Search results

Corona viruses have spiked structure and enveloped glycoproteins in their structure, along with specific proteins in membranes. The mode of action of COVID-19 consists of three phases, i.e. viral replication, immune hyperactivity and pulmonary destruction. However, angiotensin-converting enzyme is an essential receptor that causes COVID-19. After infecting any individual, COVID-19 develops in specific pattern with the following stages: replication stage and adaptive immunity stage. After adaptive immunity stage, the clinical symptoms of patient include cough, fever, runny nose, shortness of breath and many more. To cope with this COVID-19, food and nutrition played an important role. The purpose of this review article is to emphasize the significance of food and immunity to reduce COVID-19 attack among susceptibles.

In the present era, corona virus, a member of crown-shaped type, created a pandemic situation around the globe owing to its capability to move from human to animals and vice versa. This virus can transfer its 35% infection to the individuals. Contemporarily, this outbreak was observed in Wuhan, China in December 2019 and affected a huge number of communities by spreading like common flu. However, after investigation, it was revealed that COVID-19 is different from common flu regarding respiratory illness created by this novel coronavirus along with its taxonomy.

Over and above to all the aforementioned food safety concerns, it is suggested to use sufficient supply of green leafy vegetables along with fiber-rich foods such as legumes, beans, whole grains and vegetables. It is further good for health to get all these varieties whole and organic. The individuals should use probiotics (lactobacillus) on daily basis as a source of diary probiotic that can further bind to the virus directly and then can inhibit the viral attachment with body cells; also the probiotics are claimed to enhance the immune system of the body.

In the case of fruits and vegetables purchased, it must be thoroughly washed with water and then disinfected; after pat drying, these fruits and vegetables can be moved to the refrigerator and stored along with other foods. During the outbreak of COVID-19, the partially cooked or ready-to-eat foods should be avoided, especially eggs, meat, meat products, milk and milk-related products. On the other side, nuts should not be consumed; if required, then it must be properly roasted at home before consumption.

The baked product right after coming out of oven is completely safe; however, these are contaminated during handling such as crumb in case of bread and with knives; so in the breakout of any viral attack, the individuals should avoid slicing of the bread. Moreover, it is recommended to place the baked products in personal bags instead of bulk quantity. Before the consumption, the baked products should be toasted, microwaved or heated with the help of pan (traditional) to reduce the amount of viral load.

Dietary supplements are taken by most of the people on daily basis or sporadically especially when they are suffering from inflammation or cold. They have options while selecting these types of supplements such as probiotics, Echinacea, omega-3 fatty acids, vitamins D and E, calcium glucosamine, garlic and iron. These supplements are beneficial for the group of population who do not eat diversity of food as sufficient amount of essential nutrients is provided through this pathway.

This work is original in its novelty.

The Food and Agriculture Organization (FAO) of the United Nations has established that vinegar is a liquid only obtained by fermentation. Although in Pakistan mostly synthetic vinegar is marketed, production of vinegar through fermentation at industrial level is also carried out. However, vinegar produced by this method tends to be of inferior quality due to uncontrolled conditions. In industry a mixed culture of Acetobacter is used for the production of acetic acid but no attention is given towards its proper maintenance and culture is contaminated with other kinds of microorganisms and still no work has been done along these lines in Pakistan. There is a need to develop pure vinegar cultures for vinegar production so that the use of synthetic vinegar may be avoided as it is prohibited in most countries. Keeping in view all of these points, the present work was conducted to isolate a pure culture of Acetobacter aceti, the maintenance of this culture and finally its utilization in fermentation for vinegar production.

In this study efforts were made to isolate vinegar culture (Acetobacter aceti) from sugar cane juice, rotten apples, flowers, wine, canal water and vinegar as a primary source for Acetobacter by continuous sub‐culturing on standard medium glucose, yeast extract and calcium carbonate (GYC). The culture was identified on the basis of colony characteristics and morphology. It was finally confirmed by different biochemical/enzymatic tests and further specified by nutritional and temperature requirements for the growth. The isolated strain was later used for the production of vinegar through fermentation.

Among canal water, crushed apples, sugar cane juice, alcohol, vinegar and flowers, the alcohol and vinegar were found to be the most suitable sources for isolation of Acetobacter spp. The colonies of purified culture were found to be pale to off‐white, circular, raised, convex, smooth and not >3 mm in diameter with morphology of Gram‐ve, ellipsoidal, rods, squat bacilli, roundish, single, in pairs and in chains. The isolated and identified spp. gave excellent results for the production of vinegar and this vinegar was more acceptable rather than commercially available fermented vinegar.

At industrial level good quality vinegar can be produced by using pure culture of Acetobacter aceti for acetic acid fermentation.

The research carried out is one of an original type as no work has been done in Pakistan previously. Further, for the accuracy of the results, all the practices were carried out in triplicates.

The coronavirus (COVID-19) pandemic has had profound economic effects, putting women entrepreneurs at considerable risk of losing income and sales growth as a result. This study aims to examine whether the COVID-19 pandemic is a blessing or a curse for women entrepreneurs in Pakistan’s informal sector. The influence of business type, family support and other socio-economic factors on the sales volume of women’s businesses is examined.

Data were collected from 400 women entrepreneurs using a survey questionnaire. Logistic regression was used to investigate the relationships between perceived sales volume and socio-economic as well as demographic factors of women entrepreneurs.

Findings for RQ1 revealed that the pandemic was a blessing for cloth and cosmetic entrepreneurs, but a curse for those women selling dairy products. Results for RQ2 showed that age, homeownership, household size, family support and type of business were significant predictors of sales. Furthermore, women entrepreneurs were greatly influenced by their family’s desires and decisions, such that women entrepreneurs who received support from families and relatives reported higher sales than those who did not receive such support.

The results may assist policymakers in designing supportive programs to encourage women’s informal entrepreneurial activities. Creating entrepreneurial ecosystems may provide support for women entrepreneurs beyond family support. The findings provide a better understanding of women’s business effectiveness during COVID-19 pandemic. It reveals the resilience of women entrepreneurs in the face of cultural, economic and institutional constraints encountered during the pandemic.

This study is unique because it focuses on the impact of the pandemic at the household level rather than examining broad macroeconomic scenarios. To the best of the authors’ knowledge, this study is the first attempt to explore the informal, home-based business sector of women entrepreneurs in Pakistan during the pandemic.

Additive manufacturing (AM) technology has a huge influence on the real world because of its ability to manufacture massively complicated geometrics. The purpose of this study is to use CiteSpace (CS) visual analysis to identify fused deposition modeling (FDM) research and development patterns to guide researchers to decide future research and provide a framework for corporations and organizations to prepare for the development in the rapid prototyping industry. Three-dimensional printing (3DP) is defined to budget minimize manufactured input and output for aviation and the medical product industrial sectors. 3DP has implemented its potential in the Coronavirus Disease of 2019 (COVID-19) reaction.

First, 396 original publications were extracted from the web of science (WOS) with the comprehensive list and did scientometrics analysis in CS software. The parameters are specified in CS including the span (from 2011 to 2019, one year slice for the co-authorship and the co-accordance analysis), visualization (show the merged networks), specific criteria for selection (top 20%), node form (author, organization, region, reference cited; cited author, journal and keywords) and pruning (pathfinder and slicing network). Finally, correlating data was studied and showed the results of the visualization study of FDM research were shown.

The framework of FDM information is beginning to take shape. About hot research topics, there are “Morphology,” “Tensile Property by making Blends,” “Use of Carbon nanotube in 3DP” and “Topology optimization.” Regarding the latest research frontiers of FDM printing, there are “Fused Filament Fabrication,” “AM,” in FDM printing. Where “Post-processing” and “environmental impact” are the research hotspots in FDM printing. These research results can provide insight into FDM printing and useful information to consider the existing studies and developments in FDM researchers’ analysis.

Despite some important obtained results through FDM-related publications’ visualization, some deficiencies remain in this research. With >99% of articles written in English, the input data for CS was all downloaded from WOS databases, resulting in a language bias of papers in other languages and neglecting other data sources. Although, there are several challenges being faced by the FDM that limit its wide variety of applications. However, the significance of the current work concerning the technical and engineering prospects is discussed herein.

First, the novelty of this work lies in describing the FDM approach in a Scientometric way. In Scientometric investigation, leading writers, organizations, keywords, hot research and emerging knowledge points were explained. Second, this research has thoroughly and comprehensively examined the useful sustainability effects, i.e. economic sustainability, energy-based sustainability, environmental sustainability, of 3DP in industrial development in qualitative and quantitative aspects by 2025 from a global viewpoint. Third, this work also described the practical significance of FDM based on 3DP since COVID-19. 3DP has stepped up as a vital technology to support improved healthcare and other general response to emergency situations.

The purpose of this paper is to empirically investigate the relationship between working capital management (WCM) efficiency and exogenous variables of the Indian manufacturing sector along with its sub-industries that are involved in export activities.

Panel regression (fixed effects) was used on a sample of 563 Indian manufacturing firms involved in export activities, covering a time period from 2008 to 2018.

Industry-wise results showed a significant relation of leverage, net fixed asset ratio, profitability, asset turnover ratio, total asset growth rate and productivity with cash conversion cycle (CCC).

Firstly, having taken a sample from a developing economy, the results of our study may be generalizable only among developing contexts. Secondly, the time period taken in this study (2008–2018) has witnessed several economic fluctuations such as recession and demonetization which might differ for the firms or countries in normal conditions.

An improved working capital model could advance the firms' performance by reducing the CCC of the firm, thereby creating efficiency in WCM. In addition, the results of this study could be helpful for many stakeholders such as working capital managers, debt holders, investors, financial consultants and others for monitoring the firms.

This study contributes to the existing literature in the relation between WCM efficiency and exogenous variables of the Indian manufacturing firms engaged in the export activities. Moreover, this study is one of the few research studies to investigate this relationship among Indian export firms in different industries, thus filling the gap in similar work done in other countries.

(1) A mathematical model for the Hybrid nanofluids flow is used as carriers for delivering drugs. (2) The flow conditions are controlled to enable drug-loaded nanofluids to flow through the smaller gap between the two tubes. (3) Hybrid nanofluids (HNFs) made from silver (Ag) and titanium dioxide (TiO₂) nanoparticles are analyzed for applications of drug delivery. (Ag) and (TiO₂) (NPs) are suitable candidates for cancer treatment due to their excellent biocompatibility, high photoactivity, and low toxicity. (4) The new strategy of artificial neural networks (ANN) is used which is machine-based and more prominent in validation, and comparison with other techniques.

The two Tubes are settled in such a manner that the gap between them is uniform. The Control Volume Finite Element Method; Rk-4 and Artificial Neural Network (ANN).

(1) From the obtained results it is observed that the dispersion and distribution of drug-loaded nanoparticles within the body will be improved by the convective motion caused by hybrid nanofluids. The effectiveness and uniformity of drug delivery to target tissues or organs is improved based on the uniform flow and uniform gap. (2) The targeting efficiency of nanofluids is further improved with the addition of the magnetic field. (3) The size of the cylinders, and flow rate, are considered uniform to optimize the drug delivery.

(1)The flow phenomena is considered laminar, one can use the same idea through a turbulent flow case. (2) The gap is considered uniform and will be interesting if someone extends the idea as non-uniform.

(1) To deliver drugs to the targeted area, a suitable mathematical model is required. (2) The analysis of hybrid nanofluids (HNFs) derived from silver (Ag) and titanium dioxide (TiO₂) nanoparticles is conducted for the purpose of drug delivery. The biocompatibility, high photoactivity, and low toxicity of (Ag) and (TiO₂) (NPs) make them ideal candidates for cancer treatment. (3) Machine-based artificial neural networks (ANN) have a new strategy that is more prominent in validation compared to other techniques.

The drug delivery model is a useful strategy for new researchers. (1) They can extend this idea using a non-uniform gap. (2) The flow is considered uniform, the new researchers can extend the idea using a turbulent case. (3) Other hybrid nanofluids flow, in the same model for other industrial usages are possible.

All the obtained results are new. The experimental thermophysical results are used from the existing literature and references are provided.

Since the biomedical implants with an improved compressive strength, near bone elastic modulus, controlled porosity, and sufficient surface roughness, can assist in long term implantation. Therefore, the fine process tuning plays its crucial role to develop optimal settings to achieve these desired properties. This paper aims to find applications for fine process tuning in laser powder bed fusion of biomedical Ti alloys for load-bearing implants.

In this work, the parametric porosity simulations were initially performed to simulate the process-induced porosity for selective laser-melted Ti₆Al₄V as per full factorial design. Continually, the experiments were performed to validate the simulation results and perform multiresponse optimization to fine-tune the processing parameters. Three levels of each control variable, namely, laser power – Pl (180, 190, 200) W, scanning speed – Vs (1500, 1600, 1700) mm/s and scan orientation – ϴ{1(0,0), 2(0,67°), 3(0,90°)} were used to investigate the processing performance. The measured properties from this study include compressive yield strength, elastic modulus, process-induced porosity and surface roughness. Finally, confirmatory experiments and comparisons with the already published works were also performed to validate the research results.

The results of porosity parametric simulation and experiments in selective laser melting of Ti₆Al₄V were found close to each other with overall porosity (less than 10%). The fine process tuning was resulted in optimal settings [Pl (200 W), Vs (1500 mm/s), ϴ (0,90°)], [Pl (200 W), Vs (1500 mm/s), ϴ (0,67°)], [Pl (200 W), Vs (1500 mm/s), ϴ (0,0)] and [Pl (200 W), Vs (1500 mm/s), ϴ (0,0)] with higher compressive strength (672.78 MPa), near cortical bone elastic modulus (12.932 GPa), process-induced porosity (0.751%) and minimum surface roughness (2.72 µm). The morphology of the selective laser melted (SLMed) surface indicated that the lack of fusion pores was prominent because of low laser energy density among the laser and powder bed. Confirmatory experimentation revealed that an overall percent improvement of around 15% was found between predicted and the experimental values.

Since no significant works are available on the collaborative optimization and fine process tuning in laser powder bed fusion of biomedical Ti alloys for different load bearing implants. Therefore, this work involves the comprehensive investigation and multi-objective optimization to determine optimal parametric settings for better mechanical and physical properties. Another novel aspect is the parametric porosity simulation using Ansys Additive to assist in process parameters and their levels selection. As a result, selective laser melted Ti alloys at optimal settings may help in examining the possibility for manufacturing metallic implants for load-bearing applications.

In this era of rapid technological advancement, Artificial Intelligence (AI) has emerged as a crucial factor in reshaping organisational dynamics, notably in the realm of leadership. This systematic literature review (SLR) aims to investigate the emerging relationship between AI and leadership, focussing on defining AI-powered leadership, identifying prevalent themes, exploring challenges, and uncovering research gaps within the relevant literature.

A sample of 73 papers was chosen after carefully applying the inclusion and exclusion criteria to 1,387 research articles that were initially sought. Using the methodological framework presented by Denyer and Tranfield (2009), our study adopted a four-step procedure to obtain insights from the corpus of literature. The papers were analysed by employing content and thematic analysis to address four key questions.

The review explores various definitions of AI-powered leadership proposed in the literature based on real-world situations. The study further synthesises significant themes in the existing literature, such as the past, present and future of AI and AI in various facets of organisational leadership, transitional management, and urban management. The review revealed a range of key challenges in AI-powered leadership, including ethical dilemmas, complications in human-AI interactions, hurdles in AI implementation within leadership contexts, and long-term risks associated with AI integration. In addition, this study identified areas within AI-powered leadership research that require further investigation by revealing significant research gaps in the papers.

By adopting a comprehensive approach, this research advances understanding of the complex relationship between AI and leadership dynamics, thus facilitating comprehension of the current body of knowledge and enabling future scholarly investigations in the AI-powered leadership domain.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

The purpose of this paper is to investigate the relationships between technology orientations and export performance of small and medium-sized enterprises (SMEs).

A quantitative research design was adopted for this study. The paper formulates hypotheses from the literature review. These hypotheses are tested using structural equation modeling with data collected from 231 SMEs in Uganda. Data were analyzed using SPSS version 23 and AMOS.

The findings of this study showed technology orientation has a positive and significant relationship with the performance of Ugandan SMEs and that supply chain agility moderates technology orientation and export performance.

The study discusses the findings, advances limitations and managerial implications. It also suggests future research avenues. It proposes some recommendations to help Ugandan SMEs to form flexible supply chains, use the latest technology and create strong relationship ties with their partners in the supply chain.

The study suggests that managers of Ugandan SMEs should use the latest technology in production, marketing, logistics and supply chain management which will enable them to respond quickly to customer tastes and preferences leading to higher levels of export performance.

This study contributes to the literature on strategic management showing the reliability of scales used and the confirmatory of the factor structure. This study shows that in strategic management technology, orientation is critical in increasing export performance. This study has extended the resource-based view (RBV) and dynamic capabilities theories.