Search Results (9,330)

Accurate monitoring of tomato maturity and fruit number is essential for improving crop management and supporting accurate yield estimation in greenhouse environments. However, variations in lighting conditions, occlusions, and overlapping fruits often make reliable maturity classification and fruit counting challenging. This paper presents an integrated approach for tomato maturity classification and fruit number estimation using RGB and multispectral images. The proposed approach consists of tomato detection, tomato tracking and counting, and maturity classification of tomatoes. The detection model identifies tomatoes in each frame, the tracking module associates individual tomatoes across image sequences to avoid duplicate counting, and the classification models determine maturity levels. Experiments are conducted on three tomato datasets collected in greenhouse environments. The results show that the proposed method achieves a maximum maturity classification accuracy of 81%. In addition, the proposed approach facilitates consistent fruit counting across image sequences, supporting practical applications in greenhouse monitoring. These findings demonstrate the potential of integrating RGB and multispectral information for automated tomato maturity classification and fruit counting in precision agriculture. Full article

Background: Successful healing of chronic apical periodontitis after endodontic treatment requires a reduction in the size of the radiolucent area and the healing of the bone. This study aimed to compare the effects of different irrigation activation techniques on healing in single-rooted mandibular premolar teeth with periapical lesions of endodontic origin. Methods: A total of 132 systemically healthy patients with mandibular single-rooted premolar teeth and a periapical index (PAI) score ≥ 3 were assigned to five experimental groups (Sonic activation, Passive ultrasonic irrigation, Photon-Induced Photoacoustic Streaming, Shock Wave Enhanced Emission Photoacoustic Streaming and Manual dynamic activation) and a control group (Conventional Syringe Irrigation). After access cavity preparation, the canals were prepared up to three sizes larger than the initial apical diameter with 5 mL of 2.5% NaOCl used between each file. Final irrigation was performed via the assigned activation system. The root canals were obturated with gutta-percha in a single visit. The effects of the activation systems on healing were compared at 1-year follow-up. The primary outcome measure was the change in lesion diameter. PAI score and fractal dimension (FD) were evaluated as secondary outcomes. Results: At the 1-year follow-up, FD values significantly increased, PAI scores and lesion size decreased in all groups compared with baseline (p < 0.001). However, the increase in FD was comparable among the irrigation groups (p > 0.05). In contrast, lesion size reduction and PAI-based healing rates favored the laser-activated groups. The PAI scores and lesion size in the control group were significantly greater than that in the laser groups (p < 0.05). Conclusions: At the 1-year follow-up, all the groups presented similar FD increases, while the laser irrigation groups presented significantly greater reductions in lesion size than did the control group. Full article

The rapid growth of Digital Financial Services (DFSs), including what is occurring in Indonesia, necessitates evaluation methods that are capable of objectively and systematically handling multiple assessment criteria. Therefore, this study aimed to propose a hybrid FSS–CRITIC–TOPSIS framework for selecting optimal DFSs. Fuzzy soft sets (FSSs) were used to model uncertainty and subjectivity in criterion assessments. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method determined the weights objectively based on the degree of contrast and inter-criteria correlation. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to rank the alternatives based on the closeness to the ideal solution. The incorporation led to a formally defined decision operator, τ, which mapped FSS to complete preference orderings while ensuring provable stability and strong discriminative properties. The framework was applied to five major Indonesian digital wallets, namely ShopeePay, GoPay, OVO, LinkAja, and DANA, as well as being evaluated across five criteria. This framework identified DANA as the optimal alternative, with a score of 0.9282, followed by ShopeePay (0.8354) and GoPay (0.6958). Comparative analysis with other methods showed a near-perfect ranking correlation (ρ = 0.9−1) with a more proportional score distribution and ranking results that reflected actual conditions. Sensitivity analysis also confirmed robustness, with ranking changes remaining logically consistent underweight variations. In conclusion, the FSS-CRITIC-TOPSIS framework provided an effective, mathematically rigorous method for multi-criteria decision-making (MCDM) under uncertainty, which applied to digital wallet selection as well as potential extension to broader evaluation contexts supporting SDGs 8, 9, and 10. Full article

Background. Smart warehousing increasingly relies on digital twin technologies to enhance operational efficiency, real-time visibility, and decision-making in logistics systems. However, existing research primarily focuses on technological capabilities while paying limited attention to the organizational practices that shape successful implementation. Methods. This study aims to identify and prioritize the critical success factors (CSFs) for integrating digital twins into smart warehousing using the Practice-Based View (PBV) as the theoretical lens. Based on insights from prior research and expert validation, nine CSFs were identified and evaluated using the Best–Worst Method (BWM). Empirical input was obtained from six industry experts with experience in digital transformation, warehousing, and supply chain management. Results. The results indicate that collaborative learning, contextual training, and gamification elements emerge as the most influential critical success factors, highlighting the importance of organizational practices in supporting digital twin adoption in smart warehousing. Conclusions. By linking technological capabilities with organizational routines, the proposed framework provides both theoretical insights and practical guidance for implementing digital twins in smart warehouse environments. Full article

Understanding the hemodynamics of the carotid artery is essential for assessing atherosclerotic disease progression and identifying regions vulnerable to plaque formation. Background: Disturbed flow patterns and abnormal shear stresses, particularly near the carotid bifurcation, are known to influence endothelial dysfunction; therefore, this study aims to quantify the impact of patient-specific carotid artery geometry on key hemodynamic parameters associated with atherosclerotic risk. Methods: Four patient-specific carotid artery geometries were reconstructed from medical imaging data, processed using MIMICS, and analyzed using computational fluid dynamics in ANSYS Fluent, with blood modeled as an incompressible non-Newtonian fluid using the Carreau–Yasuda viscosity model under pulsatile flow conditions; velocity streamlines, pressure distribution, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were evaluated at early systole, peak systole, and peak diastole. Results: The simulations revealed complex flow behaviour, including flow reversal, pressure build-up, and low-shear regions concentrated near the carotid bulb and bifurcation, with TAWSS consistently identifying low-shear zones (<1 Pa) across all geometries and OSI exhibiting pronounced directional oscillations in models with increased curvature and wider bifurcation angles. Conclusions: These findings demonstrate that geometric characteristics such as bifurcation angle, vessel tortuosity, and asymmetry play a critical role in shaping local haemodynamics, underscoring the utility of patient-specific CFD analysis as a diagnostic and predictive tool for atherosclerotic risk assessment and supporting more informed, personalized clinical decision-making. Full article

Background: Among gynecological malignancies, ovarian cancer (OC) remains a leading cause of mortality worldwide, often characterized by the highest fatality-to-case ratio due to its asymptomatic progression and late-stage detection. Despite substantial investigation, the root cause of disease development and pathology remains unknown. Early detection is critical for improving OC prognosis. Unfortunately, because of the lack of identifiable symptoms in the early stages, the disease is frequently detected late. As a result, regular check-ups, being aware of risk factors, and paying attention to unusual symptoms can all help discover OC early. Apolipoproteins (APOs) and Annexins (ANXs) have recently been linked to OC. Aim: We conducted a cutting-edge bioinformatics investigation to find novel therapeutic targets and precise biomarkers linked to OC against APO and ANX. Methods: We started by compiling the ANX and APO families via HUGO Gene Nomenclature Committee (HGNC) homepage. Next, we accessed GEPIA2 to compare the relative messenger RNA (mRNA) expression levels of all ANX and APO family members across the cancer genome atlas (TCGA)-OC cohort and matched normal and GTEx data. Prognostic analysis of all significantly expressed ANXs and APOs was performed via Kaplan–Meier (KM) plotter. cBioPortal was used for mutational analysis of prognostic ANXs and APOs. Finally, we ran functional enrichment, molecular docking, and molecular dynamics (MD) simulation analyses. Results: Overall, the results suggest that ANXA2 and its related genetic changes represent potential focal points for precision oncology, offering a computational rationale for the development of target-driven therapeutic interventions in OC. Conclusions: Molecular docking and MD simulation analyses identified curcumin as a potential inhibitor of ANXA2, demonstrating stable binding affinity and structural conservation throughout the simulation period. These computational findings characterize curcumin as a promising candidate for targeting ANXA2 in OC, warranting further experimental validation to confirm its therapeutic efficacy. Full article

In this article I analyse the period of social and political upheaval faced by mixed Greek Catholic and Roman Catholic families living in the Subcarpathian countryside in the 1930s and 1940s. Focusing on a vernacular perspective often overlooked in nation-centric historiographies, I describe the nature of neighbourly relations and collective identity both before and after World War II. I pay particular attention to the ambiguous connections between religious and ethnic identities before the war, highlighting phenomena such as bi-ritualism and diglossia. I then juxtapose this with the specific circumstances of 1944–1945, when villagers were frequently forced to choose their ethnic identity under the threat of Polish and Ukrainian nationalist guerrillas, especially active during that time. Building on a rich body of ethnographic material, I argue that choices of ethnic identity during a “state of exception” were often unstable and shaped primarily by the imperative of survival and other pragmatic considerations. However, I also present tragic stories of mixed families, where the ethnic choices made by some individuals were rooted in their deeply held convictions. Additionally, I reference scholars who are re-evaluating and complicating the relationship between nationalism and religious identity in rural European communities living in border areas, including Norman Davies, Kate Brown, Max Bergholz, and Jarosław Syrnyk. Full article

The establishment of AI ethics bylaws in academia is needed for teaching, learning, and assessment. The adaptive parameters of these bylaws define the ethical, pedagogical, and operational standards for the use of artificial intelligence tools within academia. The main aim is to ensure that AI tools are used to enhance educational practices while preserving human judgment, safeguarding academic integrity, and promoting critical thinking. Specifically, these are intended to mentor all domains of academia to uphold the core values of fairness and transparency while adapting to the advent of modern technologies. While many are enthused by the support provided by large language models, it is also important to prevent over-reliance or misuse of AI technologies. This establishes clear responsibility for faculty, students, and administration. These significant bylaws pay more attention to these issues to provide a foundation for good governance, evaluation, and amendment of AI-related practices. To provide normative insight into the anticipated reception of these bylaws, we conducted a small exploratory pilot study with STEM faculty. The resulting observations offer preliminary indications of the feasibility of the proposed method for future research and policy development. Full article

HM Inspectorate of Probation is committed to building and utilising the evidence base for high-quality youth justice services, and to promoting excellence and having a positive impact upon those inspected and the wider sector. Research evidence and inspection findings are used to inform understanding of what helps and what hinders services and to consider system-wide change. In this article, the latest inspection and research findings in relation to the high-profile areas of serious youth violence and criminal exploitation are highlighted. The article encompasses insights from core and thematic inspections, including those from recent joint targeted area inspections (JTAIs) undertaken with other inspectorates. Alongside the JTAIs which examined multi-agency responses to serious youth violence, research was commissioned to hear directly from children and families about their experiences. Other research commissioned and published by the Inspectorate has emphasised the importance of implementing relational, child-centred and trauma-informed approaches and to optimising collaborative/partnership working across agencies and sectors. Reports have also drawn attention to the value of paying attention to the socio-ecological framework, systemic resilience, adultification biases, and both contextual and transitional safeguarding. Full article

Despite a growing interest in biochar for olive orchard fertility management, little is known about its effects on nitrogen (N) dynamics and greenhouse gas (GHG) emissions in Mediterranean soils, particularly when comparing neutral (pH 6.7) and alkaline (pH 8.2) soils using farmer-accessible flame-curtain pyrolysis biochar. In this 60-day soil mesocosm study, we hypothesized that biochar amendments in fertilized soils would enhance soil N availability and potentially reduce N₂O emissions, with effects modulated by soil pH. Treatments included: control, urea fertilizer, and urea plus biochar (5% w/w). Urea fertilization significantly increased soil ammonium (NH₄⁺) and total oxidized nitrogen (NO₃⁻ + NO₂⁻) in both soils, and co-application of biochar further increased these pools, particularly in the neutral soil (NH₄⁺: + 91% and + 62% in neutral and alkaline soil, respectively). Biochar addition consistently reduced cumulative carbon dioxide (CO₂) emissions in both soils, supporting its role in stabilizing soil organic carbon. However, impacts on nitrous oxide (N₂O) emissions were soil-pH-dependent: biochar slightly reduced N₂O emissions in neutral soil, though nearly doubled N₂O emissions in alkaline soil, highlighting that biochar’s efficacy for GHG mitigation is context-specific. These findings underscore biochar’s potential to improve soil N availability and reduce carbon losses but reveal clear limitations for N₂O mitigation in alkaline soils, necessitating site-specific application strategies that explicitly consider soil pH when targeting climate benefits in Mediterranean olive production. Full article

Education occupies an important place among the 17 Sustainable Development Goals. It plays a role in the process of spreading awareness of the concept—its directions, meaning, and goals. According to the idea of the SDG, it is to be universally available to the world’s communities, with the aim of bridging social inequalities, as well as increasing the capacity for responsible functioning and development. The authors of this study believe that knowledge about sustainable development is crucial for shaping social attitudes that determine the uninterrupted development of the world’s economies towards sustainability. In their opinion, it is essential to pay particular attention to ensuring sustainable competences in the education process, which is aimed at preparing staff to perform professional roles in the socio-economic sphere and to be competent in the field of sustainable development. Hence, the aim of this study is to examine the level of awareness of students from selected higher education schools in Poland in this area. The study was conducted on the basis of a diagnostic survey, and the analysis of the results was carried out using qualitative methods, as well as quantitative methods in an in-depth study (logistic regression, supported by PQStat software version 1.8.4.164. The research results indicated that the surveyed students’ knowledge of sustainable development is good, as confirmed by 91% of responses. A key factor in strengthening this knowledge is the educational process implemented as part of their studies (64% of responses). Events supporting the teaching process, such as conferences or meetings with experts, are particularly important for shaping this knowledge. This indicates a high level of student motivation to explore this knowledge and apply it to a model of social behavior, which is rated as responsible by 94% of respondents. In-depth research confirms the above. The odds ratio of 12.994 with a confidence interval of −95% CI: 1.894–+95% CI: 3.238 for the factor of scientific events in the process of supporting green education demonstrates the significance of the findings. Strengthening green education with thematic scientific events is, therefore, an attractive and anticipated form of gaining knowledge on the SDGs by students, and undertaking these events is a recommendation resulting from the presented research. These results are important for modeling sustainable education in terms of the development potential of human resources. Full article

The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at $p=0.318$) while retaining the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics ($p<0.001$, Cohen’s $d>0.8$). Critical scalability thresholds are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to future Python-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures. Full article

The global mental health issue is becoming increasingly prominent. The fair supply of psychological therapeutic landscape spaces in urban high-density residential areas is a core path to ensuring the physical and mental health of residents and maintaining social health equity. This study takes the theory of spatial justice as the core framework, selects 20 typical high-density residential areas in Shijiazhuang City as empirical samples, and collects basic data through structured questionnaire surveys and on-site observations to explore the justice dilemma, evaluation system, and group demand differentiation characteristics of psychological therapeutic landscape spaces in high-density residential areas. The research results show that there are three core injustice problems in the psychological therapeutic landscape spaces of high-density residential areas: insufficient spatial inclusiveness, lack of ecological space justice, and incomplete facilities and management systems. Residents’ evaluations of the spatial justice of therapeutic landscapes can be divided into four dimensions: practical, ecological, social, and management. Among them, the ecological dimension is the core dimension that residents pay the most attention to. Individual characteristics such as gender, age, identity category, community activity duration, and governance participation willingness have a significant impact on residents’ evaluations of spatial justice. This study constructs an evaluation system for the spatial justice of therapeutic landscape spaces suitable for high-density residential areas, providing theoretical support and practical guidance for the planning, design, and optimization and renewal of fair and inclusive psychological therapeutic landscapes in high-density residential areas in northern China. At the same time, it provides a scientific basis for the construction of healthy cities and the practical application of spatial justice in the field of human settlements. Full article

Rapid housing expansion exacerbates the urban heat island (UHI) effect, yet the influence of household-level awareness on sustainable housing decisions remains underexplored, particularly in tropical contexts. This study integrates the Theory of Planned Behavior (TPB) into a moderated-mediation model to examine how UHI awareness shapes the relationships among attitude, subjective norms, perceived behavioral control, socioeconomic factors, purchase intention, and willingness to pay (WTP) for heat-mitigating housing. Survey data from 441 homebuyers in Bandung City, Indonesia, were analyzed using partial least squares structural equation modeling (SEM). Results reveal that awareness fundamentally alters decision pathways: without awareness, subjective norms (β = 0.066, p-value = 0.007) and perceived behavioral control (β = 0.050, p-value = 0.005) significantly influence WTP via purchase intention; with high awareness, attitude becomes the sole significant predictor (β = 0.109, p-value = 0.035), while the effects of social pressure (β = −0.015, p-value = 0.130) and perceived control (β = −0.005, p-value = 0.376) diminish. The model explains 50.1% of the variance in purchase intention (R² = 0.501) but only 14.7% of the variance in WTP (R² = 0.147), reflecting the low-price premiums respondents are willing to pay (0–5%). These findings highlight that climate-specific awareness acts as a cognitive filter, guiding pro-environmental housing choices, and underscore the importance of awareness-driven interventions for promoting sustainable urban development in tropical cities. Full article

The SERPINE1 gene encodes the serine protease inhibitor plasminogen activator inhibitor type-1 (PAI-1), a major negative regulator of the plasmin-dependent pericellular proteolytic cascade and a crucial determinant in the program of stromal remodeling. Recent omics approaches confirmed that high tumor SERPINE1 levels are prognostic for poor disease outcomes and shorter disease-free survival in various malignancies. Kinetic analysis of biomarkers of cell cycle transit in growth-synchronized p53 dual mutant human keratinocytes confirmed that PAI-1 transcription occurred early after growth activation of quiescent (G₀) cells and prior to G1 entry. Previous evidence has confirmed that differential residence of USF family members (USF1→USF2 switch) at the PE2 region hexanucleotide E box motif (CACGTG) in the SERPINE1 proximal promoter characterizes the G₀→G₁ transition period and the transcriptional status of the SERPINE1 gene. A consensus PE2 E box motif (5′-CACGTG-3′) at nucleotides −566 to −561 is required for USF occupancy of the PE2 E box and serum-stimulated SERPINE1 transcription. Interference with USF2 occupancy of the PE2 E Box site by a double-stranded PE2 “decoy”, or induced expression of a dominant-negative USF (A-USF) construct, attenuate serum- and TGF-β1-stimulated SERPINE1 synthesis. Tet-Off activation of an A-USF insert reduced both PAI-1 and PAI-2 transcripts while increasing the fraction of proliferating (Ki-67⁺ cells). Conversely, overexpression of USF2 or adenoviral delivery of a PAI-1 vector inhibited HaCaT colony expansion. These findings are discussed in this review and collectively suggest that the USF1→USF2 transition at the PE2 E box site and subsequent SERPINE1 transcription impact serum-stimulated keratinocyte growth and, likely, cell cycle progression. Full article