Multiple Routes Research Articles

The growing volume of healthcare data presents opportunities for machine learning to improve treatment, uncover new patterns in data and predict patient outcomes. Selecting appropriate features for a machine learning model is an important step in the process as the choice of relevant variables directly influences the model's performance and interpretability. Effective feature selection can enhance both the accuracy and generalisability of the model, especially given the complexity and heterogeneity of healthcare data. The XGBoost algorithm is trained on the eICU Collaborative Research Database to predict in-hospital mortality, with focus on investigating the impact of different feature sets. The analysis cohort comprised 73210 patients. Different models are trained and tested using 20000 distinct feature sets, each containing ten features, to assess how different features influence model performance. The models are trained using a train/test split of 80/20. Shapley additive explanations (SHAP) values are used to evaluate the importance of individual features. On average, the feature sets achieve an area under the receiver operating characteristic curve (AUROC) of 0.811, with the highest AUROC of 0.832 obtained from the feature set comprising [admission diagnosis, age, albumin, creatinine, heart rate, mean blood pressure, motor (from Glasgow Coma Scale), respiratory rate, temperature, unit admit source]. Despite variations in feature composition, models exhibit comparable performance in terms of both AUROC and the area under the precision-recall curve (AUPRC). Overall, age emerges as particularly influential, appearing most frequently in the feature sets associated with the highest AUROC scores. However, this trend is not observed for AUPRC.The results show that different models can achieve similar discrimination for different feature sets and that feature importance and ranking vary accordingly. This suggests that there may be multiple routes to good performance and that evaluating several feature combinations could be more informative than focusing on a single best set. Average feature importances may not reliably indicate a variable's overall utility or real-world importance and should be interpreted within the context of specific combinations. Prospective evaluation of promising sets and attention to robustness across combinations may help guide validation and eventual clinical use.

With the increasing scarcity of critical resources, competition in the electric vehicle battery (EVB) recycling market has intensified, and the strategic establishment of efficient and resilient recycling networks is increasingly vital for maintaining raw material security. Although existing studies have explored electric vehicle battery recycling network design (EVBRND), the impact of facility disruption risks on network decisions is rarely analyzed. This study explores a novel resilient EVBRND problem under disruption risk from the perspective of fourth-party logistics. To cope with disruptions, capacity backups, multi-source allocation, multiple third-party logistics (3PL), multiple transportation routes and facility fortification strategies are systematically integrated. A two-stage stochastic programming model is developed to characterize the problem, which is subsequently reformulated into a mixed-integer linear programming model using a scenario-based approach. To overcome the computational complexity resulting from the enlarged scenario set and the additional binary variables introduced by 3PL selection, a scenario reduction and decomposition-based heuristic (SRDBH) algorithm is developed, which integrates Lagrangian relaxation, conditional relaxation, scenario reduction, and the adaptive subgradient method. The proposed model and algorithm are validated through a real-world case study. Computational results confirm that the SRDBH algorithm achieves superior performance compared with CPLEX. Furthermore, sensitivity analyses highlight the critical role of flexible risk-mitigation configurations in balancing cost minimization with the enhancement of network resilience.

Purpose In archipelagic countries, sea transport plays a vital role because islands are not always reachable by air because of the absence of airports. This study aimed to explore the use of pioneer shipping routes for delivering vaccines to these remote locations. Pioneer shipping is a service implemented by the government using “pioneer ships” to improve connectivity. This study explored the utilisation of pioneer shipping for vaccine distribution. Despite its potential, vaccine distribution using the pioneer shipping route is challenged by risks related to sea transport, such as delays, weather, sea conditions and handling errors, which have not been addressed in existing studies. Design/methodology/approach We introduce a new reinforcement learning-based model that optimises delivery while considering vaccine perishability and wave height. The model was tested in actual shipping routes by utilising historical wave-height data. Findings The numerical results indicate that without any optimisation effort, the route can cause 50% damage to the vaccine. Increasing the number of routes can help prevent damage during the voyage owing to the reduced shipping time. By drawing insights from numerical tests, managerial and policy implications can be derived, including the installation of vaccine refrigeration equipment in the vessel. If the equipment is unavailable, it is preferable to use direct shipping from port to port, as our analysis showed that wave variations significantly increase the average travel time and hinder the shipping of the vaccine. Originality/value This paper contributes to the literature by addressing the underexplored challenge of vaccine distribution in archipelagic and remote regions. This paper specifically optimises the utilisation of pioneering shipping routes by proposing a novel reinforcement learning-based vessel routing framework that optimises delivery considering goods’ perishability and wave-induced variations. The modelling framework leverages clustering and attention mechanisms to prioritise the delivery of vaccines across multiple shipping routes, enhancing both efficiency and vaccine preservation.

Persistent Environmental Toxicants PCP, TCHQ, and HCB Drive PANoptosis and RNA Granule Remodeling in Human Lung Epithelial Cells.

Dispersal dynamics of an HIV and AIDS model with multiple infection routes and patches

Monohaloacetamide disinfection by-products are potential risk factors for the quality of mouse oocytes.

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure.

Anti-Toxoplasma gondii studies of the venom peptide XYP4 from the Lycosa coelestis.

Muscone inhibits ferroptosis for neuroprotection in a Parkinson's disease model.

Pressodonta viridis (Bivalvia: Unionida) is a species of freshwater mussel considered rare and imperiled in 14 U.S. states and Ontario, Canada. Limited population-level genetic information is available for P. viridis despite the species' imperilment status. We used 13,661 single nucleotide polymorphisms (SNPs) derived from double digest restriction site-associated sequencing (ddRAD-seq) to examine patterns in population genetic diversity, structure, and differentiation among P. viridis populations in the Upper Mississippi River, Ohio and Wabash rivers, and Great Lakes watersheds. Structure analyses revealed significant differentiation related to major drainage among groups of P. viridis populations. Evidence of founder effects in Great Lakes populations by colonization routes from Upper Mississippi River and Ohio/Wabash River systems was found in admixture analyses. Limited evidence of isolation-by-distance effects was found, suggesting that multiple colonization routes and glacial refugia are responsible for current P. viridis genetic structure. A combination of ancient river hydrological patterns and more recent headwater stream capture events following the last glacial retreat likely correspond to considerable admixture found among populations of P. viridis in the Upper Mississippi, Wabash, and Ohio Rivers. Population genetic variation, structure, and differentiation described in this study should be used to inform conservation efforts of P. viridis in the future, particularly if recovery actions include population augmentation from translocations or hatchery propagation.

With the widespread use of mobile communication devices, Location-Based Services (LBS) provides diversified services to users. However, attackers can potentially infer users’ private information by analyzing location trajectories during the use of LBS. To address this issue, this paper proposes a personalized location privacy protection method to counter background knowledge attacks. The proposed method first evaluates the sensitivity of location points using user-defined sensitivity levels and historical query data, and then selects an optimal route, minimizing both privacy leakage and travel costs from multiple routes recommended by the navigation system. Based on the total sensitivity and travel distance, a personalized privacy budget is assigned to the user. To protect location privacy, Laplace noise, adhering to the differential privacy mechanism, is added to the requested location points. Security analysis and simulation results demonstrate that the proposed method effectively provides personalized location privacy protection while maintaining the quality of service.

Purpose This study aims to address critical theoretical fragmentation in knowledge management by examining how cultural industries orchestrate knowledge ecosystems during digital transformation, introducing the Knowledge Ecosystem Integration Model and conceptualising “knowledge ecosystem resonance” as a novel theoretical construct explaining value amplification through harmonised knowledge flows across organisational boundaries. Design/methodology/approach Using methodological triangulation, this study combines partial least squares structural equation modelling and fuzzy-set qualitative comparative analysis with data from 308 managers and creative professionals across Vietnam’s film, music and digital content sectors, revealing both linear relationships and configurational pathways to digital creative value. Findings Anticipatory governance emerges as a critical temporal knowledge orchestration mechanism, demonstrating substantial effects on creative behaviour and digital culture. Digital culture is crucial to transforming creative knowledge into value. Five distinct configurational pathways demonstrate equifinality, challenging linear approaches and revealing multiple routes to achieving knowledge ecosystem resonance. Originality/value This research advances knowledge management theory through: theoretical integration of knowledge creation theory, dynamic capabilities and knowledge governance frameworks addressing temporal dimensions; conceptualisation of anticipatory governance as future-oriented knowledge orchestration; empirical demonstration of knowledge ecosystem resonance as measurable phenomenon; and identification of configurational pathways revealing equifinality in ecosystem development.

In organophosphorus chemistry, several established reactions, such as the conversion of phosphorus trichloride into tertiary phosphines, followed by oxidation and quaternization to form phosphine oxides and phosphonium salts, are widely recognized and routinely applied. In contrast, other potentially valuable transformations, including reverse or complementary versions of these standard synthetic routes, remain largely unexplored or technically challenging. This work introduces two new reaction pathways that broaden the scope of organophosphorus synthesis. The first involves a P-C bond-forming process that enables interconversion of symmetrical phosphine oxides, such as triphenylphosphine oxide (Ph3PO), into P-stereogenic phosphine oxides and quaternary phosphonium salts. The second transformation is based on the distinctive reactivity of methoxymethyl (MOM)-substituted quaternary phosphonium salts. These compounds undergo a P-C bond cleavage reaction that results in de-quaternization, allowing the synthesis of mixed-substituent tertiary phosphines from triphenylphosphine as a common precursor. Together, these two processes provide multiple efficient synthetic routes to phosphines, phosphine oxides, and quaternary phosphonium salts. The overall synthetic approach is flexible, so that the target compounds can be obtained through several pathways using different substituent combinations as starting materials.

Hydrogen-powered hybrid trains equipped with fuel cells (FC) and batteries represent a promising alternative to diesel traction on non-electrified railway lines and have significant potential to support modal shifts toward more sustainable transport systems. This study presents the development of a flexible MATLAB-based tool for the dynamic simulation of fuel cell–battery hybrid powertrains. The model integrates train dynamics, rule-based energy management, system efficiencies, and component degradation, enabling both energy and cost analyses over the vehicle’s lifetime. The objective is to assess the techno-economic performance of different powertrain configurations. Sensitivity analyses were carried out by varying two sizing parameters: the nominal power of the fuel cell (parameter m) and the total battery capacity (parameter n), across multiple real-world railway routes. Results show a slight reduction in lifecycle costs as m increases (5.1 €/km for m = 0.50) mainly due to a lower FC degradation. Conversely, increasing battery capacity (n) lowers costs by reducing cycling stress for both battery and FC, from 5.3 €/km (n = 0.10) to 4.5 €/km (n = 0.20). In general, lowest values of m and n provide unviable solutions as the battery discharges completely before the end of the journey. The study highlights the critical impact of the operational profile: for a fixed powertrain configuration (m = 0.45, n = 0.20), the specific cost dramatically increases from 4.44 €/km on a long, flat route to 15.8 €/km on a hilly line and up to 76.7 €/km on a mountainous route, primarily due to severe fuel cell degradation under transient loads. These findings demonstrate that an “all-purpose” train sizing approach is inadequate, confirming the necessity of route-specific powertrain optimization to balance techno-economic performance.

Tyre additives such as p-phenylenediamines (PPDs) and benzothiazoles (BTs) are ubiquitous in the environment. They have been frequently detected in urban air and have been detected in the human body. However, few studies have examined the toxicological effects in human cells. In this study we perform cytotoxicity, oxidative stress and DNA damage assays on A549 human alveolar lung cells with N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD), N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine-quinone (6PPD-Q), diphenyl-p-phenylenediamine (DPPD), 1,3-benzothiazole (BTZ) and 2-mercaptobenzothiazole (MBT). It was found that all additives were able to cause glutathione (GSH) depletion and induce DNA strand breaks after 24 h exposure in a concentration-dependent manner. The presence of N-acetyl-L-cysteine (NAC), a GSH precursor, mitigated both GSH depletion and DNA damage from 6PPD. Although the tested concentrations of tyre additives exceeded typical levels reported for ambient air, these additives have been detected in wastewater, road runoff and road dust. Therefore, human exposure can occur through multiple routes, including inhalation, ingestion, and dermal absorption, ultimately reaching alveolar cells either directly via the lungs or indirectly through the bloodstream.

Post-translational modifications play important rolesin the regulationof protein function, with Nε acetylation being akey reversible modification affecting processes such as transcription,metabolism, and stress responses. Sirtuins, particularly SIRT1 andits ortholog, Sir2, are NAD+-dependent deacetylases that target bothhistone and nonhistone proteins, including the tumor suppressor p53.Acetylation of p53 on K382 influences its degradation and transcriptionalactivity. Despite structural studies of the Sir2/acetylated p53 complex,the role of the conserved cofactor-binding loop (CBL) in regulatingNAD+ binding and deacetylation remains unclear. Using both conventionalmolecular dynamics (MD) and parallel cascade selection MD (PaCS-MD)simulations, we investigated the conformational dynamics of the Sir2/acetylatedand nonacetylated p53 complexes, focusing on the conformational changesin the CBL of Sir2 in response to p53 acetylation. We identified openand closed states of the NAD+ binding pocket caused by CBL conformationalchanges depending on p53 acetylation and deacetylation. The forwardallosteric effect of the acetylated p53 binding was found to openthe NAD+-binding pocket, which is expected to promote NAD+ binding.In contrast, the binding of nonacetylated p53 is significantly weaker,and the reverse allosteric effect drives the pocket closure. Thesesequential allosteric effects positively accelerate the reaction cycle,which can be considered a “tandem allostery of the reactant(acetylated p53) and the product (deacetylated p53)”. Combiningthese simulations with entropy transfer analysis, K382 was found toinitiate multiple communication routes through strands β7 andβ9, and the FEG loop, ultimately converging on the CBL via thehelical small domain, the Rossmann-fold domain, or directly from p53,thereby highlighting the critical role of CBL in NAD+ binding andp53 deacetylation.

The article considers the history of creation of the park “Yermakovo Field”, its volumetric and spatial plots, layout, and symbolic filling. Architectural objects, engineering structures (pedestrian bridges) and park sculptures are presented as elements of a single system. The author emphasizes the importance of the principle of multiple walking routes as a basis for the planning structure of the park. Sculptural memorial objects are presented as independent and dominant central accents in architectural structures. The thematic cluster of Yermak-Tobolsk-Siberia-Russia became the main for the park “Yermakovo Field”.

Micro-nanoplastics inhibit extracellular polymeric substance and lactate synthesis via perturbing glucose metabolism of Lacticaseibacillus rhamnosus.

Urinary levels of selected metals and associated individual-level characteristics in Tuscany, Italy: The EPIMETAL study.

Clinical application of customized and non-customized bacteriophage therapy in patients with refractory/resistant bacterial infections: A systematic review and meta-analysis.