A system dynamics approach to food security: The case of Turkey

Predicting a sustainable food safety policy for the near future is among Turkey's priority problems. In this context, this study aims to predict Turkey's sustainable food safety policies. For this reason, the system dynamics model, which is a dynamic cycle-based method with stock and flow diagrams, is used in this paper. This study supposed the six different scenarios for 2020 and 2050. Data were selected as population, productivity rate, arable land fertility rate, and annual food consumption (per capita). The purpose of creating these scenarios; To determine the most appropriate policy to ensure food safety in Turkey. In the first scenario, we assumed that the current situation continues. In the second scenario, the average productivity rate was increased by 1.5%. The third scenario assumes that annual per capita food consumption rises to 1.2 tonnes per year. In the fourth scenario, the total fertility rate is accelerated by 2%. In the fifth scenario, we assumed that the arable land loss rate decreased by 1/3. Finally, we assumed that the sixth scenario covers all the second, third, fourth, and fifth scenarios and that 2 points reduce food losses. In conclusion, the findings show that food security responds positively in scenarios 2 and 6. However, in other scenarios, food security is negatively affected. The findings show that the sixth scenario is the best-case scenario. To ensure food security, it is necessary to reduce arable land losses and food waste. Training farmers and control of the food supply chain will be beneficial for sustainable food security in Turkey. We recommend that policymakers consider these recommendations.

Comparative Review of Aerobic and Anaerobic Composting for the Reduction of Organic Waste

Composting is a self-heating, aerobic, bio-decomposition process of organic waste that has advantages over other disposal strategies since it reduces waste volume by 40-50% and kills pathogens by the heat generated during the thermophilic phase. This process uses organic waste (food scraps, grass chipping, etc.), water, soil (for added microbes) and either incorporation of air by turning the compost (aerobic) or lack of air within the compost (anaerobic). This study is designed to comparatively assess aerobic and anaerobic composting mechanisms on the productivity rate and analyse the different variables influencing the process. Based on the results obtained the time taken to completely compost the organic materials might not always be the same, because composting time is dependent on the percentage of microorganisms, water content, temperature and C:N ratio present in the pile at the said time along with the amount of material to be composted. Finally, this study will not only help farmers but also the general public in choosing a cost-effective and environmentally friendly way of reducing organic waste from landfills and reduction of greenhouse gases in the ozone layer.

Rational and Emotional Advertising: A bibliometric Analysis (19902020)

This study analyzes trends in the scientific literature on the concepts of rational and emotional advertising. The article presents a bibliometric analysis of 96 studies on rational and emotional advertising, taken from the Web of Science database (WOS) for the period 1990-2020. The study categorizes these documents according to bibliographic indicators, i.e., most productive authors, year of publication, countries with the highest productivity rate, the journals and universities that published the most on this topic, language, type of research and field of research. This analysis provides an overview of the nature and trends of research on rational and emotional advertising. The results of the analysis reveal the research weakness for this concept, especially in terms of definitions and conceptualization. Also, the results highlight the fragmented nature of the themes addressed in the various research articles on rational and emotional advertising.

Optimization of Machining Parameters for Product Quality and Productivity in Turning Process of Aluminum

Modern production process is accompanied with new challenges in reducing the environmental impacts related to machining processes. The turning process is a manufacturing process widely used with numerous applications for creating engineering components. Accordingly, many studies have been conducted in order to optimize the machining parameters and facilitate the decision-making process. This work aims to optimize the quality of the machined products (surface finish) and the productivity rate of the turning manufacturing process. To do so, we use Aluminum as the material test to perform the turning process with cutting speed, feed rate, depth of cut, and nose radius of the cutting tool as our design factors. Product quality is quantified using surface roughness (R_a) and the productivity rate based on material removal rate (MRR). We develop a predictive and optimization model by coupling Artificial Neural Networks (ANN) and the Particle Swarm Optimization (PSO) multi-function optimization technique, as an alternative to predict the model response (R_a) first and then search for the optimal value of turning parameters to minimize the surface roughness (R_a) and maximize the material removal rate (MRR). The results obtained by the proposed models indicate good match between the predicted and experimental values proving that the proposed ANN model is capable to predict the surface roughness accurately. The optimization model PSO has provided a Pareto Front for the optimal solution determining the best machining parameters for minimum R_a and maximum MRR. The results from this study offer application in the real industry where the selection of optimal machining parameters helps to manage two conflicting objectives, which eventually facilitate the decision-making process of machined products.

ADVANTAGES, DISADVANTAGES AND PROSPECTS OF IMPLEMENTING A PIECE-RATE FORM OF PAY AT THE INDUSTRIAL ENTERPRISE ENVIRONMENT

The paper considers the conditions of expediency of using the piece-rate form of pay in the modern industrial environment. The necessity to ensure implementation of the motivational and stimulating role of the pay system is noted, as both business efficiency and employee motivation depend on this choice. The extensive use of piece-rate form of pay at industrial enterprises is related to its proven advantages, the main of which is the obvious relationship between production output and wages. This stimulates to increase individual employee productivity rate and efficiency the production system as a whole. The reason for the piece-rate form of pay to be promising to consist in its flexibility and ability to transform depending on the specifics of production environment and conditions of labour organization. Emphasized that despite mentioned advantages, implementation of piece-rate form of pay has its own certain limitations and risks. Specifically, it does not natively support accounting for a quality parameters of production, may lead to violation of production process synchronicity requirement and omit compliance to the norms of material consumption. Is determined that while choosing forms and systems of pay, it should be clearly understood that the development of an effective such system for successful monetary motivation of employees at the enterprise environment is an extremely complex task and depends on multiple factors, even if we take into consideration industrial enterprises only.

Enterprise Robotic Process Automation

Robotic Process Automation (RPA) is an emerging technology widely used across multiple sectors such as human resources, healthcare, finance, accounting, manufacturing, higher education and supply chain management, etc. RPA, also known as ‘software bots’, replaces manual, rule-based, repetitive tasks humans perform. These software bots are currently in a journey, evolving to be more sophisticated, mimicking human activities and enabling humans to achieve higher-valued tasks. Hence, RPA impacts the overall operational efficiency in organisations through multiple facets by its integration with employees, existing technologies and infrastructure, and business processes. It reduces the burden on IT as it does not disturb the underlying legacy systems. It increases reliability as bots can work 24x7 effectively. It is used as a time and cost reduction technology as it reduces the size of the manual workload. The tasks performed through RPA is accurate as it is less prone to errors. It increases compliances as it follows the rules and keeps audit trails. The productivity rate of organisations increase as the execution time through RPA is faster than tasks being performed by human employees. Furthermore, RPA is introduced as a low code technology that uses drag and drop functionalities with little to no programming knowledge [1].

Delay in payment effects on productivity of small and medium construction companies in Oman: exploration and ranking

Maintaining a stable productivity rate in a construction project is a challenge. Many external and internal factors influence it. Delay in payment is one of the factors representing the project cash flow and mirrors the company’s financial stability status. This study explores the delay in payments effects on the construction productivity of the small and medium construction companies in Oman. Also, it ranks the delay in payment among other productivity factors. Sixty-five small and medium construction companies registered in Oman Tender Board participated in the questionnaire survey. The results showed that delay in payment significantly affects the financial stability of the companies. The delay in payment was ranked third out of 21 influencing productivity factors. The results were compared with a previous study that covered large construction companies in Oman. It was found that the rank of delay in payment in the small and medium construction is significantly higher than what was found in large companies.

OPTIMIZING PRODUCTIVITY RATE TO MINIMIZE OVERDRAFT INTEREST PAYMENT IN INFRASTRUCTURE CONSTRUCTION PROJECTS

Winning a bid is a good opportunity for contractors that includes risks. After winning a project, contractors typically receive payments after two-three months of work completion that leads to negative cash flow (overdraft) throughout the duration of a project. Hence, contractors borrow from banks and pay monthly interests on the amount of overdraft they owe. To solve this problem, a hybrid model utilizing Discrete Event Simulation using SIMPHONY software, a special purpose simulation tool developed by the University of Alberta, accompanied by Markov Chain prediction technique. The developed hybrid model allows contractors to test different scenarios in search of the optimum productivity rate and payment arrangement to minimize negative cash flow. A case study utilizing a typical road construction project is used to test and validate the developed model and its ability to determine the optimum scenario. Results revealed that markup percentage and initial investment are two crucial factors to deliver the project successfully. In the harsh market, increasing the amount of cash to invest without a reasonable markup (at least 10%) will no longer make a profit. But, if the markup percentage could be increased by more than 15%, it will offer a chance to the contractor to make a profit and successfully deliver the project with initial investment reasonably low; and save a flexible productivity rate to finish the project within the schedule.

FPGA-Based Implementation of an Optimization Algorithm to Maximize the Productivity of a Microbial Electrolysis Cell

In this work, the design of the hardware architecture to implement an algorithm for optimizing the Hydrogen Productivity Rate (HPR) in a Microbial Electrolysis Cell (MEC) is presented. The HPR in the MEC is maximized by the golden section search algorithm in conjunction with a super-twisting controller. The development of the digital architecture in the implementation step of the optimization algorithm was developed in the Very High Description Language (VHDL) and synthesized in a Field Programmable Gate Array (FPGA). Numerical simulations demonstrated the feasibility of the proposed optimization strategy embedded in an FPGA Cyclone II. Results showed that only 21% of the total logic elements, 5.19% of dedicated logic registers, and 64% of the total eight-bits multipliers of the FPGA were used. On the other hand, the estimated power consumption required by the FPGA-embedded optimization algorithm was only 146 mW.

Critical insight on the recent scenario of degraded lands in India and its bioremediation status

The planet earth is composed of one-fourth of the land with a total geographical area of India as 328.2M ha. Due to various reasons, 14.64% of the land resource falls under wastelands. Rapid industrialization, intensive agricultural practices, and unsustainable urbanization lead to the degradation of land resources. Most of the time, the biophysical composition of the soil determined its utilization and productivity rate. Some are highly productive on the other hand some are less or unproductive, India has roughly 328 million hectares of land area and the land utility pattern been 43.6% agricultural, 4.6% grazing lands, 12.2% marshlands, 10.7% woodlands, 8.4% desolate land, 5.3% municipal land. The continuous increase of the population leads to pressure for uptown land. The area under non-agricultural land is used for the roads, buildings, railways, industrial establishments. In this connection, non-agricultural land is increasing which is a major concern about land degradation. Based on the secondary data this review paper discussed the contaminated land in India, respective case studies, and its remediation technologies. Many physical, chemical, and biological methods were adopted for cleaning the contaminated lands in them some are tough and some are expensive to handle.