LAB University of Applied Sciences

Honey is a complex natural product valued for its nutritional and therapeutic properties, including antioxidant, antimicrobial, and anti-inflammatory effects. Its composition varies with botanical and geographical origin, making authenticity assurance essential. However, increasing adulteration, including the addition of low-cost sweeteners and mislabeling of origin, continues to undermine consumer trust and distort global markets. Rapid and reliable analytical approaches are therefore critical for effective quality control. This review provides a comprehensive overview of spectroscopic techniques for honey authentication and adulteration detection, including Ultraviolet-Visible spectroscopy, Near- and Mid-Infrared spectroscopy, Raman spectroscopy, Nuclear Magnetic Resonance spectroscopy, fluorescence spectroscopy, Hyperspectral Imaging, Laser-Induced Breakdown Spectroscopy, portable and field-deployable spectroscopic devices. These techniques rely on characteristic spectral fingerprints that capture subtle compositional variations. When coupled with multivariate chemometric tools, they enable efficient extraction of meaningful information from complex datasets, enhancing both classification and prediction performance. Representative studies demonstrate that techniques such as Near-Infrared spectroscopy combined with Partial Least Squares Discriminant Analysis achieve classification accuracies above 95%, while Raman spectroscopy with Partial Least Squares Regression enables quantification of sugar adulteration at low levels with high accuracy (R & sup2; > 0.98). The spectroscopic-chemometric approaches offer powerful, rapid, and non-destructive solutions for ensuring honey authenticity and market integrity.

ABSTRACT Emerging evidence suggests that reduced exposure to biodiversity, including rich environmental microbiota, is associated with negative outcomes in the health and well-being of children. Biodiversity loss not only impacts individual health but also poses significant threats to planetary health. It destabilizes systems that regulate climate, purify air and water, maintain soil fertility, and support plant and microbial life essential for environmental health. Here, we review the scientific evidence on microbiome-supportive strategies in eco-centric, child-friendly playground environments. Investigating how environmental features influence soil microbiomes and exposure pathways could provide insights into how playgrounds function as living interfaces. These are places where environmental microbes shape children’s microbial colonization patterns, immune and endocrine regulatory systems, while also contributing to ecosystem services such as biodiversity support and pollutant mitigation—particularly relevant given that many pollutants are known to disrupt immune and endocrine functions in children. These dynamics have far-reaching implications for child well-being, preventive health strategies, physical activity, environmental literacy, and broader sustainability. A multi-omic systems approach offers a critical pathway to uncover the ecological and health-related impacts of nature-associated microbial exposure and characterize host–microbiome interactions underlying immune and endocrine regulation, brain development, cognition, and stress-related disorders. Our review highlights a lack of such integrative studies, underscoring the need to advance this line of research to inform evidence-based, sustainable, and health-promoting urban design.

BackgroundStroke, multiple sclerosis (MS), and Parkinson's disease (PD) cause balance and walking impairments, which increase the risk of falls and reduce quality of life. Technological advances have enabled novel reactive balance approaches.ObjectiveTo synthesize the current approaches of technology-based perturbation interventions during walking and their effects on physical function in stroke, MS, and PD.MethodsA literature search was conducted in PubMed, PEDro, and ScienceDirect for studies published from January 2014 to December 2025, supplemented by citation tracking and grey literature. Eligible studies included interventional studies utilizing technology-based perturbation during walking in stroke, MS, or PD.ResultsTen studies were included. Participants were diagnosed with stroke (n = 83, 42%), MS (n = 77, 39%), or PD (n = 38, 19%). Most interventions used treadmill-based perturbations to induce slips, trips, or lateral balance instability. Interventions comprised a median of nine sessions over three weeks (25 min per session). Outcomes were grouped into four categories: clinical assessments, patient-reported outcomes, technology-based assessments, and assessments during walking training. Between-group differences compared to similar treatments without perturbation were inconsistent, but within-group improvements in walking speed, balance, and functional mobility were commonly reported. Adverse events were rare, and safety protocols were followed. Considerable heterogeneity in intervention protocols and outcome measures limited synthesis.ConclusionTechnology-based perturbation training during walking may offer an alternative for improving physical function in stroke, MS, and PD. Evidence of superiority over non-perturbation interventions remains inconclusive due to heterogeneity and small samples. Within-group improvements suggest benefits, warranting standardized protocols and larger high-quality trials.

Academic entrepreneurs (AEs) commercializing deep technologies operate under high uncertainty conditions, where technological advances precede clearly defined industrial applications. At low TRL, laboratory validation and demonstrations provide technical feasibility signals, yet problem definitions, user contexts, and system-level requirements remain narrow with limited perspective. Market information is fragmented and typically hard to translate into practical use. Under such conditions, commercialization unfolds as an iterative process of entrepreneurial framing, innovation learning, and external alignment. This study examines how AEs structure early commercialization through stakeholder engagement. Empirically grounded in a low TRL technology project, the research draws on expert interviews, internal team reflections, university support actors, workshops and a literature review. The literature provides a conceptual baseline against which emerging empirical themes are compared, refined, and structured. The analysis applies an inductive qualitative coding process, grouping findings into preliminary thematic categories such as entrepreneurial framing, iterative experimentation, business model development, technological advancement, and stakeholder alignment. Early-stage interactions within the team and university ecosystem played a critical role in articulating preliminary benefit hypotheses and shaping initial customer-oriented narratives. As engagement expanded to external stakeholders, effective progress depended on identifying actors with contextual authority and commercialization literacy. Rather than eliciting predefined requirements, exploratory questioning surfaced existing customer technical constraints. These dialogues enabled iterative reframing of problem–solution configurations, aligning technological competitive capabilities with differentiated customer contexts and value creation. Technical development progressed from proof-of-concept experimentation toward validated lab-scale feasibility, allowing more credible hypothesis testing regarding application-specific value propositions. The study conceptualizes early deep-tech commercialization as dialogical validation under time pressure and strategic risk. By integrating narrative stakeholder inquiry, structured analytical intelligence, and iterative technical experimentation, AEs can accelerate learning while managing uncertainty.

Social impact measurement is critically important in many kinds of organisations, particularly hybrid organisations with a social mission. Practitioners struggle with impact measurement and management (IMM). Social IMM involves the complex task of balancing meaningful impact assessment with metrics that are practical yet sufficiently reflective of key values. The study examines social IMM as a sensemaking accomplishment. What is presented as social impact is shaped by the sense-making of what counts as social impact and what is feasible to measure. Drawing on qualitative analysis of public social impact communications from three hybrid company cases, the study investigates how social impact is made meaningful, which forms of impact become measurable, and which meanings are more implicit. The analysis proceeds in three phases: first, examining how organisations construct meanings of social impact, then how they argue these meanings. This study analyses how certain interpretations are measurable or reportable. Third, the findings are synthesized to reflect the sensemaking of social impact to advance theoretical perspectives. Social impact presentations reveal traces of meaning that go beyond what is formally documented or quantified. Quantitative indicators tend to foreground discrete outcomes, while more processual, relational, and long-term impacts can also be acknowledged rhetorically. Public impact communications play a central role in legitimizing particular interpretations of impact. By conceptualising social impact measurement as a sensemaking process, this study underscores the construction of social impact meanings. The central challenge in IMM is mediating between assignable outcomes and diffuse systemic transformations. The study highlights the need for greater reflexivity about how meaning, attention, and resources shape what counts as social impact.