Search Results (62)

Limited availability of veterinary medicinal products (VMPs) for small ruminants is a long-standing challenge. This mixed-methods study provides the first systematic definition and assessment of (i) shortages, (ii) lack of availability and (iii) unmet needs for sheep and goats across Europe. Survey data from 96 European veterinarians in 13 European countries (the majority of whom were from Spain, Germany, France, and Greece), a market analysis of authorized and available VMPs via the EMA Union Products Database (UPD) and expert knowledge elicitation (EKE) by 23 specialists were combined. Antimicrobials (36.7%) and nonsteroidal anti-inflammatory drugs (NSAIDs) (19.9%) were identified as the VMP categories most commonly perceived as critically important. Although nearly 5000 VMPs are authorized for small ruminants at the EU level, UPD market research reveals that there is a reported market availability of 28.9% of ovine and 32.7% of caprine authorized VMPs. Validation by EKE confirmed lack of VMP authorization to be the leading root cause of the lack of availability of ovine (31%) and caprine (43%) VMPs at both the national and EU level. The European Veterinary Medicines Gaps and Needs Compass identifies four highest-priority medicine groups lacking in availability for sheep (anthelmintics and endectocides, bacterial and viral vaccines) and two for goats (anthelmintics and bacterial vaccines). Moreover, 13 highest-priority unmet needs were identified for sheep and 14 for goats across antibacterials, analgetics and anti-inflammatories, antiparasitics and vaccines. Potential actionable solutions advised through EKE include harmonized market access pathways and targeted development (especially for vaccines, NSAIDs, and antibiotic teat injectors) to secure animal health, welfare, and One Health objectives. Full article

Accurately evaluating the intelligence of autonomous path planning remains challenging, primarily due to the interdependencies among evaluation metrics and the insufficient integration of subjective and objective weighting methods. This paper proposes Game-Theoretic Kendall’s Coefficient (GTKC) weighting framework for evaluating autonomous path planning intelligence. The framework specifies a safety–efficiency–comfort metric system with observable, reproducible, and quantifiable metrics. To account for intermetric dependence, subjective weights are elicited via an improved Analytic Network Process (ANP), while objective weights are derived using the CRITIC method to capture contrast intensity and intercriteria conflict. The credibility of the subjective and objective weights is evaluated using Kendall’s coefficient and the coefficient of variation, respectively. Subsequently, based on the principle that higher credibility should receive greater weight, a game-theoretic optimization model is employed to dynamically derive optimal combination coefficients. Experimental results on three case scenarios demonstrate that the GTKC framework significantly outperforms existing weighting approaches in terms of effectiveness (achieving a lowest Mean Absolute Error (MAE) of 0.15 and a perfect Spearman’s correlation coefficient ($\overline{\rho }=1.0$) with ground-truth rankings), stability (Mean Standard Deviation (MSD) = 0.023), and ranking consistency (Kendall’s coefficient W = 0.924). These findings validate GTKC as a theoretically grounded and practically robust mechanism that explicitly models metric interdependencies and integrates expert knowledge with empirical evidence, enabling reliable and reproducible evaluation of autonomous path planning intelligence. Full article

The authors frame greenhouse operation as a Controlled Environment Agriculture (CEA) challenge involving multiple interdependent targets: air temperature and humidity, CO₂ enrichment, photoperiod-constrained lighting, and irrigation under dynamic and limited energy availability. We propose a knowledge-driven, multi-objective Model Predictive Controller whose cost function integrates expert priorities elicited via an online Analytic Hierarchy Process (AHP) survey; these AHP-derived weights parameterize the controller’s objectives and are solved over two 72 h seasonal episodes, so the MPC can anticipate renewable availability and coordinate HVAC, (de)humidification, CO₂ dosing, LED lighting, and irrigation alongside dispatch from photovoltaic and wind sources, battery storage, and the grid. By embedding the physical interdependence of climate variables directly into the decision layer, the controller schedules energy-intensive actions around renewable peaks and avoids counterproductive actuator conflicts. Seasonal case studies (summer/high solar and winter/low solar) demonstrate robust performance: temperature tracking errors of SMAPE 2.25%/3.05% and CO₂ SMAPE 3.72–3.92%; humidity control with SMAPE 7.04–8.56%; lighting and irrigation following setpoints with low NRMSE (0.08–0.14). Summer energy was 59% renewable; winter was only 13%, increasing grid reliance to 77.5% (peaks: 4.57 kW/6.92 kW for 197.7/181.5 kWh). Under water or energy scarcity, the controller degrades gracefully, protecting high-priority agronomic variables while allowing bounded relaxation on lower-priority targets. This expert-informed, predictive, and resource-aware orchestration offers a scalable route to precision greenhouse control within the food–water–energy nexus. Full article

The emergence of Industry 5.0 brings a paradigm shift towards collaborative environments where humans and intelligent robots work side-by-side, enabling personalized, flexible, and resilient manufacturing. However, integrating humans and robots introduces new operational and safety risks that require proactive and adaptive control strategies. This study proposes an intelligent multi-criteria decision-making framework for smart risk assessment and the selection of optimal adaptive control strategies in human–robot collaborative manufacturing settings. The proposed framework integrates advanced risk analytics, real-time data processing, and expert knowledge to evaluate alternative control strategies, such as real-time wearable sensor integration, vision-based dynamic safety zones, AI-driven behavior prediction models, haptic feedback, and self-learning adaptive robot algorithms. A cross-disciplinary panel of ten experts structures six main and eighteen sub-criteria spanning safety, adaptability, ergonomics, reliability, performance, and cost, with response time and implementation/maintenance costs modeled as cost types. Safety receives the most significant weight; the most influential sub-criteria are collision avoidance efficiency, return on investment (ROI), and emergency response capability. The framework preserves linguistic semantics from elicitation to aggregation and provides a transparent, uncertainty-aware tool for selecting and phasing adaptive control strategies in Industry 5.0 collaborative cells. Full article

Remote sensing visual question answering (RSVQA) involves interpreting complex geospatial information captured by satellite imagery to answer natural language questions, making it a vital tool for observing and analyzing Earth’s surface without direct contact. Although numerous studies have addressed RSVQA, most have focused primarily on answer accuracy, often overlooking the underlying reasoning capabilities required to interpret spatial and contextual cues in satellite imagery. To address this gap, this study presents a comprehensive evaluation of four large multimodal models (LMMs) as follows: GPT-4o, Grok 3, Gemini 2.5 Pro, and Claude 3.7 Sonnet. We used a curated subset of the EarthVQA dataset consisting of 100 rural images with 29 question–answer pairs each and 100 urban images with 42 pairs each. We developed the following three task-specific frameworks: (1) Zero-GeoVision, which employs zero-shot prompting with problem-specific prompts that elicit direct answers from the pretrained knowledge base without fine-tuning; (2) CoT-GeoReason, which enhances the knowledge base with chain-of-thought prompting, guiding it through explicit steps of feature detection, spatial analysis, and answer synthesis; and (3) Self-GeoSense, which extends this approach by stochastically decoding five independent reasoning chains for each remote sensing question. Rather than merging these chains, it counts the final answers, selects the majority choice, and returns a single complete reasoning chain whose conclusion aligns with that majority. Additionally, we designed the Geo-Judge framework to employ a two-stage evaluation process. In Stage 1, a GPT-4o-mini-based LMM judge assesses reasoning coherence and answer correctness using the input image, task type, reasoning steps, generated model answer, and ground truth. In Stage 2, blinded human experts independently review the LMM’s reasoning and answer, providing unbiased validation through careful reassessment. Focusing on Self-GeoSense with Grok 3, this framework achieves superior performance with 94.69% accuracy in Basic Judging, 93.18% in Basic Counting, 89.42% in Reasoning-Based Judging, 83.29% in Reasoning-Based Counting, 77.64% in Object Situation Analysis, and 65.29% in Comprehensive Analysis, alongside RMSE values of 0.9102 in Basic Counting and 1.0551 in Reasoning-Based Counting. Full article

Background: Evidence is growing that psychedelic substances have positive effects in the setting of Palliative Care (PC), focusing on special needs in this patient population. After a scoping review of the literature, no published expert recommendations guiding best practice for psychedelic-assisted therapy (PAT) towards the end of life were identified. Objective: To draw conclusions from first-hand experienced professionals on PAT in PC (PATPC). Setting, Design, Participants: An international workshop with experts was held in Wasserfallen, Switzerland. A thematic analysis of a semi-structured, questionnaire-based qualitative study with 13 experts in PC, oncology, psychiatry/psychology, and PAT from Europe, the United States, and Oceania was made. Measurements: The questionnaire was designed to elicit the participant’s perspectives on (A) special considerations on PATPC, (B) specific characteristics of PATPC (versus mental illness), and (C) the relevance of these differences during preparation, substance dosing session, and integration in PATPC. Results: (A) Special Considerations included (non-medicalized) setting, potential need, and possibility to reduce preparation time. (B) Distinguishing characteristics included the patient’s intrinsic motivation for treatment success, the importance of anxiety, depression, and spiritual distress as indications for PATPC, and the importance of sufficient integration of the psychedelic experience into life in the face of limited time due to the life-limiting illness. (C) Flexibility in setting and timing of preparation, choosing the appropriate dosage of the psychedelic substance depending on the patient’s intended focus, low/medium (relational issues), higher for transcendental experiences, considering mental capacity and vulnerability for the individual. In addition, respondents noted that for therapists, knowledge about transcendental states, such as mystical experiences, existential aspects of life-threatening illness, and the role of therapists’ own self-experience/inner work, as well as good knowledge of the theoretical basis for treatment, was highlighted. Conclusions: This study highlights special considerations for PAT PC and could be a first step towards specific treatment recommendations (guidelines) for PATPC. Full article

Ontologies are essential tools for representing, organizing, and sharing knowledge across various domains. This study presents a methodology for ontology construction supported by large language models (LLMs), with an initial application in the automotive sector. Specifically, a user preference ontology for adaptive interfaces in autonomous machines was developed using ChatGPT-4o. Based on this case study, the results were generalized into a reusable methodology. The proposed workflow integrates classical ontology engineering methodologies with the generative and analytical capabilities of LLMs. Each phase follows well-established steps: domain definition, term elicitation, class hierarchy construction, property specification, formalization, population, and validation. A key innovation of this approach is the use of a guiding table that translates domain knowledge into structured prompts, ensuring consistency across iterative interactions with the LLM. Human experts play a continuous role throughout the process, refining definitions, resolving ambiguities, and validating outputs. The ontology was evaluated in terms of logical consistency, structural properties, semantic accuracy, and inferential completeness, confirming its correctness and coherence. Additional validation through SPARQL queries demonstrated its reasoning capabilities. This methodology is generalizable to other domains, if domain experts adapt the guiding table to the specific context. Despite the support provided by LLMs, domain expertise remains essential to guarantee conceptual rigor and practical relevance. Full article

Background and Objectives: Diabetes is a rapidly increasing global health challenge compounded by a critical shortage of diabetes care and education specialists. Robot-assisted diabetes care offers a cost-effective and scalable alternative to traditional methods such as training and dispatching human experts. This pilot study aimed to evaluate the feasibility of using robots for diabetes care consultations by examining their ability to elicit meaningful patient feedback, identify therapeutic issues, and assess their potential as substitutes for human specialists. Materials and Methods: A robot-assisted consultation programme was developed by selecting an appropriate robot, designing the programme content, and tailoring back-channel communication elements. Experienced diabetes care nurses operated the robot during the consultations. Patient feedback was collected through a 17-item questionnaire using a five-point Likert scale (evaluating functionality, impressions, and effects). Additionally, a five-item questionnaire was used to assess whether the programme helped patients reflect on the key therapeutic domains of diabetes knowledge, diet, exercise, medications, and blood glucose control. Results: This study included 32 participants (22 males; mean age, 69.7 ± 12.6 years; mean HbA1c, 7.2 ± 1.0%). None of the participants reported any discomfort during the consultation. Sixteen of the seventeen feedback items scored above the median of 3, as did all five therapeutic reflection items. The interview content analysis revealed the programme’s ability to differentiate patients facing issues in treatment compliance from those effectively managing their condition. Robots can elicit valuable patient narratives like human specialists. Conclusions: The results of this pilot study support the feasibility of robot-assisted diabetes care to assist human experts. Future research should explore the programme’s application with healthcare professionals with limited experience in diabetes care, further demonstrating its scalability and utility in diverse healthcare settings. Full article

Background/Objectives: The global spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) warrants collaborative action. Guidance should come from integrated One Health surveillance; however, a surveillance strategy is currently unavailable due to insufficient knowledge on the sources and transmission routes of CRPA. The aim of the SAMPAN study (“A Smart Surveillance Strategy for Carbapenem-resistant Pseudomonas aeruginosa”) is to develop a globally applicable surveillance strategy. Methods: First, an international cross-sectional study will be conducted to investigate CRPA in clinical and environmental settings in Rotterdam (The Netherlands), Rome (Italy), and Jakarta (Indonesia). Screening cultures and risk factor questionnaires will be taken from healthy individuals and patients upon hospital admission. Clinical CRPA isolates will also be included. Additionally, samples will be taken twice from wet hospital environments and monthly from the hospitals’ (drinking) water system, hospital and municipal wastewater treatment plants, and receiving rivers. Whole-genome sequencing will be performed to characterize CRPA isolates and determine the genetic relatedness among the isolates from different reservoirs. Findings from the cross-sectional study, combined with expert elicitation using a Delphi method, will serve as the input for the surveillance strategy. Conclusions: The SAMPAN study will provide a broader understanding of the sources and transmission routes of CRPA. Therewith, the development of a globally applicable smart surveillance strategy will be made possible, delivering information that is needed to guide actions against the spread of CRPA. Full article

Mobile apps have revolutionized almost every aspect of our daily lives, shaping the way we shop, learn and work. The transformative and unprecedented impact they have made on our lifestyle and the convenience they have offered have increased their adoption in diverse domains. Therefore, it is of paramount importance to hear from the interested audience about their desires and requirements in mobile apps. This has stressed the need to employ crowdsourcing in requirements engineering (RE) activities to harness the scattered talent in the crowd. RE tasks require certain software domain knowledge, hence, selecting a suitable subset of the crowd is crucial to obtain high-quality contributions. For that, we propose a crowd selection approach for crowdsourcing mobile app requirements engineering tasks which leverages the untapped crowd available on the social network Twitter (recently changed to X). This article is an extension of our previous work, where we present the proposed social-network-based crowd selection approach design, continue to work on the remaining component of the approach and evaluate the approach through a controlled experiment. For evaluation, the approach was utilized to select a real crowd that were invited to contribute to crowdsourcing requirements elicitation tasks for a fitness mobile app. The quality of the crowdsourced requirements was assessed by experts and the results have provided encouraging and compelling insights about the effectiveness of the proposed approach. The obtained assessment scores for the five quality factors clarity, creativity, relatedness, feasibility and diversity were respectively 4.36, 4.01, 4.29, 4.45 and 4.43 out of 5. Overall, we believe that the proposed social-network-based crowd selection approach could help in eliciting mobile app requirements and features that could cater to the needs of a large audience. Full article

Background: The evaluation of evidence, which frequently takes the form of scientific evidence, necessitates the input of experts in relevant fields. The results are presented as expert opinions or expert evaluations, which are generally accepted as a reliable representation of the facts. A further issue that remains unresolved though is the process of evaluating the expertise and knowledge of an expert in the first instance. In general, earned certificates, grades and other objective criteria are typically regarded as representative documentation to substantiate an expert status. However, there is a possibility that these may not always be sufficiently representative. Objectives: The goal of the present study was to provide evidence that the neural processing of law-relevant and law-irrelevant terms varies significantly between participants who have received training in the field of law (experts) and those who have not (novices). Methods: To this end, changes in brain activity were recorded via electroencephalography (EEG) during visual presentations of terms belonging to five different categories (fake right, democracy, filler word, basic right and rule of law). Event-related potentials (ERPs) were subsequently averaged for each category and subjected to statistical analysis. Results: The results clearly demonstrate that participants trained in law processed fake rights and filler words in a similar manner. Furthermore, both of these conditions elicited different levels of brain activity compared to all law-relevant terms. This was not the case in participants who had not received legal training. The brains of untrained participants processed all five term categories in a strikingly similar manner. In light of prior knowledge regarding language processing, the primary focus was on two distinct electrode locations: one in the left posterior region, and the other in the left frontal region. In both locations, the most prominent differences in brain activity elicited by the aforementioned term categories in law-trained participants occurred approximately 450 milliseconds after stimulus onset. The results were further corroborated by a repeated-measures ANOVA and subsequent t-tests, which also demonstrated the absence of this effect in law-untrained participants. Conclusions: The findings of this study provide empirical evidence that brain activity measurements, in particular ERPs, can be used to distinguish between experts trained in a specific field of expertise and novices in that field. Such findings have the potential to facilitate objective assessments of expertise, enabling comparisons between experts and novices that extend beyond traditional criteria such as qualifications and experience. Instead, individuals can be evaluated based on their cognitive processes, as observed through brain activity. Full article

Misalignment between second language (L2) self-perception and actual ability is often observed among L2 learners. In order to further understand this phenomenon, the current study investigated how the roles of individual differences (IDs; especially experiential and cognitive IDs) influence the learners’ self-assessment accuracy. To this end, L2 speech samples elicited from 97 Japanese learners of English were analyzed via self-evaluation and expert evaluations. Subsequently, learners’ IDs profiles, including working memory, phonological memory, implicit learning and auditory processing, were linked to (a) the gap between self- and expert evaluation scores and (b) the type of inaccurate self-evaluation (i.e., overconfident vs. underconfident evaluations). The study illustrates the complex relationships between L2 learners’ linguistic knowledge, cognitive abilities, experiential profiles and self-perception. Full article

Many systems engineering projects begin with the involvement of stakeholders to aid in decision-making processes. As an application of systems engineering, systems architecture involves the documentation of stakeholder needs gathered via elicitation and the transformation of these needs into requirements for a system. Within human–machine teaming, systems architecture allows for the creation of a system with desired characteristics elicited from stakeholders involved with the project or system. Though stakeholders can be excellent sources for expert opinion, vested interests in a project may potentially bias stakeholders and impact decision-making processes. These biases may influence the design of the system architecture, potentially resulting in a system that is developed with unbalanced and misrepresented stakeholder preferences. This paper presents an activity analysis of the Stakeholder Needs and Requirements Process as described in the Systems Engineering Body of Knowledge (SEBoK) to identify potential biases associated with this elicitation process. As part of the research presented in this paper, a workshop was conducted where currently practicing systems architects provided feedback regarding perceptions of biases encountered during the elicitation process. The findings of this research will aid systems architects, developers, and users in understanding how biases may impact stakeholder elicitation within the architecting process. Full article

Landscape restoration activities must be conducted through a transdisciplinary process, integrating social, economic, environmental, and governance aspects. Combining visions from the natural and social sciences is a challenge in highly complex territories, where unique ecosystem characteristics, economic processes, stakeholders of diverse nature, and different normativity converge. The harmonization of multiple techniques, such as multicriteria spatial analysis, expert knowledge elicitation, and social mapping, allows for an approach to defining landscape restoration areas in complex regions. This paper employs multiple techniques to define ecosystem restoration areas in a complex Colombian Andes landscape, integrating ecological and social components for sustainable development. We observed that areas of high and very high feasibility for ecological restoration, encompassing 179.5 hectares (4.84% of the study area), are predominantly located near primary forests. Although some areas have a low feasibility for conservation processes, they should not be disregarded as they still require protection. Landowners prioritize watershed and soil restoration as the most important landscape restoration activity due to their interest in improving water-related ecosystem services. This proposal enables the identification of areas with a higher restoration potential at the property level, facilitating prioritization and investment allocation for future implementation. Full article

Despite the increasing time sensitivity of climate change, many cities worldwide still heavily rely on coal. The extraction, processing, transport, and usage of coal lead to deteriorated air quality, resulting in complex environmental and public health problems for the local communities. Mapping different pollution sources in coal-centric cities is not trivial due to the hyperlocal nature of air pollution and the often low-density network of air quality monitors. This study explores the air quality issues surrounding coal-centric cities using a combination of qualitative and quantitative data from reference-grade air quality monitors, low-cost sensors (LCSs) deployed on citizens’ vehicles, and community engagement activities. It explores how LCSs can be used to characterize air quality at a high spatio-temporal resolution and how this information can be used to decode people’s perceptions of air quality issues and elicit local knowledge. We evaluated our approach in Sparwood (Canada), and Oskemen (Kazakhstan) which are very different cities, but are both heavily dependent on coal. LCSs have been proven an efficient tool to identify pollution hotspots that traditional reference monitors miss, while workshop-based activities making use of data maps and coding tools have successfully elicited information about pollution sources from non-experts, helping collaborative sense-making and informing new LCS deployment strategies. Understanding air quality in coal-centric cities as a complex socio-technical phenomenon can enable the coal industry, city officials, and residents to engage in addressing air quality issues. Full article