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This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.

Since 2008’s financial crisis, attention toward supply chain finance (SCF) has increased. However, most research investigates SCF considering single supply chain (SC) stages or buyer–supplier dyads and focuses on a single SCF solution. It is important to see how different solutions are adopted at different SC stages, by actors with different financing needs. This study aims to analyze SCF at different SC stages, to understand why different solutions are implemented at different SC stages and the contingency factors (regulation, SC stage, product category and size) influencing their adoption.

The paper is based on multiple exploratory case studies in the Italian agri-food industry, considering firms distributed at different SC stages and adopting multiple SCF solutions. The paper exploits a contingent approach (Sousa and Voss, 2008) to analyze how contingent factors influence SCF adoption at different SC stages.

Findings explain how and why different SC stages (producer, cooperative, processor and retailer) implement different SCF solutions (reverse factoring, dynamic discounting, inventory finance and Minibond), describing contingency variables’ impact on their adoption.

To the best of the authors’ knowledge, the research is original in its description of SCF at different SC stages, considering different SC actors’ drivers and barriers, and questioning the importance of a coordinated approach in SCF adoption along an entire SC. Moreover, the paper adopts a contingent approach, contributing to SCF research, seldomly based on theoretical lenses.

The United States (US) sugar program protects domestic sugar farmers from unrestricted imports of heavily-subsidized global sugar. Sugar-using firms (SUFs) criticize that program for causing US sugar prices to be higher than world sugar prices. This study examines the financial performance of publicly traded SUFs to determine if they are performing at an economic disadvantage in terms of accounting profitability, risk and economic profitability compared to other industries.

Firm-level financial accounting and market data from 2010 to 2019 were utilized to construct financial metrics for publicly traded SUFs, agribusinesses and general US firms. These financial metrics were analyzed to determine how SUFs compare to their agribusiness peer group and general US companies. The comprehensive financial analysis in this study covers: (1) accounting profit rates, (2) drivers of profitability, (3) economic profit rates, (4) trend analysis and (5) peer comparisons. Quantile regression analysis and Wilcoxon–Mann–Whitney statistics are employed for statistical comparisons.

Regarding various profitability and risk measures, SUFs outperform their agribusiness peers and the general benchmark of all US firms in terms of accounting profit rates, risk levels and economic profit rates. Furthermore, compared to other US industries using the 17 French and Fama classifications, SUFs have the highest return on investment and economic profit rate―measured by the Economic Value Added® margin―and the second-lowest opportunity cost of capital, measured by the weighted average cost of capital.

This study finds nothing to suggest that the US sugar program hinders the financial success of SUFs, contrary to recent claims by sugar-using firms. Notably in this analysis is the evaluation of economic profit rates and a series of robustness techniques.

The authors assess the development potential of cowpea beyond grain in local markets in Mali by: (1) identifying trader types and types of cowpea products sold; (2) examining trader roles; (3) estimating gross margins and their determinants; and (4) discussing policy opportunities to further develop the value chain.

The authors analyze data collected through observation and semi-structured questionnaires from 487 sellers in 26 markets, including market, seller, and product characteristics. The authors also calculate gross margins and conduct a regression analysis to identify influential factors.

The authors identify several types of cowpea sellers in local markets, including processor-retailers, retailers of fresh leaves and fodder, and grain retailers, collectors and wholesalers. Women dominate the marketing of processed products and fresh leaves. The marketing of boiled cowpeas offers retailers higher margin rates compared to fritters and pancakes. Grain sellers, who are mostly men, have lower margins but sell larger quantities. Processor-retailers bring more value to the cowpea value chain. Specialization of the seller in cowpea, regional location of the market and day of the market fair all influence gross margins.

Future work should explore consumer preferences for different types of cowpea products.

This study of the cowpea value chain in Mali has revealed the multidimensional character of the cowpea plant, which goes far beyond its grain and highlight the important roles played by women.

This paper examined the factors influencing the participation of rice processors in short supply chains and the participation impact on the amount of rice processed, per capita expenditure of household and value of sales.

The Seemingly Unrelated Regression and Doubly Robust Augmented Inverse Probability Weighting Model (AIPW) were used to analyse the determinants of short supply chain participation and the impact of short supply.

From the results, the mean value of rice processed was GH₵18385 (US$ 3,069.28), with the minimum value being GH₵ 25 (US$ 4.17) and the maximum GH₵ 67200 (US$ 1,1218.70) per annum. Processed rice aroma and grade characteristics positively influence the value of processed rice sold via short supply chains as well as the expertise rate of the processor, Farmer-Based Organisation membership, and marketing information availability. Women rice processors' per capita expenditure, total sales value and the value of processed rice was positively influenced by the short supply chain participation.

Even though the sample size was appropriate, a larger sample size could further support the study's finding since a limited geographical area with predominant domestic rice processors was studied. Again, future studies should consider behavioural theories, such as the Theory of Planned Behaviour, amongst others, in understanding the reasons for the choices of short supply chains compared to other sales outlets.

Although there is a growing body of literature on rice, most of the studies focussed on the marketing outlet of rice producers, rice processing, constraints and opportunities faced by rice farmers and processors and an out-grower scheme involving rice processors amongst rice producers with none of these on the choice of short supply chains amongst women processors. Also, amongst all the studies on rice producers, none applied a theory; however, the Women in Development (WID) Theory was used to analyse the impact of the short supply chain on the impact on household per capita expenditure (poverty), the value of sales and amount of rice processed, a modest theoretical contribution of the paper to literature.

This study aims to utilise Six Sigma in an Irish-based red meat processor to reduce process variability and improve yields.

This is a case study within an Irish meat processor where the structured Define, Measure, Analyse, Improve and Control (DMAIC) methodology was utilised along with statistical analysis to highlight areas of the meat boning process to improve.

The project led to using Six Sigma to identify and measure areas of process variation. This resulted in eliminating over-trimming of meat cuts, improving process capabilities, increasing revenue and reducing meat wastage. In addition, key performance indicators and control charts, meat-cutting templates and smart cutting lasers were implemented.

The study is one of Irish meat processors' first Six Sigma applications. The wider food and meat processing industries can leverage the learnings to understand, measure and minimise variation to enhance revenue.

Organisations can use this study to understand the benefits of adopting Six Sigma, particularly in the food industry and how measuring process variation can affect quality.

This is the first practical case study on Six sigma deployment in an Irish meat processor, and the study can be used to benchmark how Six Sigma tools can aid in understanding variation, thus benefiting key performance metrics.

Research efforts aiming to improve understanding of how various organisational relationships contribute to better food quality (FQ) in a constantly changing business environment are limited. This study examines the effects of supply chain (SC) organisations on the quality of food products across multi-tiered segments with dynamic business situations.

Guided by a conceptual research framework based on contingency theory and netchain analysis, moderation-based partial least squares structural equation modelling (PLS-SEM) was used to analyse multi-tiered data from 405 shrimpers and 238 women processors in Akwa-Ibom, Lagos and Ondo states in Nigeria.

The authors' findings show that unpredictable business environments such as market turbulence (MT), power asymmetry (PA) and distrust (DT) not only directly influence SC organisations but also moderate how organisational networks contribute to improved FQ. Further results reveal that closer vertical ties such as relational contracts are prerequisites for small-scale actors to guarantee improved FQ along multiple nodes of the food system.

This is the first study to examine, from a contingency and multi-tiered perspective, how small-scale actors can maintain FQ across interdependent nodes of a food chain in a developing country context and to explore the complex interplay between SC networks and the quality of highly perishable food products in unpredictable business environments. Relevant theoretical and policy implications are discussed.

The main purpose of the paper is timing analysis of mixed critical applications on the multicore system to identify an efficient task scheduling mechanism to achieve three main objectives improving schedulability, achieving reliability and minimizing the number of cores used. The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

The paper opted for a simulation-based study. The simulation of mixed critical applications, like air traffic control systems and synthetic workloads, is carried out using a litmus-real time testbed on an Ubuntu machine. The heuristic algorithms for task allocation based on utilization factors and task criticalities are proposed for partitioned approaches with multiple objectives.

Both partitioned earliest deadline first (EDF) with the utilization-based heuristic and EDF-virtual deadline (VD) with a criticality-based heuristic for allocation works well, as it schedules the air traffic system with a 98% success ratio (SR) using only three processor cores with transient faults being handled by the active backup of the tasks. With synthetic task loads, the proposed criticality-based heuristic works well with EDF-VD, as the SR is 94%. The validation of the proposed heuristic is done with a global and partitioned approach of scheduling, considering active backups to make the system reliable. There is an improvement in SR by 11% as compared to the global approach and a 17% improvement in comparison with the partitioned fixed-priority approach with only three processor cores being used.

The simulations of mixed critical tasks are carried out on a real-time kernel based on Linux and are generalizable in Linux-based environments.

The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

This paper fulfills an identified need to have multi-objective task scheduling in a mixed critical system. The timing analysis helps to identify performance risks and assess alternative architectures used to achieve reliability in terms of transient faults.

In the transition towards circular economy and sustainable development, effective implementation of extended producer responsibility (EPR) legislation is crucial to prevent plastic-waste generation and promote recycling activities. The purpose of this study is to undertake a qualitative analysis to examine recent EPR policy changes, implementation, barriers and enabling conditions.

In-depth interviews and group discussions with key stakeholders were undertaken to derive the barriers and facilitators of EPR implementation. Based on opinions and insights from a wide range of participants, this study identified a number of key issues faced by various parties in implementing EPR in India.

Stakeholders agree on a lack of clarity on various policy aspects, such as mandatory approval of urban local bodies, registration of recyclers/waste processors and consistency in the definition of technical terms. This paper provides useful policy inputs to address these challenges and to develop comprehensive EPR policy systems. More consultation and deliberation across various stakeholders is required to ensure the policies are effective.

India’s plastic-waste generation has increased at a rapid pace over the past five years and is expected to grow at a higher rate in the future. This research provides implications for policymakers to formulate coherent policies that align with the interests of brand owners and recyclers. Clear policy suggestions and improvements for effective plastic-waste management in India are also outlined.

This paper, based on a qualitative approach, contributes to research on plastic-waste management by integrating the perspectives of all EPR-policy stakeholders in India.

Purpose: Green computing is a way of using the computer resource in an eco-friendly while maintaining and decreasing the harmful environmental impact. Minimising toxic materials and reducing energy usage can also be used to recycle the product.

Need for the Study: The motivation of the study is to use green computing resources to decrease carbon emissions and their adverse effect on the environment.

Methodology: The study uses a qualitative method of collecting resources and data to address the opportunities, challenges, and future trends in green computing for Sustainable Future Technologies. The study focusses on multiple kinds of cloud computing services collected and executed into single remote servers. The service demand processor offers these services to the client per their needs. The simultaneous requests to access the cloud services, processing and expertly managing these requests by the processors are discussed and analysed.

Findings: The findings suggest that green computing is an upcoming and most promising area. The number of resources employed for green computing can be beneficial for lowering E-waste so that computing can be environmentally friendly and self-sustainable.

Practical Implications: Green computing applies across all industries and service sectors like healthcare, entertainment, tourism, and education. The convergence of technologies like Cloud Computing, AI, and Internet of Things (IoT) is greatly impacting Green Supply Chain Management (GSCM) market.