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This study presents a novel technological framework that integrates web-based 3D modeling and digital prototyping into interdisciplinary design education. Addressing the gap between traditional theoretical assessment and modern industry demands, the research investigates the implementation of interactive micro-websites and high-fidelity 3D product models as standard deliverables. Using a quasi-experimental design, the proposed digital workflow was tested on 53 final-year graphic design students at the University of Zagreb, divided into three groups based on the end users of their digital prototypes: real industry clients, peers, or academic mentors. The systemic reliability of the technological framework was measured through the technical quality of the final output (grades) analyzed via ANOVA, while user engagement with the digital process was tracked longitudinally. Results indicate that the implemented technological pipeline produced consistently high-quality outputs across all cohorts, with the client-facing group achieving the highest technical scores (M = 4.37; SD = 0.57). The lack of statistically significant variance between groups highlights a “ceiling effect,” demonstrating that the structured digital workflow itself is operationally stable and ensuring top-tier technical performance and prepress accuracy regardless of the evaluator. The study concludes that integrating advanced 3D web technologies and interactive public deliverables into the curriculum provides a scalable, industry-aligned technological model that successfully prepares design engineers for complex professional environments. Full article

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19 pages, 502 KB

Open AccessArticle

LSTM-Predicted Sliding Mode Control for String-Stable Vehicle Platooning in Mixed Traffic Flow

by Mei Cao and Qingman Fan

Vehicles 2026, 8(7), 147; https://doi.org/10.3390/vehicles8070147 (registering DOI) - 30 Jun 2026

Abstract

To address the issues of slow response to preceding vehicles and poor string stability in distributed platoon control of connected and autonomous vehicles (CAVs) under mixed traffic flow, this paper proposes a sliding mode control method based on LSTM trajectory prediction, denoted as LSTM-SMC, within a multi-agent framework. The LSTM model is trained using the HighD naturalistic driving dataset to achieve high-precision prediction of the leader vehicle’s trajectory over a horizon of 3 s, with root mean square errors (RMSE) of 8.52 m in the X-direction and 0.896 m in the Y-direction. The predicted trajectory information is converted into a preview error and embedded directly into the design of the sliding surface, enabling each following vehicle to anticipate disturbances before they propagate. A diminishing preview gain strategy (

γ_{1} = 0.4

γ_{2} = 0.2

γ_{3} = 0.1

) is employed to suppress error propagation along the platoon, while a saturation function is introduced to eliminate chattering and ensure smooth control inputs. Three simulation scenarios—prescribed leading, HDV (human-driven vehicle) leading, and curved road scenario—are constructed to validate the proposed method against traditional constant time headway (CTH) control, pure sliding mode control (SMC), and LSTM-MPC. Results demonstrate that under extreme conditions, the proposed method reduces the speed RMSE of the 3rd following vehicle by 18.3% compared to CTH and by 39.7% compared to SMC. Under HDV leading conditions, all string stability amplification factors are less than 1, and the position RMSE of the 3rd vehicle is only 5.03 m in the curved road scenario. Compared with LSTM-MPC, the proposed LSTM-SMC achieves comparable tracking accuracy while reducing computational cost by 1.43–3.51×. The proposed method achieves a native integration of prediction and robust control, significantly improving tracking accuracy, string stability, and computational efficiency across diverse operating conditions in mixed traffic flow. Full article

(This article belongs to the Special Issue Trajectory Tracking of Autonomous Vehicles)

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32 pages, 4683 KB

Open AccessReview

Microalgae-Mediated Nanotechnology for Sustainable Agriculture: Applications, Advances, and Future Prospects

by Yu Xie, Zirui Yang, Shoukai Guo, Liqin Sun, Hongli Cui and Zhongliang Sun

Int. J. Mol. Sci. 2026, 27(13), 5875; https://doi.org/10.3390/ijms27135875 (registering DOI) - 30 Jun 2026

Abstract

The overreliance on chemical pesticides has caused severe environmental contamination, health risks, and increasing pest and pathogen resistance, creating an urgent need for greener and more efficient alternatives in sustainable agriculture. Microalgae-mediated green nano-synthesis has emerged as a promising strategy because of its environmental compatibility, cost-effectiveness, and multifunctional potential. This review critically summarizes recent advances in microalgae-derived nanomaterials for agricultural applications. First, we discuss the biochemical basis of nanoparticle biosynthesis, highlighting the roles of microalgal polysaccharides, proteins, photosynthetic pigments, extracellular polymeric substances, and secondary metabolites as reducing, capping, and stabilizing agents. We then summarize intracellular and extracellular synthesis pathways, advanced synthesis strategies, and key reaction parameters, including temperature, pH, and metal precursor concentration, which regulate nanoparticle size, morphology, stability, and yield. Subsequently, major microalgae-derived nanomaterials, including gold, silver, selenium, zinc oxide, bimetallic, and other functional nanoparticles, are discussed in relation to their agricultural applications. These nanomaterials show potential in bacterial, fungal, and viral disease control, biofilm disruption, plant growth promotion, yield enhancement, and abiotic stress mitigation. Their agronomic effects are associated with multiple mechanisms, including reactive oxygen species generation, pathogen membrane disruption, inhibition of biofilm formation, enhanced nutrient bioavailability, antioxidant regulation, and activation of plant systemic resistance. In addition, this review evaluates the phytotoxicity, biocompatibility, soil microbial impacts, and environmental safety of microalgae-derived nanomaterials, emphasizing that green synthesis does not automatically guarantee biosafety. Finally, we discuss their integration into circular agriculture through CO₂ capture and wastewater-derived metal recovery, while highlighting remaining challenges in scale-up, quality control, economic feasibility, regulatory classification, and public acceptance. Overall, microalgae-mediated nanotechnology offers a promising platform for developing safer, more efficient, and circular agricultural inputs. Full article

(This article belongs to the Section Molecular Nanoscience)

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19 pages, 16490 KB

Open AccessArticle

Effects of Ascorbic Acid on Apoptosis, Metabolism, and Muscle Quality in Ammonia-Stressed Rainbow Trout (Oncorhynchus mykiss)

by Siliang Yuan, Yiwen Wu, Yuxuan Pi, Chenxin Wang, Guangquan Xiong, Wenjin Wu, Liu Shi, Tao Yin, Hao Du, Lan Wang and Sheng Chen

Foods 2026, 15(13), 2316; https://doi.org/10.3390/foods15132316 (registering DOI) - 30 Jun 2026

Abstract

The present study aimed to evaluate the role of ascorbic acid in alleviating ammonia-induced muscle quality deterioration and to clarify its regulatory effects on apoptosis, texture, and flavor-related metabolites in rainbow trout (Oncorhynchus mykiss). The results demonstrated that ascorbic acid alleviated ammonia stress-induced inflammatory and apoptotic damage by regulating toll like receptor 5 (TLR5), myeloid differentiation primary response 88 (MyD88), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) expression, thereby contributing to the restoration of myofibrillar integrity, reduced extracellular gaps, and increased shear force from 14.18 N to 18.26 N (p < 0.05). Ascorbic acid modulated ammonia handling and ion-exchange responses by upregulating glutamine synthetase (GS) expression from approximately 2.3-fold to 6.7-fold and increasing ornithine and citrulline accumulation. Alterations in tricarboxylic acid cycle-related metabolites further suggested that energy metabolism may be involved in the physiological adaptation to ammonia stress. Meanwhile, the ascorbic acid reduced the accumulation of key off-flavor compounds (1-octene-3-alcohol and (E)-2-nonenal), attenuating the earthy–moldy and fishy flavor. This research proposes a potential strategy to improve muscle quality in live transportation. Full article

(This article belongs to the Section Food Quality and Safety)

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22 pages, 4961 KB

Open AccessReview

Spatial Heterogeneity of Intratumoral Microbiota and Its Roles in Tumor–Microbiota Interactions and Therapeutic Implications

by Li Li, Xiaoqian Shi, Mingyang Liu, Tongzhen Xu, Yinan Chen, Ranjiaxi Wang, Qiyue Zhang and Dan Li

Pathogens 2026, 15(7), 687; https://doi.org/10.3390/pathogens15070687 (registering DOI) - 30 Jun 2026

Abstract

The intratumoral microbiota has emerged as a critical component of the tumor microenvironment (TME), with accumulating evidence indicating that its biological functions are influenced not only by microbial composition but also by their spatial organization within tumor tissues. This review summarizes the historical development and potential origins of intratumoral microbiota, and elaborates on the concept and biological significance of spatial heterogeneity. Based on recurrent spatial distribution patterns reported across different tumor types, we propose a conceptual framework comprising several putative spatial niches, including hypoxic/necrotic, immune-enriched, stromal-associated, invasive/metastatic, and intracellular niches. We further discuss the potential mechanisms contributing to the establishment and maintenance of spatial heterogeneity. The clinical significance of spatial microbial signatures is critically evaluated, alongside a comprehensive overview of spatial analytical methodologies, ranging from in situ hybridization and immunology-based approaches to emerging spatial omics and multi-omics integration strategies. Finally, we address key challenges and limitations, including contamination control, causal inference, barriers to clinical translation, and the underexplored spatial dimensions of the intratumoral mycobiome and virome. By synthesizing current knowledge and identifying critical gaps, this review aims to provide a conceptual and methodological framework for advancing spatially resolved investigations of intratumoral microbiota and facilitating their potential translational applications in precision oncology. Full article

(This article belongs to the Section Bacterial Pathogens)

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17 pages, 8141 KB

Open AccessArticle

Natural Clogging Design for Tailings Pond Filters

by Jingyu Song, Faning Dang, Weikang Bai, Haibin Xue, Fan Feng, Bin Hou, Zhongji Dong and Jihong Zhang

Water 2026, 18(13), 1589; https://doi.org/10.3390/w18131589 (registering DOI) - 30 Jun 2026

Abstract

Filters serve as critical facilities for ensuring the seepage stability of earth-rock dams and tailings dams; their failure poses severe threats to dam safety. Traditional filter design criteria are constrained by the diversity of soil types and fail to account for the influence of pore characteristics (e.g., constriction size) on the soil retention and hydraulic conductivity of filters. Design methods recommended in design codes only provide gradation envelope boundaries without specifying exact gradation curves. This paper proposes a filter design approach based on the natural clogging concept. Using Terzaghi’s interlayer coefficient as the initial parameter, this method induces stable clogging layers of base soil within the filter through sediment-laden seepage, adopting the post-clogging gradation as the design gradation. Experimental results demonstrate that: (1) when the initial interlayer coefficient α of the filter is ≤10.4, the base soil retention rate exceeds 97% (soil loss < 3%), surpassing the conservative limit of α < 4 in Terzaghi’s criterion; (2) the final interlayer coefficient α of filters ZS-2 to ZS-5 ranges between 1.15 and 2.48, with i_b/i_f values between 6.26 and 23.68, simultaneously satisfying Terzaghi’s requirements for soil retention and hydraulic conductivity; (3) this method explicitly defines the specific gradation curve of the filter, with the final gradation curve of ZS-5 largely falling within the envelope recommended by design codes. The proposed method integrates Terzaghi’s interlayer coefficient criterion with the influence of pore characteristics on filter performance, offering a new design strategy for tailings dam filters with fine-grained base soils, preliminarily validated under laboratory conditions. Full article

(This article belongs to the Special Issue Advances in Water Related Geotechnical Engineering)

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16 pages, 14882 KB

Open AccessArticle

Physics-Informed Machine Learning Framework for Fatigue Life Prediction of Additively Manufactured Alloys

by Hyoju Ahn, Jongwon Lee, Saurabh Tiwari and Nokeun Park

Appl. Sci. 2026, 16(13), 6493; https://doi.org/10.3390/app16136493 (registering DOI) - 30 Jun 2026

Abstract

The fatigue life prediction of additively manufactured (AM) alloys remains challenging owing to process-induced defects, microstructural variability, and complex loading conditions of the alloys. This study presents a domain-knowledge-informed machine learning (ML) and deep learning (DL) framework for fatigue life prediction, in which physically motivated fatigue descriptors are integrated into the feature space using experimentally obtained stress–life (S–N) data. Four physics-guided engineered descriptors, namely the normalized stress (σ_a/UTS), R-modified stress amplitude, UTS/YS ratio, and elastic strain energy density, were incorporated into the modelling framework to improve mechanistically grounded learning across diverse alloy systems. Five ML/DL models, namely Deep Artificial Neural Network (DANN), XGBoost, Extra Trees, Stacking Ensemble, and Random Forest, were benchmarked against the classical Basquin stress–life baseline. DANN achieved the best test-set performance (R² = 0.7114, RMSE = 0.5205 log cycles), whereas XGBoost exhibited the highest cross-validation performance (R² = 0.7547 ± 0.056). Ablation analysis confirmed the positive contributions of both the engineered descriptors (ΔR² = +0.115) and runout indicator (ΔR² = +0.107) to the predictive capability. The runout flag is appropriate for retrospective database modelling. For prospective applications, the no-runout configuration (R² = 0.5504) substantially outperformed the Basquin baseline (R² = 0.1244) and is recommended when runout information is unavailable. TreeSHAP analysis identified normalized stress and elongation as dominant predictors, with σ_a/UTS showing substantially greater importance than did the raw stress amplitude. The results demonstrate that physics-informed feature engineering substantially improves fatigue life prediction across the alloy systems and processing conditions represented in the dataset; however, further validation is required for under-represented additive manufacturing processes and alloy classes. Full article

(This article belongs to the Special Issue Mechanical Properties and Numerical Modeling of Advanced Materials)

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14 pages, 2543 KB

Open AccessArticle

Virtual Reality in Craniomaxillofacial Surgical Planning Education: A Feasibility Study on Usability, Cognitive Load, and Perceived Educational Outcomes

by Neha Sharma, Valentina Foehn, Jokin Zubizarreta Oteiza, Daniel Seiler and Florian M. Thieringer

Appl. Sci. 2026, 16(13), 6492; https://doi.org/10.3390/app16136492 (registering DOI) - 30 Jun 2026

Abstract

Introduction: Digital surgical planning in craniomaxillofacial (CMF) surgery requires biomedical engineers who can navigate complex 3D anatomical data confidently, yet most engineering training programmes still rely on static 2D methods. This study evaluated the usability, cognitive demands, and perceived educational outcomes of a clinically derived virtual reality (VR) surgical planning platform for master’s-level biomedical engineering students. Methods: A cross-sectional feasibility study was conducted assessing usability with the System Usability Scale (SUS), cognitive load with a modified NASA Task Load Index (NASA-TLX), and perceived educational outcomes using domain-specific rating scales, with open-ended responses analysed thematically. Results: Twelve of 15 enrolled students completed the survey (80% response rate). The platform achieved a mean SUS score of 72.5 (Above Average), with comparable scores across prior VR experience levels. All NASA-TLX demand dimensions remained below the scale midpoint. All participants rated VR as more engaging than traditional methods, and 91.7% rated the virtual anatomical models as realistic. Self-reported spatial reasoning benefits were most notable in landmark identification, while outcomes for translating digital-to-surgical planning were more limited. Haptic feedback was the most requested enhancement. Conclusions: VR surgical planning tools appear feasible to integrate into biomedical engineering training. Future studies should incorporate objective outcome measures and comparison groups to establish effectiveness. Full article

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16 pages, 1339 KB

Open AccessArticle

Research on VLF Ionospheric Propagation Method Based on the Dynamic Stratification Transmission Matrix

by Lin Zhao, Zhiting Zhan and Hui Xie

Atmosphere 2026, 17(7), 648; https://doi.org/10.3390/atmos17070648 (registering DOI) - 30 Jun 2026

Abstract

To address the poor computational efficiency of traditional fixed-stratification methods in very low frequency (VLF) ionospheric propagation modeling, this paper proposes a dynamic stratification algorithm. First, filtering optimization is applied to the electron density, and dynamic adaptive stratification is implemented in the vertical direction. By establishing a nonlinear mapping relationship between the electron density gradient and the stratification thickness, the algorithm integrates dynamic ionospheric stratification with a hybrid regularization algorithm for the transmission matrix. Specifically, Singular Value Decomposition (SVD) and dynamic truncation techniques are employed to process the transmission matrix, effectively resolving the numerical ill-posedness in regions with abrupt ionospheric changes. This enables high-precision calculation of reflection coefficients in the 3–30 kHz frequency band. By tuning parameters such as the reference stratification thickness and adjustment factors, an optimized stratification model and an algorithm quality evaluation coefficient are obtained. The simulation results demonstrate that, compared with fixed stratification, the proposed algorithm achieves an average relative error of 4.7% for the reflection coefficient in the VLF range while improving computational efficiency by more than 50%. This provides a promising approach for efficient and high-precision prediction of VLF wave propagation. Full article

(This article belongs to the Special Issue Intelligent Modeling of the Ionosphere and Troposphere for Radio Application (2nd Edition))

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18 pages, 2814 KB

Open AccessArticle

Simulation-Based Design of Ultra-Fast Dynamic Torque Control for Electric Vehicle Permanent Magnet Motor Drives

by Abdullatif Hakami

Energies 2026, 19(13), 3085; https://doi.org/10.3390/en19133085 (registering DOI) - 30 Jun 2026

Abstract

Electric Vehicle drive systems must provide fast torque response, low or minimal torque ripple, robustness to both parameter variations and external disturbances. Permanent Magnet Synchronous Motors (PMSMs) are commonly found in electric vehicle propulsion applications due to their high power density, high efficiency, and excellent dynamic performance. However, performance degradation in torque control of PMSMs under time-varying conditions arises from the nonlinear characteristics of motors and their high sensitivity to changes in system parameters. This paper presents a torque-control method with high dynamic bandwidth that combines three techniques: (1) Nonlinear Sliding Mode Torque Control; (2) Predictive Current Control; and (3) Disturbance Estimation. The sliding mode controller provides improved robustness against uncertainties about the system. In addition, the predictive current control provides improved accuracy in current tracking and significantly reduces the time required to achieve a steady state. A disturbance observer is used to compensate for load disturbances and model errors in the motor model. The integrated control architecture is simulated and modeled in MATLAB/Simulink for a typical EV driving environment. The simulation framework produced faster and more accurate torque tracking than conventional PI-type vector controllers, as well as reduced torque ripple and improved disturbance rejection under similar operating conditions. The results demonstrate that the proposed method is a viable candidate for high-performance EV propulsion systems while acknowledging practical limitations such as chattering, tuning complexity, sampling time sensitivity, and the need for further experimental validation. Full article

(This article belongs to the Special Issue Control, Energy Management and Battery Management of Electric Vehicles)

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12 pages, 343 KB

Open AccessArticle

Patient and Staff Safety Incidents in Korean Dental Practice: Implications for Quality of Care and Safer Healthcare Delivery

by Kyeol Koh and Se Hoon Kahm

Healthcare 2026, 14(13), 1895; https://doi.org/10.3390/healthcare14131895 (registering DOI) - 30 Jun 2026

Abstract

Objectives: Patient safety is central to healthcare quality, yet dental practice also involves occupational risks for professionals. This study examined the lifetime prevalence and types of patient- and staff-safety incidents among Korean dental professionals and explored associated demographic, professional, and institutional factors. Methods: A cross-sectional survey was conducted among 439 dental professionals in South Korea. Participants reported lifetime experience of predefined safety incidents, institutional safety factors, and demographic and occupational characteristics. Descriptive statistics, profession-based comparisons, and multivariable logistic regression were applied. Results: Overall, 89.1% of respondents reported at least one safety incident. The most common patient-safety events were aspiration or ingestion of teeth or prosthetic materials and instrument-related injury, whereas sharps injuries and verbal abuse were the leading staff-safety issues. Dentists and dental hygienists differed significantly in response knowledge, liability insurance coverage, and safety education. The presence of institutional safety protocols was associated with higher reported incident experience, which may reflect greater recognition and reporting rather than a causal increase in harm. Conclusions: Safety incidents are highly prevalent in Korean dental practice and represent an underrecognized quality-of-care and workforce-safety issue. Integrated strategies including occupational-hygiene measures, structured safety education, non-punitive reporting, and stronger organizational preparedness are needed to improve dental healthcare delivery. Full article

(This article belongs to the Special Issue Quality of Healthcare and Patient Safety Across Disciplines: Innovations, Evidence, and Practice)

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34 pages, 3008 KB

Open AccessSystematic Review

Machine Learning Applications in Emergency Resource Allocation in Europe: A Systematic Review and Future Research Agenda

by Stavros Kalogiannidis, Konstantinos Spinthiropoulos, Fotios Chatzitheodoridis, Dimitrios Parris and Angel Valsamopoulos

Mach. Learn. Knowl. Extr. 2026, 8(7), 182; https://doi.org/10.3390/make8070182 (registering DOI) - 30 Jun 2026

Abstract

This study systematically reviews the application of machine learning (ML) in emergency resource allocation across Europe, with the aim of synthesizing current evidence and identifying future research directions. A systematic literature review (SLR) was conducted following PRISMA guidelines. Data were collected from major academic databases (2018–2025) using predefined inclusion and exclusion criteria. A total of 52 relevant studies were analyzed through qualitative thematic synthesis. The review finds that ML significantly enhances predictive analytics, enabling accurate forecasting of emergency demand and proactive resource allocation. ML-driven optimization improves ambulance dispatch, hospital resource management, and logistics efficiency, while real-time decision support systems strengthen situational awareness and coordination. However, challenges persist, including data quality issues, system fragmentation, ethical concerns (bias, transparency), and limited interoperability across European systems. ML has transformative potential in shifting emergency resource allocation from reactive to data-driven, predictive systems. Its effectiveness, however, depends on robust data infrastructure, ethical governance, and system integration. The study recommends strengthening data systems, adopting hybrid ML-optimization models, enhancing ethical frameworks, investing in human capacity, and promoting cross-border collaboration. Full article

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18 pages, 1065 KB

Open AccessArticle

Microbially Matured Phytomedicines from Sesame Hull (Sesamum indicum L.) Cell-Wall Oligosaccharides: Lactobacillus-Generated Pre-Postbiotics with Antioxidant, Enzyme-Inhibitory and Anti-Helicobacter pylori Activity in a Functional Beverage

by Fatemeh Naderi, Maryam Salami, Seyed Hadi Razavi, Mona Miran, Michael J. Serpe, Marleny D. A. Saldaña, Raimar Loebenberg, Marlon C. Mallillin, Shengnan Zhao and Neal M. Davies

J. Phytomed. 2026, 1(2), 7; https://doi.org/10.3390/jphytomed1020007 (registering DOI) - 30 Jun 2026

Abstract

Many bioactive constituents of medicinal plants depend on microbial biotransformation for their pharmacological activity, positioning postbiotics from plant substrates as microbially matured phytomedicines. An emerging framework integrates prebiotic phytochemicals with probiotic strains to modulate gut microbiota and host health. In this study, we explored the functional properties of heat-inactivated Lactobacillus strains following the fermentation of oligosaccharides obtained from sesame hulls (Sesamum indicum L.), underutilised agro-industrial residues. Cell-wall oligosaccharides were obtained by alkaline or enzymatic (Celluclast^® 1.5 L (Novonesis, Copenhagen, Denmark)) extraction with Ultraflo^® L (Novonesis, Copenhagen, Denmark) hydrolysis and fermented with Lactobacillus acidophilus, L. casei, or L. paracasei. Heat-inactivated pre-postbiotic preparations were profiled for antioxidant capacity, inhibition of metabolic enzymes implicated in obesity and type 2 diabetes, and anti-Helicobacter pylori urease activity. Moreover, these preparations were incorporated into a barley malt (Hordeum vulgare L.) beverage. Bioactivity was strain- and substrate-dependent: L. casei-derived postbiotics most strongly inhibited pancreatic lipase (47.82%) and α-glucosidase (52.14%); L. acidophilus most strongly inhibited α-amylase (43.67%); and L. paracasei exhibited the strongest urease inhibition (20.66%). All strains displayed enhanced antioxidant activity, with ABTS scavenging reaching 87.02%. The supplemented beverages improved antioxidant activity by ~20%. The fermentation of these oligosaccharides thus yields a microbially matured phytomedicine with multi-target activity, supporting postbiotics as active mediators of plant-based therapeutics. Full article

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Graphical abstract

35 pages, 431 KB

Open AccessArticle

Prioritizing Digital Economy Drivers of Inflation Using an Intelligent-Based Fuzzy Decision Framework: Implications for Financial Risk Management

by Seniye Zeynep Aslıyüce, Serkan Eti, Sümeyye Özdemir, Serhat Yüksel, Hasan Dinçer and Merve Acar

J. Risk Financial Manag. 2026, 19(7), 478; https://doi.org/10.3390/jrfm19070478 (registering DOI) - 30 Jun 2026

Abstract

This study aims to identify and prioritize digital economy factors affecting inflation and to determine effective policy strategies for managing digitally driven inflationary pressures in the context of financial systems and risk dynamics. The analysis considers twelve key digital economy indicators, including e-commerce penetration, digital payment systems, internet infrastructure, price transparency, digital advertising, Industry 4.0 technologies, data-driven inventory and demand systems, fintech adoption, cryptocurrency usage, and digital financial access. In parallel, eight policy strategies are evaluated, covering digital price transparency, expansion of digital payments, digital logistics optimization, digital public services, smart manufacturing, intelligent-based demand forecasting, fintech integration, and digital workforce development. The study employs a novel intelligent-supported decision-making framework integrating an attention-based expert weighting approach, generalized fractal fuzzy sets, the MEREC method, and the ARLON technique. The empirical design is based on expert evaluations obtained from ten specialists with at least 12 years of experience in digital economy, finance, and policymaking. Rather than relying on country-specific or time-series inflation datasets, the study examines the structural relationship between digitalization and inflation through a multi-criteria expert-based approach, with data collected in 2025. The findings indicate that e-commerce penetration and the prevalence of digital payment systems are the most influential factors affecting inflation. In addition, digital price transparency and the expansion of digital payment systems emerge as the most effective strategies for mitigating inflationary pressures. These results provide important insights into how digital transformation reshapes inflation dynamics, monetary transmission mechanisms, and inflation-related financial risks. The proposed model offers a robust and systematic framework for analyzing inflation in digitalized economies and supports policymakers and financial decision-makers in managing emerging risks in intelligent-driven economic environments. Full article

(This article belongs to the Section Economics and Finance)

10 pages, 659 KB

Open AccessArticle

Detection and Isolation of stx2e-Positive O139:H1 Shiga Toxin–Producing Escherichia coli from Surface Waters of Apulia Region (Southern Italy)

by Maria Grazia Basanisi, Gaia Nobili, Annachiara Cocomazzi, Rosa Coppola, Annita Maria Damato, Emilio Coniglio, Nicola Pugliese and Giovanna La Salandra

Appl. Sci. 2026, 16(13), 6490; https://doi.org/10.3390/app16136490 (registering DOI) - 30 Jun 2026

Abstract

Shiga toxin–producing Escherichia coli (STEC) are important zoonotic pathogens that can disseminate through environmental water systems, yet data from Southern Italy remain scarce. The aim of this study was to investigate the occurrence and genetic characteristics of STEC isolated from surface water samples collected from rivers and lakes in the Apulia region (Southern Italy). A total of 120 samples were processed according to ISO/TS 13136:2012, followed by whole genome sequencing (WGS) for isolate confirmation and characterization. Overall, 20% of the samples were stx-positive in screening. STEC strains were isolated from 4.2% of stx-positive enrichments, corresponding to one sample out of a total of 120 (0.8%). The isolate was identified as O139:H1, carrying the stx2e subtype and belonging to sequence type ST1. Genomic analysis revealed multiple virulence-associated determinants, including the complete F18 fimbrial operon (fedA-F), hlyA, csgA, gad, chuA, yehA-D, and ompT, along with stress-resistance and tellurite-resistance genes. The strain was susceptible to all antibiotics tested. The genomic profile suggests a swine-associated lineage with multiple environmental persistence traits but limited antimicrobial resistance. The detection of a swine-associated STEC strain in surface waters highlights potential environmental dissemination pathways and underscores the importance of continued monitoring within integrated water–livestock surveillance frameworks. Full article

(This article belongs to the Special Issue Microbiology and Antibiotic Resistance in Environment)

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