Search Results (742)

Berry pomace is a valuable source of dietary fiber (DF) with promising applications in functional food development. This study aimed to evaluate and compare the technological and rheological properties of soluble (SDF) and insoluble (IDF) fiber fractions isolated from cranberry, black currant, lingonberry, and sea buckthorn pomace. SDF fractions demonstrated higher water solubility and lower swelling capacity, compared with IDF fractions. Meanwhile, water and oil retention capacities depended on fiber type and the sources of pomace. Fractionation notably affected color parameters, with SDFs generally being lighter. Rheological analysis revealed pseudoplastic, shear-thinning behavior in all SDF samples, with viscosity dependent on both pH and shear rate. In particular, the black currant SDF demonstrated higher yield stress compared to other SDFs, suggesting enhanced resistance to deformation and superior structural stability under low shear conditions. The consistency coefficient varied across samples, indicating differences in gel-forming potential. These findings highlight the importance of berry source and fiber fraction in determining functionality. The distinct hydration, binding, and rheological properties suggest that both SDF and IDF from berry pomace can be strategically applied as thickeners, stabilizers, or texture enhancers in food systems. This study supports the valorization of berry by-products as sustainable and functional ingredients in the formulation of fiber-enriched foods. Full article

Rapid advancements in artificial intelligence and the Internet of Things (IoT) have fueled the growth of furniture, transforming traditional home environments into intelligent living spaces. As consumer adoption accelerates, understanding user concerns and sentiment trends becomes crucial for brands to refine product offerings and enhance market competitiveness. This study systematically investigates consumer concerns and sentiment trends toward furniture products by analyzing user-generated reviews across two major e-commerce platforms: Jingdong and Taobao. Leveraging advanced text-mining methods including TF-IDF keyword extraction, hierarchical clustering, Graph of Words–Latent Dirichlet Allocation (GoW-LDA) topic modeling, and BERT-based sentiment analysis, this research identifies critical user preferences, product satisfaction factors, and platform-specific behavioral patterns. Results reveal distinct cross-platform differences; Jingdong users prioritize service quality, brand trust, and logistical efficiency, whereas Taobao users emphasize product aesthetics, material selection, and cost-effectiveness. The sentiment analysis demonstrates that Jingdong users exhibit more consistent and positive feedback, while sentiment on Taobao displays higher variability due to product-quality discrepancies and price sensitivity. Full article

Problem: Amid digital disruption and the cross-fertilization of RBV, DCV, and KBV, strategic management knowledge has grown fragmented with blurred boundaries. Conventional mapping (citation/co-word, LDA) lacks semantic and temporal resolution, obscuring overlaps, divergences, and turning points and hindering links to actionable indicators (e.g., the Balanced Scorecard). Hence, an integrated, semantically faithful, time-stamped map is needed to bridge research and operational metrics. Gap: Prior maps rely on citation/co-word signals, miss textual meaning, and treat RBV/DCV/KBV in isolation—lacking a theory-aligned, time-stamped, manager-oriented synthesis. Objectives: This study aims to (1) reveal how RBV, DCV, and KBV evolve and interrelate over time; (2) produce an integrated, semantically grounded map; and (3) translate selected themes into actionable managerial indicators. Method: We analyzed 25,907 WoS articles (1980–2025) with BERTopic (Sentence-BERT + UMAP + HDBSCAN + c-TF-IDF). We used an RBV/DCV/KBV lexicon to guide retrieval/interpretation (not to constrain modeling). We discovered 230 topics, retained 33 via coherence (C_V), and benchmarked them against LDA. Key findings: A concise set of 33 high-quality themes with a higher C_V than LDA on this corpus was established. A Fish-Scale view (overlapping subfields across economics, management, sociology) that clarifies RBV–DCV–KBV intersections was achieved. Era-sliced prevalence shows how themes emerge and recombine over 1980–2025. Selected themes mapped to Balanced Scorecard (BSC) indicators linking capabilities → processes → customer outcomes → financial results. Contribution: A clear, time-aware synthesis of RBV–DCV–KBV and a scalable, reproducible pipeline for structuring fragmented theory landscapes are presented in this study—bridging scholarly integration with managerial application via BSC mapping. Full article

Background/Objectives: Outpatient parenteral antimicrobial therapy (OPAT) has been implemented throughout the world for the treatment of most infections. Published studies have focused on OPAT delivery, with limited data on coordination and monitoring practices. Methods: A mixed methods study, using an exploratory sequential design, was conducted at a tertiary hospital in Paris, France. Ten semi-structured interviews were conducted with prescribing physicians and professionals involved in OPAT coordination and monitoring. A general inductive approach was used to analyze verbatim data and build a framework for OPAT model characterization. Cost estimates, using a standardized scenario, were applied to each model. Results: Five OPAT coordination and monitoring models were identified. All OPATs were administered by visiting nurses in the patient’s home. Referral to an infectious disease physician was not systematic, and three models, with 3 to 50 OPAT episodes/year each, outsourced hospital-to-home coordination and monitoring to external medical service and device providers. Only one OPAT model, with 450 OPATs annually, included a nurse specialist within the unit to coordinate and monitor treatment. Clinically and/or socially vulnerable patients received OPAT through hospital at home services, which reported 30 OPATs/year. Under the standardized clinical scenario applied to each OPAT model, weekly costs ranged from EUR 1445 to EUR 2308. Conclusions: The diversity of OPAT coordination and monitoring practices identified within a single hospital suggests that similar trends may be observed in other settings, in France and elsewhere. Identifying the most cost-effective OPAT service model could guide stakeholders and facilitate the implementation of best practice recommendations in line with antimicrobial stewardship principles. Full article

Objectives: In response to the high prevalence of global obesity and associated metabolic diseases, this study aimed to investigate the effects of Pickering emulsions stabilized by cellulase-hydrolyzed pomelo peel insoluble dietary fiber (IDF), namely EPI and its octenyl succinic anhydride (OSA)-modified form (OSA-EPI), on alleviating high-fat diet (HFD)-induced metabolic disorders in mice. Methods: Male C57BL/6J mice were subjected to an HFD-induced obesity model. Biochemical index determination, histopathological examination, gut microbiota analysis, and short-chain fatty acids (SCFAs) analysis were used to study the potential efficacy of pomelo peel IDF-based emulsion (EPI and OSA-EPI) in alleviating obesity and related metabolic diseases. Results: The findings demonstrated that both emulsions effectively mitigated HFD-induced health impairments: reduced weight gain, improved blood glucose and lipid profiles, attenuated tissue steatosis and inflammation, and lowered oxidative stress. Furthermore, both EPI and OSA-EPI restored gut microbiota diversity, promoted the proliferation of beneficial bacterial taxa (e.g., Akkermansia), and inhibited the growth of harmful genera (e.g., Muribaculum, Faecalibaculum). These changes were accompanied by increased production of SCFAs. Conclusions: This study confirms that modified pomelo peel IDF can effectively exert the health intervention effect of IDF on obesity when used as an emulsion stabilizer, providing a robust scientific foundation for the application of emulsified dietary fibers in combating obesity and related metabolic disorders. Full article

A necessary condition for the success of the 7 October 2023 Hamas deadly incursion into Israel was the absence of the Israel Defense Forces (IDF) from that region. The IDF was involved in helping the settlers in their conflicts with Palestinians on the West Bank, many miles from the Gaza border. Absent the settlers, it is likely that either the Hamas attack might not have occurred or would have been blunted at the outset, yielding a much more measured Israeli response. Hence it is imperative that we understand the origins of the settler movement. It is to be found in Biblical injunctions that were to be amplified considerably by the outcomes of the extraordinarily successful Six-Day war of 1967 and its sequel the Yom Kippur war of 1973. In the third chapter of the Book of Genesis, that is, of the entire Hebrew Bible, God commands Abraham to leave his current domicile and travel to Canaan where a great nation would be formed. Effectively, this is the religious foundation of the connection between the people of Israel and the land of Israel, then called Canaan. The contrast between the outcomes of 1967 and 1973 was striking. Instead of a lopsided victory in the earlier war, the human losses in 1973 were surprising, even terrifying. This intense ephemeral gain combined with a world view defense engendered by mortality salience established the basis for later religious Zionist extremism. The vastly increased number of casualties in 1973 set the stage for the victory of Likud, much more amenable to West Bank settlements than the ousted Labor government had been. Religious Zionists leaped at this opportunity, justifying this activity by referring to God’s commandment to settle the entire land of Israel in the West Bank territories using their Biblical Hebrew names: Yehuda (Judea) and Shomron (Samaria), whatever the cost in violent Palestinian land dispossession. Full article

Intensity–duration–frequency (IDF) curves, which relate rainfall intensity (i), storm duration (d), and return period (T), are cornerstone tools for planning, designing, and operating hydraulic works. Since Sherman’s pioneering formulation in 1931, many modern implementations have systematically omitted the duration-shifting parameter C, causing predicted intensities to diverge to infinity as $d\to 0$. This mathematical paradox becomes especially problematic under extreme hydrological regimes and convective storms exceeding 300 mm/h, where an accurate curve fit is critical. Here, we first review conventional IDF curve fitting techniques and their limitations. We then introduce IDF-GtzLo, a novel, intuitive formulation that reinstates and calibrates C directly from observed storm statistics, ensuring finite intensities for all durations. Applied to 36 automatic weather stations across Mexico, our method reduces the root mean square error by 23 % compared to the classical model. By eliminating the infinite intensity paradox and improving statistical performance, IDF-GtzLo offers a more reliable foundation for hydrological risk assessment and the design of infrastructure resilient to climate-driven extremes. Full article

Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited. Full article

Terrorism remains a critical global challenge, and the proliferation of social media has created new avenues for monitoring extremist discourse. This study investigates stance detection as a method to identify Arabic tweets expressing support for or opposition to specific organizations associated with extremist activities, using Hezbollah as a case study. Thousands of relevant Arabic tweets were collected and manually annotated by expert annotators. After extensive preprocessing and feature extraction using term frequency–inverse document frequency (tf-idf), we implemented traditional machine learning (ML) classifiers—Support Vector Machines (SVMs) with multiple kernels, Multinomial Naïve Bayes, and Weighted K-Nearest Neighbors. ML models were selected over deep learning (DL) approaches due to (1) limited annotated Arabic data availability for effective DL training; (2) computational efficiency for resource-constrained environments; and (3) the critical need for interpretability in counterterrorism applications. While interpretability is not a core focus of this work, the use of traditional ML models (rather than DL) makes the system inherently more transparent and readily adaptable for future integration of interpretability techniques. Comparative experiments using FastText word embeddings and tf-idf with supervised classifiers revealed superior performance with the latter approach. Our best result achieved a macro F-score of 78.62% using SVMs with the RBF kernel, demonstrating that interpretable ML frameworks offer a viable and resource-efficient approach for monitoring extremist discourse in Arabic social media. These findings highlight the potential of such frameworks to support scalable and explainable counterterrorism tools in low-resource linguistic settings. Full article

Under the twin imperatives of climate change mitigation and sustainable development, achieving a low-carbon transformation of power systems has become a national priority. To clarify this objective, China issued the Blue Book on the Development of New Power System, which comprehensively defines the guiding concepts and characteristic features of a new power system. In this study, natural language processing-based keyword extraction techniques were applied to the document, employing both the TF-IDF and TextRank algorithms to identify its high-frequency terms as characteristic keywords. These keywords were then used as topic queries in the Web of Science Core Collection, yielding 1568 relevant publications. CiteSpace was employed to perform a bibliometric analysis of these records, extracting research hotspots in the new power system domain and tracing their evolutionary trajectories. The analysis revealed that “renewable energy” appeared 247 times as the core high-frequency term, while “energy storage” exhibited both high frequency and high centrality, acting as a bridge across multiple subfields. This pattern suggests that research in the new power system field has evolved from a foundation in renewable energy and storage toward smart grids, market mechanisms, carbon capture, and artificial intelligence applications. Taken together, these results indicate that early research was primarily grounded in renewable energy and storage technologies, which provided the technical basis for subsequent exploration of smart grids and market mechanisms. In the more recent stage, under the dual-carbon policy and digital intelligence imperatives, research hotspots have further expanded toward carbon capture, utilization, and storage (CCUS) and artificial intelligence applications. Looking ahead, interdisciplinary studies focusing on intelligent dispatch and low-carbon transition are poised to emerge as the next major research frontier. Full article

Background: Critical Incident Reporting Systems (CIRS) play a key role in improving patient safety but facess limitations due to the unstructured nature of narrative data. Systematic analysis of such data to identify latent risk patterns remains challenging. While artificial intelligence (AI) shows promise in healthcare, its application to CIRS analysis is still underexplored. Methods: This study presents a transformer-based approach to classify incident reports into predefined risk categories and support clinical risk managers in identifying safety hazards. We compared a traditional TF-IDF/logistic regression model with a transformer-based German BERT (GBERT) model using 617 anonymized CIRS reports. Reports were categorized manually into four classes: Organization, Treatment, Documentation, and Consent/Communication. Models were evaluated using stratified 5-fold cross-validation. Interpretability was ensured via Shapley Additive Explanations (SHAP). Results: GBERT outperformed the baseline across all metrics, achieving macro averaged-F1 of 0.44 and a weighted-F1 of 0.75 versus 0.35 and 0.71. SHAP analysis revealed clinically plausible feature attributions. Conclusions: In summary, transformer-based models such as GBERT improve classification of incident report data and enable interpretable, systematic risk stratification. These findings highlight the potential of explainable AI to enhance learning from critical incidents. Full article

Rice bran represents an exceptional natural source of dietary fiber (DF), and its physicochemical properties and therapeutic potential are closely associated with its origin and processing methods. Herein, rice bran was subjected to extrusion, fermentation, and a combined treatment of fermentation and extrusion to explore the alternations in the structural, physicochemical, and functional properties of the resulting insoluble dietary fiber (IDF). All treatments induced substantial microstructural alterations in IDF, producing fiber matrices with enhanced porosity and looser architectures. The employed processing treatments significantly enhanced the functional properties of rice bran IDF over the unprocessed sample, with 1.37- to 1.78-fold increases in oil-holding capacity, 1.31- to 1.48-fold increases in cholesterol-adsorption capacity, 2.89- to 5.90-fold increases in α-amylase-inhibitory activity, and 2.41- to 3.70-fold increases in glucose-adsorption capacity. Among them, extrusion proved more effective than fermentation in enhancing the water-holding capacity, sodium cholate binding, and cholesterol-adsorption capacity of rice bran IDF. However, fermented rice bran-derived IDF exhibited the optimum α-amylase-inhibitory and glucose-absorption capacities among all employed IDF samples. These findings provide valuable insights for the development of rice bran-based functional foods with enhanced health benefits. Full article

Environmental, Social, and Governance (ESG) ratings are widely used for investment and regulatory decision-making, yet they often suffer from symbolic compliance and information asymmetry. To address these limitations, this study introduces a hybrid ESG risk assessment model that integrates court ruling data with traditional ESG ratings to detect latent sustainability risks. Using a dataset of 213 ESG-related U.S. court rulings from January 2023 to May 2025, we apply natural language processing (TF-IDF, Legal-BERT) and explainable AI (SHAP) techniques to extract structured features from legal texts. We construct and compare classification models—including Random Forest, XGBoost, and a Legal-BERT-based hybrid model—to predict firms’ litigation risk. The hybrid model significantly outperforms the baseline ESG-only model in all key metrics: F1-score (0.81), precision (0.79), recall (0.84), and AUC-ROC (0.87). SHAP analysis reveals that legal features such as regulatory sanctions and governance violations are the most influential predictors. This study demonstrates the empirical value of integrating adjudicated legal evidence into ESG modeling and offers a transparent, verifiable framework to enhance ESG risk evaluation and reduce information asymmetry in sustainability assessments. Full article

This study presents a comprehensive analysis of heat transfer enhancement, flow resistance, and thermal performance in rectangular channels equipped with three baffle configurations: conventional transverse baffles (TBs), in-line downward-facing notched baffles (IDF-NBs), and staggered downward-facing notched baffles (SDF-NBs). The influence of the pitch-to-baffle height ratio (P/e), ranging from 2.0 to 10, was examined across Reynolds numbers from 6000 to 24,000. Results indicate that a P/e ratio of 6.0 consistently yielded the highest Nusselt numbers across all configurations. While the TB configuration produced significant heat transfer at P/e= 6.0, it experienced a substantial friction penalty, with its best thermal enhancement factor (TEF = 1.168) observed at P/e = 8.0. The IDF-NB configuration achieved optimal performance at P/e = 6.0 with a TEF of 1.257, offering a better balance between heat transfer and flow resistance. The SDF-NB arrangement outperformed all other cases, delivering the highest Nusselt number (Nu = 116.9), TEF (1.362), and improved flow reattachment, primarily due to enhanced mixing from the staggered layout. These findings demonstrate that the staggered notched baffle configuration at P/e = 6.0 offers the most effective thermal performance enhancement among the configurations studied. Full article

This paper presents the study of integrating a deep motion model into simple online and real-time tracking for video multi-object tracking. The tracking-by-detection paradigm faces significant challenges in handling nonlinear motion and occlusions. Although conventional Kalman-filter-based methods such as the SORT are efficient, they suffer from error accumulation because of their linear motion assumption. We propose KalmanFormer, a novel framework that enhances Kalman-filter-based tracking through adaptive motion modeling for video sequences. KalmanFormer consists of three key components. First, the inner-trajectory motion corrector, built upon the transformer architecture, refines Kalman filter predictions by learning nonlinear residuals from historical trajectories, thereby improving adaptability to complex motion patterns in videos. Second, the cross-trajectory attention module captures interobject motion correlations, significantly boosting object association under occlusions. Third, a pseudo-observation generator is integrated to provide neural-based predictions when detections are missing, stabilizing the Kalman filter update process. To validate our approach, we conduct comprehensive evaluations on the video benchmarks DanceTrack, MOT17, and MOT20 to demonstrate its effectiveness in handling complex motion and occlusion. The experimental results on the DanceTrack, MOT17, and MOT20 benchmarks demonstrate that KalmanFormer achieves competitive performance, with HOTA scores of 66.6 on MOT17 and 63.2 on MOT20, and strong identity preservation, IDF1: 82.0% and 80.1%, respectively. Full article