Academic literature on the topic 'Matrix linear regression'

Hard coal mining is characterised by features that pose numerous challenges to its current operations and cause strategic and operational problems in planning its development. The most important of these include the high capital intensity of mining investment projects and the dynamically changing environment in which the sector operates, while the long-term role of the sector is dependent on factors originating at both national and international level. At the same time, the conditions for coal mining are deteriorating, the resources more readily available in active mines are being exhausted, mining depths are increasing, temperature levels in pits are rising, transport routes for staff and materials are getting longer, effective working time is decreasing, natural hazards are increasing, and seams with an increasing content of waste rock are being mined. The mining industry is currently in a very difficult situation, both in technical (mining) and economic terms. It cannot be ignored, however, that the difficult financial situation of Polish mining companies is largely exacerbated by their high operating costs. The cost of obtaining coal and its price are two key elements that determine the level of efficiency of Polish mines. This situation could be improved by streamlining the planning processes. This would involve striving for production planning that is as predictable as possible and, on the other hand, economically efficient. In this respect, it is helpful to plan the production from operating longwalls with full awareness of the complexity of geological and mining conditions and the resulting economic consequences. The constraints on increasing the efficiency of the mining process are due to the technical potential of the mining process, organisational factors and, above all, geological and mining conditions. The main objective of the monograph is to identify relations between geological and mining parameters and the level of longwall mining costs, and their daily output. In view of the above, it was assumed that it was possible to present the relationship between the costs of longwall mining and the daily coal output from a longwall as a function of onerous geological and mining factors. The monograph presents two models of onerous geological and mining conditions, including natural hazards, deposit (seam) parameters, mining (technical) parameters and environmental factors. The models were used to calculate two onerousness indicators, Wue and WUt, which synthetically define the level of impact of onerous geological and mining conditions on the mining process in relation to: —— operating costs at longwall faces – indicator WUe, —— daily longwall mining output – indicator WUt. In the next research step, the analysis of direct relationships of selected geological and mining factors with longwall costs and the mining output level was conducted. For this purpose, two statistical models were built for the following dependent variables: unit operating cost (Model 1) and daily longwall mining output (Model 2). The models served two additional sub-objectives: interpretation of the influence of independent variables on dependent variables and point forecasting. The models were also used for forecasting purposes. Statistical models were built on the basis of historical production results of selected seven Polish mines. On the basis of variability of geological and mining conditions at 120 longwalls, the influence of individual parameters on longwall mining between 2010 and 2019 was determined. The identified relationships made it possible to formulate numerical forecast of unit production cost and daily longwall mining output in relation to the level of expected onerousness. The projection period was assumed to be 2020–2030. On this basis, an opinion was formulated on the forecast of the expected unit production costs and the output of the 259 longwalls planned to be mined at these mines. A procedure scheme was developed using the following methods: 1) Analytic Hierarchy Process (AHP) – mathematical multi-criteria decision-making method, 2) comparative multivariate analysis, 3) regression analysis, 4) Monte Carlo simulation. The utilitarian purpose of the monograph is to provide the research community with the concept of building models that can be used to solve real decision-making problems during longwall planning in hard coal mines. The layout of the monograph, consisting of an introduction, eight main sections and a conclusion, follows the objectives set out above. Section One presents the methodology used to assess the impact of onerous geological and mining conditions on the mining process. Multi-Criteria Decision Analysis (MCDA) is reviewed and basic definitions used in the following part of the paper are introduced. The section includes a description of AHP which was used in the presented analysis. Individual factors resulting from natural hazards, from the geological structure of the deposit (seam), from limitations caused by technical requirements, from the impact of mining on the environment, which affect the mining process, are described exhaustively in Section Two. Sections Three and Four present the construction of two hierarchical models of geological and mining conditions onerousness: the first in the context of extraction costs and the second in relation to daily longwall mining. The procedure for valuing the importance of their components by a group of experts (pairwise comparison of criteria and sub-criteria on the basis of Saaty’s 9-point comparison scale) is presented. The AHP method is very sensitive to even small changes in the value of the comparison matrix. In order to determine the stability of the valuation of both onerousness models, a sensitivity analysis was carried out, which is described in detail in Section Five. Section Six is devoted to the issue of constructing aggregate indices, WUe and WUt, which synthetically measure the impact of onerous geological and mining conditions on the mining process in individual longwalls and allow for a linear ordering of longwalls according to increasing levels of onerousness. Section Seven opens the research part of the work, which analyses the results of the developed models and indicators in individual mines. A detailed analysis is presented of the assessment of the impact of onerous mining conditions on mining costs in selected seams of the analysed mines, and in the case of the impact of onerous mining on daily longwall mining output, the variability of this process in individual fields (lots) of the mines is characterised. Section Eight presents the regression equations for the dependence of the costs and level of extraction on the aggregated onerousness indicators, WUe and WUt. The regression models f(KJC_N) and f(W) developed in this way are used to forecast the unit mining costs and daily output of the designed longwalls in the context of diversified geological and mining conditions. The use of regression models is of great practical importance. It makes it possible to approximate unit costs and daily output for newly designed longwall workings. The use of this knowledge may significantly improve the quality of planning processes and the effectiveness of the mining process.

Esta obra tem por objetivo apresentar produções acadêmicas que possuem em comum a grande área Ciências Agrárias. Permeando por este vasto tema, nas próximas páginas serão apresentados trabalhos que abordam sobre Ciência e Tecnologia de Alimentos, Engenharia Agrícola, bem como na Ciência Animal. Desta forma, para melhor direcionar o fluxo da leitura, o livro está dividido em capítulos, nos quais os primeiros apresentados abordam o tema “Ciência e Tecnologia em Alimentos” e apresenta trabalhos desenvolvidos com ênfase em controle de qualidade, aproveitamento de subprodutos e planejamento experimental. Acredito que o controle de qualidade de alimentos e o aproveitamento de subprodutos são temas de grande relevância para nosso país e desta forma, nós como professores e pesquisadores, devemos, por meio da ciência sempre trazer novas pesquisas a fim de preencher lacunas no conhecimento e apresentar novas possibilidades e soluções para o melhor aproveitamento e utilização dos alimentos. Na sequência, são apresentados trabalhos desenvolvidos na temática de produção e caracterização de forrageiras de cereais de inverno, predição da produtividade da cultura da soja por meio da aplicação de modelos de regressão linear, bem como relatar um estudo casos de onfalite em bezerros. Neste sentido, os trabalhos aqui apresentados, alinham-se a estas demandas e trazem novas analises que condizem com as necessidades emergentes da nossa sociedade. Profª. Drª. Heloisa Gabriel Falcão. Instituto Federal de Educação (IFG) – Campus Inhumas O crescimento da economia e da taxa de urbanização de alguns países, especialmente da Ásia, resultou em significativas mudanças no estilo de vida das populações neles residentes, com incrementos no consumo de bens duráveis, energia e alimentos. Além disso, estima-se que a população mundial ultrapassará 8,5 bilhões de pessoas até 2030 e que a maior porção desse crescimento demográfico ocorrerá na China, Índia e Indonésia. Esse contexto representa um desafio para a segurança alimentar e energética mundial, uma vez que, se as tendências atuais forem mantidas, a área agrícola deverá aumentar em cerca de 42 milhões hectares até 2027. Contudo, a limitação de terras agricultáveis permitirá um crescimento de apenas 10% em escala mundial, sendo que, quase metade disso se dará no Brasil e na Argentina. Assim, a América do Sul será a mais importante fonte de expansão agrícola do mundo. Com abundantes recursos naturais e grande potencial de desenvolvimento agropecuário, a América do Sul configura importante elemento estratégico para melhorar a segurança alimentar global. Em particular, o setor agropecuário brasileiro é reconhecido internacionalmente pela elevada inserção no mercado globalizado, com destaque para produção de carne de frango, açúcar, suco de laranja, fumo, café e soja... produtos do agronegócio brasileiro que são campeões no ranking de exportações do mercado global. Outros produtos agropecuários brasileiros que merecem grande destaque por configurarem entre as primeiras posições no ranking mundial de produção e exportação são: carne bovina, óleo de soja, farelo de soja, milho e leite bovino. A pandemia de Covid-19 impactou negativamente a economia mundial em razão das necessidades sanitárias e de distanciamento social. Ainda assim, mesmo em momentos de maiores restrições de circulação e transportes, vários segmentos agropecuários do Brasil experimentaram expressiva elevação na produção e vendas nacionais e internacionais. Isso ocorreu em razão das políticas preventivas de vários países no sentido de garantir a segurança alimentar de suas populações, restringindo as exportações e aumentando as importações de alimentos para ampliar suas reservas estratégicas. Essas políticas preventivas não foram adotadas pelo Brasil e, devido ao desmonte dos estoques reguladores e da redução substancial dos recursos destinados a agricultura familiar desde 2017, o mercado interno foi drasticamente afetado pelas exportações record de 2020 e 2021. A redução da quantidade de milho, soja e carnes, principalmente bovina, no mercado interno promoveu expressivo aumento dos preços num momento onde houve aumento de desemprego e queda de renda das classes menos abastadas da população brasileira. O Brasil, que já tinha voltado ao mapa da fome em 2018, sofreu um aumento de 14% no número de domicílios com algum tipo de insegurança alimentar entre 2018 e 2020. Estima-se que mais de 55% da população brasileira sofreu de insegurança alimentar entre 2020 e 2021, conforme dados da rede Penssan e da Organização das Nações Unidas. Nesse contexto, apesar das reduções dramáticas no volume de recursos públicos destinados a produção cientifica no Brasil, tornou-se ainda mais imprescindível a produção de pesquisas e a disseminação do conhecimento resultante delas. Composto por sete capítulos que apresentam pesquisas relevantes, esse livro pretende contribuir com subsídios significativos para o enfrentamento desse imenso desafio que se apresenta, ainda mais intenso nesses tempos de pós-Covid-19, que é elevar a eficiência da produção agropecuária a fim de garantir melhores condições de segurança alimentar para a população brasileira. O primeiro capítulo apresenta uma proposta de utilização da farinha de okara para o enriquecimento do hamburguer de carne bovina. Um dos produtos mais conhecidos do processamento da soja é o leite de soja ou extrato aquoso de soja. Ele é obtido a partir da lavagem, maceração, aquecimento e filtração dos grãos de soja. O okara é o subproduto solido do processo de filtração que separa o leite de soja. Aproximadamente, 250 g de farinha de okara são obtidos a partir do processamento de cada quilo de soja. Trata-se de um alimento altamente nutritivo, fonte de isoflavonas, antioxidantes, fibras solúveis e insolúveis que, além de auxiliar na redução de colesterol e triglicerídeos, previne a ação carcinogênica do bolo fecal. Os capítulos 2 e 3 apresentam um estudo que desenvolveu e avaliou as características químicas, físicas e funcionais de biscoitos, tipo cookie, com substituição parcial de farinha de trigo por farinha de gérmen de milho. Essa proposta se mostra extremamente relevante do ponto de vista econômico e nutricional. Uma vez que o advento do conflito bélico entre Rússia e Ucrânia tende a reduzir a oferta de trigo no mercado global e elevar seus preços. O Brasil é o segundo maior produtor de milho do planeta e apenas o 21º produtor de trigo. O resultado disso é que o Brasil importa cerca de 50% do trigo consumido no mercado interno. Além disso, o aumento da prevalência de pessoas com sensibilidade ao glúten, apontado pela pesquisa nacional de saúde do IBGE em 2017, torna esse tipo de experimento, muito relevante para o aumento de alternativas alimentares para esse público. O capítulo 4 compreende um estudo que identificou os agentes causadores de mastite em vacas leiteiras. Além disso, avaliou a relação entre a sua ocorrência de mastite e a qualidade do leite. A mastite é uma reação inflamatória da glândula mamária, geralmente associada à presença de microrganismos, que reduz a qualidade do leite e seus derivados, bem como a segurança do consumidor em razão de alterações na composição físico-química e sensorial dos produtos. Trata-se de uma pesquisa de grande relevância, uma vez que a retomada das exportações de leite para a China em 2021 tende a reduzir a oferta no mercado interno. Ainda sem as exportações para a China, o Brasil vendeu cerca de 29 milhões de toneladas de leite para Argélia, Venezuela, Estados Unidos, Argentina e Uruguai em 2021. Isso explica parte da pressão inflacionaria sobre o produto desde o início das medidas de contenção da Covid-19. Nesse contexto, contribuições que auxiliem na melhoria da qualidade e aumento da produtividade são salutares. O capitulo 5 nos relata um experimento que analisou as características químicas e bromatológicas de forragens de cereais de inverno em duas alturas de corte do solo e os benefícios da manutenção da cobertura vegetal na forma de matéria seca. Cereais de inverno, como centeio, trigo, triticale, cevada e aveia, além de produzirem grãos utilizados na alimentação humana, podem servir de alimento para aves, suínos, bovinos de corte, ovinos e, principalmente, vacas leiteiras. Na região Sul do Brasil, durante o inverno, não é incomum que grande parte de áreas agrícolas e máquinas fiquem ociosas. Dessa forma, a produção de cereais de inverno para forragear os rebanhos e para formar reservas para épocas de escassez parece ser uma estratégia viável para melhorar a constância da produtividade animal, gerando renda e diluindo os custos fixos da propriedade rural. Ademais, a manutenção de matéria seca no solo contribui para a redução de custos por meio da conservação da fertilidade do solo e redução da perda de carbono e necessidade de insumos. O sexto capítulo trata da utilização de técnicas de sensoriamento remoto para estimar a produtividade da cultura da soja, com a utilização de imagens de satélite. São apresentados modelos de regressão múltipla para prever a produtividade a partir de índices de vegetação (NDVI, SAVI, NDWI e EVI2). Ainda que pesquisas oficiais com as do IBGE e CONAB estimem a produtividade da soja com relativa precisão em escala estadual, elas são baseadas em abordagens qualitativas com grupos focais. Assim, o desenvolvimento de novas técnicas para o acompanhamento das culturas em escala microrregional pode contribuir para a redução de custos e maior precisão nas pesquisas oficiais. Além disso, os produtores e operadores do agronegócio podem fazer uso de insumos específicos para o planejamento da cultura e tomada de decisões. O capitulo 7, último desse livro, relata um estudo de 30 casos de onfalite em bezerros, dos quais 15 animais foram tratados conservadoramente e 15 submetidos ao tratamento cirúrgico. A onfalite constitui uma infecção dos remanescentes umbilicais cuja evolução pode resultar em óbito do animal ou comprometer o crescimento e rentabilidade do sistema produtivo desse. Os escassos estudos epidemiológicos brasileiros, a respeito dessa afecção umbilical, relatam que entre 21% e 45% dos bezerros neonatos desenvolverão algum nível dessa infecção e desses, entre 5,5% e 10% irão a óbito. Os resultados do estudo descrito nesse capítulo são extremamente relevantes para que criadores, zootecnistas e médicos veterinários tenham maios evidencias na tomada de decisão a respeito dos procedimentos a serem adotadas diante de tal situação. João Francisco Severo Santos. Doutor em Ciências do Ambiente – UFT. Analista de Pesquisas Agropecuárias - IBGE

With the rapid increase of complex and high-dimensional sparse data, demands for new methods to select features by exploiting both labeled and unlabeled data have increased. Least regression based feature selection methods usually learn a projection matrix and evaluate the importances of features using the projection matrix, which is lack of theoretical explanation. Moreover, these methods cannot find both global and sparse solution of the projection matrix. In this paper, we propose a novel semi-supervised feature selection method which can learn both global and sparse solution of the projection matrix. The new method extends the least square regression model by rescaling the regression coefficients in the least square regression with a set of scale factors, which are used for ranking the features. It has shown that the new model can learn global and sparse solution. Moreover, the introduction of scale factors provides a theoretical explanation for why we can use the projection matrix to rank the features. A simple yet effective algorithm with proved convergence is proposed to optimize the new model. Experimental results on eight real-life data sets show the superiority of the method.

This paper intends to analyze classification models that can predict whether breast cancer is cancerous or noncancerous through the attributes of the selected dataset with the application of machine learning algorithms. The high dimensional Breast Cancer Wisconsin (Diagnostic) Dataset is reduced through principal component analysis (PCA). Six major components were extracted with a cumulative variance of 91.063%. The selected classifier models that were evaluated were logistic regression and linear discriminant analysis (LDA). After training and testing, the model’s performance was evaluated using a confusion matrix where metrics such as accuracy, precision, recall, and F1-score were calculated. Experimental results obtained a commendable prediction with 99.7% accuracy for logistic regression and 95.4% accuracy for LDA. The LDA model was observed to have greater misclassified diagnoses than logistic regression. Thus, it is inferred that in binary classification problems like breast cancer detection, logistic regression produces more promising predictions.

Objectives. To support a planned update of the Dietary Recommended Intake of macronutrients, including dietary fiber, we conducted a systematic review of the effect of fiber on laxation outcomes (related to gut motility) in people with normal bowel function. Data sources and review methods. We searched PubMed, Embase, and existing systematic reviews to March 4, 2025 for randomized trials in the general population of people in any life stage (without bowel dysfunction) that compared amounts, types, or forms of fiber. Outcomes of interest were stool consistency, fecal weight, fecal frequency, gut transit time, and constipation. Standard Agency for Healthcare Research and Quality methods were followed. We performed hierarchical Bayesian regression models, including spline analyses, analyses of fiber characteristics and format, and standard meta-analyses; and assessed strength of evidence (SoE). Results. We found 118 eligible trials, with mostly low or moderate risk of bias, but high heterogeneity. Eligible trials evaluated 64 different fibers or fiber sources across a wide range of fiber amounts. No study directly addressed the research question. Metaregression models provided moderate SoE (downgraded because of indirectness) of linear associations between increasing fiber intake (per g/day) and softening stool consistency, increasing total and dry fecal weight, increasing fecal frequency, and shortening gut transit times. However, the effects of fiber on laxation outcomes in generally healthy people were typically small. The associations between increasing total or added fiber and outcomes varied across the range of fiber intake amounts. Low solubility fibers and low fermentability fibers had the strongest associations with laxation outcomes, with some inconsistencies (low-medium SoE). Fructo-oligosaccharides (FOS) probably increase fecal frequency, wheat bran fiber probably increases fecal weight, and fiber from evaluated vegetables probably increases fecal weight; however, inulin probably does not affect fecal frequency and fiber from evaluated fruits probably has no effect on stool consistency (all moderate SoE). In infants, fiber added to formula or infant cereal probably softens stools and increases fecal frequency. There is insufficient evidence specific to other children, to older adults, or to people who are pregnant, or by sex. Conclusion. Randomized controlled trials support small to modest effects of dietary fiber on laxation in generally healthy people. Low solubility fibers and low fermentability fibers had the strongest associations with most laxation outcomes. The evidence is most applicable to generally healthy adults with normal bowel function, with insufficient evidence specific to people in other life stages. Future studies to address the key question of this systematic review need to be specifically designed and powered to fully evaluate fibers that people typically consume within the food matrix and the effect of incrementally adding fiber to their diets. Future studies should also better report the characteristics of the fibers being evaluated and should more comprehensively describe the total fiber intake of study participants.

A major goal in Israeli and U.S. agroecosystems is to maximize nitrogen availability to crops while minimizing nitrogen losses to air and water resources. This goal has presented a significant challenge to global agronomists and scientists because crops require large inputs of nitrogen (N) fertilizer to maximize yield, but N fertilizers are easily lost to surrounding ecosystems where they contribute to water pollution and greenhouse gas concentrations. Determination of the optimum N fertilizer input is complex because the amount of N produced from soil organic matter varies with time, space and management. Indicators of soil N availability may help to guide requirements for N fertilizer inputs and are increasingly viewed as indicators of soil health To address these challenges and improve N availability indicators, project 4550 “Improving nitrogen availability indicators by emphasizing correlations between gross nitrogen mineralization and the quality and quantity of labile organic matter fractions” addressed the following objectives: Link the quantity and quality of labile soil organic matter fractions to indicators of soil fertility and environmental quality including: i) laboratory potential net N mineralization ii) in situ gross N mineralization iii) in situ N accumulation on ion exchange resins iv) crop uptake of N from mineralized soil organic matter sources (non-fertilizer N), and v) soil nitrate pool size. Evaluate and compare the potential for hot water extractable organic matter (HWEOM) and particulate organic matter quantity and quality to characterize soil N dynamics in biophysically variable Israeli and U.S. agroecosystems that are managed with different N fertility sources. Ultimately, we sought to determine if nitrogen availability indicators are the same for i) gross vs. potential net N mineralization processes, ii) diverse agroecosystems (Israel vs. US) and, iii) management strategies (organic vs. inorganic N fertility sources). Nitrogen availability indicators significantly differed for gross vs. potential N mineralization processes. These results highlight that different mechanisms control each process. Although most research on N availability indicators focuses on potential net N mineralization, new research highlights that gross N mineralization may better reflect plant N availability. Results from this project identify the use of ion exchange resin (IERs) beads as a potential technical advance to improve N mineralization assays and predictors of N availability. The IERs mimic the rhizosphere by protecting mineralized N from loss and immobilization. As a result, the IERs may save time and money by providing a measurement of N mineralization that is more similar to the costly and time consuming measurement of gross N mineralization. In further search of more accurate and cost-effective predictors of N dynamics, Excitation- Emission Matrix (EEM) spectroscopy analysis of HWEOM solution has the potential to provide reliable indicators for changes in HWEOM over time. These results demonstrated that conventional methods of labile soil organic matter quantity (HWEOM) coupled with new analyses (EEM) may be used to obtain more detailed information about N dynamics. Across Israeli and US soils with organic and inorganic based N fertility sources, multiple linear regression models were developed to predict gross and potential N mineralization. The use of N availability indicators is increasing as they are incorporated into soil health assessments and agroecosystem models that guide N inputs. Results from this project suggest that some soil variables can universally predict these important ecosystem process across diverse soils, climate and agronomic management. BARD Report - Project4550 Page 2 of 249

The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,