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The main objective of this study is to produce a systematic literature review that analyses key performance indicators (KPI) in the context of efficient and sustainable building renovation. Efficiency and sustainability, in combination with building renovation, are important themes due to the increasing need for creating sustainable renovations worldwide. The identification and monitoring of KPIs is fundamental in decision-making processes, but also in the monitoring of short-term and long-term project goals. In the current academic literature, existing research gaps, especially in the social aspects of sustainability and research, have also been analyzed in terms of regional differences in the approach to each KPI. The systematic literature review examined 29 studies published between 2014 and 2024, based on a literature search conducted in 2024, using databases such as Scopus and Web of Science, with the final search performed in June 2024. The inclusion criteria focused on peer-reviewed studies addressing KPIs in sustainable building renovation, while studies not directly related to renovation processes or lacking KPI analysis were excluded. The research results show that the majority of studies focus on economic and environmental factors, which are the most commonly addressed, while research on other KPIs is significantly behind. The results were synthesized using a qualitative comparative analysis of identified KPI categories. This study also highlights the importance of addressing effective and sustainable renovation for historic buildings with a focus on heritage preservation and the need to further analyze the use of KPIs with a focus on historic buildings. The limitations include the limited number of studies and the underrepresentation of social sustainability aspects. Full article

Human serum albumin (HSA) is the most abundant protein in plasma, and the redox state of circulating HSA has been used as a biomarker of systemic oxidative stress (OS) for decades. While informative, many traditional biomarkers of OS measure short-lived or downstream products of oxidative damage that offer limited perspectives on the dynamic and integrated processes that govern systemic redox biology within human populations. By moving beyond single-analyte damage markers and towards coordinated patterns of protein modifications, HSA-Cys³⁴ adductomics offers a systems-level approach that simultaneously captures change in multiple layers of OS. Because of its high abundance in plasma and HSA’s unique and highly reactive single free thiol (Cys³⁴), HSA-Cys³⁴ serves as an ideal sentinel target for monitoring reactions with reactive oxygen species (ROS), reactive nitrogen species (RNS), and electrophilic species produced by endogenous metabolism and responses to exogenous chemical exposures. The reaction of HSA with ROS, RNS, and reactive electrophiles yields a diverse array of protein modifications, including direct oxidation products (sulfenic, sulfinic, and sulfonic acid), low molecular weight thiol-disulfide exchange, and lipid peroxidation (LPO)-derived reactive aldehydes. With a mean residence time of about a month, these accumulated adducts serve as an integrated picture of oxidative and electrophilic stress that together function as a molecular record of systemic redox physiology. Previous studies using high-resolution mass spectrometry-based adductomics have enabled global untargeted analysis of HSA-Cys³⁴ modifications, yielding an expansive inventory of novel redox signatures of environmental stressors and disease states. In this paper we review the chemistry and biology underlying OS-related modifications of HSA-Cys³⁴ and highlight the important role of HSA-Cys³⁴ adducts as integrative biomarkers of OS at the interface of molecular biology, exposure assessment, and public health research. Full article

This paper presents a study on green value co-creation behavior in the relationship to tourists’ behavior, including perceived green empowerment, experience value, satisfaction and brand advocacy, specifically considering the role of perceived greenwashing in these relationships based on Service-Dominant Logic. A quantitative study using the structural equation modeling analysis technique was conducted with customers of tourism businesses in Vietnam. The result of the study shows that green value co-creation behavior has a positive effect on perceived green empowerment, experience value, satisfaction and brand advocacy. In addition, the study also shows the positive effect of perceived green empowerment, experience value and satisfaction on brand advocacy. Perceived greenwashing reduces the impact of green value co-creation behavior on perceived green empowerment, experience value and satisfaction. The research results provide empirical evidence confirming the important positive role of green value co-creation and the barrier role of perceived greenwashing in achieving positive outcomes on customer behavior of tourism businesses. At the same time, the study provides useful information for managers in increasing perceived green empowerment, experience value, satisfaction and brand advocacy through customer green value co-creation activities, based on honest and standard green practices. Full article

Pacific white shrimp (Litopenaeus vannamei) is a major aquaculture product worldwide. For consumers, discriminating domestic from imported sources of shrimp meat, and individual domestic sources, can be highly desirable because of the different meat quality and environmental contamination from geographically different origins of shrimp. This study evaluated the potential of stable isotope analysis (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) with chemometric models to authenticate the origins of Pacific white shrimp sold in China. Shrimp samples from domestic (Guangxi, Fujian, Shandong, Inner Mongolia) and foreign (Ecuador) sources were analyzed, using statistical analyses. The four-isotope model achieved 89.3% cross-validation accuracy in distinguishing domestic and foreign shrimp, with an overall prediction Area Under the Curve (AUC) of 0.901 (95% CI: 0.819–0.983)—significantly outperforming single-isotope models. Differences in δ¹³C and δ¹⁵N reflected feed source variations, while δ²H and δ¹⁸O (Variable Importance in the Projection (VIP) > 1, key discriminatory indicators) mirrored geographic environmental difference. Although δ¹⁵N did not differ significantly among groups, the combination of all four isotopes reduced limitations of individual δ²H/δ¹⁸O use. This approach enhanced the precision, reliability, and applicability of stable isotope analysis for origin authentication by leveraging complementary isotopic data and robust statistical frameworks. These findings demonstrate the proposed model’s potential as a cost-effective, copyright-compliant framework for shrimp origin authentication, with implications for isotopic traceability across food science fields. Full article

Carbon dioxide (CO₂) enrichment using fuel combustion is widely applied in greenhouse production. However, its implications for air quality and occupational safety under real operating conditions remain insufficiently characterized. This study evaluates a propane-based CO₂ enrichment system in an advanced greenhouse. The analysis integrates CO₂ dynamics, combustion-derived pollutants, and occupational exposure. High-resolution monitoring at 5 min intervals was conducted in an enriched module and a control module over a five-month period. Two operational modes were assessed: continuous and diurnal-only enrichment. The system maintained CO₂ concentrations within agronomic targets. Mean values reached 1200 ppm and 940 ppm for continuous and diurnal operation, respectively. However, significant CO₂ losses were observed due to ventilation. The maximum enrichment efficiency, expressed as the Combustion Efficiency Index (CEI), was 2.67 × 10⁻³. Combustion-related pollutants (CO, NO, NO₂, SO₂, and O₃) showed transient peaks during burner activation. However, concentrations remained below occupational exposure limits when evaluated using time-weighted averages. The incomplete combustion ratio (ICR) remained stable at approximately 1.9 × 10⁻³. This indicates predominantly complete combustion. These results provide field-based evidence on the performance and safety of propane-based CO₂ enrichment systems. They also highlight the importance of continuous monitoring and improved CO₂ retention strategies in semi-confined greenhouse environments. Full article

International tourism plays an important role in the global service economy, contributing to trade, employment, and regional development. For this reason, identifying the factors that influence tourist flows is an important issue for tourism policy, market strategy, and infrastructure planning. A large body of research has applied gravity models to analyze tourism flows between countries. While this approach provides a clear economic interpretation, it is usually based on linear specifications and may therefore capture only part of the relationships present in tourism data. This study examines the economic and geographic determinants of international tourism flows to Mongolia using a framework that combines a traditional gravity model with machine learning techniques. Mongolia serves as an instructive empirical setting, a landlocked, geographically peripheral destination whose inbound demand determinants have received limited systematic empirical attention. The analysis uses panel data for 27 origin countries covering the period from 2000 to 2024. In the first stage, a gravity model is estimated to assess how tourism flows relate to economic size and geographic distance. The results show that tourism flows tend to increase with the economic size of origin and destination countries, while greater geographical distance is associated with lower tourism flows. The estimated distance elasticity ranges from approximately −1.85 to −2.10 across model specifications, which is larger in absolute terms than the values typically reported in cross-country studies. This result is consistent with the relatively high travel cost barriers associated with Mongolia’s geographic location. These findings are consistent with the distance decay relationship commonly reported in the tourism literature. In the second stage, machine learning algorithms, including Random Forest, LightGBM, and XGBoost, are used as complementary interpretive instruments rather than forecasting tools to explore possible nonlinear relationships among the explanatory variables. To make the results more interpretable, the contribution of individual variables is examined using SHAP (Shapley Additive Explanations). The machine learning results indicate that some relationships in tourism demand may be nonlinear and not fully captured by the linear gravity specification. Specifically, distance sensitivity is approximately 6.5 times greater in nearby markets than in long-haul markets, with a structural inflexion at around 5700 km. Further analysis suggests that the influence of geographical distance is not uniform across all markets. In particular, tourism flows originating from middle-income countries appear to be more sensitive to increases in travel distance than those from higher-income countries. Overall, the findings indicate that economic size and geographical distance remain key determinants of international tourism flows to Mongolia. At the same time, the use of machine learning methods provides additional insight into potential nonlinear patterns in tourism demand. By combining econometric modelling with explainable machine learning techniques, the study offers an integrated analytical perspective for examining international tourism flows at geographically peripheral destinations where standard gravity assumptions may be insufficient. Full article

The load characteristics of the helicopter tail rotor system are critical to flight safety and handling performance, and flight testing remains the most direct and reliable means to obtain authentic load data. In this paper, the well-established Wheatstone bridge strain measurement method is adopted to carry out accurate load testing on the helicopter tail rotor system. The tail rotor assembly mainly consists of the tail rotor shaft, pitch link, and tail rotor blades, which undertake different load transfer tasks during flight. Under actual operating conditions, the tail rotor shaft bears significant axial tension as well as combined lateral and vertical bending moments; the pitch link is primarily subjected to alternating axial tension and compression; and the tail rotor blades withstand complex loads including flapping bending, lagwise bending, and torsional moments. According to the distinct stress characteristics and force transmission paths of each component, targeted flight test maneuvers are reasonably designed. These maneuvers include steady-level flight at low, medium, and high speeds, zigzag climbing flight, near-ground side-rear flight, as well as deceleration-to-sprint and obstacle slope maneuvers specified in ADS-33E. Key flight parameters are selected for in-depth analysis to reveal the load distribution and dynamic variation patterns of the tail rotor under typical operating conditions. On this basis, a helicopter load risk test point matrix is established to identify high-risk working conditions and key monitoring positions. This study provides a solid theoretical and data foundation for subsequent flight test monitoring and structural strength verification. It effectively reduces flight test risks, improves monitoring efficiency and accuracy, and helps cut down the human, material, and financial costs associated with flight test monitoring. The research results can also provide important references for the design optimization and safety evaluation of helicopter tail rotor systems. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

Salmonella enteritidis (SE) is recognized as a primary etiological agent of foodborne infection and food poisoning. Selective and sensitive determination of SE in animal-derived products is of great importance for ensuring safety in the food industry. Here, we report a highly sensitive and specific competition assay for detecting SE in eggs without interference from background fluorescence, by using persistent luminescent nanoparticles (PLNPs) as luminescent probes in combination with aptamer recognition and magnetic separation. Initially, the SE-specific aptamer (SEapt), as previously reported, was conjugated onto the surface of Fe₃O₄ magnetic nanoparticles to serve as both the recognition and separation unit. Meanwhile, the ZnGa₂O₄:Cr (PLNPs) were functionalized with the aptamer-complementary DNA (cDNA), serving as the PL signal generator. The constructed PL aptasensor is composed of the aptamer-conjugated MNPs (MNPs-SEapt) and cDNA-functionalized PLNPs (PLNPs-cDNA), integrating the merits of the long-lasting luminescence of PLNPs, the magnetic separation ability of MNPs and the selectivity of the aptamer. This integration offers a promising approach for autofluorescence-free determination of SE in food samples. The proposed aptasensor exhibited excellent linearity in the range from 1.0 × 10²–1.0 × 10⁷ CFU mL⁻¹ with a limit of detection as low as 32 CFU mL⁻¹. The precision for 11 replicate determinations of 1.0 × 10³ CFU mL⁻¹ SE was 3.4% (relative standard deviation). The developed aptasensor achieved recoveries ranging from 98.8% to 102.8% for the determination of SE in the presence of common foodborne bacterial interferents. The method was successfully applied to the analysis of Salmonella genus in egg samples. In principle, the proposed platform may be adapted to other food matrices by substituting the target-specific aptamer, pending target-dependent optimization and validation. Full article

Injection molding process optimization has predominantly relied on simulation-generated data, which cannot capture machine-specific variability and stochastic process noise inherent in real manufacturing environments. This paper presents an experiment-driven machine learning framework for multi-objective optimization of injection molding process parameters targeting volumetric shrinkage, warpage, cycle time, and part weight. Physical experiments were conducted on an industrial injection molding machine using high-density polyethylene with a face-centered central composite design. Systematic benchmarking of four machine learning algorithms under identical cross-validation protocols identified Gaussian process regression as the best-performing surrogate model for the majority of quality metrics, while warpage prediction remained challenging across all algorithms due to its complex thermo-mechanical origins. Permutation-based feature importance analysis established a clear parameter hierarchy, identifying holding time as the dominant factor governing multiple quality responses. Constrained Bayesian optimization with progressive constraint tightening was employed to identify optimal parameter sets and fundamental process capability boundaries. The resulting parameter configurations were validated against a held-out test set. This work demonstrates that rigorous, data-driven optimization using exclusively experimental data provides a viable and practically achievable alternative to simulation-based approaches, contributing to experiment-centric smart manufacturing in polymer processing. Full article

This narrative review integrates landmark studies, recent publications, and major clinical guidelines to highlight the current state of the art concerning acute pancreatitis, a well-known yet still challenging condition. We will focus on recent practice transitions and future perspectives arising from advances in diagnostic imaging and interventional endoscopy. Pathogenesis and etiology: We carry out an overview of the fundamental mechanisms underlying acute pancreatitis, followed by an analysis of both common and uncommon causes, along with emerging evidence regarding idiopathic forms. Diagnosis and risk stratification: We pursue two objectives: on one hand, to emphasize the enduring importance of clinical assessment in the diagnosis of acute pancreatitis; on the other, to analyze the increasingly central role that imaging has acquired over recent decades. Identification of patients at higher risk for complications or an unfavorable prognosis is crucial. Several scoring systems have been proposed over the past decades, but with limited impact on daily clinical practice. Treatment: Therapeutic approaches have undergone significant revisions over time. Our objective is to provide an overview of the current standards together with best evidence-based medical approaches, targeted and interventional therapies, with focus on the endoscopic ones. Furthermore, we want to clarify the importance of nutrition and its proper management. Conclusions: Acute pancreatitis continues to stimulate discoveries and improvements in clinical management. We will place emphasis on unmet needs and emerging innovations that may importantly influence future practice also promoting evidenced-based standards of care. Full article

Objectives: Adolescent idiopathic scoliosis (AIS) may negatively affect both the quality of life of adolescents and the quality of life of their families (FQOL). Therefore, the analysis of objective and subjective determinants of FQOL in families of children and adolescents with AIS undergoing long-term conservative treatment becomes important and will allow for a better understanding of factors that may have a significant impact on the prognosis and clinical treatment outcomes. Methods: The analysis covered a total of 200 families of children and adolescents aged 7–18 from the Podkarpackie region (Poland). The medical history chart and the original physical examination card, as well as the Family Adaptability and Cohesion Scales (FACES III) and the Family Quality of Life Scale (FQOL), were used in this study. Results: Families of adolescents without AIS demonstrated significantly higher levels of family cohesion and adaptability compared with families of adolescents with AIS (p < 0.001). The mean overall FQOL score was significantly lower in the AIS group (75.33 ± 9.18) than in the control group (86.97 ± 7.91; p < 0.001, r_rb = 0.58). Multivariate analysis indicated that family adaptability was an independent predictor of FQOL in the AIS group, with higher adaptability associated with lower overall FQOL and reduced scores in parental functioning and emotional well-being domains. Conclusions: A long process of AIS treatment can cause crisis situations for patients and their families and influences both the physical and mental health of patients by changing their family’s quality of life (FQOL). Early identification of families characterized by diminished cohesion and adaptability enables the integration of psychopedagogical support and family consultations into standardized care. Such a multidimensional approach may enhance therapeutic prognosis and accelerate the rehabilitation process. Full article

Crypto-asset markets have been rapidly evolving during the past years, being under the spotlight of a diverse set of actors in the financial ecosystem, including investors, financial institutions, regulators and academics. Their potential interconnections with the traditional financial markets are important, and identifying them can provide useful insight in a diversity of areas such as risk contagion and mitigation, price formation, portfolio management and regulatory framework design. In order to identify such interconnections, various lines of research are followed. Specifically, the correlation between prominent stock market indices and crypto-assets from 2018 to 2025 is examined, while their volatility is also evaluated. Furthermore, the relevant effect of news, events and announcements is explored. The results are based on both daily and high-frequency datasets, with the use of the latter focusing on intra-day variation. The analysis of the results identifies existing interconnections between 2020 and 2025, as well as the important respective impact of news and announcements. An additional generic outcome is the usefulness of high-frequency datasets in the crypto-asset context. The conclusions are useful for all actors in the financial ecosystem. Future work can focus on the extension of the research to additional markets or crypto-assets. Full article

The emergence of variant strains of Avian orthoreovirus (ARV) has caused substantial economic losses in the poultry industry worldwide, but the molecular features of goose-origin strains and viral transmission among different avian species remain poorly understood. Here, we describe a goose-origin avian orthoreovirus strain, SD0407, associated with growth retardation and joint swelling. Complete genome analysis identified ten double-stranded RNA segments. Sequence comparison indicated that SD0407 is closely related to previously reported duck-origin reovirus strains. Phylogenetic and recombination analyses showed that most segments clustered with duck-origin strains, indicating close genetic relatedness among waterfowl-origin orthoreoviruses. Sequence and structural analysis of the σC attachment protein revealed ten unique amino acid substitutions, including D250 within the DE-loop region involved in receptor-binding. Molecular docking suggested that σC interacts with the conserved AnxA2-S100A10 heterotetrameric receptor complex, providing a possible structural basis for receptor compatibility across avian species. Although SD0407 replicated efficiently in goose embryo fibroblasts, it did not induce expression of type I, II or III interferons. Transcriptome profiling revealed weak activation of innate immune signaling and downregulation of metabolic and cytoskeletal genes, consistent with effective suppression of antiviral responses. These findings demonstrate that SD0407 combines structural variability with immune evasion to enhance host adaptability and underscore the importance of sustained ARV surveillance in waterfowl populations. Full article