The safe running of trains is seriously affected by the high-frequency excitation generated by wheel polygonal wear. This paper proposes the polygonal evolution theory and the wear updating strategy of maximum wear cycle for the case of ‘Indivisible’ wear, focusing on the lateral self-excited vibration characteristics of resilient wheels. By combining the wheel/rail rigid-flexible coupling dynamics model and the wheel circumferential wear model, an iteration model of long-term circumferential wear for the resilient wheel is established. The wear mechanism of the resilient wheel is explained by analyzing the evolution process of polygonal wear under different operating speeds. The actual wheel wear is basically consistent with the theoretical calculation results. The results indicate that, due to the combined effect of wheel intrinsic properties and operating conditions, the phase deviation of rolling wear will lead to the occurrence of ‘Indivisible’ wear and affects the polygonization process to some extent when the relationship between wheel rotational frequency and lateral vibration frequency is not approximate multiple.

1. Organisms have evolved diverse strategies to manage parasite infections. Broadly, hosts may avoid infection by altering behaviour, resist infection by targeting parasites, or tolerate infection by repairing associated damage. Effectiveness of a strategy depends on interactions between, e.g., resource availability, parasite traits (virulence, life-history) and the host itself (nutritional status, immunopathology). 2. To understand how these factors shape host parasite-mitigation strategies, we developed a mathematical model of within-host, parasite-immune dynamics in the context of helminth infections. The model incorporated host nutrition and resource allocation to different mechanisms of immune response: larval parasite prevention; adult parasite clearance; damage repair (tolerance). We also considered a non-immune strategy: avoidance via anorexia, reducing intake of infective stages. Resources not allocated to immune processes promoted host condition, whereas harm due to parasites and immunopathology diminished it. Maximising condition (a proxy for fitness), we determined optimal host investment for each parasite-mitigation strategy, singly and combined, across different environmental resource levels and parasite trait values. 3. Which strategy was optimal varied with scenario. Tolerance generally performed well, especially with high resources. Success of the different resistance strategies (larval prevention or adult clearance) tracked relative virulence of larval and adult parasites: slowly maturing, highly damaging larvae favoured prevention; rapidly maturing, less harmful larvae favoured clearance. Anorexia was viable only in the short-term, due to reduced host nutrition. Combined strategies always outperformed any lone strategy: these were dominated by tolerance, with some investment in resistance. 4. Choice of parasite mitigation strategy has profound consequences for hosts, impacting their condition, survival and reproductive success. We show the efficacy of different strategies is highly dependent on timescale, parasite traits and resource availability. Models that integrate such factors can inform the collection and interpretation of empirical data, to understand how those drivers interact to shape host immune responses in natural systems.

Diagnostic modeling of voice disorders is important to improve the efficiency of patient diagnosis. However, due to sample imbalance and complexity of voice data analysis, basic acoustic features and traditional machine learning methods are not effective in predicting voice status. This letter proposes a three-layer ensemble learning model with two innovations: 1) At the feature level, acoustic features are expanded using Multilayer Perceptron and SincNet; 2) Structurally, a tree-based ensemble learning model is proposed that utilizes XGBoost for feature selection and feature transformation, with LightGBM as the classifier. Experiments have shown that the proposed method in this paper outperforms traditional machine learning models.

Background Nirmatrelvir/ritonavir is recommended for high-risk patients with COVID-19 to reduce disease progression and mortality. Ritonavir significantly increases the bioavailability of nirmatrelvir and is the most potent irreversible cytochrome P 450 3A4 inhibitor in clinical use, resulting in a substantial risk for drug-drug interactions (DDI). We aimed to analyze the incidence of potential DDI (pDDI) in critically ill patients with SARS-CoV-2 infection. 7.2 Methods This is a retrospective single-center study in a quaternary care center in Northern Germany. We reviewed electronic health records for demographic characteristics, comorbid conditions, and medication history. The pre-existing comedication was screened for pDDI with nirmatrelvir/ritonavir using publicly available databases. Binary logistic regression was used to identify patient characteristics associated with pDDI. 7.3 Results Of 500 critically ill patients with SARS-CoV-2 infection, 362 (72.4%) received pre-existing comedication. A total of 241/500 patients (48.2%) had a medication history prone to pDDI. Antidiabetics, lipid-lowering drugs, and anticoagulants were among the most frequently used agents with a pDDI. Higher age (OR 1.043; 1.028-1.058; p<0.01) and the number of comorbidities (OR 1.229; 1.119-1.350; p<0.01) were significantly associated with pDDI. 7.4 Conclusion The very patient population that may benefit most from treatment with nirmatrelvir/ritonavir also has the greatest risk of pDDI. Polypharmacy is frequently present in these patients and a conscientious check of the comedication is mandatory before a treatment with nirmatrelvir/ritonavir can be initiated.

Inspired by the effect of mesh segmentation, a novel planar mesh segmentation microchannel device (PMSM) was developed to investigate the extraction efficiency of Mg (Ⅱ) from liquid-liquid and gas-liquid-liquid extraction by utilizing the segmented flow and segmentation effect of metal mesh, as well as the continuous and multi-stage process. When O/A = 1 and total flow rate Q = 4 mL/min, the extraction equilibrium value was reached in only 79.5 s, while conventional stirred extraction required 300 s. When aqueous phase residence time was kept constant and Q g = 2.5 mL/min, the maximum extraction efficiency of 31.04% was achieved in only 26.5s, which exceeded the equilibrium value of the conventional one-stage extraction. In addition, the flow patterns, total volume mass transfer coefficient (K La), extraction kinetics and the correlation diagram of We aq-We o-g-E were used to further reveal the mechanism of mass transfer enhancement.

The research proposes a hybrid algorithm model that combines model-driven and data-driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi-objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi-source heterogeneous data, selection of key sub-parameters using Principal Component Analysis (PCA), enhanced K-means clustering analysis, determination of structural component target thresholds, time-dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double-layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub-parameters that have significant impacts on the performance of structural components. Enhanced K-means clustering analysis is carried out to effectively group and classify the identified key sub-parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi-parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan-Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double-layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and the bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.

We present an analysis of the performance data of a monitored PV system onboard a light commercial electric vehicle during parking and driving conditions. The PV system’s nominal power is 2180 WP with flat silicon modules on the vehicle’s roof, rear, left, and right sides, as well as other electronic components needed to charge the vehicle’s high-voltage (HV) battery. The analysis indicated that after 488.92 hours of operation, the modules mounted on the vehicle roof produced 133.32 kWh of electricity during parking compared to 15.4, 30.67, and 22.99 kWh for the modules mounted on the rear, left, and right sides, respectively. During the trips, after 31.99 hours of operation, 6.12, 0.68, 1.08, and 1.86 kWh of electricity were produced by the modules on the roof, rear, left, and right sides, respectively. The overall system efficiency was in the 60-65 % range. The aggregated usable electricity reaching the HV battery after multiple conversion stages generated by the system at the two parking locations was 129.39 kWh. PV electricity generated at the two parking locations enabled a range extension of approximately 530 km, which is 30% of the total distance driven during the measurement period between April and July 2021.

A document by ALI Khalifa. Click on the document to view its contents.

Background Motor and emotional variables, plus other factors like gender, influence how preadolescent schoolchildren perceive their motor competence level. Objective By means of a hierarchical linear regression analysis, this longitudinal study aimed to determine the predictive value that coordination, anxiety and self-esteem at the age of 9 years can have on the self-perceived motor competence variable at the age between 11 and 12 years. Methods Data collection was performed with 224 pupils at a first time point with grade-4 Primary Education (PEd) pupils (mean age 9.11±.32 years) and a second time point with grade-6 PEd pupils (11.49±.50 years). Results The results evidenced that coordination, anxiety and self-esteem at the age of 9 years predicted self-perceived motor competence at the age between 11 and 12 years. For the female gender, age explained 5% of the variance for self-perceived motor competence, coordination explained 19% and anxiety 8%. The male gender explained 7% of the variance for self-perception, coordination explained 23% of further variance, anxiety 16% of variance and self-esteem 11%. Conclusions The evidence found reveals the importance of more globally understanding motor competence to promote more suitable and facilitating conditions that you need to perform physical activity at school and elsewhere.

There are two ways to organize factors in Bronfenbrenner’s Process–Person–Context–Time (PPCT) model using quantitative data: a latent growth curve model with a moderation variable and a mediation model. This study’s objectives are to examine these ways, discuss whether they meet some of the necessary criteria based on the definition of the elements and the theory propositions, and consider the advantages and disadvantages of each model. The analysis will help researchers to decide which model to use in different situations.

Aims Corticosteroids are the treatment of choice for many inflammatory diseases, but often lead to adverse effects, including hyperglycemia. This study investigated the mechanisms driving differential effects on glucose control for AZD9567, a novel oral, non-steroidal, selective glucocorticoid receptor modulator, versus prednisolone in 46 patients with type 2 diabetes mellitus. Methods In this randomized, double-blind, 2-way cross-over study (NCT04556760), participants received either AZD9567 72 mg and prednisolone 40 mg daily (Cohort 1); AZD9567 40 mg and prednisolone 20 mg daily (Cohort 2); or placebo and prednisolone 5 mg daily (Cohort 3). Treatment duration was 3 days with a 3-week washout between treatment periods. Glycemic control was assessed after a standardized meal and with continuous glucose monitoring. Results A significant difference between AZD9567 and prednisolone in favour of AZD9567 was observed for the change from baseline to Day 4 glucose excursions post-meal in Cohort 1 (glucose AUC0-4h -4.54%; 95% CI: -8.88, -0.01; p=0.049), but not in Cohort 2 (-5.77%; 95% CI: -20.92, 12.29; p=0.435). In Cohort 1, significant differences between AZD9567 and prednisolone were also seen for the change from baseline to Day 4 in insulin and glucagon secretion post-meal (p<0.001 and p=0.005, respectively), and change from baseline to Day 4 in GLP-1 response (p=0.022). Significant differences between AZD9567 and prednisolone for 24-hour glucose control were observed for both Cohort 1 (-1.507 mmol/L; 95% CI: -2.0820, -0.9314; p<0.001), and Cohort 2 (-1.110 mmol/L; 95% CI -1.7257, -0.4941; p<0.001). Conclusions AZD9567 significantly reduced treatment-induced hyperglycemia compared with prednisolone.

Case Report: Metastatic Squamous Cell Carcinoma in the Parotid Disguised as Syndrome of Inappropriate Antidiuretic Hormone SecretionAuthor Details: Aditya K. Ghosh, M.D., Nivedha Balaji, D.O., Sonu Gupta, M.D.Aditya K. Ghosh – Aghosh@sgu.eduNivedha Balaji – Nivedha.Balaji@nghs.comSonu Gupta – Sonu.Gupta@nghs.comNortheast Georgia Medical Center, 743 Spring St NE, Gainesville, Georgia 30501Financial Disclosures: NoneInformed Consent: The patient in this study provided informed consent prior to participation.Conflict of Interest: None

Aims: To investigate the effects of the CYP3A4*18B, CYP3A5*3, and MDR1C3435T gene polymorphisms on the pharmacokinetics of tacrolimus in healthy Chinese subjects. Methods: Thirty healthy Chinese subjects received single oral doses of 5 mg tacrolimus and were genotyped for CYP3A4*18B, CYP3A5*3, and MDR1C3435T using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The blood concentrations of tacrolimus were determined by liquid chromatography-tandem mass spectrometry (LC/MS/MS) up to 96 h after dosing. Results: The mean tacrolimus AUC0-96, AUC0-∞ and Cmax for the CYP3A4*1/*1 carriers were 282.81±202.13 ng·h·mL-1, 308.68±211.16 ng·h·mL-1 and 38.05±30.30 ng·mL-1, respectively, which were 3.96-fold, 3.70-fold and 3.88-fold greater than those of the CYP3A4*18B/*18B carriers (71.39±21.61 ng·h·mL-1, 83.38±29.00 ng·h·mL-1 and 9.80±2.60 ng·mL-1, respectively) (P= 0.001, 0.001 and 0.004, respectively). Similarly, the AUC0–96, AUC0-∞ and Cmax for the CYP3A5*3/*3 carriers were 238.27±178.82 ng·h·mL-1, 263.78±190.72 ng·h·mL-1 and 32.53±24.52 ng·mL-1, respectively, which were 2.52-, 2.48- and 2.27-fold greater than those of the CYP3A5*1 carriers (94.69±37.70 ng·h·mL-1, 106.28±41.46 ng·h·mL-1 and 14.30±5.77 ng·mL-1, respectively) (P=0.001, 0.001 and 0.004, respectively). Although there were no significant differences in pharmacokinetics among MDR1C3435T genotypes (P>0.05), the Tmax for MDR1 CC (1.25±0.27 h) homozygotes in CYP3A4 expressers (*18B/*18B or *1/*18B genotype) was much lower than that for the MDR1 T carriers (1.82±0.72 h) (P<0.05). In addition, sex, age, alanine aminotransferase (ALT) level, haematocrit (HCT) level, serum creatinine (SCr) level, gamma-glutamyl transferase (GGT) level, and CYP3A4*18B and CYP3A5*3 gene polymorphisms affected the pharmacokinetics of tacrolimus. Conclusions: CYP3A4*18B and CYP3A5*3 are important genetic factors influencing the pharmacokinetics of tacrolimus in the Chinese population.

A Novel Approach for Challenging Coronary Angiography in Ascending Aortic Aneurysm: Utilizing an Angioplasty Wire Zaid haitham Abduljabbar 1, Ahmed Qasim Mohammed Alhatemi 2 , Hashim Talib Hashim 3, Al Talib Hashim 4Al Nasiriyah Heart Centre, coronary care unit, Thi Qar, Iraq.Al Nasiriyah Teaching Hospital, Thi Qar, Iraq.Warith Al Anbiyaa university, college of medicine, Karbala, Iraq.Golestan University of medical sciences, Gorgan, Iran.Corresponding author: Ali Talib Hashim, Golestan University of medical sciences, Gorgan, Iran. Talibhashim42@gmail.com

Comorbidity Influence in Observational Studies: Why Ignore the Real World ?Comorbidity is any coexisting disease or medical condition with another medical condition, at a particular time point or during a particular period, in a patient. As such they can be acute or of chronic nature. Comorbidities may or may not interact with each other. Indeed, comorbid conditions may elevate the risk of the development of other conditions, increase the risk of death, reduce the quality of life, worsen the mental and physical health and impair the general functional ability of the patient. However, the literature on empirical evidence of treatment effect modification by comorbidity from randomized clinical trials (RCTs) is very limited, although this has been attributed to the underrepresentation of people with comorbidities in trials.1 Despite this, the standard assumption that efficacy is constant across subgroups of patients by levels of comorbidity in trials, is often criticised despite the power granted by treatment randomization to such studies.2 In contrast, the influence of comorbidity on health outcomes and treatment effectiveness is well established from observational studies.3-4In most observational studies, the influence of comorbidity on treatment effect is modelled as constant regardless of how long the patient has had the condition up to the start of follow-up, with the inherent assumption that the duration does not influence the outcome of interest. The question we inadvertently fail to address is whether it is reasonable to assume a constant influence for each of the comorbidities- namely, a level of influence that is independent of how long the patient has had each comorbid condition since diagnosis? We readily ignore the reality that there may be no evidence on the assumption of a constant, unchanging effect over time by the comorbidity of interest. For example, in assessing the treatment effect of a particular drug in a diabetic cohort, we generally consider the risk associated with each of the comorbidities of interest, such as hypertension as the same between patients diagnosed five years ago and those diagnosed recently. By so doing, we run the risk of not properly controlling for the influence of some of these comorbidities on the outcome of interest. Indeed, this assumption was challenged in a study that demonstrated the duration-dependent nature of the influence of certain comorbidities on survival among chronic obstructive pulmonary disease (COPD) patients in the UK’s Clinical Practice Research Datalink (CPRD) and since then, using similar approaches that are easy to apply, two other studies have also demonstrated the duration-dependent nature of certain comorbidities within the real-world evidence generation space.5-7Another common practice we find in observational studies is the modelling of and adjusting for comorbidity influence additively, either as the number of comorbidities or as an index (such as the Charlson Comorbidity Index), and we do so often without regard to the specific outcome of interest, which may be different from the outcome used in the derivation of the index.8-10 In so doing, we are inadvertently assuming that (1) the outcome is associated linearly with the comorbidity count or index and (2) the impact of every comorbidity is one directional- namely, to elevate the risk of the outcome and hence, the weights we derive from combining the set of comorbidities must be positive (i.e. the null hypothesis that outcome worsens with increasing comorbidity is inadvertently assumed as already proven). Although these common practices are generally suitable in many observational studies, there is evidence some of these assumptions may not necessary hold true in some disease populations, such as in COPD patients.11-13 One such evidence is that as a result of adequate control of COPD by medications and lifestyle changes, among others, its influence on the risk of an outcome of interest, may not necessarily be as a risk elevator - suggesting, risk reduction by an adequately managed comorbid condition is possible in real-life studies. An additional, possible consequence of these approaches is our likely loss of ability to adequately identify specific interactions between those comorbidities that influence prognosis.Some of the problems associated with our current simplistic approach to handling comorbidity influence were first highlighted in a 2012 paper on the conduct of prospective observational post-authorization safety studies.14 The paper recommends assessing the suitability of the assumptions that underpin the methodologies we intend to apply to the data within the context of the specific outcome(s) of interest as the least we could do with real-life data. In other words, we need to demonstrate that such assumptions are appropriate and reasonable on the basis of the results from exploratory assessments of the data. We need not ignore what the specific study data may reveal about the nature of the true association between the outcome and each specific comorbidity and/or combination of comorbidities. We particularly need not ignore evidence from routine clinical practice. The current common assumption in observation studies, that the influence of every comorbid condition on any health outcome of interest is independent of both its duration and management in routine clinical practice, is unlikely to be tenable in the real world.Acknowledgement :The authors are grateful to Professor Gilbert MacKenzie for his valuable contributions to initial discussions on the subject.Prof Victor A Kiri MPH, PhDEpidemiology DirectorMarket Access Consulting, RWE & AnalyticsFortreaMaidenhead, Berkshire SL6 3QHUnited Kingdomvictor.kiri@fortrea.comORCID iD: https://orcid.org/0000-0001-7171-2011Dr Maurille Feudjo-Tepie PhDVice PresidentHead of GlobalRWE & Digital sciences Head of Global RWE & Digital sciencesUCBSlough, Berkshire SL1 3WEUnited Kingdommaurille.feudjotepie@ucb.comReferencesHanlon P, Hannigan L, Rodriguez-Perez J, et al. Representation of people with comorbidity and multimorbidity in clinical trials of novel drug therapies: an individual-level participant data analysis. BMC Med. 2019; 17(1):201Hanlon P, Butterly EW, Shah ASV, et.al. Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials. PLoS Med 2023; 20(6): e1004176Iversen LH, Nørgaard M, Jacobsen J, et al. The impact of comorbidity on survival of Danish colorectal cancer patients from 1995 to 2006 – a population-based cohort study. Dis Colon Rectum 2009;52(1):71–78Lund L, Jacobsen J, Nørgaard M, et al. The prognostic impact of comorbidities on renal cancer, 1995 to 2006: a Danish population based study. J Urol. 2009;182(1):35–40; discussion 40.Kiri VA, Oyee J. Assessing the time-dependency nature of comorbidity influence in COPD, Pharmacoepidemiology and Drug Safety (2006); 15 (Suppl 2): S8Plana-Ripoll O, Pedersen CB, Holtz Y, et al. Exploring comorbidity within mental disorders among a Danish national population. JAMA Psychiatry 2019b; 76: 259–270McGrath JJ, Lim CCW, Plana-Ripoll O, et al. Comorbidity within mental disorders: A comprehensive analysis based on 145,990 survey respondents from 27 countries. Epidemiology and Psychiatric Sciences 2020; 29: [e153]Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40 (5):373-383Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.Yngvar Nilssen, Trond-Eirik Strand, Robert Wiik, et al. Utilizing national patient-register data to control for comorbidity in prognostic studies, Clinical Epidemiology 2014;395-404Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27Kiri VA, Visick G, Muellerova H, MacKenzie G. A Novel Methodology for Measuring the Influence of Comorbidity in Disease Outcome Studies, Pharmacoepidemiology and Drug Safety (2005); 14 (Suppl 2): S135Kiri VA, Muellerova H, Visick G, MacKenzie G. Assessing the multivariate structure and influence of comorbidity in COPD, Eur Respir J (2005); 26 (Suppl 49):A470sKiri VA. A Pathway to Improved Prospective Observational Post-Authorization Safety Studies. Drug Saf 2012; 35 (9): 711-724

This study was designed to investigate the presence of potential human pathogenic bacteria, bacterial load, and their incidence in ready-to-eat leafy greens viz., coriander, lettuce, and mint leaves sold at diverse marketplaces in Dhaka City. Multiple identification methods including cultural, morphological, biochemical, and molecular analysis were employed in the Plant Pathology Laboratory of Sher-e-Bangla Agricultural University to identify the human pathogenic bacteria. In molecular analysis, the DNA samples were put through PCR using bacterial primer 27F: AGAGT TTG ATC MTG

ABDOMINAL COCOON AND RECURRENT HEMORRHAGIC ASCITES, UNEXPECTED FINDINGS IN ENDOMETRIOSIS: A CASE REPORTRosheen Jamil1

TITLE: PITUITARY STALK INTERRUPTION SYNDROME: A RARE BUT TREATABLE CAUSE OF SHORT STATURE

The precise localization of mobile robots in unstructured environments is of utmost importance for many industrial and field applications, especially when the mobile robot is part of a more complex kinematic chain, such as a mobile manipulator. Being able to precisely localize affects the outcome of tasks that rely on an open-loop kinematic computation, such as work-station docking procedures. To achieve a repeatable and precise localization and positioning, mobile robots generally rely on onboard sensors, most commonly 2D laser scanners, whose readings are subjected to noise and numerous disturbing factors (e.g., materials reluctance). Problems arise when precise localization is needed in dynamic and unstructured environments where generally applicable methods won’t perform adequately or might be time-consuming to set up. In this work, we propose a cloud-edge computing architecture to deploy a recurrent neural network (RNN) based registration system, which uses a pair of consecutive LiDAR readings to estimate a fixed transformation. The capability of RNNs to process contiguous inputs will help neglect errors embedded in punctual laser scanner readings and output a more precise registration estimation. In such a way, the RNN can estimate a displacement error based on multiple consecutive readings and act as a sensor to be employed in a closed-loop control scheme. To tackle the dynamic and unstructured environments, the model is firstly tuned on synthetic LiDAR data to embed rigid transformations into the deep learning model, for then rapidly fine-tuned on local scenarios. After model architecture and optimization of hyperparameters, the devised model is tested in different scenarios, comparing the precise positioning capability of the AMR(autonomous mobile robot) with that of a classical registration algorithm. The results suggest that an RNN model can greatly improve the registration precision of laser scanner signals and, consequently, the precise positioning efficiency of AMRs.

Chirality and luminescence are important for both chemistry and biology, which are highly influenced by aggregation. In this work, we reported a pair of metalated tetraphenylethylene(TPE)-based organic cage enantiomers, which feature a quadrangular prismatic cage structure. These homochiral cages exhibit concentration-dependent chiral behaviors alongside a propensity for thermodynamic aggregation. Aggregation caused quench effect is found for these cages accompanying the increasing of the concentrations. When a poor solvent is added to produce a kinetical aggregation, the aggregation-annihilation circular dichroism and aggregation-induced emission behaviours are observed for these enantiomeric cages. By comparing these observations with the photophysical behaviors of a pair of structurally similar organic molecular enantiomers, the unique photophysical properties observed are intricately linked to the metal-integrated TPE-functionalized cage structures.

The heat transfer in power-law fluids across three corrugated circular cylinders placed in triangular pitch arrangement is studied computationally in a confined channel. Continuity, momentum and energy balance equations were solved using ANSYS FLUENT (Version 18.0). The flow is assumed to be steady, incompressible, 2D and laminar. A square domain of side 300 Dh is selected after detailed domain study. An optimized grid with 98187 cells is used in the study. The convergence criteria of 10 -7 for the continuity, x-momentum and y-momentum balances and 10 -12 for energy equation were used. Constant density and non-Newtonian power-law viscosity modules were used. Diffusive term is discretized using central difference scheme. Convective terms are discretized using Second Order Upwind (SOU) scheme. Pressure-velocity coupling between continuity and momentum equations was implemented using the SIMPLE (Semi-Implicit Method for Pressure Linked Equation) scheme. Streamlines show wake development behind the cylinders, which is much dominant at large ReN and n. Isotherm contours are cramped at higher values of ReN and PrN, implying higher heat transfer. Global parameters like Cd and Nu are computed for the wide ranges of controlling dimensionless parameters, such as power-law index (0.3 ≤ n ≤ 1.5), Reynolds (0.1 ≤ ReN ≤ 40) and Prandtl numbers (0.72 ≤ PrN ≤ 500). The NuLocal plot attains a pitch near corrugation of surface due to abrupt change in velocity and temperature gradients. Nu increases with ReN and/or PrN and decreases with n under otherwise identical situations. Nu is correlated with pertinent parameters, namely, ReN , PrN and n.

Decoupling electrical and thermal properties to enhance the figure of merit of thermoelectric materials underscores an in-depth understanding of the mechanisms that govern the transfer of charge carriers. Typically, a factor that contributes to the optimization of thermal conductivity is often found to be detrimental to the electrical transport properties. Here, we systematically investigated 26 dimeric MX2-type compounds (where M represents a metal and X represents a non-metal element) to explore the influence of the electronic configurations of metal cations on lattice thermal transport and thermoelectric performance using first-principles calculations. A principled scheme has been identified that the filled outer orbitals of the cation lead to a significantly lower lattice thermal conductivity compared to that of the partly occupied case for MX2, due to the much weakened bonds manifested by the shallow potential well, smaller interatomic force constants, and higher atomic displacement parameters. Based on these findings, we propose two ionic compounds, BaAs and BaSe2, to realize reasonable high electrical conductivities through the structural anisotropy caused by the inserted covalent X2 dimers, while still maintaining the large lattice anharmonicity. The combined superior electrical and thermal properties of BaSe2 lead to a high n-type thermoelectric ZT value of 2.3 at 500 K. This work clarifies the structural origin of the heat transport properties in dimeric MX2-type compounds and provides an insightful strategy for developing promising thermoelectric materials.

Co-evolutionary adaptation of hookworms with their mammalian hosts has selected for immunoregulatory excretory/secretory (E/S) products. However, it is not known whether, or if so, how host immunological status impacts the secreted profile of hematophagous adult worms. This study interrogated the impact of host Signal and transducer of activator of transcription 6 (STAT6) expression during experimental evolution of hookworms through sequential passage of the life-cycle in either STAT6 deficient or WT C57BL/6 mice. Proteomic analysis of E/S products by LC-MS showed increased abundance of 15 proteins, including myosin-3, related to muscle function, and aconitate hydratase, related to iron homeostasis. However, most E/S proteins (174 of 337 unique identities) were decreased, including those in the Ancylostoma-secreted protein (ASP) category, and metallopeptidases. Several identified proteins are established immune-modulators such as fatty acid-binding protein homolog, cystatin, and acetylcholinesterase. Enrichment analysis of InterPro functional categories showed down-regulation of Cysteine-rich secretory proteins, Antigen 5, and Pathogenesis-related 1 proteins (CAP), Astacin-like metallopeptidase, Glycoside hydrolase, and Transthyretin-like protein groups in STAT6 KO adapted worms. Taken together, these data indicate that in an environment lacking Type 2 immunity, hookworms alter their secretome by reducing immune evasion proteins- and increasing locomotor- and feeding-associated proteins.

The recent COVID-19 pandemic, which had a significant impact on businesses in 2020 and 2021, highlighted the issue of analyzing the financial performance of banks in times of crisis. Practical experience has shown that banks with higher resilience perform better in different crises. This paper studies different banking business models and examines their resilience. In this way, by analyzing the banks' resilience variables, their situation for different types of banking business models is examined. Resilience in banks will be considered through the control of risky assets and deposits, leverage rate, capital adequacy, liquidity coverage ratio, net stable funding ratio, and diversification. Through examining the data of 33 Iranian banks and analyzing their different business models in a soft clustering method, it was found that the resilience of merchandise-oriented banks has been more than the others. Detailed analysis revealed that reducing the investment and increasing the variety of loans would reduce the systemic risk of the pandemic and its effect on other banks leading to an increase in resilience.