Abstract: Curcumin, a major phytochemical derived from Curcuma longa, has been shown to enhance the efficacy of chemotherapeutic agents such as doxorubicin, 5-fluorouracil, and cisplatin by overcoming drug resistance, making it a promising adjunct in the treatment of glioblastoma. However, the global gene expression changes triggered by curcumin in glioblastoma remain underexplored. In this study, we investigated the effects of curcumin on human glioblastoma (U87 MG) cells, where it significantly reduced cell viability and proliferation in a dose- and time-dependent manner and induced apoptosis without affecting senescence. Transcriptomic analysis revealed 5,036 differentially expressed genes, with pathway enrichment identifying 13 dysregulated cancer-associated pathways. Notably, curcumin modulated several key regulators involved in MAPK, Ras, TGF-β, Wnt, Cytokine, and TNF signalling pathways. Several apoptosis and cell cycle-associated genes, including PRKCG, GDF7, GDF9, GDF15, GDF5, FZD1, FZD2, FZD8, AIFM3, TP53AIP1, CRD14, NIBAN3, BOK, BCL2L10, BCL2L14, BNIPL, FASLG, GZMM, TNFSF10, TNFSF11, and TNFSF4, were significantly altered. Importantly, RUNX3, a key tumour suppressor, was markedly upregulated following curcumin treatment, emphasizing its potential role in curcumin-mediated anti-tumour effects. This study provides insight into the molecular mechanisms underlying curcumin's action against glioblastoma.

Abstract: A one-dimensional plug-flow reactor modeling procedure was developed and used to investigate the performance of a membrane reactor (MR) for hydrogen separation from syngas. A feed syngas enters from one side, while a sweep gas of nitrogen enters from the opposite side. The model treats the membrane reactor as a series of 200 segments with a constant cross section and temperature. The adopted spatial resolution was verified to be accurate based on a conducted resolution sensitivity analysis. Permeation is modeled as happening through thin palladium membranes that are selectively permeable to hydrogen, depending on the temperature and membrane thickness. After analyzing the hydrogen permeation profile in a base case corresponding to reference operational temperature and pressures, the temperature of the module, the retentate-side pressure, and the permeate-side pressure were varied individually and their influence on the permeation performance was investigated. In all the simulation cases, fixed targets of 95% hydrogen recovery and 40% mole-fraction of hydrogen at the permeate exit were demanded. The module length is allowed to change to satisfy these targets, with a shorter module requiring less space and reflecting better hydrogen permeation mass flux. Other dependent permeation-performance variables that were investigated include the logarithmic mean pressure-square-root difference, the hydrogen apparent permeance, and the efficiency factor. Various linear and nonlinear regression models were proposed based on the obtained results. This work gives general insights about hydrogen permeation via palladium membranes.

Abstract: This paper presents OpenRSSI, a novel motion capture system that leverages ultra-wideband (UWB) radio signal strength indicators combined with inertial measurement units (IMUs) to achieve high-precision tracking without the positional drift common in pure inertial systems. Our approach utilizes an adaptive sensor fusion algorithm that dynamically adjusts to environmental conditions and movement patterns, providing robust tracking across varied use cases.

Abstract: In this study, we propose \textit{CrossFusionTokens (XFT)}, a novel channel-aware method for integrating visual and linguistic information in multimodal representation learning. Our work is motivated by the increasing demand for robust systems capable of interpreting and reasoning over both visual and textual data. Tasks such as Visual Question Answering (VQA) and Visual Entailment require precise alignment and fusion between language semantics and visual perception, where traditional approaches like unimodal concatenation and symmetric cross-attention fall short in maintaining coherence across modalities. Our method introduces a dual cross-attention mechanism that facilitates bidirectional querying between modalities—first using visual tokens to extract text features, and then reversing the process using text tokens to retrieve visual information. These paired outputs are fused along the channel dimension to form compound representations that encapsulate rich, contextualized information from both inputs. Unlike prior methods that concatenate tokens along the sequence axis, our fusion along the channel dimension maintains token compactness while enriching feature semantics. We validate XFT across three widely used benchmarks—GQA, VQA2.0, and SNLI-VE—demonstrating superior performance to several state-of-the-art fusion approaches. Notably, XFT provides a unified pipeline that combines the advantages of co-attention and merged attention mechanisms without incurring excessive computational costs. This research contributes a scalable and effective solution for advancing vision-language reasoning, paving the way for more general-purpose multimodal understanding systems.

Abstract: The integration of Artificial Intelligence (AI) into manufacturing processes is revolutionizing the industry, significantly enhancing operational efficiency and productivity. This comprehensive review explores the multifaceted impact of AI on manufacturing efficiency, analyzing various AI technologies such as machine learning, robotics, computer vision, and natural language processing. By automating production processes and enabling predictive maintenance, AI minimizes downtime and reduces human error, leading to streamlined workflows and optimized resource allocation. The review highlights advancements in quality control through real-time defect detection and improved supply chain management facilitated by AI-driven demand forecasting and inventory optimization.Case studies from diverse industries, including automotive, electronics, and aerospace, illustrate successful AI implementations, showcasing measurable efficiency gains and enhanced competitiveness. However, the adoption of AI in manufacturing is not without challenges. Issues such as data quality, resistance to organizational change, workforce skills gaps, and ethical considerations pose significant barriers to effective implementation. The review also addresses future directions for AI in manufacturing, emphasizing emerging technologies and their potential to further transform the industry.Overall, this review underscores the critical role of AI in reshaping manufacturing efficiency, offering insights for practitioners and researchers alike. It concludes with recommendations for future research to address existing challenges and leverage AI's full potential in the manufacturing sector.

Abstract: The growing environmental concerns associated with petroleum-based plastics require the development of sustainable, biodegradable alternatives. Polyhydroxyalkanoates (PHAs), a family of biodegradable bioplastics, offer promising potential as eco-friendly substitutes due to their renewable origin and favorable degradation properties. This research investigates the use of synthetic condensate mimicking the liquid fraction from drying and shredding of household food waste as a viable substrate for PHA production using mixed microbial cultures. Two draw-fill reactors (DFRs) operated under different organic loading rates (2.0 ± 0.5 and 3.8 ± 0.6 g COD/L), maintaining a consistent carbon-to-nitrogen ratio to selectively enrich microorganisms capable of accumulating PHAs through alternating nutrient availability and deficiency. Both reactors achieved efficient organic pollutant removal (>95% soluble COD removal), stable biomass growth, and optimal pH levels. Notably, the reactor with the higher organic load (DFR-2) demonstrated a modest increase in PHA accumulation (19.05 ± 7.18%) compared to the lower-loaded reactor (DFR-1; 15.19 ± 6.00%), alongside significantly enhanced biomass productivity. Polymer characterization revealed the formation of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), influenced by the substrate composition. Microbial community analysis showed an adaptive shift towards Proteobacteria dominance, signifying successful enrichment of effective PHA producers.

Abstract: Background/Objectives: Ferroptosis, an iron-dependent form of cell death, shows promise as a target for therapy-resistant cancers that exhibit increased iron metabolism. Ferroptosis is primarily characterised by lipid peroxidation within cell membranes. However, many cancers evade ferroptosis by upregulation of specialised ferroptosis defence mechanisms. This study investigates ferroptosis susceptibility in Tuberous Sclerosis Complex (TSC) model, ovarian and breast cancer cell lines to identify ferroptosis resistance mechanisms and therapeutic targets. Methods: We assessed ferroptosis susceptibility using the ferroptosis inducers, RSL3 and erastin. We explored ferroptosis resistance genes using inhibitors of NRF2 (ML385) and FSP1 (iFSP1). RNA-sequencing was conducted to identify dysregulated ferroptosis resistance genes and to better characterise NRF2 target genes. Results: TSC2-deficient cells exhibited resistance to RSL3 and erastin-induced ferroptosis, which correlated with increased ferroptosis defence gene expression, including NRF2 and downstream targets. NRF2 inhibition re-sensitised TSC2-deficient cells to ferroptosis, confirming its protective role. However, FSP1 inhibition did not re-sensitise TSC2-deficient angiomyolipoma kidney tumor cells to RSL3. In contrast, FSP1 knockdown significantly enhanced ferroptosis sensitivity in ovarian (PEO1, PEO4, OVCAR3) and breast (MDA-MB-436) cancer cell lines. Notably, in MDA-MB-436 cells, FSP1 knockdown was more effective than NRF2 inhibition in reversing ferroptosis resistance. Conclusions: Our findings highlight NRF2 and FSP1 as key regulators of ferroptosis resistance in TSC2-deficient and cancer cells. However, the differential efficacy of targeting these pathways suggests that patient stratification may be necessary for optimal therapeutic strategies. Targeting NRF2 and FSP1 could enhance ferroptosis susceptibility, offering a potential therapeutic approach for ferroptosis resistance cancers.

Abstract: Specific occupational factors among midwives—such as shift work, night duties, and significant professional responsibilities—are likely to contribute to a high perceived workload within this group. These factors may increase the risk of nutrition-related health issues, including abnormal body weight and disordered eating behaviors. Despite growing awareness of these concerns, research examining the predictors of nutrition-related health risks among midwives remains limited. The present study aimed to assess the prevalence and key occupational predictors of such risks in this population. A cross-sectional preliminary study was conducted among 703 midwives in Poland. Disordered eating behaviors were evaluated using the Eating Attitudes Test-26 (EAT-26), while workload intensity was measured with the Quantitative Workload Inventory (QWI). Additional data on BMI classification, work experience, and night shift history were collected. A Classification and Regression Tree (C&RT) model was used to identify key predictors of nutritional health risk, defined as meeting at least one of the following criteria: abnormal BMI, EAT-26 score > 20, behavioral indicators of disordered eating, or a history of treatment for an eating disorder. Of the participants, 56.76% (n = 399) were classified as being at nutritional health risk. The most salient predictors included work experience (more than 17.5 years), duration of night shift work, and QWI score. Among midwives with over 17.5 years of experience, both night shift duration and QWI score were significant predictors. For those with 17.5 years or less of experience, QWI score was the strongest predictor. Night shift work alone was not a significant factor in the model. Work experience and workload intensity are key predictors of nutrition-related health risks among midwives. Targeted workplace interventions—including schedule optimization, stress management programs, and nutrition-focused education—may help mitigate these risks. Further research is warranted to explore the long-term health consequences of occupational stress in this professional group.

Abstract: Symbolic Field Theory (SFT) proposes that irreducible mathematical structures—such as prime numbers—emerge not from randomness, but from compression minima in symbolic curvature fields. These fields are generated by projection functions ψ(x), which map integers into symbolic space, and a curvature operator κ(x), which quantifies local symbolic deviation. Structural emergence is hypothesized to occur at local minima ofκ(x), termed collapse zones. This paper empirically validates the predictive power of symbolic collapse geometry by applying a Monte Carlo enrichment framework to various projection-defined curvature fields. A hybrid projection function—combining modular residue and factor complexity—was tested over the first million natural numbers. Results show that collapse zones identified by κ(x) align with prime locations at a rate over three times higher than chance (enrichment ratio: 3.35; Z = 412.655). In contrast, entropy-weighted and factor-count projections yield collapse zones that repel primes, confirming that symbolic curvature fields can both attract and exclude irreducibles. We present a fully reproducible methodology for symbolic curvature analysis, including the definition of projection operators, a formal collapse detector, and statistical controls. Collapse zone prime frequencies are compared against randomized controls of equal size, demonstrating that observed enrichment is not a sampling artifact but an emergent property of symbolic alignment. The framework generalizes across domains and projections, enabling the detection of irreducible structure in symbolic systems ranging from mathematics to language. This work positions SFT as a foundational step toward a unified symbolic science of structural emergence.

Abstract: Infective endocarditis continues to represent a challenge for healthcare systems, requiring careful management and resources. It appears that there may have been something of a shift, in recent years, from Streptococcus sp a to Staphylococcus sp and Enterococcus sp as the primary pathogens of concern. This shift is of concern as it is associated with Staphylococcus Aureus which has a high virulence rate and a tendency to form a biofilm, meaning that non-surgical therapy may not be effective. It is imperative to deliberate on the likelihood of platelet blood clot formation, which may be accompanied by bacterial infestation and the development of a biofilm. An endocarditis lesion is believed to comprise primarily a fibrin and platelet blood clot infested with bacteria, which adheres to the cardiac valves. Consequently, infective endocarditis serves as a paradigm of immunothrombosis that has developed in an unfavorable manner. The concept of immunothrombosis involves a multifacetered interaction among the coagulation system, innate immunity, and the function of coagulation in isolating and eliminating invasive pathogens. However, in the context of infective endocarditis, immunothrombosis unintentionally establishes an optimal environment that is conducive to bacteria proliferation. The process of immunothrombosis functions to impede the host immune system, thus enabling bacterial proliferation in a manner that is largely uninhibited. The coagulation system plays a pivotal role in the progression of this condition at each stage. It has been demonstrated that the coagulation system plays a pivotal role in the initial adhesion of bacteria to the leaflets, the subsequent proliferation and maturation of vegetations, and the development of complications such as embolization and valve dysfunction. Furthermore, the primary etiological agent of infective endocarditis, Staphylococcus aureus, has been demonstrated to manipulate immunothrombosis, thriving within the fibrin-rich milieu of an endocarditis vegetation. Given its central role in infective endocarditis, the coagulation system emerges as an attractive therapeutic target for this deadly disease. However, it is crucial to exercise caution, as the use of antithrombotic agents in patients with endocarditis frequently accompanies an elevated bleeding risk.

Abstract: The integration of Artificial Intelligence (AI) in supply chain management represents a transformative shift aimed at enhancing operational efficiency and precision. This study investigates the role of AI in optimizing supply chain processes, particularly focusing on its impact on Enterprise Resource Planning (ERP) systems and the reduction of errors inherent in traditional management methods.Supply chains are complex networks that require real-time data processing and accurate forecasting to function effectively. ERP systems serve as the backbone of these networks, facilitating seamless integration across various business functions. However, many organizations face challenges such as data entry errors, integration issues, and inefficiencies that hinder performance. This research delineates how AI technologies—such as machine learning, predictive analytics, and natural language processing—can address these challenges by improving data accuracy, enhancing demand forecasting, and optimizing inventory management.The paper presents a comprehensive literature review to contextualize the current state of AI applications in supply chain optimization. It further examines case studies where organizations have successfully implemented AI solutions to minimize errors in ERP systems, illustrating the tangible benefits of increased precision and operational agility. Key findings highlight that AI not only reduces human error through automation but also empowers decision-makers with actionable insights derived from real-time data analysis.Despite the promising potential of AI, the study also discusses significant challenges, including implementation costs, organizational resistance, and data privacy concerns. These barriers necessitate a strategic approach to AI adoption, emphasizing the importance of training, change management, and robust data governance frameworks.In conclusion, this research underscores the critical need for organizations to embrace AI-driven strategies within their supply chain operations. By leveraging advanced technologies, companies can enhance their ERP systems, reduce operational errors, and ultimately achieve a competitive advantage in an increasingly dynamic market landscape. The findings contribute to the growing body of knowledge in supply chain management and provide actionable recommendations for practitioners seeking to navigate the complexities of AI integration.

Abstract: As emergencies and disasters continue to strain public health systems globally, integrating psychosocial support into national response frameworks has emerged as a critical, though often overlooked, priority. This study examines the role of the Red Cross of Serbia in delivering Psychoscial First Aid (PFA), highlighting it as a vital yet underrepresented component within the broader scope of emergency response. Grounded in a theoretical framework, the paper outlines the core principles of PFA, its significance during crises, and the psychosocial impact of disasters on individuals and communities. It further explores the relationship between mental health and community resilience, underscoring the importance of mental recovery in disaster contexts. Utilising a qualitative approach, the study draws on an expert interview with a representative of the Red Cross of Serbia to investigate the current state of PFA implementation. Particular attention is given to institutional integration, training protocols, and prevailing public perceptions of psychosocial support. The findings point to a marked disparity between the established provision of medical first aid and the marginal position of psychosocial assistance. Key barriers identified include insufficient institutional acknowledgment, a lack of structured and standardised training programs, and limited awareness of PFA as a distinct and necessary intervention. Despite these challenges, the Red Cross of Serbia has initiated several promising efforts—such as developing educational materials, training volunteers, and establishing internal support mechanisms—which provide a solid foundation for future advancement. This paper argues for systematically including psychosocial support in emergency management strategies. It emphasises the need for coordinated, evidence-informed, and person-centered approaches to fostering health and resilience in disaster-affected communities.

Abstract:

Human psychology has been playing major contributory factors in the calibration of human medicine, as it is cognitive perception that has ultimately shaped the trajectory of medical progress. Such perceptive patterns are dependent upon the integrity of emotional and intellectual levels of intelligence, meaning that good emotional states can significantly contribute to shaping medical and scientific progress. Throughout the paper, the topic of the progressive loss of balance in societal perspectives, attitudes and behaviours will be thoroughly assessed, given that such loss of balance often results in a phenomenon known as “throwing the baby out with the bathwater”, in which good values are rooted out with the bad habits infiltrated into emerged branches. For example, the increasing epidemic of loneliness, isolation and deprivation of affection has resulted in the creation of an inaccurate perception upon the importance of solitude and self-reflection due to a generated excessive emotion of craving for human affection, which has often translated into practices of dependency upon social contexts, attachment to mismatching relationships, promiscuity and unhealthy, unexplained abandonment. Such increasing events have created unprecedented frictions within societies, which resulted in the skyrocketed extent of trust issues and isolation among people and consequently, to a steep decline in the average extent of human mental health and emotional wellbeing. Such societal frictions have significantly manifested even within biological families, which itself represents a direct factor for the recent increase in the number of people registered as “homeless”. It is therefore evident that loneliness and homelessness represent two opposite ends of the same sequence of events, as homelessness is ultimately dependent upon loneliness and isolation. The author will be presenting an extensive set of theoretical and practical solutions against the ongoing and growing problem of the existing frictions within human relationships by encouraging proportional workshops and novel lifestyles aimed at gradually repairing the created damages of human trust, with an emphasis upon existing projects of “mental health first aid”, “cuddle therapy”, “cuddled bed & breakfast” and even similar practices to be incorporated into regular housing, which may be regarded as “cuddled renting” or “housing”, as well as workshops in retreat and camping settings, alongside the creation of theoretical and practical courses to help each participating member apprehend the depth of the details covering consent, boundaries, as well as health and safety. Given that life emerges from the water and that, immediately after the new-born human is separated from the amniotic water after nine months of pregnancy, is united with the mother in a long and profound hug, affection is as important for human survival as water. Platonic intimacy represents the most important, profound and sophisticated form of art that brings all forms of sensorial art into a complete state of “oneness”, reflecting the objective of human existence herself.

Abstract: With the increasing detection of artemisinin resistance to front-line antimalarials in Africa and notwithstanding the planned roll-out of RTS’S and R21 in Africa, the search for new vaccines with high efficacy remains an imperative. Towards this endeavor, we performed in silico screening to identify Plasmodium falciparum gametocyte stage genes that could be targets of protection or diagnosis. Through the analysis, we identified a gene, Pf3D7_1105800, coding for a Plasmodium falciparum subtilisin-like do-main-containing protein (PfSDP). Genetic diversity assessment revealed the Pfsdp gene to be relatively conserved across continents with signs of directional selection. Using RT qPCR and western blots, we observed that Pfsdp is expressed in all parasite developmental stages at the transcript and protein levels. Immunofluorescence assays found PfSDP protein colocalizing with PfMSP-1 and partially with Pfs48/45 at the asexual and sexual stages, respectively. Further, we demonstrated that anti-PfSDP peptide-specific antibodies inhibited erythrocyte invasion by 20-60% in a dose-dependent manner, suggesting that PfSDP protein might play a role in merozoites invasion. We also discovered that PfSDP protein is immunogenic in children from different endemic areas with antibody level increasing from acute infection to day 7 post-treatment, followed by a gradual decay. The limited effect of antibodies on erythrocyte invasion could imply that it might be more involved in other processes in the development of the parasite.

Abstract: Facial reconstruction presents complex challenges due to the intricate nature of craniofacial anatomy and the necessity for individualized treatment. Conventional reconstructive methods—such as autologous bone grafts and prefabricated alloplastic implants—pose limitations, including donor site morbidity, implant rejection, and suboptimal aesthetic results. The emergence of 3D printing technology has introduced patient-specific implants (PSIs) that enhance anatomical fit, functional restoration, and biocompatibility. This review outlines the evolution of 3D-printed implants, key materials, computer-assisted design (CAD), and their applications across trauma, oncology, congenital conditions, and aesthetics. It also addresses current challenges and explores future directions such as bioprinting, smart implants, and drug-eluting coatings.

Abstract: The interaction between cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC) in exerting anticonvulsant effects has been scarcely studied. To improve this knowledge, we evaluated CBD and THC, combined or not, in two seizure models in mice, using an improved vehicle formula. Firstly, acute seizures were induced by intraperitoneal (i.p.) pentylenetetrazole (PTZ, 80 mg/kg), and mice received CBD or THC at 1, 3, 6, and 10 mg/kg, or a CBD/THC 1:1 combination at 1.5, 3, and 6 mg/kg, per os (p.o.), one hour before PTZ administration. Secondly, mice received p.o. CBD (10 mg/kg), CBD/THC (1.5, 3, and 6 mg/kg), valproic acid (50 mg/kg), or vehicle, one hour before PTZ (30 mg/kg, i.p.) every other day for 21 days. Behavioral, biochemical, and immunohistochemical analyses were performed to assess the response to PTZ, oxidative stress, and astroglial activation. In the acute model, CBD and THC at 3-10 mg/kg, and their combinations, significantly increased latency to generalized seizures and death, and improved survival rates. In the chronic model, similarly to valproic acid, CBD 10 mg/kg and CBD/THC at 1.5 and 3 mg/kg delayed kindling acquisition, while CBD/THC 6 mg/kg had no effect. CBD and CBD/THC treatments reduced oxidative and nitrosative stress and attenuated astrogliosis, as indicated by decreased glial fibrillary acidic protein and GABA transporter 1 expression, and increased inwardly rectifying potassium channel 4.1 expression in hippocampal regions. However, no cannabinoid treatment prevented the impairment in novel object recognition and Y maze tests. These findings support the potential role of cannabinoids in counteracting seizures, possibly by reducing oxidative stress and astrogliosis. The study also highlights the importance of nanoemulsions as a delivery vehicle to enhance cannabinoid effectiveness while considering the risks associated with direct cannabinoid receptor activation.

Abstract: Silicon modulators play a crucial role in optical communication systems. Over the past thirty years, there has been a notable enhancement in the bandwidth of silicon modulators; however, their operational speed remains constrained by the resistance-capacitance (RC) time constant. This study presents a silicon strip waveguide-based modulator configured in a series push-pull (SPP) arrangement, which effectively mitigates the limitations imposed by the RC time factor. The modulator exhibits a flat electro-optic (EO) response extending up to 68 GHz. Additionally, it achieves a phase shift of 0.022 radians for a C-band optical wave when subjected to a 15 GHz radio-frequency (RF) modulation signal with an amplitude of 2.45 V.

Abstract: The thermal and singularity assumptions of the standard Big Bang model are re-examined through the lens of Multifaceted Coherence (MC) and the Superluminal Graviton Condensate Vacuum (SGCV). While Big Bang Nucleosynthesis (BBN) successfully explains the primordial abundances of hydrogen and helium, it overpredicts the concentration of lithium-7 by a factor of three. It underpredicts lithium-6 by several orders of magnitude—a persistent discrepancy that remains unresolved within standard cosmology. These anomalies are attributed not to observational error but to a fundamental mischaracterization of the early universe as a thermally equilibrated, isotropic plasma. In contrast, structured quantum coherence fields, discrete curvature geometries, and entropy–coherence couplings are proposed as dominant mechanisms shaping nucleosynthetic outcomes. The observable projection psi_s^star emerges from a deeper coherence substrate psi_s within the SGCV. As coherence decays, energy is redistributed through ghost fields, vacuum fluctuations, and curvature memory, selectively suppressing lithium-7 and enhancing lithium-6 abundance in high-curvature domains. The integration of the Dodecahedron Linear String Field Hypothesis (DLSFH) further reveals how discrete topological constraints modulate nuclear reaction cross-sections and resonance pathways. Nucleosynthesis is reformulated using coherence-weighted yield equations, replacing classical thermodynamic predictions. This coherence-based framework resolves the lithium problem without invoking a primordial singularity, restores informational continuity across early cosmic epochs, and establishes a quantum-geometric foundation for cosmogenesis.

Abstract: The cross-border port serves as a crucial cross-border travel connecting mainland China with Hong Kong and Macau, directly impacting the overall satisfaction of cross-border travel. While previous studies on neighborhoods, communities, and other areas have thoroughly examined the heterogeneity and asymmetry in satisfaction, research on the satisfaction of cross-border travel at ports remains notably limited. This paper explores the heterogeneity and asymmetry of cross-border travel satisfaction using gradient boosted decision trees (GBDT) and k-means cluster analysis under the framework of three-factor theory, aiming to demonstrate the latest scientific research results on the fundamental theories and applications of artificial intelligence. The results show that prevalent asymmetric relationships between factors and cross-border travel satisfaction, with the factor structure exhibiting heterogeneity across different groups. High-income individuals were more likely to prioritize the reliability of cross-border travel, whereas low-income individuals tended to emphasize the convenience of travel. Finally, this paper proposes improvement priorities for different types of passengers, reflecting the practical application of advanced mathematical methods in artificial intelligence to drive intelligent decision-making.

Abstract: Background/Objectives: Ketamine anesthesia frequently causes postoperative cognitive dysfunction and behavioral disorders, with limited effective therapeutic options. This study explores novel neuroprotective compounds targeting multiple pathways in neurological disorders following ketamine anesthesia through the design, synthesis, and evaluation of 2'-R-6'H-spiro(cycloalkyl-, heterocyclyl)[1,2,4]triazolo[1,5-c]quinazolines. Methods: Using fragment-oriented design, we synthesized 40 spiro-triazoloquinazolines via [5+1]-cyclocondensation. Molecular docking assessed binding affinities to glutamate receptor GluA3, while ADMET analyses evaluated pharmacokinetic properties. Selected compounds were tested in a ketamine-induced cognitive impairment rat model with behavioral assessment via open field test. Neurobiochemical analyses measured inflammatory markers, apoptotic regulators, and gene expression in hippocampal tissue. Results: Molecular docking showed superior binding affinities to GluA3 compared to reference nootropics, while ADMET analyses confirmed favorable drug-likeness profiles. In vivo evaluation demonstrated compounds 25, 26, and 32 effectively normalized ketamine-disrupted behavioral parameters, reducing anxiety and improving cognitive function more effectively than piracetam and fabomotizole. Neurobiochemical analyses revealed compound-specific mechanisms: compound 31 showed potent anti-inflammatory effects (72% reduction in IL-1β, 80% reduction in caspase-1), while compound 26 enhanced cell survival pathways (96% increase in Bcl-2) and hypoxic adaptation (3.5-fold increase in HIF-1 mRNA). Structure-activity relationship analyses established that spiro-junction type and 2'-position substituent critically determine pharmacological profiles. Conclusions: These novel spiro-triazoloquinazolines demonstrate promising neuroprotective properties for treating cognitive and behavioral disorders associated with ketamine anesthesia through multiple mechanisms including anti-inflammatory, anti-apoptotic, and adaptive pathway modulation. Their superior efficacy compared to current treatments positions them as candidates for further development in post-anesthetic cognitive dysfunction and potentially in post-viral and trauma-related neurological conditions.