Abstract: The proliferation of 4D point cloud videos highlights their potential, but the high cost of obtaining large-scale annotated data severely limits supervised methods. Consequently, self-supervised learning (SSL) is vital for learning generalizable representations from unlabeled 4D data. While existing SSL frameworks, such as Uni4D, have made progress, they often struggle with fine-grained motion understanding in extremely dynamic scenes, maintaining robustness under severe occlusion, and developing explicit predictive capabilities. To address these, we propose Dynamic4D, a novel and robust self-supervised framework tailored for dynamic 4D point cloud understanding. Dynamic4D introduces an Adaptive Causal Temporal Attention (ACTA) mechanism in the encoder for explicit causal temporal modeling and dynamic region-focused learning. Its decoder employs Motion Prediction Tokens (MPT) to directly infer motion vectors for masked regions. A novel adaptive motion-sensitive masking strategy further enhances robustness by intelligently prioritizing high-dynamic zones. Our multi-objective pre-training strategy integrates a new Dynamic Perception Loss alongside geometric reconstruction and latent-space alignment. Extensive experiments on diverse challenging benchmarks demonstrate that Dynamic4D consistently achieves state-of-the-art performance. It substantially outperforms prior methods, validating its superior capacity to learn highly robust, generalizable, and motion-aware representations for complex dynamic 4D point cloud scenes.

Abstract: SARS-CoV-2 is a zoonotic virus with a proven ability to infect various animal species, including domestic cats. In the post-pandemic period of COVID 19, there are still limited data on the clinical course, shedding of infectious virus and diagnostic features in cats. The aim of this study was to investigate the spread of SARS-CoV-2 in cats in 2023, the clinical manifestations of the infection, the diagnostic algorithm including molecular detection of viral components, differential diagnosis of co-infection with FHV, FCV, Mycoplasma spp. and Chlamydia felis, serology and isolation of infectious SARS-CoV-2. The immunomodulatory therapy in animals with a standalone SARS-CoV-2 infection was applied. The study included oropharyngeal, conjunctival and nasal swab samples from 102 domestic cats with clinical signs. Of them, 20.6% (21/102) were positive for SARS-CoV-2, with 16.67% (17/102) of the cats showing various variants of co-infection with FHV, FCV, Mycoplasma spp. and Chlamydia felis. Four of the cats had a standalone SARS-CoV-2 with mild clinical manifestations that included serous discharges from the eyes, without change in the general condition. The virus was isolated from these samples. These four cats and their owners were positive for antibodies to the virus, and the owners were PCR-negative. The treatment of SARS-CoV-2 infection included the preparations Viusid, RX immunosuport, Vetomun and Lisymun. This is one of the first post-pandemic study covering FHV, FCV, Mycoplasma spp. and Chlamydia felis in domestic cats with SARS-CoV-2 infection and further expands on the essential main idea including the specified pathogens of interest.

Abstract: Intraoperative hypotension (IOH) is a critical complication during surgical procedures that can lead to severe adverse outcomes including myocardial injury, acute kidney injury, and increased mortality. Early prediction of hypotensive events remains a significant challenge in perioperative medicine. This study leverages the Medical Informatics Operating Room Vitals and Events Repository (MOVER) dataset, a comprehensive collection of intraoperative physiological signals and clinical events, to develop and evaluate machine learning models for predicting hypotensive events 5, 10, and 15 minutes before onset.The MOVER dataset contains high-frequency vital sign measurements including heart rate, blood pressure, oxygen saturation, and respiratory metrics from over 5,000 surgical procedures. Extensive preprocessing and feature engineering were performed to extract statistical, temporal, and interaction features across multiple time windows. Multiple machine learning algorithms were implemented and compared including XGBoost, Random Forest, Histogram-based Gradient Boosting (HGB), Support Vector Machines (SVM) with RBF kernel, Long Short-Term Memory (LSTM) networks, Multilayer Perceptron (MLP), and K-Nearest Neighbors (KNN).Experimental results demonstrate that XGBoost achieves the highest predictive performance with an accuracy of 94.2%, precision of 93.8%, recall of 94.5%, and AUC-ROC of 0.973 for 5-minute prediction windows. Performance remained strong for 10-minute (AUC-ROC = 0.942) and 15-minute (AUC-ROC = 0.908) predictions. Feature importance analysis revealed that mean arterial pressure (MAP) trends, heart rate variability, shock index, and time since last vasopressor administration were the most significant predictors. Error analysis identified borderline MAP values and rapid hemodynamic changes as primary sources of misclassification.The proposed models demonstrate strong potential for real-time clinical decision support systems to alert anesthesiologists of impending hypotensive events, enabling proactive interventions and improved patient outcomes. This research represents the first comprehensive comparison of multiple machine learning algorithms on the MOVER dataset for hypotension prediction, providing a foundation for future clinical implementation and prospective validation studies.

Abstract: The retention of Zillennial employees (born 1990–2005) presents significant challenges for contemporary organizations navigating competitive labor markets. This study investigates the relationships among perceived organizational support (POS), employee well-being (EWB), career development (CD), employee engagement (EE), and turnover intention (TI) within this workforce segment in Indonesia. Grounded in social exchange theory and complemented by conservation of resources theory, this research employed a quantitative cross-sectional survey design, collecting data from 360 Zillennial employees across multiple industries. Partial least squares structural equation modeling (PLS-SEM) tested the hypothesized relationships. Results indicate that POS (β = -0.285, p < 0.001) and CD (β = -0.198, p < 0.01) demonstrate significant negative direct effects on turnover intention, while EWB shows no significant direct relationship (β = -0.082, p > 0.05). All three antecedent variables significantly predicted employee engagement, which exhibited a strong negative relationship with turnover intention (β = -0.387, p < 0.001). Mediation analyses confirmed that employee engagement fully mediates the well-being–turnover relationship and partially mediates the effects of POS and CD. The model explained 64.8% of variance in turnover intention. These findings suggest that organizations seeking to retain Zillennial talent in Indonesia should prioritize organizational support systems, career development opportunities, and engagement-fostering initiatives. This study contributes to the literature by empirically examining these integrated relationships within an understudied demographic and cultural context, while acknowledging limitations inherent in cross-sectional, self-report designs.

Abstract: What psychological and behavioral factors distinguish those who produce exceptional, original contributions from those who achieve competence without breakthrough impact? This article synthesizes research from cognitive psychology, motivation science, expertise studies, and the sociology of knowledge to propose an integrative framework for understanding exceptional achievement. Drawing on both empirical research and theoretical analysis, the paper identifies four sequential phases through which great work emerges—domain selection, frontier attainment, gap identification, and persistent exploration—and examines three enabling conditions that sustain the process: deep curiosity, earnest engagement, and resilient morale. The framework reconciles deliberate practice models with creativity research, addresses the role of social and institutional factors, and offers implications for education, mentorship, and self-directed development. The analysis suggests that exceptional achievement, while rare, follows discernible patterns that can inform both individual practice and institutional design.

Abstract: Theories guide scientific inquiry by describing, explaining, and predicting human behavior and development across the lifespan. However, the social sciences have been largely shaped by theories rooted in Western philosophy, with Indigenous theories notably underrepresented. This scoping review identified Indigenous theories of human development and examined how they conceptualize development across the lifespan. Searches across four databases yielded 18 articles and 21 theories. Across theories, three developmental domains were prioritized (identity, relationships, and spirituality) embedded within four life stages: prenatal/childhood, youthhood, adulthood, and elderhood. Indigenous theories overwhelmingly centered community wellbeing and interconnectedness at each life stage. Last, rather than a linear, age-related progression, Indigenous theories reflected relational, cyclical, and narrative developmental trajectories- each with shared expectations for how development unfolds across the lifespan. These findings elevate Indigenous frameworks within developmental science and offer a foundation for theoretical and empirical innovation.

Abstract: The article describes the results of research on the power supply quality of selected fluorescent lamps and solid-state light sources powered by voltage with different waveforms and supply voltage values. The power factor, total harmonic distortion (THD) factor and values of individual harmonics were measured and their compliance with international standards was assessed. The measurement set-up used and the measurement results obtained with it are described. The results of the experimental research showed that the light sources under consideration did not meet the criteria specified in international standards for the THD factor and the values of individual harmonics, regardless of the shape of the supply voltage waveform. However, it was shown that supplying some light sources with a triangular voltage waveform can increase the illuminance value. On the other hand, the use of a rectangular voltage waveform leads to an increase in the power factor and a decrease in reactive power.

Abstract: Background/Objectives: Macrophage phenotype and function are highly sensitive to environmental cues; however, most in vitro studies rely on 2D culture systems that lack physiologically relevant structural context. The spatial dimensionality can influence immune cell signaling, yet the roles of these cells in regulating macrophage behavior remain incompletely understood. This study aimed to investigate how cultural dimensionality affects the phenotype, signaling, and functional activity of monocyte-derived macrophages. Methods: GFP-expressing THP-1 monocytes were differentiated into M0, M1, and M2 macrophages and cultured either on planar substrates or within 3D matrices composed of Matrigel or type I collagen. Macrophage morphology and viability were monitored. Membrane receptor expression and secreted cytokines were examined and quantified. Functional activity was further assessed through coculture experiments with RFP-expressing MDA-MB-231 breast cancer cells. Results: Compared with 2D culture, 3D environments induced distinct morphological and viability changes in macrophages. Collagen matrices supported sustained growth, subtype-specific morphologies, and enhanced functional activity, whereas Matrigel promoted aggregation and reduced viability. Core lineage markers remained stable across conditions, but activation-associated receptors and cytokine profiles were strongly influenced by dimensionality. 3D culture enhanced TNF-α expression and altered serglycin glycosylation patterns. In coculture assays, macrophage effects on tumor cell growth depended on polarization state and were more pronounced in 3D systems. Conclusions: These findings demonstrate that culture dimensionality and ECM composition are key regulators of macrophage phenotype and function. Collagen-based 3D systems better reproduce physiologically relevant macrophage behaviors than conventional 2D platforms, highlighting the value of structurally biomimetic models for immunological studies and therapeutic screening.

Abstract: Telecommunication networks operate as highly distributed, multi-vendor, and mis-sion-critical infrastructures, making them prime targets for sophisticated cyber threats. As networks evolve toward cloud-native, virtualized, and software-defined architec-tures, traditional perimeter-based security models have become insufficient. Zero-Trust Architecture (ZTA) has therefore emerged as a key security paradigm in telecommu-nications, enabling continuous verification, fine-grained access control, and improved protection of network and information assets. While ZTA strengthens technical security and operational resilience, its large-scale deployment introduces significant so-cio-technical and governance challenges that extend beyond network engineering. This study examines the implementation of ZTA in a multinational telecommunications in-frastructure organization using a four-wave longitudinal design (2020 - 2023). Drawing on an extended Technology Acceptance Model incorporating Perceived Trust, we ana-lyze employee perceptions of productivity, ease of use, usefulness, and trust before and after ZTA deployment, and following a structured governance intervention. Results reveal a substantial decline in the composite TAM index following ZTA enforcement (−25%, Cohen’s d = 1.12), with no meaningful spontaneous recovery over time (d = 0.08). A Communication Campaign emphasizing transparency and stakeholder engagement produced a partial but incomplete recovery (d ~ 0.52), indicating that trust erosion under Zero-Trust conditions is measurable and contingent upon governance design rather than technological determinism. The findings demonstrate that ZTA functions not merely as a technical safeguard but as a socio-technical governance mechanism that restructures organizational trust. The study advances a Proactive Trust Management framework tailored to telecommunications environments, integrating security en-forcement with transparency, participatory oversight, and ethical calibration to sustain operational resilience in cloud-native infrastructures.

Abstract: Zone 2 thoracic endovascular aortic repair (TEVAR) frequently requires left subclavian artery (LSA) preservation to maintain vertebrobasilar and upper-extremity perfusion while obtaining a durable proximal seal. Dedicated single-branch endografts were de-veloped to standardize this step and to convert a traditionally hybrid scenario into a reproducible fully endovascular strategy. Two different concepts currently dominate this field: integrated unibody branch platforms, represented by Castor and the sec-ond-generation Cratos, and modular retrograde-branch systems, represented by the Gore TAG Thoracic Branch Endoprosthesis (TBE). The Castor/Cratos evidence base is broader, older, and much more heavily weighted toward type B aortic dissection, including long-term prospective multicenter data and several large real-world cohorts with fa-vorable branch patency and aortic remodeling. By contrast, TBE evidence is expanding rapidly and is supported by prospective midterm data in arch aneurysms as well as by increasingly large post-commercial series and comparative analyses across zones 0–2. Beyond outcomes, the two platforms differ substantially in branch directionality, con-tribution to proximal fixation, modularity, branch diameter range, proximal landing requirements, access profile, and regulatory/off-the-shelf availability, all of which have direct consequences for anatomical suitability in dissection, aneurysm disease, and trauma. This narrative review synthesizes current evidence and proposes an anato-my-first, pathology-aware framework for selecting between Castor/Cratos and TBE in totally endovascular zone 2 TEVAR with LSA revascularization.

Abstract: Tumor Treating Fields (TTFields) represent a novel, non-invasive therapeutic modality in oncology that employs low-intensity, intermediate-frequency alternating electric fields to disrupt mitotic processes and induce cancer cell death. This review integrates mechanistic, preclinical, and emerging clinical evidence supporting the integration of TTFields with immunotherapeutic strategies in pancreatic ductal adenocarcinoma (PDAC). Although immunotherapy has transformed the treatment landscape across multiple malignancies, its efficacy in PDAC remains limited due to the tumor’s dense stroma, immunosuppressive microenvironment, and low immunogenicity. Preclinical investigations suggest that TTFields may potentiate immune-based therapies by enhancing antigen presentation, modulating the tumor microenvironment, and attenuating mechanisms of immune resistance. We highlight studies evaluating TTFields in combination with immune checkpoint inhibitors, adoptive cellular therapies, and cancer vaccines, emphasizing their potential synergistic effects in PDAC. Clinically, the phase II PANOVA-2 trial demonstrated feasibility and encouraging survival outcomes with TTFields in combination with gemcitabine and nab-paclitaxel, providing the rationale for the ongoing phase III PANOVA-3 trial and the phase II PANOVA-4 trial which combines TTFields with chemotherapy and atezolizumab. Additional clinical experiences in glioblastoma and non-small cell lung cancer further substantiate the broader applicability of TTFields as an immunomodulatory adjunct. Remaining challenges include optimizing treatment sequencing, identifying predictive biomarkers, and managing TTFields-associated toxicities. Collectively, current evidence positions TTFields as a promising strategy to augment immunotherapy in PDAC, warranting further translational and clinical investigation to establish its role in reshaping therapeutic paradigms.

Abstract: Elodea species, including Egeria densa, are globally recognized as invasive aquatic macrophytes that significantly disrupt aquatic ecosystems through the formation of dense floating mats thereby inducing anoxic conditions. However, their potential as a source of high-value bioactive compounds remains mostly under-explored. This study aimed to evaluate the phytochemical profile and antioxidant capacity of Elodea methanolic extract to assess its potential for industrial application. Total soluble protein was quantified via the Bicinchoninic Acid (BCA) assay, while total phenolic content (TPC) and total flavonoid content (TFC) were determined using colorimetric methods. The antioxidant activity of the extract was assessed using the Ferric Reducing Antioxidant Power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assays, supplemented by qualitative screening for secondary metabolites. Results revealed that the extract possessed a mean soluble protein content of 4.5 ± 0.19 mg BSA eq./g FW, a TPC content of 4.015 ± 0.3 mg GAE/g FW, and a 1.86 ± 0.12 mg QE/g FW. Qualitative screening of the extract revealed the presence of alkaloids and flavonoids. The extract displayed high reducing power (FRAP: 196.6 ± 0.5 µmol Fe²⁺ g⁻¹ FW) and significant radical scavenging activity with an IC50 of 65.5 µg/mL, comparable to the commercial standard, ascorbic acid (IC50 of 61.98 µg/mL). These findings showed that E. densa is rich in pharmacologically active bioactive components; alkaloids, proteins, phenolics, and flavonoids, thereby highlighting its potential as a reservoir of natural antioxidants. It also suggests that the biochemical synergy between soluble proteins and phenolics drives the high antioxidant efficacy of the extract. The results also suggest that E. densa biomass, often overlooked as an invasive species may serve as a sustainable source of bioactive metabolites in the pharmaceutical or food preservative industries.

Abstract: This article proposes a novel and replicable computational methodology named CoLiRa (Computational Literature Review & Analysis) Framework to quantitatively analyze and map the evolution of a scientific field. As a multi-stage approach, the CoLiRa Framework first uses Latent Dirichlet Allocation (LDA) to identify core research topics from a body of literature. Second, it applies cluster analysis (K-Means and Multidimensional Scaling) to map the conceptual structure of the field’s key terms. Finally, it uses linear regression analysis to quantitatively assess the development trends of these topics over time. We demonstrate our proposal through a semi-systematic literature review on the semantic enrichment of tabular data, which covers studies (up to 2024) that utilize Semantic Web ontologies, Linked Data, and knowledge graphs. The analysis of this case study revealed three core research topics and found no statistically significant evidence of a shift in topic prevalence, indicating a stable research ecosystem. This work thus offers a validated computational approach for conducting literature reviews and mapping research trends.

Abstract: Technology adoption theories developed in institutionally mature contexts assume stable hierarchies among determinants, with perceived usefulness typically dominating. This paper qualifies this assumption by proposing that adoption hierarchies are institutionally contingent. Drawing on institutional voids theory and digital finance research, the paper develops a framework identifying three adoption regimes that function as ideal types which may overlap within contexts: (a) institutional trust dominant, where strong market￾supporting institutions enable usefulness-centered adoption; (b) vendor trust compensatory, where institutional voids elevate vendor-specific trust to primary importance; and (c) infrastructure-constrained, where basic access functions as a direct behavioral determinant. The framework extends technology acceptance theory by specifying when hierarchies change, theorizing trust as a compensatory mechanism, infrastructure as a hard constraint based on physical feasibility rather than perceptions, and a digital leveling effect explaining selective cultural influence. We derive propositions and outline a research agenda for cross-country and longitudinal validation, with implications for technology acceptance theory and digital financial inclusion practice.

Abstract: Drought stress severely limits maize growth and productivity worldwide. In this study, we examined the effects of foliar-applied carbon nanoparticles (CNPs) on morphological and physiological traits in maize plants exposed to drought stress for 25 days. Two maize hybrids one drought-tolerant (LG 36745 PRO4) and one drought-sensitive (AG 8088 PRO2) were treated with 0 or 1.0 mL L⁻¹ of a CNP-based nanofertilizer and exposed to three drought levels: 0 MPa (control), -0.4 MPa (moderate stress), and -0.8 MPa (severe stress). The experiment followed a 2 × 2 × 3 factorial design with four replicates. Results indicated that drought stress adversely affected most morphological and physiological traits, particularly in the drought-sensitive hybrid. However, foliar CNP application showed strong potential to alleviate drought's adverse effects in maize under moderate and severe stress, primarily by preserving plant water status, enhancing water use efficiency, carboxylation efficiency, photosynthetic rate, and early growth in challenging environments. These findings will provide the basis for future research on management practices adopted to control drought and ensure the development of modern and sustainable agriculture.

Abstract: This paper examines how veterinary science intertwines with the different ontologies of resilience. As resilience has increasingly become an influential yet conceptually diverse framework, its different ontologies shape and are shaped by veterinary science thinking. This paper will begin with a brief overview of the origins of the resilience concept and its three major ontologies: engineering, psychological and ecological resiliencies. Following these different ontologies, the paper then explores animal level resilience, where engineering framings emphasise disease response and production stability, while welfare-oriented perspectives focus resilience on the affective experience and the lived realities of animals. It then considers veterinary professional resilience, highlighting how emotional labour, workload pressures and structural constraints shape wellbeing across the profession. Finally, it analyses how veterinary science contributes to socio ecological resilience through One Health approaches in public health, food systems and climate adaptation. Across these domains, resilience is often framed as a desirable at-tribute, yet it remains a value laden concept that can obscure inequities or normalise preventable harms. This paper calls for critical, justice-oriented engagement with resilience to ensure it supports ethically grounded veterinary practice and promotes healthier-happier animals, more equitable systems, and sustainable professional environments.

Abstract: Objective: To compare fetal MAPSE and TAPSE values in preeclamptic pregnancies with those in healthy pregnancies and to examine the changes in these parameters according to the severity of preeclampsia. Methods: This prospective case–control study enrolled 77 women with preeclampsia and 81 healthy pregnant controls. Fetal MAPSE and TAPSE were obtained under standardized conditions by experienced operators using M-mode ultrasonography. Results: Fetal mitral annular plane systolic excursion (MAPSE) and tricuspid annular plane systolic excursion (TAPSE) values were found to be significantly lower in the preeclampsia group compared with the control group (p < 0.001). In analyses evaluating preeclampsia cases within themselves, fetal MAPSE and TAPSE values were found to be more significantly reduced in the preeclampsia with severe features group compared to the preeclampsia without severe features group. Conclusion: Fetal MAPSE and TAPSE values, measured by M-mode ultrasonography, were found to be significantly lower in the preeclampsia group compared to the control group. The more pronounced decrease in these values, particularly in preeclampsia with severe features cases, suggests that MAPSE and TAPSE measurements may be early indicators of fetal cardiac adaptation to the impaired intrauterine environment.

Abstract: Background: Helicobacter pylori is a well-established risk factor for gastric carcinogenesis. Increasing evidence suggests that the oral cavity may serve as an extragastric reservoir for the bacterium, potentially contributing to persistent infection and reinfection. Orthodontic appliances can modify oral biofilm ecology and may facilitate bacterial colonization. This study aimed to investigate the association between oral H. pylori colonization and gastric cancer, while exploring the potential modifying role of fixed orthodontic appliances. Materials and Methods: In this cross-sectional observational study, 212 participants were recruited from gastroenterology and dental clinics between January 2023 and March 2025. Oral samples were collected and analyzed for H. pylori DNA using polymerase chain reaction (PCR). Gastric diagnoses were established through endoscopic examination and histopathological evaluation, classifying participants into gastric cancer, precancerous gastric lesions, non-atrophic gastritis, and control groups. Demographic, clinical, and oral health variables were recorded. Multivariable logistic regression models were used to evaluate the association between oral H. pylori detection and gastric cancer while adjusting for potential confounders, including age, sex, smoking status, oral hygiene indicators, and socioeconomic factors. Results: Oral H. pylori DNA was detected more frequently in participants with gastric cancer compared with controls. After adjustment for potential confounders, the presence of oral H. pylori was significantly associated with increased odds of gastric cancer. Interaction analysis suggested that individuals with fixed orthodontic appliances demonstrated higher rates of oral H. pylori detection, supporting the hypothesis that orthodontic biofilm retention may facilitate bacterial persistence within the oral cavity. Conclusions: Our findings support the concept of an oral–gastric microbial axis in H. pylori–associated disease and suggest that the oral cavity may represent a potential reservoir contributing to gastric infection dynamics. The presence of orthodontic appliances may influence oral microbial ecology and could play a role in sustaining H. pylori colonization. These results highlight the importance of interdisciplinary approaches integrating dentistry and gastroenterology in the understanding and management of H. pylori infection and gastric cancer risk.

Abstract: Land use planning plays an important role in advancing sustainable development by integrating environmental, social, and economic dimensions to optimize land utilization and bolster climate resilience. The adoption of efficient practices contributes to the mitigation of land degradation, while strategically planned agricultural systems enhance food security and promote ecological balance. This study focused on the development of an environmental conservation framework for sustainable land use planning in Zambia. Employing a mixed-methods research design, data were collected from a sample of 150 respondents. Quantitative data were analysed using descriptive and inferential statistics, including regression analysis, while qualitative data were subjected to thematic analysis. The research identified key conflicts between agriculture and environmental conservation, including unsustainable farming practices (30.8%), resource competition (24.2%), and deforestation (23.3%). Approximately 40.3% of respondents reported occasional conflicts, while 33% experienced them often. Major barriers to sustainable land development included inadequate financial support (35%) and lack of knowledge (30%). Awareness of sustainable agricultural practices varied, with 38% of respondents indicating high awareness and 35.8% reporting low awareness. Conventional agriculture (35.8%), crop rotation (30%), and conservation agriculture (11.7%) were the most common practices, with crop rotation being the easiest to implement (42.2%), and climate-smart agriculture being the most challenging (37.8%). A chi-square analysis revealed no significant association between awareness levels and perceived barrier impacts (p=0.327). Regression analysis indicated that age negatively correlated with the type of conflict (β=-0.0283, p< 0.001), while location influenced conflict experiences, with certain areas, such as Section D (β=1.3799, p< 0.001) and Section G (β=1.6554, p< 0.001), reporting more frequent conflicts. Additionally, sex had a positive but marginally significant effect (β=0.2640, p=0.062). Qualitative findings highlighted the tension between agricultural production and environmental conservation, with economic pressures driving environmental degradation, such as deforestation and water pollution. Participants also pointed to limited knowledge, training, and financial barriers, including high costs and restricted access to credit, as key obstacles. The study proposed an environmental conservation framework to address these conflicts, integrating sustainable agricultural practices with effective land use planning. The framework advocates a multi-stakeholder approach involving policymakers, farmers, and environmental experts to promote balanced sustainable land use. The findings enhance the body of knowledge by providing empirical evidence on the conflicts between agriculture and environmental conservation in land use planning, highlighting key socio-economic and spatial factors influencing sustainability challenges. The proposed environmental conservation framework offers a practical guide for policymakers and stakeholders to integrate sustainable agricultural practices into land use planning.

Abstract: Missing data remains a pervasive challenge in air quality data analysis, where inappropriate imputation techniques can introduce hidden biases and compromise the reliability of time-series models such as AutoRegressive Integrated Moving Average (ARIMA). This paper examines the impact of linear interpolation and mean/median imputation on the performance of the ARIMA model and biases in the prediction of particulate matter 2.5 (PM2.5) concentration, together with a detailed analysis of ARIMA generated error metrics and their implications for the accuracy and reliability of the prediction. The findings reveal that package-default imputation significantly influences ARIMA forecasts, while mean/median imputation consistently delivers superior predictive performance, highlighting its robustness for handling missing environmental data. Moreover, imputation during the data transformation stage exerts a greater influence on model outcomes than methods applied at later analysis stages.