Search Results (38)

The semiconductor manufacturing industry depends on effective data collection and analysis for critical processes such as Root Cause Analysis (RCA) and Risk Assessment (RA). Both processes involve software-driven data collection and subsequent analysis by domain experts to support informed decision-making. However, the increasing complexity, volume, and decentralized nature of manufacturing data pose significant challenges for effective data collection. Data is distributed across multiple systems with varying formats and ownership, making conventional programming paradigms and manual data collection scripts inadequate for handling this decentralized data landscape. To address these challenges, this study proposes integrating Action-Oriented Programming (AcOP) with Automatic Agent Generation (AAG) as a novel solution. AcOP emphasizes actions as fundamental execution units, separating system behavior and data. Complementing this, AAG uses large language models (LLMs) to autonomously generate intelligent agents, which manage these actions and perform preliminary data analysis with domain-specific knowledge. Our experimental setup compares three microservice applications supporting RCA and RA: Object-Oriented Programming (OOP), AcOP, and AcOP integrated with AAG. Evaluation results indicate that AcOP improves modularity, adaptability, and error handling in decentralized systems. Integrating AAG enhances automation, provides a flexible, low-maintenance solution for data collection and analysis pipelines, and promotes autonomous microservice architectures in data-intensive environments. Full article

The contemporary cyber-threat landscape is becoming increasingly diverse and complex, creating a persistent gap between situational awareness and operational response. This study presents a framework designed to bridge this gap by transforming up-to-date cyber-threat intelligence (CTI) into standardized knowledge structures and actionable defense measures. First, the proposed framework integrates the threat data collected from OpenCTI and normalizes them based on the MITRE ATT&CK tactics and techniques matrix. It then leverages a large language model to automatically generate diverse threat scenarios based on the analyzed intelligence. Each scenario is organized as a tactic sequence, and individual techniques are mapped to MITRE D3FEND defensive categories based on official ATT&CK–D3FEND relationships and structured contextual interpretation. Finally, the framework produces outputs in the form of a Defense Description that includes the corresponding technique IDs, recommended defense strategies, supporting rationales, and prerequisites. An evaluation using several recent cases demonstrates that the proposed framework effectively connects current threat intelligence with practical defense strategies. In summary, the proposed framework strengthens proactive cyber defense by directly linking structured attack flows to actionable context-aware defensive techniques. In addition, this framework provides a structured pipeline that systematizes and automates steps conventionally performed manually, thereby reducing repetitive analyst effort. Full article

This paper presents a systematic, data-driven literature review of research on Artificial Intelligence (AI) agents based on the top 100 Google Scholar publications related to the search terms “AI agent” and “AI agents”. The rapid advancement of AI agents, driven in particular by recent progress in Large Language Models, has resulted in a diverse and fragmented research landscape that lacks comprehensive quantitative overviews. To address this gap, we implement and apply a fully automated, AI-driven analysis pipeline to the domain of AI agents. The collected publications are processed using a Large Language Model accessed via a Python-based Application Programming Interface (API), enabling an automated analysis of the literature without manual categorization. Based on this approach, the publications are grouped into data-driven thematic clusters reflecting dominant research perspectives in the field. Specifically, the identified clusters comprise “Architecture & Frameworks”, “Multi-Agent Systems”, “Applications”, “Safety” and “Ethics, Accountability & Governance”. By synthesizing the literature in a structured and automated manner, this work provides a consolidated overview of central research patterns, identifies key operational and structural challenges and highlights fragmentation across AI agent research. The findings support a more systematic understanding of AI agents and provide a foundation for future research on robust, scalable and trustworthy AI agent systems. Full article

In the context of urban renewal, the spatial interface of historic commercial districts functions as both a carrier of historical character and a key setting for commercial activity, public life, and local cultural expression. To address the limitations of conventional studies that rely heavily on field observation and qualitative description, this study takes Xiaohe Zhijie in Hangzhou as a case and develops a multi-level street-view recognition framework for the quantitative analysis of spatial interface characteristics. Based on street-view image collection and standardized preprocessing, a sample database was established at the sampling-point scale. Semantic segmentation, automated commercial object detection, and manual interpretation were combined to identify interface elements, including buildings, sky, greenery, pavement, vehicles, pedestrians, and commercial objects, while commercial content was assessed in terms of locality and homogenization. The results show that Xiaohe Zhijie exhibits a building-dominated and relatively enclosed interface pattern, with greenery and pavement forming the basic environmental ground, weak vehicle interference, and localized enhancement of vitality through commercial objects and pedestrian activities. Significant differences were found among street segments in openness, commercial coverage, and local expression. Three interface types were identified: commercial–cultural composite, local life-oriented, and waterfront landscape–cultural composite. The main challenge lies not in commercialization itself, but in stronger visual locality than content locality and increasing homogenization, resulting in a pattern of “localized form but homogenized content.” Full article

Dirofilaria immitis constitutes a significant global veterinary burden and an emerging zoonotic risk. Despite decades of study, the structural evolution of its scientific landscape remains unexplored. This study provides a comprehensive longitudinal analysis of global research on D. immitis to evaluate its trajectory, intellectual structure, and conceptual shifts over the last eight decades. A systematic bibliometric analysis was conducted following PRISMA guidelines adapted for bibliometrics. Data were retrieved from Web of Science Core Collection and Scopus, covering the period from 1945 to 2025. After deduplication and manual screening, a final corpus of 3589 documents was analyzed using performance indicators and science mapping techniques to assess growth patterns, geographic leadership, collaboration networks, and thematic evolution. The field exhibits a mature profile with a sustained mean annual growth rate of 2.39%. Production is geographically polarized, with the United States and Italy acting as the primary research hubs, though international collaboration networks are increasingly integrating endemic regions in the Global South. Thematic analysis reveals a profound paradigm shift: while early research (1945–1980) focused on parasite morphology and clinical description, the 21st century is characterized by a multidisciplinary approach dominated by molecular biology, the study of the endosymbiont Wolbachia, and the genetic mechanisms of macrocyclic lactone resistance. The intellectual structure is currently organized into distinct but interconnected clusters, linking established clinical pathology with emerging genomic and environmental control strategies. Research on D. immitis has evolved from a classical parasitology discipline into a complex biomedical ecosystem aligned with the One Health framework. The persistence of the disease, driven by drug resistance and climate-mediated vector expansion, has catalyzed a transition toward integrative research models. Future control strategies must transcend geographic borders, combining advanced genomic surveillance with ecological modeling to mitigate the impact of this transboundary disease on both animal and human health. Full article

In this work, we present the Snow Depth and Snow Water Equivalent Dataset for specific areas located in the East Kazakhstan Region that can be exploited to monitor and understand water resource dynamics in mountain regions. The present dataset represents a georeferenced collection of snow depth, snow density, and derived snow water equivalent (SWE) measurements obtained through manual snow surveys. Snow survey observations were conducted during field campaigns in the East Kazakhstan Region during the period of maximum snow accumulation from 27 February to 6 March 2025. Snow survey sites were selected to maximize coverage of diverse landscape settings and snow accumulation conditions. In total, 111 snow survey sites were established across the East Kazakhstan Region, and 2331 snow depth measurements and 555 snow density measurements were collected. In post-field (laboratory) processing, snow water equivalent (SWE) was calculated for all snow survey sites based on measured snow depth and snow density values. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

The growing demand for transparency in sustainability reporting has compelled enterprises to look far beyond the boundaries of classical financial ratios when assessing their own performance. Environmental, social, and governance (ESG) indicators have dominated recent academic debate—primarily because of mounting regulatory and societal pressure. By contrast, the significance of other non-financial performance indicators (NFPIs), such as operational efficiency, quality management, and employee turnover, has been insufficiently explored, despite their importance for long-term competitiveness. Existing research is fragmented and provides limited integrative insights, which creates a clear gap regarding how ESG and non-ESG indicators collectively influence organisational performance. To address this gap, this study synthesises the NFPI landscape through (1) a combined bibliometric and systematic literature review, (2) detailed manual content analysis used to construct a theoretical framework integrating ESG and non-ESG indicators, and (3) expert validation to recommend a concise set of NFPIs for the manufacturing sector. Findings indicate that experts prioritise sustainability-related indicators, even when presented with a broader NFPI framework. This highlights a practical misalignment between theoretical expectations and industry focus. The study contributes a validated NFPI set and an integrative framework that aids more informed managerial decisions. Full article

Aspect-based sentiment analysis (ABSA) plays a pivotal role in understanding the nuances of sentiment expressed in text, particularly in the context of diverse languages and cultures. This paper presents a novel deep convolutional neural network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, as Hausa is an underrepresented language with limited resources and presence in sentiment analysis research. One of the primary implications of this work is the creation of a comprehensive Hausa ABSA dataset, which addresses a significant gap in the availability of resources for sentiment analysis in underrepresented languages. This dataset fosters a more inclusive sentiment analysis landscape and advances research in languages with limited resources. The collected dataset was first preprocessed using Sci-Kit Learn to perform TF-IDF transformation for extracting feature word vector weights. Aspect-level feature ontology words within the analyzed text were derived, and the sentiment of the reviewed texts was manually annotated. The proposed model combines convolutional neural networks (CNNs) with an attention mechanism to aid aspect word prediction. The model utilizes sentences from the corpus and feature words as vector inputs to enhance prediction accuracy. The proposed model leverages the advantages of the convolutional and attention layers to extract contextual information and sentiment polarities from Hausa movie reviews. The performance demonstrates the applicability of such models to underrepresented languages. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model excels in aspect identification and sentiment analysis, offering insights into specific aspects of interest and their associated sentiments. The proposed model outperformed traditional machine models in both aspect word and polarity prediction. Through the creation of the Hausa ABSA dataset and the development of an effective model, this study makes significant advances in ABSA research. It has wide-ranging implications for the sentiment analysis field in the context of underrepresented languages. Full article

Evaluating the restoration quality of university outdoor spaces is often constrained by subjective surveys and manual assessment, limiting scalability and objectivity. This study addresses this gap by applying explainable machine learning to predict restorative quality from campus imagery, enabling large-scale, data-driven evaluation and capturing complex nonlinear relationships that traditional methods may overlook. Using Fujian Agriculture and Forestry University as a case study, this study extracted road network data, generated 297 coordinates at 50-m intervals, and collected 1197 images. Surveys were conducted to obtain restorative quality scores. The Mask2Former model was used to extract landscape features, and decision tree algorithms (RF, XGBoost, GBR) were selected based on MAE, MSE, and EVS metrics. The combination of optimal algorithms and SHAP was employed to predict restoration quality and identify key features. This research also used a multivariate linear regression model to identify features with significant statistical impact but lower features importance ranking. Finally, the study also analyzed heterogeneity in scores for three restoration indicators and five campus zones using k-means clustering. Empirical results show that natural elements like vegetation and water positively affect psychological perception, while structural components like walls and fences have negative or nonlinear effects. On this basis, this study proposes spatial optimization strategies for different campus areas, offering a foundation for creating high-quality outdoor environments with restorative and social functions. Full article

This study employs social network analysis to illuminate the intricate relationships within Daoist exorcism rituals from the Southern Song to the Yuan dynasty, as documented in two pivotal compilations: Pearls Left Behind from the Sea of Ritual (Fahai Yizhu 法海遺珠) and Collected Essentials of Daoist Methods (Daofa Huiyuan 道法會元). While previous scholarship focused on individual rituals or generals using traditional document analysis, this article introduces a novel digital humanities methodology. By treating the Daoist generals summoned in these rituals as network nodes, we map and analyze their co-occurrence patterns, offering a comprehensive understanding of the evolving ritual landscape. Our analysis reveals a significant expansion in the scale of exorcism rituals from Fahai Yizhu to Daofa Huiyuan, indicating a shift from concise manuals to more systematic frameworks with clearer factional organization. Specifically, the Great Demon-Subjugating Ritual of Shangqing Tianpeng (Shangqing Tianpeng Fumu Dafa 上清天蓬伏魔大法) and various Marshal Zhao exorcism rituals exhibit the largest scales, reflecting the widespread popularity of Heavenly Commander Tianpeng (Tianpeng 天蓬) beliefs and Marshal Zhao’s capacity to integrate diverse pantheons, including local deities, plague gods, thunder generals, and “rampant soldiers” (changing 猖兵). Key figures like Yin Jiao (殷郊), Zhao Gongming (趙公明), Zhang Yuanbo (張元伯), Ma Sheng (馬勝), Deng Bowen (鄧伯溫), and Guan Yu (關羽) demonstrate high centrality. Notably, Ma Sheng, Zhao Gongming (趙公明), and Guan Yu (關羽) play increasingly pivotal roles in Daofa Huiyuan, while Zhang Yuanbo (張元伯) and Song Wuji (宋無忌) experience hierarchical reversals, suggesting an augmented importance of local deities after the Southern Song. This pioneering SNA application offers a robust framework for understanding these complex interconnections. Full article

The reference area (RA) approach has been frequently used in soil surveying and mapping projects, since it allows for reduced costs. However, a crucial point in using this approach is the choice or delineation of an RA, which can compromise the accuracy of prediction models. In this study, an innovative algorithm that delineates RA (autoRA—automatic reference areas) is presented, and its efficiency is evaluated in Sátiro Dias, Bahia, Brazil. autoRA integrates multiple environmental covariates (e.g., geomorphology, geology, digital elevation models, temperature, precipitation, etc.) using the Gower’s Dissimilarity Index to capture landscape variability more comprehensively. One hundred and two soil profiles were collected under a specialist’s manual delineation to establish baseline mapping soil taxonomy. We tested autoRA coverages ranging from 10% to 50%, comparing them to RA manual delineation and a conventional “Total Area” (TA) approach. Environmental heterogeneity was insufficiently sampled at lower coverages (autoRA at 10–20%), resulting in poor classification accuracy (0.11–0.14). In contrast, larger coverages significantly improved performance: 30% yielded an accuracy of 0.85, while 40% and 50% reached 0.96. Notably, 40% struck the best balance between high accuracy (kappa = 0.65) and minimal redundancy, outperforming RA manual delineation (accuracy = 0.75) and closely matching the best TA outcomes. These findings underscore the advantage of applying an automated, diversity-driven strategy like autoRA before field campaigns, ensuring the representative sampling of critical environmental gradients to improve DSM workflows. Full article

Agro-ecosystem models are useful tools to assess crop diversification strategies or management adaptations to within-field heterogeneities, but require proper simulation of soil water dynamics, which are crucial for crop growth. To simulate these, the model requires soil hydraulic parameter inputs which are often derived using pedotransfer functions (PTFs). Various PTFs are available and show varying performance; therefore, in this study, we calibrated and validated an agro-ecosystem model using the Hypres PTF and the German Manual of Soil Mapping approach and adjusting bulk density for the top- and subsoil. Experimental data were collected at the “patchCROP” landscape laboratory in Brandenburg, Germany. The daily volumetric soil water content (SWC) at 12 locations and above ground biomass at flowering were used to evaluate model performance. The findings highlight the importance of calibrating agro-ecosystem models for spatially heterogeneous soil conditions not only for crop growth parameters, but also for soil water-related processes—in this case by PTF choice—in order to capture the interplay of top- and especially subsoil heterogeneity, climate, crop management, soil moisture dynamics and crop growth and their variability within a field. The results showed that while the impact of bulk density was rather small, the PTF choice led to differences in simulating SWC and biomass. Employing the Hypres PTF, the model was able to simulate the climate and seasonal crop growth interactions at contrasting soil conditions for soil moisture and biomass reasonably well. The model error in SWC was largest after intense rainfall events for locations with a loamy subsoil texture. The validated model has the potential to be used to study the impact of management practices on soil moisture dynamics under heterogeneous soil and crop conditions. Full article

This study investigates how religious rhetoric and popular mobilisation contributed to the preservation and propagation of Daoist traditions at the mountain Maoshan 茅山 from late Qing to Republican China (1864–1937), focusing particularly on the corpus of religious texts related to Maoshan and its tutelary gods, the Three Mao Lords 三茅真君. Through a detailed analysis of primary sources, including editions of the Maoshan Gazetteer, liturgical manuals such as the scripture (jing 經), litany (chan 懺), and performative texts such as the precious scroll (baojuan 寶卷) of the Three Mao Lords, this study identifies six key rhetoric strategies employed by Maoshan Daoists, using the acronym IMPACT: (1) Incorporation: Appending miracle tales (lingyan ji 靈驗記) and divine medicine (xianfang 仙方) to address immediate and practical needs of contemporary society; (2) Memory: Preserving doctrinal continuity while invoking cultural nostalgia to reinforce connections to traditional values and heritage; (3) Performance: Collaborating with professional storytellers to disseminate vernacularized texts through oral performances, thereby reaching broader audiences including the illiterate. (4) Abridgment: Condensing lengthy texts into concise and accessible formats; (5) Canonization: Elevating the divine status of deities through spirit-writing, thereby enhancing their religious authority; (6) Translation: Rendering classical texts into vernacular language for broader accessibility. Building upon J.L. Austin’s speech act theory, this study reconceptualizes these textual innovations as a form of “text acts”, arguing that Maoshan texts did not merely transmit religious doctrine but actively shaped pilgrimages and devotional practices through their illocutionary and perlocutionary force. Additionally, this study also highlights the crucial role of social networks, particularly the efforts of key individuals such as Zhang Hefeng 張鶴峰 (fl. 1860–1864), Long Zehou 龍澤厚 (1860–1945), Jiang Daomin 江導岷 (1867–1939), Wang Yiting 王一亭 (1867–1938) and Teng Ruizhi 滕瑞芝 (fl. 1920–1947) who facilitated the reconstruction, reprinting and dissemination of these texts. Furthermore, this study considers pilgrimages to Maoshan as a form of popular mobilisation and resistance to anti-clerical and anti-superstition campaigns, illustrating how, against all odds, Maoshan emerged as a site where religious devotion and economic activity coalesced to sustain the local communities. Ultimately, despite the challenges identified in applying speech act theory to textual practices, the findings conclude that the survival and revival of Daoist traditions at Maoshan was not only a result of textual retention and innovation but also a testament to how religious rhetoric, when coupled with strategic social engagement, can fuel popular mobilisation, reignite collective devotion, and reshape cultural landscapes in transformative ways. Full article

The biodiversity of invertebrate animals is largely affected by climatic changes. This study evaluates the seasonal abundance and diversity of non-insect arthropods in the King Abdulaziz Royal Reserve (KARR), Saudi Arabia, over four collection periods (summer, autumn, winter, and spring) during 2023. Sampling was conducted across multiple sites in the reserve using both active (manual collection and active surveying for the diurnal species) and passive (pitfall traps and malaise traps for the nocturnal species) methods. A total of 586 non-insect arthropod specimens were collected, representing four classes: Arachnida, Chilopoda, Branchiopoda, and Malacostraca. The results show that the most abundant species was the jumping spider Plexippus paykulli, which dominated collections across two seasons, with a peak abundance of 50.7% in late summer. Seasonal variations in non-insect arthropod diversity were observed, with a lower diversity recorded during January–March (4 species, and this may be attributed to this period revealing the lowest temperature reading recorded during the study period) and higher diversity in August–September (end of summer) and October–November (mid of autumn), with 14 species. Scorpions, particularly species from the families Buthidae and Scorpionidae, were common during the summer months, while solifuges and centipedes showed sporadic occurrences across seasons. These findings align with the results for arthropod distribution in arid regions, with temperature and resource availability as key drivers of biodiversity in desert environments because of their direct effects on the biochemical processes of these creatures. This study contributes valuable baseline data on the non-insect arthropod fauna of the KARR. The insights gained from this study can aid in conservation efforts and provide a foundation for further research on non-insect arthropod ecology in arid landscapes. Full article