Browse Articles

Survival analysis of electric vehicle charging behavior and the temporal evolution of feature effects

Abstract This study proposes a survival-based modeling framework that combines behavioral features with interpretable machine learning to understand and predict user churn in electric vehicle charging services. Using a dataset of 1,074 users and 107,531 charging sessions from Central European countries, we modeled time-to-churn while handling censored observations. The best-performing model, a Stacked Weibull survival model based on gradient boosting, achieved a concordance index of 0.826 ± 0.041 and Integrated Brier Score of 0.078 ± 0.008 (5-fold cross-validation), with strong calibration relative to Kaplan-Meier survival estimates. Interpretability analyses identified sustained session frequency, positive engagement trends, and temporal regularity in charging behavior as key predictors of reduced churn risk. These findings highlight the potential of survival modeling integrated with behavioral analytics to predict churn risk and inform retention strategies in electric vehicle charging networks.

Rethinking tube feeding in palliative care: Impact on pneumonia, depression, and mortality in patients with dysphagia and life-limiting illness

Introduction Tube feeding continues to be recommended for nutrition when a patient has dysphagia amid life-limiting illness. Tube feeding is frequently claimed to be a safer alternative to oral feeding. Oral feeding, including careful hand feeding, has emerged as a viable and evidence-based alternative for patients who desire nutrition at the end of life but have trouble swallowing. There is a critical need to determine the best option for a feeding route at the end of life. This novel, pilot, prospective study was conducted to contribute toward that need. This is the first study of its kind. Aims Researchers aimed to investigate the impact of feeding route (oral or tube) on patient outcomes, contributing to scientific knowledge as related to nutritional decision-making when patients are critically or terminally ill, desiring nutrition, and diagnosed with dysphagia. Methods Participants (N = 65) were admitted to a tertiary center, diagnosed with dysphagia and life-limiting illness, and treated by Palliative or Family Medicine. Data were collected as related to demographic (age, sex, race, ethnicity) and clinical statistics (mortality risk/severity of illness, primary diagnoses, patient outcomes [pneumonia, depression, and mortality], and feeding route [oral and tube]). Logistic regression modeling (unadjusted and adjusted by mortality risk and age) and propensity score matching were used to analyze data. Results Results indicated a greater likelihood of negative clinical outcomes as related to tube versus oral feeding. Findings challenge default use of tube feeding as a safer alternative for nutrition. Specifically, results revealed tube feeding to be significantly (p < 0.001) associated with increased risk of pneumonia (adjusted OR = 19.28, p < 0.01) and significantly (p < 0.001) associated with depression (adjusted OR = 17.25, p < 0.01), with mortality also trending higher (adjusted OR = 2.78, p = 0.147). Moreover, a composite outcome analysis (pneumonia, depression, or mortality) revealed impressively greater odds of an adverse event occurring due to tube versus oral feeding (adjusted OR = 55.64, p < 0.01), underscoring the need to consider further research and a possible paradigm shift in nutritional decision-making for palliative care patients. The propensity score analysis yielded balanced, matched groups (n = 30), 15 participants who were oral-fed and 15 who were tube-fed. Results also revealed significantly higher rates of pneumonia (p < 0.001) and depression (p = 0.035) in participants who received tube versus oral feeding. Again, mortality trended higher but significant results were not achieved. Clinical implications These results have substantial clinical implications. Results support reconsidering routine tube feeding in favor of individualized, patient-centered approaches that prioritize quality of life and informed decision-making. This pilot study contributes to ongoing discussions about evidence-based palliative care and has the potential to influence guidelines on feeding practices for terminally-ill patients.

Nutritional imbalances and reduced forage production under intra-seasonal drought are alleviated by irrigation in tropical forage grasses

A study on factors influencing the national carbon emission trading price in China

On 16 July 2021, China officially launched its national carbon emissions trading market, which has since become the largest market in the world in terms of coverage of greenhouse gas emissions and plays an important role in combating global climate change. This study selects the national carbon emissions trading price data from July 16, 2021 to August 31, 2024, which records the price fluctuation characteristics in the early stage of the market (covering only the stage of the electric power industry), which not only provides a historical reference for the subsequent inclusion of the industry, but also provides an important basis for evaluating the effectiveness of the market construction and the optimization of the policy. At the same time, the VEC model is used to study the dynamic relationship between the national carbon emission trading price and key variables, including energy prices, macroeconomic conditions, the development of the power industry, international carbon prices, carbon emissions from the power industry, and the trading volume of national carbon emission quotas. The results show that there is a long-term equilibrium relationship between the national carbon trading price and the variables, which provides a scientific basis for setting a reasonable fluctuation range of the carbon price. Furthermore, impulse response and variance decomposition analyses were conducted to evaluate the short-term dynamic effects of each variable on the national carbon emission trading price. The results reveal that the trading volume of national carbon emission quotas and the development of the power industry exert significant influence on the national carbon emission trading price. In addition, the carbon emissions of the electric power industry have a positive impact on the national carbon emissions trading price in the short-term situation, and in the long-term situation, they have a negative impact. This provides a parameter calibration basis for the expansion of the industry and a reference for policy making, which is important for the construction of the national carbon emission trading market with international influence. The marginal contributions of this study are:(1) forming a new combination of variables for a comprehensive analysis of the national carbon emissions trading market; (2) the empirical results uncover new insights into the dynamics of the national carbon price; and (3) providing empirical evidence for improving the institutional system of the national carbon emissions trading market.

Current status of knowledge, attitudes, and practices among ICU physicians and nurses regarding palliative care in China

Stochastic evolution model for international migration

We present a new international migration model that combines stochastic sampling techniques with dynamic accounting of flows by means of evolution equations. Migration flows are sampled from paramaterized probability distributions based on reported migration flow data that is partitioned by socio-economic covariates. This method allows for non-trivial time evolution that goes beyond extrapolation, while requiring minimal prior knowledge about the elusive processes driving migration flows. It thus combines the advantages of different existing modeling approaches. In hindcasts our model compares well with bilateral migrant stock data in many world regions and country income groups. Moreover, we observe a significant difference between the full model and its deterministic formulation, which highlights the non-Gaussian and interdependent nature of migration flow distributions and corroborates the use of a stochastic dynamic approach. Our model can be flexibly extended with additional information, e.g. regional migration policies, which are expected to further improve the agreement with data.

Population-based variance-reduced evolution over stochastic landscapes

Abstract Black-box stochastic optimization involves sampling in both the solution and data spaces. Traditional variance reduction methods mainly designed for reducing the data sampling noise may suffer from slow convergence if the noise in the solution space is poorly handled. In this paper, we present a novel zeroth-order optimization method, termed Population-based Variance-Reduced Evolution (PVRE), which simultaneously mitigates noise in both the solution and data spaces. PVRE uses a normalized-momentum mechanism to guide the search and reduce the noise due to data sampling. A population-based gradient estimation scheme, a well-established evolutionary optimization technique, is incorporated to further reduce noise in the solution space. We show that PVRE exhibits the convergence properties of theory-backed optimization algorithms and the adaptability of evolutionary algorithms. In particular, PVRE achieves the best-known function evaluation complexity of $$\mathscr {O}(n\epsilon ^{-3})$$ for finding an $$\epsilon$$ -accurate first-order optimal solution, up to a logarithmic factor, with any initial step-size. We assess the performance of PVRE through numerical experiments on benchmark problems as well as a real-world task involving adversarial attacks against neural image classifiers.

Does China’s corporate two-way foreign direct investment mitigate environmental pollution?

The global push for low-carbon growth highlights the urgent need to examine how corporate internationalization shapes environmental performance. However, the environmental implications of corporate two-way foreign direct investment (CTFDI, integrating inward and outward FDI) remain insufficiently explored. This study investigates whether CTFDI mitigates corporate pollution emissions in line with the pollution halo hypothesis. This study employs a multiple fixed-effects OLS model using 43,410 firm-year observations from 2,894 A-share listed Chinese firms over 2008–2022. The results indicate that CTFDI significantly reduces emissions, with a one-unit increase associated with an average 0.15% decline. Mechanism analysis demonstrates that research and development investment and the adoption of digital and intelligent technologies are primary channels through which CTFDI exerts this effect. Heterogeneity analysis further reveals that non-state-owned enterprises and firms in non-polluting industries experience more pronounced benefits. Overall, the findings provide robust empirical evidence supporting the pollution halo hypothesis from the perspective of two-way FDI and highlight the role of economic openness in advancing green corporate development.

Cooperation between competitive electric vehicle manufacturers: a strategic analysis of charging pile construction

Mitochondrial genomics and phylogeny of noctuoid moths: Implications for Macroheterocera

The majority of the Lepidoptera species belongs to the Macroheterocera clade. The macroheteroceran superfamilies’ phylogenetic relationships are still unstable. The construction of a robust phylogenetic tree and comprehensive analysis can be facilitated by an increased availability of mitochondrial genome data. In this study, the mitochondrial genomes of five species such as Episparis tortuosalis, Pandesma quenavadi, Erebus macrops, Polydesma boarmoides and Xanthodes albago from two families in the superfamily Noctuoidea were sequenced, assembled, and annotated. The mitochondrial genomes have characteristic circular double-stranded structures observed in other lepidopteran moths, including 13 protein-coding genes, 22 transfer RNAs, two ribosomal RNAs, and the control region. All PCGs typically start with ATN codons, but nad4 and nad4l in E. tortuosalis are not starting with standard initiation codons. Phylogenetic analysis was performed using Bayesian Inference (BI) and Maximum Likelihood (ML) through PhyloSuite v1.2.3 based on amino acid sequences of 13 mitochondrial PCGs. The tree indicates close ancestry of E. tortuosalis with Noctuidae insects rather than with Erebidae. Major superfamilies in Macroheterocera and their phylogenetic relationships were as follows: ((Geometroidea)+ ((Lasiocampoidea+ Bombycoidea)+ (Drepanoidea)+ (Noctuoidea))))); this showed a novel relationship compared to previous analyses. This analysis significantly enhanced the Noctuoidea mitogenome database and reinforced the high-level phylogenetic relationships of macroheterocera clade.

The role of Faraday effect on the one-dimensional n-doped Si photonic crystals towards magnetic field sensing applications

Estimated glucose disposal rate predicts frailty through diabetes: Evidence from machine learning and mediation models in NHANES

Objective As an emerging insulin resistance marker, the relationship between estimated glucose disposal rate (eGDR) and frailty needs further exploration. This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention. Methods Using National Health and Nutrition Examination Survey (NHANES) 2005–2010 data, we analyzed glucose disposal and frailty associations. Feature selection used LASSO, and class imbalance was handled by SMOTEN. The resampled data were split 7:3 into a training set (n = 29,309) and a test set (n = 12,561).Ten machine learning models were built, with discrimination, calibration, and clinical utility evaluated to identify the optimal model. Confusion matrices visualized performance. Mediation analysis assessed DM’s role in the eGDR-frailty relationship. Results Among 26,282 participants, eGDR negatively correlated with frailty. Higher eGDR significantly reduced frailty risk in subgroups: women, age ≤ 60, normal/high BMI, never/current smokers, and alcohol users. LASSO selected 12 predictors. Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. Confusion matrices validated classification accuracy. Mediation analysis showed DM partially mediated the eGDR-frailty relationship: indirect effect β=−0.003 (95% CI −0.003 to −0.002; P < 0.001), mediation proportion = 8.71%. Conclusion This first NHANES-based study demonstrates a significant negative correlation between eGDR and frailty, confirming DM’s partial mediating role. The developed machine learning models effectively support early frailty risk assessment and intervention.

Distinct trajectories of perceived control over aversive stimulation predict affective reactions to stressors over and above objective control

Abstract Psychological theories and evidence from animal and human studies highlight the importance of stressor controllability for affective stress reactions. In addition to objective control, i.e. action-outcome contingencies, higher subjective perceptions of control and trait-like control beliefs such as self-efficacy have been linked to more resilient stress outcomes. Hence, facets of perceived control may compensate for an objective lack of control. In a randomized, controlled behavioral study in healthy young adults, we studied the effect of experimentally manipulated objective control over aversive stimulation and perceived control as rated by the participants, on affective responses and tested whether a self-efficacy manipulation would buffer against the negative effects of uncontrollable stress. 168 participants were assigned to groups experiencing no (NO-STRESS), controllable (CON), uncontrollable (UNCON), or uncontrollable aversive stimulation preceded by an autobiographical self-efficacy manipulation (UNCON-HSE) while concurrently rating perceived control. The CON group reported lower helplessness and, for women, lower negative affect than the UNCON group. The self-efficacy manipulation had no effect on self-efficacy or affect. Latent class growth analysis of perceived control trajectories revealed three classes with low, rising, and medium perceived control. Compared to the rising and medium classes, the low class reported more helplessness and depression and, for women, a greater increase in negative affect. Perceived control may thus be a valuable target for resilience interventions and depression therapy.

Risk factors influencing Coxiella burnetii seropositivity in water Buffalo (Bubalus bubalis) populations of Egypt’s Nile Delta

This study investigated the seroprevalence of Coxiella burnetii in buffaloes across several governorates in Egypt’s Nile Delta. No significant variation was observed between regions (P > 0.05), although the highest prevalence was recorded in Kafr El-Sheikh (15.5%) and the lowest in Menofia (9.1%). While sex was not statistically significant, females showed a higher seroprevalence (12.4%) than males (9%). Age had a significant impact, with buffaloes over 8 years of age showed a higher prevalence (21.6%) compared to the younger age groups. Tick infestation was also significantly associated with infection, with a prevalence of 22.4% in infested animals. Buffaloes exposed to communal grazing (13.8%) or kept in contact with small ruminants (16.3%) showed increased seropositivity. Notably, animals with a history of abortion had a markedly higher prevalence (26.7%). Multivariate logistic regression analysis identified age above 8 years (OR = 6.7), tick infestation (OR = 3.0), contact with small ruminants (OR = 3.0), and abortion history (OR = 3.2) as significant risk factors. Communal grazing (OR = 1.9) and age between 5 and 8 years (OR = 2.2) were also associated with increased odds of seropositivity. These findings highlight key epidemiological factors contributing to C. burnetii infection risk in buffaloes.

Retraction Note: Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm

Histopathological change of age-related hearing loss in female advance-aged CBA/CaJ mice

With the increase in the older population, the number of individuals with age-related hearing loss is also growing explosively. Therefore, there is an urgent need to identify the detailed pathology of age-related hearing loss and develop novel treatment strategies. In this study, we have investigated the audiological physiology and cochlear pathology of advanced-age CBA/CaJ mice, a strain that resists early pathological hearing loss. The subjects were naturally aged close to their lifespan limit (> two years) under normal in vivo conditions. We used 11 CBA/CaJ mice aged between 129 and 138 weeks to establish an aged group. To compare the electrophysiological function and histological changes, a young group was established using 12 young mice aged between 9 and 14 weeks. The loss of outer hair cells peaked at 11.3 kHz, and the greatest synapse loss was observed in the 5.6 kHz region, which was covered by the dominant frequency in the ambient sound. Furthermore, atrophy and microthrombus formation occurred in the stria vascularis, with endolymphatic hydrops observed in the cochlear apical turn. In the spiral ganglion and cochlear nerve, a reduction in the number of cells was accompanied by morphological changes indicative of cell aging. Increased levels of derivative-reactive oxygen metabolites, an oxidative stress marker, were observed in aged mice. These results indicate that age-related hearing loss involves a combined pathology of acoustic cochlear damage, which is potentially associated with chronic sound exposure and metabolic changes owing to mitochondrial dysfunction and oxidative stress accumulation. Accordingly, these two distinct etiologies must be addressed to prevent and treat age-related hearing loss.

Synthesis of a cross-linked polymer using a diallylammonium monomer containing 12-crown-4 motifs for the selective extraction of lithium ions

Effects of virtual reality-based disaster simulation education on nursing students

Background This study aimed to confirm the effectiveness of disaster simulation education that uses virtual reality (VR). Method This quasi-experimental study was conducted using a non-equivalent control group pretest-posttest design. Participants in this study were third- and fourth-year nursing students at two universities. A total of 67 nursing students were included in the analysis, with 33 in the experimental group and 34 in the control group. The experimental group underwent VR disaster simulation education based on cognitive continuum theory. The control group underwent written case simulation. Results Disaster triage accuracy (t = 6.11, p < .001), disaster triage confidence (t = 2.69, p = .009), learning immersion (t = 6.88, p < .001), and cognitive flexibility (t = 5.47, p < .001) significantly increased in the experimental group. Conclusion The possibility of using the VR simulation program based on cognitive continuum theory developed through this study should be further explored. An effective educational strategy is presented to strengthen prospective health care professionals’ disaster capabilities through VR disaster simulation education training.

Exploring the relationship between muscle activity, jaw behaviour and pain

Abstract While muscle overload is commonly implicated in musculoskeletal pain conditions, real-time assessment of associated behavioural and physiological features is challenging. This study aims to investigate the relationship between self-reported awake bruxism using Ecological Momentary Assessment (EMA) and jaw muscle activity registered by surface electromyography (sEMG), and differences between individuals with and without temporomandibular disorder (TMD) pain. Seventy participants (38 women, 32 men), of which 31% reported pain, completed 3-day EMA using a smartphone application combined with a sEMG device only for day 1. Overload, defined as muscle activity exceeding 20% of maximum voluntary contraction (MVC), was evaluated for duration and area under curve (AUC). A strong correlation was observed between EMA-reported bruxism and sEMG overload duration (ρ = 0.62, p < 0.001). AUC showed a correlation with EMA only in the TMD group. Participants with TMD pain exhibited shorter high-intensity bursts (60–79% MVC, p ≤ 0.005) but prolonged low-intensity muscle activity (20–39% MVC, p < 0.001). Bruxism behaviour and stress levels were higher in women and in individuals with pain. The results suggest that combining EMA and sEMG provides valid assessment of musculoskeletal overload, capturing both perceptual and physiological dimensions. Incorporating EMA in pain management can identify pain-related risk behaviours, thus supporting tailored patient-centred interventions.

Optimal bus bridging service for urban rail transit disruptions with stochastic passenger demand

Disruptions in urban rail transit (URT) systems can significantly impact operational efficiency, while well-designed bus bridging service (BBS) can effectively mitigate such effects. To address the surge in travel demand caused by disruptions, this study comprehensively considers alternative transportation modes that affected passengers may adopt (including taxis, shared bicycles, bridging buses, and walking), aiming to minimize both the operational costs of bridging buses and the total travel time of passengers. A travel choice model based on the random regret minimization (RRM) theory is developed to characterize passengers’ decision-making behavior following station disruptions. Demand uncertainty is represented using trapezoidal fuzzy variables, and a distributionally robust credibility optimization model is established. An innovative reinforcement learning-based parallel genetic algorithm (RPGA) is proposed for solving the model. A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. This method effectively enhances the system’s ability to cope with demand fluctuations and provides decision-making support for emergency scheduling in urban rail transit.