SEEKING A COMMON THEME: A STUDY OF CERAMIC EFFIGY ARTIFACTS IN THE PRE-HISPANIC AMERICAN SOUTHWEST AND NORTHERN MEXICO USING COMPUTER IMAGE PATTERN RECOGNITION AND PHYLOGENETIC ANALYSIS

Effigy artifacts are found throughout the Pre-Hispanic American Southwest and Northern Mexico (PHASNM), as well as in other cultures around the world, with many sharing the same forms and design features. The earliest figurines within the PHASNM were partial anthropomorphic figurines made from fired clay, dating to between A.D. 287 and A.D. 312 (Morss 1954:27). They were found in a pit house village of Bluff Ruin in the Forestdale Valley of eastern Arizona, and they appeared to be associated with the Mogollon culture. The temporal range of the samples examined in this study is from approximately 200 A.D. to 1650 A.D., and the geographical range includes the Southwestern United States (Arizona, New Mexico, Texas, Colorado, and Utah) and the northcentral section of Mexico (Casas Grandes and the surrounding area). This research looks at the similarities among the markings of ceramic effigy artifacts from the PHASNM, using computer image pattern recognition, design analysis, and phylogenetics, to determine whether their ceramic traditions share a common theme and whether the specific method of social learning responsible for the transmission of information relating to ceramic effigy decoration can be identified. Transmission is possible in one of three ways: vertical transmission, where parents/teachers distribute information by encouraging imitation and sharing learned traditions with children/students (Richerson and Boyd 2005; Shennan 2002); horizontal transmission, where information is transmitted among peers, either from within the individual’s group or from interaction with peers from neighboring populations (Borgerhoff Mulder et al. 2006), and where the individual comes into contact with a wide range of attributes related to the item of interest and then adopts those that allow for the fastest, most economical methods of production and distribution (Eerkens et al 2006; Rogers 1983); and oblique transmission, where information is transmitted by adults, masters, or institutions of elite or higher social status, either internally or externally to the adopting cultural Type (Jensen 2016; Jordan 2014), and where particular traits are adopted or left out in disproportionate ways, creating patterns in localized traditions that can be empirically identified. Horizontal transmission can be broken into two types: unlimited, where contact is not confined to a particular group; and limited, where contact is restricted to a particular set of contacts. Using criteria for each of the categories as set forth by the New Mexico Office of Archaeological Studies Pottery Typology Project, the samples were classified in terms of cultural area (culture), branch, tradition, ware, and type. The research v group consisted of 360 photographic samples represented by 868 images that were resized to a 640x640 pixel format. The images were then examined through computer image pattern recognition (using YOLOv5) and through manual observation. This study resulted in a database representing 230 traits. These traits were assembled into groups by cultural area, branch, tradition, ware, and type, and phylogenetic analysis was applied to show how the different entities transfer information among each other

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Green food supply chain design considering risk and post-harvest losses: a case study

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Food industry supply chain planning with product quality indicators

Quantitative supply chain modelling has contributed substantially to a number of fields, such as the automotive industry, logistics and computer hardware. The inherent methods and optimisation techniques could also be explored in relation to the food industry in order to offer potential benefits. One of the major issues of the food industry is to overcome supply seasonality and on-shelf demand. On the shelf demand is the consumer’s in store demand which could also be seasonal. Objective of this work is to add flexibility to seasonal products (i.e. soup) in order to meet the on-shelf demand. In order to achieve this, a preparation process is introduced and integrated into the manufacturing system. This process increases the shelf-life of raw materials before starting the production process. This process, however, affects the quality of fresh raw materials and requires energy. Therefore, a supply chain model is developed, which is based on the link between the quality of the raw material and the processing conditions, which have an effect on the process’ energy consumption and on the overall product quality. It is challenging to quantify the quality by looking at the processing conditions (degrees of freedom) and by linking it with energy in order to control and optimise the quality and energy consumption for each product. The degrees of freedom are defined differently for each process and state. Therefore, the developed model could be applied to all states and processes in order to generate an optimum solution. Moreover, based on the developed model, we have determined key factors in the whole chain, which are most likely to affect the product quality and consequently overall demand. There are two main quality indicator classes to be optimised, which are both considered in the model: static and time dependent indicators. Also, this work considers three different preparation processes – the air-dry, freeze-dry and freezing process – in order to increase the shelf-life of fresh raw materials and to add flexibility to them. A model based on the interrelationship between the quality and the processing conditions has been developed. This new methodology simplifies and enables the model to find the optimum processing conditions in order to obtain optimum quality across all quality indicators, whilst ensuring minimum energy consumption. This model is later integrated into the supply chain system, where it generates optimum solutions, which are then fed into the supply chain model. The supply chain model optimises the quality in terms of customer satisfaction, energy consumption and wastage of the system linked to environmental issues, and cost, so that the final products are more economical. In this system, both the manufacturing and inventory systems are optimised. This model is later implemented with a real world industrial case study (provided by the industrial collaborator). Two case studies are considered (soya milk and soup) and interestingly enough only one of them (soup) corresponds with this model. The advantage of this model is that it compares the two systems and then establishes which system generates an optimum end product.Open Acces

Optimization Models for Iraq’s Water Allocation System

abstract: In the recent past, Iraq was considered relatively rich considering its water resources compared to its surroundings. Currently, the magnitude of water resource shortages in Iraq represents an important factor in the stability of the country and in protecting sustained economic development. The need for a practical, applicable, and sustainable river basin management for the Tigris and Euphrates Rivers in Iraq is essential. Applicable water resources allocation scenarios are important to minimize the potential future water crises in connection with water quality and quantity. The allocation of the available fresh water resources in addition to reclaimed water to different users in a sustainable manner is of the urgent necessities to maintain good water quantity and quality. In this dissertation, predictive water allocation optimization models were developed which can be used to easily identify good alternatives for water management that can then be discussed, debated, adjusted, and simulated in greater detail. This study provides guidance for decision makers in Iraq for potential future conditions, where water supplies are reduced, and demonstrates how it is feasible to adopt an efficient water allocation strategy with flexibility in providing equitable water resource allocation considering alternative resource. Using reclaimed water will help in reducing the potential negative environmental impacts of treated or/and partially treated wastewater discharges while increasing the potential uses of reclaimed water for agriculture and other applications. Using reclaimed water for irrigation is logical and efficient to enhance the economy of farmers and the environment while providing a diversity of crops, especially since most of Iraq’s built or under construction wastewater treatment plants are located in or adjacent to agricultural lands. Adopting an optimization modelling approach can assist decision makers, ensuring their decisions will benefit the economy by incorporating global experiences to control water allocations in Iraq especially considering diminished water supplies.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Modelación matemática en estudio de agro-cadenas: una revisión de literatura

The agricultural sector is the fundamental axis that moves the world economy, it allows the generation of agricultural and livestock products to supply small and large cities. In underdeveloped countries, the participation of industry and academia is necessary to strengthen production systems, this based on the injection of technology, as well as the transfer and appropriation of knowledge in the sector. An approach used to strengthen the sector is the study of agricultural supply chains (agro-chains) based on mathematical modeling, that allows data processing and facilitates strategic, tactical or operational decision-making. We conducted a review of the literature on the application of mathematical models in the study of agricultural chains during the last 20 years. The study concludes that there is a fairly great interest by the academic-scientific community to strengthen the agricultural sector in different countries such as the United States, Brazil, India and the Netherlands, among others. Stochastic simulation models are used in 36% of the consulted works, allowing complex problems involving uncertainty in data behavior to be addressed. Also, in 70% of the works consulted, heuristic models are used to solve design and distribution problems in agro-chains, and the remaining 30% require the use of metaheuristics because they require solving problems with multiple responses given the complexity of the data. Mathematical modeling has become a very useful tool for solving latent problems in agro-chains, it facilitates data processing and complex decision-making, mainly during chain design, product supply and control of costs, delivery times and environmental impacts, among other important variables.El sector agrícola es el eje fundamental que mueve la economía del mundo, permite la generación de productos agrícolas y pecuarios para el abastecimiento de pequeñas y grandes ciudades. En los países subdesarrollados es necesaria la participación de la industria y la academia para el fortalecimiento de los sistemas productivos, esto a partir de la inyección de tecnología, así como la transferencia y apropiación de conocimiento en el sector. Un enfoque usado para el fortalecimiento del sector, es el estudio de las cadenas de suministro agrícolas (agro-cadenas) a partir de la modelación matemática, la cual permite el tratamiento de datos y facilita la toma de decisiones de orden estratégico, táctico y/o operativo. En el presente trabajo se realizó una revisión de literatura sobre la aplicación de la modelación matemática en el estudio de las Agro-cadenas durante los últimos 20 años. Se concluye del estudio que, existe un interés bastante grande por la comunidad académico-científica por fortalecer el sector agrícola en diferentes países como Estados Unidos, Brasil, india y Holanda entre otros. En el 36% de los trabajos consultados se emplean modelos de simulación estocástica, permitiendo abordar problemas complejos que involucran incertidumbre en con comportamiento de los datos. Además, en el 70% de los trabajos consultados, se utilizan modelos heurísticos para resolver problemas de diseño y distribución en agrocadenas, y el 30% restante requiere el uso de meta-heurísticas porque requieren resolver problemas con múltiples respuestas dada la complejidad de los datos. La modelación matemática se ha convertido en una herramienta de gran utilidad para la solución de problemas latentes en la agro-cadenas, facilita el tratamiento de datos y la toma de decisiones complejas, principalmente durante el diseño de cadena, el abastecimiento de producto y control de costos, tiempos de entrega e impactos ambientales, entre otras variables importantes.El sector agrícola es el eje fundamental que mueve la economía del mundo, permite la generación de productos agrícolas y pecuarios para el abastecimiento de pequeñas y grandes ciudades. En los países subdesarrollados es necesaria la participación de la industria y la academia para el fortalecimiento de los sistemas productivos, esto a partir de la inyección de tecnología, así como la transferencia y apropiación de conocimiento en el sector. Un enfoque usado para el fortalecimiento del sector, es el estudio de las cadenas de suministro agrícolas (agro-cadenas) a partir de la modelación matemática, la cual permite el tratamiento de datos y facilita la toma de decisiones de orden estratégico, táctico y/o operativo. En el presente trabajo se realizó una revisión de literatura sobre la aplicación de la modelación matemática en el estudio de las Agro-cadenas durante los últimos 20 años. Se concluye del estudio que, existe un interés bastante grande por la comunidad académico-científica por fortalecer el sector agrícola en diferentes países como Estados Unidos, Brasil, india y Holanda entre otros. En el 36% de los trabajos consultados se emplean modelos de simulación estocástica, permitiendo abordar problemas complejos que involucran incertidumbre en con comportamiento de los datos. Además, en el 70% de los trabajos consultados, se utilizan modelos heurísticos para resolver problemas de diseño y distribución en agrocadenas, y el 30% restante requiere el uso de meta-heurísticas porque requieren resolver problemas con múltiples respuestas dada la complejidad de los datos. La modelación matemática se ha convertido en una herramienta de gran utilidad para la solución de problemas latentes en la agro-cadenas, facilita el tratamiento de datos y la toma de decisiones complejas, principalmente durante el diseño de cadena, el abastecimiento de producto y control de costos, tiempos de entrega e impactos ambientales, entre otras variables importantes.The agricultural sector is the fundamental axis that moves the world economy, it allows the generation of agricultural and livestock products to supply small and large cities. In underdeveloped countries, the participation of industry and academia is necessary to strengthen production systems, this based on the injection of technology, as well as the transfer and appropriation of knowledge in the sector. An approach used to strengthen the sector is the study of agricultural supply chains (agro-chains) based on mathematical modeling, that allows data processing and facilitates strategic, tactical or operational decision-making. We conducted a review of the literature on the application of mathematical models in the study of agricultural chains during the last 20 years. The study concludes that there is a fairly great interest by the academic-scientific community to strengthen the agricultural sector in different countries such as the United States, Brazil, India and the Netherlands, among others. Stochastic simulation models are used in 36% of the consulted works, allowing complex problems involving uncertainty in data behavior to be addressed. Also, in 70% of the works consulted, heuristic models are used to solve design and distribution problems in agro-chains, and the remaining 30% require the use of metaheuristics because they require solving problems with multiple responses given the complexity of the data. Mathematical modeling has become a very useful tool for solving latent problems in agro-chains, it facilitates data processing and complex decision-making, mainly during chain design, product supply and control of costs, delivery times and environmental impacts, among other important variables