomo, generando raccomandazioni interpretabili.
La ricerca applica il CoA alla sicurezza antincendio, uno dei settori più complessi della gestione del patrimonio costruito. Attraverso casi di studio basati su incidenti storici riportato dalla National Fire Protection Association (NFPA), il sistema mostra come gli LLM possano estrarre parametri, derivare regole e proporre strategie di prevenzione ed evacuazione a partire dalle informazioni presenti nei testi e planimetrie bidimensionali. Esperimenti preliminari eseguiti con GPT-4.1, Gemini 2.5 Flash e Ollama LLaMA-3 (one-shot, few-shot e Chain-of-Thought) hanno valutato le prestazioni dei modelli e identificato Gemini 2.5 Flash come il più adatto per le esecuzioni CoA. Infatti, il confronto tra CoA e prompt one-shot mostra che, mentre quest'ultimo ha prodotto output incompleti e generici, CoA ha generato risultati strutturati e sensibili al contesto.
Le valutazioni effettuate da ingegneri qualificati nel settore della progettazione antincendio, confermano che il sistema CoA migliora spiegabilità, coerenza e solidità tecnica rispetto ai singoli LLM. Gli LLM mostrano tuttavia una tendenza all’eccessivo ottimismo, evidenziando la necessità di audit umani d coinvolgere neo ragionamento. Il meccanismo AI-in-the-loop permette infatti agli esperti di verificare e correggere iterativamente la conoscenza generata, riducendo allucinazioni e garantendo la presa di responsabilità. Combinando adattabilità degli LLM e rigore del knowledge engineering, il framework offre un’automazione affidabile ed esplicabile.
In conclusione, la ricerca propone un modello epistemico che considera la creazione della conoscenza come un processo dinamico e iterativo, supervisionato dall’uomo e supportato dall’AI. Questo modello orienta il Facility Management verso un paradigma centrato sulla conoscenza, permettendo la generazione in pochi minuti di sistemi adattati al caso specifico. Sebbene validato sulla sicurezza antincendio degli edifici, la metodologia offre basi per la generazione di sistemi adattivi anche in altri ambiti dell’ambiente costruito, spiegabili e affidabili. Ricerche future potranno valutare l’allineamento agli standard e l’applicazione in altri domini di applicazione rappresentativi.The increasing complexity of decision-making in construction facility management demands intelligent systems capable of integrating human expertise with adaptive technologies. Traditional expert systems, while reliable and explainable, remain static and brittle, struggling to cope with dynamic, data-rich domains such as fire safety management where decisions must reconcile unstructured information, regulations, and life-critical constraints. On the contrary, Large Language Models (LLMs) offer remarkable capabilities for knowledge extraction and reasoning but lack the structural rigor, transparency, and accountability required for high-stake domains. This thesis proposes a hybrid human-AI knowledge engineering framework that combines the structured formalism of CommonKADS with the adaptive reasoning of LLMs through a novel Chain-of-Agents (CoA) methodology.
The CoA framework redefines knowledge engineering as a continuous design process rather than a static model-building exercise. Instead of constructing a classical expert system, the approach generates an intelligent decision support system that dynamically adapts to specific cases. Drawing on Allen Newell’s Knowledge Level theory, the framework distinguishes between a meta-level (knowledge design) and an application-level (knowledge execution), enabling real-time co-evolution between problem formulation and solution generation. At the meta-level, under the supervision of human experts, LLMs carry out the knowledge design by sequentially transforming unstructured information (narratives, building layouts) into structured case-specific parameters and generates a CoA workflow execution for the decision support. Each output is validated by human experts, ensuring accountability, traceability, and domain alignment. At the application level, the validated chain of LLM-agents executes reasoning tasks autonomously, producing interpretable, domain-specific recommendations.
This research applies the CoA framework to fire safety management, one of the most complex and safety-critical areas of facility management. Using case studies based on historical fire incidents reported by the National Fire Protection Association (NFPA) (including the Rhythm Club, Cocoanut Grove, and Beverly Hills Supper Club fires) the system demonstrates how LLMs can extract parameters, derive reasoning rules, and propose fire prevention and evacuation strategies from both text narratives and 2D layouts. Preliminary experiments were conducted using GPT-4.1, Gemini 2.5 Flash, and Ollama LLaMA-3 under one-shot, few-shot, and Chain-of-Thought prompting in two phases to understand how single agent performs under simple or complex tasks, and to select most suitable model for CoA executions. Then, selected model, GEMINI 2.5 Flash, run CoA workflows. Later, this agentic structure was compared with one-shot prompting for complex decision making. Results show that while simple one-shot prompting led to incomplete and generic outputs, the CoA-based multi-agent reasoning produced structured, transparent, and context-aware results.
Quantitative and qualitative evaluations involving certified fire safety engineers confirmed that the hybrid CoA system significantly improved explainability, coherence, and technical soundness compared to single-agent LLM outputs. However, LLMs often exhibited overconfidence in evaluating their outputs, highlighting the necessity of human auditing in complex reasoning. Therefore, AI-in-the-loop mechanism enabled human experts to audit, refine, and correct LLM-generated knowledge iteratively, mitigating hallucinations and ensuring ethical accountability. By aligning LLM adaptability with structured knowledge engineering, the proposed framework achieves a good balance: explainable and responsible automation with artificial intelligence.
In conclusion, this research presents a new epistemic model that approaches knowledge creation as a dynamic and iterative design process under human oversight and supported by artificial intelligence. This model advances facility management toward a knowledge-centric paradigm, enabling the creation of dynamic, situation-specific systems within minutes to support complex decision-making processes under uncertainty. Beyond fire safety, the proposed methodology establishes the theoretical and methodological foundation for the next generation of adaptive, explainable, and accountable knowledge-based systems across the built environment. Future research should investigate the alignment of the outputs with regulations and conduct experiments in different complex domains
Dietary Glyphosate Exposure Disrupts Hepatic and Reproductive Function in Female Zebrafish at Regulatory Safe Levels
Full text linkGlyphosate (GLY), the active ingredient in widely used herbicides, was long considered specific to plants and bacteria, yet mounting evidence shows it can impair endocrine and reproductive functions in animals. Given its widespread use and environmental persistence, assessing its effects at regulatory-approved doses is critical. Here, adult female zebrafish (Danio rerio) were exposed for 21 days to different concentrations of dietary GLY at 0.5 mg/kg body weight/day (GLY0.5, acceptable daily intake, ADI), 5 mg/kg/day (GLY5), and 50 mg/kg/day (GLY50, no-observed-adverse-effect level, NOAEL). Our findings show that dietary GLY induces dose-dependent perturbations along the hepato-gonadal axis. At the highest dose, chronic stress responses were evident through elevated cortisol and cortisone, accompanied by hepatic glycogen accumulation and ferroptotic stress. Although follicle histology appeared normal, alterations in several genes involved in oocyte maturation and estrogen receptor signaling translated into reduced fertilization, revealing compromised gamete quality rather than overt follicular development abnormality. Likewise, the lowest dose triggered modifications in genes crucial for oogenesis without altering the follicle development, although in this case, potential compensatory mechanisms could have led to enhanced fertilization. GLY5 did not alter the number of fertilized eggs but significantly increased embryo mortality. Overall, dietary GLY disrupted hepatic metabolism, endocrine signaling, and reproduction in a non-monotonic manner, even at levels considered safe by EFSA. These findings highlight the need to reevaluate current safety thresholds with attention to female-specific reproductive risks
Artificial Intelligence-Based Vision Systems for Automated Footwear Disassembly
No full textThe increasing consumer preference for shortlifespan products has led to significant environmental challenges in waste management. This process needs to be automated to make the disassembly of footwear easier and thus more widespread; to this scope vision-based systems are paramount. This paper presents the concept of an automated disassembly line for footwear disassembly based on artificial intelligence-based vision systems. The first vision system extracts morphological features, which are useful for identifying and processing the different components of the shoe, while another system extracts the contour of the shoe to be used as a trajectory for the robot in charge of removing the stitching that joins the sole and upper. Experimental results confirm the efficiency of Artificial Intelligence (AI) in extracting morphological features and measuring geometry of the contour crucial for automation