Punjabi University

During long standing hyperglycaemic state in diabetes mellitus, glucose forms covalent adducts with the plasma proteins through a non-enzymatic process known as glycation. Protein glycation and formation of advanced glycation end products (AGEs) play an important role in the pathogenesis of diabetic complications like retinopathy, nephropathy, neuropathy, cardiomyopathy along with some other diseases such as rheumatoid arthritis, osteoporosis and aging. Glycation of proteins interferes with their normal functions by disrupting molecular conformation, altering enzymatic activity, and interfering with receptor functioning. AGEs form intra- and extracellular cross linking not only with proteins, but with some other endogenous key molecules including lipids and nucleic acids to contribute in the development of diabetic complications. Recent studies suggest that AGEs interact with plasma membrane localized receptors for AGEs (RAGE) to alter intracellular signaling, gene expression, release of pro-inflammatory molecules and free radicals. The present review discusses the glycation of plasma proteins such as albumin, fibrinogen, globulins and collagen to form different types of AGEs. Furthermore, the role of AGEs in the pathogenesis of diabetic complications including retinopathy, cataract, neuropathy, nephropathy and cardiomyopathy is also discussed.

Particle swarm optimization (PSO) is one of the most famous swarm-based optimization techniques inspired by nature. Due to its properties of flexibility and easy implementation, there is an enormous increase in the popularity of this nature-inspired technique. Particle swarm optimization (PSO) has gained prompt attention from every field of researchers. Since its origin in 1995 till now, researchers have improved the original Particle swarm optimization (PSO) in varying ways. They have derived new versions of it, such as the published theoretical studies on various parameters of PSO, proposed many variants of the algorithm and numerous other advances. In the present paper, an overview of the PSO algorithm is presented. On the one hand, the basic concepts and parameters of PSO are explained, on the other hand, various advances in relation to PSO, including its modifications, extensions, hybridization, theoretical analysis, are included.

Purpose The purpose of this paper is to review the literature on Total Productive Maintenance (TPM) and to present an overview of TPM implementation practices adopted by the manufacturing organizations. It also seeks to highlight appropriate enablers and success factors for eliminating barriers in successful TPM implementation. Design/methodology/approach The paper systematically categorizes the published literature and then analyzes and reviews it methodically. Findings The paper reveals the important issues in Total Productive Maintenance ranging from maintenance techniques, framework of TPM, overall equipment effectiveness (OEE), TPM implementation practices, barriers and success factors in TPM implementation, etc. The contributions of strategic TPM programmes towards improving manufacturing competencies of the organizations have also been highlighted here. Practical implications The literature on classification of Total Productive Maintenance has so far been very limited. The paper reviews a large number of papers in this field and presents the overview of various TPM implementation practices demonstrated by manufacturing organizations globally. It also highlights the approaches suggested by various researchers and practitioners and critically evaluates the reasons behind failure of TPM programmes in the organizations. Further, the enablers and success factors for TPM implementation have also been highlighted for ensuring smooth and effective TPM implementation in the organizations. Originality/value The paper contains a comprehensive listing of publications on the field in question and their classification according to various attributes. It will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the significance of TPM.

The Internet was originally designed to facilitate communication and research activities. However, the dramatic increase in the use of the Internet in recent years has led to pathological use (Internet addiction). This study is a preliminary investigation of the extent of Internet addiction in school children 16-18 years old in India. The Davis Online Cognition Scale (DOCS) was used to assess pathological Internet use. On the basis of total scores obtained (N = 100) on the DOCS, two groups were identified--dependents (18) and non-dependents (21), using mean +/- 1/2 SD as the criterion for selection. The UCLA loneliness scale was also administered to the subjects. Significant behavioral and functional usage differences were revealed between the two groups. Dependents were found to delay other work to spend time online, lose sleep due to late-night logons, and feel life would be boring without the Internet. The hours spent on the Internet by dependents were greater than those of non-dependents. On the loneliness measure, significant differences were found between the two groups, with the dependents scoring higher than the non-dependents.

Cymbopogon citratus, Stapf (Lemon grass) is a widely used herb in tropical countries, especially in Southeast Asia. The essential oil of the plant is used in aromatherapy. The compounds identified in Cymbopogon citratus are mainly terpenes, alcohols, ketones, aldehyde and esters. Some of the reported phytoconstituents are essential oils that contain Citral α, Citral β, Nerol Geraniol, Citronellal, Terpinolene, Geranyl acetate, Myrecene and Terpinol Methylheptenone. The plant also contains reported phytoconstituents such as flavonoids and phenolic compounds, which consist of luteolin, isoorientin 2'-O-rhamnoside, quercetin, kaempferol and apiginin. Studies indicate that Cymbopogon citratus possesses various pharmacological activities such as anti-amoebic, antibacterial, antidiarrheal, antifilarial, antifungal and anti-inflammatory properties. Various other effects like antimalarial, antimutagenicity, antimycobacterial, antioxidants, hypoglycemic and neurobehaviorial have also been studied. These results are very encouraging and indicate that this herb should be studied more extensively to confirm these results and reveal other potential therapeutic effects.

Ulcerative colitis and Crohn's disease are a set of chronic, idiopathic, immunological and relapsing inflammatory disorders of the gastrointestinal tract referred to as inflammatory bowel disorder (IBD). Although the etiological factors involved in the perpetuation of IBD remain uncertain, development of various animal models provides new insights to unveil the onset and the progression of IBD. Various chemical-induced colitis models are widely used on laboratory scale. Furthermore, these models closely mimic morphological, histopathological and symptomatical features of human IBD. Among the chemical-induced colitis models, trinitrobenzene sulfonic acid (TNBS)-induced colitis, oxazolone induced-colitis and dextran sulphate sodium (DSS)-induced colitis models are most widely used. TNBS elicits Th-1 driven immune response, whereas oxazolone predominantly exhibits immune response of Th-2 phenotype. DSS-induced colitis model also induces changes in Th-1/Th-2 cytokine profile. The present review discusses the methodology and rationale of using various chemical-induced colitis models for evaluating the pathogenesis of IBD.

The application of artificial intelligence is machine learning which is one of the current topics in the computer field as well as for the new COVID-19 pandemic. Researchers have given a lot of input to enhance the precision of machine learning algorithms and lot of work is carried out rapidly to enhance the intelligence of machines. Learning, a natural process in human behaviour that also becomes a vital part of machines as well. Besides this, another concept of deep learning comes to play its major role. Deep neural network (deep learning) is a subgroup of machine learning. Deep learning had been analysed and implemented in various applications and had shown remarkable results thus this field needs wider exploration which can be helpful for further real-world applications. The main objective of this paper is to provide insight survey for machine learning along with deep learning applications in various domains. Also, some applications with new normal COVID-19 blues. A review on already present applications and currently going on applications in several domains, for machine learning along with deep neural learning are exemplified.

Animal models are pivotal for understanding the mechanism of neuropathic pain and development of effective therapy for its optimal management. A battery of neuropathic pain models has been developed to simulate the clinical pain conditions with diverse etiology. The present review exhaustively discusses the methodology, behavioral alterations, limitations, and advantages of about 40 different animal models of neuropathic pain along with their modifications. Development of these models has contributed immensely in understanding the chronic pain and underlying peripheral as well as central pathogenic mechanisms. Furthermore, research has resulted in the development of new therapeutic agents for neuropathic pain management, and the preclinical data obtained using these animal models have been successively translated to effective pain management in clinical setup also. As each animal model has been created with specific methodology and results tend to vary largely with the slight changes related to methodology, therefore, it is essential that data from different models should be reported and interpreted in the context of the specific pain model.

BACKGROUND: The APOE gene and its protein product is associated with a number of plasma proteins like very-low density lipoprotein (VLDL), high density lipoprotein (HDL) chylomicrons, chylomicron remnants, and plays a crucial role in lipid metabolism. The APOE gene is polymorphic and common alleles (*E2, *E3 and *E4) have been associated with a number of common and complex diseases in different populations. Due to their crucial role in metabolism and clinical significance, it is imperative that allelic variation in different populations is analysed to evaluate the usage of APOE in an evolutionary and clinical context. AIM: We report allelic variation at the APOE locus in three European and four Indian populations and evaluate global patterns of genetic variation at this locus. The large, intricate and unexpected heterogeneity of this locus in its global perspective may have insightful consequences, which we have explored in this paper. SUBJECT AND METHODS: Apolipoprotein E genotypes were determined in four population groups (Punjabi Sikhs, Punjabi Hindus, Maria Gonds and Koch, total individuals = 497) of India and three regionally sub-divided British populations (Nottinghamshire, East Midlands and West Midlands, total individuals = 621). The extent and distribution of APOE allele frequencies were compared with 292 populations of the world using a variety of multivariate methods. RESULTS: Three alleles, APOE*E2, APOE*E3 and APOE*E4, were observed with contrasting variation, although *E4 was absent in the tribal population of Koch. Higher heterozygosities (>43%) in British populations reflected their greater genetic diversity at this locus. The overall pattern of allelic diversity among these populations is comparable to many European and Indian populations. At a global level, higher frequencies of the *E2 allele were observed in Africa and Oceania (0.099 +/- 0.083 and 0.111 +/- 0.052, respectively). Similarly, *E4 allele averages were higher in Oceania (0.221 +/- 0.149) and Africa (0.209 +/- 0.090), while Indian and Asian populations showed the highest frequencies of *E3 allele. The coefficient of gene differentiation was found to be highest in South America (9.6%), although the highest genetic diversity was observed in Oceania (48.7%) and Africa (46.3%). APOE*E2 revealed a statistically significant decreasing cline towards the north in Asia (r = -0.407, d.f. = 70, p < 0.05), which is not compatible with the coronary heart disease statistics in this continent. APOE*E4 showed a significant increasing cline in North European populations. Spatial autocorrelation analysis shows that the variation at this locus is influenced by 'isolation by distance' with a strong positive correlation for lower distances up to 1313 km. CONCLUSION: Overall APOE allelic variation in UK and Indian populations is comparable to previous studies but in tribal populations *E4 allele frequency was very low or absent. At a global level allelic variation shows that geography, isolation by distance, genetic drift and possibly pre-historical selection are responsible for shaping the spectrum of genetic variation at the APOE gene. Overall, APOE is a good anthropogenetic and clinical diagnostic marker.

Abstract Aerosol emissions from biomass burning are of specific interest over the globe due to their strong radiative impacts and climate implications. The present study examines the impact of paddy crop residue burning over northern India during the postmonsoon (October–November) season of 2012 on modification of aerosol properties, as well as the long‐range transport of smoke plumes, altitude characteristics, and affected areas via the synergy of ground‐based measurements and satellite observations. During this period, Moderate Resolution Imaging Spectroradiometer (MODIS) images show a thick smoke/hazy aerosol layer below 2–2.5 km in the atmosphere covering nearly the whole Indo‐Gangetic Plains (IGP). The air mass trajectories originating from the biomass‐burning source region over Punjab at 500 m reveal a potential aerosol transport pathway along the Ganges valley from west to east, resulting in a strong aerosol optical depth (AOD) gradient. Sometimes, depending upon the wind direction and meteorological conditions, the plumes also influence central India, the Arabian Sea, and the Bay of Bengal, thus contributing to Asian pollution outflow. The increased number of fire counts (Terra and Aqua MODIS data) is associated with severe aerosol‐laden atmospheres (AOD 500 nm &gt; 1.0) over six IGP locations, high values of Ångström exponent (&gt;1.2), high particulate mass 2.5 (PM 2.5 ) concentrations (&gt;100–150 µgm −3 ), and enhanced Ozone Monitoring Instrument Aerosol Index gradient (~2.5) and NO 2 concentrations (~6 × 10 15 mol/cm 2 ), indicating the dominance of smoke aerosols from agricultural crop residue burning. The aerosol size distribution is shifted toward the fine‐mode fraction, also exhibiting an increase in the radius of fine aerosols due to coagulation processes in a highly turbid environment. The spectral variation of the single‐scattering albedo reveals enhanced dominance of moderately absorbing aerosols, while the aerosol properties, modification, and mixing atmospheric processes differentiate along the IGP sites depending on the distance from the aerosol source, urban influence, and local characteristics.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

Arsenic is an environmental pollutant and its contamination in the drinking water is considered as a serious worldwide environmental health threat. The chronic arsenic exposure is a cause of immense health distress as it accounts for the increased risk of various disorders such as cardiovascular abnormalities, diabetes mellitus, neurotoxicity, and nephrotoxicity. In addition, the exposure to arsenic has been suggested to affect the liver function and to induce hepatotoxicity. Moreover, few studies demonstrated the induction of carcinogenicity especially cancer of the skin, bladder, and lungs after the chronic exposure to arsenic. The present review addresses diverse mechanisms involved in the pathogenesis of arsenic-induced toxicity and end-organ damage.

Abstract Academic and corporate interest in ethical leadership, corporate social responsibility (CSR), and firm performance has attracted a lot of attention in recent years. In fact, many research papers and journal special issues have been focused on these three domains. In this context, this paper conducts a systematic review on the concepts of ethical leadership and CSR and their impact on firm performance. 114 papers published over a period of 58 years (1958–2016) were selected and analyzed according to descriptive and content perspectives to propose a conceptual framework and define a future research agenda. In fact, the main results allow us to derive six main propositions representing possible areas of investigation to direct research on the topic. More in details, the body of literature highlights that financial factors are the main barriers affecting the adoption of CSR practices. On the contrary, internal and external environment was found to represent a critical success factor in the adoption of CSR practices. Finally, the results highlight that personal values have impact on ethical leadership that in turn has direct positive impact on CSR and direct and indirect impact on firm performance.

In present study, we evaluated the effects of Jasmonic acid (JA) on physio-biochemical attributes, antioxidant enzyme activity, and gene expression in soybean (Glycine max L.) plants subjected to nickel (Ni) stress. Ni stress decreases the shoot and root length and chlorophyll content by 37.23, 38.31, and 39.21%, respectively, over the control. However, application of JA was found to improve the chlorophyll content and length of shoot and root of Ni-fed seedlings. Plants supplemented with JA restores the chlorophyll fluorescence, which was disturbed by Ni stress. The present study demonstrated increase in proline, glycinebetaine, total protein, and total soluble sugar (TSS) by 33.09, 51.26, 22.58, and 49.15%, respectively, under Ni toxicity over the control. Addition of JA to Ni stressed plants further enhanced the above parameters. Ni stress increases hydrogen peroxide (H2O2) by 68.49%, lipid peroxidation (MDA) by 50.57% and NADPH oxidase by 50.92% over the control. Supplementation of JA minimizes the accumulation of H2O2, MDA, and NADPH oxidase, which helps in stabilization of biomolecules. The activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) increases by 40.04, 28.22, 48.53, and 56.79%, respectively, over the control in Ni treated seedlings and further enhancement in the antioxidant activity was observed by the application of JA. Ni treated soybean seedlings showed increase in expression of Fe-SOD by 77.62, CAT by 15.25, POD by 58.33, and APX by 80.58% over the control. Nevertheless, application of JA further enhanced the expression of the above genes in the present study. Our results signified that Ni stress caused negative impacts on soybean seedlings, but, co-application of JA facilitate the seedlings to combat the detrimental effects of Ni through enhanced osmolytes, activity of antioxidant enzymes and gene expression.

A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants’ strength. Some unimodal, multimodal, and fixed-dimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.

This review gives an overview of the developments in the analysis of drugs of abuse and other illicit substances by Raman spectroscopy for forensic purpose. The review covers the brief overview of basic principle and instrumentation of Raman spectroscopy along with selected and recent applications for characterization of drugs of abuse using this technique. These applications show the potential value of Raman spectroscopy in the qualitative and quantitative analysis of trace amounts of drugs of abuse and other illicit substances on different matrices such as cloth, currency notes, fiber etc., without extensive sample preparation in a non-destructive manner.

Abstract The first regional synthesis of long‐term (back to ~ 25 years at some stations) primary data (from direct measurement) on aerosol optical depth from the ARFINET (network of aerosol observatories established under the Aerosol Radiative Forcing over India (ARFI) project of Indian Space Research Organization over Indian subcontinent) have revealed a statistically significant increasing trend with a significant seasonal variability. Examining the current values of turbidity coefficients with those reported ~ 50 years ago reveals the phenomenal nature of the increase in aerosol loading. Seasonally, the rate of increase is consistently high during the dry months (December to March) over the entire region whereas the trends are rather inconsistent and weak during the premonsoon (April to May) and summer monsoon period (June to September). The trends in the spectral variation of aerosol optical depth (AOD) reveal the significance of anthropogenic activities on the increasing trend in AOD. Examining these with climate variables such as seasonal and regional rainfall, it is seen that the dry season depicts a decreasing trend in the total number of rainy days over the Indian region. The insignificant trend in AOD observed over the Indo‐Gangetic Plain, a regional hot spot of aerosols, during the premonsoon and summer monsoon season is mainly attributed to the competing effects of dust transport and wet removal of aerosols by the monsoon rain. Contributions of different aerosol chemical species to the total dust, simulated using Goddard Chemistry Aerosol Radiation and Transport model over the ARFINET stations, showed an increasing trend for all the anthropogenic components and a decreasing trend for dust, consistent with the inference deduced from trend in Angstrom exponent.

Educational Data Mining field concentrate on Prediction more often as compare to generate exact results for future purpose. In order to keep a check on the changes occurring in curriculum patterns, a regular analysis is must of educational databases. This paper focus on identifying the slow learners among students and displaying it by a predictive data mining model using classification based algorithms. Real World data set from a high school is taken and filtration of desired potential variables is done using WEKA an Open Source Tool. The dataset of student academic records is tested and applied on various classification algorithms such as Multilayer Perception, Naïve Bayes, SMO, J48 and REPTree using WEKA an Open source tool. As a result, statistics are generated based on all classification algorithms and comparison of all five classifiers is also done in order to predict the accuracy and to find the best performing classification algorithm among all. In this paper, a knowledge flow model is also shown among all five classifiers. This paper showcases the importance of Prediction and Classification based data mining algorithms in the field of education and also presents some promising future lines.

Sharing of fake news on social media platforms is a global concern, with research offering little insight into the motives behind such sharing. This study adopts a mixed-method approach to explore fake-news sharing behaviour. To begin with, qualitative data from 58 open-ended essays was analysed to identify six behavioural manifestations associated with sharing fake news. Thereafter, research model hypothesizing the association between these behaviours was proposed using the honeycomb framework and the third-person effect hypothesis. Age and gender were the control variables. Two data sets obtained from cross-sectional surveys with 471 and 374 social media users were utilized to test the proposed model. The study results suggest that instantaneous sharing of news for creating awareness had positive effect on sharing fake news due to lack of time and religiosity. However, authenticating news before sharing had no effect on sharing fake news due to lack of time and religiosity. The study results also suggest that social media users who engage in active corrective action are unlikely to share fake news due to lack of time. These results have significant theoretical and practical implications.

This article comprises detailed information about L-asparaginase, encompassing topics such as microbial and plant sources of L-asparaginase, treatment with L-asparaginase, mechanism of action of L-asparaginase, production, purification, properties, expression and characteristics of l-asparaginase along with information about studies on the structure of L-asparaginase. Although L-asparaginase has been reviewed by Savitri and Azmi (2003), our effort has been to include recent and updated information about the enzyme covering new aspects such as structural modification and immobilization of L-asparaginase, recombinant L-asparaginase, resistance to L-asparaginase, methods of assay of L-asparagine and L-asparaginase activity using the biosensor approach, L-asparaginase activity in soil and the factors affecting it. Also, side-effects of L-asparaginase treatment in acute lymphoblastic leukemia (ALL) have been discussed in the current review. L-asparaginase has been and is still one of the most widely studied therapeutic enzymes by researchers and scientists worldwide.