Search Results (2,916)

Objective: This paper investigates how Generation Z students perceive the quality, accessibility, and capacity of higher education in the Czech Republic, with relevance for the wider Visegrád (V4) region and for the design of educational platforms. Methodology: The study draws on a questionnaire survey of 819 students and analyses 38 Likert-type statements aggregated into five domains: digitalisation and technology, innovation in teaching, practical orientation and mobility, support and well-being, and capacity constraints and infrastructure. The analysis combines descriptive statistics, reliability and dimensionality checks, Pearson correlation analysis with normality and robustness diagnostics, and subgroup comparisons by gender, level of study, field of study, institution type, and age group. Results: Students report the strongest agreement with digitalisation and technology (M = 3.90) and practical orientation and mobility (M = 3.89), while support and well-being (M = 3.40) and capacity constraints and infrastructure (M = 3.36) remain weaker. Perceived adequacy of the student–teacher ratio is positively associated with perceived teacher availability (r = 0.37) and emotional support (r = 0.34), while teacher availability is positively associated with emotional support (r = 0.44). Subgroup tests indicate limited but meaningful differences, particularly by institution type, field of study, and age group. Conclusion: The findings suggest that Generation Z students value digitally enabled, practice-oriented and innovative educational platforms, but sustainable quality improvement also requires investment in human capacity, teacher availability, and student-facing support systems. Full article

Consumer resistance remains a major barrier to the effectiveness of AI-enabled social media marketing despite advances in content personalisation, influencer marketing, and intelligent recommendation systems. This study investigates how content personalisation, influencer trust, and platform interactivity influence consumer resistance, user engagement, and purchase intention by proposing a behaviourally informed Deep Reinforcement Learning (DRL) framework that integrates empirical behavioural modelling with adaptive optimisation. Survey data were collected from 619 higher education students in Saudi Arabia and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), Multi-Group Analysis (MGA), and a Deep Q-Network (DQN)-based optimisation framework. The results show that content personalisation, influencer trust, and platform interactivity significantly increase user engagement while reducing consumer resistance. User engagement positively influences purchase intention, whereas consumer resistance negatively affects purchasing behaviour. Multi-Group Analysis revealed that Generation Z responded more strongly to personalisation and platform interactivity, whereas Generation Y showed greater responsiveness to influencer trust. The proposed behaviourally informed DQN framework incorporated latent behavioural constructs and statistically validated structural relationships into the reinforcement learning environment to generate adaptive marketing policies. Compared with conventional static and rule-based strategies, the proposed framework achieved approximately 36% higher optimisation performance across repeated behavioural simulations. The study contributes by positioning consumer resistance as the central behavioural construct, introducing an integrated behavioural–computational framework that embeds empirical behavioural relationships into the DRL state representation, reward mechanism, and policy-learning process, and providing practical guidance for developing transparent, trust-sensitive, and adaptive social media marketing strategies that enhance user engagement, reduce consumer resistance, and improve purchase intention in digital commerce environments. Full article

The Standard Model accurately describes particle phenomena through continuous gauge fields, color, chirality, generations, and Yukawa couplings, but it does not derive these labels from a deeper structural principle. This paper proposes a carrier-resolution interpretation in which particle species are carrier-readable manifestations of a common loop-detectable codimension-two archetype defect. The carrier supplies Lorentzian propagation and globally available $U\left(1\right)$ phase closure, while particle labels arise through holonomy, embedding, closure, and read-out conditions. The first persistent asymmetric resolution contains a lepton-like $Z_2$-Lorentz branch and a hadron-supporting branch with confined $Z_3$ closure. The $Z_2$ branch accounts for spinorial and chiral read-out through twofold holonomy and Lorentz embedding, while the three observed fermion generations are interpreted as the three leading saturated projective embedding layers of the common $Z_2$-Lorentz branch, not as consequences of the $Z_3$ color-like layer. In this framework, $Z_3$ supplies hadronic sectorality, and higher $Z_n$ refinements provide suppressed mass and response corrections rather than additional ordinary generations. The usual $SU{\left(3\right)}_C$ QCD description is retained as the effective after-read-out continuum gauge theory of color dynamics revealed by high-energy probes. The proposal does not replace QCD; instead, it interprets confined $Z_3$ closure as a pre-read-out structural condition whose incomplete sectors are not carrier-readable as isolated hadrons. As a quantitative test, the neutron–proton magnetic-moment ratio is derived from an ideal $Z_3$-complete baseline, a rule-generated closure-interface sequence, and a neutral-parent magnetic completion. The same-branch sequence reaches a sub-ppm residual and then saturates, so the remaining discrepancy is assigned to a neutral magnetic-completion seam rather than to deeper $Z_n$ terms. The resulting prediction is $-0.684979364944$, differing from the CODATA value of $-0.68497935\left(16\right)$ by about $0.022$ ppm, or $0.093$ standard deviations. No coefficient is adjusted to fit the observed value. The result is presented as a sharp no-fit test of carrier-resolution and neutral-parent closure, not as a replacement for QCD or a complete theory of all baryon magnetic moments. Full article

Pharmaceutical residues in wastewater pose persistent ecological and public health risks, creating an urgent need for efficient and sustainable remediation technologies. MXene-based photocatalysts have attracted growing interest owing to their high electrical conductivity, tunable surface chemistry, abundant active sites, and excellent charge-transfer capability. This review summarizes recent advances in MXene-based photocatalytic systems for pharmaceutical wastewater treatment and renewable energy production. Key topics include pharmaceutical degradation pathways, reactive oxygen species generation, ecotoxicological implications, and the multifunctional roles of MXenes as conductive supports, electron mediators, and cocatalysts. Interfacial engineering strategies, including Z-scheme, S-scheme, and Schottky heterojunctions, are discussed with respect to light absorption, charge separation, and interfacial redox reactions. Practical considerations, such as reactor design, life cycle assessment, and techno-economic feasibility, are also addressed. Finally, current challenges and future directions are highlighted, particularly scalable fluorine-free synthesis, improved oxidative stability, and machine learning-assisted material design. This review provides a concise framework for developing stable, efficient, and scalable MXene-based photocatalytic platforms for pharmaceutical wastewater remediation and sustainable energy generation. Full article

Objectives: This study investigates the repeatability and reproducibility of radiomic features extracted from different energy levels of virtual monoenergetic reconstruction (VMER) and polyenergetic reconstruction (PER) obtained with photon-counting computed tomography (PCCT). Methods: Sixteen organic phantoms were scanned twice in a test–retest format using a 120 kV tube potential and tube currents of 10, 50, and 100 mAs. After rotating the phantoms 90° around their z-axis, additional test–retest scans were performed. A PER and 16 VMERs were generated. Segmentation and extraction of 105 original radiomic features followed. The repeatability and reproducibility of these features were assessed using the concordance correlation coefficient (CCC) for agreement and the intraclass correlation coefficient (ICC) for reliability, excluding 14 shape-based features from the analysis. Results: On average, 85 out of 91 radiomic features from VMER showed high repeatability. Approximately 30% of features demonstrated high intra-scan and inter-scan reproducibility when comparing PER and VMER. For different energy levels of VMER, around 78% showed high intra-scan reproducibility, and 74% showed high inter-scan reproducibility. Comparing the average values of test and retest scans in both the initial and rotated states revealed that 65% of features showed high agreement and 73% high reliability for PER, while for VMER, these values were 51% and 55%, respectively. Conclusions: Radiomic features from VMERs showed high test–retest repeatability, whereas reproducibility across reconstruction types and widely separated energy levels was more limited. These findings suggest that energy levels should be carefully standardized when radiomic features are extracted from PCCT-derived VMER images. Full article

Accurate prediction of natural gas thermophysical properties is essential for applications in production and transportation engineering, including reservoir simulation and flow modeling. Although machine learning (ML) techniques have been widely used, most studies focus on the estimation of these properties, with limited integration into practical applications. In this study, we propose a supervised model based on a Backpropagation Neural Network for simultaneous estimation of four interdependent properties: compressibility factor (Z), viscosity (μ), density (ρ) and gas formation volume factor (B_g). The multi-output model was trained on 58,165 data points generated from thermodynamic correlations, using pressure, temperature, composition (mole fractions of N₂, CO₂ and H₂S), and gas specific gravity as inputs. The results yielded RMSE values of 5.56 × 10⁻⁴, 3.24 × 10⁻⁵, 3.01 × 10⁻², and 6.33 × 10⁻⁴ for Z, μ, ρ and B_g, respectively, with R² coefficients close to unity. The model’s applicability was evaluated by integrating the Z-factor into pressure drop calculations in pipelines using the Cullender and Smith method, resulting in a mean percentage error of 3.78%, close to the traditional method (3.83%). The results indicate that the model is an efficient and consistent alternative, highlighting the potential for integrating ML with classical hydraulic models. Full article

Generating satellite-based deforestation alerts with actionable latency requires frequent imaging, creating an imperative to use different sensors together. We introduce a simple and open-source framework called the Disturbance Index Alert System (DIAS), which is based upon transformation of imagery from different sources into an interoperable stream of Disturbance Index (DI) values. Whereas most alert systems target divergence of forested pixels from historical states, DIAS targets movement of a pixel’s Z-score position relative to the image-wide population of forest pixels along a forest-sensitive axis. This strategy provides the following practical benefits: (1) it reduces the need to process the historical archive; (2) it reduces dependence upon stable sensor calibration; (3) it allows Z-score-based DI values to be combined across sensors; and (4) it accommodates changes to the group of sensors providing measurements. We demonstrated in Madagascar that sensor integration through DIAS can provide more timely alerts than both conventional individual-sensor systems and additive combination of such systems. Across our study sites, using a commercial source of daily imaging (PlanetScope) in conjunction with imagery from public sources (Landsat, Sentinels-1 and -2) allowed high-confidence detection (false alert rate of approximately 20%) of two-thirds of deforestation occurring at 10 m reference pixels within one month; 40% were detected in that timeframe with public data alone. As commercial options for Earth observation proliferate, flexible and computationally lightweight approaches such as DIAS are needed to accommodate diverse and sometimes only loosely calibrated instruments in support of timely forest monitoring. Full article

Background/Objectives: The nutrition transition in low- and middle-income countries has produced a double burden of malnutrition (coexistence of excess weight, undernutrition, and micronutrient deficiencies), with scarce evidence in schoolchildren from vulnerable peri-urban areas of Paraguay. The objective was to characterize, in a multidimensional way, the nutritional status of children and adolescents from Limpio and to explore its associations with anemia and clinical, dietary, and environmental variables, in particular, reported intestinal parasitosis. Methods: Cross-sectional observational study in 90 participants aged 6 to 16 years recruited by convenience at six community settings. Anthropometry, body composition, capillary hemoglobin, dietary patterns, and environment were assessed. Weight status was classified using the WHO 2007 references (z-scores), anemia was described using WHO thresholds, and central obesity was assessed using a waist-to-height ratio > 0.5. Non-parametric tests, Fisher’s exact test, Spearman correlations, and multivariable logistic regression were used. Results: Overweight or obesity affected 39.3% (obesity, 16.7%) and central obesity 22.4%, with no cases of thinness, coexisting with anemia (27.0%), stunting (8.2%), and reported intestinal parasitosis (24.1%). Anemia was more frequent in children with reported intestinal parasitosis (45% versus 20%; adjusted OR 5.44; 95% CI 1.44–20.51). Height-for-age was inversely associated with the number of siblings (ρ = −0.25). Conclusions: This population showed a double burden of malnutrition. The association between reported, non-laboratory-confirmed intestinal parasitosis and capillary-hemoglobin-defined anemia was exploratory and non-causal, given the cross-sectional design. Together with the high burden of anemia, these findings raise the hypothesis of a possible triple burden of malnutrition, which would require confirmation through stool parasitological testing and biomarkers of iron status, inflammation, and other micronutrients. These findings are compatible with integrated community strategies addressing dietary quality, sanitation, and access to safe water; decisions on deworming and micronutrient supplementation should be guided by local parasitological surveillance and biomarker-based assessment rather than by these data alone. Because the study used a convenience sample from a single peri-urban community during one fieldwork period, the findings should not be generalized beyond similar vulnerable settings without further confirmation. Full article

This work presents an automated vision-based measurement system designed for the precise dimensional characterization of high-aspect-ratio micro-holes, achieving a relative dimensional error of less than 1% for characterizing high-aspect-ratio damage geometries. The system integrates coaxial microscopic imaging with a precision motorized scanning stage. To ensure high-fidelity measurements in early-stage warning applications, depth is determined using a focus variation method driven by a robust data fusion strategy. By capturing a sequence of images along the Z-axis, the focal planes of the defect’s surface orifice and internal base are automatically identified using a data fusion algorithm based on a consensus evaluation of three parallel sharpness metrics (Tenengrad, Laplacian, and Brenner variants). The Z-axis scanning module, featuring encoder feedback and bi-directional compensation, achieves a repeated positioning error of ±0.5 µm. For lateral damage assessment, the system’s high magnification provides an effective sampling resolution of 0.09 µm. The equivalent diameter of the focused orifice image is calculated through a robust pipeline involving adaptive thresholding, morphological filtering, and sub-pixel ellipse fitting, which serves as a highly sensitive indicator for early-stage structural deformation. The entire process can be completed within five minutes, demonstrating a rapid, highly accurate, and localized optical inspection solution that generates high-precision dimensional data crucial for quality inspection in aerospace and precision engineering. Full article

Objectives: To assess the feasibility and reproducibility of predicting antimicrobial resistance (AMR) in Escherichia coli from MALDI-TOF mass spectrometry data using a standardized, open-source machine learning (ML) workflow, we systematically compared four ML algorithms, evaluated the impact of culture conditions, extract storage, and spectral preprocessing on model performance, and validated results through nested cross-validation with statistical significance testing. Methods: A total of 282 clinical E. coli isolates were analyzed. Two MALDI-TOF MS datasets were generated from freshly cultured extracts (T₁) and recultured isolates one year later (T₃), yielding 4468 spectra. A third dataset from the T₁ extracts stored at −20 °C for one year (T₂) was evaluated for spectral stability but excluded from primary modeling likely due to storage-induced degradation. Protein spectra (m/z 2000–15,000) were preprocessed using an in-house developed MALDI-TOF preprocessing pipeline (MTPP) comprising variance stabilization, Savitzky–Golay smoothing, SNIP baseline correction, TIC normalization, LOWESS alignment, and MAD-based peak detection (SNR ≥ 3), yielding 121 m/z features. Four classifiers—Random Forest (RF), Logistic Regression, Support Vector Machine, and Gradient Boosting—were trained to predict resistance to 11 antibiotics using nested cross-validation: outer GroupShuffleSplit (5-fold, isolate-level) for evaluation and inner GroupKFold for recursive feature elimination (RFECV) and hyperparameter tuning (RandomizedSearchCV). Classification thresholds were optimized via the precision–recall curve. Model performance was assessed using AUROC, AUPRC, F1-score, Matthews Correlation Coefficient (MCC), and bootstrap 95% confidence intervals (1000 replicates). Pairwise model comparisons were tested with McNemar’s chi-squared test. Results: Among the 12 antibiotics included in the analysis (meropenem excluded for absence of resistance), resistance prevalence ranged from 1.1% (colistin) to 59.9% (amoxicillin). Colistin was subsequently also excluded from ML modeling due to insufficient resistant isolates (n = 3), leaving 11 antibiotics for prediction. The best predictive performance was observed for ciprofloxacin (AUROC 0.76 [95% CI 0.74–0.77]; F1 0.54; MCC 0.38) and ceftazidime (AUROC 0.68 [0.65–0.71]; F1 0.36; MCC 0.29), using 13 and 37 RFECV-selected features, respectively. Amoxicillin achieved the highest F1-score (0.76), driven by high recall (0.98) but modest AUROC (0.58). No meaningful predictive signal was detected for amikacin, cefepime, or tigecycline (AUROC ≤ 0.57, F1 ≤ 0.17), attributable to extreme class imbalance, and no robust multi-peak resistance signature was detected in this dataset. McNemar’s test confirmed that RF significantly outperformed Logistic Regression for all antibiotics (p < 0.01), while Gradient Boosting performed comparably to RF for ciprofloxacin (p = 0.17) and ceftazidime (p = 0.28). Frozen extracts (T₂) produced lower spectral similarity and were excluded from model training; the aligned T₁₊₃ dataset yielded the most stable performance across metrics. Conclusions: Machine learning analysis of MALDI-TOF spectra enables reproducible AMR prediction for selected antibiotics in E. coli, with ciprofloxacin and ceftazidime showing the strongest signal. Nested isolate-level cross-validation, multi-model comparison with statistical testing, and open-source code provide a transparent, reproducible foundation for integrating ML-assisted MALDI-TOF analysis into diagnostic AMR surveillance. Extract storage at −20 °C degrades spectral quality and should be avoided in ML training workflows. Full article

Background/Objectives: Childhood and adolescence are critical periods for bone mineral accrual and future skeletal reserve. Milk intake, sun exposure and caffeinated energy drink consumption are familiar lifestyle concepts, but they differ substantially in biological proximity and evidential strength. This structured narrative review critically evaluates these exposures in relation to peak bone mass accrual in youth. Methods: PubMed/MEDLINE, Scopus, Web of Science, the Cochrane Library and Google Scholar were searched from database inception to 23 June 2026. Search terms combined pediatric population terms with bone outcomes and exposure terms related to milk/dairy, calcium, vitamin D, sun exposure, physical activity, sleep, caffeine and energy drinks. A literature collection flowchart and a GRADE-informed evidence appraisal table are provided to improve transparency and clinical interpretability. Results: Evidence is strongest for adequate calcium intake, calcium-rich foods and weight-bearing physical activity as modifiable contributors to skeletal accrual. Vitamin D is essential for mineral homeostasis, but supplementation effects on bone density in otherwise healthy children are context-dependent and appear most relevant for deficiency prevention or treatment. Milk intake is best interpreted as a practical marker of calcium-rich dietary patterns rather than as the only route to calcium adequacy. Sun exposure is an indirect determinant of vitamin D status and is modified by season, latitude, skin pigmentation, clothing, sunscreen, adiposity and outdoor behavior. Direct evidence linking caffeinated energy drinks to impaired pediatric bone accrual is very limited. The relevance of caffeinated energy drink intake is better framed as indirect and hypothesis-generating, through possible displacement of calcium-rich beverages, sleep disruption and clustering with poorer lifestyle patterns. Conclusions: A prevention framework for pediatric bone health should emphasize calcium adequacy, avoidance of vitamin D deficiency, mechanical loading and correct pediatric DXA interpretation using Z-scores. Energy drinks can be included as a lifestyle concern, but conclusions should remain cautious because direct skeletal evidence is limited. Full article

Background: The utilization of robot-assisted surgery in pediatric patients is increasing, with particularly notable advantages in complex reconstructive procedures. This study aims to evaluate the safety and efficacy of robotic-assisted resection of choledochal cysts in infants aged less than 3 months. Methods: A total of 73 infants with choledochal cysts who were admitted to the Department of General Surgery, Children’s Hospital of Zhejiang University School of Medicine, between April 2019 and December 2025 were included. The patients were divided into a robotic-assisted surgery (RAS) group (n = 39) and a laparoscopic-assisted surgery (LAS) group (n = 34). Clinical data, including demographic information, laboratory indexes, surgical data, and prognostic data, were retrospectively reviewed, and the Mann–Whitney U test, independent-samples t-test, and Fisher’s exact test were used for statistical analysis. Results: The groups were comparable in terms of age, sex, weight, pre- and postoperative biochemical markers, fasting time, cyst diameter, and operative time. Overall, 80.8% of cases were prenatally detected. The RAS group had a significantly shorter postoperative hospital stay (p = 0.004, Z = −2.864), drainage tube duration (p = 0.002, Z = −3.100), and hepaticojejunostomy time (p < 0.0001, df = 71, 95%CI (−5.70, −3.04)) compared to the LAS group. In the LAS group, three patients developed anastomotic fistulas, all of whom required reoperation, and one patient developed adhesive bowel obstruction, whereas in the RAS group, one patient developed incision infection, one developed cholangitis, one developed adhesive bowel obstruction, and one presented with postoperative liver function abnormalities. The hospitalization cost in the LAS group was significantly lower than that in the RAS group (p < 0.0001, Z = −5.468). Conclusions: In experienced pediatric centers, robotic-assisted resection of choledochal cysts is safe and effective for infants aged less than 3 months and deserves further exploration. Full article

Background: Anxiety and depression are major contributors to mental-health burden and frequently co-occur in clinical practice. In tele–mental health, routinely captured operational variables such as consultation duration, visit frequency, and follow-up cadence may provide clinical digital phenotypes that complement conventional symptom scales. This study aimed to characterize anxiety–depression comorbidity in a large real-world tele–mental health cohort and to determine whether symptom severity was associated with distinct patterns of healthcare utilization. Methods: We conducted a retrospective real-world study of 3467 patients followed in psychiatry and psychology teleconsultations. Patients were classified as anxiety only, depression only, comorbid anxiety–depression, or neither. Symptom severity was categorized as mild, moderate, or severe using validated questionnaire-based measures; to improve comparability across instruments, scores were additionally harmonized using z-score normalization. Associations between anxiety and depression severity within the comorbid subgroup were examined using a chi-square framework. Telehealth utilization endpoints included consultation duration, number of consultations, and inter-visit interval, analysed overall and stratified by sex, age group, and symptom severity. Results: Anxiety and/or depression were present in 61.7% of the cohort (2140/3467), and anxiety–depression comorbidity accounted for 43.8% of all patients (1520/3467), indicating substantial real-world overlap. Within comorbid cases, anxiety and depression severity were strongly coupled, with depression severity varying systematically across anxiety severity strata (chi-square p = 9.88 × 10⁻¹⁰²). Compared with isolated anxiety or depression, comorbidity was associated with a more intensive healthcare-utilization profile, characterized by a higher mean number of consultations and shorter inter-visit intervals. Among comorbid patients, females showed greater longitudinal service use than males, with more visits and closer follow-up. Resource use also varied according to symptom burden, mainly in depression, supporting a graded relationship between clinical severity and operational care demand. Conclusions: In this large real-world tele–mental health cohort, anxiety–depression comorbidity was highly prevalent, clinically structured, and associated with distinct and measurable resource-use signatures. These findings highlight the novelty and practical value of integrating symptom severity with operational telehealth data to derive pragmatic digital phenotypes of care intensity. Such phenotypes may support risk stratification, triage, follow-up scheduling, and capacity planning in tele–mental health, with potential translational relevance for broader mental healthcare systems. However, these findings should be considered descriptive and hypothesis-generating and warrant further longitudinal validation in other clinical settings. Full article

Generation Z consumers are reshaping food consumption patterns in urban digital environments, particularly in restaurant contexts characterized by high choice complexity and uncertainty. In Mexico, the evolution of the restaurant industry has intensified the need to understand how digital cues shape consumer food choices. Addressing this gap, this study examines how Social Media Marketing (SMM), Social Media electronic Word of Mouth (Social Media eWOM), and Social Media Influencers (SMIs) shape food consumption intention among Generation Z in Mexico City. Grounded in the Stimulus–Organism–Response (SOR) model and integrating the attitudinal foundations of the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB), this study analyzes how these digital factors impact food consumption intention (operationalized as restaurant purchase intention) through the mediating psychological mechanism of Consumer Brand Engagement (CBE). A quantitative, non-experimental design was employed using a sample of 406 respondents, and data were analyzed through Structural Equation Modeling (SEM). The results indicate that the model explains 73.6% of the variance in food consumption intention. SMM emerged as the strongest direct predictor, followed by Social Media eWOM and SMIs. Crucially, CBE mediates only the relationship between influencers and consumption intention. Conversely, both SMM and Social Media eWOM exert direct effects that bypass affective engagement. These findings highlight the role of digital ecosystems as cognitive proxies in restaurant selection, providing actionable insights for restaurant SMEs to optimize digital strategies and enhance economic resilience. They also suggest potential implications for healthier and more sustainable urban food environments. Full article

Background: According to well-known dietary recommendations, herbs and spices are part of a healthy, balanced diet, and their consumption may contribute to improved health. Globalisation fosters greater exposure to other cultures and cuisines, including the use of spices. This study aimed to assess the awareness and attitudes towards spices among Polish students in a rural area. Methods: A survey study was conducted among 278 Generation Z students (aged 18–28 years old) from a university located in a small town in southeastern Poland. Questions concerning, a.o., preparing meals, awareness of spices’ properties, and the use of seasoning were included. Results: Most of the respondents declared using a lot of spices beyond salt and pepper (61.2%), more often women than men (67.9% vs. 45.1%; p = 0.0004), and more often participants aged ≥23 years than ≤22 years (82.9% vs. 58.0%; p = 0.005). Participants who grew their own spices and often watched TV culinary programs used more seasonings (72.4%; p = 0.001 and 85.2%; p = 0.0002, respectively). Less than half of the respondents (45%) recognised health properties in some of the spices, and 28.1% of them recognised health properties in spices in general, with more older participants (45.7%; p = 0.0402). Respondents with the highest awareness of the health properties of spices used them more often to improve their health (42.3%; p < 0.00). Conclusions: Exposure to cuisines from other cultures and their spices and the willingness to try new flavours among respondents were low. This might be due to sociodemographic factors, including origin from small, rural, traditional communities, where attachment to familiar recipes might be observed. Moreover, awareness of the healing benefits of spice use was low. Therefore, education about the composition and use of local spices might be helpful in increasing their intake for the benefit of health. Full article