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This paper presents a novel method for conducting load flow (LF) calculations in radial distribution networks. The method is based on solving LF calculations as a constrained minimization problem using nonlinear programming CONOPT solver, integrated into general algebraic modeling system (GAMS) environment. The performance of the proposed CONOPT solver is evaluated on IEEE 33-bus and IEEE 69-bus test systems, where four cases were considered. In the first case, LF calculations were performed on standard test systems, and the results obtained using the proposed CONOPT solver were compared with three well-known power systems analysis software: EPSA, NEPLAN, and DigSilent. In the second and third cases, CONOPT is used for LF calculation in the presence of distributed generators (DGs) and shunt capacitors (SCs), respectively. In the fourth case, the proposed CONOPT solver is employed for LF calculation in the presence of electric vehicles (EVs). The simulation results show that the CONOPT solver outputs are accurate and fast in their convergence to global solutions in standard test systems in the presence of DGs, SCs, and EVs. [ABSTRACT FROM AUTHOR]

Industrial exhaust gases and gas fields are two significant sources of carbon dioxide (CO2) emissions that contribute to the rising levels of CO2 in the atmosphere. Among the various emission reduction systems, the CCSU (Carbon Capture, Storage, and Utilization) system has garnered extensive attention and research. This research aims to obtain the superstructure network sequentially in the CCSU system using GAMS (General Algebraic Modeling System). A mathematical approach was developed to optimize the amount of CO2 stored and utilized by varying the time difference (dt) between the source and sink from 0 to 10 years. After calculating the economic potential (EP), it was found that the Carbon Capture and Storage (CCS) system for both sources has a negative impact. In contrast, the CCSU system enhances the economic potential (EP) by generating a positive value. This is possible as the captured CO2 can be sold to the utilization sink, thereby creating a revenue stream. The EP for CO2 reduction from gas fields is greater than that from the industry, 21.68 × 106 USD compared to 12.50 × 106 USD at dt min10 years. The CCSU system, when utilizing CO2 sources from gas fields, is more profitable compared to using industrial emissions. [ABSTRACT FROM AUTHOR]

Generalized Additive Models (GAMs) are widely employed in ecological research, serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors. Nevertheless, evaluating the relative importance of predictors with concurvity (analogous to collinearity) on response variables in GAMs remains a challenge. To address this challenge, we developed an R package named gam.hp. gam.hp calculates individual R2 values for predictors, based on the concept of ‘average shared variance’, a method previously introduced for multiple regression and canonical analyses. Through these individual R2s, which add up to the overall R2, researchers can evaluate the relative importance of each predictor within GAMs. We illustrate the utility of the gam.hp package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London, UK. We believe that the gam.hp package will improve the interpretation of results obtained from GAMs.

Glioblastoma (GBM) displays an infiltrative growth characteristic that recruits neighboring normal cells to facilitate tumor growth, maintenance, and invasion into the brain. While the blood-brain barrier serves as a critical natural defense mechanism for the central nervous system, GBM disrupts this barrier, resulting in the infiltration of macrophages from the peripheral bone marrow and the activation of resident microglia. Recent advancements in single-cell transcriptomics and spatial transcriptomics have refined the categorization of cells within the tumor microenvironment for precise identification. The intricate interactions and influences on cell growth within the tumor microenvironment under multi-omics conditions are succinctly outlined. The factors and mechanisms involving microglia, macrophages, endothelial cells, and T cells that impact the growth of GBM are individually examined. The collaborative mechanisms of tumor cell-immune cell interactions within the tumor microenvironment synergistically promote the growth, infiltration, and metastasis of gliomas, while also influencing the immune status and therapeutic response of the tumor microenvironment. As immunotherapy continues to progress, targeting the cells within the inter-tumor microenvironment emerges as a promising novel therapeutic approach for GBM. By comprehensively understanding and intervening in the intricate cellular interactions within the tumor microenvironment, novel therapeutic modalities may be developed to enhance treatment outcomes for patients with GBM. [ABSTRACT FROM AUTHOR]

Understanding the influence and impact of environmental factors on manta ray sightings is critical to understanding the spatial and temporal ecology of a highly mobile species. Therefore, this study aims to determine the influence and impact of environmental factors as indicated by the parameters of wind speed, chlorophyll-a, SST, salinity, pH, dissolved oxygen, and the number of phytoplankton and zooplankton species. The mapped chlorophyll-a was reanalyzed based on the seasonal period throughout 2021 downloaded from marine copernicus and analyzed by kriging method. The influence and effects of environmental parameters on the short-term appearance of eye rays were studied using an adaptive model (GAM). The analysis showed a significant influence of environmental factors on manta ray sightings in Raja Ampat, namely Calanoid spp, Oithona nana, Acartia clausi, Calanus helgoladicus, and Oithona brevicornis. Based on this model, zooplankton is an important parameter that can describe the influence of environmental parameters on manta ray sightings at observation points in Raja Ampat MPA. The results of the reanalysis of chlorophyll-a concentrations were highest in the eastern to transitional seasons, which were scattered on the west side of Raja Ampat waters. Meanwhile, chlorophyll-a concentrations were low in the west to transitional season on the east side. This mechanism may drive the foraging strategy of manta rays, which visit shallow waters where zooplankton density and biomass are abundant. Adopting the BHS MPA network concept, as it has been implemented, would be in line with broader conservation expectations for the sustainability of manta rays in Raja Ampat. [ABSTRACT FROM AUTHOR]

The Galician estuaries are Europe's foremost supplier of mussels, generating millions of euros annually and offering substantial employment opportunities for its population. One of the most critical threats to shellfish production is the occurrence of harmful algal blooms (HABs), contaminating bivalves with phytotoxins. To successfully tackle this problem, there needs to be a collaborative effort between the scientific community and decision-makers to establish a dynamic and effective monitoring system. This could enable early warnings and preventive actions to avert the loss of millions of tons of shellfish. Remote sensing, despite its limitations, requires commitment and effort by experts to devise effective methods for detecting target optical constituents mixed with other undesired target but that exhibit strong signals. Therefore, the essential necessity arises to identify approaches for mitigating the shortcomings of the undertaken efforts. The objective of this study is to assess the main environmental drivers of potential harmful genera (Pseudo-nitzschia, Dinophysis, Alexandrium, and Gymnodinium) in the Rías Baixas from 2015 to 2022, developing regression-based models and customizing Generalized Additive Models (GAMs) to investigate their spatial-temporal dynamics. Risk-susceptible bloom zones were identified in the river mouth of the Ría Pontevedra and from the center to offshore of the Ría Vigo. Early upwelling events triggered peaks in chlorophyll-a (Chl-a), driving Dinophysis grazing on phytoplankton communities mainly dominated by diatoms, flagellates, and ciliates. Subsequently, the upwelling intensity favoring Pseudo-nitzschia or Alexandrium minutum growth as larger diatoms declined. A. minutum exhibited elevated incidence over the past five years. Gymnodinium catenatum presented a rare occurrence across the three studied estuaries, which limited the assessment of its spatial dynamics in the region. This study emphasizes the need to integrate remote sensing evaluation of high-risk bloom areas (July to September), in-situ cell count collection, and enhanced efforts for forecasting future critical occurrences of HABs. [ABSTRACT FROM AUTHOR]

Environmental issues have become a global crisis since the 1990s, primarily due to energy consumption. Despite cities occupying only 2% of the world's surface, they consume between 60 and 80% of the world's energy. In recent years, there has been a global shift towards reducing the use of fossil fuels, driven by resource depletion and environmental concerns. For instance, the European Union mandates its members to construct and operate zero-energy buildings that produce more renewable energy than they consume throughout the year. This research proposes a method for long-term renewable energy management planning for three communities. Studies have shown that renewable energy has gained significant importance in various sectors. Therefore, the proposed research suggests an optimal combination of materials and active and passive air conditioning systems for a commercial complex in Tehran, using a mathematical planning model with two main objectives: reducing solar energy-related costs and consumption. The proposed model was implemented and validated based on real data and simulation in the GAMS software environment. To validate the results, the composition of building materials was determined, and the thermal performance of the commercial complex was investigated in the BCS19 software environment by calculating heat transfer coefficients and thermal power of the walls. • Application of renewable energy sources to solve the negative impact of fossil fuels. • Clean energy sources considering in large commercial buildings by profit increasing. • Introducing shift towards sustainability by analyzing solar energy cost & benefits. [ABSTRACT FROM AUTHOR]

We present an application of quantile generalized additive models (QGAMs) to study spatially compounding climate extremes, namely extremes that occur (near‐) simultaneously in geographically remote regions. We take as an example wintertime cold spells in North America and co‐occurring wet or windy extremes in Western Europe, which we collectively term Pan‐Atlantic compound extremes. QGAMS are largely novel in climate science applications and present a number of key advantages over conventional statistical models of weather extremes. Specifically, they remove the need for a direct identification and parametrization of the extremes themselves, since they model all quantiles of the distributions of interest. They thus make use of all information available, and not only of a small number of extreme values. Moreover, they do not require any a priori knowledge of the functional relationship between the predictors and the dependent variable. Here, we use QGAMs to both characterize the co‐occurrence statistics and investigate the role of possible dynamical drivers of the Pan‐Atlantic compound extremes. We find that cold spells in North America are a useful predictor of subsequent wet or windy extremes in Western Europe, and that QGAMs can predict those extremes more accurately than conventional peak‐over‐threshold models. Plain Language Summary: In this paper we propose a new data‐driven method to study climate extremes occurring simultaneously in multiple, possibly remote, locations. Such extremes can pose a greater threat to human societies than single, isolated extremes, as their effects may exacerbate each other and lead to correlated losses. The method we suggest requires fewer assumptions than conventional extreme value statistical techniques, and can help us to identify previously unknown relationships between the extremes themselves and their possible drivers. We exemplify its use by studying the co‐occurrence of periods of unusually cold weather in North America and subsequent uncommonly strong wind and abundant precipitation in Western Europe. We find that the new method has better predictive power for the European extremes than conventional statistical approaches. Furthermore, we confirm the results of previous studies suggesting an association between the wintertime extremes in North America and Western Europe. Key Points: Quantile general additive models (QGAMs) can model the relationship between compound climate extremes flexibly and robustlyNorth American cold spells show some predictive skill for wet or windy extremes in Western Europe, even when accounting for confoundersGiven relevant atmospheric predictors, QGAMs can predict these extremes more accurately than peak‐over‐threshold models in most regions [ABSTRACT FROM AUTHOR]

Sweet sorghum (Sorghum bicolor) lines M81-E and Colman were previously shown to differ in responses to Fusarium thapsinum and Macrophomina phaseolina, stalk rot pathogens that can reduce the yields and quality of biomass and extracted sugars. Inoculated tissues were compared for transcriptomic, phenolic metabolite, and enzymatic activity during disease development 3 and 13 days after inoculation (DAI). At 13 DAI, M81-E had shorter mean lesion lengths than Colman when inoculated with either pathogen. Transcripts encoding monolignol biosynthetic and modification enzymes were associated with transcriptional wound (control) responses of both lines at 3 DAI. Monolignol biosynthetic genes were differentially coexpressed with transcriptional activator SbMyb76 in all Colman inoculations, but only following M. phaseolina inoculation in M81-E, suggesting that SbMyb76 is associated with lignin biosynthesis during pathogen responses. In control inoculations, defense-related genes were expressed at higher levels in M81 -E than Colman. Line, treatment, and timepoint differences observed in phenolic metabolite and enzyme activities did not account for observed differences in lesions. However, generalized additive models were able to relate metabolites, but not enzyme activities, to lesion length for quantitatively modeling disease progression: in M81 -E, but not Colman, sinapic acid levels positively predicted lesion length at 3 DAI when cell wall-bound syringic acid was low, soluble caffeic acid was high, and lactic acid was high, suggesting that sinapic acid may contribute to responses at 3 DAI. These results provide potential gene targets for development of sweet sorghum varieties with increased stalk rot resistance to ensure biomass and sugar quality. [ABSTRACT FROM AUTHOR]

Summary: Regression models that ignore measurement error in predictors may produce highly biased estimates leading to erroneous inferences. It is well known that it is extremely difficult to take measurement error into account in Gaussian non‐parametric regression. This problem becomes even more difficult when considering other families such as binary, Poisson and negative binomial regression. We present a novel method aiming to correct for measurement error when estimating regression functions. Our approach is sufficiently flexible to cover virtually all distributions and link functions regularly considered in generalised linear models. This approach depends on approximating the first and the second moment of the response after integrating out the true unobserved predictors in any semi‐parametric generalised regression model. By the latter is meant a model with both linear and non‐parametric effects that are connected to the mean response by a link function and with a response distribution in an exponential family or quasi‐likelihood model. Unlike previous methods, the method we now propose is not restricted to truncated splines and can utilise various basis functions. Moreover, it can operate without making any distributional assumption about the unobserved predictor. Through extensive simulation studies, we study the performance of our method under many scenarios. [ABSTRACT FROM AUTHOR]

The Galician estuaries are Europe’s foremost supplier of mussels, generating millions of euros annually and offering substantial employment opportunities for its population. One of the most critical threats to shellfish production is the occurrence of harmful algal blooms (HABs), contaminating bivalves with phytotoxins. To successfully tackle this problem, there needs to be a collaborative effort between the scientific community and decision-makers to establish a dynamic and effective monitoring system. This could enable early warnings and preventive actions to avert the loss of millions of tons of shellfish. Remote sensing, despite its limitations, requires commitment and effort by experts to devise effective methods for detecting target optical constituents mixed with other undesired target but that exhibit strong signals. Therefore, the essential necessity arises to identify approaches for mitigating the shortcomings of the undertaken efforts. The objective of this study is to assess the main environmental drivers of potential harmful genera (Pseudo-nitzschia, Dinophysis, Alexandrium, and Gymnodinium) in the Rías Baixas from 2015 to 2022, developing regression-based models and customizing Generalized Additive Models (GAMs) to investigate their spatial-temporal dynamics. Risk-susceptible bloom zones were identified in the river mouth of the Ría Pontevedra and from the center to offshore of the Ría Vigo. Early upwelling events triggered peaks in chlorophyll-a (Chl-a), driving Dinophysis grazing on phytoplankton communities mainly dominated by diatoms, flagellates, and ciliates. Subsequently, the upwelling intensity favoring Pseudo-nitzschia or Alexandrium minutum growth as larger diatoms declined. A. minutum exhibited elevated incidence over the past five years. Gymnodinium catenatum presented a rare occurrence across the three studied estuaries, which limited the assessment of its spatial dynamics in the region. This study emphasizes the need to integrate remote sensing evaluation of high-risk bloom areas (July to September), in-situ cell count collection, and enhanced efforts for forecasting future critical occurrences of HABs.

Cladophora glomerata is the main macroalgae species responsible for the appearance of large mass compact algae formations, known as mats, in the fresh water of Lake Oporzynskie and the river Nielba, in western Poland. The mats are usually created by single species. In order to achieve seasonal domination, the algae secrete to the environment the allelopathic substances, which are phenolic compounds. In the period of intensive population growth, the thread-like thalli of these algae are richly incrusted with epiphytic diatoms. The amount of the secreted phenolic compounds changes in time within the vegetation period and depends on the weather conditions. The most abundant secretion of phenolic compounds takes place in July when, with increasing concentration of phenolic compounds in water, their content in the cells decreases.The main aim of the study was to establish the effect of the phenolic compounds secreted by C. glomerata thalli on the diatoms growing on them. According to our results, polyphenolic compounds have an impact not only on the abundance of diatom communities, but also on the structure of their communities. With increasing concentration of polyphenols, the abundance of Achnathidium minutissimum, Gomphonema olivaceum, Cocconeis pediculus, Navicula tripunctata decreased, while that of Amphora ovalis, Navicula rostellata, Staurosira construens, Cymbella lange-bartelotti increased. The species Cocconeis placentula reached domination thanks to the favorable influence of the environment and the effect of polyphenols secreted by the macroalgae. The variation in the diatom communities' structure was proved to also depend on the habitat parameters, mainly: EC, Turbidity, N-NH4, N-NO3, TDS.

The Northwest Pacific Ocean is one of the most productive fisheries in the world, with small pelagic fish constituting a substantial portion of its fishery resources. Changes in abiotic factors can affect the distribution of marine fish, but there are limited studies on the relationship between abiotic factors and fish density in a vertical direction. This study used acoustic measurement data collected by the scientific research vessel “Songhang” equipped with a Simrad EK 80 scientific echosounder from June to July 2022, to estimate the small pelagic fish resource density in different water layers from 0 to 200 m. Furthermore, Generalized Additive Models (GAMs) were established for various water layers, combined with remote sensing data such as temperature (Temp), salinity (Sal), net primary productivity (Nppv), as well as latitude (Lat) and longitude (Lon) data, to analyze the influence of abiotic factors on abundance density of small pelagic fish species. The results showed that mean abundance and biomass densities estimated based on the acoustic method were 3.12×105±1.42×106 ind/n mile2 and 5768.39±26224.76 kg/n mile2, respectively. Small pelagic fish resources exhibited a predominant concentration within the 0–50 m layers and displayed a significant accumulation phenomenon near the Kuroshio Extension area in each water layer. The GAMs results indicated that abiotic factors affecting fish density vary across different water layers. Temp and Lon factors appeared in optimal GAMs for all water layers and significantly contributed to fish density. Variations in temperature lead to differences in the vertical distribution of fishery resources. Our results emphasized the necessity of exploring the relationship between abiotic factors and fish density within different water layers. This research can provide scientific support for developing and managing fishery resources in the Northwest Pacific Ocean.

Due to the limited availability of fossil fuels and environmental concerns over greenhouse gas emissions, micro-grid systems (MGSs) have become crucial for integrating a high penetration of renewables. Nonetheless, the complexity of the operation of MGS increases in the actual operating process due to the irregular nature of renewables, electrical loads (load demands), and uncertain market prices. The core aim of this paper is to suggest a bi-objective optimization model for managing energy efficiently in a grid-connected MG while considering the unsteadiness of electrical load demands, market prices, and renewable output powers. Also, the suggested model tries to improve the MG’s scheduling while considering the wind turbine, photovoltaic, loads, and market pricing uncertainty. MG operators can develop strategies that balance cost savings with risk management and ensure long-term sustainability by considering the uncertainty of the different generation/demand resources and markets. The suggested optimization model includes two distinct objective functions – minimizing the 24-hour horizon’s total projected cost of the grid-connected MG and reducing MG emissions during the considered period. Further, a simultaneous reduction of the overall operating cost and the pollutant emission MG over 24 h is presented. The general algebraic modeling system (GAMS) has been used to solve the presented multi-objective problem. The findings clearly demonstrate the efficacy of the suggested energy management (EM) methodology for improving the techno-economic-environmental performance of grid-connected MGs under uncertain situations.

The maximum clique problem is a problem that takes many forms in optimization and related graph theory problems, and also has many applications. Because of its NP-completeness (nondeterministic polynomial time), the question arises of its solvability for larger instances. Instead of the traditional approaches based on the use of approximate or stochastic heuristic methods, we focus here on the use of integer programming models in the GAMS (General Algebraic Modelling System) environment, which is based on exact methods and sophisticated deterministic heuristics incorporated in it. We propose modifications of integer models, derive their time complexities and show their direct use in GAMS. GAMS makes it possible to find optimal solutions to the maximum clique problem for instances with hundreds of vertices and thousands of edges within minutes at most. For extremely large instances, good approximations of the optimum are given in a reasonable amount of time. A great advantage of this approach over all the mentioned algorithms is that even if GAMS does not find the best known solution within the chosen time limit, it displays its value at the end of the calculation as a reachable bound. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agriculture & Nature / Kahramanmaraş Sütçü İmam Üniversitesi Tarım & Doğa Dergisi is the property of Kahramanmaras Sutcu Imam Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background There are regional differences in the effect of green space on mortality of Chronic obstructive pulmonary disease (COPD). We conduct an ecological study, using the administrative divisions of Chongqing townships in China as the basic unit, to investigate the association between COPD mortality and green space based on data of 313,013 COPD deaths in Chongqing from 2012 to 2020. Green space is defined by Fractional vegetation cover (FVC), which is further calculated based on the normalised vegetation index (NDVI) from satellite remote sensing imagery maps. Methods After processing the data, the non-linear relationship between green space and COPD mortality is revealed by generalised additive models; the spatial differences between green space and COPD mortality is described by geographically weighted regression models; and finally, the interpretive power and interaction of each factor on the spatial distribution of COPD mortality is examined by a geographic probe. Results The results show that the FVC local regression coefficients ranged from − 0.0397 to 0.0478, 63.0% of the regions in Chongqing have a positive correlation between green space and COPD mortality while 37.0% of the regions mainly in the northeast and west have a negative correlation. The interpretive power of the FVC factor on the spatial distribution of COPD mortality is 0.08. Conclusions Green space may be a potential risk factor for increased COPD mortality in some regions of Chongqing. This study is the first to reveal the relationship between COPD mortality and green space in Chongqing at the township scale, providing a basis for public health policy formulation in Chongqing.

Homogeneous (HM) and functionally graded (FG) TiN thin films were produced by the grid-assisted magnetron sputtering (GAMS) technique. The joint use of ball-cratering micro-abrasive wear test (BCMA) and nanoscratching (NN) test was proposed and carried out to evaluate the wear resistance of TiN films. The results showed an increase of 33.3% wear resistance in the FG TiN thin film, when compared with the HM TiN thin film. This result was justified by the higher resistance to nucleation and propagation of cracks and higher level of adhesion to the substrate by the FG TiN thin film, validated through the NN tests.

This paper presents empirical models developed through stepwise multiple linear regression to estimate the live fuel moisture content (LFMC) in a Mediterranean area. The models are based on LFMC data measured in 50 field plots, considering four groups with similar bioclimatic characteristics and vegetation types (trees and shrubs). We also applied a species-specific LFMC model for Rosmarinus officinalis in plots with this dominant species. Spectral indices extracted from Sentinel-2 images and their averages over the study time period in each plot with a spatial resolution of 10 m were used as predictors, together with interpolated meteorological, topographic, and seasonal variables. The models achieved adjusted R2 values ranging between 52.1% and 74.4%. Spatial and temporal variations of LFMC in shrub areas were represented on a map. The results highlight the feasibility of developing satellite-derived LFMC operational empirical models in areas with various vegetation types and taking into account bioclimatic zones. The adjustment of data through GAM (generalized additive models) is also addressed in this study. The different error metrics obtained reflect that these models provided a better fit (most adjusted R2 values ranged between 65% and 74.1%) than the linear models, due to GAMs being more versatile and suitable for addressing complex problems such as LFMC behavior. [ABSTRACT FROM AUTHOR]

Background: Harmful algal blooms (HABs) of the toxic dinoflagellate Karenia brevis impact the overall ecosystem health. Methods: K. brevis cell counts were extracted from Harmful Algal BloomS Observing System (HABSOS) in situ data and matched with 0.25º resolution environmental information from the Copernicus database to generate spatio-temporal maps of HABs in the Gulf of Mexico (GoM) between 2010 and 2020. The data was used to analyze the relationship between spatial and temporal variability in the presence/absence of K. brevis blooms (≥100,000 cells/L) and biotic and abiotic variables using Generalized Additive Models (GAM). Results: The variability of blooms was strongly linked to geographic location (latitude and salinity), and temporal variables (month and year). A higher probability of K. brevis blooms presence was predicted in areas with negative sea surface height (SSH) values, silicate concentration (0, 30-35 mmol. m -3), sea surface temperature of 22-28 oC, and water currents moving south-westward (225º). The smooth effect of each environmental variable shows a bimodal pattern common in semi-enclosed basins such as GoM. The spatial predictions from the model identified an important permanent area in (1) Southwest Florida (25.8-27.4 o latitude), and four seasonally important areas, (2) North Central Florida (3) Central West Florida, (4) Alabama on Gulf Shores and (5) Mississippi with higher bloom probabilities during the fall to winter season (November-January). Results also suggest that HABs can extend until ≥ 300 km offshore; starting to form in March and reaching a peak in September, and were swept to the coastal area during fall and winter. This suggests the role of upwelling and water circulation in GoM for the accumulation of cells and HABs. Information on the spatio-temporal dynamics of K. brevis blooms and understanding the environmental drivers are crucial to support more holistic spatial management to decrease K. brevis blooms incidence in bodies of water.

Modular assembly systems are a category of changeable manufacturing systems, which can handle the rapid change in customer demands, product design change and market fluctuations. On the operational level, jigs and fixtures are fundamental elements of assembly systems. They are used to hold parts and subassemblies in place, and directly affect assembly cost, quality and time. Therefore, modular fixtures that can adapt to different geometries are becoming a very important enabler for changeable manufacturing. In this paper, two mathematical models are presented to optimise the use of a passive modular assembly fixture plan in an automated assembly system by considering different production scenarios and constraints. These models optimise the changeability plan of the modular fixture by minimising the number of dowel replacements between different part geometries assuming that the candidate dowels locations for each part have been determined using existing methods in the literature by considering different assembly requirements. The first model, LRTE, considers all possible part rotations and translations on the fixture to minimise setup time. In addition, the second model, SLRTE, enables the system to simultaneously optimise job sequence. This paper presents various examples in different sizes, and the results show that the model can effectively reduce the fixture setup time up to %50. [ABSTRACT FROM AUTHOR]

In this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective ones, whose results are regarded as ideal points for the objective functions in the bi-objective model. Anti-ideal points are also searched similarly. Then, using the ideal and anti-ideal points, the bi-objective model is redefined as a single-objective one and solved. The difficulties of solving the models, which are basically non-linear, are discussed. Furthermore, the models are linearized, in which case how the number of variables and constraints changes is discussed. Mathematical models are implemented in Python's Pulp library, Lingo, IBM ILOG CPLEX Optimization Studio, and GAMS software, and the obtained results are presented. Furthermore, metaheuristics available in Python's Pymoo library are utilized to solve the models' single- and bi-objective versions. In the experimental results section, benchmarks of different sizes are derived for the problem, and the results are presented. It is observed that the solvers do not perform satisfactorily in solving models; of all of them, GAMS achieves the best results. The utilized metaheuristics from the Pymoo library gain feasible results in reasonable times. In the conclusion section, suggestions are given for solving similar problems. Furthermore, this article summarizes the managerial applications of the sectorization problems.

This study shows the modeling of the convective drying operation of quinoa (Chenopodium quinoa Willd. var. Hualhuas) grains implemented in the General Algebraic Modeling System (GAMS) software. The proposed model was based on Fick’s second law. The drying experiences were carried out using a pilot-scale oven. The drying air conditions were: 40, 60, and 80°C and 0.2 and 0.7 m s-1. The mathematical modeling was employed to describe the behavior of the drying operation according to variations of the average moisture over time. The effective diffusivity of moisture and mass transfer were studied for the different operating conditions. The model was validated by experimental data. It was possible to model the quinoa grains drying process, obtaining a high precision between the experimental and estimated values. Quinoa drying curves can be represented properly by the studied model. In the operating ranges tested, the effective diffusivity values of moisture were between 2.52 10-10 and 1 10-9 m2 s-1 and the mass transfer values were between 7.20 and 11.47 cm s-1. The effective diffusivity (Deff) showed significant differences (P

University course scheduling (UCS) is one of the most important and time-consuming issues that all educational institutions face yearly. Most of the existing techniques to model and solve UCS problems have applied approximate methods, which differ in terms of efficiency, performance, and optimization speed. Accordingly, this research aims to apply an exact optimization method to provide an optimal solution to the course scheduling problem. In other words, in this research, an integer programming model is presented to solve the USC problem. In this model, the constraints include the facilities of classrooms, courses of different levels and compression of students' curriculum, courses outside the faculty and planning for them, and the limited time allocated to the professors. The objective is to maximize the weighted sum of allocating available times to professors based on their preferences in all periods. To evaluate the presented model's feasibility, it is implemented using the GAMS software. Finally, the presented model is solved in a larger dimension using a real data set from a college in China and compared with the current program in the same college. The obtained results show that considering the mathematical model's constraints and objective function, the faculty courses' timetable is reduced from 4 days a week to 3 working days. Moreover, master courses are planned in two days, and the courses in the educational groups do not interfere with each other. Furthermore, by implementing the proposed model for the real case study, the maximum teaching hours of the professors are significantly reduced. The results demonstrate the efficiency of the proposed model and solution method in terms of optimization speed and solution accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Agronomía Colombiana is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cell-free protein synthesis (CFPS) is a method utilized for producing proteins without the limits of cell viability. The plug-and-play utility of CFPS is a key advantage over traditional plasmid-based expression systems and is foundational to the potential of this biotechnology. A key limitation of CFPS is the varying stability of DNA types, limiting the effectiveness of cell-free protein synthesis reactions. Researchers generally rely on plasmid DNA for its ability to support robust protein expression in vitro. However, the overhead required to clone, propagate, and purify plasmids reduces the potential of CFPS for rapid prototyping. While linear templates overcome the limits of plasmid DNA preparation, linear expression templates (LETs) were under-utilized due to their rapid degradation in extract based CFPS systems, limiting protein synthesis. To reach the potential of CFPS using LETs, researchers have made notable progress toward protection and stabilization of linear templates throughout the reaction. The current advancements range from modular solutions, such as supplementing nuclease inhibitors and genome engineering to produce strains lacking nuclease activity. Effective application of LET protection techniques improves expression yields of target proteins to match that of plasmid-based expression. The outcome of LET utilization in CFPS is rapid design–build–test–learn cycles to support synthetic biology applications. This review describes the various protection mechanisms for linear expression templates, methodological insights for implementation, and proposals for continued efforts that may further advance the field. [ABSTRACT FROM AUTHOR]

Background: There are regional differences in the effect of green space on mortality of Chronic obstructive pulmonary disease (COPD). We conduct an ecological study, using the administrative divisions of Chongqing townships in China as the basic unit, to investigate the association between COPD mortality and green space based on data of 313,013 COPD deaths in Chongqing from 2012 to 2020. Green space is defined by Fractional vegetation cover (FVC), which is further calculated based on the normalised vegetation index (NDVI) from satellite remote sensing imagery maps. Methods: After processing the data, the non-linear relationship between green space and COPD mortality is revealed by generalised additive models; the spatial differences between green space and COPD mortality is described by geographically weighted regression models; and finally, the interpretive power and interaction of each factor on the spatial distribution of COPD mortality is examined by a geographic probe. Results: The results show that the FVC local regression coefficients ranged from − 0.0397 to 0.0478, 63.0% of the regions in Chongqing have a positive correlation between green space and COPD mortality while 37.0% of the regions mainly in the northeast and west have a negative correlation. The interpretive power of the FVC factor on the spatial distribution of COPD mortality is 0.08. Conclusions: Green space may be a potential risk factor for increased COPD mortality in some regions of Chongqing. This study is the first to reveal the relationship between COPD mortality and green space in Chongqing at the township scale, providing a basis for public health policy formulation in Chongqing. [ABSTRACT FROM AUTHOR]

آب به عنوان یکی از نهاده‌های اساسی در تولیدات بخش کشاورزی، از جایگاه ممتازی در توسعه این بخش برخوردار می‌باشد. در دهه‌های اخیر با رشد جمعیت، افزایش تقاضا برای محصولات کشاورزی و همچنین توسعه صنعت و کشاورزی، برداشت از منابع آب زیرزمینی بعنوان مهم‌ترین تامین کننده آب در مناطق خشک و نیمه‌خشک نیز بطور چشمگیری افزایش یافته و منجر به پیشی گرفتن تقاضا بر عرضه منابع آب و ایجاد بحران در اغلب این مناطق شده است. بی‌شک یکی از مهم‌ترین ابزارها در کنترل و مدیریت تقاضای منابع آب و کاهش بحران حاصل از آن، بهره گیری از ابزارهای اقتصادی و لحاظ نمودن ارزش اقتصادی آب در فعالیت های کشاورزی بعنوان بزرگترین مصرف کننده آن می‌باشد. مطالعه حاضر با هدف ارزشگذاری اقتصادی منابع آب زیر زمینی در دشت همدان- بهار با بهره‌گیری از الگوی برنامه‌ریزی پویا با استفاده از نرم‌افزار GAMS در سال زراعی 96-1395 می‌‌باشد. بر اساس نتایج تحقیق ارزش اقتصادی آب زیرزمینی به ازای هر متر مکعب در چهار ناحیه دشت همدان-بهار شامل منطقه همدان 3543 ریال، منطقه لالجین 4538 ریال، منطقه بهار 4015 ریال و منطقه صالح‌آباد 3690 ریال محاسبه گردید. هم‌چنین بازده ناخالص حاصل از فعالیت های کشاورزی هر یک از مناطق به‌ترتیب، همدان (708/6887810)، لالجین (150/7148527)، بهار (755/4741399)، صالح‌آباد (005/3639706) میلیون‌ ریال، بازده ناخالص کل مناطق (22417440) میلیون ‌ریال و میزان حجم آب مصرفی کل معادل 185629200 متر مکعب برآورد شد. بررسی و مقایسه ارزش اقتصادی برآورد شده با قیمت آب در نواحی مورد مطالعه نشان داد که ارزش اقتصادی محاسبه شده هر متر مکعب آب بیشتر از قیمت فعلی آب در منطقه می‌باشد، بطوریکه افزایش هزینه استفاده این نهاده از طریق ابزارهای مختلف سیاستی نظیر وضع قیمت آب می‌تواند نقش موثری در کنترل بهره‌برداری و تخلیه آبخوان داشته باشد.

Yangjiang coastal waters provide vital spawning grounds, feeding grounds, and nursery areas for many commercial fish species. It is important to understand the spatial distribution of fish for the management, development, and protection of fishery resources. In this study, an acoustic survey was conducted from 29 July to 5 June 2021. Meanwhile, remote sensing data were collected, including sea surface temperature (SST), chlorophyll concentration (Chla), sea surface salinity (SSS), and sea surface temperature anomaly (SSTA). The spatial distribution of density and biomass of fish was analyzed based on acoustic survey data using the geostatistical method. Combining with remote sensing data, we explored the relation between fish density and the environment based on the GAMs model. The results showed that fish are mainly small individuals. The horizontal distri-bution of fish density had a characteristic of high nearshore and low offshore. In the vertical direc-tion, fish are mainly distributed in surface-middle layers in shallow waters (<10 m) and in middle-bottom layers in deeper waters (>10 m), respectively. The deviance explained in the optimal GAM model was 59.2%. SST, Chla, SSS, and longitude were significant factors influencing fish density distribu-tion with a contribution of 35.3%, 11.8%, 6.5%, and 5.6%, respectively. This study can pro-vide a scientific foundation and data support for rational developing and protecting fishery re-sources in Yangjiang coastal waters. [ABSTRACT FROM AUTHOR]

This article presents new quantitative results on the distribution of residential green space for people with disabilities in the United States, building on and bridging scholarly research in two distinct domains: one involving approaches that quantify disparities in green space access among racialized minorities and socioeconomically disadvantaged groups, and the other using qualitative methods that demonstrate that most green spaces remain inaccessible and unwelcoming to disabled visitors. Using generalized additive models (GAMs) that controlled for demographic factors and climatological characteristics, we find that residential areas with more green space generally have a higher proportion of disabled residents. The statistical results run counter to expectations from the literature, thus complicating the prevailing narrative and indicating a need for mixed-methods research to examine multiple dimensions of access and environmental justice. Using cluster analysis to assess spatial trends, we detect residential clusters of high disability and low green space and find that they are located in predominantly non-White, urban, and more socioeconomically disadvantaged neighborhoods compared to clusters of high disability and high green space. Cluster analysis results suggest that there are inequities in green space access at the intersection of disability, race, and class, as well as across the urban–rural continuum. [ABSTRACT FROM AUTHOR]

One of the key topics in scientific research on the effect of global climate change is to study the variations in the global carbon cycle. In this respect, net primary production (NPP) is an important component of carbon storage and a key indicator for evaluating ecosystem functions. However, there are many climatic factors and variables, both at the local and global levels, that can influence NPP variations at various time scales, including daily, monthly, seasonal, and even annual. So, it is of crucial significance for stakeholders and ecosystem managers to understand these variables and their relationship with NPP variations in different ecosystems with different vegetation types, especially in arid and semi-arid regions. Thus, this research investigated the spatiotemporal linear and nonlinear relationships between NPP variations of different bioclimatic zones of Iran and the atmospheric-oceanic oscillations on a monthly and seasonal basis from 2000 to 2016 using generalized linear and additive regression models (GLMs and GAMs). For this purpose, the NPP values were derived from the MODIS satellite products to check the effect of teleconnection indices including Arctic Oscillation (AO), Antarctic Oscillation (AAO), Atlantic Multi-decadal Oscillation (AMO), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), Western Mediterranean Oscillation (WMO), and El Niño-Southern Oscillation indices (NINO) including NINO1.2, NINO3.4, NINO3, and NINO4 on it. Findings showed that almost all of the studied climatic indices were influential on the seasonal NPP variations in Iran's bioclimatic regions with various temporal and spatial patterns. It is revealed that AO, AAO, AMO, NAO, SOI, and WMO indices influence the bioclimatic zones perpetually over the year. However, some indices, such as NINO3.4 in spring, NINO.4 and SOI in summer, and NAO and AMO during warm phases in autumn, have more significant impacts on NPP variations in a given season of the year. However, the model adequacy statistics showed that the NINO family indices and the NAO index were more influential on NPP variations, especially in the winter and spring, than the other climatic indices. In general, the results revealed that the relationship between the NINO family indices and NPP was ascending, either in the linear form or in the nonlinear form. In other words, the NPP value will increase in the bioclimatic regions of Iran during the El Niño phases. [ABSTRACT FROM AUTHOR]

Water sector planning and policy making in arid and semi-arid regions are challenging because many drivers and decision criteria require consideration. In this study, a multi-period mixed-integer linear programming model was developed to integrate and economically evaluate water management options for water supply in arid regions. The applicability of the proposed approach was demonstrated through a case study in the Emirate of Abu Dhabi (EAD), United Arab Emirates. The model was programmed in general algebraic modeling system (GAMS) and solved using the Cplex solver. The model determined the optimal economic and environmental costs, capacity expansion of treatment plants and water transmission systems, and other environmental aspects including the carbon footprint and brine discharge. Results show that the capacity contribution of reverse osmosis for desalination is expected to increase from 5.1% in 2021 to 18.1% in 2050. Based on the model's results for the case study, it was concluded that even with moderate consideration of environmental aspects, desalination plants in the EAD need a major technology transformation from thermal desalination to reverse osmosis The proposed model is proved to be effective for integrated water resources management and infrastructure planning in the EAD, and has the potential for effective application in other arid or semi-arid countries.

Understanding changes in extreme compound hazard events is important for climate mitigation and policy. By definition, such events are rare so robust quantification of their future changes is challenging. An approach is presented, for probabilistic modelling and simulation of climate model data, which is invariant to the event definition since it models the underlying weather variables. The approach is based on the idea of a ‘moving window’ in conjunction with Generalised Additive Models (GAMs) and Bayesian inference. As such, it is robust to the data size and completely parallelizable, while it fully quantifies uncertainty allowing also for comprehensive model checking. Lastly, Gaussian anamorphosis is used to capture dependency across weather variables. The approach results in probabilistic simulations to enable extrapolation beyond the original data range and thus robust quantification of future changes of rare events. We illustrate by application to daily temperature, humidity and precipitation from a regional climate model.

The Charlie-Gibbs Fracture Zone (CGFZ) is a prominent geological feature offsetting the Mid-Atlantic Ridge (MAR), consisting of two parallel fractures, creating a highly variable seafloor bathymetry. It has been defined as the most important latitudinal biodiversity transitional zone on the MAR. Despite this recognition, the faunal communities living on the fracture zone have not been extensively described. A remotely operated vehicle (ROV) was utilised during the TOSCA (Tectonic Ocean Spreading at the Charlie-Gibbs Fracture Zone) survey. The survey included five ROV video transects at depths between 560 and 2900 m. The objectives of the study were to use this video footage to quantify benthic megafaunal density and biodiversity patterns on the CGFZ and their environmental drivers. Species accumulation curves and generalised additive modelling show that depth and the presence of hard substrates play an important role in explaining species richness and abundance at the CGFZ. Coral taxa showed highest abundance between depths of 1500 and 2000 m, while sponge taxa were more abundant between 1750 and 2250 m. A dense sponge aggregation was identified on a ridge feature at 2250 m depth. The high biodiversity and presence of dense sponge aggregations and coral gardens found in this study highlight the need for detailed surveys to help support decisions made by governing bodies on the protection status of the CGFZ. [ABSTRACT FROM AUTHOR]

Today’s power systems are characterized by a considerable penetration of electricity production from renewable energy sources. However, renewable energy sources are characterized by intermittent non-stationary output, which leads to more complicated regulation of voltage, power flows, and frequency in power systems. Energy storage systems (ESSs) have been identified as one of the more efficient solutions to overcome some of these problems. In the literature, ESSs have been predominantly viewed as producers (in discharging) or consumers (in charging) of active power. Using bi-directional converters (BDCs), ESSs can regulate the connection point’s reactive power. Therefore, this paper aims to consider the effects of the coordination of BDCs and ESSs on modern power systems. The CONOPT solver embedded in the general algebraic modeling system (GAMS) was used to analyze this problem and find the optimal location of ESSs in the system. All calculations were performed on both IEEE 39- and IEEE 30-test networks, while the objective function was minimizing the power loss. The presented results show that losses in the power system can be reduced by correctly allocating ESSs. Furthermore, the simultaneous allocation of ESSs and BDCs efficiently reduce power system losses using the CONOPT solver.

BackgroundIn 2021, an estimated 38 million people were living with human immunodeficiency virus (HIV) globally, with over two-thirds living in African regions. In South Africa, ~20% of South African adults are living with HIV. Accurate estimation of the risk factors and spatial patterns of HIV risk using individual-level data from a nationally representative sample is invaluable for designing geographically targeted intervention and control programs.MethodsData were obtained from the 2016 South Africa Demographic and Health Survey (SDHS16). The study involved all men and women aged 15 years and older, who responded to questions and tested for HIV in the SDHS. Generalized additive models (GAMs) were fitted to our data with a nonparametric bivariate smooth term of spatial location parameters (X and Y coordinates). The GAMs were used to assess the spatial disparities and the potential contribution of sociodemographic, biological, and behavioral factors to the spatial patterns of HIV prevalence in South Africa.ResultsA significantly highest risk of HIV was observed in east coast, central and north-eastern regions. South African men and women who are widowed and divorced had higher odds of HIV as compared to their counterparts. Additionally, men and women who are unemployed had higher odds of HIV as compared to the employed. Surprisingly, the odds of HIV infection among men residing in rural areas were 1.60 times higher (AOR 1.60, 95% CI 1.12, 2.29) as compared to those in urban areas. But men who were circumcised had lower odds of HIV (AOR 0.73, 95% CI 0.52, 0.98), while those who had STI in the last 12 months prior to the survey had higher odds of HIV (AOR 1.76, 95% CI 1.44, 3.68).ConclusionSpatial heterogeneity in HIV risk persisted even after covariate adjustment but differed by sex, suggesting that there are plausible unobserved influencing factors contributing to HIV uneven variation. This study's findings could guide geographically targeted public health policy and effective HIV intervention in South Africa.