Experimental investigation on punch shear strength of poly lactic acid specimens for biomedical applications.

The designed biomedical implants require excellent shear strength primarily for mechanical stability against forces in human body. However, metallic implants undergo stress shielding with release of toxic ions in the body. Thus, Fused Deposition Modeling (FDM) has made significant progress in the biomedical field through the production of customized implants. The mechanical behavior is highly dependent on printing parameters, however, the effect of these parameters on punch shear strength of ASTM D732-02 standard specimens has not been explored. Thus, in the current study, the effect of infill density (IFD), printing speed (PTS), wall thickness (WLT), and layer thickness (LYT) has been investigated on the punch shear strength using Response Surface Methodology. The Analysis of Variance (ANOVA) has been performed for predicting statistical model with 95% confidence interval. During the statistical analysis, the terms with p-value lower than 0.05 were considered significant and the influence of process parameters has been examined using microscopic images. The surface plots have been used for discussing the effect of interactions between printing parameters. The statistical results revealed IFD as the most significant contributing factor, followed by PTS, LYT, and WLT. The study concluded by optimization of printing parameters for obtaining the highest punch shear strength.

Optimization of cassava peel ash concrete using central composite design method.

Cassava peel ash (CPA) is an abundant agricultural byproduct that has shown promise as an additional cementitious material in concrete manufacturing. This research study aims to optimize the incorporation of CPA in concrete blends using the central composite design (CCD) methodology to determine the most effective combination of ingredients for maximizing concrete performance. The investigation involves a physicochemical analysis of CPA to assess its pozzolanic characteristics. Laboratory experiments are then conducted to assess the compressive and flexural strengths of concrete mixtures formulated with varying proportions of CPA, cement, and aggregates. The results show that a mix ratio of 0.2:0.0875:0.3625:0.4625 for cement, CPA, fine, and coarse aggregates, respectively, yields a maximum compressive strength of 28.51 MPa. Additionally, a maximum flexural strength of 10.36 MPa is achieved with a mix ratio of 0.2:0.0875:0.3625:0.525. The experimental data were used to develop quadratic predictive models, followed by statistical analyses. The culmination of the research resulted in the identification of an optimal concrete blend that significantly enhances both compressive and flexural strength. To ensure the reliability of the model, rigorous validation was conducted using student's t-test, revealing a strong correlation between laboratory findings and simulated values, with computed p-values of 0.9987 and 0.9912 for compressive and flexural strength responses, respectively. This study underscores the potential for enhancing concrete properties and reducing waste through the effective utilization of CPA in the construction sector.

Comparison of industrial engineering programs in Colombia based on standardized test results.

This study aims to evaluate the performance of industrial engineering programs across various Higher Education Institutions (HEIs) in Colombia, focusing on the SaberPRO test outcomes from 2016 to 2018. The evaluation considers three primary dimensions: the type of funding (public vs. private HEIs), geographical region, and program accreditation status. The methodology involved gathering and analyzing Saber Pro test results from students enrolled in industrial engineering programs at 82 Colombian HEIs during the specified period. In Colombia, the SaberPRO Exam, administered by the Colombian Institute for the Evaluation of Education (ICFES), is a critical measure for assessing the quality of future industrial engineering graduates. This research employs an analytical framework that includes Analysis of Variance (ANOVA) and hypothesis testing for mean comparisons to scrutinize the performance of these programs. Findings reveal notable disparities in performance between public and private institutions and between accredited and non-accredited programs. Additionally, through Multiple Factor Analysis (MFA), the study identifies the competencies with the highest discriminative capacity, recognizing 15 exemplary programs out of the 82 evaluated.

Application of analysis of variance to determine important features of signals for diagnostic classifiers of displacement pumps.

This paper presents the use of one-way analysis of variance ANOVA as an effective tool for ranking the features calculated from diagnostic signals and evaluates their impact on the accuracy of the machine learning system's classification of displacement pump wear.The first part includes a review of contemporary diagnostic systems and a description of typical damage of multi-piston displacement pumps and Its causes. The work also contains description of a diagnostic experiment which was conducted in order to obtain the matrix of vibration signals and the matrix of pressures measured at selected locations on the pump housing and at the pump pressure line. The measured signals were subjected to time-frequency analysis. The features of signals calculated in the time and frequency domains were ranked using the ANOVA. The next step involved the use the available classifiers in pump wear evaluation, conducting tests and assessing their effectiveness in terms of the ranking of features and the origin of diagnostic signals.

Role of Magnetic Resonance Imaging in the Evaluation of Age- and Gender-Related Changes in the Dimensions of the Pituitary Gland in the Indian Population.

Background MRI is the standard tool for imaging the pituitary gland. MRI is useful in detecting pathological conditions in the pituitary. Changes in the size and shape of the pituitary among different age groups are seen in MRI. Linear growth is seen in the pituitary during puberty except for growth spurts at the 1st, 10th, and 15th years, followed by a decline in pituitary height and cross-sectional area with increasing age. A convex upper margin was seen in females more than in males. There is a shortage of information about pituitary dimensions and volume in various age groups and among both genders in the Indian population. Hence, a study is needed to assess these parameters. Materials and methods A retrospective cross-sectional study was done in the MRI unit of Radiology, Saveetha Medical College and Hospital, Chennai. A total of 200 patients in the age group of 11-80 years who underwent MRI free from neuroendocrine, neurological, and psychiatric disorders were included in this study. Statistical analysis Measurements were made of the pituitary gland's height, volume, and anteroposterior and transverse dimensions. Using SPSS Statistics software (IBM Corp. IBM SPSS Statistics for Windows. Armonk, NY: IBM Corp.), the data was input and examined. The ANOVA test revealed the relationship between anteroposterior dimension, transverse dimension, height, and volume with age. In contrast, an independent t-test determined the association of the same parameters with sex. The Chi-square test was used to assess the association of the shape of the pituitary gland with age and sex. Results Anteroposterior dimension, height, and volume of the pituitary gland were found to be statistically significant with age (p<0.05), but the transverse dimension was not significant with age (p>0.05). However, the independent t-test showed highly significant differences between the anteroposterior dimension in males and females. The shape of the pituitary gland was found to be statistically significant with age and gender. In contrast, the pituitary gland's transverse diameter, height, and volume showed no significance. Conclusion The study helps identify the substantial changes in the pituitary gland during a person's lifespan, which are affected by age and gender. The pituitary height and volume will reflect physiological neuroendocrine differences between younger and older male and female subjects.

The impact of prior care experience on nursing students' compassionate values and behaviours: A mixed methods study.

BACKGROUND: Compassion is critical to the provision of high-quality healthcare and is foregrounded internationally as an issue of contemporary concern. Paid care experience prior to nurse training has been suggested as a potential means of improving compassion, which has been characterised by the values and behaviours of care, compassion, competence, communication, courage, and commitment. There is however a dearth of evidence to support the effectiveness of prior care experience as a means of improving compassion in nursing. OBJECTIVE: To explore the impact of paid prior care experience on the values and behaviours of pre-registration nursing students indicated as characterising compassionate care. DESIGN: Longitudinal mixed methods design employing a modified concurrent triangulation strategy, comprising two work packages. Work package 1 was qualitative, and work package 2 adopted a concurrent embedded strategy with a dominant quantitative component. Research is reported in accordance with the Good Reporting of a Mixed Methods Study framework. SETTING(S): Three United Kingdom universities. PARTICIPANTS: Pre-registration nursing students attending one of three universities, and individuals who had previously participated in a Health Education England paid prior care experience pilot. Participant numbers at time point 1 were questionnaires n = 220, telephone interviews n = 10, and focus groups n = 8. METHODS: Work package 1 consisted of longitudinal semi-structured telephone interviews. Work package 2 comprised validated online questionnaires measuring emotional intelligence, compassion satisfaction and fatigue, resilience, psychological empowerment, and career commitment (as proxies of compassionate values and behaviours), and focus groups. Qualitative data were thematically analysed. Quantitative data were analysed via Analysis of Variance in SPSS v 26. RESULTS: Qualitative findings suggest that prior care experience has both positive and negative effects on students' compassionate values and behaviours, however positive effects do not extend to qualification. No statistically significant differences were found in any of the quantitative outcome measures between participants with and without paid prior care experience. A statistically significant increase in compassion fatigue was identified in both groups of participants post-qualification. Paid prior care experience did not prevent participants from experiencing reality shock on becoming a student or on qualification. CONCLUSIONS: There is insufficient evidence of longitudinal beneficial impact to recommend paid prior care experience as an effective intervention to foster nursing students' compassionate values and behaviours. These findings do not support mandating a period of paid care experience as a prerequisite for entry into nurse education. REGISTRATION: N/A. Tweetable abstract Insufficient evidence of longitudinal beneficial impact to recommend prior care experience as an effective intervention to foster nursing student compassion @PriorCareExp @Sarah_F_R.

Using a centered general linear model for detection of interactions among biomarkers.

The dummy variable based general linear model (gLM) is commonly used to model categorical factors and their interactions. However, the main factors and their interactions in a general linear model are often correlated even when the factors are independently distributed. Alternatively, the classical two-way factorial analysis of variance (ANOVA) model can avoid the correlation between the main factors and their interactions when the main factors are independent. But the ANOVA model is hardly applicable to a regular linear regression model especially in the presence of other covariates due to constraints on its model parameters. In this study, a centered general linear model (cgLM) is proposed for modeling interactions between categorical factors based on their centered dummy variables. We show that the cgLM can avoid the correlation between the main factors and their interactions as the ANOVA model when the main factors are independent. Meanwhile, similar to gLM, it can be used in regular regression and fitted conveniently using the standard least square approach by choosing appropriate baselines to avoid constraints on its model parameters. The potential advantage of cgLM over gLM for detection of interactions in model building procedures is also illustrated and compared via a simulation study. Finally, the cgLM is applied to a postmortem brain gene expression data set.

Insitu synthesis of Al- MgAl2O4 composites and parametric optimization of tribological characteristics.

In this research, multiobjective optimization of tribological characteristics of Al-4Mg/in-situ MgAl2O4 composites fabricated via ultrasonic cavitation treatment assisted stir casting technique was carried out. Al-4Mg alloy dispersed with 0.5, 1 and 2 wt% in-situ MgAl2O4 was prepared and the microstructural and mechanical characterisation of the same has been carried out. Reinforcement addition, load and sliding velocity at 3 different levels was considered to attain the responses wear rate and friction coefficient. To identify optimised process condition for the developed composites to attain reduced friction coefficient and wear rate condition, grey analysis is tried out. Experimental results analysed via Grey relation and analysis of variance (ANOVA) proved wt.% of MgAl2O4 particles as significant parameter trailed by load and speed. Based on grey relational grade, minimal wear loss at lowest frictional coefficient can be attained for the composite dispersed with 2 wt% of in-situ MgAl2O4 at 20 N load and 2 m/s sliding velocity. Mechanisms behind the wear loss was analysed from worn out surface micrographs.

Field Measurement and Analysis on the Mechanical Response of Asphalt Pavement Using Large-Particle-Size Crushed Stone Base Treated with Fly Ash and Slag Powder.

The mechanical response of asphalt pavement under vehicular loading is an essential reference for crack-resistant pavement design. However, limited research focuses on the mechanical response measurement of asphalt pavement using a large-particle-size crushed stone base treated with fly ash and slag powder. Therefore, two types of asphalt pavements were constructed. The first type of asphalt pavement uses a large-particle-size crushed stone base treated with fly ash and slag powder, where the slag powder uses granulated blast furnace slag powder. The second type uses a conventional cement-stabilized crushed stone base and serves as a reference structure. Firstly, the strain gauges and temperature sensors were installed during the construction of asphalt pavements. Secondly, the mechanical response of the pavement was tested at different speeds and service time conditions. Then, sensitivity analysis and three-factor analysis of variance (ANOVA) were carried out. Finally, the prediction equations were developed. The results show that the longitudinal strain pulse of the asphalt layer exhibited a "compression-tension-compression" characteristic. For the transverse strain pulse of the asphalt layer, the base layer's transverse and longitudinal strain pulses were only shown as "tensile" characteristics. The vehicular speed significantly affected the strain values for the base and asphalt layers, showing a decreasing trend with increasing speed. For the asphalt layer, the strain values showed an increasing trend with increasing temperature; for the base layer, the strain values showed a decreasing trend with increasing service time. The type of base layer had a significant effect on the strain value. Compared with the conventional base layer, the large-particle-size crushed stone base treated with fly ash and slag powder had lower strain at the base layer and a lower position of the asphalt layer, which could better prevent bottom-up fatigue cracking. Finally, the strain prediction model of the pavement under the speed and temperature (service time) was fitted to obtain a model that can predict the mechanical response of the pavement under different operating conditions. The findings of this research can provide a reference for the design of asphalt pavement using a large-particle-size crushed stone base treated with fly ash and slag powder.

Safety climate in Pakistani universities' laboratories: an assessment of individual factors.

Objectives. Current research aims to identify factors that affect the occupational safety climate in university laboratories despite their perception as low-risk areas compared to industrial environments. Methods. A safety climate survey was conducted in science laboratories across various engineering universities in Pakistan. The survey questionnaire was administered to 406 personnel, and a quantitative method for analysis was selected to examine the socio-demographic variables. A 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) was used to perceive responses from participants. Additionally, a scale reliability test was conducted, and multivariate analysis of variance was performed to determine the relationship between selected dependent and independent variables. Results. The study found an overall safety climate score of 3.16 ± 0.55, indicating a moderate to high perception of safety on a scale of 1-5. Parameters such as role in the laboratory, departments/disciplines, accident experience and safety training significantly affected the safety climate score, while gender, age group, duration in university and accident witnessing did not. Conclusion. Upper management involvement, safety communication and direct supervision are crucial for improving the safety climate of university laboratories. The study recommends the consideration of the identified significant safety climate dimensions in laboratory safety policy-making at academic institutes.

Clustering of lifestyle behaviours and analysis of their associations with MAFLD: a cross-sectional study of 196,515 individuals in China.

BACKGROUND: The aggregation of lifestyle behaviours and their association with metabolic-associated fatty liver disease (MAFLD) remain unclear. We identified lifestyle patterns and investigated their association with the risk of developing MAFLD in a sample of Chinese adults who underwent annual physical examinations. METHODS: Annual physical examination data of Chinese adults from January 2016 to December 2020 were used in this study. We created a scoring system for lifestyle items combining a statistical method (multivariate analysis of variance) and clinical expertise (Delphi method). Subsequently, principal component analysis and two-step cluster analysis were implemented to derive the lifestyle patterns of men and women. Binary logistic regression analysis was used to explore the prevalence risk of MAFLD among lifestyle patterns stratified by sex. RESULTS: A total of 196,515 subjects were included in the analysis. Based on the defined lifestyle scoring system, nine and four lifestyle patterns were identified for men and women, respectively, which included "healthy or unhealthy" patterns and mixed patterns containing a combination of healthy and risky lifestyle behaviours. This study showed that subjects with an unhealthy or mixed pattern had a significantly higher risk of developing MAFLD than subjects with a relatively healthy pattern, especially among men. CONCLUSIONS: Clusters of unfavourable behaviours are more prominent in men than in women. Lifestyle patterns, as important factors influencing the development of MAFLD, show significant sex differences in the risk of MAFLD. There is a strong need for future research to develop targeted MAFLD interventions based on the identified behavioural clusters by sex stratification.

Multi-component analyses of raspberry: Optimization of extraction procedure and network pharmacology.

The contents of ellagic acid and kaempferol-3-O-rutinoside, the chief active components of raspberry, are considered the quality control indices of raspberry. This work employed the ant colony neural network (ACO-BPNN) to optimize their extraction processes, and the combination of network pharmacology and molecular docking technology to unveil the potential pharmacological effects of these components. Based on the single-factor test (ultrasonic time, ethanol concentration, ultrasonic temperature, and solid-liquid ratio), a factorial experiment with 4-factors and 3-levels was conducted in parallel for 3 times. The multi-factor analysis of variance results revealed high-order interactions among the factors. Then, the ACO-BPNN model was established to characterize the complex relationship of experimental data. After further verification, relative errors were all less than 8 %, implying the model's effectiveness and reliability. Moreover, with the network pharmacology, 66 key targets were screened out and mainly concentrated in PI3K-AKT, MAPK, and Ras signal pathways. Molecular docking revealed the binding sites between active components and key targets.

The Study of Factors Affecting the Covid-19 Death Rate.

This study focuses on the analysis of the effects of potential factors on the Death Rate of Covid-19. The factors, in both demographic and economic aspects, have been discussed in the study through the extensive application of statistical inference, such as the Analysis of Variance (ANOVA), Univariate Linear Regression, and Multivariate Linear Regression. In the result section, the significance of the effects of these factors on Covid-19 has been carefully discussed.

Xanthohumol ameliorates cardiac injury induced by sepsis in a mice model: role of toll-like receptor 4.

Sepsis, a life-threatening condition arising from infection, often results in multi-organ failure, including cardiac dysfunction. This study investigated Xanthohumol, a natural compound, and its potential mechanism of action to enhance heart function following sepsis. A total of twenty-four adult male Swiss albino mice were allocated randomly to one of four equal groups (n=6): sham, CLP, vehicle Xanthohumol the same amount of DMSO injected IP 10 minutes before the CLP, and Xanthohumol group (0.4 mg/kg of Xanthohumol administered IP before the CLP process). Toll-like receptor 4, pro-inflammatory mediators, anti-inflammatory markers, oxidative stress indicators, apoptosis markers, and serum cardiac damage biomarkers were measured in the cardiac tissue using ELISA. Data with normal distribution were analyzed using t-test and ANOVA tests (p<0.05). In comparison to the sham group, the sepsis group had significantly higher levels of TLR-4, IL-6, TNF-α, MIF, F2-isoprostane, caspase-3, cTn-I, and CK-MB, while the pre-treated group with Xanthohumol had significantly lower levels (p<0.05) of these markers than the sepsis group. Bcl-2 showed no significant difference in Xanthohumol pre-treated group relative to the sepsis group, while IL-10 was significantly elevated. Xanthohumol dramatically reduced cardiac tissue injury (p<0.05) relative to the CLP group. By blocking the downstream signal transduction pathways of TLR-4 and NF-kB, Xanthohumol was shown to lessen cardiac damage in male mice during CLP-induced polymicrobial sepsis.

Effect of Sulforaphane on cardiac injury induced by sepsis in a mouse model: Role of toll-like receptor 4.

As sepsis is associated with a 50% increase in mortality, sepsis-induced cardiomyopathy has become a critical topic. A multidisciplinary approach is required for the diagnosis and treatment of septic cardiomyopathy. This study looked at Sulforaphane, a natural product that aims to evaluate cardiac function after sepsis, and its likely mechanism of action. Twenty-four adult male Swiss albino mice were randomly divided into 4 equal groups (n=6): sham, CLP, vehicle Sulforaphane (the same amount of DMSO injected IP one hour before the CLP), and Sulforaphane group (one hour before the CLP, a 5mg/kg dose of Sulforaphane was injected). Cardiac tissue levels of toll-like receptor 4 (TLR-4), pro-inflammatory mediators, anti-inflammatory markers, oxidative stress markers, apoptosis markers, and serum cardiac damage biomarkers were assessed using ELISA. Statistical analyses, including t-tests and ANOVA tests, were performed with a significance level of 0.05 for normally distributed data. Compared to the sham group, the sepsis group had significantly elevated levels of TLR-4, IL-6, TNF-α, MIF, F2-isoprostane, caspase-3, cTn-I, and CK-MB (p<0.05). In contrast, the Sulforaphane pre-treated group demonstrated significantly lower levels of these markers (p<0.05). Additionally, Bcl-2 levels were significantly reduced (p<0.05) in the Sulforaphane group. Sulforaphane administration also significantly attenuated cardiac tissue injury (p<0.05). The findings suggest that Sulforaphane can decrease heart damage in male mice during CLP-induced polymicrobial sepsis by suppressing TLR-4/NF-kB downstream signal transduction pathways.

Spark erosion behavior in the machining of MoSi2-SiC ceramic composites for improving dimensional accuracy.

The novel applications of MoSi2 and SiC as matrix and reinforcing materials in the creation of high-performance composites were investigated in this work. In particular, Spark Erosion Machining's geometric tolerances were studied in order to shed light on the technique's potential for precision manufacture in the realm of MoSi2-SiC composites. Our research focused on evaluating critical parameters and their impact on machining performance, including material removal rate, surface roughness, wear rate and drilled hole accuracy. In-depth research revealed the critical input factors that had the greatest impact on the machining procedure. Notably, parameters such as current (32%), sparking on time (23%), sparking gap voltage (12%), dielectric pressure (12%), and sparking off time (17%) emerged as the most influential factors, as determined by ANOVA analysis. These findings provide valuable insights into optimizing the Sparking EDM approach for MoSi2-SiC composite materials. This study further demonstrated the improvement in composite desirability ratings across multiple performance criteria, highlighting the effectiveness of Sparking EDM in enhancing machining outcomes (e.g., from 0.8523 to 0.9527). Correlations between the EDM's output responses were found to be quite high when geometric tolerances and the coefficient of determination (R2) were used (0.7858, 0.9625, 0.8427, 0.8678, 0.8474, 0.8368, 0.8344, 0.8671). Consider that, for the sake of a more complete understanding of the procedure's approach, the emphasis is on the methodology rather than the multifaceted metal removal mechanisms involved. This research doesn't dive further into the physical concerns of Spark Erosion Machining, but it does provide insights into the practical application of this technique in the precision manufacturing of MoSi2-SiC composite materials. For real-world medical applications such implanted devices, dental implants, surgical instruments, biological sensors and diagnostics, this study provides a valuable and encouraging approach. A validation experiment verifies the results, proving the feasibility of improved spark erosion in high-precision production. The results of this research show that EDM methods can be fine-tuned to produce ceramic composites with much greater MRR, superior surface finishes and a marked decrease in subsurface cracking and microstructural modifications. This is essential for protecting the integrity of materials used in life-saving medical equipment.

Study on the Shape Characteristics and the Allometry of Phalaenopsis Leaves for Greenhouse Management.

Phalaenopsis orchids are highly economical ornamental potted plants. Controlling their production schedule requires information on the leaf development characteristics of the orchids. Phalaenopsis leaves affect the plant's photosynthesis, respiration, and transpiration. The leaf growth conditions can serve as a development index for greenhouse management. The use of the growth characteristics of Phalaenopsis leaves as the basis for greenhouse cultivation and management needs to be studied. The allometry of Phalaenopsis leaves is worth studying. The goal of this research was to investigate the allometry of Phalaenopsis leaves and develop prediction models of the total leaf area. Then, these total leaf area models were developed and validated. In this study, five Phalaenopsis varieties (amabilis, Sin-Yuan beauty, Ruey Lish beauty, Ishin KHM1095, and Sogo F1091) were selected. Each sample had five mature leaves. The lengths, widths, and areas of the sequential leaves were measured, and then the length ratios, width ratios, and area ratios were calculated. The top and bottom models were used to calculate the total leaf areas. The results indicate that no significant differences could be found in the length ratios, width ratios, and area ratios of the sequential leaves from the same variety. However, significant differences were found in these leaf characteristics between different varieties. The observation of leaf growth characteristics can be used to provide useful information for Phalaenopsis management. Comparing the predictive criteria of the two models, the top model had a better predictive ability than the bottom model. From a practical viewpoint, measuring the top leaf area is easier than measuring the bottom leaf area in a greenhouse operation. Comparing the effects of the sample numbers on the predictive ability of the model, the sample number of 30 was sufficient to ensure the accuracy of the total leaf area measurements. We provide an easy and accurate method to measure the total leaf area of Phalaenopsis. The calculated values of total leaf areas can be incorporated into decision models for smart management.

Dataset of manual metal arc welded heterogeneous thin steel plates AISI1018 AND AISI4340.

Advancement in technology demands the joining of heterogeneous metals of low and high-carbon steel grades. In this investigation, AISI1018 low and AIS4340 medium carbon steels were welded to form a heterogeneous thin metal joint using the Manual Metal Arc Welding (MMAW) method. Experimental variations of welding current, electrode position, and weld orientation are selected as the MMAW parameters. The trials are planned using the Full Factorial Design (FFD) and the trial results are analysed using Analysis of Variance (ANOVA) and regression methods. A computerized tensile testing machine (TM2101N) was used to test the tensile strength of the welded specimens that were prepared in accordance with the ASTM E646 - 98 standards. The prediction model for tensile strength was generated based on regression analysis. The ANOVA and prediction model helped in studying the effect of the MMAW parameters.

Mathematical Modelling and Optimization for Facile Synthesis of Structured Activated Carbon (ACs) from Adansonia kilima (Baobab) Wood Chips Integrating Microwave-Assisted Pyrolysis for the Elimination of Lead (II) Cations from Wastewater Effluents.

In this research, activated carbon (AC) was synthesized from ligno-cellulosic residues of Adansonia kilima (Baobab) wood chips (AKTW) using two-step semi-carbonization and subsequent pyrolysis using microwave-induced heating (MWP) in the presence of a mild activating agent of K2CO3. The influence of process input variables of microwave power (x1), residence time (y1), and amount of K2CO3 (z1) were analysed to yield superior quality carbon having maximum removal efficiencies (R1) for lead (II) cations from waste effluents, fixed carbon percentages (R2), and carbon yield percentages (R3). Analysis of variance (ANOVA) was used to develop relevant mathematical models, with an appropriate statistical assessment of errors. Level factorial response surface methodology (RSM) relying on the Box-Behnken design (BBD) was implemented for the experimental design. The surface area and porous texture of the samples were determined using Brunauer, Emmett, and Teller (BET) adsorption/desorption curves based on the N2 isotherm. Surface morphological structure was observed using field emission scanning electron microscopic (FESEM) analysis. Thermogravimetric analysis (TGA) was carried out to observe the thermal stability of the sample. Change in the carbon content of the samples was determined using ultimate analysis. X-ray diffraction (XRD) analysis was performed to observe the crystalline and amorphous texture of the samples. The retention of a higher proportion of fixed carbon (80.01%) ensures that the synthesized adsorbent (AKTWAC) will have a greater adsorption capacity while avoiding unwanted catalytic activity for our synthesized final sample.

Assessment of quality of life in patients having Poly-Cystic Ovarian Syndrome: A cross-sectional facility-based study.

BACKGROUND: Polycystic ovarian syndrome (PCOS) is a complex and heterogeneous disorder affecting various body organs. Menstrual irregularity, anovulation, and many cosmetic issues faced by PCOS patients endanger the essence of being a woman and may have a deleterious impact on their health-related quality of life (HRQOL). This study aimed to assess HRQOL in patients with PCOS and to identify the clinical and socio-demographic factors that might predict poor HRQOL. MATERIALS AND METHODS: This cross-sectional study was carried out in the tertiary care hospital in India. A total of 275 women visiting the same setting and diagnosed with PCOS were included. The participants' quality of life was studied using a disease-specific HRQOL questionnaire. Information regarding clinical and socio-demographics was collected using the interviewer schedule. For evaluating the predictors of HRQOL in PCOS subjects, analysis of variance and independent t-test was applied. For subgroup analysis, the post hoc (Gabriel) test was applied. RESULTS: The average total score of HRQOL of the study participants was 125.41 ± 29.1. The lowest weighted mean score was for menstrual problems. Among the socio-demographic variables, age and educational level influenced the HRQOL scores. Highly educated women reported the poorest HRQOL. The analysis of variance also indicated a significant variation in HRQOL scores among body mass index categories [F (4,270) = 5.09, P = <.001] and hirsutism status [F (2,272) = 14.222, P =<.001]. CONCLUSIONS: Menstrual irregularity, hirsutism, increased body mass index, educational status, and age are critical in altering HRQOL in PCOS cases. Clinicians should inquire about the HRQOL of patients with severe clinical manifestations and appropriate support must be provided during patient care.