Specific Framework Research Articles (Page 1)

In this manuscript, we present new ideas concerning the domain of soft BCK/BCI-algebras and outline specific categorical frameworks, including equalizers and finite products. Additionally, we demonstrate that the category of soft BCK/BCI-algebras conforms to a topological construct. Moreover, we establish that the category of soft BCK/BCI-algebras features distinctive elements such as terminal objects, initial objects, and zero objects.

Anxiety and emotional stress are pervasive psychological challenges that profoundly impact human health in today’s fast-paced society. Traditional assessment methods, such as self-reports and clinical interviews, often suffer from subjective biases and lack the capability for objective, real-time evaluation of mental states. However, the integration of physiological signals—including electroencephalography (EEG), heart rate (HR), electrodermal activity (EDA), and eye movements—with advanced machine learning (ML) techniques, offers a promising approach to automate and objectify mental health assessments. A systematic review was conducted to explore recent advances in the early detection of anxiety and stress by combining physiological signals and ML methods. To assess methodological quality, a specific analysis framework was designed for the 113 studies included, which identified significant deficiencies in the literature. This highlights the urgent need to adopt standardized reporting guidelines in this field. The role of these technologies in feature extraction, classification, and predictive modeling was analyzed, also addressing critical challenges related to data quality, model interpretability, and the influence of intersectional factors like gender and age. Ethical and privacy considerations in the current research were also included. Finally, potential avenues for future research were summarized, highlighting the potential of ML technologies for early detection and proactive intervention in mental disorders.

The mass of galaxy cluster systems can be determined by calculating the hydrostatic equation for such systems. In this study, we derive the hydrostatic mass of galaxy cluster systems within the Eddington-inspired Born-Infeld (EiBI) theory, a modified theory of gravity. The EiBI theory is further modified by incorporating the generalized uncertainty principle (GUP) into its formulation. The GUP affects the mathematical expression of the temperature in galaxy clusters, leading to modifications in the clusters' equation of state (EoS), which is also an essential mathematical tool in hydrostatic equation calculations. This incorporation is motivated by the need to explore quantum gravitational effects on cosmological scales, bridging a fundamental gap between a modified theory of gravity and quantum mechanics. This work is significant in that it introduces the effect of the GUP, implemented through a modification of the temperature, within the framework of EiBI gravity. Using the derived formulation, we calculate the mass of 12 galaxy clusters and compare the results with observational data. The calculations reveal a significant reduction in the masses of these galaxy clusters to the order of 10-19 M⊙. A result which is profoundly inconsistent with observational data, thereby challenging the physical viability of this specific EiBI-GUP framework for modelling large-scale structures like galaxy clusters.

Abstract As global challenges intensify, integrating sustainability into STEM education and careers is urgent. We conducted a convergent mixed-methods review that combines scientometric and narrative approaches. Scientometric analyses help map the macro-level scope of the field—tracking publication trends, identifying dominant clusters, and pinpointing emerging areas. Narrative reviews offer complementary depth, revealing how specific research methods and theoretical frameworks shape our understanding of sustainability within STEM. The scientometric analysis encompassed 2721 publications from 1992 to February 2025, utilizing CiteSpace and VOSviewer for mapping. In addition, a narrative synthesis was conducted on 113 studies selected through title and abstract screening. Scientometric analysis shows a post-2010 surge in sustainability-focused STEM work, coalescing into four clusters: (1) education and technology integration, (2) human factors and institutional structures, (3) sustainability and policy, and (4) innovation and economic impact. Narrative findings identify three dominant methodological traditions (quantitative, qualitative, and mixed) and five recurrent theoretical lenses (SCCT, identity, SDT, ecological systems, and integrated models). Triangulation reveals three persistent gaps: (i) limited research in non-Western contexts, (ii) scarce longitudinal studies following learners into early careers, and (iii) weak integration of person-centred and structural theories. Together, the two strands provide a roadmap for context-sensitive, interdisciplinary, and longitudinal inquiry that can better equip the global STEM workforce to advance sustainability.

This paper presents a TensorFlow-native implementation for automated crack detection in concrete structures, addressing the critical need for efficient and objective infrastructure monitoring. Leveraging a Convolutional Neural Network architecture with 24.8 million parameters, the model was trained on a large-scale dataset of 40,000 images, each with a 227x227 RGB resolution. The methodology, incorporating specific framework optimizations and a rigorous training configuration, achieved a remarkable overall classification accuracy of 99.375% on the validation dataset. The model demonstrated balanced performance with precision values of 0.993 and 0.994, recall values of 0.994 and 0.993, and F1-scores of 0.994 and 0.994 for both "No Crack" and "Crack" classes. This high accuracy, coupled with balanced metrics, underscores the model's effectiveness and reliability for practical applications. The proposed solution significantly enhances real-time structural health monitoring systems, mitigating the limitations of traditional manual inspections and facilitating proactive maintenance strategies for concrete infrastructure.

This paper presents an ontology developed to address challenges such as com munication gaps, risks of errors, and inconsistencies during the manual process of creating software requirement specifications (SRS). The proposed ontology offers a systematic and formal depiction of the requirements, enhancing consis tency and communication among stakeholders. The ontology has been devel oped from the software requirements documents to facilitate the development process. This paper discusses the process of creating the ontology and demon strates using Pellet Reasoner for inference and Prot´eg´e for ontology construction to save and reuse information. The ontology seems to be efficient in manag ing complex software projects, enabling accurate requirement retrieval through SPARQL queries. This study emphasizes how incorporating ontologies into re quirement engineering can significantly enhance the quality and reliability of SRS.

The subject of the article are Christian cemeteries discovered during archaeological research in the area of the town of Chełm, dating back to the medieval and modern period – from the 13th to the end of the 18th century. Written sources from that period mention 12 Christian churches in Chełm, of which only four have survived to the present day. As a result of archaeological research, 11 burial sites have been identified which are dated within a specific chronological framework. Several cemeteries were also located in places that are not currently sacred spaces. Burials discovered in the very centre of the town may be linked to 13th–15th century temples, while graves located in the suburbs, outside the former city walls, were linked to several temples mentioned from the 15th–16th century, which no longer exist. However, due to the small extent of the discoveries, they require further archaeological research. Their more complete identification would allow to more accurately reconstruct the former spatial layout of Chełm in the medieval and modern periods, as well as to supplement the knowledge of the burial customs of its inhabitants.

The widespread adoption of Kubernetes as a platform for orchestrating containerized applications has heightened the need for effective security mechanisms, particularly to counter Denial-of-Service (DoS) attacks. This article proposes an approach to DoS attack detection based on two key components the use of comprehensive metrics and the application of ensemble Machine Learning models. The approach involves the collection and analysis of comprehensive metrics from node-level (CPU, memory) and application-level (network activity, file descriptors) data from containers running on various frameworks (Flask, Django, FastAPI, Node.js , Golang). To implement this approach, a dataset containing 49,990 instances of network activity, characterized by 28 features (comprehensive metrics), was created. Statistical analysis (Student’s t-test, Pearson correlation) identified the metrics most relevant for attack detection, including total CPU time (cpu_sec_total) and resident memory usage (resident_memory_total). A comparison of nine Machine Learning models for attack detection was conducted, including ensemble methods (Random Forest, XGBoost, LightGBM) which demonstrated the highest effectiveness, achieving 100 % accuracy (F1-score equals 1.0) and perfect class separation (AUC equals 1.0). The XGBoost model also eliminated false positives (precision equals 1.0). Feature importance analysis revealed the most significant metrics for classification: CPU usage (cpu_sec_total, cpu_sec_idle), network packet transmission (transmit_packets), system load average, and memory usage (virtual_memory_total, resident_ memory_total). The work emphasizes the importance of integrating multi-level metrics for building resilient anomaly detection systems. The proposed approach is scalable and independent of specific frameworks, making it applicable for protecting containerized environments. The research results serve as a foundation for developing proactive Kubernetes security systems capable of countering sophisticated attack vectors.

The joint venture agreement is a form of business collaboration increasingly utilized in commercial practice by both domestic and foreign parties. However, Indonesian legislation does not explicitly regulate such agreements, raising legal questions regarding their standing and enforceability under national law. This study aims to examine the legal position of joint venture agreements within the Indonesian civil law system and assess their compliance with the validity requirements of contracts as stipulated in Article 1320 of the Indonesian Civil Code (KUH Perdata). This is normative legal research employing a statute-based and conceptual approach. The findings show that joint venture agreements are legally acknowledged and enforceable based on the principle of freedom of contract as articulated in Article 1338 of the Civil Code As long as the agreement meets the four requirements for a valid agreement, namely agreement, capacity, a specific object, and a lawful cause it is considered valid and binding under Indonesian civil law. Although there is no specific statutory framework governing joint ventures, their legal force is supported through legal doctrines and judicial precedents. This study concludes that the absence of explicit regulation does not invalidate joint venture agreements but highlights the importance of carefully interpreting foundational civil law principles to ensure legal certainty and protection for the contracting parties.

Understanding how transcription factors regulate organized cellular diversity in developing tissues remains a major challenge due to their pleiotropic functions. We addressed this by monitoring and genetically modulating the activity of PAX3 and PAX7 during the specification of neural progenitor pools in the embryonic spinal cord. Using mouse models, we show that the balance between the transcriptional activating and repressing functions of these factors is modulated along the dorsoventral axis and is instructive to the patterning of spinal progenitor pools. By combining loss-of-function experiments with functional genomics in spinal organoids, we demonstrate that PAX-mediated repression and activation rely on distinct cis-regulatory genomic modules. This enables both the coexistence of their dual activity in dorsal cell progenitors and the specific control of two major differentiation programs. PAX promote H3K27me3 deposition at silencers to repress ventral identities, while at enhancers, they act as pioneer factors, opening and activating cis-regulatory modules to specify dorsal-most identities. Finally, we show that this pioneer activity is restricted to cells exposed to BMP morphogens, ensuring spatial specificity. These findings reveal how PAX proteins, modulated by morphogen gradients, orchestrate neuronal diversity in the spinal cord, providing a robust framework for neural subtype specification.

Systemic social oppression is a chronic, complex, transmissible, and cumulative source of collective trauma that targets groups of people based on shared identities and experiences. While various disciplines rely on collective trauma as a theoretical framework to elaborate on the pervasive impacts of systemic oppression, other disciplines allude to the study and measurement of this phenomenon but neglect to characterize it under this specific framework. Without fully integrating these multidisciplinary perspectives and developing reliable methods of experimentation and data collection, researchers will remain limited in their ability to provide a truly holistic perspective of collective trauma's impact on the health and well-being of marginalized communities. This integrative, interdisciplinary analysis draws from various disciplines to define and conceptualize collective trauma resulting from systemic social oppression. Furthermore, this article seeks to explore how collective trauma contributes to inequities in health and social conditions among historically marginalized groups. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

This research investigates the integration of English language instruction into Nigerian vocational artisan training programs to enhance communicative competence, professional performance, and economic inclusion among tradespeople. Grounded in English for Specific Purposes (ESP) theory and workplace literacy frameworks, the study employed a mixed-methods research design, combining structured surveys with semi-structured interviews. A total of 63 artisans across five trades—carpentry, tailoring, welding, automotive repair, and electrical installation—participated in the study conducted in Kogi State, Nigeria. Quantitative findings revealed critically low English proficiency levels, with only 12.4% of respondents demonstrating high proficiency in reading technical documents and 10.0% in writing estimates and invoices. Communication-related challenges occurred frequently in workplace contexts, with misunderstanding client requests (M = 3.31) and inability to interpret safety instructions (M = 3.25) emerging as the most prevalent issues. Qualitative data identified specific trade-based English needs, including reading technical manuals, writing job quotations, using digital marketing platforms, and understanding safety regulations. Based on these findings, the study proposes a Trade-Specific English Curriculum (TSEC) framework designed to integrate task-based language instruction with authentic workplace demands through five interconnected components: Needs Analysis, Task-Based Modules, Authentic Materials, Collaborative Learning, and Performance-Based Assessment. The study concludes that language integration within vocational programs represents both a pedagogical and economic imperative, with implications for workforce competitiveness, occupational safety, and formal economic participation. The proposed framework offers a replicable model for vocational education reform aligned with Nigeria’s sustainable development objectives.

In research on females’ participation in the drug economy, Chinese scholarship continues to focus predominantly on the roles and division of labor undertaken by females, with the aim of refining law enforcement practices and sentencing procedures. This orientation overlooks females’ subjectivities within drug trades and simplifies the complex entanglements between gender, embodiment, and illicit economies. This study centers on three cases from the southwestern border, foregrounding the narratives and understandings of female participants regarding their bodily experiences before recruitment, during participation in pregnancy-based drug trafficking, and across different stages of their post-trafficking lives. Through the lens of body politics, the analysis reveals how females’ bodies, whether within patriarchal structures or the drug economy, are objects. These bodies undergo a trajectory of construction from sanctification to desacralisation, yet at no point do females attain true agency over the interpretation, shaping, or autonomy of their corporeality. Pregnancy-based drug trafficking not only reconfigures the function of the body but also undermines females’ moral standing within patriarchal social norms, leaving them to endure intensified trauma and profound experiences of meaninglessness in the aftermath of trafficking. By situating these cases within the specific framework of prohibition-centered drug policy, this article contributes to cross-regional dialogue and offers empirical insights into gendered embodiment. It highlights how state power, illicit economies, and social violence intersect to shape women’s corporeal and moral lives.

The emergence and effects of burnout syndrome in the workplace encompass numerous important issues that directly affect employees' physical and mental health, and consequently their productivity. This syndrome is typically shaped by a combination of various psychological and environmental factors, such as excessive workload, low sense of control, feelings of inadequacy, perceived worthlessness, and exhaustion. Burnout can have serious and lasting consequences not only at the individual level but also at the organisational level; it significantly reduces employee motivation, negatively affects work performance, and threatens long-term organisational success. Developing and implementing psychosocial support programmes is crucial for effectively preventing and managing this condition. These programmes are carefully designed within a specific framework to enhance employees' stress management skills, support their psychological well-being, create healthy communication environments in the workplace, and strengthen work relationships. Furthermore, the effectiveness of these support mechanisms can yield the most effective results when they are integrated into organisational policies, not just at the individual level, to ensure sustainability.

The prevalent view of project governance is that it involves structures, principles, and processes that control project development and steer it toward desirable goals. However, the concept is not static, and this article highlights how its theoretical underpinnings have evolved over recent decades. A systematic review of 636 research articles reveals that almost 54% of these works are explicitly grounded in specific theoretical frameworks. We chart the trajectory and consolidation of the theories employed in these articles. Our analysis documents how a cohesive research stream of project governance emerged from a set of theoretically fragmented case studies in the early 2000s by gradually coalescing around a few strongly interconnected streams of studies employing agency theory and stakeholder theory together with transaction cost and institutional theoretical lenses. We highlight five approaches in project governance literature on a spectrum from traditional, theory-based approaches to adaptive, practice-based perspectives. We observe growth in applications of complexity, network, game, leadership, organizational, and social exchange theories. Building on the review, we propose that currently emerging technologies with novel applications to project governance necessitate more research addressing their ethical risks and their implications for project governance decentralization and anticipatory governance. Finally, we call for project governance research to further expand beyond traditionally studied industries to better tackle global challenges and support systemic sustainability transitions.

A physically informed domain-independent data-driven inundation forecast model.

Software development landscapes have undergone a remarkable transformation as coding tools increasingly incorporate intelligence features that function as active partners throughout project lifecycles. These advanced systems now contribute to numerous programming phases from initial code drafting to refinement, evaluation, and documentation tasks. The text presents specific interaction challenges and structural frameworks characterizing such applications, particularly highlighting visual organization principles, situational comprehension abilities, and response mechanisms influencing developer engagement. The presence of computational assistants within programming workflows significantly affects established engineering approaches, potentially altering skill expectations, reducing delivery timeframes, and creating opportunities for innovative problem resolution techniques. Evidence indicates successful deployment depends substantially on appropriate division between automated functions and manual control, comprehensible suggestion rationales, and accurate interpretation of programming environments. These insights offer valuable direction for application designers pursuing more effective collaboration between programmers and intelligent systems. For technical specialists and industry professionals alike, these patterns provide evaluation structures for existing solutions and conceptual guidelines for developing next-generation programming platforms that meaningfully strengthen developer capabilities while addressing practical integration limitations.

This article presents a general formal framework for describing the relationship between a criticality-aware scheduler, a set of application jobs that are assigned different criticality levels, and an environment that generates both work and faults that the run-time system must control. The proposed formalism extends the rely-guarantee approach, which facilitates formal reasoning about the functional behaviour of concurrent systems, to address real-time properties. The exposition of the general framework is supplemented by a seven step approach that enables it to be instantiated to deliver the formal specification of any proposed mixed-criticality scheduling protocol. The expressive power of the approach is explored via a non-trivial instantiation.

This paper introduces Contextual Object Grouping (COG), a specific computer vision framework that enables automatic interpretation of technical security diagrams through dynamic legend learning for intelligent sensing applications. Unlike traditional object detection approaches that rely on post-processing heuristics to establish relationships between the detected elements, COG embeds contextual understanding directly into the detection process by treating spatially and functionally related objects as unified semantic entities. We demonstrate this approach in the context of Cyber-Physical Security Systems (CPPS) assessment, where the same symbol may represent different security devices across different designers and projects. Our proof-of-concept implementation using YOLOv8 achieves robust detection of legend components (mAP50 ≈ 0.99, mAP50–95 ≈ 0.81) and successfully establishes symbol–label relationships for automated security asset identification. The framework introduces a new ontological class—the contextual COG class that bridges atomic object detection and semantic interpretation, enabling intelligent sensing systems to perceive context rather than infer it through post-processing reasoning. This proof-of-concept appears to validate the COG hypothesis and suggests new research directions for structured visual understanding in smart sensing environments, with applications potentially extending to building automation and cyber-physical security assessment.

Abstract Like standard Slovenian, Polish, and several Romance languages, the Slovenian dialect of Resia forms impersonal clauses by combining transitive and intransitive verbs with reflexive clitics. Due to their syntactic and semantic properties, these reflexive impersonals have been analyzed as involving a subject with an arbitrary human referent realized by the reflexive clitic. Based on data from a recent translation of The little prince and other Resian resources, this paper brings forward hitherto unnoticed empirical evidence supporting this analysis. Unlike other Slavic languages, Resian has developed a set of subject clitics whose position in the clause differs from that of object clitics. Interestingly, when forming impersonal constructions, reflexive clitics are placed like subject clitics and not like object clitics as they are in other contexts. Furthermore, the distribution of the reflexive clitic variants se and sa in The little prince suggests that the latter is becoming the sole impersonal marker while the former is used to express all other reflexive meanings. This implies that sa is developing into a separate subject clitic. The general significance of these findings lies in the fact that they demonstrate a link between impersonal reflexives and subjects which does not depend on the assumption of a specific theoretical framework.