Search Results (40)

This paper presents a combinatorial perspective on evaluating the permanent for a rectangular matrix. It proves that the permanent can be computed using the permanents of its largest square submatrices. The proof employs a structured combinatorial method and reveals a connection to the subset-sum problem, known as the grid shading problem. Furthermore, this study uncovers an inherent symmetry in the distribution of terms, highlighting structured patterns within permanent computation. This perspective bridges combinatorial principles with matrix theory, offering new insights into their interplay. Full article

Background/Objectives: Accurate diagnosis during ultrasound examinations of patients with kidney and gallbladder stones is crucial. Although stone areas typically show posterior acoustic shadowing on ultrasound images, their accurate diagnosis can be challenging if the shaded areas are vague. This study proposes a method to improve the diagnostic accuracy of kidney and gallbladder stones through texture analysis of ultrasound images. Methods: Two doctors and three sonographers evaluated abdominal ultrasound images and categorized kidney and gallbladder stones into groups based on their predicted likelihood of being present: 50–60%, 60–80%, and ≥80%. The texture analysis method for the posterior acoustic shadows generated from ultrasound images of stones was modeled using a gray level co-occurrence matrix (GLCM). Average values and 95% confidence intervals were used to evaluate the method. Results: The three prediction classes were clearly distinguished when GLCM_Contrast was applied to the ultrasound images of patients with kidney and gallbladder stones. However, GLCM_Correlation, GLCM_Energy, and GLCM_Homogeneity were found to be difficult for analyzing the texture of shadowed areas in ultrasound images because they did not clearly or completely distinguish between the three classes. Conclusions: Accurate diagnosis of kidney and gallbladder stones may be possible using the GLCM texture analysis method applied to ultrasound images. Full article

Background/Objectives: This study tested the influence of in vitro digestion on the release of organic acids and low molecular weight saccharides of matcha. Methods: The concentrations of analytes in the raw and undigested portion of matcha were measured using HPLC with spectrometric and refractometric detection to establish their residual values after a two-step enzymatic digestion that was finally presented as a retention factor. Results: It was established that dry matter digestibility values after simulated gastric and both gastric and intestinal phases were 67.3 and 85.9%, respectively. Native matcha, citric acid (44.8 mg/g), malic acid (32.2 mg/g), trehalose (36.1 mg/g), and L-arabinose (8.20 mg/g) reached the highest values and were predominant, whereas D-fructose, xylose, maltose, and saccharose were not detected. Regarding gastric phase digestion, succinic and malic acids, trehalose and D-glucose were the worst-releasing compounds and their remaining factors reached 34, 19, 18, and 50%, respectively, whereas L-arabinose was completely released. Focusing on gastric and small intestinal digestion, the least-releasing compounds of matcha tea leaves were succinic acid and trehalose, with their retention factors at 7 and 13%, which can proceed with the leaf matrix to the large intestine. Conclusions: Malic, oxalic, and citric acids, the carbohydrates D-glucose, L-arabinose, and L-rhamnose, are almost entirely released from matcha tea during digestion in the stomach and small intestine and can be available for absorption in the small intestine. In the measurement of oxalic acid, considering that the process of shading tea leaves increases the concentration of this acid and its retention factor value is too small, it would be appropriate in the future to evaluate the recommended maximum daily intake of matcha tea for people sensitive to the formation of urinal stones. Full article

The application of the resilience concept within socioecological systems has recently received much attention. Assessing the characteristics of cocoa agroecosystems in the dry and moist semi-deciduous ecological zones has become critical for resilience analysis in this era of climate change and the constant shrinking of cocoa suitability areas. Previous studies have used one of the dimensions of resilience to analyse complex adaptive systems, excluding critical factors and variables. This study applied a multi-criteria decision-making process, the Analytic Hierarchy Process (AHP) that accommodates the three dimensions of resilience, i.e., buffer capacity, adaptive capacity and self-organisation. The AHP is a multi-criteria decision-making tool that proceeds with the design of a hierarchy system for the goal, criteria, attributes and variables. Selected cocoa farmers were assigned weights related to criteria, attributes and variables in a comparison matrix. The resilience of the cocoa agroecosystems in Offinso Municipal and Adansi North Districts was 2.75 ± 0.06 (mean ± SD) and 3.23 ± 0.10 (mean ± SD), respectively. Buffer capacity contributed the highest proportion (44.3%) in the Offinso Municipal District, followed by adaptive capacity (38.7%) and self-organisation (17%). A similar trend was recorded for the Adansi North District: buffer capacity (42.9%), adaptive capacity (42.9%) and self-organisation (14.3%). Across the two study areas, shade trees, crop diversification, soil quality, cocoa variety, farm size, farm age, alternative livelihood, annual income and co-operative membership contributed prominently to the construction of cocoa agroecosystem resilience. The assessment of agroecosystem resilience is location-specific, and the study provides a simplified methodology for evaluating resilience. The paper aims to understand the importance of the components of the cocoa agroecosystem, and a simplified methodology for evaluating its resilience to perturbations. It presents a conceptual and methodological framework for the analysis and measurement of agroecosystem resilience in a participatory manner. Full article

Background: The choice of the correct color is of paramount importance in esthetic dentistry; however, there is still no consensus on the best technique to determine it. The aim of the present study is to compare the accuracy of a recently introduced colorimeter in shade matching with human vision. In addition, possible variables affecting color-matching by human eye have been analysed. Methods: 18 disc-shaped composite samples with identical size and shape were produced from a composite flow system (Enamel plus HriHF, Micerium): Nine were considered control samples (UD 0-UD 6), and nine were test samples with identical flow composite shade to the control ones. Parallelly, 70 individuals (dental students and dental field professionals) were individually instructed to sit in a dark room illuminated with D55 light and to perform visual shade matching between control and test discs. An error matrix containing ΔE₉₄ between control and test discs was generated, containing four match-clusters depending on perceptibility and acceptability thresholds. The frequency and severity of errors were examined. Results: The colorimeter achieved a 100% perfect matching, while individuals only achieved a 78%. A higher occurrence of mismatches was noted for intermediate composite shades without a statistically significant difference. No statistically significant differences were reported for age, sex, and experience. A statistically significant difference was present among the Optishade match and the visual determination. Conclusions: The instrumental shade-matching evaluation proved to be significantly more reliable than the human visual system. Further research is needed to determine whether the same outcomes are achieved in a clinical setting directly on patients. Full article

The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) and analyzed various radiomic features and gray-level co-occurrence matrix (GLCM) features. These variables and patient clinicopathologic characteristics were compared between EC and MMMTs using the Wilcoxon Rank sum and Fisher’s exact test. The area under the curve of the receiving operating characteristics (AUC ROC) was calculated for univariate analysis in predicting EC status. Logistic regression with elastic net regularization was performed for texture feature selection. This study showed that skewness (p = 0.045) and tumor volume (p = 0.007) significantly differed between EC and MMMTs. The range of cluster shade, the angular variance of cluster shade, and the range of the sum of squares variance were significant predictors of EC status (p ≤ 0.05). The regularized Cox regression analysis identified the “256 Angular Variance of Energy” texture feature as significantly associated with OS independently of the EC/MMMT grouping (p = 0.004). The volume and texture features of the tumor region may help distinguish between EC and MMMTs and predict patient outcomes. Full article

The main aim of the present work is to evaluate and characterize the mechanical, morphological and thermal properties of wastes coming from the textile industry, mainly composed of cotton and polyester. These wastes will be thereafter implemented in commodity plastic such as polyamide, in order to develop new formulations of environmentally friendly materials. The composites were produced by extrusion and injection-molded processes in amounts between 15 wt.% and 60 wt.% of textile waste. With the objective of improving the properties of the materials, silanes were used as a compatibilizer between the textile fibers and the polymeric matrix. The effect of the compatibilizer in the composites was studied together with the effect of the amount of textile fiber added to the composites. Mechanical, thermal, morphological and wettability properties were analyzed for each composite. The results show that the use of silanes improves the interaction especially in those composites with a higher amount of textile waste, offering a balanced mechanical behavior with significantly high quantities. On the other hand, the melting temperature does not vary significantly with the introduction of silanes and textile waste content, although the incorporation of textile waste slightly reduces up to 23% the degradation temperature of the resulting composites. The wettability of the composites is also increased up to 16% with the incorporation of textile waste. Finally, the appearance of the composites with textile waste is strongly influenced by the incorporation of the reinforcement, offering shades close to dark brown in the whole range of compositions. Full article

Photovoltaic systems for pumping water, based on direct current powered motor pumps, have great application in small rural regions without electrical networks. In addition, these systems provide environmental benefits by replacing fossil fuels. However, these systems reduce their performance due to partial shading, which is magnified by the internal mismatch of the PV modules. This work proposes an intelligent, low-cost, and automatic method to mitigate these effects through the electrical reconfiguration of the PV array. Unlike other reported techniques, this method considers the pump head variations. For that, the global voltage and current supplied by the PV array to the motor pump subsystem are introduced to an artificial neural network and to a third-order equation, which locates the shaded PV module and detects the pump head, respectively. A connection control implements the optimal electrical rearrangement. The selection is based on the identified partial shading pattern and pump head. Finally, the switching matrix modifies the electrical connections between the PV modules on the PV array without changing the interconnection scheme, PV array dimension, or physical location of the PVMs. The proposed approach was implemented in a real PV water pumping system. Low-cost and commercial electronic devices were used. The experimental results show that the output power of the PV array increased by 8.43%, which maintains a more stable level of water extraction and, therefore, a constant flow level. Full article

The field of interior home design has witnessed a growing utilization of machine learning. However, the subjective nature of aesthetics poses a significant challenge due to its variability among individuals and cultures. This paper proposes an applied machine learning method to enhance manufactured custom doors in a proper and aesthetic home design environment. Since there are millions of possible custom door models based on door types, wood species, dyeing, paint, and glass types, it is impossible to foresee a home design model fitting every custom door. To generate the classification data, a home design expert has to label thousands of door/home design combinations with the different colors and shades utilized in home designs. These data train a random forest classifier in a supervised learning context. The classifier predicts a home design according to a particular custom door. This method is applied in the following context: A web page displays a choice of doors to a customer. The customer selects the desired door properties, which are sent to a server that returns an aesthetic home design model for this door. This door configuration generates a series of images through the Unity 3D engine module, which are returned to the web client. The customer finally visualizes their door in an aesthetic home design context. The results show the random forest classifier’s good performance, with an accuracy level of 86.8%, in predicting suitable home design, marking the way for future developments requiring subjective evaluations. The results are also explained using a feature importance graphic, a decision tree, a confusion matrix, and text. Full article

The purpose of this study was to evaluate the color stability of repaired aesthetic restorative resin matrix materials after immersion in coffee and the effect of polishing systems after staining. One hundred and eighty cylindrical discs (8 mm × 2 mm) were prepared using a conventional nano-fill resin composite (Clearfil Majesty Esthetic A2 shade) with round cavities (3 × 1 mm). Cavities were repaired by three resin composite materials: Clearfil Majesty Esthetic A2 shade, one-shaded nano-fill resin composite (Omnichroma) and group-shaded nano-hybrid resin composite (Optishade, medium shade). Each group was polished with three polishing systems (n = 20); aluminum oxide (Soflex Spiral Wheels, 3M ESPE), silicon carbide (Occlubrush, Kerr, CA, USA) and diamond particulate (Twist Dia Spiral Wheels, Kuraray, Okayama, Japan). Color change (ΔE₀₀) measurements were performed with a spectrophotometer at the baseline. Half of the polished samples were either kept in distilled water or immersed in coffee for 15 days, and color measurements were repeated before and after polishing. Statistical analysis was performed using the Kruskal–Wallis test. Repaired samples showed different color correspondence values in all groups. All three restorative materials showed significant color changes (ΔE₀₀) after immersion in coffee (p ≤ 0.05). Repolishing of stained samples showed color improvement values in all groups. The content of the polishing system played an important role in removing the stains. Full article

After prolonged operation, external objects may obstruct the photovoltaic (PV) array, resulting in prolonged partial shading. The dynamic reconfiguration of PV arrays uses a switch matrix to change the electrical positions of the PV cells in the array, and it is an effective method to solve the problem of partial shading. Most of the current dynamic reconfigurations only consider the optimization of power output. Neglecting the switch actions will increase the number of switch matrix actions, making the switch control more complex and reducing the lifespan of devices. To address power optimization and switch action optimization simultaneously during dynamic reconfiguration, this paper introduces a novel objective function. This function combines power optimization and switch action optimization in a weighted manner. Based on the novel function, the algorithm prioritizes optimizing the electrical positions of PV cells with larger shading values. This ensures that the PV array can improve its output while significantly reducing the number of switch actions. The Pelican Optimization Algorithm (POA) is improved and employed to optimize the proposed objective function. In terms of the output power optimization, the effectiveness of the novel objective function with the improved POA is validated by comparing and analyzing the reconfiguration results with the conventional objective functions under four shading scenarios. The results demonstrate that the novel objective function with the improved POA increases the output power by 30% in short and wide shadow and achieves the highest power output. Moreover, the tests conducted on dynamic reconfiguration results with different weights validate the effectiveness of the novel objective function in minimizing switching actions while improving power output. Full article

Remote sensing approaches are often used to monitor land cover change. However, the small physical size (about 1–2 hectare area) of smallholder orchards and the cultivation of cocoa (Theobroma cocoa L.) under shade trees make the use of many popular satellite sensors inefficient to distinguish cocoa orchards from forest areas. Nevertheless, high-resolution satellite imagery combined with novel signal extraction methods facilitates the differentiation of coconut palms (Cocos nucifera L.) from forests. Cocoa grows well under established coconut shade, and underplanting provides a viable opportunity to intensify production and meet demand and government targets. In this study, we combined grey-level co-occurrence matrix (GLCM) textural features and vegetation indices from Sentinel datasets to evaluate the sustainability of cocoa expansion given land suitability for agriculture and soil capability classes. Additionally, it sheds light on underexploited areas with agricultural potential. The mapping of areas where cocoa smallholder orchards already exist or can be grown involved three main components. Firstly, the use of the fine-resolution C-band synthetic aperture radar and multispectral instruments from Sentinel-1 and Sentinel-2 satellites, respectively. Secondly, the processing of imagery (Sentinel-1 and Sentinel-2) for feature extraction using 22 variables. Lastly, fitting a random forest (RF) model to detect and distinguish potential cocoa orchards from non-cocoa areas. The RF classification scheme differentiated cocoa (for consistency, the coconut–cocoa areas in this manuscript will be referred to as cocoa regions or orchards) and non-cocoa regions with 97 percent overall accuracy and over 90 percent producer’s and user’s accuracies for the cocoa regions when trained on a combination of spectral indices and GLCM textural feature sets. The top five variables that contributed the most to the model were the red band (B4), red edge curve index (RECI), blue band (B2), near-infrared (NIR) entropy, and enhanced vegetation index (EVI), indicating the importance of vegetation indices and entropy values. By comparing the classified map created in this study with the soil and land capability legacy information of Bougainville, we observed that potential cocoa regions are already rated as highly suitable. This implies that cocoa expansion has reached one of many intersecting limits, including land suitability, political, social, economic, educational, health, labour, and infrastructure. Understanding how these interactions limit cocoa productivity at present will inform further sustainable growth. The tool provides inexpensive and rapid monitoring of land use, suitable for a sustainable planning framework that supports responsible agricultural land use management. The study developed a heuristic tool for monitoring land cover changes for cocoa production, informing sustainable development that balances the needs and aspirations of the government and farming communities with the protection of the environment. Full article

The structure of phytobenthic diatom communities was studied to reveal differences between natural and channelized river sections in Slovenia. As part of the national monitoring of surface waters, samples of phytobenthos were collected at 85 sites throughout the country according to standard protocols. At the same time, basic environmental parameters were also assessed. Trophic (TI) and saprobic (SI) indices were calculated based on diatoms and other algae, while diversity indices and gradient analyses were performed only for the diatom community. The results showed that channelized rivers harbor significantly more diverse benthic diatom communities than natural sections, mainly due to the significantly higher number of motile diatom taxa that are able to take advantage of more nutrient-rich and less-shaded river sections because of their high adaptability. Selected environmental parameters explained 34% of the variability in diatom community structure when taxa were classified into ecological types. The removal of Achnanthidium minutissimum yielded clearer results (24.1%) than the total species matrix (22.6%). Therefore, we suggest excluding this taxon from calculations of TI, SI, or other indices when it is determined as A. minutissimum complex, because A. minutissimum complex was most abundant in both types of reaches in our study and has a wide ecological amplitude, which reduces the indicative power of the diatom community in the evaluation of environmental conditions and ecological status. Full article

Building-integrated photovoltaics (BIPVs) and vehicle-integrated photovoltaics (VIPVs) receive solar irradiance through non-uniform shading objects. Standard scalar calculations cannot accurately determine the solar irradiance of BIPV and VIPV systems. This study proposes a matrix model using an aperture matrix to accurately calculate the horizontal and vertical planes affected by non-uniform shading objects. This can be extended to the solar irradiance on a VIPV by applying a local coordinate system. The 3D model is validated by a simultaneous measurement of five orientations (roof and four sides, front, left, tail, and right) of solar irradiance on a car body. An accumulated logistic function can approximate the shading probability. Furthermore, the combined use of the 3D solar irradiance model is effective in assessing the energy performance of solar electric vehicles in various zones, including buildings, residential areas, and open spaces. Unlike standard solar energy systems, the energy yield of a VIPV is affected by the shading environment. This, in turn, is affected mainly by the location of vehicle travel or parking in the city rather than by the climate zones of the city. Full article

Urban green space takes a dominant role in alleviating the urban heat island (UHI) effect. Most investigations into the effects of cooling factors from urban green spaces on the UHI have evaluated the correlation between each factor and land surface temperature (LST) separately, and the contribution weights of various typical cooling factors in mitigating the thermal environment have rarely been analyzed. For this research, three periods of Landsat 8 data captured between 2014 and 2018 of Xuzhou during the summer and autumn seasons were selected along with corresponding meteorological and flux measurements. The mono-window method was employed to retrieve LST. Based on the characteristics of the vegetation and spatial features of the green space, eight factors related to green space were selected and computed, consisting of three indices that measure vegetation and five metrics that evaluate landscape patterns: vegetation density (VD), evapotranspiration (ET), green space shading degree (GSSD), patch area ratio (PLAND), largest patch index (LPI), patch natural connectivity (COHESION), patch aggregation (AI), and patch mean shape index distribution (SHPAE_MN). Linear regression and bivariate spatial autocorrelation analyses between each green space factor and LST showed that there were significant negative linear and spatial correlations between all factors and LST, which proved that the eight factors were all cooling factors. In addition, LST was strongly correlated with all factors (|r| > 0.5) except for SHPAE_MN, which was moderately correlated (0.3 < |r| < 0.5). Based on this, two principal components were extracted by applying principal component analysis with all standardized green space factors as the original variables. To determine the contribution weight of each green space factor in mitigating the urban heat island (UHI) effect, we multiplied the influence coefficient matrix of the initial variables with the standardized multiple linear regression coefficients between the two principal component variables and LST. The final results indicated that the vegetation indices of green space contribute more to the alleviation of the UHI than its landscape pattern metrics, and the contribution weights are ranked as VD ≥ ET > GSSD > PLAND ≈ LPI > COHESION > AI > SHAPE_MN. Our study suggests that increasing vegetation density is preferred in urban planning to mitigate urban thermal environment, and increasing broadleaf forests with high evapotranspiration and shade levels in urban greening is also an effective way to reduce ambient temperature. For urban green space planning, a priority is to multiply the regional green space proportion or the area of largest patches. Second, improving the connectivity or aggregation among patches of green space can enhance their ability to cool the surrounding environment. Altering the green space spatial shape is likely the least significant factor to consider. Full article