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Sufficient production, consistent food supply, and environmental protection in urban +settings are major global concerns for future sustainable cities. Currently, sustainable food supply is under intense pressure due to exponential population growth, expanding urban dwellings, climate change, and limited natural resources. The recent novel coronavirus 2019 (COVID-19) pandemic crisis has impacted sustainable fresh food supply, and has disrupted the food supply chain and prices significantly. Under these circumstances, urban horticulture and crop cultivation have emerged as potential ways to expand to new locations through urban green infrastructure. Therefore, the objective of this study is to review the salient features of contemporary urban horticulture, in addition to illustrating traditional and innovative developments occurring in urban environments. Current urban cropping systems, such as home gardening, community gardens, edible landscape, and indoor planting systems, can be enhanced with new techniques, such as vertical gardening, hydroponics, aeroponics, aquaponics, and rooftop gardening. These modern techniques are ecofriendly, energy- saving, and promise food security through steady supplies of fresh fruits and vegetables to urban neighborhoods. There is a need, in this modern era, to integrate information technology tools in urban horticulture, which could help in maintaining consistent food supply during (and after) a pandemic, as well as make agriculture more sustainable.

The COVID-19 pandemic has brought rampant changes in skill needed in the labor market. It has accentuated technological disruption leaving millions in dire need of reskilling and upskilling. In this paper, we empirically analyze the impact of the COVID-19 pandemic related lockdown on the thrust of skills upgradation among people. By analyzing the Google trends data of 13 countries, we test the effect of the lockdown implementations on the urge to upgrade the skills through online searches for skills enhancement. Using difference-in-difference estimation approach, we found a substantial hike in the frequency of search terms related to skills upgradation. Our results suggest that people are utilizing the excess time, made available due to lockdowns, by exploring avenues to enhance their skills to accumulate human capital. The online educational platforms have been proven vital. The findings of this study establish the causal link between use of online education platforms and human capital development.

The Corona Virus Disease 2019 (COVID-19) has been a tough test on companies in the global food sector supply chain, exacerbating the realities and deficiencies it presents in developing economies. This situation has been reflected in the firm’s performance (F.P.) due to the lack of business conditions to respond to the current pandemic. However, in some companies, the adverse effects of COVID-19 have been counteracted due to endowment and technological capabilities. Thus, this study examined the role of technological innovation (T.I.) and business data analytics (B.D.A.) in the F.P. of foods in Ecuador during COVID-19. A questionnaire collected the information from the food firms. Then, Covariance-Based Structural Equation Modeling processed the collected information. We found that (B.D.A.) mechanisms and different levels of T.I. within the developing market significantly shape the F.P. The results showed that the B.D.A. enables circular economy (C.E.) practices and the improvement of product delivery services, which constitutes an improvement of the F.P. The COVID-19 outbreak did not significantly affect T.I., unlike what happened with B.D.A. This study concluded that firms with the most extraordinary technological production processes have been the least affected during COVID-19. This study suggested that policy measures should boost food firms’ technological endowment to improve their resilience in uncertainty and risk scenarios.

Pollution by organic wastes and manures is an important problem in tropical and sub-tropical countries and novel solutions for their proper management and valorization are needed. Waste-derived organic manures may increase metal load in the soil–plant ecosystem and food chain, with potential risks to public health. The aim of this work was to evaluate the impact of three manures (poultry waste (PW), press mud (PM), and farmyard manure (FYM)) on heavy metals (HMs) (Cd, Co, Cr, Cu, Pb, Zn, Fe, Mn) toxicity in a soil and coriander (Coriandrum sativum L.) system and their environmental impact (bioaccumulation, pollution load) and the consequent risk to human health via consumption. Results demonstrated that HMs in coriander fluctuated from 0.40 to 0.43 for Cd, 1.84 to 3.52 for Co, 0.15 to 0.16 for Cr, 1.32 to 1.40 for Cu, 0.05 to 0.09 for Pb, 1.32 to 2.51 for Fe, 0.10 to 0.32 for Mn, and 2.01 to 8.70 mg/kg for Zn, respectively. Highest pollution load index value was 2.89 for Cd and Mn showed the lowest (0.005). Daily intake of metal was noticed to be higher for Zn (0.049 mg/kg/day) for PW and lower for Mn (0.0005) at FYM treatment. The health risk index value was <1 and in the range of 2.30–2.50 for Cd showing potential carcinogenicity. It was concluded that as the organic amendments have the widest application in vegetables, it should be prudent to avoid their contamination and mobilization in plant–soil ecosystems to protect public health perspectives.

Drought and low amounts of mineral nutrients in the soil are the two leading global constraints in arid and semiarid regions. Their detrimental effects on soils and crops can be alleviated by applying controlled release and biodegradable fertilizers to better and sustain the crops. On a global scale, spinach (Spinacia oleracea L.) is an essential leafy green vegetable that is biologically considered a reliable source of essential nutrients and minerals for human health. A comprehensive approach is needed to manage water stress to mitigate the impacts of stress-caused damage and to examine this for better and increased plant production. An experiment was conducted using potassium-nitrate-containing chitosan/montmorillonite microparticles (150 mg) under mild and severe drought stress (MDS: 50% and SDS: 35% FC, respectively). The treatments include control (no KNO3 and 70% FC as normal irrigation (NI)), KNO3 + NI, 50% FC as mild drought stress (MDS), KNO3 + MDS, 35% FC as severe drought stress (SDS) and KNO3 + SDS. Results revealed that drought stress decreased all studied physiological parameters and increased oxidative stress indicators in spinach. Applying KN significantly increased root (122%) and shoot length (4%), shoot fresh weight (32%) and shoot dry weight (71%), chlorophyll a (88%), carotenoids (39%), total soluble proteins (50%), soluble sugars (51%), potassium (80%), and phosphorous (32%) concentrations over No KN at severe drought. While stress indicators, like glycine betaine, malondialdehyde, hydrogen peroxide, electrolyte leakage, peroxidase, superoxide dismutase, and ascorbic acid levels, were increased in stress. Treatment KN was proved efficient and effective in improving spinach physiological status in both MDS and SDS.

Heavy metals play an important role in the homeostasis of living cells. However, these elements induce several adverse environmental effects and toxicities, and therefore seriously affect living cells and organisms. In recent years, some heavy metal pollutants have been reported to cause harmful effects on crop quality, and thus affect both food security and human health. For example, chromium, cadmium, copper, lead, and mercury were detected in natural foods. Evidence suggests that these elements are environmental contaminants in natural foods. Consequently, this review highlights the risks of heavy metal contamination of the soil and food crops, and their impact on human health. The data were retrieved from different databases such as Science Direct, PubMed, Google scholar, and the Directory of Open Access Journals. Results show that vegetable and fruit crops grown in polluted soil accumulate higher levels of heavy metals than crops grown in unpolluted soil. Moreover, heavy metals in water, air, and soil can reduce the benefits of eating fruits and vegetables. A healthy diet requires a rational consumption of foods. Physical, chemical, and biological processes have been developed to reduce heavy metal concentration and bioavailability to reduce heavy metal aggregation in the ecosystem. However, mechanisms by which these heavy metals exhibit their action on human health are not well elucidated. In addition, the positive and negative effects of heavy metals are not very well established, suggesting the need for further investigation.

Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students’ performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.