Search Results (142)

To address the one-class collaborative filtering (OCCF) issue in e-commerce recommendation with only positive implicit feedback, mainstream methods adopt pairwise preference learning represented by Bayesian Personalized Ranking (BPR). However, BPR relies on an invalid assumption and suffers from severe data sparsity. This paper proposes Multi-pairwise Ranking with Heterogeneous Implicit Feedback (MPIF), which exploits heterogeneous implicit and auxiliary information to mine deep user preferences, constructs six pairwise preferences for classified items, and optimizes the model via stochastic gradient descent (SGD). Experiments on three real-world datasets verify that MPIF+ outperforms all state-of-the-art baselines on Normalized Discounted Cumulative Gain at rank 5 (NDCG@5), Precision at rank 5 (Pre@5), Recall at rank 5 (Rec@5), and Area Under Curve (AUC). It yields maximum improvements of 34.2%, 5.5%, and 32.9% on NDCG@5 for the Sobazaar, Retailrocket, and REES46 datasets, respectively, achieving significant and stable recommendation gains. Full article

Jailbreak attacks (JAs) represent a sophisticated subclass of adversarial threats wherein malicious actors craft strategically engineered prompts that subvert the intended operational boundaries of large language models (LLMs). These attacks exploit latent vulnerabilities in generative AI architectures, allowing adversaries to circumvent established safety protocols and illicitly induce the model to output prohibited, unethical, or harmful content. The emergence of such exploits underscores critical gaps in the security and controllability of modern AI systems, raising profound concerns about their societal impact and deployment in sensitive environments. In response, this study introduces an innovative defense framework that synergistically integrates language model perplexity analysis with a Multi-Agent System (MAS)-oriented detection architecture. This hybrid design aims to fortify the resilience of LLMs by proactively identifying and neutralizing jailbreak attempts, thereby ensuring the protection of user privacy and ethical integrity. The experimental setup adopts a query-driven adversarial probing strategy, in which jailbreak prompts are dynamically generated and injected into the open-source LLaMA-2 model to systematically explore potential vulnerabilities. To ensure rigorous validation, the proposed framework will be evaluated using a custom jailbreak detection benchmark encompassing metrics such as Attack Success Rate (ASR), Defense Success Rate (DSR), Defense Pass Rate (DPR), False Positive Rate, Benign Pass Rate (BPR), and End-to-End Latency. Through iterative experimentation and continuous refinement, this work endeavors to advance the defensive capabilities of LLM-based systems, enabling more trustworthy, secure, and ethically aligned deployment of generative AI in real-world environments. Full article

This research explores the effects of milling on fecal sludge (FS) biochar with an emphasis on milling time (5, 10, and 15 min) and ball-to-powder ratio (BPR) (4.533 g/g, 9.067 g/g, and 10.5 g/g). FS biochar was prepared through slow co-pyrolysis of a 50:50 mixture (by weight) of fecal sludge and rice husk powder at 550 °C. The resultant FS biochar with good qualities was subjected to methylene blue (MB) dye adsorption at varying FS biochar weights (0.05 g, 0.1 g, and 0.15 g) and adsorption durations. The BSA peaked at 50 m²/g for a BPR of 10.5 g/g and a milling duration of 10 min. Prolonged milling (15 min) led to structural degradation and reduced BET surface area (BSA). The pore volume peaked at a BPR of 9.067 g/g for shorter milling times and 10.5 g/g for extended milling. The SEM revealed that a milling time of 10 min at a BPR of 9.067 g/g provided the best balance between particle size reduction and uniform morphology, minimizing agglomeration. MB adsorption revealed that FS biochar milled for 10 min and 9.067 g/g BPR demonstrated the best properties. These findings highlight the potential of FS biochar for applications in environmental remediation and agricultural fields, contributing to resource recovery from FS. Full article

A comprehensive study of the structural–phase transformations and hydrogen desorption kinetics in the Mg₅₆Al₄₄ system was conducted using a multistage approach combining thermodynamic modeling CALPHAD, Thermo-Calc 2025a, mechanical synthesis (MS), spark plasma sintering (SPS), and subsequent dispersion treatment. Thermodynamic modeling revealed a stable existence region of the intermetallic compound Mg₁₇Al₁₂, exhibiting Cp-T anomalies at 303 and 351 °C that closely corresponded to the experimental DSC/TGA results. Microstructural analysis showed that varying the ball-to-powder ratio BPR 20:1 and BPR 30:1 determines the defect density, crystallite size 25–45 nm, and lattice strain 1.5–3.0 × 10⁻³, all of which directly influence the hydrogen desorption kinetics. For the samples synthesized at BPR 30:1, the onset temperature of hydrogen release decreased to 180–200 °C while maintaining a hydrogen storage capacity of 4.9 wt.%, accompanied by a reduction in the apparent activation energy of desorption from 92 to 74 kJ·mol⁻¹ according to the Kissinger method. The dispersion stage partially disrupted and redistributed the surface MgO layer, leading to a reduction in its overall contribution and improvement in structural homogeneity, rather than complete oxide removal. The combined MS-SPS-dispersion processing route enabled controlled nanostructure formation, reduced the hydrogen desorption temperature by approximately 100 °C compared to conventional MgH₂-based materials, and significantly enhanced the thermokinetic performance. These findings demonstrate that Mg-Al alloys are promising candidates for solid-state hydrogen storage systems with improved desorption kinetics and reduced activation barriers. Full article

Background: Clozapine is the only antipsychotic medication proven effective in patients with treatment-resistant schizophrenia (TRS). However, many patients have serum concentrations outside the recommended therapeutic window, and clozapine exhibits substantial interindividual variability. This study aimed to (1) examine clozapine dosage and blood concentrations in patients with TRS; (2) investigate the effects of sex and age on dosage and blood concentrations; (3) assess clinical response to clozapine treatment; and (4) develop a random forest (RF) model to predict therapeutic response using clinical and therapeutic drug monitoring (TDM) data. Methods: Dried blood spots were used to measure concentrations of clozapine, norclozapine, and clozapine N-oxide. Clinical symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS). The RF algorithm was applied to analyze the relationships between biochemical and demographic factors and clinical response to clozapine. Results: A total of 754 blood samples from 167 patients were analyzed. Men received higher doses than women, and glucose levels were elevated in both sexes. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.986 for the training set and 0.852 for the testing set. Accuracy, precision, recall, and F1-score (training/testing) were 0.938/0.786, 0.936/0.736, 0.934/0.780, and 0.935/0.757, respectively. The SHapley Additive exPlanations (SHAP) analysis indicated that baseline BPRS score, treatment duration, age, and clozapine concentration were important variables contributing to the output of the model. Conclusions: Our model achieved satisfactory predictive performance for clinical response and provides valuable insights into personalized prediction of clozapine efficacy. Full article

In this work, the results of the structural and phase state of LaNi₅-based alloys modified with Ti, Mn, and Co elements, obtained by mechanical alloying and subsequent spark plasma sintering, are presented. X-ray diffraction analysis was carried out to determine the phase composition, lattice parameters, microstrain, and average crystallite size, as well as to study the morphology and microstructure of the synthesized samples. It was established that the ball-to-powder ratio (BPR) and the milling speed affect the degree of intermetallic phase formation and the level of accumulated microstrains. The optimal mechanical alloying parameters make it possible to form the necessary precursor components for subsequent spark plasma sintering (SPS). It was determined that the SPS process effectively promotes the formation of intermetallic phases such as TiNi, LaNi₄Mn, LaNi₃Mn₂, and LaNi₄Co, ensuring high crystallinity and a reduction in defects accumulated during mechanical alloying. The morphology and microstructure of the samples with titanium, manganese, and cobalt additions showed that at the mechanical alloying stage, all systems are characterized by a dispersed and agglomerated structure, a wide particle size distribution, and a developed surface. After SPS, all series exhibited material consolidation and the formation of a dense matrix with distinct grain boundaries. Full article

Against the backdrop of accelerating global trade and rising consumer expectations, cross-border e-commerce must urgently increase consumers’ willingness to reuse them. This study uses social exchange theory (SET) and resource dependency theory (RDT) to look at how business process reengineering (BPR) in cross-border e-commerce logistics services helps with digitalising the services, resulting in consumers being more likely to keep using the platform. The study also investigates information sharing and supply chain integration (SCI) as variables. This study used a sample of Chinese cross-border e-commerce enterprises and employed confirmatory factor analysis and structural equation modelling (SEM) as analytical methods. The findings indicate a positive relationship between logistics service BPR and logistics service digitisation. Our results also show that SCI positively moderates the relationship between BPR and logistics service digitalization by enhancing cross-organizational collaboration and information flow. We further find that greater information sharing cross-border e-commerce platforms and logistics service providers strengthens SCI’s moderating effect, indicating a secondary moderating role of information sharing. This study proposes an innovative interactive perspective and, drawing on SET, constructs three models to identify the boundary conditions influencing the relationship. It provides a theoretical foundation and practical reference for cross-border e-commerce platforms seeking to optimize digital logistics services and enhance consumers’ willingness to reuse the platform. Full article

Background/Objectives: Bacillus probiotic mixtures have been used as a novel source of antimicrobial production. However, there is a need to emphasize the potential of co-cultured Bacillus strains and to understand the underlying mechanisms involved in their combination formulation. Methods: Bacillus strains BPR-11, BPR-16, and BPR-17, were cultured either as mono or multi-cultures. The bacterial growth and density were evaluated by measuring their optical density. The chemical profiles of their bioactive extracts were analysed by LC-MS, and their antimicrobial activity were tested against selected pathogens. Results: Our results reveal that co-cultured strains significantly increased bacterial growth, with an optical density level of, 2.67 which was significantly higher than the monocultures of BPR-11 (2.24), BPR-16 (2.32), and BPR-17 (2.22). Furthermore, the ethyl acetate extracts from these bacterial cultures showed that the combined co-culture (F1) exhibited the highest antimicrobial activity, with MICs of 25 µg/mL against C. perfringens, E. coli, and S. aureus, and 50 µg/mL against P. aeruginosa and S. enterica. In contrast, the monocultures BPR-16 and BPR-17 showed moderate activity with MICs of 50 µg/mL against C. perfringens, E. coli, and S. aureus, and 100 µg/mL against P. aeruginosa and S. enterica. BPR-11 had the lowest antimicrobial activity, with MICs of 100 µg/mL against C. perfringens, E. coli, and S. aureus, and no activity against P. aeruginosa and S. enterica. Metabolite profiling showed that the extract from the co-culture had a marked increase in the production of antimicrobial metabolites, including C₁₃-C₁₆ surfactin C. Lastly, the metabolism study of surfactin C analogues suggested that they were highly stable (99%) when incubated with cytochromes P450 over 120 min. Conclusions: Together, these findings highlight the potential for multi-strain co-culturing to develop new antimicrobials and provide valuable insights into the synergistic effects for antimicrobial production. Full article

The impact of milling process parameters on the physicochemical properties of waste plum stones was investigated to enable their further utilization as a functional material. The experiments were conducted using a planetary ball mill, with variations in milling duration (1–3 h), the ball-to-powder ratio (bpr) (10:1 and 20:1), and the rotation speed (250 and 500 rpm). Transformations of material in a function of process parameters were assessed by XRD, FTIR, and SEM analysis, revealing differences in particle size distribution, functional group composition, and surface morphology. Optimization of milling process parameters was focused on promoting fine particle formation and surface activation without causing significant material degradation. The best result was achieved with the PS-M10 sample, processed at a speed of 500 rpm and a bpr of 20:1 during a short milling time of 1 h. The milled sample demonstrated promising potential for further applications, particularly for heavy metal ion (Pb²⁺ and Cu²⁺) removal from aqueous solutions through adsorption. Full article

Background and Objectives: Suicide is one of the leading causes of death worldwide. Identifying psychopathological factors and personality traits associated with suicidal ideation is crucial for improving prevention. This study investigates the association between current suicidal ideation—measured by item 4 of the Brief Psychiatric Rating Scale-24 items (BPRS-24)—and personality traits assessed with the Personality Inventory for DSM-5 (PID-5) in a transdiagnostic outpatient psychiatric population. The association of BPRS-24 item 4 with early traumatic experiences, attachment styles, and dissociative phenomena is also explored as a secondary objective. Materials and Methods: We propose a secondary analysis on a sample of 137 individuals receiving care at an academic community mental health center. Personality traits were assessed using the PID-5, while attachment styles were assessed with the Experiences in Close Relationships-Revised (ERC-R), childhood traumas with the Childhood Experience of Care and Abuse Questionnaire (CECA.Q), and dissociative experiences with the Dissociative Experience Scale II (DES-II). Suicidal ideation was measured with item 4 of the BPRS-24. Associations were examined using Spearman’s correlation and ordinal logistic regression models, adjusted for age, sex assigned at birth, and global clinical severity (CGI-S). Results: We found statistically significant associations between suicidal ideation and the PID-5 trait of depressivity (OR = 1.80, 95 % CI 1.23–2.63, p = 0.002, p-value adjusted Holm’s method 0.012). However, this association lost significance after adjusting for depressive symptoms (BPRS-24 item 3), suggesting a mediating role of depression. We found no significant associations with childhood trauma, attachment styles, or dissociative experiences. Conclusions: Our findings suggest a potential link between specific personality traits and suicidal ideation, mediated by depressive symptomatology. We explore how future studies may evaluate PID-5 as a clinical tool to support the identification of individuals at long-term risk of suicidality or for targeting specific populations for tailored interventions. Full article

This paper investigates a repeated stochastic security game against a non-stationary attacker. Most of the work to date assumes that the defender has a repeated interaction with a fixed type of attacker. In fact, the defender is more likely to encounter changing attackers in multi-round games. A defender faces an attacker whose identity is unknown. The attacker type changes stochastically over time and the defender cannot detect when these changes occur. We adopt the BPR (Bayesian Policy Reuse) algorithm to detect the switches of the attacker, and the defender could play the accurate policy correspondingly. The experiment results show that BPR algorithm could accurately detect switches and help the defender gain more utilities than the EXP3-S algorithm. Full article

The Internet of Drones (IoD) is rapidly expanding into sensitive applications, necessitating robust and efficient authentication. Traditional methods struggle against prevalent attacks, especially considering the unique vulnerabilities of the IoD, such as drone physical capture. This paper proposes Bio-2FA-IoD, a novel biometric-enhanced two-factor authentication protocol designed for secure IoD operations. Drawing on established 2FA principles and fuzzy extractor technology, Bio-2FA-IoD achieves strong mutual authentication between an operator (via an operator device), a drone (as a relay), and a ground control station (GCS), supported by a trusted authority. We detail the protocol’s registration and authentication phases, emphasizing reliable biometric key generation. A formal security analysis using BAN logic demonstrates secure belief establishment and key agreement, while a proof sketch under the Bellare–Pointcheval–Rogaway (BPR) model confirms its security against active adversaries in Authenticated Key Exchange (AKE) contexts. Furthermore, a comprehensive performance evaluation conducted using the Contiki OS and Cooja simulator illustrates Bio-2FA-IoD’s superior efficiency in computational and communication costs, alongside very low latency, high packet delivery rate, and minimal energy consumption. This positions it as a highly viable and lightweight solution for resource-constrained IoD environments. Additionally, this paper conceptually explores potential extensions to Bio-2FA-IoD, including the integration of Diffie–Hellman for enhanced perfect forward secrecy and a Sybil-free pseudonym management scheme for improved user anonymity and unlinkability. Full article

The accelerated uptake of unmanned aerial vehicles (UAVs) has significantly altered communication and data exchange landscapes but has also introduced substantial security challenges, especially in open-access UAV communication environments. To address these, Elliptic curve cryptography (ECC) offers robust security with computational efficiency, ideal for resource-constrained Internet of Drones (IoD) systems. This study proposes a Secure and Efficient Three-Way Key Exchange (SETKE) protocol using ECC, specifically tailored for IoD. The SETKE protocol’s security was rigorously analyzed within an extended Bellare–Pointcheval–Rogaway (BPR) model under the random oracle assumption, demonstrating its resilience. Formal verification using the AVISPA tool confirmed the protocol’s safety against man-in-the-middle (MITM) attacks, and formal proofs establish its Authenticated Key Exchange (AKE) security. In terms of performance, SETKE is highly efficient, requiring only 3 ECC scalar multiplications for the Service Requester drone, 4 for the Service Provider drone, and 3 for the Control Server, which is demonstrably lower than several existing schemes. My approach achieves this robust protection with minimal communication overhead (e.g., a maximum payload of 844 bits per session), ensuring its practicality for resource-limited IoD environments. The significance of this work for the IoD field lies in providing a provably secure, lightweight, and computationally efficient key exchange mechanism vital for addressing critical security challenges in IoD systems. Full article

Background/Objectives: Cancer stem cells (CSCs) represent a minor yet critical subpopulation within tumors, endowed with self-renewal and differentiation capacities, and are implicated in tumor initiation, progression, metastasis, therapeutic resistance, and recurrence. Reliable in vitro functional assays to characterize CSCs are pivotal for the development of personalized oncology strategies. This study sought to establish and validate a microfluidic device (MD) platform for the enrichment, functional assessment, and therapeutic evaluation of CSC populations derived from experimental models and primary tumor samples. Methods: Murine (LM38LP) and human (BPR6) breast cancer cell lines were cultured within MDs to promote sphere formation. CSC enrichment was confirmed through the expression analysis of pluripotency-associated genes (Oct4, Sox2, Nanog, and CD44) by quantitative PCR (qPCR) and immunofluorescence. Sphere number, size, and gene expression profiles were quantitatively assessed before (control) and after chemotherapeutic exposure. To validate the MD platform against conventional scale, parallel experiments were performed in 12 well plates. To extend translational relevance, three primary canine tumor samples (solid thyroid carcinoma, simple tubular carcinoma, and reactive lymph node) were mechanically disaggregated and processed within MDs for CSC characterization. Results: The MD platform enabled the consistent enrichment of CSC populations, showing significant modulation of sphere growth parameters and stemness marker expression following chemotherapeutic treatment. Beyond its comparability with conventional culture, the MD also supported immunofluorescence staining and allowed real-time monitoring of individual cell growth. Sphere formation efficiency (SFE) and CSC marker expression were similarly demonstrated in primary veterinary tumor cultures, highlighting the device’s cross-species applicability. Conclusions: Microfluidic-based sphere assays represent a robust, reproducible, and scalable platform for the functional interrogation of CSC dynamics and therapeutic responses. This methodology holds great promise for advancing CSC-targeted therapies and supporting personalized oncology in both human and veterinary settings. Full article

Empirical studies have suggested that travelers’ risk attitudes affect their choice behavior when travel conditions are stochastic. By considering the travelers’ risk attitudes, we extend the classical two-route model, in which road capacities vary due to such shocks as bad weather, accidents, and special events. Two information regimes have been investigated. In the zero-information regime, we postulate that travelers acquire the variability in route travel time based on past experiences and choose the route to minimize the travel time budget. In the full-information regime, travelers have pre-trip information of the road capacities and thus choose the route to minimize the travel time. User equilibrium states of the two regimes have been analyzed, based on the canonical BPR travel time function with power coefficient $p$. In the special case $p=1$, the closed form solutions have been derived. Three cases and eleven subcases have been classified concerning the dependence of expected total travel times on the risk attitude in the zero-information regime. In the general condition $p>0$, although we are not able to derive the closed form solutions, we proved that the results are qualitatively unchanged. We have studied the benefit gains/losses by shifting from the zero-information to the full-information regime. The circumstance under which pre-trip information is beneficial has been identified. A numerical analysis is conducted to further illustrate the theoretical findings. Full article