Mitigating the environmental concerns through total quality management and green manufacturing practices.

This study seeks to explore the intricate relationship between total quality management (TQM) and environmental performance (EP), incorporating the mediating role of green manufacturing practices (GMPs). Additionally, the study examines the moderating impact of environmental strategy on the connections between GMPs and EP. Data were collected through a questionnaire distributed to managers of manufacturing small and medium-sized enterprises (SMEs) and were subjected to analysis using structural equation modeling. The results reveal a positive and significant impact of TQM on EP. Furthermore, the findings suggest that GMPs partially mediate the association between TQM and EP, while the anticipated moderating effect of environmental strategy between GMPs and EP is also supported in this study. These outcomes hold valuable implications for enhancing the environmental performance of SMEs through the integration of TQM and GMPs. It is important to note that this research exclusively focuses on manufacturing SMEs; therefore, future studies should extend their examination of this concept to other industries. Additionally, the study's findings provide a valuable roadmap for SME administrators aiming to elevate their environmental performance.

Potential application of 2D nano-layered MXene in analysing and remediating endocrine disruptor compounds and heavy metals in water.

With the advancement of technologies and growth of the economy, it is inevitable that more complex processes are deployed, producing more heterogeneous wastewater that comes from biomedical, biochemical and various biotechnological industries. While the conventional way of wastewater treatment could effectively reduce the chemical oxygen demand, pH and turbidity of wastewater, trace pollutants, specifically the endocrine disruptor compounds (EDCs) that exist in µg L-1 or ng L-1 have further hardened the detection and removal of these biochemical pollutants. Even in small amounts, EDC could interfere human's hormone, causing severe implications on human body. Hence, this review elucidates the recent insights regarding the effectiveness of an advanced 2D material based on titanium carbide (Ti3C2Tx), also known as MXene, in detecting and removing EDCs. MXene's highly tunable feature also allows its surface chemistry to be adjusted by adding chemicals with different functional groups to adsorb different kinds of EDCs for biochemical pollution mitigation. At the same time, the incorporation of MXene into sample matrices also further eases the analysis of trace pollutants down to ng L-1 levels, thereby making way for a more cleaner and comprehensive wastewater treatment. In that sense, this review also highlights the progress in synthesizing MXene from the conventional method to the more modern approaches, together with their respective key parameters. To further understand and attest to the efficacy of MXene, the limitations and current gaps of this potential agent are also accentuated, targeting to seek resolutions for a more sustainable application.

Modelling photoperiod in enhancing hydrogen production from Chlorella vulgaris sp. while bioremediating ammonium and organic pollutants in municipal wastewater.

Municipal wastewater is ubiquitously laden with myriad pollutants discharged primarily from a combination of domestic and industrial activities. These heterogeneous pollutants are threating the natural environments when the traditional activated sludge system fails sporadically to reduce the pollutants' toxicities. Besides, the activated sludge system is very energy intensive, bringing conundrums for decarbonization. This research endeavoured to employ Chlorella vulgaris sp. In converting pollutants from municipal wastewater into hydrogen via alternate light and dark fermentative process. The microalgae in attached form onto 1 cm3 of polyurethane foam cubes were adopted in optimizing light intensity and photoperiod during the light exposure duration. The highest hydrogen production was recorded at 52 mL amidst the synergistic light intensity and photoperiod of 200 µmolm-2s-1 and 12:12 h (light:dark h), respectively. At this lighting condition, the removals of chemical oxygen demand (COD) and ammoniacal nitrogen were both achieved at about 80%. The sustainability of microalgal fermentative performances was verified in recyclability study using similar immobilization support material. There were negligible diminishments of hydrogen production as well as both COD and ammoniacal nitrogen removals after five cycles, heralding inconsequential microalgal cells' washout from the polyurethane support when replacing the municipal wastewater medium at each cycle. The collected dataset was finally modelled into enhanced Monod equation aided by Python software tool of machine learning. The derived model was capable to predict the performances of microalgae to execute the fermentative process in producing hydrogen while subsisting municipal wastewater at arbitrary photoperiod. The enhanced model had a best fitting of R2 of 0.9857 as validated using an independent dataset. Concisely, the outcomes had contributed towards the advancement of municipal wastewater treatment via microalgal fermentative process in producing green hydrogen as a clean energy source to decarbonize the wastewater treatment facilities.

Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis.

BACKGROUND: Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres. METHODS: For this meta-analysis, we identified individual participant data from a WHO public call for data on the management of tuberculosis in children and adolescents and referral from childhood tuberculosis experts. We included studies that prospectively recruited consecutive participants younger than 10 years attending health-care centres in countries with a high tuberculosis incidence for clinical evaluation of pulmonary tuberculosis. We collated individual participant data including clinical, bacteriological, and radiological information and a standardised reference classification of pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms. We then used the data to develop two multivariable prediction models that included features used in clinical evaluation of pulmonary tuberculosis-one with chest x-ray features and one without-and we investigated each model's generalisability using internal-external cross-validation. The parameter coefficient estimates of the two models were scaled into two scoring systems to classify tuberculosis with a prespecified sensitivity target. The two scoring systems were used to develop two pragmatic, treatment-decision algorithms for use in primary health-care settings. FINDINGS: Of 4718 children from 13 studies from 12 countries, 1811 (38·4%) were classified as having pulmonary tuberculosis: 541 (29·9%) bacteriologically confirmed and 1270 (70·1%) unconfirmed. Existing treatment-decision algorithms had highly variable diagnostic performance. The scoring system derived from the prediction model that included clinical features and features from chest x-ray had a combined sensitivity of 0·86 [95% CI 0·68-0·94] and specificity of 0·37 [0·15-0·66] against a composite reference standard. The scoring system derived from the model that included only clinical features had a combined sensitivity of 0·84 [95% CI 0·66-0·93] and specificity of 0·30 [0·13-0·56] against a composite reference standard. The scoring system from each model was placed after triage steps, including assessment of illness acuity and risk of poor tuberculosis-related outcomes, to develop treatment-decision algorithms. INTERPRETATION: We adopted an evidence-based approach to develop pragmatic algorithms to guide tuberculosis treatment decisions in children, irrespective of the resources locally available. This approach will empower health workers in primary health-care settings with high tuberculosis incidence and limited resources to initiate tuberculosis treatment in children to improve access to care and reduce tuberculosis-related mortality. These algorithms have been included in the operational handbook accompanying the latest WHO guidelines on the management of tuberculosis in children and adolescents. Future prospective evaluation of algorithms, including those developed in this work, is necessary to investigate clinical performance. FUNDING: WHO, US National Institutes of Health.

Drug-Resistant Tuberculosis Treatment Outcomes among Children and Adolescents in Karachi, Pakistan.

BACKGROUND: Significant data gaps exist for children and adolescents with drug-resistant (DR) TB, particularly from high TB incidence settings. This report provides a descriptive analysis of programmatic outcomes among children and adolescents treated for DR-TB in Pakistan. METHODS: We extracted programmatic data from January 2014 to December 2019 from a tertiary care hospital with specialised child and adolescent DR-TB services. A physician assessed all children and adolescents (0-19 years) with presumptive DR-TB, including details of exposure to DR-TB, medical history, radiology, and laboratory results. All patients received treatment as per national DR-TB management guidelines based on WHO recommendations. RESULTS: There were 262 treatment episodes for 247 patients enrolled during the study period. The median age of the cohort was 16 years (IQR: 13-18 years) with 16 (6.1%) children being under 5 years; 237 (90.5%) patients had pulmonary TB. The majority of the patients (194 or 74.1%) experienced a favourable treatment outcome and 26 (9.9%) died while on treatment. Female patients (78.5%) were more likely to experience favourable outcomes compared to males (64.7%; chi-sqr p-value = 0.02). CONCLUSIONS: We found high rates of favourable outcomes in children and adolescents treated for DR-TB. However, there were few young children in our cohort and there was a considerable gender gap that enhanced efforts to diagnose DR-TB in young children and to elucidate and mitigate the reasons for poor outcomes amongst males.

Digital Students' Satisfaction With and Intention to Use Online Teaching Modes, Role of Big Five Personality Traits.

During the COVID-19 pandemic, online teaching modes were found vital to continue students' learning process, but sustainable implementation of online teaching models is an area of concern for policymakers. Psychiatrists are also eager to know students' behavior toward learning and modes of teaching during COVID-19. We have drawn a model based on the big five personality traits to study students' satisfaction with online teaching modes and their adoption intentions toward online teaching modes. We have collected data from 718 bachelor's and master's level students from four different universities. We have applied the SEM-ANN dual-stage approach to test personality traits' influence and ranked them based on their normalized importance. The results revealed that agreeableness, conscientiousness, neuroticism, and openness positively influence students' satisfaction with online teaching models, but that extraversion negatively influences their satisfaction. Agreeableness, extraversion, and neuroticism positively impact, but openness negatively influences. Conscientiousness does not affect adoption intention. Furthermore, agreeableness is the most significant, and conscientiousness is the least important factor for students to adopt online teaching modes. The findings of the study have useful perceptiveness for educational policymakers, academics, and psychiatrists.

Does Health Consciousness Matter to Adopt New Technology? An Integrated Model of UTAUT2 With SEM-fsQCA Approach.

Every emerging technology has its pros and cons; health-conscious users pay more importance to healthy and environment-friendly technologies. Based on the UTAUT2 model, we proposed a comprehensive novel model to study the factors influencing consumers' decision-making to adopt the technology. Compared to prior studies that focused on linear models to investigate consumers' technology adoption intentions and use behavior. This study used a Structural Equation Modeling-fuzzy set qualitative comparative analysis (SEM-fsQCA) approach to account for the complexity of customers' decision-making processes in adopting new technology. We collected valid responses from 830 consumers, analyzed them, and evaluated them using a deep learning SEM-fsQCA technique to capture symmetric and asymmetric relations between variables. We have extensively incorporated a health-consciousness attitude as a predictor and mediator to understand better the decision-making toward technology adoption, specifically 5G technology. All the factors tested in our model are statistically significant except the economic factors. Health-consciousness attitude (HCA) and behavioral intention (BI) found significant predictors and valid mediators in the process of 5G technology adoption. FsQCA provided six configurations to achieve high 5G adoption. The findings have significant practical ramifications for telecom corporations, advertisers, government officials, and key policymakers. Additionally, the study added substantial theoretical literature to technology adoption, particularly the adoption of 5G technology.

Impact of Role Conflict on Intention to Leave Job With the Moderating Role of Job Embeddedness in Banking Sector Employees.

This study investigates why some employees intend to leave their jobs when facing conflict between family responsibilities and job routines. The present study also reveals the moderating role of on-the-job embeddedness between role conflict and intention to leave the job. Drawing on conservation of resources theory, the paper investigates the buffering effect of the three on-the-job embeddedness components (fit, links, and sacrifice). Data were collected from banking officers because most of the employees have to face role conflict between family and job responsibilities, as banking is considered among the most stressful jobs. Collected data were analyzed by applying structural equation modeling. Results indicate that the role conflict significantly influences intention to leave the job. Furthermore, the study shows that on-the-job embeddedness moderates the relationship between role conflict and intention to leave. The results suggest that organizations can reduce turnover intention during times of work and life conflict by developing employee on-the-job embeddedness. This study provides some insights to managers on why many employees leave their jobs and how to overcome this problem. Management should also offer extra and available resources in periods of greater tension to minimize early thinking regarding quitting.

A rapid and efficient DNA extraction method suitable for marine macroalgae.

Macroalgae are a diverse group of organisms. Marine macroalgae, in particular, have numerous medicinal and industrial applications. Molecular studies of macroalgae require suitable concentrations of DNA free of contaminants. At present, numerous protocols exist for DNA extraction from macroalgae. However, they are either time consuming, expensive or work only with few species. The method described in this study is rapid and efficient and applicable to different types of marine macroalgae. This method yields an average of 3.85 µg of DNA per 50 mg of algal tissue, with an average purity of 1.88. The isolated DNA was suitable for PCR amplification of universal plastid region of macroalgae.

Expression and characterization of alkaline protease from the metagenomic library of tannery activated sludge.

Metagenomics has the potential to facilitate the discovery of novel enzymes; however, to date, only a few alkaline proteases have been characterized from environmentally-sourced DNA. We report the identification and characterization of an alkaline serine protease designated as Prt1A from the metagenomic library of tannery activated sludge. Sequence analysis revealed that Prt1A is closely related to S8A family subtilisins with a catalytic triad of Asp143, His173, and Ser326. The putative protease gene (prt-1A) was subcloned in pET 28a (+) vector and overexpressed in Escherichia coli BL21(DE3)pLysS cells. This 38.8 KDa recombinant protease was purified to homogeneity by nickel affinity chromatography and exhibited optimal enzyme activity at elevated pH (11.0) and temperature (55°C). The enzyme activity was enhanced by the addition of 5 mM Ca2+ ions, and was stable in the presence of anionic detergent, oxidizing agent and various organic solvents. The enzyme displayed high affinity and catalytic efficiency for casein under standard assay conditions (Vmax = 279 U/mg/min, Km = 1.70 mg/mL) and was also compatible with commercial detergents. These results suggest that Prt1A protease could act as an efficient enzyme in various industrial applications.

Direct utilization of waste water algal biomass for ethanol production by cellulolytic Clostridium phytofermentans DSM1183.

Direct bioconversion of waste water algal biomass into ethanol using Clostridium phytofermentans DSM1183 was demonstrated in this study. Fermentation of 2% (w/v) autoclaved algal biomass produced ethanol concentration of 0.52 g L(-1) (solvent yield of 0.19 g/g) where as fermentation of acid pretreated algal biomass (2%, w/v) produced ethanol concentration of 4.6 g L(-1) in GS2 media (solvent yield of 0.26 g/g). The control experiment with 2% (w/v) glucose in GS2 media produced ethanol concentration of 2.8 g L(-1) (solvent yield of 0.25 g/g). The microalgal strains from waste water algal biomass were identified as Chlamydomonas dorsoventralis, Graesiella emersonii, Coelastrum proboscideum, Scenedesmus obliquus, Micractinium sp., Desmodesmus sp., and Chlorella sp., based on ITS-2 molecular marker. The presence of glucose, galactose, xylose and rhamnose were detected by high performance liquid chromatography in the algal biomass. Scanning Electron Microscopy observations of fermentation samples showed characteristic morphological changes in algal cells and bioaccessibility of C. phytofermentans.

A Rapid and Economical Method for Efficient DNA Extraction from Diverse Soils Suitable for Metagenomic Applications.

A rapid, cost effective method of metagenomic DNA extraction from soil is a useful tool for environmental microbiology. The present work describes an improved method of DNA extraction namely "powdered glass method" from diverse soils. The method involves the use of sterile glass powder for cell lysis followed by addition of 1% powdered activated charcoal (PAC) as purifying agent to remove humic substances. The method yielded substantial DNA (5.87 ± 0.04 µg/g of soil) with high purity (A260/280: 1.76 ± 0.05) and reduced humic substances (A340: 0.047 ± 0.03). The quality of the extracted DNA was compared against five different methods based on 16S rDNA PCR amplification, BamHI digestion and validated using quantitative PCR. The digested DNA was used for a metagenomic library construction with the transformation efficiency of 4 X 106 CFU mL-1. Besides providing rapid, efficient and economical extraction of metgenomic DNA from diverse soils, this method's applicability is also demonstrated for cultivated organisms (Gram positive B. subtilis NRRL-B-201, Gram negative E. coli MTCC40, and a microalgae C. sorokiniana UTEX#1666).

Agrobacterium-mediated transformation of three freshwater microalgal strains.

Microalgal transformation has gained interest in recent years. Agrobacterium-mediated transformation remains as the most efficient method for the development of transgenic plants and microalgae due to its wide host range, inexpensive procedure and transfer of large segments of DNA. In the present study, three different microalgal species were isolated from freshwater environment and identified based on the morphological characteristics and ITS-2 region amplification. Agrobacterium-mediated transformation was successful for the isolates Chlorella sp., Ankistrodesmus sp and Scenedesmus bajacalifornicus. Gene integration and expression was confirmed by PCR amplification of hptII and GUS histochemical assay. A. tumifaciens contamination was checked by amplification of npt II gene (kanamycin resistant) which lies outside the T-border. Based on GUS assay, transformation efficiencies were found to be 12.25% for Chlorella sp. 2.96% for Scenedesmus bajacalifornicus and 3.5% for Ankistrodesmus sp.