Search Results (191)

With the popularization of social media, information related to public health events has seen explosive growth online, making it essential to accurately identify informative tweets with decision-making and management value for public health emergency response and risk monitoring. However, existing methods often suffer performance degradation during cross-event transfer due to differences in data distribution, and research specifically targeting public health events remains limited. To address this, we propose the Label Confusion Domain Adversarial Network (LCDAN), which innovatively integrates label confusion with domain adaptation to enhance the detection of informative tweets across different public health events. First, LCDAN employs an adversarial domain adaptation model to learn cross-domain feature representation. Second, it dynamically evaluates the importance of different source domain samples to the target domain through label confusion to optimize the migration effect. Experiments were conducted on datasets related to COVID-19, Ebola disease, and Middle East Respiratory Syndrome public health events. The results demonstrate that LCDAN significantly outperforms existing methods across all tasks. This research provides an effective tool for information detection during public health emergencies, with substantial theoretical and practical implications. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

Quantum Dots (QDs) are small semiconductor nanoparticles (<10 nm) with strong, relatively stable, and tunable luminescent properties, which are increasingly applied in the sensing and detection of various analytes, including metal ions, biomarkers, explosives, proteins, RNA/DNA fragments, pesticides, drugs, and pollutants. In this review, we critically assess recent developments and advancements in luminescent QD-based sensors from an analytical perspective. We collected, tabulated, and analyzed relevant data reported in 124 peer-reviewed articles. The key analytical figures of merit, including the limit of detection (LOD), excitation and emission wavelengths, and size of the particles were extracted, tabulated, and analyzed with graphical representations. We calculated the geometric mean and median LODs from those tabulated publications. We found the following geometric mean LODs: 38 nM for QD-fluorescent-based sensors, 26 nM for QD-phosphorescent-based sensors, and an impressively low 0.109 pM for QD-chemiluminescent-based sensors, which demonstrate by far the best sensitivity in QD-based detection. Moreover, AI-based sensing methods, including the ATTBeadNet model, optimized principal component analysis(OPCA) model, and Support Vector Machine (SVM)-based system, were reviewed as they enhance the analytical performance of the detection. Despite these advances, there are still challenges that include improvements in recovery values, biocompatibility, stability, and overall performance. This review highlights trends to guide the future design of robust, high-performance, QD-based luminescent sensors. Full article

With the rapid development of the Internet, the speed and scope of sudden public events disseminating in cyberspace have grown significantly. Current methods of quantifying public opinion heat often neglect emotion-driven factors and user interaction behaviors, making it difficult to accurately capture fluctuations during dissemination. To address these issues, first, this study addressed the complexity of interaction behaviors by introducing an approach that employs the information gain ratio as a weighting indicator to measure the “interaction heat” contributed by different interaction attributes during event evolution. Second, this study built on SnowNLP and expanded textual features to conduct in-depth sentiment mining of large-scale opinion texts, defining the variance of netizens’ emotional tendencies as an indicator of emotional fluctuations, thereby capturing “emotional heat”. We then integrated interactive behavior and emotional conflict assessment to achieve comprehensive heat index to quantification and dynamic evolution analysis of online public opinion heat. Subsequently, we used Hodrick–Prescott filter to separate long-term trends and short-term fluctuations, extract six key quantitative features (number of peaks, time of first peak, maximum amplitude, decay time, peak emotional conflict, and overall duration), and applied K-means clustering algorithm (K-means) to classify events into three propagation patterns, which are extreme burst, normal burst, and long-tail. Finally, this study conducted ablation experiments on critical external intervention nodes to quantify the distinct contribution of each intervention to the propagation trend by observing changes in the model’s goodness-of-fit ($R^2$) after removing different interventions. Through an empirical analysis of six representative public opinion events from 2024, this study verified the effectiveness of the proposed framework and uncovered critical characteristics of opinion dissemination, including explosiveness versus persistence, multi-round dissemination with recurring emotional fluctuations, and the interplay of multiple driving factors. Full article

Prolonged conflicts in Iraq over the past four decades have profoundly disrupted environmental systems, not only through immediate post-conflict emissions—such as residues from munitions and explosives—but also via long-term infrastructural collapse, population displacement, and unsustainable resource practices. Despite growing concern over air quality in conflict-affected regions, comprehensive assessments integrating long-term data and localized measurements remain scarce. This study addresses this gap by analyzing the environmental consequences of sustained instability in Mosul, focusing on air pollution trends using both remote sensing data (1983–2023) and in situ monitoring of key pollutants—including PM_2.5, PM₁₀, TVOCs, NO₂, SO₂, and formaldehyde—at six urban sites during 2022–2023. The results indicate marked seasonal variations, with winter peaks in combustion-related pollutants (NO₂, SO₂) and elevated particulate concentrations in summer driven by sandstorm activity. Annual average concentrations of all six pollutants increased by 14–51%, frequently exceeding WHO air quality guidelines. These patterns coincide with worsening meteorological conditions, including higher temperatures, reduced rainfall, and more frequent storms, suggesting synergistic effects between climate stress and pollution. The findings highlight severe public health risks and emphasize the urgent need for integrated urban recovery strategies that promote sustainable infrastructure, environmental restoration, and resilience to climate change. Full article

Lebanon is grappling with a multifaceted transportation crisis driven by the high population density, limited public transit infrastructure, and overwhelming dependence on private vehicles. These longstanding issues have been exacerbated by compounding national shocks, including the October 2019 economic collapse, the COVID-19 pandemic, and the catastrophic Beirut Port explosion in August 2020. This study investigates the implications of Lebanon’s unreliable public transportation system amid the ongoing economic instability. Using a structured Likert scale survey distributed among residents, this research analyses the key determinants influencing the modal shift from private to public transport. The results identify three dominant factors shaping this transition: the deteriorating economic conditions, the sociocultural attributes of commuters, and the perceived reliability and adequacy of public transport infrastructure. Notably, 15% of respondents cited transport reliability as the main factor influencing their commuting behaviour, while only 3% attributed their decisions solely to financial pressures. However, a majority acknowledged a confluence of both. The sharp escalation in fuel prices, triggered by the financial crisis, has amplified public interest in alternative transportation options. These findings underscore an urgent need to revamp and modernise Lebanon’s public transport system as a strategic response to mitigate congestion, enhance accessibility, and reduce economic burdens on citizens. Addressing the infrastructural gaps and improving service reliability are critical to fostering a sustainable and inclusive mobility ecosystem in the face of persistent national adversity. Full article

Water regeneration in PVC production is a key issue to consider, given the high freshwater consumption rate of the process. This research evaluates the inherent safety of poly(vinyl chloride) (PVC) production via suspension polymerization by implementing mass and energy integration strategies in combination with wastewater regeneration under a zero-liquid-discharge (ZLD) approach. The impact of these integrations on process safety was examined by considering the risks associated with the handling of hazardous materials and critical operations, as well as the reduction in waste generation. To this end, the Inherent Safety Index (ISI) methodology was employed, which quantifies hazards based on factors such as toxicity and flammability, enabling the identification of risks arising from system condition changes due to the implementation of sustainable water treatment technologies. Although the ISI methodology has been applied to various chemical processes, there are few documented cases of its specific application in PVC plants that adopt circular production strategies and water resource sustainability. Therefore, in this study, ISI was used to thoroughly evaluate each stage of the process, providing a comprehensive picture of the safety risks associated with the use of sustainable technologies. The assessment was carried out using simulation software, computer-aided process engineering (CAPE) methodologies, and information obtained from safety repositories and expert publications. Specifically, the Chemical Safety Index score was 22 points, with the highest risk associated with flammability, which scored 4 points, followed by toxicity (5 points), explosiveness (2 points), and chemical interactions, with 4 points attributed to vinyl chloride monomer (VCM). In the toxicity sub-index, both VCM and PVC received 5 points, while substances such as sodium hydroxide (NaOH) and sodium chloride (NaCl) scored 4 points. In the heat of reaction sub-index, the main reaction scored 3 points due to its high heat of reaction (−1600 kJ/kg), while the secondary reactions from PVA biodegradation scored 0 points for the anoxic reaction (−156.5 kJ/kg) and 3 points for the aerobic reaction (−2304 kJ/kg), significantly increasing the total index. The Process Safety Index scored 15 points, with the highest risk found in the inventory of hazardous substances within the inside battery limits (ISBL) of the plant, where a flow rate of 3241.75 t/h was reported (5 points). The safe equipment sub-index received 4 points due to the presence of boilers, burners, compressors, and reactors. The process structure scored 3 points, temperature 2, and pressure 1, reflecting the criticality of certain operating conditions. Despite sustainability improvements, the process still presented significant chemical and operational risks. However, the implementation of control strategies and safety measures could optimize the process, balancing sustainability and safety without compromising system viability. Full article

The management of penetrating rectal trauma has evolved from a historic 4-D algorithm (Divert, Drain, Direct repair, and Distal washout) to a more selective approach. This case report describes a patient with multiple wounds, including a high-grade extraperitoneal rectal injury resulting from a pelvic explosive injury. The patient was successfully treated with intraluminal vacuum therapy (ILVT). This case highlights ILVT as a novel method for managing complicated blast-related rectal injuries. While there are limited publications on combat-related penetrating rectal injuries that provide evidence-based guidelines, we suggest an aggressive surgical approach combined with negative pressure wound therapy for optimal patient outcomes. Full article

Firework-related injuries remain a serious public health issue in Germany, especially during New Year’s Eve. While many injuries are minor, the misuse of illegal or homemade fireworks can cause severe trauma resembling military combat injuries and can heavily burden emergency services. Notably, injury rates declined during the COVID-19 firework bans, underscoring the impact of preventive measures. We report two cases of young males with severe injuries from illicit fireworks. The first is a case of a 16-year-old that detonated an illegal Polish firework ball bomb, sustaining 9% total body surface area (TBSA) burns (second- to third-degree), hand fractures, compartment syndrome of the hand, and soft-tissue trauma. He underwent multiple surgeries, including fasciotomy, osteosynthesis, and skin grafting. The other case presented is a 19-year-old man who was injured by a homemade device made of bundled firecrackers, suffering deep facial and bilateral hand burns. He required prolonged ventilation, surgical debridement, and treatment with Kerecis^® fish skin and Epicite^® dressings. Both required intensive ICU care, interdisciplinary management, and lengthy rehabilitation. Total hospital costs amounted to €58,459.52 and €94,230.23, respectively, as calculated according to the standardized German DRG. These cases illustrate the devastating impact of illegal fireworks. The devastating consequences of explosive trauma are often difficult to treat and may lead to long-term functional and psychological impairments. Prevention through public education, stricter regulations, and preparedness is essential. Pandemic-era injury reductions support sustained policy efforts. Full article

Bimetals—materials composed of two metal components with dissimilar standard reduction–oxidation (redox) potentials—offer unique electronic, optical, and catalytic properties, surpassing monometallic systems. These materials exhibit not only the combined attributes of their constituent metals but also new and novel properties arising from their synergy. Although many reviews have explored the synthesis, properties, and applications of bimetallic systems, none have focused exclusively on iron (Fe)- and aluminum (Al)-based bimetals. This systematic review addresses this gap by providing a comprehensive overview of conventional and emerging techniques for Fe-based and Al-based bimetal synthesis. Specifically, this work systematically reviewed recent studies from 2014 to 2023 using the Scopus, Web of Science (WoS), and Google Scholar databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and was registered under INPLASY with the registration number INPLASY202540026. Articles were excluded if they were inaccessible, non-English, review articles, conference papers, book chapters, or not directly related to the synthesis of Fe- or Al-based bimetals. Additionally, a bibliometric analysis was performed to evaluate the research trends on the synthesis of Fe-based and Al-based bimetals. Based on the 122 articles analyzed, Fe-based and Al-based bimetal synthesis methods were classified into three types: (i) physical, (ii) chemical, and (iii) biological techniques. Physical methods include mechanical alloying, radiolysis, sonochemical methods, the electrical explosion of metal wires, and magnetic field-assisted laser ablation in liquid (MF-LAL). In comparison, chemical protocols covered reduction, dealloying, supported particle methods, thermogravimetric methods, seed-mediated growth, galvanic replacement, and electrochemical synthesis. Meanwhile, biological techniques utilized plant extracts, chitosan, alginate, and cellulose-based materials as reducing agents and stabilizers during bimetal synthesis. Research works on the synthesis of Fe-based and Al-based bimetals initially declined but increased in 2018, followed by a stable trend, with 50% of the total studies conducted in the last five years. China led in the number of publications (62.3%), followed by Russia, Australia, and India, while Saudi Arabia had the highest number of citations per document (95). RSC Advances was the most active journal, publishing eight papers from 2014 to 2023, while Applied Catalysis B: Environmental had the highest number of citations per document at 203. Among the three synthesis methods, chemical techniques dominated, particularly supported particles, galvanic replacement, and chemical reduction, while biological and physical methods have started gaining interest. Iron–copper (Fe/Cu), iron–aluminum (Fe/Al), and iron–nickel (Fe/Ni) were the most commonly synthesized bimetals in the last 10 years. Finally, this work was funded by DOST-PCIEERD and DOST-ERDT. Full article

The detection of explosives is highly important for the investigation of explosion cases and public safety management. However, the detection of trace explosive residues in complex matrices remains a major challenge. Molecularly imprinted polymers (MIPs), which mimic the antigen–antibody recognition mechanism, can selectively recognize and bind target explosive molecules. They offer advantages such as high efficiency, specificity, renewability, and ease of preparation, and they have shown significant potential for the efficient extraction and highly sensitive detection of trace explosive residues in complex matrices. This review comprehensively discusses the applications of MIPs in the analysis of explosives; systematically summarizes the preparation methods; and evaluates their performance in detecting nitroaromatic explosives, nitrate esters, nitroamine explosives, and peroxide explosives. Finally, this review explores the future potential of emerging technologies in enhancing the MIP-based analysis of explosives. The aim is to support the further application of MIPs in the investigation of explosion cases and safety management, providing more effective technical solutions for public safety. Full article

In non-intrusive load monitoring (NILM), single-dimensional features exhibit limited representational capacity, while feature fusion at the feature layer often leads to information loss due to dimensional transformation, as well as the risk of dimensional explosion caused by the newly added features. To address these challenges, this paper proposes a non-intrusive load identification method based on multivariate features and information entropy-weighted ensemble. Specifically, one-dimensional numerical features related to power and current are input into traditional machine learning models, and two-dimensional image features of binary V-I trajectory are processed by the deep neural network model Swin Transformer. Information entropy is employed to adaptively determine the weight of each classification model, and a weighted voting strategy is utilized to combine the decisions of multiple models to obtain the final identification result. This approach achieves feature fusion at the decision layer, effectively avoiding dimensional transformations and fully leveraging the complementary advantages of features from different dimensions. Experimental results show that the proposed method achieves identification accuracies of 99.48% and 99.54% on the public datasets PLAID and WHITED, respectively. Full article

Gas distribution stations (GDSs), pivotal nodes in long-distance natural gas transportation networks, are susceptible to catastrophic fire and explosion accidents stemming from system failures, thereby emphasizing the urgency for robust safety measures. While previous studies have mainly focused on gas transmission pipelines, GDSs have received less attention, and existing risk assessment methodologies for GDSs may have limitations in providing accurate and reliable accident probability predictions and fault diagnoses, especially under data uncertainty. This paper introduces a novel dynamic accident probability assessment framework tailored for GDS under data uncertainty. By integrating Bayesian network (BN) modeling with fuzzy expert judgments, frequentist estimation, and Bayesian updating, the framework offers a comprehensive approach. It encompasses accident modeling, root event (RE) probability estimation, undesired event (UE) predictive analysis, probability adaptation, and accident diagnosis analysis. A case study demonstrates the framework’s reliability and effectiveness, revealing that the occurrence probability of major hazards like vapor cloud explosions and long-duration jet fires diminishes significantly with effective safety barriers. Crucially, the framework acknowledges the dynamic nature of risk by incorporating observed failure incidents or near-misses into the assessment, promptly adjusting risk indicators like UE probabilities and RE criticality. This underscores the importance for decision-makers to maintain a heightened awareness of these dynamics, enabling swift adjustments to maintenance strategies and resource allocation prioritization. By mitigating assessment uncertainty and enhancing precision in maintenance strategies, the framework represents a significant advancement in GDS safety management, ultimately striving to elevate safety and reliability standards, mitigate natural gas distribution risks, and safeguard public safety and the environment. Full article

In recent years, balloon-borne threats carrying hazardous or explosive materials have emerged as a novel form of asymmetric terrorism, posing serious challenges to public safety. In response to this evolving threat, this study presents an AI-driven autonomous drone defense system capable of real-time detection, tracking, and neutralization of airborne hazards. The proposed framework integrates state-of-the-art deep learning models, including YOLO (You Only Look Once) for fast and accurate object detection, and convolutional neural networks (CNNs) for X-ray image analysis, enabling precise identification of hazardous payloads. This multi-stage system ensures safe interception and retrieval while minimizing the risk of secondary damage from debris dispersion. Moreover, a robust data collection and storage architecture supports continuous model improvement, ensuring scalability and adaptability for future counter-terrorism operations. As balloon-based threats represent a new and unconventional security risk, this research offers a practical and deployable solution. Beyond immediate applicability, the system also provides a foundational platform for the development of next-generation autonomous security infrastructures in both civilian and defense contexts. Full article

The rapid growth of information technology has led to an explosion of data, posing a significant challenge for data processing. Recommendation systems aim to address this by providing personalized content recommendations to users from vast datasets. Recently, multimodal recommendation systems have gained considerable attention due to their ability to leverage diverse data modalities (e.g., images and text) for more accurate recommendations. However, effectively fusing these modalities to accurately represent user preferences remains a challenging task, despite progress made by existing multimodal recommendation approaches. To address this challenge, we propose a novel method which we call GC4MRec (Generative-Contrastive for Multimodal Recommendation). On the one hand, we design a bilateral information flow module using two graph convolutional networks (GCNs). This module captures modal features from two distinct perspectives—standard and generatively augmented—to extract latent preferences. On the other hand, we introduce a novel modality fusion module that dynamically represents user multimodal fusion preferences, enabling the construction of accurate user preference profiles. Finally, we evaluate our proposed method, GC4MRec, on three public real-world datasets and demonstrate its effectiveness compared to the state-of-the-art methods. Full article