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This study aims to assess the effect of water variation on bioethanol production from cassava peels (CP) using Saccharomyces cerevisiae yeast as the ethanologenic agent.

The milled CP was divided into three treatment groups in a small-scale flask experiment where each 20 g CP was subjected to two-stage hydrolysis. Different amount of water was added to the fermentation process of CP. The fermented samples were collected every 24 h for various analyses.

The results of the fermentation revealed that the highest ethanol productivity and fermentation efficiency was obtained at 17.38 ± 0.30% and 0.139 ± 0.003 gL⁻¹ h⁻¹. The study affirmed that ethanol production was increased for the addition of water up to 35% for the CP hydrolysate process.

The finding of this study demonstrates that S. cerevisiae is the key player in industrial ethanol production among a variety of yeasts that produce ethanol through sugar fermentation. In order to design truly sustainable processes, it should be expanded to include a thorough analysis and the gradual scaling-up of this process to an industrial level.

This paper is an original research work dealing with bioethanol production from CP using S. cerevisiae microbe.

1. Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

2. Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

3. Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

4. Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

Is there a secret recipe for economic growth?

No, there is no recipe, but we can extrapolate some pieces of advice from Adam Smith.

An economy can leave behind its “dull” stagnant state and grow when its markets expand, when the productivity of its workers increases thanks to high compensations, which are seen as incentives to work harder and when lobbying and cronyism are kept at bay. Luck plays a role too, but these three ingredients are necessary, even if not sufficient, for an economy to grow and thus be “cheerful.”

These three aspects – expansion of market, liberal compensation of workers and lobbying – especially combined, have often been underestimated in Smith’s understanding of the possible sources of economic growth.

In this paper we examine the validity of the J-curve hypothesis in four Southeast Asian economies (Indonesia, Malaysia, the Philippines and Thailand) over the 1980–2017 period.

We employ the linear autoregressive distributed lags (ARDL) model that captures the dynamic relationships between the variables and additionally use the nonlinear ARDL model that considers the asymmetric effects of the real exchange rate changes.

The estimated models were diagnostically sound, and the variables were found to be cointegrated. However, with the exception of Malaysia, the short- and long-run relationships did not attest to the presence of the J-curve effect. The trade flows were affected asymmetrically in Malaysia and the Philippines, suggesting the appropriateness of nonlinear ARDL in these countries.

The previous research tended to examine the effects of the real exchange rate changes on the agricultural trade balance and specifically the J-curve effect (deterioration of the trade balance followed by its improvement) in the developed economies and rarely in the developing ones. In this paper, we address this omission.

Diabetes is one of the life-threatening chronic diseases, which is already affecting 422m people globally based on (World Health Organization) WHO report as at 2018. This costs individuals, government and groups a whole lot; right from its diagnosis stage to the treatment stage. The reason for this cost, among others, is that it is a long-term treatment disease. This disease is likely to continue to affect more people because of its long asymptotic phase, which makes its early detection not feasible.

In this study, the authors have presented machine learning models with feature selection, which can detect diabetes disease at its early stage. Also, the models presented are not costly and available to everyone, including those in the remote areas.

The study result shows that feature selection helps in getting better model, as it prevents overfitting and removes redundant data. Hence, the study result when compared with previous research shows the better result has been achieved, after it was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at diagnosing diabetes disease at its early stage.

This study has not been published anywhere else.

Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.

In this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.

By conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.

In this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality involve labor-intensive processes and rely on the expertise of connoisseurs proficient in identifying taste profiles and key quality factors. In this research, we introduce an innovative and efficient approach centered on the analysis of volatile organic compounds (VOCs) signals using an electronic nose, thereby empowering nonexperts to accurately assess wine quality.

To devise an optimal algorithm for this purpose, we conducted four computational experiments, culminating in the development of a specialized deep learning network. This network seamlessly integrates 1D-convolutional and long-short-term memory layers, tailor-made for the intricate task at hand. Rigorous validation ensued, employing a leave-one-out cross-validation methodology to scrutinize the efficacy of our design.

The outcomes of these e-demonstrates were subjected to meticulous evaluation and analysis, which unequivocally demonstrate that our proposed architecture consistently attains promising recognition accuracies, ranging impressively from 87.8% to an astonishing 99.41%. All this is achieved within a remarkably brief timeframe of a mere 4 seconds. These compelling findings have far-reaching implications, promising to revolutionize the assessment and tracking of wine quality, ultimately affording substantial benefits to the wine industry and all its stakeholders, with a particular focus on the critical aspect of VOCs signal analysis.

This research has not been published anywhere else.

The primacy of commerce in livelihood security cannot be overstated. However, in a rural context defined by involuntary socio-ecological displacement, commerce can assume a sociologically distinct character, with far-reaching implications. Based on first-hand encounters with victims of the devastating 2014 flood in Tokwe-Mukorsi, Zimbabwe, this paper analyses how the processes of “recreating” village markets in the resettlement site of Chingwizi impacted the victims’ experiences of resource provisioning and livelihood security.

Qualitative data were collected through 10 in-depth interviews, 10 key informant interviews and two focus group discussions, five years into the flood victims’ resettlement in Chingwizi. The data analysis focused on the dynamics around the recreation of village markets, and the consequences of this on the household economic standing of the resettled flood victims.

The paper reveals how the formation of village markets in Chingwizi was influenced not primarily by the ethno-commercial and ethno-economic impulses reminiscent of life in their ancestral home but mostly by new, disruptive dynamics and challenges unique to the resettlement site. The paper elucidates the constellation of factors that, together, exacerbated the flood victims’ overall socio-economic dislocation and disadvantage.

The study provides a systematic understanding of the dynamics of ethno-commerce, particularly on the evolution of village market activities and livelihoods, among Zimbabwe’s Chingwizi community over a period of five years into their resettlement. It brings to the fore, the often ignored, but significant nuances that 'village market' formation and livelihoods recreation takes in a resettlement context.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2023-0682

This study addresses the COVID-19 infection and its relationship with the city’s constructive intensity, commuting time to work and labor market dynamics during the lockdown period.

Microdata from formal workers in Recife was used to adjust a probability model for disease contraction.

The authors' results indicate that greater distance to employment increases the probability of infection. The same applies to constructive intensity, suggesting that residences in denser areas, such as apartments in buildings, condominiums and informal settlements, elevate the chances of contracting the disease. It is also observed that formal workers with completed higher education have lower infection risks, while healthcare professionals on the frontlines of combating the disease face higher risks than others. The lockdown effectively reduced contagion by limiting people’s mobility during the specified period.

The research shows important causal relationships, making it possible to think about public policies for the health of individuals both when commuting to work and in living conditions, aiming to control contagion by COVID-19.

The lockdown effectively reduced contagion by limiting people’s mobility during the specified period.

It is also observed that formal workers with completed higher education have lower infection risks, while healthcare professionals on the frontlines of combating the disease face higher risks than others.

The authors identified positive and significant relationships between these urban characteristics and increased contagion, controlling for neighborhood, individual characteristics, comorbidities, occupations and economic activities.

This study aimed to measure the effects of the Russian–Ukrainian war on the value of imports and the food trade balance in Saudi Arabia.

Estimating the suggested model using econometric analysis for the years 1990–2021.

The amount of deficit increased in the food trade balance from 11.58 billion riyals in 1990 to 72.98 billion riyals in 2021. As for the increase in the index of food production by 10%, it leads to a decrease in the value of food imports for Saudi Arabia by 1.88%. Also, the value of the deficit in Saudi Arabia's food trade balance decreases by 5.24% as a result of a 10% rise in food exports to the country.

In light of the increase in the food price index to 145.8, the value of food imports and the deficit in the food trade balance exceed their counterparts in the current situation for the year 2021, at a rate of 37.1% and 44.5% for each respectively. In view of achieving huge financial surpluses as a result of the rise in oil prices, the Saudi Arabia is able to bear the high import bill and the amount of food trade balance deficit. Finally, the Russian–Ukrainian war leads to an increase in the cost of obtaining food commodities and their unavailability in the markets and thus affects the food security environment. Therefore, this study recommends the necessity of conducting more studies on the impact of the war on the food security of the Kingdom of Saudi Arabia.

Extant literature in supply chain management tends to address a portion of the product flow to make food accessible to clients in need. The authors present a broader view of food insecurity and present nuances relevant to appreciate the complexities of dealing with this social problem.

The authors conducted an inductive study to reveal the deep meaning of the context as managers of nonprofit organizations (NPO) define and address food insecurity. The focus was on a delimited geographic area for capturing interactions among NPOs which have not been described previously.

This study describes the role of supply chains collaborating in unexpected ways in the not-for-profit context, leading to interesting insights for the conceptual development of service ecosystems. This is relevant because the solution for the food insecure stems from the orchestration of assistance provided by the many supply chains for social assistance.

The authors introduce two concepts: customer sharing and customer release. Customer sharing enables these supply chains behave like an ecosystem with no focal organization. Customer release is the opposite to customer retention, when the food insecure stops needing assistance.

The authors describe the use of customer-centric measures of success such improved health measured. The solution to food insecurity for an individual is likely to be the result of the orchestration of assistance provided by several supply chains.

The authors started asking who the client is and how the NPOs define food insecurity, leading to discussing contrasts between food access and utilization, between hunger relief and nourishment, between assistance and solution of the problem, and between supply chains and ecosystems.