Production Model Research Articles

Intersectional equity in Brazil's remote rural municipalities: the road to efficiency and effectiveness in local health systems.

The Brazilian remote rurality has been classified more reliably only recently, according to demographic density, proportion of urban population, and accessibility to urban centers. It comprises 5.8% of the municipalities, in nearly half of the states, with a population of 3,524,597 (1.85%). Remote rural localities (RRL) have reduced political/economic power, facing greater distances and barriers. Most health strategies are developed with the urban space in mind. We aim to understand how RRL are positioned concerning efficiency/effectiveness in health, compared to other urban-rural typologies of Brazilian localities, focusing on Primary Health Care (PHC), and its organizational models. We evaluated the efficiency and effectiveness of the organizational models using the health production model, from 2010-2019, gradually deepening the immersion into the RRL reality. We analyzed the human and financial resources dimensions, emphasizing teams, the results of PHC actions, and health levels. We used the fixed effects model and data envelopment analysis, cross-sectioned by intersectional inequities. We compared the Brazilian states with and without RRL, Brazilian municipalities according to rural-urban typologies, and RRL clusters. Brazilian RRL states show superior resource/health efficiency through services utilization according to health needs. The remote rural typology demonstrated greater efficiency and effectiveness in health than the other typologies in the RRL states. The organizational models with the Family Health Strategy (FHS) teams and the Community Health Worker (CHW) visits played a key role, together with local per capita health expenditures and intergovernmental transfers. Thus, financial resources and health professionals are essential to achieve efficient/effective results in health services. Among the RRL, the Amazon region clusters stand out, denoting the importance of riverine and fluvial health teams, the proportion of diagnostic/treatment units in addition to the proportion of illiteracy and adolescent mothers along with the inequity of reaching high levels of schooling between gender/ethnicity. Hopefully, these elements might contribute to gains in efficiency and effectiveness, prioritizing the allocation of financial/human resources, mobile FHS teams, availability of local diagnosis/treatment, and basic sanitation. Finally, one should aim for equity of gender/ethnicity in income and education and, above all, of place, perceived in its entirety.

Discussion on the production mechanism of deep coalbed methane in the eastern margin of the Ordos Basin

This study builds upon the research progress in the theories of CBM desorption, diffusion, and seepage flow to explore the production mechanisms of deep coalbed methane (CBM) in the Daing-Jixian block, aiming to achieve scientific and reasonable control of gas wells. Theoretical analysis suggests that CBM adsorption belongs to liquid–solid interfacial adsorption, encompassing four stages: liquid phase adsorption—liquid phase desorption—composite desorption—gas phase desorption. Most of the desorbed gas is driven by a pressure differential in a Darcy's flow process. By calculating the Knudsen number (Kn) under various temperature, pressure, and fracture diameter conditions, the flow state can be identified. Whole-diameter CT scanning reveals a multi-scale pore-fracture system ranging from millimeters to micrometers to nanometers. Calculations show that during the gas well drainage and depressurization process, fractures of millimeter scale and larger exhibit Darcy's flow, while micron-scale fractures maintain Darcy's flow status above a reservoir pressure of 5 MPa; other scales primarily exhibit non-Darcy flow without significant macroscopic movement. In summary, starting from the fundamental mechanisms of the original multiscale tri-level pore-permeability system of the coal reservoir, through the post-fracturing transformation forming three diversion zones of high, medium, and low conductive regions, and transitioning from primarily free gas to desorbed gas in three production stages, an ideal comprehensive production model schematic for the study area has been established, providing theoretical support for on-site production management.

Integrating Order Splitting and Acceptance with Batch Delivery in Parallel Machine Scheduling

Multiple production lines can work together to efficiently manufacture certain products. Thus, when capacity is insufficient, it is necessary to decide whether to develop new production lines to ensure the timely completion of all orders. For example, running a new production line for a small number of orders is not cost-effective. Therefore, decision-making involves choosing between paying tardiness costs for a few orders, abandoning some orders, or developing new production lines to maximize efficiency. Additionally, the timely transportation of completed orders is crucial and depends on vehicle usage efficiency. From a transportation perspective, fully loading vehicles is the most efficient, but this may impact the timeliness of orders, leading to potential tardiness costs. By comprehensively considering these aspects, a multi-machine production model is constructed that incorporates transportation batch sequences and uses heuristic algorithms to solve the problem. Finally, designed case examples validate the effectiveness of the model and algorithm.

The Impact of African Swine Fever on the Efficiency of China’s Pig Farming Industry

African Swine Fever (ASF) is a severe viral disease that has significantly impacted the pig farming industry in China. It first broke out in China in 2018 and quickly spread to multiple provinces, significantly affecting the production efficiency of the pig farming industry. This study utilized pig production data from 17 provinces in China from 2010 to 2022 and applied the Malmquist production efficiency index and panel regression methods to assess the impact of the ASF epidemic on the efficiency of the pig farming industry. The results indicated that the outbreak of ASF significantly reduced overall production efficiency, which magnified the vulnerabilities of the production system. Although there was a general decline in technological change and pure technical efficiency, the increase in scale efficiency suggested effective resource optimization by farmers under resource-constrained conditions. In light of these findings, it is recommended to strengthen biosecurity education and epidemic prevention measures in the pig farming industry and to enhance technological innovation and the application of smart technologies to improve production efficiency and disease response capabilities. Additionally, timely adjustments in farming scale and resource optimization will be key to addressing future challenges. Through these strategies, the pig farming industry can maintain stable production efficiency during future epidemics and push towards a more efficient and refined production model.

An Evolutionary Game Study of Collaborative Innovation across the Whole Industry Chain of Rural E-Commerce under Digital Empowerment

With the profound integration of digital technology and traditional agriculture, the whole industry chain of rural e-commerce, as an advanced system, is reshaping the production, sales, and management models of agriculture and is emerging as a new catalyst for the advancement of digital agriculture through significant innovation. This paper focuses on the digital empowerment attributes and strategic attributes of the whole industrial chain of rural e-commerce, and evolutionary game models under market mechanisms and regulations are constructed. It examines the influence of various elements, such as the digital technology empowerment coefficient, on collaborative innovation within the whole industry chain of rural e-commerce. Using case simulations, this paper discusses the role of government regulation and intervention when market mechanisms are inadequate. The study reveals that factors such as the digital technology empowerment coefficient, technology absorptive capacity, and the amount of technology and information stored in collaborative innovation positively influence the whole industry chain. Potential risk losses and free-rider gains have negative effects on the evolution of the system toward collaborative innovation. When market mechanisms are inadequate, a combination of operational cost subsidies and incentive measures can yield more effective policy synergy, with the incentive impact of cost subsidies being particularly notable. The strategic application of enterprise-liquidated damage and government penalties will aid in regulating and directing corporate behavior. The findings of this research not only offer a new microlevel explanation for understanding the decision-making mechanism behind collaborative innovation in the whole industrial chain of rural e-commerce but also serve as a reference for government policy formulation to ensure the stability of the whole rural e-commerce industry chain.

A framework of insole blanking robot based on adaptive edge detection and FSPS-BIT* path planning

Insole blanking production technology plays a vital role in contemporary machining and manufacturing industries. Existing insole blanking production models have limitations because most robots are required to accurately position the workpiece to a predetermined location, and special auxiliary equipment is usually required to ensure the precise positioning of the robot. In this paper, we present an adaptive blanking robotic system for different lighting environments, which consists of an industrial robot arm, an RGB-D camera configuration, and a customized insole blanking table and mold. We introduce an innovative edge detection framework that utilizes color features and morphological parameters optimized through particle swarm optimization (PSO) techniques to Adaptive recognition of insole edge contours. A path planning framework based on FSPS-BIT* is also introduced, which integrates the BIT* algorithm with the FSPS algorithm for efficient path planning of the robotic arm.

Development and modeling of power, hydrogen and freshwater production based on a novel double-flash geothermal power plant

In this article, a novel geothermal energy-assisted multigeneration plant is proposed and analyzed from the thermodynamic, economic, and environmental perspectives. In the design of the geothermal power system, a transcritical Rankine cycle, which employs CO2 as the working fluid (tCO2-RC), a Kalina cycle (KC), a hydrogen generation sub-plant, and a desalination unit are considered. The reverse osmosis (RO) desalination process is used for freshwater production, while the proton exchanger membrane (PEM) electrolyzer is utilized for hydrogen generation. In addition, parametric analyses are conducted to ascertain the impacts of the temperature of the geothermal source, mass flow rate, and flash chamber pressure on system performance. According to the results, the plant's overall energetic and exergetic efficiencies are determined to be 8.6 % and 34.9 %, respectively where the net power production is calculated as 3317.94 kW, and the system's total irreversibility is found to be 5852.88 kW. Furthermore, the hourly H2, O2, and freshwater generation amounts are 8.5 kg, 49.1 kg, and 9.3 m3, respectively. Finally, the levelized energy cost (LEC) and sustainability index of the system are found to be 0.128 USD/kWh and 0.215.

An assessment framework for creative production in computing and engineering disciplines

With the increasing expansion of creative computing courses among higher education institutions, there is a growing demand for more objective and efficient assessment frameworks that cater to the multidisciplinary skills that students engage in during the production of such work. This research addresses persistent gaps for a systematic approach of assessing creative content in technical courses. Existing theories are reviewed including creative requirements framework and computing production model, which leads to the establishment of a newly proposed assessment framework. A mixture of research methods is employed combining both inductive and deductive approaches, evidence- based analysis, and case study, based on data collected over three academic years at a British university. The proposed assessment framework incorporates three factors of production (function, usability, and management) and two attributes of creativity (core and optional requirements), which is evaluated to determine its suitability for accurately reflecting the work undertaken by students. Creativity is quantified in the assessment based on technical contribution. The proposed framework can be applied in the delivery of courses involving creative production in the computer science and engineering disciplines in which teaching usually focuses on technical content rather than contextual design, creativity, or aesthetics. The findings reveal positive feedback and a number of pertinent outcomes, including promoting creativity, student satisfaction, and efficient assessments. The framework may be adopted by educators for similar courses that are underpinned by technical skills and creativity.

Long-term power forecasting of photovoltaic plants using artificial neural networks

Increasing the robustness of forecasting energy production models from a photovoltaic plant is crucial in investment decisions, while profitability is closely linked to its location since its energy production depends on the local climate conditions. This study aimed to mitigate uncertainty regarding the installation of photovoltaic systems by addressing the challenge of forecasting electrical energy production over the long term. Artificial Neural Network models were used for this purpose, predicting the power output of a photovoltaic plant based on the ambient temperature, cell temperature, and solar irradiance. Data recorded every minute over one year at an experimental photovoltaic plant revealed a strong correlation between energy production and the input variables. This research compared the performance of multilayer perceptron, feedforward, long short-term memory, and modular artificial neural networks architectures. Validation was carried out using mean squared error, mean absolute error, mean absolute percentage error, root mean squared error, and coefficient of determination as key metrics. Notably, the long short-term memory network demonstrated the highest accuracy in energy forecasting achieving a mean squared error of 0.0089 p.u, a mean absolute error of 0.0527 p.u, a root mean squared error of 0.0944 p.u, and a mean absolute percentage error of 49.0124 %. Nonetheless, the modular model came close to long short-term memory accuracy but with lower computational cost.

Production Prediction and Influencing Factors Analysis of Horizontal Well Plunger Gas Lift Based on Interpretable Machine Learning

With the development of unconventional natural gas resources, plunger gas lift technology has gained widespread application. Accurately predicting gas production from unconventional gas reservoirs is a crucial step in evaluating the effectiveness of plunger gas lift technology and optimizing its design. However, most existing prediction methods are mechanism-driven, incorporating numerous assumptions and simplifications that make it challenging to fully capture the complex physical processes involved in plunger gas lift technology, ultimately leading to significant errors in capacity prediction. Furthermore, engineering design factors and production system factors associated with plunger gas lift technology can contribute to substantial deviations in gas production forecasts. This study employs three powerful regression algorithms, XGBoost, Random Forest, and SVR, to predict gas production in plunger gas lift wells. This method comprehensively leverages various types of data, including collected engineering design, production system, and production data, directly extracting the underlying patterns within the data through machine learning algorithms to establish a prediction model for gas production in plunger gas lift wells. Among these, the XGBoost algorithm stands out due to its robustness and numerous advantages, such as high accuracy, ability to effectively handle outliers, and reduced risk of overfitting. The results indicate that the XGBoost algorithm exhibits impressive performance, achieving an R2 (coefficient of determination) value of 0.87 for six-fold cross-validation and 0.85 for the test set. Furthermore, to address the “black box” problem (the inability to know the internal working structure and workings of the model and to directly understand the decision-making process), which is commonly associated with conventional machine learning models, the SHAP (Shapley additive explanations) method was utilized to globally and locally interpret the established machine learning model, analyze the main factors (such as starting time of wells, gas–liquid ratio, catcher well inclination angle, etc.) influencing gas production, and enhance the credibility and transparency of the model. Taking plunger gas lift wells in southwest China as an example, the effectiveness and practicality of this method are demonstrated, providing reliable data support for shale gas production prediction, and offering valuable guidance for actual on-site production.

Research on Optimization of Intermittent Production Process System

As gas wells enter later production stages, the formation pressure decreases and liquid accumulates at the bottom of the gas well. The formation pressure is insufficient to lift the accumulated liquid from the bottom of the well to the surface. At this time, a large number of gas wells need to undergo intermittent production to maintain their production capacity. This article focuses on the four stages of intermittent production in gas wells, considering the changes in slip gas holdup, pressure, and gas–liquid flow in and out of tubing and casing, and establishes a transient mathematical model for intermittent production in gas wells in stages. By using the dynamic tracking technology of moving liquid slugs to divide the wellbore grid and solve it in stages, the optimal shut-in time for intermittent production of gas wells was obtained. The transient mathematical model developed for intermittent gas well production achieved a high historical fit accuracy of over 90%. This indicates that the simulation results are in line with the actual situation of gas well intermittent production and can effectively guide intermittent production. The optimized intermittent production system of gas wells has a higher cumulative gas production compared to the original system, achieving the optimization of intermittent production system. This method is beneficial for guiding efficient production of gas wells in low-pressure formations.

Undoing cultural studies: cultural economy at the Open University (1979–1997)

ABSTRACT This introduction article establishes the territory for the Symposium What Was Cultural Economy? After setting out the reasoning behind the collection and its eight contributions, the introduction describes the terms on which the ‘cultural economy’ concept was launched, within the Open University’s Faculty of Social Sciences, at the end of the 1990s. Noting the collective work involved, I highlight the role of Stuart Hall, and the brand of cultural studies he represents, as a key vector. Hall’s position as totemic public intellectual obscures his work as a teacher, curriculum designer and colleague, channelling the ‘cultural turn’ through this internal academic context. As such, I also stress the OU’s course team model of intellectual production, drawing from recent historiography on workshops as devices for the practice of collective intellectual and cultural work, coupling this to the figure of the ‘vanishing mediator’ as a driver of material and symbolic transformation. I then move to sketch out some of the key interpersonal relations, institutional contexts and intellectual trajectories through which cultural economy was first formulated. In conclusion, I ask what implications this little history might hold for a present-day journal engaged in the workshopping of ideas at increasingly international scale.

Accessible Type 2 diabetes medication through stable expression of Exendin-4 in Saccharomyces cerevisiae

Diabetes mellitus affects roughly one in ten people globally and is the world’s ninth leading cause of death. However, a significant portion of chronic complications that contribute to mortality can be prevented with proper treatment and medication. Glucagon-like peptide 1 receptor agonists, such as Exendin-4, are one of the leading classes of Type 2 diabetes treatments but are prohibitively expensive. In this study, experimental models for recombinant Exendin-4 protein production were designed in both Escherichia coli and Saccharomyces cerevisiae. Protein expression in the chromosomally integrated S. cerevisiae strain was observed at the expected size of Exendin-4 and confirmed by immunoassay. This provides a foundation for the use of this Generally Regarded as Safe organism as an affordable treatment for Type 2 diabetes that can be propagated, prepared, and distributed locally.

The role of animacy in language production: evidence from bare noun naming.

According to Levelt's language production model, to name an object, speakers must first conceptualise and lexicalise the object before its name can be articulated. Conceptualisation is conducted through the semantic network that exists at the conceptual level, with the highly activated concept(s) activating lexical items at the lemma level, that is, lexicalisation. So far, research focused mostly on semantic categories (i.e., semantic interference) but less so on animacy-a concept that is correlated with semantic categories. To investigate the role of this semantic feature in language production, we conducted a picture-word interference study in Mandarin Chinese, varying animacy congruency and controlling for classifier congruency while recording behavioural and electrophysiological responses. We observed an animacy interference effect together with a larger N400 component for animacy-incongruent versus congruent picture-word pairs, suggesting animacy-congruent concepts may be in closer proximity and hence lead to a stronger spreading of activation relative to animacy-incongruent concepts. Furthermore, a larger P600 component was observed for classifier-incongruent versus classifier-congruent picture-word pairs, suggesting syntactically driven processing of classifiers at the lemma level.

Probabilistic Analysis of Green Hydrogen Production from a Mix of Solar and Wind Energy

This article describes an example of using the measurement data from photovoltaic systems and wind turbines to perform practical probabilistic calculations around green hydrogen generation. First, the power generated in one month by a ground-mounted photovoltaic system with a peak power of 3 MWp is described. Using the Metalog family of probability distributions, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is calculated. Identical calculations are performed for the simulation data, allowing us to determine the power produced by a wind turbine with a maximum power of 3.45 MW. After interpolating both time series of the power generated by the renewable energy sources to a common sampling time, they are summed. For the sum of the power produced by the photovoltaic system and the wind turbine, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is again calculated. The presented calculations allow us to determine, with probability distribution accuracy, the amount of hydrogen generated from the energy sources constituting a mix of photovoltaics and wind. The green hydrogen production model includes the hardware and the geographic context. It can be used to determine the preliminary assumptions related to the production of large amounts of green hydrogen in selected locations. The calculations presented in this article are a practical example of Business Intelligence.

Livelihood Transformation of The K’Ho Sre Ethnic Minority in Lam Dong Province

The K’Ho Sre people in Lam Dong province were the largest local group of the K’Ho ethnic group. Their traditional livelihood activities were farming, animal husbandry, making traditional handicrafts, and participating in predatory economic activities (hunting, gathering, fishing). Nowadays, the K’Ho Sre ethnic group has been adapting to converting their livelihoods from traditional agricultural production models to agricultural production models by applying science and technology. This economic transformation was manifested in many aspects, notably the strong introduction of new farm production methods completely different from traditional production methods regarding cultivation techniques, seedling structure, and forms of exploitation and product consumption. Besides, there were some relatively new activities such as services, buying and selling, hiring, etc. The research was conducted through the following methods: synthesis, observation, survey, and analysis of field data for 400 K’Ho Sre ethnic families in Lam Dong to point out changes in livelihood activities and propose recommendations to contribute to hunger eradication, poverty reduction, and sustainable development.

Validated 3D finite-element model of the Risso’s dolphin (Grampus griseus) head anatomy demonstrates gular sound reception and channelling through the mandibular fats

Like other odontocetes, Risso's dolphins actively emit clicks and passively listen to the echoes during echolocation. However, the head anatomy of Risso's dolphins differs from that of other odontocetes by a unique vertical cleft along the anterior surface of the forehead and a differently-shaped lower jaw. In this study, 3D finite-element sound reception and production models were constructed based on computed tomography (CT) data of a deceased Risso's dolphin. Our results were verified by finding good agreement with experimental measurements of hearing sensitivity. Moreover, the acoustic pathway for sounds to travel from the seawater into the dolphin's tympanoperiotic complexes (TPCs) was computed. The gular reception mechanism, previously discovered inDelphinus delphisandZiphius cavirostris, was also found in this species. The received sound pressure levels and relative displacement at TPC surfaces were compared between the cases with and without the mandibular fats or mandible. The results demonstrate a pronounced wave-guiding role of the mandibular fats and a limited bone-conductor role of the mandible. For sound production modelling, we digitally filled the cleft with neighbouring soft tissues, creating a hypothetical 'cleftless' head. Comparison between sound travelling through a 'cleftless' head vs. an original head indicates that the distinctive cleft plays a limited role in biosonar sound propagation.

Prophet modeling for oil production forecasting in an enhanced oil recovery field

Accurate daily oil production forecasting is essential for efficient reservoir management and investment strategy. Forecasting oil production in enhanced oil recovery (EOR) and conformance-dominated fields is a complex process due to the nonlinear, voluminous, and often uncertain nature of reservoir parameters and hidden factors. As a result, conventional tools such as decline curve analysis frequently fail to accurately predict daily oil production in conformance-controlled areas. In contrast, machine learning works efficiently for large datasets, even if the parameter values are unknown. The current study employs a Prophet time series forecasting method for five oil production wells in an EOR applied field, but it fails to achieve the desired sweep efficiency. This study compares the results of conventional decline curve analysis (DCA) and popular autoregressive integrated moving average time series forecasting methods with the Prophet model. This is the first attempt to use Prophet for oil well production forecasting, where polymer flooding is used. In all, 60% of the data are used for training, and the remaining 40% are used for testing. The Prophet shows the best performance for all the wells. This study is also the first to handle shut-in data using the Prophet model for oil production. Well-2 achieves the highest accuracy after incorporating shut-in results, with an R2 score of 92%. The result shows that though the DCA performs reasonably well with higher linearity and trend stationary data, Prophet modeling shows superior results than conventional DCA for all EOR applied producing wells.

A fuzzy multi-objective model for aggregate production planning considering energy consumption, carbon emission, chemical, solid waste, and wastewater

With a growing focus on environmental conservation, enterprises continuously reevaluate their conventional profit-driven business models. This shift has led to the development of green production planning models, attracting considerable attention in recent years. Previously, energy consumption or carbon emission were the most considered green parameters in the production planning problems. Nevertheless, several other essential green aspects such as reductions in chemical, solid waste, and wastewater are usually neglected. Therefore, this study proposes a multi-objective model for environmentally conscious aggregate production planning, simultaneously considering the above-mentioned green objectives. Additionally, the uncertain parameters of the model, including the green parameters, were modelled through fuzzy programming. Furthermore, this study incorporated a contradicting relationship between energy consumption cost and associated carbon emission, which was previously presented as non-contradicting. This relationship was illustrated by utilizing the time-of-use electricity tariff scheme. To solve the model, the initial step involved the defuzzification of uncertain attributes, followed by the transformation of the multi-objective model into a single-objective model. Finally, the IBM ILOG CPLEX Optimization Studio solver was utilized to solve the model based on the data collected from a precision engineering company. The generated production plan achieved an overall satisfaction level of 71%, which is reasonably acceptable to the decision maker. Moreover, sensitivity analysis has been conducted for the degree of optimism of the objective functions and the feasibility degree of the constraints. A general overview of the results associated with the performed sensitivity analysis indicated that the optimum values of the objective functions deteriorate as the parameters increase, though all remain within an acceptable range. The proposed study will assist decision makers in generating production plans that are both economically and environmentally feasible.

Forecasting models for area, production and productivity of wheat in North Gujarat

Forecasting models for area, production and productivity of wheat in North Gujarat