Present Research Research Articles

Biopharmaceutical distribution and pharmacodynamic evaluation of intra nasal in-situ gel of Lamotrigine for brain targeted drug delivery

Background: The present research investigates the nasal delivery of Lamotrigine by incorporating it into a natural in-situ gelling system. Additionally, the retention of the drug in the nasal cavity was enhanced by employing the natural mucoadhesive polymer locust bean gum (LBG). A preliminary investigation was conducted to determine the optimal concentration of gellan gum. The dosage of the drug was calculated using the Robinson Erikson equation. The central composite design was utilized to optimize the influence of individual variables such as gellan gum and locust bean gum on various responses, including gelation time, gel viscosity, mucoadhesive strength, and the time taken for the drug to release half of its initial concentration (t50). The goal of the current study was to evaluate the in-vivo effectiveness of intra nasal in-situ gel of Lamotrigine. Methodology: The pharmacokinetic and tissue distribution studies were carried out to evaluate the brain targeting efficiency of lamotrigine. Blood samples and tissues of various vital organs like brain, liver, kidneys and heart were obtained at different time intervals, plasma and tissue concentration of Lamotrigine was estimated by reverse phase HPLC. Results: According to the pharmacokinetic analysis, Cmax and AUC0-α is found to be significantly more (P<0.05) for nasal route compared to oral route. In comparison to the oral route, Cmax and AUC0-α was 7 and 6.5 folds more for IN route. The absolute bioavailability was found to be 159.07%. with regard to the oral group, minimal drug was present in any of the other tissue samples. In the pharmacodynamic data also the formulation through nasal route showed a significant difference compared to oral route (pure drug suspension) delivery in PTZ induced study. Keywords: Lamotrigine, in-vivo study, tissue distribution, pharmacodynamics

Attentional Orienting and Disfluency-Related Memory Boost Are Intact in Adults With Moderate-Severe Traumatic Brain Injury.

Traumatic brain injury (TBI) is associated with a range of cognitive-communicative deficits that interfere with everyday communication and social interaction. Considerable effort has been directed at characterizing the nature and scope of cognitive-communication disorders in TBI, yet the underlying mechanisms of impairment are largely unspecified. The present research examines sensitivity to a common communicative cue, disfluency, and its impact on memory for spoken language in TBI. Fifty-three participants with moderate-severe TBI and 53 noninjured comparison participants listened to a series of sentences, some of which contained disfluencies. A subsequent memory test probed memory for critical words in the sentences. Participants with TBI successfully remembered the spoken words (b = 1.57, p < .0001) at a similar level to noninjured comparison participants. Critically, participants with TBI also exhibited better recognition memory for words preceded by disfluency compared to words from fluent sentences (b = 0.57, p = .02). These findings advance mechanistic accounts of cognitive-communication disorder by revealing that, when isolated for experimental study, individuals with moderate-severe TBI are sensitive to attentional orienting cues in speech and exhibit enhanced recognition of individual words preceded by disfluency. These results suggest that some aspects of cognitive-communication disorders may not emerge from an inability to perceive and use individual communication cues, but rather from disruptions in managing (i.e., attending, weighting, integrating) multiple cognitive, communicative, and social cues in complex and dynamic interactions. This hypothesis warrants further investigation.

Are women more risk-Averse than men regarding investment decision?

This paper emphasizes the significance of how gender influences the decision of an individual while taking investment decisions. It investigates the possible impact of gender on investment behavior, risk tolerance, and preferred investment avenues. Prior research has investigated of female investors having lower risk tolerance than men and tends to steer clear of high-risk investment options. The study made an effort to highlight the disparities between men's and women's investing options and risk tolerance. Differential gender-based investing patterns have been shaped by cultural expectations and traditional gender stereotypes. In several past literatures women have always been underestimated by researchers in the magnitude of investment decision making as several past papers have shown they have encountered various social stigma and that are the possible reasons which have a greater impact on their investment choices. According to research, women frequently have more risk-averse attitudes than men do when it comes to their financial activities. In our present research paper we clearly get a striking disparity in the investment options and a remarkably distinct tolerance level for risk aptitude for male and female investors.

Population characteristics and self-assessment of speaking and singing voice in Polish contemporary commercial music singers-an exploratory, cross-sectional study.

The domination of the Contemporary Commercial Music (CCM) industry in music markets has led to a significant increase in the number of CCM performers. Performing in a wide variety of singing styles involves exposing CCM singers to specific risk factors potentially leading to voice problems. This, in turn, necessitates the consideration of this particular group of voice users in the Occupational Health framework. The aim of the present research was threefold. First, it sought to profile the group of Polish CCM singers. Second, it was designed to explore the prevalence of self-reported voice problems and voice quality in this population, in both speech and singing. Third, it aimed to explore the relationships between voice problems and lifetime singing involvement, occupational voice use, smoking, alcohol consumption, vocal training, and microphone use, as potential voice risk factors. The study was conducted in Poland from January 2020 to April 2023. An online survey included socio-demographic information, singing involvement characteristics, and singers' voice self-assessment. The prevalence of voice problems was assessed by the Polish versions of the Vocal Tract Discomfort Scale (VTDS) and the Singing Voice Handicap Index (SVHI). Also, a self-reported dysphonia symptoms protocol was applied. The perceived overall voice quality was assessed by a Visual Analogue Scale (VAS) of 100 mm. 412 singers, 310 women and 102 men, completed the survey. Nearly half of the studied population declared lifetime singing experience over 10 years with an average daily singing time of 1 or 2 h. 283 participants received vocal training. For 11.4% of respondents, singing was the primary income source, and 42% defined their career goals as voice-related. The median scores of the VTDS were 11.00 (0-44) and 12.00 (0-40) for the Frequency and Severity subscales, respectively. The median SVHI score of 33 (0-139) was significantly higher than the normative values determined in a systematic review and meta-analysis (2018). Strong positive correlations were observed between SVHI and both VTD subscales: Frequency (r = 0.632, p < 0.001) and Severity (r = 0.611, p < 0.001). The relationships between most of the other variables studied were weak or negligible. The examined CCM singers exhibited substantial diversity with regard to musical genre preferences, aspirations pertaining to singing endeavors, career affiliations, and source of income. Singing voice assessment revealed a greater degree of voice problems in the examined cohort than so far reported in the literature, based on the SVH and VTDS.

Virtual reality and museum tourism in the anchoring paradigm

ABSTRACT The present research designed a series of two studies and examined the impacts of the additional VR tour on museum tourist behaviour, learning and satisfaction, considering tourists’ belief of technology in the anchoring paradigm. It finds that museum tourists with an additional VR tour of cultural exhibitions spend less time and show a targeted temporal–spatial pattern in the on-site visit than those without VR. The findings indicated that participants who read the poster about VR are advanced and helpful in museum visits and reported higher learning and satisfaction compared with those who did not read the poster, no matter whether they took the additional VR tour or not. This finding suggests that external anchors significantly influence and even bias people’s judgements in visitor learning and satisfaction with their visit to cultural exhibitions. This study is among the first to look into the human-technology relationship, particularly the sentiment of ‘technological optimism’ in the tourism context. Using the anchoring theory, this study investigates museum tourists’ cognitive mechanism involving their belief in technology. It is also innovative in employing interdisciplinary methods, such as spatial-temporal analysis, in-depth interviews, observations and experiments.

Experimental and theoretical insights into Artemisia Stems aqueous extract as a sustainable and eco-friendly corrosion inhibitor for mild steel in 1M HCl environment.

The present research demonstrates an innovative investigation of environmentally friendly mild steel (M-steel) corrosion inhibition using the artemisia stems aqueous extract (ASAEx) as an inhibitor in hydrochloric acid 1M. The standard extraction technique of hydrodistillation was used for producing the aqueous solutions of ASAEx. To assess the ratios of the chemical components, phytochemical screening was used to identify the stems of this plant. We used a variety of methods and techniques in our research on corrosion inhibition, including weight loss measures, surface analysis methods like XPS and SEM/EDS, electrochemical testing like PDP and EIS, as well as computational lead compound evaluation. Maximum inhibitory efficacy was achieved with 400mg/L ASAEx in 1M HCl at 303K, i.e. 90%. The PDP investigation verified the mixed-kind inhibitor status of the ASAEx extract. To describe the surface of M-steel, fitting and synthetic data were used to identify a constant phase element (CPE). SEM surface analysis was also used to detect the ASAEx effect on the surface of M-steel. X-ray photoelectron spectroscopy (XPS) analysis shows the presence of trace molecules of ASAEx on M-steel surface characterizing the bands in Maj-ASAEx (major compound of ASAEx). Density functional theory (DFT) and molecular dynamics simulations (MDs) were used in computational chemistry to clarify the adsorption mechanism and inhibitory impact.

Why politicize the cultural game? (Emil) Cioran’s ethos: mysticism, religion and ethnic philosophy

The present research aims at understanding some of Emil Cioran's spiritual positions in several of his writings. The tools of analysis are the philosophical and cultural approaches, but also elements of positivist determinism, such as climate, disease, etc. The oeuvres studied belong to different periods of creation. The working hypothesis is that the essayist remained a lifelong thinker oscillating between the extreme right and the extreme left, as well as a stubborn and metaphysically rejected progressive thinker, but always obsessed with metaphysics and the transcendent. Among the commonplaces of Cioran's artistic and geopolitical critique of reality and the real are narcissism, humor, victimhood and diatribe. His work is underpinned by intuition and Utopian projection and much less by logic. Cioran becomes dangerous if treated as a programmatic philosopher, but otherwise he is a spectacular source of artistic and literary-philosophical insights; an experimental essayist, dangerous as an influencer. The fate of his thinking has been heavily marked by the ideologies of his interpreters.

Examining the effects of international mindedness among prospective teachers on intercultural competence in higher education institutions: an empirical analysis

PurposeThe purpose of this empirical study is to examine prospective teachers' international mindedness and intercultural competence, grounded in sociocultural theory. The research aims to understand the factors that foster these traits in prospective teachers and their significance in promoting competence in culturally diverse environments.Design/methodology/approachThe study utilized structured equation modeling with maximum likelihood estimation to analyze data from 425 participants of two higher education institutions. The participants were selected through simple random probability sampling. This methodology allowed the researchers to explore the relationship between international mindedness and intercultural competence in prospective teachers.FindingsThe results of the study demonstrated a positive correlation between international mindedness and intercultural competence in prospective teachers. The findings suggest that by cultivating international mindedness, educators can enhance their intercultural competence, thereby fostering more effective interactions in diverse settings.Practical implicationsThe present research holds practical implications for teacher education programs and higher education institutions. By understanding how international mindedness impacts intercultural competence, educators can implement targeted interventions and training to nurture these traits among prospective teachers. This can lead to the creation of culturally inclusive learning environments, promoting mutual respect and appreciation of diversity among students.Originality/valueThe originality of this study lies in its investigation of the link between international mindedness and intercultural competence in the context of prospective teachers enrolled in B.Ed. program in higher education institution. By establishing a causal relationship between these traits, the research adds to the understanding of how teachers can be prepared to engage effectively in culturally diverse classrooms. The findings hold value for educators and policymakers seeking to improve teaching practices and foster global citizenship among future educators and their students.

Cellular automata-based MapReduce design: Migrating a big data processing model from Industry 4.0 to Industry 5.0

A successful deployment of Industry 5.0 is significantly dependent on the synergetic integration of several advanced technologies such as big data processing, Artificial Intelligence (AI) integration, and several effective digitization techniques that emphasize the uses of Robotics, Internet of Things (IoTs), Cloud Computing, etc. with active participations from human workers. Several researchers have explored the importance of big data processing in view of Industry 4.0 as it facilitated enhanced production at any smart manufacturing line by ensuring efficient process management, typically involving big data processing. Researchers presented several MapReduce-based data processing models at smart manufacturing lines to facilitate big data processing. Among several others, the Elementary Cellular Automata (ECAs)-based MapReduce model was introduced as an energy-efficient low-cost model for big data processing in view of the Industry 4.0 scenario. In the present research, an investigation is further proposed to explore the true potential (if any) for the ECAs-based MapReduce model with reference to available several MapReduce models, to migrate an existing big data processing model from Industry 4.0 into the future i.e., Industry 5.0. Investigation results as achieved from the comparison among several MapReduce models and further examinations about the inherent quality of shuffle in those MapReduce blocks, explore the inherent advantages of ECAs-based MapReduce model towards its choice for big data processing in Industry 5.0 scenario.

Development and Validation of a Comprehensive Nursing Competence Assessment Questionnaire (CNCAQ) to Determine the Perceived Clinical Competence of Nursing Graduates

Background: An assessment of the clinical competency of nurses at the time of their graduation is essential to get a fair estimate of their level of competence. It helps to lessen the burden on the healthcare system. A “Comprehensive Nursing Competence Assessment Questionnaire (CNCAQ)” has been constructed to fulfil this purpose. Aim: This study aims to develop and validate a CNCAQ to determine the perceived clinical competence of final-year nursing graduates who are about to graduate within three months from selected private and government nursing colleges in India. Methods: A mixed methodology was used and completed in two phases. In phase 1, along with an extensive literature review, focus group discussions, expert evaluation, and pretesting were done, in which 10 nursing experts participated. In phase 2, data were collected from 649 nursing graduates to establish the validity of the construct. The study period was from August to October 2022. Results: The developed CNCAQ has 36 items, comprising four sections: (a) conceptual knowledge and skill in routine nursing care; (b) skill in basic nursing procedures; (c) skill in advanced nursing procedures; and (d) assessment of infusion/transfusion-related nursing procedures. The overall reliability of the tool was 0.963, indicating its high reliability. Overall, there was no difference in clinical competency skills between female and male upcoming nursing graduates. However, statistically significant differences in basic nursing skills and advanced and analytical nursing skills were observed. Conclusions: Assessing nursing students’ competencies is crucial to ensure that they possess the necessary knowledge, skills, and attitudes to provide safe and effective patient care. The development and validation of a valid and reliable instrument for assessing nursing competencies are essential to evaluate nursing students’ clinical performance accurately. In the present research, a tool with a total of 36 items was used. It demonstrates high reliability; however, it needs to be applied to a larger population of nursing graduates with diverse backgrounds. Nurse educators and administrators can use it to assess the level of competence and, accordingly, a tailor-made induction program based on identified weak areas in theoretical and practical knowledge or skill can be developed. No patient’s were involved in the study. It involved developing a CNCAQ and validating it on a selected group of upcoming nursing graduates.

An integration method of a hybrid genetic algorithm and the Levenberg–Marquardt algorithm for ultrasonic testing

The accurate measurement of time-of-flight (TOF) is essential in ultrasonic testing. Further, noise interference is the key factor affecting the measurement accuracy. Therefore, to develop a reliable computational method of TOF for test pieces working in noisy environments, an integration method of a hybrid genetic algorithm and the Levenberg–Marquardt algorithm (GA–LM) for ultrasonic thickness measurement is proposed in the present research. A Gaussian model is first established for an echo signal. Further, the model-based parameter estimation is converted into a nonlinear optimization problem by applying the least square method. As the parameter estimation methods are easily affected by the initial value, an integrating innovation of the GA–LM algorithm is proposed. The initial values of the model parameters are selected by GA to obtain an approximate global optimal solution. Subsequently, this approximate solution is used as the initial value for the LM algorithm to perform iterations. The accurate global optimal solution of the Gaussian model is obtained through these iterations. Finally, the measuring accuracy and robustness of the GA–LM algorithm for TOF computation are verified by both numerical simulation and experiment data

A deep learning approach for prediction of air quality index in smart city

Industrial developments and consumption of massive amount of fossil fuels, vehicle pollution, and other calamities upsurges the AQI (Air Quality Index) of major cities in a drastic manner. Owing to these factors, it is important to take proactive measures for reducing the air pollution in order to avoid life- threatening consequence. Therefore, prediction of air quality is significant for improving the health of living beings as highly polluted regions have a higher concentration of pollutants mixed in the air, affecting the respiratory system and reducing the lifetime. To control pollution, AQI is used as a measure for estimating the pollutant content in the air. Even though many existing techniques have predicted AQI, enhancement is required in prediction algorithms with minimized loss. To address the challenges in traditional algorithms, the proposed smart cities-based AQI prediction intends to utilize the proposed regression algorithm in the dataset, namely Air- Quality-Data, which collected harmful pollutants on an hourly and daily basis from multiple cities in India between 2015 to 2020. To achieve prediction efficiency with reduced loss, pre-processing of input data is being performed using Deep GAN (Generative Adversarial Network). It performs the imputation of data in place of missing values to improve accurate prediction. Additionally, feature scaling normalizes independent real-data features to a fixed scale. With the processed data, regression is done through modified Stacked Attention GRU with KL divergence, which predicts Ernakulam, Chennai and Ahmedabad cities with higher, medium, and low levels of AQI in India. The performance of the proposed regression algorithm is measured using metrics such as MAE (Mean Absolute Error), MSE (Mean Square Error), R2 (Coefficient of determination), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) and better MAE, MSE, R2, MAPE and RMSE obtained by the model is 0.1013, 0.0134, 0.9479, 0.1152 and 0.1156. Internal assessment and comparative analysis performed with existing regression algorithms exhibit lower loss values obtained from the present research, which determines the efficacy of the proposed model.

'They are conspiring against us': How outgroup conspiracy theories stimulate environmental neglect in intergroup resource dilemmas.

Natural resources are limited, and people often share these limited resources in groups, which creates an intergroup resource dilemma. To understand individuals' sustainable behaviours in intergroup resource dilemmas in the context of group interactions, the present research systematically investigates the effect of outgroup conspiracy theories on sustainable behaviours and preliminarily explores the internal mechanism underlying this effect. First, a survey study (Study 1) relying on real-world intergroup relations first confirmed the negative correlation between outgroup conspiracy beliefs and sustainable intentions in intergroup resource dilemmas. Then, an online experimental study that utilized the real situation of a region in China (Study 2) tested the causal relationship between exposure to an outgroup conspiracy theory and sustainable intentions, as well as showing the mediating role of intergroup threat perception underlying this relationship. Finally, a preregistered experimental laboratory study (Study 3) further verified the causal effect of exposure to an outgroup conspiracy theory on sustainable behaviours, again confirming the mediating role of intergroup threat perception. In general, our research demonstrates that exposure to an outgroup conspiracy theory stimulates individuals' environmental neglect and reduces their sustainable behaviours by increasing their perceptions of intergroup threat when faced with intergroup resource dilemmas.

Transmitter diversity and OAM incorporated 40 Gbps free space optical system

Abstract The present research evaluates optical angular momentum’s (OAM) performance in challenging atmospheric conditions and emphasizes its significance in free space optical (FSO) communication systems. It has been demonstrated that implementing the transmitter diversity (TD) technique effectively suppresses inter-channel interference, improving system performance as a whole. The best option among the studied encodings is found to be the combination of non-return-to-zero (NRZ) and carrier suppressed RZ (CSRZ), which performs better in a variety of weather scenarios and covers a wide FSO range from 72 m to 1450 m. Proposed system offered distance enhancement of 81.25 % under clear sky, 16.66 % under light rain, 10.22 % under moderate rain, 3.4 % under heavy rain, 10 % under light haze, 4 % under moderate haze, 4.44 % under heavy haze, 12.5 % under light fog, 4 % under moderate fog, 7.8 % under heavy fog, 5.8 % under light dust, 7.6 % under medium dust and 12.5 % under heavy dust as compared to existing workIn particular, during bad weather, this research offers significant insights into the design and optimisation of high-speed FSO systems.

Pine needle gasification-based electricity production: Understanding the effect of supply chain.

Pine needles (pine tree leaves), found abundantly across continents such as North America, Asia, Europe, South America, Africa, and parts of the Southern Hemisphere, are a significant global concern due to their high susceptibility to catching fire, especially in dry and hot climates. The same issue persists in the Uttarakhand state of India, which boasts ample pine forests, yielding a substantial 1.67 × 109kg of pine needles annually. In the present study, the annual potential emissions from forest fires in Uttarakhand were estimated to be 58.37 × 109kg of CO2 equivalent. Therefore, the present research aims to unlock pine needles' potential via gasification for green electricity and biochar production, offering an alternative to coal-based plants while reducing forest fire frequencies. Nevertheless, obstacles hindering pine needle gasification include an unsteady supply chain, limited collection windows (100days), and plant expenses, including transportation and operational costs. The primary focus of the research is to design and assess the performance of a gasification-based supply chain for pine needles in the Almora District of Uttarakhand. Ten plant capacity scenarios were considered, ranging from 25 to 250kW. The study incorporated critical factors, encompassing diverse losses within the supply chain, selecting potential plant sites, minimizing transportation distance, and evaluating the supply chain's economic and environmental performance. The economic analysis revealed that the 250-kW plant scenario exhibited a minimum discounted payback period (DPP) of 3.93years, alongside an internal rate of return (IRR) of 19% and a net present value (NPV) of 653.32 million INR without government subsidies. With subsidies included, the DPP decreased to 1.30years, improving the IRR to 69% with an NPV of 916.17 million INR. The emission analysis indicated that gasification plant capacity scenarios could potentially reduce 44.63 × 106 to 46.16 × 106kg of CO2 equivalent emissions annually compared to grid electricity while meeting nearly 5.5% of the electricity demand of Almora district. The present study aligns with SDG-7 (Affordable and Clean Energy), SDG-13 (Climate Action), SDG-9 (Industry, Innovation, and Infrastructure), SDG-11 (Sustainable Cities and Communities), SDG-3 (Good Health and Well-being), and SDG-15 (Life on Land).

Novel adaptive approach for anomaly detection in nonlinear and time-varying industrial systems

Abstract The present research describes a novel adaptive anomaly detection method to optimize the performance of nonlinear and time-varying systems. The proposal integrates a centroid-based approach with the real-time identification technique Recursive Least Squares. In order to find anomalies, the approach compares the present system dynamics with the average (centroid) of the dynamics found in earlier states for a given setpoint. The system labels the dynamics difference as an anomaly if it rises over a determinate threshold. To validate the proposal, two different datasets obtained from a level control plant operation have been used, to which anomalies have been artificially added. The results shown have determined a satisfactory performance of the method, especially in those processes with low noise.

Indoor air quality prediction modeling for a naturally ventilated fitness building using RNN-LSTM artificial neural networks

Indoor air quality prediction modeling for a naturally ventilated fitness building using RNN-LSTM artificial neural networks

The Importance of Analysis of Judicial Circumstances in the Sentence Dosimetry Process

The Constitution plays an important role as a point of reference for the critical analysis of the criminal system, not only because it derives from the legal assets susceptible to punitive protection, but also and among other reasons, because the principles enshrined as a government norm in the Penal Code governing The imposition of sanctions is a clear development of the constitutional text and doctrine that the Constitutional Court has been building with its pronouncements, in the form that criminal law must acquire. The dosimetry of punishment in Brazil takes a three-step approach, pushing the system of fixed sanctions implemented by the legislature or the full discretion of the judge, promoting, on the other hand, a distribution of functions between the two, as he chooses the minimum and maximum sentence levels and dosage parameters, and this determines the sanction in the specific case from the legal criteria. In this sense, the present research aims to analyze the importance of analyzing judicial circumstances in the process of penalty dosimetry. The specific objectives are: to present a brief history of criminal legislation in Brazil, to review the most recent literature on the application of the penalty and its principles, to analyze the relationship between the dosimetry of the penalty and judicial circumstances and to point out cases in the jurisprudence that materialize the revised theory. Thus, given the relevance of the theme in the current legal scenario, the research is justified by discussing a contemporary theme. Through research on the principles of exceptionality, state of innocence, motivation of decisions and proportionality and the dosimetry of the penalty, possible judgments of the judge that relate the dosimetry of the penalty with the principles of human rights will be discussed. The judge should take charge of the evidence produced in relation to the facts that, in his opinion, influence the minor or major measure of the exact penalty to be imposed. The lack of motivation currently constitutes a violation of the constitutional guarantee of due process and absolute grounds for invalidity under the Criminal Procedure Code.

Development and validation of career decision making proficiency scale

The purpose of the present research was to develop and examine the psychometric properties of the Career Decision Making Proficiency Scale (CDMPS). The new tool amalgamates various conceptual domains of career exploration process and validates its usage for students and young adults (N=565). The researcher used a survey research design that involved the theoretical evaluation by experts, mandating two subscale divisions for the original 40 items. A deductive approach was considered for the scale development. The content validity assessment by means of CVR- Lawshe’s Test, Fleiss’ Kappa statistics, and the CVI-proportion agreement method retained 34 items. R Studio software was used for dimension reduction techniques and inter-factor bi-variate correlations. The results of the item analysis confirmed 30 items clustered into five dormant constructs. The reliability coefficients of the two sub-scales were found to be high (α= 0.80 and 0.87). Furthermore, the exploratory factor analysis explained 52.45 percent and 50.99 percent of total variance, confirming the factorial internal structural validity of the two sub-scales, respectively. The scale can be administered by career and school counsellors to guide students with their career decisions, to assess career decision difficulties faced by weak students and potential dropouts or repeaters, and to assess students’ confidence, motivation, and abilities in handling problematic situations at the time of deciding a career path.

Enhancing photodegradation of thiamethoxam insecticide using SnS2/NCL as a photocatalyst: Mechanistic insights and environmental implications

The current research highlights the fabrication of a novel SnS2/CO32−@Ni–Co LDH (SnS2/NCL) by precipitating Ni–Co LDH over hydrothermally synthesized SnS2 nanoparticles for the enhanced degradation of thiamethoxam (THM) insecticide through the advanced oxidation process. The effect of several reaction parameters was optimized, and a maximum degradation of 98.1 ± 1.2 % with a rate constant of 0.0541 min−1 of 10 ppm THM was reached at a catalyst loading of 0.16 gL-1 using 0.3 mM of H2O2 within 70 min of visible light irradiation. The effect of metal cations, inorganic anions, dissolved organic matter, organic compounds and water samples on the photodegradation performance of SnS2/NCL nanocomposite was also examined to evaluate the prepared photocatalyst's suitability for use in actual wastewater conditions. The metal cations blocked the active sites of the photocatalyst and reduced the degradation efficiency except for Fe2+ ions, since it is a Fenton reagent and increased the production of hydroxyl radicals. Inorganic anions are the scavengers of hydroxyl radicals and hinder photocatalytic activity. Meanwhile, lake water containing varying degrees of co-existing ions shows the lowest degradation efficiency among other water samples. The SnS2/NCL nanocomposite could be reused for five cycles while maintaining a photocatalytic efficiency of 83.6 ± 0.3 % in the fifth run. The prepared SnS2/NCL nanocomposite also showed excellent photodegradation of several other emerging organic pollutants with an efficiency of over 80 % under optimum conditions. Incorporating Ni–Co LDH with SnS2 helped to delocalize photoinduced charges, leading to increased photocatalytic activity and a slower electron-hole recombination rate. The present research highlights the photocatalytic activity of SnS2/NCL photocatalysts for the photocatalytic degradation of emerging contaminants from wastewater.