Total Ranking Research Articles

Burns Across Seasons: Seasonal Effect on the Mechanism of Burn Injuries in Pediatric Patients Requiring Hospitalization

Background: Burn injuries are a major cause of trauma in pediatric patients, comprising 17–25% of total burn admissions and ranking among the five most deadly childhood injuries. Seasonal variations significantly influence burn mechanisms due to changes in environmental conditions and activities. Scald injuries peak during colder months with increased use of hot water and heating appliances, while warmer months see more contact and electrical burns from outdoor activities and power surges. Despite the high burden in developing nations, limited research explores these seasonal patterns. Objectives: This study aimed to investigate the seasonal relationship with burn injury mechanisms in pediatric patients under 13 years of age. Specifically, it sought to identify seasonal variations in burn types, quantify hospitalizations across seasons, and assess associated hospital stay durations. Methods: A prospective analytical cross-sectional study was conducted at Mayo Hospital Lahore/KEMU from December 2023 to August

Assessing the effects of water stress and bio-organic fertilizers on English and French Lavandula species in different locations

A study was conducted utilizing a factorial split-plot design within complete randomized blocks to evaluate the effects of water stress (at 95±5, 75±5, 50±5, and 25±5% of the field capacity (FC)) and various fertilizer sources (Azotobacter-Psedomonas Bacterial (APB), arbuscular mycorrhizal fungi (AMFs), and biochar (BI), applied individually and in combination, along with a control treatment on the two species of lavender (French and English) in two distinct climates (Fars and Hamedan) in Iran. The results revealed that severe water deficit stress reduced the growth, yield, and essential oil content in both lavender species. APB+AMFs+BI and APB+BI led to the highest flower yield for French lavender in Fars and Hamedan while subjected to well-watered conditions. The essential oil yield significantly decreased in both experimental regions under 25% FC. Employing APB+BI under 75 and 50% FC treatments led to the highest essential oil content (4.23 and 1.56%, respectively) in English lavender. Linalool showed the highest average (22.46 to 30.06%) in both species. French and English lavender in Fars showed significant differences in fatty acid compositions, but no such differences were reported in Hamedan. APB+BI emerged as the most effective fertilizer combination in terms of total fatty acids and rank index. Simultaneous use of bio-organic fertilizers yielded appropriate results under water-deficient conditions. Finally, the study underscored the strong correlation between climatic parameters such as rainfall and temperature, as well as the efficacy of different fertilizer sources, emphasizing that climatic condition and plant species can significantly impact the effectiveness of fertilizer use.

On general fiber product rings, Poincaré series and their structure

The present paper deals with the investigation of the structure of general fiber product rings R × T S , where R, S and T are local rings with common residue field. We show that the Poincaré series of any R-module over the fiber product ring R × T S is bounded by a rational function. In addition, we give a description of depth ( R × T S ) , which is an open problem in this theory. As a biproduct, using the characterization of the Betti numbers over R × T S obtained, we provide certain cases of the Cohen-Macaulayness of R × T S and, in particular, we show that R × T S is always non-regular. Some positive answers for the Buchsbaum-Eisenbud-Horrocks and Total rank conjectures over R × T S are also established.

Effect of Different Fertilization Strategies on Infestation of Brown Wheat Mite and Wheat Productivity

The brown wheat mite, Petrobia tritici, poses a significant threat to wheat fields. While fertilizers can increase crop productivity, imbalanced application may exacerbate plant susceptibility to pests. This study aimed to evaluate the impact of various NPK fertilization programs on P. tritici infestations over two consecutive cropping seasons. The results revealed significant differences in mite infestation among the treatment groups (p < 0.001). The lowest populations (1.1 and 3.0 mites/leaf) were observed in the treatments sprayed with phosphoric acid (at 0.75 and 1.00 cm/L), where the infestation appeared approximately 120 days after sowing; in contrast, it appeared early at 75 days in the other treatments. Conversely, treatments lacking potassium fertilizer presented the greatest degree of mite injury levels (49.5–57.7 mites/leaf). Although these treatments provided moderate leaf nutrition and crop yield, the highest nutritional content and total yield (10.49 and 9.71 1 t/ha for the two years) were observed in the treatment that received 224:70:100 kg fad−1 commercial fertilizers (=178:25:114 kg ha−1 NPK units) as soil fertilization, which was followed by the treatment with a foliar application of phosphoric acid (1.00 cm/L) with a total yield of 9.34 and 8.53 1 t/ha for the two years. In this treatment, the P. tritici density was moderately high at 9.40 and 6.32 mites/leaf over the two years, respectively. The consistency of P. tritici density and total yield ranking across both years indicated reliable estimates of the impact of fertilization. This study suggests that potassium sulfate application is crucial for reducing P. tritici density and that foliar phosphoric acid application instead of soil application reduces the number of P. tritici and delays its occurrence.

Gradation optimization of AC-20 asphalt mixture based on the fuzzy analytic hierarchy process and comprehensive evaluation method

The aggregate gradation of asphalt mixture is one of the most important factors affecting the service life of asphalt pavement. In order to study the gradation of asphalt mixture with the best comprehensive performance, this study puts forward a new method of mineral aggregate gradation optimization based on the fuzzy analytic hierarchy process and comprehensive evaluation method, aiming at the multi-level and multi-index evaluation of the road performance of asphalt mixture. First, the orthogonal test design method is applied to design nine target gradations with coarse aggregate (>4.75 mm) and fine aggregate (<4.75 mm) of AC-20 (asphalt concrete) mixture serving as two factors and the upper, middle, and lower positions of the gradation curve as three levels, and then the road performance test research is carried out. Second, a comprehensive model for evaluation of road performance of mineral aggregate gradation is established. A fuzzy complementary judgment matrix is constructed, and the index weights of each level and the hierarchical total ranking weight are calculated. Then, the membership function is introduced into the comprehensive evaluation model for the road performance of mineral aggregate gradation, and the membership values of each index of asphalt mixture road performance are obtained. Finally, the fuzzy comprehensive evaluation method is used to find out the comprehensive evaluation value of the road performance of the nine graded asphalt mixtures, and the mineral aggregate gradation is optimized. The research results show that the 1# gradation of the asphalt mixture has the highest comprehensive road performance evaluation value, and the combination of the fuzzy hierarchical analysis process and comprehensive evaluation method can more objectively and comprehensively evaluate the comprehensive road performance of the asphalt mixture and provide a useful reference for the optimal selection of asphalt mixture mineral gradation.

The matroid of a graphing

Graphings serve as limit objects for bounded-degree graphs. We define the “cycle matroid” of a graphing as a submodular setfunction, with values in [0,1], which generalizes (up to normalization) the cycle matroid of finite graphs. We prove that for a Benjamini–Schramm convergent sequence of graphs, the total rank, normalized by the number of nodes, converges to the total rank of the limit graphing.

Assessment of adaptive properties of winter triticale cultivars by yield in the Middle Urals

Triticale is a versatile crop with high yield potential but insufficient resistance to unfavourable growing conditions. Therefore, the combination of high yield, adaptability, plasticity, stability and stress resistance of varieties is of great importance. The purpose of the research is to assess the adaptive potential of winter triticale cultivars according to the "yield" trait in agroecological conditions of the Middle Urals. Field studies were conducted in the Udmurt Republic in the years with contrasting weather conditions (2021–2023), the index of environmental conditions varied from -128 to 152. Forty-five cultivars of various ecological and geographical origin were studied. The analysis of the adaptive potential of the cultivars was carried out using various statistical methods. As a result, the most adaptive cultivars for the agroecological conditions of the Middle Urals were identified. The cultivars ‘Beta’, ‘Tribune’, ‘Topaz’, ‘Linda’ (Russia), ‘Mayak’, ‘Ales’ (Belarus), ‘Torchinsky’ (Ukraine) were char- acterized by high responsiveness to improving environmental conditions (bi = 1.82 2.01). The cultivars ‘Giray’, ‘Bereket’, ‘Ataman Platov’ (Russia), ‘Amulet’ (Belarus) showed relatively high stress resistance. The cultivars ‘Beta’, ‘Zimogor’, AD 1405, ‘Topaz’, ‘Kornet’ (Russia) were distinguished by a high compensatory ability. The cultivars ‘Bereket’ and ‘Ataman Platov’ had the lowest yield variability and at the same time high homeostaticity. When ranking the tested cultivars according to the listed indicators, the top ten most stable and plastic cultivars were ‘Zimogor’, ‘Kornet’, ‘Ataman Platov’, ‘Bereket’, ‘Nemchinovsky 56’, ‘Izhevskaya 2’, ‘Imprint’, ‘Bard’ (Russia), ‘Impulse’ (Belarus), ‘Chernobrivets’ (Ukraine), with a total ranking score from 55 to 95 points. The listed cultivars can be classified as the most valuable. In the agroecological conditions of the Middle Urals, it is advisable to cultivate varieties that combine high yields and adaptability – ‘Zimogor’, ‘Kornet’, ‘Izhevskaya 2’, and according to intensive technology – ‘Beta’ cultivar. The cultivars ‘Ataman Platov’, ‘Chernobrivets’, ‘Bereket’, ‘Bard’, ‘Impulse’, ‘Nemchinovsky 56’ also have practical value as a source material in the breeding of winter triticale to increase productivity and adaptability. A comparison of different methods for determining the adaptability of cultivars to specific environmental conditions showed the practical equivalence of the indicators of yield range, yield variation coefficient and homeostaticity.

Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage

Artificial intelligence (AI) requires complex neurocomputing algorithms (NCAs) for robustness in predictive modeling. Since 1990, a plethora of NCAs have evolved, challenging selection of the most intelligent and error-tolerant model for predicting fuel consumption rate (ØFuel) in tillage. This study adopted 12 recent NCAs in modeling 72 neurocomputing architectures and evaluated their metacognitive robustness in predicting ØFuel using 80 tillage datasets. These included Levenberg-Marquardt (trainlm), Quasi-newton (trainbfg), Powell-Beale conjugate gradient (traincgb), Scaled conjugate gradient (trainscg), Fletcher-reeves conjugate gradient (traincgf), Polak-ribiére conjugate gradient (traincgp), Onestep secant (trainoss), Bayesian regularization (trainbr), Resilient backpropagation (trainrp), Gradient descent (traingd), Learning rate gradient descent (traingdx) and Gradient descent momentum (traingdm). Tillage variables were sequentially subjected to NCA architectures learning on logsig, Purelin, and tansig neuro-activation functions, targeting 1000 epochs. Neuro-cognitive performance was evaluated through broad multi-criteria of regressed neuron input–output functions using heuristic and accuracy metrics, i.e., training time (t), number of hidden layers (NL), and neurons (Nn), optimal epoch (Eopt), Coefficient of correlation (R) and determination (R2), Mean square error (MSE), Root mean square error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE), Sum square error (SSE), Prediction scatter (Tscatter), Coefficient of variation (CV) and Prediction accuracy (PA). Prediction reliability and robustness were evaluated using a20-index (a20), Willmott’s index of agreement (IOA), Index of scatter (IOS), Variance accounted for (VAF), Performance index (PI), Total score rank (ξtotal), Sensitivity index (SAij) and Anderson-Darling (AD) test. Results indicated that varying NL and Nn did not correspond with t, Eopt and PA but only occurred in single-layered trainbr and double-layered trainlm. Although PA increased at reduced convergence error limits, relationships among Eopt, t, and PA were unclear. Single-layered trainbr (14-20-1) outperformed all NCAs with R (0.9993), R2 (0.9986), MSE (6.34e-06), RMSE (0.0025), MAE (0.003), MAPE (1.4e-06), SSE (8e-04), R2All (0.9998) Tvalue (1.0), CV (3.232 %) and PA (99.81 %). However, trainlm (14-20-10-1) most accurately modeled double-layered architectures i.e. R (0.9932), R2 (0.9864), MSE (7.4e-05), RMSE (0.009), MAE (0.004), MAPE (0.003), SSE (0.003), R2All (0.9929), Tvalue (0.9998), CV (3.212 %) and PA (97.8 %). Nonetheless, trainbr (14-20-1) achieved the best reliability metrics of a20 (100 %), VAF (99.98 %), IOA (1.00), PI (1.9971), IOS (0.0115), ξtotal (284) and AD-test (2.6852). Sensitivity analysis revealed that tractive force had the most significant influence on ØFuel while tire inflation pressure had the least. Learning on transig neuro-activation function in single-layered neural network, metacognitive robustness of trainbr is superiorly merited for predicting ØFuel during tillage.

Representativeness of U.S. News & World Report Outcome and Volume Measures for Otolaryngology.

Although outcomes account for 45% of the total ranking score in otolaryngology in the 2023-2024 U.S. News Best Hospitals rankings, little attention has been paid to the representativeness of their outcomes or volume analyses. Through retrospective review of finance data from an academic otolaryngology department, we found the overall 2023-2024 USNWR volume estimate accounted for only 10.0% (n = 2, usw 024/20,334) of all adult admissions and outpatient procedures and did not adequately represent the overall case mix or caseload.

The Impact of Conclusion-Checking Technique on Altering Catastrophic Thinking among Those about to Divorce

The current research aimed to investigate the impact of the inference checking technique on altering catastrophic thinking among those about to divorce. To achieve the research objective, the researchers constructed the Catastrophic Thinking Scale based on Beck's Cognitive Therapy theory. After verifying the psychometric properties of the scale, it was administered to a research sample consisting of 200 males seeking divorce, distributed across the Personal Status Courts in Basra province. They were selected randomly. Ten individuals who scored highest on the catastrophic thinking scale were selected and divided into two groups randomly: one control and one experimental. Equivalence between the two groups was ensured. Both groups underwent pre-testing, after which the experimental group was exposed to a counseling program based on the inference-checking approach, while the control group did not undergo any activities or programs. After completing the counseling program, the experimental group underwent a post-test. The researchers arrived at the following results: 1- There were statistically significant differences between the pre-test and post-test results for the experimental group. Therefore, the researchers reject the null hypothesis and accept the alternative hypothesis, which states that there are statistically significant differences between the experimental group's performance on the pre-test and post-test.2-There were statistically significant differences between the dimensional measurement results of the experimental and control groups, indicating significant statistical differences between the two groups. When comparing the total ranks of the two groups, the differences favored the experimental group. The total rank sum for the first experimental group was (15.00), smaller than the total rank sum for the control group, which was (40.00). In light of the researchers' findings, they concluded with a set of recommendations and proposals.

Greenhouse gas emissions of environmentally sustainable diets: Insights from the Icelandic National Dietary Survey 2019–2021

BackgroundHealth authorities are increasingly integrating environmental sustainability considerations into food-based dietary guidelines. However, concerns persist about the accuracy of the data used to assess environmental impacts, as well as the extent to which these guidelines are followed in practice. AimTo compare dietary greenhouse gas (GHG) emissions estimates using different top-down and bottom-up life cycle assessment (LCA) databases; and to estimate GHG emissions of food consumption within the ranges set for meat and dairy in recently proposed environmentally sustainable diets. MethodsDietary GHG emissions were estimated for participants in the 2019–2021 Icelandic National Dietary Survey (n = 822) using three publicly available LCA databases from Denmark, the US, and France. GHG emissions among participants whose consumption was aligned with the EAT-Lancet diet, the 2021 Danish food-based dietary guidelines and the 2023 Nordic Nutrition Recommendations were also quantified. ResultsThe mean dietary GHG emissions among participants were 6.3, 6.1, and 6.1 kg CO2-eq/day based on the Danish (top-down), US (bottom-up), and French (bottom-up) databases, respectively. The relative ranking of foods was also consistent across all three databases. For example, the relative contribution of total CO2-eq (% range for the three databases) was highest for red meat (39–51%), followed by dairy (10–17%) and beverages (9–13%). The contribution from plant-based foods (6–10%), seafood (4–11%), and poultry/eggs (<5%) was modest. The dietary habits of most participants (86%) were outside the ranges for meat and dairy consumption as set by the three sustainable diets. However, participants reporting consumption within the ranges for meat and dairy had mean GHG emissions ranging between 4.2 and 4.7 kg CO2-eq/day, depending on the diet. In comparison, the mean for participants not adhering to the sustainable diets was 7.7 kg CO2-eq/day. These results are higher than those reported in other Nordic and European studies, likely due to high consumption of lamb, beef, and dairy, and low consumption of plant-based food. ConclusionAll three LCA databases provided similar estimates for total dietary GHG emissions and relative ranking of different food groups. Based on current dietary habits in Iceland, adherence to environmentally sustainable diets would lead to a substantial reduction in dietary GHG emissions.

Risk Factors for Periprosthetic Femoral Fractures After Cementless Total Hip Arthroplasty

BackgroundThe present study aimed to identify the risk factors of periprosthetic femoral fracture (PFF) after cementless total hip arthroplasty and rank them based on importance. MethodsThe age, sex, body mass index (BMI), osteoporosis, canal flare index (CFI), canal bone ratio (CBR), canal calcar ratio (CCR), stem design, and stem canal fill ratio (P1, P2, P3, and P4) of the proximal femoral medullary cavity of 111 total hip arthroplasty patients who had PFF and 388 who did not have PFF were analyzed. Independent-samples student t-tests were used for continuous variables, and Chi-square tests were used for categorical variables. The importance rankings of influencing factors were assessed using a random forest algorithm. Dimensionally reduced variables were then incorporated into a binary logistic regression model to determine the PFF-related risk factors. ResultsThe mean age, BMI, CBR, CCR, and incidence of osteoporosis were higher in cases of PFF (all P < .001), while the mean CFI, P1, P2, P3, and P4 were lower in cases of PFF (P < .001, P = .033, P = .008, P < .001, and P < .001, respectively). Additionally, the stem design was also statistically associated with PFF (P < .001). Multivariate logistic regression revealed that advanced age, higher BMI, osteoporosis, stem design, lower CFI, higher CBR, higher CCR, lower P1, lower P2, lower P3, and lower P4 were the risk factors of PFF (P < .001, P < .001, P < .001, P < .001, P < .001, P = .010, P < .001, P = .002, P < .001, P < .001, and P = .007, respectively). The ranked importance of the risk factors for PFF was P3, CFI, osteoporosis, CBR, age, P4, P1, stem design, CCR, BMI, and P2. ConclusionsLower P3, lower CFI, osteoporosis, higher CBR, advanced age, lower P4, lower P1, stem design, higher CCR, higher BMI, and lower P2 increased the risk of PFF.

Mapping and Prioritizing Flash-Flood Susceptible Watersheds in the Warana Basin, India: A Morphometric Analysis and Total Ranking Approach for Resilience Planning

Mapping and Prioritizing Flash-Flood Susceptible Watersheds in the Warana Basin, India: A Morphometric Analysis and Total Ranking Approach for Resilience Planning

A Novel Slacks-Based Interval DEA Model and Application

This paper proposes a novel slacks-based interval DEA approach that computes interval targets, slacks, and crisp inefficiency scores. It uses interval arithmetic and requires solving a mixed-integer linear program. The corresponding super-efficiency formulation to discriminate among the efficient units is also presented. We also provide a case study of its application to sustainable tourism in the Mediterranean region, assessing the sustainable tourism efficiency of twelve Mediterranean regions to validate the proposed approach. The inputs and outputs cover the three sustainability dimensions and include GHG emissions as an undesirable output. Three regions were found to be inefficient, and the corresponding inputs and output improvements were computed. A total rank of the regions was also obtained using the super-efficiency model.

A total distance ranking approach to fuzzy AHP-based MCDM method for selecting sustainable manufacturing facility location

Selecting a sustainable facility location is a crucial strategy for manufacturing companies to achieve long-term success in today’s competitive environment. Various quantitative and qualitative criteria with different importance in a multiple level structure must be considered and aggregated to assist the company in decision-making. How to determine these criteria weights and select the sustainable manufacturing facility locations have become research questions. To resolve this problem, this paper proposes a total distance ranking approach to fuzzy analytic hierarchical process (AHP)-based multiple criteria decision-making (MCDM) method. Currently, the membership functions of fuzzy weighted ratings in the MCDM with a multilevel structure cannot be developed. A ranking method is needed to defuzzify those fuzzy numbers used for fuzzy AHP and qualitative criteria in the MCDM for better executing decision-making procedure. The total distance ranking method related to the centroid on x-axis, centroid on y-axis and the distances of centroids point to the two tangent lines of a fuzzy number are considered in the proposed ranking approach. Formulas of the proposed approach will be presented, and some properties will be investigated to derive formulas for trapezoidal and triangular fuzzy numbers. A comparison with relevant ranking methods will be made to show the advantages of the proposed ranking approach. The proposed ranking approach is then applied to defuzzify the fuzzy numbers used in fuzzy AHP and linguistic values under qualitative criteria to obtain the criteria weights under multi-level structure and crisp values under qualitative criteria, respectively. The final scores of alternatives can be obtained by aggregating crisp criteria values and their corresponding weights by simple additive weighting method to obtain the ranking result. A numerical example will be conducted to show the effectiveness of the proposed model. Finally, a comparison with Best-Worst method (BWM) will be presented to show the persuasiveness of the proposed method.

Tunnel boring machine performance assessment and prediction applying hybrid artificial intelligence

A machine for tunnel boring machine (TBM) is recognized as productive equipment for tunnel construction. A dependable and precise tunnel boring machine’s performance (such as penetration rate (ROP)) prediction could reduce the cost and help choose the suitable construction method. Hence, this research develops new integrated artificial intelligence methods, i.e., biogeography-based multilayer perceptron neural network (BMLP) and biogeography-based support vector regression (BSVR), to forecast TBMPR. Using the biogeography-based optimization (BBO) algorithm aims to improve the developed model’s performance by determining the optimized neuron number of hidden layers for MLP models and the ideal values of the essential variables of SVR method. The results show that advanced methods can productively make a nonlinear relation among the ROP and its forecasters to obtain a satisfying forecast. Amongst the BMLP models with several hidden substrates, BM5L with five hidden substrates could attain the total ranking score (TRS) greatest rate, with root mean squared error (RMSE) and coefficient of determination (R2) equal to 0.017 and 0.9969. Simultaneously, the BSVR was the supreme model because of the fewer RMSE (0.00497 m/hr) and a larger R2 (0.999) compared with BMLP models. Overall, the acquired TRSs show that the BSVR outperforms the BMLP in terms of performance. As a consequence, the BSVR model may have been chosen as the suggested model if it had been able to accurately forecast the observed value even better than BM5L.

Exploration of Fine Management Mode of College Logistics Based on Digital Twin Technology

Abstract Colleges and universities are important institutions for talent cultivation, scientific and technological innovation, social service, and cultural inheritance. College logistics management is an indispensable part of the daily operation of colleges and universities, and the high and low level of college and university logistics management directly affects the strength of the overall strength of colleges and universities. This paper puts forward the optimization strategy of college logistics management from three aspects: leadership level, personnel quality, and work system. It adopts digital twin technology to realize the construction of fine management mode and, in order to effectively assess the efficiency of college logistics management, explores the level of college logistics management by constructing the FAHP model. Finally, using M College as the research object, the refined management model of college logistics based on digital twin technology is analyzed. Management system and execution have a weight value of 0.3762 among the first-level indicators, which is higher than the other two factors. The hierarchical total ranking is used to identify the factors that have a greater impact on college and university logistics efficiency in the second-level indicators. The comprehensive score of logistics management efficiency for college M is 92, which belongs to the range of excellent grades. Therefore, for the level of fine management of college logistics, the management system and execution is the key, which can be realized by strictly charging according to the regulations, sound rules and regulations and regular training of relevant managers.

A new approach for solving global optimization and engineering problems based on modified sea horse optimizer

Abstract Sea horse optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named modified sea horse optimizer (mSHO). The enhancement primarily focuses on bolstering SHO’s exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm’s search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon’s rank-sum and Friedman’s tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and efficiency of the mSHO method as an exemplary tool for tackling an array of optimization quandaries. The results show that the proposed mSHO algorithm has a total rank of 1 for CEC2020 test functions. In contrast, the mSHO achieved the best value for the engineering problems, recording a value of 0.012 665, 2993.634, 0.01 266, 1.724 967, 263.8915, 0.032 255, 58 507.14, 1.339 956, and 0.23 524 for the pressure vessel design, speed reducer design, tension/compression spring, welded beam design, three-bar truss engineering design, industrial refrigeration system, multi-product batch plant, cantilever beam problem, and multiple disc clutch brake problems, respectively. Source codes of mSHO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/135882-improved-sea-horse-algorithm.

ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES

Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like data mining. Therefore, this study aimed to evaluate three types of support vector regression (linear, SVR, libSVR) in predicting the air pollutant concentration and identify the best model. This study also would like to calculate the API by using the proposed model. The secondary daily data is used in this study from year 2002 to 2020 from the Department of Environment (DoE) Malaysia which located at Petaling Jaya monitoring station. There are six major pollutants that have been focusing in this work like PM10, PM2.5, SO2, NO2, CO, and O3. The root means square error (RMSE), mean absolute error (MAE) and relative error (RE) were used to evaluate the performance of the regression models. Experimental results showed that the best model is linear SVR with average of RMSE = 5.548, MAE = 3.490, and RE = 27.98% because had the lowest total rank value of RMSE, MAE, and RE for five air pollutants (PM10, PM2.5, SO2, CO, O3) in this study. Unlikely for NO2, the best model is support vector regression (SVR) with RMSE = 0.007, MAE = 0.006, and RE = 20.75% in predicting the air pollutant concentration. This work also illustrates that combining data mining with air pollutants prediction is an efficient and convenient way to solve some related environment problems. The best model has the potential to be applied as an early warning system to inform local authorities about the air quality and can reliably predict the daily air pollution events over three consecutive days. Besides, good air quality plays a significant role in supporting biodiversity and maintaning healthy ecosystems.

Assessment of soybean raw material in regard to plant adaptability in the climatic conditions of the Middle Volga Region

The ecological flexibility, stability, and adaptability of the “seed weight per plant” trait of soybean raw materialwere assessed in the forest-steppe conditions of the Middle Volga Region. The most valuable varieties for use in the breeding process for this trait were selected. The experimental work was carried out in 2019—2021. In total, 14 soybean varieties of various ecological and geographical origins were assessed. The regionalized agricultural variety of local selection, Yuzhanka, was set as the standard. The soil at the experimental plot was represented by typical medium-humus, medium-thick, medium-clay chernozem. The humus content was 5.8—6.9%, the content of mobile forms of phosphorus, 133.6—156.5 mg/kg, exchangeable potassium, 154.0—180.0 mg/kg. In 2019—2021, in the forest-steppe of the Middle Volga Region, average individual productivity of soybean plants was 7.47—12.17 g. The highest individual seed productivity was registered in the varieties 680-11 (Ukraine), 422 (Kazakhstan), OX 299 (Canada), L-60/2018 and Nika (Russia), exceeding the standard by 4.97—5.43 g. The lowest total rank was found for varieties 422 (Kazakhstan) – 23, Nika (Russia) – 28, Gessener (Yugoslavia) – 28, L-59/2018 (Russia) – 29 , OX 299 (Canada) – 31, and 680-11 (Ukraine) – 34, while the standard variety refers to 72. These varieties are promising raw material for developing soybean varieties with high productivity potential due to the effective use of the soil and climatic conditions of the region and resistance to dominant environmental stress factors.