Abstract: Commercial manufacturing of prebiotics relies on diverse processing steps tailored to the raw material and finished product. Commercial manufacturing operations are distinct from processes suggested in the research literature, accounting for scalability, cost and environmental metrics, and process reproducibility. Common prebiotic production processes involve extraction, hydrolysis using enzymes, acids or hot water, synthesis, condensation polymerization and precision fermentation. Crude extracts are purified using ion exchange, activated carbon, and membrane separation processes to remove impurities and produce prebiotic oligosaccharides with the targeted composition and degree of polymerization. Purified extracts are often concentrated using evaporation systems and may be dried or crystallized to produce a dry finished product. Though not an all encompassing list of prebiotics, detailed descriptions of processes for the production of some of the more common prebiotics, including acacia, fructo-oligosaccharides (FOS), galacto-oligosaccharides (GOS), human milk oligosaccharides (HMOs), inulin, mannan-oligosaccharides (MOS), certain types of resistant starch (RS), xylo-oligosaccharides (XOS), and arabinoxylan oligosaccharides (AXOS) are provided in this manuscript, along with descriptions of commercial scale unit operations that may be applied more generally to newer compounds being investigated for their prebiotic properties. The unique attributes of each type of prebiotic and prebiotic formulation, particularly the degree of polymerization and chemical structure, are strongly controlled by the processes employed in their manufacture. Strategic selection of enzymes (or hydrolysis processes in general), fermentation systems, and extraction systems/solvents will influence the product composition and degree of polymerization, leading to a diverse array of products. Regulatory requirements and quality control systems employed during manufacturing of prebiotics ensure that finished products are safe and effective for consumers, delivering the expected health and physiological benefits.

Abstract: The notion of a vortex is fundamental in fluid dynamics, where it broadly refers to rotary fluid motions of various forms. Yet a precise, universal definition has remained elusive. Two fundamental challenges persist in the existing definitions of a vortex. The first is the gap between the analytic perspective that adopts a framework of motion decomposition and the synthetic perspective that emphasizes the geometric patterns of the composite motion. The second is the gap between precisely defined local measures of rotation and intuitive large-scale descriptions of vortices. This paper develops a geometric theory of smooth tangent vector fields on oriented closed surfaces that bridges the analytic and synthetic perspectives, and provides a nonlocal definition of a vortex core. Working within the frameworks of the irreducible symmetric-antisymmetric decomposition (iSAD), eigenvalue decomposition (EVD) and Helmholtz-Hodge decomposition (HHD), we prove two principal results for such vortex cores. First, all streamlines therein wind in the same direction, indicating a nonlocal rotary motion in the entire vortex core. Second, each vortex core can have at most one axis (center or focus). The theory is illustrated with examples on spheres and tori of various curvature, demonstrating how geometry and topology shape the shape of vortex cores. The results are purely mathematical and extend naturally to open surfaces, offering a rigorous foundation for vortex identification across disciplines.

Abstract: Background: Thalassemia represents the world’s most prevalent inherited hemoglobin disorder, affecting approximately 4.4 per 10,000 live births globally. Accurate genetic characterization is indispensable both for definitive diagnosis and for lifetime clinical monitoring. The past two decades have witnessed a paradigm shift from conventional protein-based assays toward comprehensive molecular techniques, including next-generation sequencing (NGS), third-generation (long-read) sequencing, and preimplantation genetic testing for monogenic disease (PGT-M). Objectives: (1) To systematically evaluate the molecular techniques available for confirming the diagnosis of alpha- and beta-thalassemia, including their diagnostic accuracy, indications, and limitations; (2) to examine how genotype–phenotype correlation and genetic modifier profiling inform clinical prognosis and therapeutic decision-making; and (3) to define evidence-based genetic monitoring parameters for longitudinal follow-up of patients receiving transfusions, iron chelation, and novel curative therapies including gene therapy. Methods: A comprehensive narrative review was conducted by systematically searching PubMed/MEDLINE for English-language peer-reviewed articles published between January 2000 and December 2024. Forty-three studies were ultimately included after applying predefined inclusion and exclusion criteria. Quality of included studies was assessed using SANRA (Scale for the Assessment of Narrative Review Articles). Results: HPLC and capillary electrophoresis remain first-line phenotyping tools; DNA-based confirmation is mandatory for complete genotyping. NGS-based targeted panels detect >95% of common mutations but require MLPA co-testing or long-read sequencing for structural variants. Genotype–phenotype prediction is substantially enhanced by profiling three major modifier loci: XmnI (Gγ), BCL11A, and HBS1L-MYB. PGT-M using NGS achieves near-complete genotyping accuracy (>99%) with live birth rates of 40–60% per frozen embryo transfer cycle. For patients receiving curative gene therapy (exagamglogene autotemcel / Casgevy), molecular follow-up protocols spanning 15 years are now recommended. Cardiac T2* MRI remains the most reliable non-invasive tool for iron overload follow-up, superior to serum ferritin alone. Conclusion: A tiered, genotype-informed approach—combining HPLC/CE phenotyping, targeted molecular diagnostics, genetic modifier profiling, and periodic re-evaluation—optimizes diagnostic precision and guides individualized management across the thalassemia spectrum. Integration of PGT-M and long-read sequencing into standard care pathways, alongside robust gene therapy follow-up protocols, will define the next era of thalassemia genetics.

Abstract: We describe a harmonic analysis system for predicting annual peak snow water equivalent (SWE) at SNOTEL monitoring stations operated by the Natural Resources Conservation Service (NRCS) across the western United States. The algorithm, frqsrchX, performs greedy harmonic regression on historical SWE records, identifying persistent periodic climate signals and superimposing volcanic impulse functions to account for episodic radiative forcing from major eruptions. A five-phase characterization pipeline applies distinct band-limited search strategies per site, and a two-winner selection system identifies optimal configurations by both maximum pass rate and a reliability score that balances accuracy with period stability. Validation uses out-of-sample holdout testing across 15–18 years (2008–2025), graded by an asymmetric scale that penalizes over-prediction more harshly than under-prediction. We report results for 771 SNOTEL and SNOW SENSOR stations across eight western states. Average pass rates range from 88.4% (Montana, 94 sites) to 49.3% (California, 122 sites, including 87 SNOW SENSOR stations). The three commercially targeted states—Colorado (113 sites), Montana (94 sites), and Wyoming (87 sites)—achieve average pass rates of 86.4%, 88.4%, and 84.2% respectively, with 84–90% of sites meeting the ≥80% operational pass-rate threshold using identical universal parameter search procedures and no state-specific tuning. Idaho (85 sites) and Washington (76 sites) show strong intermediate performance at 83.3% and 81.5%. Utah and Oregon show mixed results, while California falls well below operational thresholds. Period stability analysis indicates that 55–62% of qualifying sites in the five strongest states achieve stable signal detection, demonstrating consistent identification of physical climate periodicities. These results demonstrate that periodic climate signals—principally in the ENSO band (2,700–2,900 mY), a mid-range band (~6,000–7,500 mY), and an extended long-period band (10,500–17,000 mY)—carry actionable predictive information about annual peak snowpack at individual station scale. The 2026 season, the first fully prospective test, produced a near-universal over-prediction across all commercially targeted states. A station-level decomposition for Colorado attributes this outcome predominantly to rain-versus-snow partitioning during a warm snow drought (53% of the SWE shortfall) rather than to a precipitation-forecasting failure. We accordingly treat warm-drought years as outside the method’s scope and ungraded, while precipitation-driven (dry) snow drought—a deficit the method does forecast—remains in scope. The historical validation reported here is unaffected.

Abstract: Bacterial meningitis is a life-threatening central nervous system infection in which rapid and accurate pathogen identification is essential for effective treatment; however, conven-tional culture methods often show limited sensitivity due to prior antibiotic exposure, low microbial load, or fastidious organisms. Background: This study aimed to investigate the cerebrospinal fluid (CSF) microbiota in patients with bacterial meningitis and to evaluate the diagnostic performance of 16S rRNA gene-based metagenomic next-generation se-quencing (mNGS), particularly in culture-negative cases. Methods: CSF samples from 26 patients collected between September 2023 and July 2025 were analyzed. Standard aerobic culture and PCR were performed. DNA was extracted using the ZymoBIOMICS kit and sequenced on the Oxford Nanopore MinION platform using the ONT 16S Barcoding Kit. Sequencing data were processed using Dorado basecalling, FastQC quality control, and taxonomic classification against NCBI and proprietary databases. Results: Conventional culture identified pathogens in 3/26 samples (Klebsiella pneumoniae, Enterobacter aerogenes, Enterococcus spp.), and one sample was PCR-positive for Mycobacterium tuberculosis. In contrast, mNGS detected bacterial pathogens in five samples, confirming all cul-ture-positive organisms and additionally identifying Mycobacterium spp. and Neisseria meningitidis operational taxonomic units in culture-negative cases. Conclusions: 16S rRNA-based mNGS demonstrated higher diagnostic yield than conventional culture and provided complementary value in pathogen detection, particularly in culture-negative meningitis, and may improve clinical diagnostic workflows.

Abstract: Agentic AI systems depend on classical public-key cryptography for agent identity, tool invocation, inter-agent communication, model integrity, and persistent state, exposing them to a cryptographically relevant quantum computer (CRQC) along two axes: confidentiality (harvest-now-decrypt-later) and integrity (harvest-now-forge-later). Existing post-quantum migration guidance addresses static, operator-controlled enterprise estates, while emerging agent-identity work omits post-quantum cryptography entirely; neither treats non-human-identity-dense, runtime-negotiated agentic systems as a distinct migration class. This paper develops a conceptual framework that does. It organizes agentic cryptography into seven migration surfaces and separates each identity into a credential layer (symmetric, operator-held, low-risk) and a trust-anchor layer (the asymmetric roots that underwrite the fleet). These layers scale inversely: a small set of trust anchors carries a forge-later blast radius equal to the population beneath it, so migration effort and forge-later risk rank the work in opposite orders. A migration matrix and a parametric effort-and-risk model formalize this, yielding the core sequencing rule: migrate anchors first. Because agentic adoption is ongoing, it also reframes migration from a finite inventory into a continuously regenerating problem, distinguishing remediation of the installed base from prevention of new classical-cryptographic debt in future deployments. It closes with oversight and procurement implications for federal post-quantum readiness.

Abstract: Limited access to quality obstetric ultrasound and trained providers remains a barrier to early pregnancy risk detection in low-resource settings. This qualitative phenomenological study explored trainer and trainee experiences of Zimbabwe’s six-week Basic Obstetric Ultrasound Short Course, delivered through the Fetal Medicine Units at Sally Mugabe Central Hospital and Parirenyatwa Group of Hospitals, and examined its perceived relevance to promoting healthy pregnancy. Sixteen semi-structured interviews were conducted between September and October 2023 with eight trainers and eight trainees purposively selected from training records. Audio-recorded interviews were transcribed and analysed using Moustakas’ transcendental phenomenological approach, including horizontalisation, clustering of significant statements, and theme synthesis. Four themes emerged: interactive and hands-on teaching and learning; confidence and skill development through supervised practice; interprofessional learning that reduced hierarchical barriers; and sustainability concerns related to equipment, refresher training, mentorship, and post-training practice. Participants described the course as practical, confidence-building, and relevant to earlier recognition of pregnancy complications, while also emphasising the need for continued supervision and system support. The findings suggest that structured basic obstetric ultrasound training may support healthy pregnancy promotion by strengthening frontline capacity for antenatal risk detection, triage, and referral, although clinical outcome effects require further evaluation.

Abstract: Fast synthetic aperture radar (SAR) imaging simulation is required by many computer vision applications. Although the shooting and bouncing ray (SBR) method has significantly accelerated electric field calculation, the number of ray tubes is still the bottleneck for SAR image simulation speed. This letter proposes an innovative adaptive SBR method driven by Q-learning for accelerated SAR imaging simulation. The core strategy is to convert the ray tube allocation into a reinforcement learning problem. The ray-shooting plane is dynamically partitioned into localized patches, where a Q-learning agent intelligently scales the ray density in real-time. By observing the geometric features of the target surface, the agent learns to employ coarser ray tubes in flat regions to eliminate redundant computation, while deploying denser ray tubes in complex areas. A multi-objective reward function is designed to balance accuracy against computational resource consumption. Numerical experiments demonstrate that the proposed Q-learning-based SBR method drastically reduces computational cost while preserving imaging similarity.

Abstract:

Toxoplasma gondii is a zoonotic protozoan transmitted by environmentally persistent oocysts and by tissue cysts in infected prey or meat. This structured narrative review compares infection evidence in five wild cervid species and three wild canid species to examine how feeding ecology shapes exposure and to assess their complementary value in wildlife surveillance. Peer-reviewed literature published between 2004 and 2025 was retrieved from PubMed, Scopus, ScienceDirect, and Google Scholar. Studies reporting evidence of T. gondii exposure or infection in wild cervids or wild canids were included, with serological evidence evaluated separately from molecular or histological detection. Cervids showed geographically variable exposure consistent with ingestion of oocysts from contaminated vegetation, soil, and water, supporting their use as sentinels of environmental contamination. Wild canids often showed higher reported seropositivity, although direct comparisons were limited by assay, sampling, and demographic heterogeneity. Their predatory, scavenging, and omnivorous diets allow access to both environmental oocysts and tissue cysts. Cervids and canids should therefore be treated as complementary rather than interchangeable indicators: cervids primarily reflect environmental exposure, whereas canids integrate environmental and trophic transmission. Standardized diagnostics, paired host–environment sampling, and explicit ecological metadata are needed to strengthen One Health surveillance and food-safety assessment.

Abstract: The use of industrial control systems (ICS) in the nuclear sector is widespread and is already considered one of the critical assets to evaluate in defining possible attack surfaces for malicious actors linked to crime or hostile nations. The risk of insider threats, both internal and related to equipment suppliers and the supply chain, is always high and must be monitored in dedicated manner with specific protocols, penetration tests and stress analysis. The typical approach to cybersecurity in IT (information technology) systems is not suitable for managing Operational Technology (OT) systems security. The advent of artificial intelligence (AI) is certainly an additional tool for enhancing defense, but it can also be a tool for possible attacks and a risk for the nuclear system in its intimate control of critical process. In this paper, we intend to review and compare various relevant standards that offer deep and comparable rules on ICS and general issues pertaining to the use of AI in most systems contextualized in nuclear domains, what institutions and international organizations are doing to exploit its advantages, and highlight the possible risks, referred to standards and guidelines.

Abstract:

Let \(X = \left( X_{t} \right)_{0 \leq t \leq T}\) be a real-valued continuous process. For a threshold \(a\), the sub-threshold time set \[E_{T}(a) = \{ t \in \lbrack 0,T\rbrack:X_{t} \leq a\}\] encodes several different threshold observables. The most elementary one is the cumulative occupation time \[A_{T}(a) = \int_{0}^{T}\mathbf{1}_{\{ X_{t} \leq a\}}\, dt.\] For a regular one-dimensional diffusion, the classical occupation density formula gives \[A_{T}(a) = \int_{- \infty}^{a}\frac{L_{T}^{y}(X)}{\sigma^{2}(y)}\, dy,\] and hence \[\frac{\partial A_{T}}{\partial a}(a) = \frac{L_{T}^{a}(X)}{\sigma^{2}(a)}.\] Thus additive threshold occupation admits a local-time sensitivity calculus. In the terminology of barrier contracts, this additive clock is the cumulative, non-resetting Parisian clock, also called the Parasian clock. The purpose of this paper is to contrast this additive/Parasian regime with the behavior of resetting Parisian burst functionals. The connected components of \(E_{T}(a)\) represent sub-threshold episodes. We study in particular the longest burst \[M_{T}(a) = \sup\{|I|:I\text{ is a connected component of }E_{T}(a)\}.\] While \(A_{T}\) is locally controlled by local time, \(M_{T}\) is governed by the connectivity of the sub-threshold time set. We prove that \(M_{T}\) is monotone, that its supremum is attained, and that the weak-sublevel version is right-continuous with left limits, while the strict-sublevel version is its left-continuous regularization. The jump at a level is the increase in the maximal connected-component length produced by adjoining the level set. This gives a deterministic càdlàg/càglàd calculus for longest-burst profiles. For regular one-dimensional diffusions, this yields a sharp structural contrast. At deterministic levels which are almost surely not local-extreme values, the weak and strict longest bursts agree almost surely. Whenever the path has a unique interior maximum, the level-indexed longest-burst profile has a positive jump at the maximum level and is therefore not absolutely continuous. Brownian motion satisfies this criterion almost surely. We further identify the deterministic mechanism behind this instability: small threshold increases may fill short temporal bridges and merge large sub-threshold components. Finally, we show that the longest burst is exactly a one-sided continuous Parisian functional. This yields an exact Laplace-transform representation of its Brownian law through the Chesney--Jeanblanc-Picqué--Yor [1] Parisian transform, and an excursion-measure formulation in which local time enters only as the Itô excursion intensity. We also discuss smoothed burst statistics, moving thresholds, and diffusion examples. The paper is intended as a threshold-sensitivity comparison: local time controls cumulative Parasian occupation, whereas resetting Parisian burst observables are controlled by component mergers and excursion structure.

Abstract: This study investigated retinal proteomic alterations associated with type-1 diabetes mellitus (T1DM) using two mouse models of diabetic retinopathy (DR): the genetic Ins2akita/+ (Akita) model and streptozotocin (STZ)-induced diabetes. Retinas were collected from Akita (n=4) and STZ-induced diabetic mice (n=6) 15–16 weeks after diabetes onset and compared with age-matched controls. Quantitative mass spectrometry identified 7,933 proteins in Akita retinas and 7,399 proteins in STZ retinas. Differentially expressed proteins were identified using adjusted p-values and log₂ fold-change, ranked by Man-hattan distance, and visualized with volcano plots and heatmaps. The top 20 dysregulat-ed proteins in each model were subjected to canonical pathway analysis. Both models demonstrated upregulation of inflammatory and angiogenesis-associated proteins, in-cluding LRRC58, coronin-2A, S100-A4, and COL4A2, supporting a pro-inflammatory and vascular remodeling microenvironment. However, there were distinctive changes in some proteins between the two models. For example, Crystallins were downregulated in the STZ model but upregulated in the Akita model. Canonical pathway analysis revealed ac-tivation of platelet-related signaling pathways, enrichment of lipid metabolic networks, and significant alterations in extracellular matrix organization. These findings indicate coordinated inflammatory, metabolic, and structural remodeling in DR and identify can-didate molecular pathways for further investigation and therapeutic targeting.

Abstract: Tiny Machine Learning (TinyML) has emerged as a significant advancement in embedded Artificial Intelligence (AI), enabling machine learning inference directly on resource-constrained microcontrollers and ultra-low-power edge devices. By integrating lightweight machine learning models with embedded systems, TinyML facilitates real-time, energy-efficient, and privacy-preserving intelligence at the edge of Internet of Things (IoT) ecosystems. This chapter presents a comprehensive introduction to TinyML, examining its evolution from conventional cloud-centric AI and Edge AI architectures toward distributed embedded intelligence. The chapter discusses the fundamental architecture of TinyML systems, key model optimization and deployment techniques, including quantization, pruning, and model compression, as well as hardware-aware design strategies for efficient on-device inference. Furthermore, major application domains such as healthcare, consumer electronics, industrial automation, agriculture, and environmental monitoring are explored to demonstrate the practical relevance of TinyML across diverse sectors. The chapter also evaluates the principal advantages and limitations of TinyML and outlines a practical development workflow encompassing hardware selection, software frameworks, data acquisition, model training, optimization, and deployment. Overall, TinyML represents a critical enabling technology for scalable, low-power, and autonomous intelligent systems, supporting the next generation of edge computing and IoT applications.

Abstract: Huizhou Intangible Cultural Heritages, represented by wood carving, brick carving, stone carving, and paper cutting, have intricate patterns and stylistic diversity. Existing image classification approaches rely heavily on manual curation, lacking scalability and interpretability for large-scale digital exhibitions. This study proposes a deep learning framework that adapts large-scale vision–language models to Huizhou Intangible Cultural Heritages through information-maximization self-distillation. Specifically, we employ Contrastive Language-Image Pre-Training (CLIP) as a teacher model to generate pseudo-labels for unlabeled Huizhou heritage images, while a student model is adapted through parameter-efficient tuning of visual prompts and LayerNorm parameters. An entropy-based information maximization objective further enhances model confidence and diversity. Extensive experiments demonstrate improvements over zero-shot and existing adaptation baselines. Overall, MM-SHAP offers a novel, information-theoretic paradigm for unsupervised and efficient fine-tuning in exhibition-focused visual classification tasks, balancing performance and efficiency.

Abstract: The rise in the presence and identification of biorefractory pollutants, also known as emerging contaminants (ECs) like microplastics, in the environment has been notable in recent times, attributed to factors such as population growth, changes in lifestyle, and rapid industrialization. A variety of pollutant substances are necessitated suitable remediation. Bioelectrochemical systems (BESs) represent sustainable technologies that can be utilized. In the current bibliometric investigation, the connection between bioelectrochemistry and pollutants in the environment is examined. Data obtained from the Web of Science database were utilized for the bibliometric analysis employing VOSviewer and R. According to the results, a highly integrated and rapidly maturing research landscape, characterized by a clear transition from fundamental technological development to large-scale environmental applications. The study serves as the essential bridge between the two primary pillars of the field: sustainable energy recovery and environmental remediation. The keyword co-occurrence networks illustrate a sophisticated synergy where the oxidative biodegradation of organic pollutants is directly coupled with electricity generation. A key discovery is the inherent synergy between the biodegradation of pollutants and the generation of electricity, which characterizes the contemporary ‘waste-to-energy’ model.

Abstract: Background: Community-based exercise programmes are increasingly recognised as an important component of chronic disease management and long-term self-management. As healthcare systems seek to promote prevention, integrated care, and community-based service delivery, there is growing interest in pathways that support sustained physical activity participation beyond traditional clinical settings. In Ireland, the Physical Activity for Health Officer (PAfHO) role has emerged as a novel initiative designed to strengthen links between healthcare services and community-based physical activity opportunities. Objective: To review contemporary evidence relating to community exercise programmes for chronic disease and examine the emerging role and potential contribution of the Physical Activity for Health Officer model within the Irish health system. Methods: A narrative review of peer-reviewed and grey literature published between 2020 and 2025 was conducted. Evidence relating to community exercise programmes, exercise referral and signposting pathways, integrated care models, and the Physical Activity for Health Officer pilot in Ireland was examined and synthesised. Results: Community exercise programmes consistently demonstrate improvements in physical activity participation, functional capacity, and health-related quality of life among people living with chronic disease. Programmes incorporating personalised exercise prescription, behavioural support, and integrated care pathways appear to achieve the strongest outcomes. Emerging evidence from Ireland suggests that the PAfHO role supports the development of referral and signposting pathways, increases access to community-based physical activity opportunities, and enhances collaboration between healthcare, local authority, and community sectors. Conclusions: Community exercise programmes represent an effective and scalable approach to supporting individuals with chronic disease. The PAfHO model aligns with international evidence supporting integrated care and community-based physical activity pathways and may provide an important mechanism for connecting healthcare services with long-term community participation. Further evaluation is required to establish its impact on health outcomes, service utilisation, and health system performance.

Abstract: This study examines the relationship between corporate FinTech disclosure and ESG reporting performance among non financial firms listed on the Saudi Stock Exchange (Tadawul), with a focus on post Covid period from 2021 to 2024. Using an ESG Disclosure Index constructed from annual reports and a textual measure of FinTech adoption, the analysis provides market-level evidence on the evolution of digital transformation and ESG disclosure in Saudi Arabia. Descriptive results indicate that ESG reporting among Tadawul firms is moderate yet heterogeneous, with governance disclosure consistently stronger than environmental and social components. Correlation analysis indicates a positive association between FinTech disclosure and overall ESG disclosure, particularly within the environmental pillar. Regression results further show that the firms with stronger FinTech disclosure tend to report higher ESGDI scores. The two way fixed effects (TWFE) model yields statistically significant results and the direction of the relationship remains consistent with theoretical expectations. Pillar level analysis suggests that digital transformation is most closely aligned with environmental reporting. Taken together, the results indicate that sustainability disclosure and digital capabilities appear to co-develop in the Tadawul market. Businesses may improve their ability to track, organize, and disseminate ESG-related data by investing in digital reporting systems, analytics, and technology modernization. In this way, FinTech serves as a governance-supporting instrument that improves transparency and reporting discipline in addition to being a financial innovation. The study adds to the expanding body of knowledge by providing important emerging-market-level evidence from the Saudi capital market and highlighting how FinTech can support sustainability-driven growth in an institutional context undergoing rapid transformation.

Abstract: This study investigates the impact of sustainable service to customer-related outcomes in the hospitality industry by focusing on environmental awareness, perceived value, customer trust, and revisit intention. Grounded in Stakeholder Theory, the research posits and empirically examines the model with the concept of perceived value and customer trust as mediators and environmental awareness as a moderator. Data was collected from a total of 457 hospitality customers, proportionately drawn from Accra (184), Kumasi (152) and Tamale (121), and analysed using partial least squares structural equation modelling (PLS-SEM). The results show that sustainable service practises have a significant impact on perceived value, customer trust and environmental awareness in a positive way. In addition, perceived value and customer trust partially mediate the relationship between sustainable service practises and environmental awareness, in which customer trust is the more effective mediating mechanism. Contrary to expectations, environmental awareness does not moderate the association between sustainable service practises and revisit intention to a great extent. Novelty: The outcome of the study accentuates the relevance of incorporating perceived value and customer trust into sustainable service practises offering both theoretical insights via stakeholder theory and practical direction for enhancing guest revisit intention in the hospitality sector. On the whole, the findings emphasize the strategic necessity of incorporating the concept of sustainability into the core service delivery and achieving better customer perceptions and relational outcomes.

Abstract:

Porous BaTiO₃ (BTO) ceramics with controlled porosity were successfully fabricated using a simple and cost-effective sucrose-assisted route. Porosity was introduced by incorporating 10–50 vol% sucrose as a pore-forming agent, followed by sintering at 1350 °C for 2 h. The use of sucrose as an effective pore-forming agent is corroborated by the systematic reduction in bulk density from ~5.92 to ~4.1 g.cm^-3. X-ray diffraction and Raman spectroscopy analysis revealed the retention of the tetragonal phase across all samples, indicating that the introduction of porosity does not alter either the average crystal or local structure. Microstructural analysis demonstrated well-developed grains with heterogeneously distributed and interconnected porosity upon sucrose addition, while maintaining good grain connectivity. Electrical characterisation showed a gradual decrease in maximum polarisation (P_max) from ~21 µC.cm^-2 for dense BTO to ~12 µC.cm^-2 for 50 vol% sucrose samples. Despite increased porosity, the electric field-induced strain response exhibited only a marginal reduction (~0.137% to 0.10%), indicating preserved electromechanical functionality with enhanced large-signal piezoelectric coefficient ~468 pm.V^-1 for the 20 vol% sucrose sample, whereas the 10 vol% counterpart shows the largest ɛ_RT ~1150 with tan δ = 0.005. These results demonstrate that sucrose-assisted fabrication enables effective porosity engineering in BTO without compromising its ferroelectric nature, offering a promising approach for the development of porous ferroelectric ceramics with tunable electromechanical properties.

Abstract: Oil extraction and agricultural production are central to North Dakota’s economy, yet their spatial coexistence and implications for sustainable land-use planning remain poorly understood. This study conducts a statewide geospatial analysis integrating OpenStreetMap data, GIS processing, DBSCAN clustering, and spatial statistics to examine the colocation of 7,102 oil well and 4,277 grain silo sites. Hotspot and spatial heterogeneity tests using the Getis–Ord Gi* statistic and local Moran’s I reveal a pronounced spatial divide: oil activity is tightly clustered in the western Bakken region, whereas grain storage facilities concentrate across central and eastern counties. The limited geographic overlap suggests minimal systemic land-use overlap, though localized high-intensity co-location patterns emerge in McKenzie, Dunn, and Mountrail counties. These patterns provide stakeholders with insight into locations where shared logistics pressures and land-use tensions may influence sustainable infrastructure planning. More broadly, the study demonstrates the value of spatial data mining techniques applied to free, publicly available data for identifying intersectoral infrastructure patterns that inform sustainable land-use, infrastructure, and regional development planning across North Dakota.