Output Levels Research Articles

Value Chain Analysis of Haricot Bean in East Shewa Zone, Oromia Regional State, Ethiopia

The key objectives of the study were identifying haricot bean value chain actors, their functions and relationship among them, examining actor’s performance along the chain and analyzing factors affecting supply to the market and factors affecting market outlet choice in the study area. Both primary and secondary sources were used for data collection. The primary data was collected through interviewing 180 sample household farmers and 40 traders using semi-structured questionnaires. STATA version 15 Software was used for data analyzing. As the descriptive result indicate that, on average farmers allocate 0.72ha of land for haricot bean production, from which 21.72qt/ha of output gained. In the value chain of haricot bean input suppliers, farmers, collectors, traders, transporters and consumers are the major value chain actors who perform various value chain. From the study result, six marketing channels of haricot bean are exhibited in the study areas. Estimates of the multiple linear regression models indicate that the level of haricot bean supplied to the market is determined by educational level of household, haricot bean farming experience, land allocated for haricot bean, market price of haricot bean, TLU, access to credit, and access to market information. Therefore, policy makers should focus more on the abovementioned variables in the future intervention to increase volume of haricot bean supplied to the market. As the multivariate probit model result indicated that the market outlet choice of rural assemblers was positively and significantly influenced by distance to the nearest market center, distance to all weather road, and educational level whereas it’s negatively and significantly influenced by output level where as selling direct to consumers outlet choice was negatively and significantly affected by sex of household, educational level, farm experience of household in haricot bean, and output level. Urban traders/wholesalers market outlet was positively and significantly affected by sex of household, educational level, haricot bean farm experience, access to credit, access to market information, membership of cooperative, and output level whereas it’s negatively influenced by distance to the nearest market center, distance to all weather road and extension service. Based on the findings of this study, some relevant implications can be drawn that can assist to design appropriate intervention mechanisms to improve market outlets choice of haricot bean farmers in the study area.

Ireland and the Antarctic Treaty System

Abstract Ireland has a rich Antarctic history, with Bransfield, Crozier, Shackleton, Crean and others acting as key Irish individuals in Antarctic exploration. Recognized as a source of Irish national pride, memorials, stamps, coins and a government research vessel all commemorate their polar feats. Today, a large proportion of Ireland's citizens visit the Antarctic region and Irish researchers produce high levels of academic outputs on Antarctic topics relative to many other nations. However, Ireland has not acceded to any Antarctic Treaty System instruments, despite a 20 year campaign to do so by cross-party Irish politicians. Ireland has the largest population of any nation in the European Union (EU) yet to accede to the Treaty and is much larger than some existing Treaty signatory states. However, Treaty accession provides no entitlement to participate in Antarctic governance, which is reserved for Consultative Parties, and therefore undertaking the associated legal and administrative work may be considered a poor use of available resources. Ireland's attainment of consultative status would be an even more complex and resource-intensive goal, but collaboration with the EU and other global partners could be a cost-effective solution that may also indirectly support Ireland's ambitions for Observer status at the Arctic Council.

Wind flow characteristics through shrouded wind turbine using ANSYS

Wind energy is a mature and quick-growing source of energy in many nations. New wind energy technologies must be introduced to increase effectiveness and reduce the cost of wind turbine installation. Wind energy conversion experts believe that shrouded wind turbines are a promising technology that could raise output and efficiency levels. A low-pressure zone is created behind the turbine because of the shroud’s unique structure. As a result, the turbine draws in greater mass flow to produce more power. With the aid of this innovative technology, wind turbines could be used in populated areas where lower wind speeds could be captured. In this study, a comparative analysis has been applied to different designs of shrouded wind turbines to analyze the wind speed, pressure, output power and power coefficient. ANSYS software has been conducted in this paper where the results obtained that the output power could be increased up to 3-4 times compared to the bare wind turbine, also a high-power coefficient could be reached up to 83.1%.

Measurement of intelligent computing via Levenberg Marquardt algorithm (LMA) for accurate prediction of fluid forces in a transient non-Newtonian thermal flow

Predicting precise results for the quantities of interest in time-dependent Computational Fluid Dynamics (CFD) simulations requires a significant investment of computational resources and time. To get around these issues, CFD simulations have been joined with Artificial Neural Networks (ANN). An optimally configured artificial neural network (ANN) is given the training and validation data sets produced by computational fluid dynamics (CFD). The flow around a cylinder, which is a well-known benchmark problem for incompressible flows, has been taken into consideration by the hybrid-CFD system. The mathematical model is based on the non-stationary Navier-Stokes and energy equations with viscosity. The basic architecture of the ANN model consists of 10 hidden layers, three output levels, and five input layers. Fast second-order LMA, a top-tier approach, was used to train the network. Both the Mean Square Error (MSE) and the coefficient of determination (R) provide statistical evidence that the ANN projected values for the drag and lift coefficients and average Nusselt number obtained from the finite element analysis are accurate. This analysis suggests that ANNs have the potential to significantly cut down on the amount of time and energy needed to run time-dependent simulations.

ECONOMIC ANALYSIS OF MILLET PRODUCTION IN JERE LOCAL GOVERNMENT AREA, BORNO STATE, NIGERIA.

This research was carried out to determine the profitability of millet production in Jere local Government area of Borno state. Random sampling technique was adopted in this research through which data was collected from ninety (90) farms with the aid of a well-structured questionnaire. Data was analysed using descriptive statistics, gross margin analysis and production functions. Result indicated that 58.9% of respondents were in their economically active age group, majority (76.7%) of the farmers were male, 66.7% were married, while 42.2% had farming experience of between 21 – 30 years in millet production and it was revealed that farms were on a small-scale basis. Result further showed that millet production was profitable with gross margin and net farm income of N29,743.18 and N12,868.00 per hectare respectively. Regression model estimated revealed that double-log was the lead equation with R2 of 68.5%. Also, land, labour and agrochemical had significant impact on the level of millet output at P<0.01 level of probability and F-ratio 35.212. Estimated efficiency ratios showed that land; labour and fertilizer were under-utilised, while agrochemical was over utilised. Return to scale gave a value of 1.27 which implies that millet production was in stage 1 of the production region. It can be concluded that millet production in the study area is profitably. The study recommends that farmers should employ more land; labour and fertilizer, while the use of agrochemical should be cut down in order to improved millet production output for profit maximization.

High-Speed Three-Dimensional-Digital Image Correlation and Schlieren Imaging Integrated With Shock Tube Loading for Investigating Dynamic Response of Human Tympanic Membrane Exposed to Blasts.

Investigating the dynamic response of human tympanic membranes (TMs) exposed to blasts requires full-field-of-view and three-dimensional (3D) methodologies. Our paper introduces a system that combines high-speed 3D digital image correlation (HS 3D-DIC) and Schlieren imaging (HS-SI) with a custom-designed shock tube for generating blast waves. This integrated system allows us to measure TM surface motions under intense transient loading, capturing full-field-of-view shape deformations exceeding 100 μm with a temporal resolution of 10 μs. System characterization encompasses (i) measuring the shock tube's output levels and repeatability, (ii) assessment of the spatial and temporal resolutions of the imaging techniques, and (iii) identification of overall system limitations. Optimizing these factors is crucial for improving the reliability of our system to ensure the accurate measurement of deformations. To assess our shock tube's reliability in generating repeated blast waves, we instrumented it with high-pressure (HP) and high-frequency (HF) pressure sensors along the blast wave pathway to record overpressure waveforms and compared them with Schlieren imaging visualized blast waves. We validate our HS 3D-DIC measured deformations by comparing them with deformations measured using single-point laser Doppler vibrometry (LDV), establishing a comprehensive assessment of the TM's dynamic response and potential fracture mechanics under blast. Finally, we test our approach with 3D-printed TM-like samples and a real cadaveric human TM. This methodology lays the groundwork for further investigations of blast-related auditory damage and the invention of more effective protective and medical solutions.

Penerapan Jaringan Syaraf Tiruan untuk Pengenalan Pola Huruf Menggunakan Metode Bidirectional Associative Memory (BAM)

The process in artificial intelligence is similar to the process that occurs in the human brain. The pattern recognition process is to determine whether the input and output values ​​are the same so that the system can detect the pattern. A reciprocal relationship between the input and output levels is made possible by the two interconnected layers of the Bidirectional Associative Memory (BAM) technique. As a result, this technique can serve as an associative memory, meaning that some of the data stored in it can be called upon.. The load signal is transmitted from the input layer X to the output layer Y in this procedure. The weight value will be modified by computing the outcomes between the ranks [1,0]. In this work, the active sigmoid function is employed. Three input characters—the letters S, O, and X—are used in this study's 5x5 order matrix. A pattern [27 4] that matches the target is produced by the target letter S [1,-1], a pattern [27 -3] that does not match the target is produced by the target letter O [1.1], and a pattern [-37 21] that matches the target is produced by the target letter X [-1,1]. Consequently, not all patterns are able to meet the established objectives.

Comparative analysis of submodules and design of seventeen-level modular multilevel converter using a five-level submodule block

Abstract Multilevel inverters and various modulation techniques have been proven to be the best solutions to overcome all the limitations of conventional two- or three-level voltage source inverters. In recent years, among all topologies, modular multilevel converter (MMC) has advantages like low total harmonic distortion (THD), reduced filter requirement, fault-tolerant operation, scalability, modularity, transformerless operation, etc. The main application of MMC is found in high-voltage DC transmission (HVDC). This study describes and analyses the performance of two distinct MMC submodule (SM) topologies: the Half Bridge submodule (HBSM) and the Full Bridge submodule (FBSM). Switching pattern is simpler in HBSM topology but it has the disadvantage that it does not have DC fault current blocking capability. Whereas FBSM not only inherently can block the DC fault current but also has advantages like reduced volume of MMC and better performance. Further, having the capability to produce three voltage levels, it can be operated in boost mode. Also, by using a proper switching pattern, MMC can be designed to generate a various number of output levels by using the same five-level submodule block. Out of which 17 level MMC gives the best output. The MATLAB-Simulink model is used to simulate the circuit, and the findings are confirmed for a 17-level output voltage.

Context-aware feature reconstruction for class-incremental anomaly detection and localization

With the development of deep learning, the unsupervised visual anomaly detection and localization task has gained significant attention in both academia and industry, where only normal data are used for training. Existing methods for this task typically train using all training data simultaneously. However, in practical industrial scenarios, new product classes are usually introduced incrementally, leading to the sequential availability of training data. Such scenarios demand methods for class-incremental anomaly detection and localization (CADL). The main challenge of class-incremental learning is to retain knowledge of old classes when learning new classes. In this article, we aim to effectively leverage limited exemplars of old classes to retain knowledge for the CADL task. Achieving this goal requires a model that can efficiently capture anomaly-identification-related knowledge from limited exemplars. Considering that pixel-level anomaly identification requires an understanding of the surrounding context, we treat context within inputs as valuable anomaly-identification-related knowledge and design a context-aware feature reconstruction (CFR) model to capture such knowledge. Moreover, to avoid inter-class context conflict that may arise with class increments, we design an intermediate feature organization strategy. This strategy and output-level knowledge distillation jointly form dual constraints to regularize the model at both mid-feature and output levels. Utilizing the CFR model with Dual Constraints, the proposed CFRDC can effectively retain old-class knowledge while learning new classes, thus addressing the CADL task. Experimental results on the commonly-used MVTec-AD dataset demonstrate the effectiveness and outstanding performance of the proposed method in CADL.

Enhancing conical solar still performance using high conductive hollow cylindrical copper fins embedded by PCM

The present study aims to enhance the efficiency of the conical solar distiller in producing water during both day and night. This will be achieved through design enhancements, such as increasing the surface area of the distiller to maximize solar energy absorption and improve condensation rates. High thermal-conductivity cylindrical fins have been used, and their role will be investigated. These fins may be hollow and extended, with or without the inclusion of phase change material (PCM). Consequently, copper tubes are positioned at the base of the distiller basin, both with and without the inclusion of phase-changing paraffin wax material inside the hollow tubes. A comparison is made between the performance of three configurations: the first configuration is traditional, the second configuration includes fins without the incorporation of PCM, and the third configuration includes PCM-based fins. This comparison is crucial as it provides a clear understanding of the impact of each design enhancement on the distiller’s performance. The study findings indicated that the arrangement with hollow fins filled with PCM had the highest level of freshwater output. Furthermore, the results showed that the combined output of conical solar distillers equipped with fins made of copper tubes, both with and without PCM, were 7.50 and 6.75 L/m2/day, respectively. The cumulative production of conventional conical distillers is 4.85 L/m2/day. Freshwater output improved by 54.64 and 39.17 % when using hollow copper tubes with and without PCM within 24 h, compared with the conventional configuration. During night-time hours, it was observed that the freshwater output of distilled water from salt water significantly improved by 550 % when utilizing copper tubes loaded with PCM, compared to distillation using empty copper tubes. The maximum daily efficiency of conical distillers with fins with and without PCM were 73.6 % and 80.3 %, respectively. These results showed that conical distillers using copper tubes hollow filled with PCM represent good options for obtaining higher performance than distillation.

Study on the Performance of a Novel Double-Section Full-Open Absorption Heat Pump for Flue Gas Waste Heat Recovery

Open absorption heat pumps are considered one of the most promising methods for efficiently utilizing low-grade waste heat, reducing energy consumption, and lowering greenhouse gas emissions. However, traditional heat pumps have significant limitations in the range of flue gas temperatures they can recover, and their relatively low system performance further restricts practical applications. In this study, we propose a novel double-section full-open absorption heat pump driven by flue gas from the desulfurization tower. By designing the absorber with a double-layer structure, the system can recover more latent and sensible heat from the flue gas, significantly enhancing its thermal recovery capability. Additionally, replacing the traditional LiBr/H2O working pair with LiCl/H2O significantly reduces the risks of solution crystallization and equipment corrosion. Through comprehensive research, the strengths and weaknesses of the system were explored. The results indicate that this system effectively recovers flue gas waste heat within the temperature range of 30–70 °C. Specifically, at a flue gas temperature of 70 °C and a flow rate of 3 kg/s, the system achieves a COP of 1.838, along with a heating capacity of 158.83 kW and a ROI of 34.1%. These metrics demonstrate that the system not only delivers high performance but also exhibits excellent economic viability. Additionally, when the solution temperature is lowered to 10 °C, the system’s maximum COP reaches 1.96, reflecting a significant 30.67% improvement over traditional heat pumps. These findings highlight the system’s potential for application in coal-fired power plants, where varying levels of power output can benefit from enhanced thermal recovery and efficiency.

Do consumers gain or lose when managers become socially concerned?

The Friedman Doctrine states, “an entity's greatest responsibility lies in the satisfaction of the shareholders.” This leads us to consider managerial firms’ corporate social responsibility (CSR) activities delegated by profit-maximizing owners. Our research follows the Ferstman-Judd-Skilvas framework to examine if profit-maximizing owners could ask managers to be socially rather than privately concerned and its impacts on social welfare, contributing to the literature of CSR by presenting the following results. First, CSR delegation is a dominant strategy. Second, CSR delegation reduces the industry output level. We further show that consumers’ surplus and social welfare decrease in the number of managerial firms adopting CSR delegation. The above results imply that socially concerned managers with profit-maximizing owners are socially undesirable in a market with output delegation.

Simulasi karakteristik mesin termoakustik pembangkit listrik dengan penambahan model kerugian minor dari dua segmen konis

Acoustic energy output level from regenerator segment of a thermoacoustic engine model is attenuated along it’s loop due to several conditions including minor losses. This article discusses the result of Delta EC simulation of a thermoacoustic engine model acting as simple electric power generator that inserted with two conical segments. The cone segments are capable to lower the energy loss which in turn improve the overall performance of the engine in term of nett heat to acoustic energy conversion efficiency. Combined acoustic energy loss induced by both cones is equivalent to 4.94 watts minor losses. At this condition, regenerator segment amplifies the incoming acoustic energy flow of 57.02 watt up to 93.57 watt, which is equals to 36.55 watts acoustic amplification. It leads to increasing of overall engines heat to acoustic efficiency into 14.05%, which is 1.29% higher than those at the case of without cones. This engine performance improvement addressed to smoother streamline of working fluid flow inside the loop.

Measurement and coupling analysis of the efficiency of resource‐based treatment of municipal solid waste in Chengdu–Chongqing area

AbstractMunicipal solid waste (MSW) is crucial for carbon cycle, residents' lives, and green circular economy development, but quantitative research is limited in efficiency and regional synergy. Considering the regional characteristics of Chengdu and Chongqing, this study establishes the input–output indicator system of MSW treatment in 16 cities in the Chengdu–Chongqing area, constructs the Data Envelopment Analysis model and the coupling coordination degree model, and makes an in‐depth study on the efficiency and environmental benefits of MSW resource treatment from 2012 to 2021. The research conclusion is as follows: first, the improvement of MSW treatment efficiency can be enhanced through reasonable policies and measures to promote sustainable urban development and socioeconomic benefits. The input and output levels of MSW treatment in 16 cities all show an upward trend. Second, the 16 MSW treatment systems have good average pure technical efficiency (Chongqing 0.9879, Chengdu 0.9805), average scale efficiency (Nanchong 0.9396), and average comprehensive efficiency (Nanchong 0.9148), but the overall comprehensive efficiency needs improvement. Third, in 2021, Chengdu's ecological and environmental benefits are better than Chongqing's, while Chongqing has a certain degree of input redundancy and output deficiency. Fourth, in the coupling analysis of resource treatment efficiency and environmental improvement benefits in Chengdu and Chongqing between 2012 and 2021, the coordination between resource treatment and environmental improvement in Chengdu lags behind that in Chongqing, but overall, the coupling is improving. Finally, suggestions for improving the efficiency of MSW resource treatment and promoting environmental improvement are presented from the front, middle, and back ends, respectively, to provide a reference for the policy‐making of city managers.

Numerical Analysis of Hydrogen-Enriched Natural Gas on Combustion and Emission Characteristics

AbstractThe integration of hydrogen into natural gas infrastructure presents a viable strategy for mitigating greenhouse gas emissions and advancing toward carbon neutrality. This study investigates the combustion characteristics and emissions profiles of hydrogen-enriched natural gas mixtures, specifically focusing on the composition of Russian pipeline natural gas. A comprehensive mathematical model was developed to predict emission concentrations and simulate fuel mixture combustion using MATLAB Simulink software. This versatile model facilitates further analysis within the MATLAB ecosystem. The simulation results demonstrate a significant correlation between the hydrogen content in the natural gas mixture and the resulting heat power output. With a constant fuel consumption rate, a notable decrease in heat power was observed as the hydrogen concentration increased, reaching a maximum reduction of 44.9% at a 45% hydrogen content. These findings underscore the feasibility of partially substituting natural gas with hydrogen, while also highlighting the necessity for increased fuel flow rates to maintain equivalent power output levels. This poses additional challenges for natural gas grid operators, necessitating infrastructure adaptations to accommodate higher fuel demands. The insights gained from this research contribute to the growing body of knowledge surrounding hydrogen integration in the energy sector, offering valuable implications for decarbonization strategies and the optimization of natural gas infrastructure.

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of 'predatory'-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.

Tunable pattern recognition of optical QPSK data using optical correlation and direct detection.

Performing pattern recognition via correlation in the optical domain has potential advantages, including: (i) high-speed operation at the line rate and (ii) tunability and scalability by operating on the optical wave properties. Such pattern recognition might be performed on quadrature-phase-shift-keying (QPSK) data transmitted over an optical network, which generally requires using coherent detection to distinguish the phase levels of the correlator output. To enable simpler detection, we combine optical correlation with optical biasing to experimentally demonstrate tunable and scalable QPSK pattern recognition using direct detection. The pattern is applied by adjusting the relative phases of the local pumps. Delayed QPSK signals, a coherent bias tone, and local pumps undergo nonlinear wave-mixing in a periodically poled lithium niobate (PPLN) waveguide to perform optical correlation and biasing. The biased correlator output is captured using direct detection, where the highest power level corresponds only to the pattern. Multiple QPSK pattern recognitions are achieved error-free over 3072 symbols using power thresholding values of (i) 0.78 at a 5-Gbaud rate and 0.73 at a 10-Gbaud rate for 2-symbol pattern recognition and (ii) 0.81 at a 5-Gbaud rate and 0.79 at a 10-Gbaud rate for 3-symbol pattern recognition.

TriLA: Triple-Level Alignment Based Unsupervised Domain Adaptation for Joint Segmentation of Optic Disc and Optic Cup.

Cross-domain joint segmentation of optic disc and optic cup on fundus images is essential, yet challenging, for effective glaucoma screening. Although many unsupervised domain adaptation (UDA) methods have been proposed, these methods can hardly achieve complete domain alignment, leading to suboptimal performance. In this paper, we propose a triple-level alignment (TriLA) model to address this issue by aligning the source and target domains at the input level, feature level, and output level simultaneously. At the input level, a learnable Fourier domain adaptation (LFDA) module is developed to learn the cut-off frequency adaptively for frequency-domain translation. At the feature level, we disentangle the style and content features and align them in the corresponding feature spaces using consistency constraints. At the output level, we design a segmentation consistency constraint to emphasize the segmentation consistency across domains. The proposed model is trained on the RIGA+ dataset and widely evaluated on six different UDA scenarios. Our comprehensive results not only demonstrate that the proposed TriLA substantially outperforms other state-of-the-art UDA methods in joint segmentation of optic disc and optic cup, but also suggest the effectiveness of the triple-level alignment strategy.

Risk-benefit comparative analysis of different control methods for reservoirs under the water to electricity mode

Abstract For some small hydropower stations that are not under the control of power dispatching department, there are many ways to implement the reservoir operation scheme formulated under the premise of determining power by water. This paper respectively selects the output, flow and water level process as the control conditions, explores the risks and benefits of reservoir operation with different control variables, and selects the optimal operation scheme through comprehensive comparative analysis. Simulation results of a hydropower station in the Han River Basin show that when the output is taken as the control condition, the waste water is small, the power generation benefit is high, and the risk of the water level outside boundary is within the acceptable range. Considering the risk and benefit of reservoir operation and taking output as the control condition, it has significant advantages over water level and flow.

Do Protective Tariffs Influence Firms’ Competitive Stance? Application of a Competitive Leverage Model in The Presence of Simultaneous Equation and Sample Selectivity Bias

ABSTRACT: We propose a Competitive Leverage model, and apply it to analyze the impact of tariffs on the attitudes of domestic import-competing firms, especially regarding their disincentives to maintain a Competitive Stance against foreign competitors in the domestic market. We propose that tariffs are a disincentive to the pursuit and maintenance of production efficiency in firms and industries. When countries implement tariff protection for their domestic firms, the most common excuse sounds reasonable, except that it is also economically flawed. While the short-run impacts of such a measure might seem plausible, its long-term effects appear to be less than favorable, and may, in fact, turn out to be damaging to the economy. The paper applies the competitive leverage concept in a unique way to highlight how tariffs distort the output levels of domestic firms, and gives an empirical analysis of the model, using USA data to verify the predictions of the model. Particularly, we develop a Cournot-competition type Competitive Leverage model, and use it to examine the role played by tariffs as a disincentive to a typical firm’s pursuit and maintenance of production efficiency. The competitive leverage model is formulated and applied to verify the effects of tariff protection from the typical domestic firm’s strategic interaction standpoint. We further apply cross-sectional data to carry out an empirical analysis of the model, to verify how tariffs impact the domestic firm’s output and pricing decisions. Our findings would enable us to contribute to the debate about the need to remove protective tariffs, on the grounds that tariffs do not only enable domestic firms to operate inefficiently, but also tends to distort the output and pricing outcomes that would otherwise have been realized in the free market. The results from the study are used to propose some major policy implications of imposing tariff protection, among which are that there is need for implementation of more deliberately sustained Research and development program on the part of domestic firms in the economy, to enable them withstand vigorous competition in a free-trade world. Also, since protective tariffs significantly increase firms’ profit rates, which in turn enables them to unduly earn and accumulate economic rents that amount to inefficiencies and distortions in free-market prices and quantities outcomes, governments should always resist any pressures from big businesses to impose protective tariffs against competition from international rivals. Yet a clear public policy implication from this paper is that, at the macro level, as the results suggest that a government policy that aims at protecting domestic firms through imposition of tariffs may have an adverse effect on the firms’ competitive stance in the industry, a practical message is for the government to impose only a moderate tariff at the most, if it must, especially as it relates to agricultural products.