Graph polynomials is one of the important research directions in mathematical chemistry. The coefficients of some graph polynomials, such as matching polynomial and permanental polynomial, are related to structural properties of graphs. The Hosoya index of a graph is the sum of the absolute value of all coefficients for the matching polynomial. And the permanental sum of a graph is the sum of the absolute value of all coefficients of the permanental polynomial. In this paper, we characterize the second to sixth minimal Hosoya indices of all bicyclic graphs. Furthermore, using the results, the second to sixth minimal permanental sums of all bicyclic graphs are also characterized. Full article

Background and Objectives: The concurrent occurrence of tuberculosis and COVID-19 coinfection poses significant clinical complexities, warranting a nuanced approach to diagnosis, management, and patient care. Materials and Methods: A retrospective, cross-sectional study was conducted on two groups: one comprising 32 patients with pulmonary TB (PTB) and COVID-19 co-infection, and one including 100 patients with COVID-19 alone. Data was collected from medical records, including patient history, clinical parameters, laboratory, imaging results, and patient outcome. Results: A lower BMI emerges as a significant marker suggesting underlying PTB in patients with SARS-CoV-2 co-infection. Type 2 diabetes mellitus increases the risk of death in PTB-SARS-CoV-2 co-infection. Co-infected patients show lymphocytopenia and higher neutrophil levels, CRP, transaminases, and D-dimer levels. Elevated CRP and ALT levels are linked to increased co-infection likelihood. Certain parameters like SpO2, CRP, ALT, AST, and D-dimer effectively differentiate between co-infected and COVID-19 patients. Platelet-to-lymphocyte ratio is notably higher in co-infected individuals. Lesion severity on imaging is significantly associated with co-infection, highlighting imaging’s diagnostic importance. Longer hospital stays are linked to co-infection but not significantly to death risk. Conclusions: Certain clinical and biological factors may serve as potential indicators of PTB co-infection in patients with SARS-CoV-2. Full article

Background: Atherosclerosis is a multi-factorial disease, and low-density lipoprotein cholesterol (LDL-C) is a critical risk factor in developing atherosclerotic cardiovascular disease (ASCVD). Cholesteryl-ester transfer-protein (CETP), synthesized by the liver, regulates LDL-C and high-density lipoprotein cholesterol (HDL-C) through the bidirectional transfer of lipids. The novelty of CETP inhibitors (CETPis) has granted new focus towards increasing HDL-C, besides lowering LDL-C strategies. To date, five CETPis that are projected to improve lipid profiles, torcetrapib, dalcetrapib, evacetrapib, anacetrapib, and obicetrapib, have reached late-stage clinical development for ASCVD risk reduction. Early trials failed to reduce atherosclerotic cardiovascular occurrences. Given the advent of some recent large-scale clinical trials (ACCELERATE, HPS3/TIMI55-REVEAL Collaborative Group), conducting a meta-analysis is essential to investigate CETPis’ efficacy. Methods: We conducted a thorough search of randomized controlled trials (RCTs) that commenced between 2003 and 2023; CETPi versus placebo studies with a ≥6-month follow-up and defined outcomes were eligible. Primary outcomes: major adverse cardiovascular events (MACEs), cardiovascular disease (CVD)-related mortality, all-cause mortality. Secondary outcomes: stroke, revascularization, hospitalization due to acute coronary syndrome, myocardial infarction (MI). Results: Nine RCTs revealed that the use of a CETPi significantly reduced CVD-related mortality (RR = 0.89; 95% CI: 0.81–0.98; p = 0.02; I2 = 0%); the same studies also reduced the risk of MI (RR = 0.92; 95% CI: 0.86–0.98; p = 0.01; I2 = 0%), which was primarily attributed to anacetrapib. The use of a CETPi did not reduce the likelihood any other outcomes. Conclusions: Our meta-analysis shows, for the first time, that CETPis are associated with reduced CVD-related mortality and MI. Full article

We examine the optimal mass transport problem in ${\mathbb{R}}^n$ between densities with transitioning compact support by considering the geometry of a continuous interpolating support boundary $\mathsf{\Gamma }$ in space-time within which the mass density evolves according to the fluid dynamical framework of Benamou and Brenier. We treat the geometry of this space-time embedding in terms of points, vectors, and sets in ${\mathbb{R}}^{n+1}=\mathbb{R}\times {\mathbb{R}}^n$ and blend the mass density and velocity as well into a space-time solenoidal vector field $\mathbf{W}|\mathsf{\Omega }\to {\mathbb{R}}^{n+1}$ over a compact set $\mathsf{\Omega }\subset {\mathbb{R}}^{n+1}$. We then formulate a joint optimization for $\mathbf{W}$ and its support using the shaped gradient of the space-time surface $\mathsf{\Gamma }$ outlining the support boundary $\partial \mathsf{\Omega }$. This easily accommodates spatiotemporal constraints, including obstacles or mandatory regions to visit. Full article

In this paper, by using the Riccati transformation and integral inequality technique, we establish several oscillation criteria for second-order Emden–Fowler neutral delay differential equations under the canonical case and non-canonical case, respectively. Compared with some recent results reported in the literature, we extend the range of the neutral coefficient. Therefore, our results generalize to some of the results presented in the literature. Furthermore, several examples are provided to illustrate our conclusions. Full article

During co-action between contact elements in reciprocating motion, different working conditions exist in outer and inner zones of stationary elements. Because the tribological effects of surface texturing depend on the operating conditions, various dimple patterns were created in the middle part of the steel disc and near the reversal points. The behaviors of variable dimple patterns were compared with those of uniform texturing and untexturing. It was found that the dimple patterns in the middle disc zone depended on the resistance to motion. The best tribological behavior was obtained for a pit area ratio of 13% and diameter of 0.4 mm in the inner zone, and pit area ratio of 3% and diameter of 0.2 mm in the outer zones. Low resistance to motion and the smallest friction variation of all tested sliding pairs were achieved. For the same pit area ratio of 13% in a disc of 0.4 mm, the dimple diameter behaved better than in the 0.2 mm diameter disc. The greatest decrease in the coefficient of friction of 85% compared to untextured sliding pair was achieved for uniform laser texturing with a pit area ratio of 13% and dimple diameter of 0.4 mm, when the normal load was 40 N and frequency of displacement was 20 Hz. Full article

Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems. While machine learning technology is widely employed for industrial equipment fault diagnosis, its effective application relies on the availability of a large dataset with annotated fault data for model training. However, in practice, the availability of informational data samples is often insufficient, with most of them being unlabeled. The challenge arises when traditional machine learning methods encounter a scarcity of training data, leading to overfitting due to limited information. To address this issue, this paper proposes a novel few-shot learning method for high-speed train fault diagnosis, incorporating sensor-perturbation injection and meta-confidence learning to improve detection accuracy. Experimental results demonstrate the superior performance of the proposed method, which introduces perturbations, compared to existing methods. The impact of perturbation effects and class numbers on fault detection is analyzed, confirming the effectiveness of our learning strategy. Full article

The Digital Product Passport (DPP) as a product-specific data set is a powerful tool that provides information on the origin or composition of products and increases transparency and traceability. This recycling case study accompanies the production of 2192 frisbees, which originated from collected beverage bottle caps. In total, 486.7 kg of feedstock was collected and transformed into 363.2 kg of final product with verified traceability through all process steps via a DPP, provided by the R-Cycle initiative and based on the GS1 standard. This demanded a generally agreed dataset, the availability of technical infrastructure, and additional effort in the processing steps to collect and process the data. R-Cycle offers a one-layer DPP where the data structure is lean and information is visible to everyone. This is beneficial to a variety of stakeholders in terms of transparency. However, it does not allow the sharing of sensitive information. On the one hand, the DPP has a high potential to be an enabler for customer engagement, origin verification, or as a starting point for more efficient and advanced recycling of plastics. On the other hand, the DPP involves a certain effort in data generation and handling, which must be justified by the benefits. For small, simple packaging items, the DPP may not be the perfect solution for all problems. However, with a broader societal mindset and legislative push, the DPP can become a widely used and trusted declaration tool. This can support the plastics industry in its journey towards a circular economy. Full article

Background: Biomarkers development for prognostication or prediction of perioperative myocardial disease is critical for the evolution of treatment options in patients undergoing cardiac surgery. The aim of our prospective monocentric study was to investigate the role of selenoprotein 1 (SEEP 1) as a potential biomarker for assessing the risk of myocardial injury after cardiac surgery. Methods: Circulating SEPP1 was measured in the blood of 45 patients before surgery and at 4 h, 8 h and 12 h after CPB by enzyme-linked immunosorbent assay (ELISA); (3) Results: circulating SEPP-1 levels measured 4 h after surgery were strongly correlated with CK-MB levels measured at 48 h (R = 0.598, p < 0.0001) and at 72 h (R = 0.308, p = 0.05). Close correlations were also found between 4 h SEPP-1 and Hs-c troponin values measured at 24 h (R = 0.532, p < 0.0001), 48 h (R = 0.348, p = 0.01) and 72 h (R = 0.377, p = 0.02), as well as with cardiopulmonary bypass (CPB) (R = 0.389, p = 0.008) and cross-clamp time (R = 0.374, p = 0.001); (4) Conclusions: Early SEPP1 measurement after CPB may hold great potential for identifying cardiac surgery patients at risk of developing perioperative myocardial injury. Full article

We study the critical behaviors of systems undergoing fractal time processes above the upper critical dimension. We derive a set of novel critical exponents, irrespective of the order of the fractional time derivative or the particular form of interaction in the Hamiltonian. For fractal time processes, we not only discover new universality classes with a dimensional constant but also decompose the dangerous irrelevant variables to obtain corrections for critical dynamic behavior and static critical properties. This contrasts with the traditional theory of critical phenomena, which posits that static critical exponents are unrelated to the dynamical processes. Simulations of the Landau–Ginzburg model for fractal time processes and the Ising model with temporal long-range interactions both show good agreement with our set of critical exponents, verifying its universality. The discovery of this new universality class provides a method for examining whether a system is undergoing a fractal time process near the critical point. Full article

Developing biostimulants is a promising approach for sustainable agriculture under a saline environment. Thymol is a plant-derived compound with a potential antioxidative capacity. However, little is known about whether and how the antioxidative property of thymol plays a role in inducing plant tolerance against abiotic stresses. Here, we find that thymol induces saline tolerance in Chinese cabbage seedlings via enhancing the antioxidative capacity. Treatment with NaCl (100 mM) decreased the seedling fresh weight by 59.9% as compared to a control. Thymol at 20 μM showed the greatest effect on promoting seedling growth under saline stress, with the seedling fresh weight being increased by 71.0% as compared to NaCl treatment. Thymol remarkably decreased the overaccumulation of ROS (hydrogen peroxide and a superoxide radical); cell membrane damage (evaluated by lipid oxidation, membrane integrity, and relative conductivity); and cell death in seedlings under saline stress. Thymol induced three antioxidative systems to lower the ROS level in salt-treated seedlings. First, thymol remarkably activated a set of antioxidative enzymes, such as SOD (superoxide dismutase), APX (ascorbate peroxidase), CAT (catalase), and POD (peroxidase). Second, thymol balanced the cellular redox status by increasing the ratio of AsA/DHA (ascorbic acid/dehydroascorbic acid) and GSH/GSSG (glutathione/oxidized glutathione). Third, thymol significantly enhanced the level-two kinds of antioxidants (total phenol and flavonoid). All of these physiological responses were observed in both the shoots and the roots. In sum, thymol deploys multiple antioxidative systems to help Chinese cabbage seedlings against saline stress. Such findings suggest that thymol has great potential to be developed as a novel biostimulant enhancing crop tolerance against saline stress. Full article

Using a simple acid-base neutralization method, a Ch-PW solid catalyst was synthesized by mixing choline hydroxide (ChOH) and phosphotungstic acid (HPW) at a 2:1 molar ratio in an aqueous solution. This catalyst was combined with a 20 wt.% potassium peroxymonosulfate (PMS) solution, using acetonitrile (ACN) as the extraction solvent to create an extraction catalytic oxidative desulfurization system. The optimal desulfurization conditions were determined through response surface methodology, targeting the highest desulfurization rate: 0.99 g of Ch-PW, 1.07 g of PMS, 2.5 g of extraction solvent, at a temperature of 50.48 °C. The predicted desulfurization rate was 90.79%, compared to an experimental rate of 93.64%, with a deviation of 3.04%. A quadratic model correlating the desulfurization rate with the four conditions was developed and validated using ANOVA, which also quantified the impact of each factor on the desulfurization rate: PMS > ACN > Ch-PW > temperature. GC-MS analysis identified the main oxidation product as DBTO₂, and the mechanism of desulfurization in this system was further explored. Full article

The inhibition of co-stimulation during T-cell activation has been shown to provide effective immunosuppression in kidney transplantation (KT). Hence, the conversion from calcineurin inhibitor (CNI) to belatacept is emerging as a potential alternate maintenance immunosuppressive therapy in those with transplant-associated thrombotic microangiopathy (TA-TMA) or in the prevention of TA-TMA. We present a 17-year-old male who presented with biopsy-proven CNI-associated TA-TMA immediately post-KT. The administration of eculizumab led to the reversal of TMA. Tacrolimus was converted to belatacept with excellent efficacy and safety during a short-term follow-up of one year. Further larger controlled studies are required to demonstrate the efficacy of this approach in children who present with early-onset TMA post-KT. Full article

With the advancement of remote sensing technology, the demand for the accurate monitoring and tracking of various targets utilizing unmanned aerial vehicles (UAVs) is increasing. However, challenges such as object deformation, motion blur, and object occlusion during the tracking process could significantly affect tracking performance and ultimately lead to tracking drift. To address this issue, this paper introduces a high-precision target-tracking method with anomaly tracking status detection and recovery. An adaptive feature fusion strategy is proposed to improve the adaptability of the traditional sum of template and pixel-wise learners (Staple) algorithm to changes in target appearance and environmental conditions. Additionally, the Moth Flame Optimization (MFO) algorithm, known for its strong global search capability, is introduced as a re-detection algorithm in case of tracking failure. Furthermore, a trajectory-guided Gaussian initialization technique and an iteration speed update strategy are proposed based on sexual pheromone density to enhance the tracking performance of the introduced re-detection algorithm. Comparative experiments conducted on UAV123 and UAVDT datasets demonstrate the excellent stability and robustness of the proposed algorithm. Full article

Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs. Full article

In this paper, an absorber with multi-band, tunable, high Q, and high sensitivity, based on terahertz periodic two-dimensional patterned graphene surface plasmon resonance (SPR), is proposed. The absorber consists of a bottom metal film separated by a periodically patterned graphene metamaterial structure and a SiO₂ dielectric layer, where the patterned graphene layer is etched by “+” and “L” shapes and circles. It has simple structural features that can greatly simplify the fabrication process. We have analyzed the optical properties of a graphene surface plasmon perfect metamaterial absorber based on graphene in the terahertz region using the finite-difference method in time domain (FDTD). The results show that the absorber device exhibits three perfect absorption peaks in the terahertz bands of f1 = 1.55 THz, f2 = 4.19 THz, and f3 = 6.92 THz, with absorption rates as high as 98.70%, 99.63%, and 99.42%, respectively. By discussing the effects of parameters such as the geometrical dimensions of patterned graphene metamaterial structure “+” width W1, “L” width W2, circular width R, and the thickness of the dielectric layer on the absorption performance of absorber, as well as investigating the chemical potential and relaxation time of patterned-layer graphene material, it was found that the amplitude of the absorption peaks and the frequency of resonance of absorber devices can be dynamically adjusted. Finally, we simulated the spectra as the surrounding refractive index n varied to better evaluate the sensing performance of the structure, yielding structural sensitivities up to 382 GHz/RIU. Based on this study, we find that the results of our research will open new doors for the use of multi-band, tunable, polarization-independent metamaterial absorbers that are insensitive to large-angle oblique incidence. Full article

As society continues to age, people are becoming more concerned about their health care. This has led to an increase in the demand for medical products in urban areas, emphasizing the need for regular and prompt deliveries. However, the existing logistics centers are located in the suburbs of Seoul, a metropolitan city, which makes it challenging to ensure timely delivery. To address this issue, this study aims to establish new logistics centers in urban areas, particularly in Seoul, while minimizing CO₂ emissions from delivery vehicles in alignment with sustainability efforts. The scientific gap addressed in and the novelty of this paper is that the input parameters are prepared based on actual data from a medical company in Korea to reflect reality, and the mathematical model-based optimization technique is applied to determine the optimal location of a new logistics center. The genetic algorithm is developed to solve the proposed mathematical model by deriving optimal or near-optimal solutions. Furthermore, the numerical experiment examined the impact of establishing a new logistics center in one of the candidate areas of local governments in Seoul by considering environmental impact. As a result, the new logistics network can reduce CO₂ emissions by approximately 66.74% compared to the existing logistics network. Full article

In-vehicle networks (IVNs) are networks that allow communication between different electronic components in a vehicle, such as infotainment systems, sensors, and control units. As these networks become more complex and interconnected, they become more vulnerable to cyber-attacks that can compromise safety and privacy. Anomaly detection is an important tool for detecting potential threats and preventing cyber-attacks in IVNs. The proposed machine learning-based anomaly detection technique uses deep learning and feature engineering to identify anomalous behavior in real-time. Feature engineering involves selecting and extracting relevant features from the data that are useful for detecting anomalies. Deep learning involves using neural networks to learn complex patterns and relationships in the data. Our experiments show that the proposed technique have achieved high accuracy in detecting anomalies and outperforms existing state-of-the-art methods. This technique can be used to enhance the security of IVNs and prevent cyber-attacks that can have serious consequences for drivers and passengers. Full article

Many communities are working to enhance the sustainability of their physical, economic, and social systems. While economic and physical systems are routinely measured (e.g., money and energy), psychological and behavioral elements of social systems (norms, attitudes, and individual behavior) are seldom tracked. The objective of this research was to evaluate a potentially scalable approach to measure the impact of sustainability initiatives on these variables in a community engaged in holistic sustainability programming. Online survey data were collected in 2012 (N = 155) and 2016 (N = 137), measuring pro-environmental thought and behavior in two towns in Ohio: Oberlin, a community engaged in holistic efforts to enhance environmental sustainability; and a similar community (Berea) used as a control. Survey links were distributed via recruitment letters mailed to randomly selected community residents from a purchased mailing list. We used two (town) by two (time) between subjects’ ANOVAs to evaluate whether Oberlin saw predicted increases in sustainable thought and behavior from 2012 to 2016, compared to the control community. Despite verifiable participation in and awareness of sustainability programs in Oberlin, our survey results did not provide strong evidence that programs resulted in the desired changes in attitudes, norms, and individual behaviors. Recycling attitudes and LED bulb installation were two exceptions. We conclude that assessing the psychological and behavioral dimensions of sustainability poses particular challenges. We encountered ceiling effects and inadequate statistical power. Possibly, norms and attitudes are not easily influenced even by a holistic community-wide effort. Full article

Background/Objectives: Obesity is currently considered a public health problem in both developed and developing countries. Gender- and age-specific body mass index (BMI) growth standards or references are particularly effective in monitoring the global obesity pandemic. This study aimed to report disparities in age-, gender- and ethnic-specific statistical estimates of overweight and obesity for 2–18 years aged Pakistani children and adolescents using the World Health Organization (WHO), the Center for Disease Control (CDC) 2000 references, the International Obesity Task Force (IOTF) and Pakistani references for BMI. Methods: The study used secondary data of 10,668 pediatric population, aged 2–18 years. Demographic information like age (years), gender, city and anthropometric examinations, i.e., height (cm) and weight (kg) were used in this study. The recommended age- and gender-specific BMI cut-offs of the WHO, CDC 2000 and the IOTF references were used to classify the children sampled as overweight and obese. For the Pakistani reference, overweight and obesity were defined as BMI-for-age ≥ 85th percentile and BMI-for-age ≥ 95th percentile, respectively. Cohen’s κ statistic was used to assess the agreement between the international references and local study population references in the classification of overweight/obesity. Results: The statistical estimates (%) of the participants for overweight and obesity varied according to the reference used: WHO (7.4% and 2.2%), CDC (4.9% and 2.1%), IOTF (5.2% and 2.0%) and Pakistan (8.8% and 6.0%), respectively; suggesting higher levels of overweight and obesity prevalence when local study references are used. The Kappa statistic shows a moderate to excellent agreement (κ ≥ 0.6) among three international references when classifying child overweight and obesity and poor agreement between local references and the WHO (0.45, 0.52), CDC (0.25, 0.50) and IOTF references (0.16, 0.31), for overweight and obesity, respectively. Conclusions: The results of the study showed a visible difference in the estimates of excess body weight after applying the WHO, CDC, IOTF and local BMI references to the study population. Based on the disparity results and poor agreement between international references and the local study reference, this study recommends using local BMI references in identifying children with overweight and obesity. Full article

Participation is important for children’s quality of life (QOL). This study aimed to identify participation factors that influence QOL among Japanese children with neurodevelopmental disorders. Ninety-two Japanese parents of children with neurodevelopmental disorders participated in this study. The parents completed the parent version of the Kid- and Kiddo-KINDL health-related QOL questionnaire and the Participation and Environment Measure for Children and Youth. The data were examined using the random forest algorithm to analyze the participation factors that affected the children’s QOL. The analyses revealed that school and community environmental factors that affected participation were the most important predictors of QOL among children. As school and community environments can significantly impact the QOL of children with neurodevelopmental disorders, greater focus should be placed on participation in environmental contexts. Full article

This paper analyzes how export margins responded to an intermediate input supply shock caused by the 2020 lockdown in China. We use regression decomposition with triple and quadruple difference-in-differences models to identify causal impacts and mitigate potential heterogeneity in transaction-level customs data from the Bangladesh apparel manufacturing industry. The triple difference estimate shows that the average export value per firm–product–destination combination declined by approximately 65%, leading to a decrease in overall exports of woven apparel from Bangladesh. The input supply shock also adversely affected the subgroups of firms across various firm-level characteristics along the intensive margin. Moreover, the export market share decomposition reveals that the shock significantly affected intensive margins by decreasing incumbents’ market allocation by 9%. An equivalent increase in extensive margins led to a readjustment in the market allocation, leading to fewer market leavers and slightly more new market entrants. Our results indicate that Bangladesh’s exports mostly decreased due to the smaller quantities of products exported rather than there being fewer firms, destinations, or products involved in export trade. There were significant market share reallocations that occurred after the Chinese input supply shock. An appropriate policy stance is required for sustainable export sector growth strategies, which will enhance the country’s defense against potential future shocks and foster the achievement of sustainable development goals (SDGs) in Bangladesh. Full article