Combination Scheme Research Articles

COMBINED SOLAR-ELECTRIC HEAT SUPPLY SYSTEMS

The article examines combined solar-electric heating systems that integrate renewable and traditional energy sources to provide heating and hot water. An alternative to existing light-absorbing coatings in solar collectors based on low-vacuum aluminum condensates is proposed. Coatings manufactured using vacuum technologies are recommended, when a combined cluster and molecular flow of matter is deposited on the substrate due to a forced increase in pressure. Choosing aluminum as a material for low-vacuum coating adds advantages to this technology, because aluminum has a fairly low melting point, and the controlled formation of oxide phases on the surface of the deposited film significantly increases the corrosion resistance of the coating. The designs of solar collectors, their principle of operation and the advantages of using selective light-absorbing coatings, which can increase the efficiency of collectors by 18–22 %. An innovative infrared heating system is proposed, which ensures increased heat transfer efficiency, reduced energy consumption, and stable operation even at a low level of insolation. the task of the proposed installation is to use energy carriers more efficiently.This becomes possible due to a more rational combination of traditional energy (supply from the network) and the use of secondary energy resources; active turbulation of the heat carrier mass supplied by the circulation pump into the internal volume of the annular chamber; regulation of the heating temperature; equalization of temperature field gradients in the inner volume of the ring chamber; possibilities of regulating the productivity of the heating installation, increasing technological reliability; unification of heating elements and modes; the possibility of power supply from the network and non-traditional sources. The use of heat accumulators and multilayer systems, which allow to increase the duration of hot water selection, have been analyzed. The possibilities of combined schemes with additional energy sources, such as electric boilers or biofuel boilers, which reduce dependence on fossil energy resources, are considered. These solutions contribute to increasing energy efficiency and sustainable development of the energy industry. The conclusions emphasize the potential of integrating such systems into modern energy networks, ensuring environmental friendliness, energy independence and reducing operating costs.

Consumer preference study for the interface design of traditional Chinese medicine applications using conjoint analysis method

The widespread use of mobile applications (apps) offers a new platform for sustaining traditional culture, yet insufficient focus on interface design has hindered user experience. This paper focuses on traditional Chinese medicine (TCM) apps, examining user preferences for interface design elements and their combinations across four dimensions: visual effects, functional attributes, layout, and interaction modes. Utilizing Conjoint Analysis Method (CAM), this study quantitatively explores user preferences for the combination schemes of 18 orthogonal designs. The study collected 464 users’ data through questionnaires and processed the data using CAM and Multivariate Analysis of Variance (MANOVA). The findings indicate that users have a high demand for theme colors (35.09%), text styles (27.32%), and icon styles (14.78%) in medical consultation apps. For health education apps, users exhibit preferences for text styles (24.95%), theme colors (24.58%), and functional architecture (16.30%). In professional learning apps, the preferred elements are theme colors (24.07%), text styles (18.54%), and icon styles (18.03%). Based on these results, the paper proposes corresponding design strategies for each type of TCM app. This study reveals the characteristics of user preferences for different categories of TCM apps, enriching the theories and methods of interaction design for similar mobile applications. Ultimately, this facilitates the integration of traditional Chinese medicine culture with modern digital technology, promoting the digital and sustainable transmission of traditional culture.

Unveiling the Potential of Waste Materials: Investigating the Novel Approach of Using Scrap Fabric-Inner Tubes Composites for Sound Absorption

Scrap fabric and used inner tubes, typically considered waste with no economic value and potential environmental issues, are being repurposed as an alternative composite material. This study uses the compression molding method to analyze the use of polyester fabric scrap and inner tubes as soundproofing composites. The process variables include temperature, pressure, and the composition of the used tire fabric rubber. The temperature variables used are 140, 150, and 160°C. The pressure variables used are 8, 10, and 12 ton-forces (tf). The composition variables of the fabric-rubber weight ratio used are 50:50, 60:40, and 40:60. Samples printed with compression molding according to the process variable combination scheme are then tested using an impedance tube tester to determine the effectiveness of sound absorption that the composite can absorb. The soundproofing test standard with impedance tubes is ISO 10140, with 125, 250, 500, 750, 1000, and 2000 Hz test frequencies. The test results show that samples with a fabric-rubber ratio of 60:40, a pressure of 10 tf, and a temperature of 150°C have the highest sound absorption coefficient value at 2000 Hz, which is 0.90817, and a Noise Reduction Coefficient of 0.8114. This result shows that the combination of pressure, temperature, and weight composition affects the composite's density and porosity, which will then affect the soundproofing performance of the developed composite.

Investigation and application of data balancing and combined discriminant model in rock burst severity prediction

In the development of intelligent rock burst prediction models, issues such as incomplete data coverage and data imbalance are frequently encountered. These issues may lead to risks of overfitting in predictive models, poor generalization capabilities, and increased bias, which in turn may result in misjudgments and unpredictable losses. To accurately predict rock burst disasters and mitigate or eliminate related threats, this paper proposes a composite prediction model that integrates Density-Based Nonlinear Resampling (DBNR)-Tomek Link data balancing algorithms with Bayesian Optimization (BO)-Multilayer Perceptron (MLP)-Random Forest (RF). Initially, this study collected and organized a total of 301 recorded rock burst disaster field observation data, covering various tectonic plates, engineering types, rock origins, rock textures, and rock burst types. Subsequently, from a data analysis perspective, we employed the PCA-SSA-K-means unsupervised clustering algorithm to delve into the underlying information contained within the data, thereby validating the rationality of categorizing rock bursts into four grades. Then, using the L2 norm to optimize the dimensionality of the indicators and supplementing with indicator importance ranking and hypothesis testing, we selected the maximum tangential stress of the surrounding rock, the ratio of the maximum tangential stress of the surrounding rock to the uniaxial compressive strength of the rock (stress coefficient), and the elastic energy index as the criteria for rock burst intensity grading. Following that, the DBNR-Tomek Link sampling method was applied to balance the sample data, optimizing the data sample ratio and ultimately expanding the sample size to 396, improving the proportion of data samples from 2:3:4:1 to 1:1:2:1, thereby enhancing the model’s generalization performance. Ultimately, a BO-MLP-RF composite prediction model was constructed based on Bayesian Optimization (BO), Multilayer Perceptron (MLP), and Random Forest (RF) algorithms, with the Bayesian Optimization method ensuring that the model fits the training data well and generalizes to the test data. The results of tenfold cross-validation demonstrated that the model’s accuracy is consistently around 92.5%, combining the training results of rock burst models with imbalanced datasets, proving that the MLP model, adept at modeling nonlinear data, and the RF model, skilled in modeling large-scale data, serve as basic classifiers. This demonstrates that the application of data balancing and combined discriminative model schemes have enhanced the model’s predictive performance and stability. The model is capable of providing high-accuracy, high-efficiency early warning monitoring services for rock burst phenomena in rock engineering, thereby ensuring engineering safety.

A breathable, waterproof and battery-free wearable e-nose with high flexibility based on MEMS gas sensors for accurate identification of volatile aromatic hydrocarbons

Benzene, toluene, ethylbenzene, xylene, and styrene (BTEXS) are harmful gases indoors and in the cabin. Electronic nose (e-nose) technology has shown great potential in detecting BTEXS. However, to maintain the normal operation of the e-nose and improve the recognition accuracy, it often employs a large array of metal oxide semiconductor (MOS) gas sensors, along with extensive signal processing circuits and high-power solid-state batteries. The bulky structure makes it difficult for the e-nose to realize the portable real-time detection of BTEXS. Herein, a breathable, waterproof, battery-free, and highly flexible wearable e-nose (BWBFW e-nose) is fabricated for BTEXS detections. In detail, we first prepare a combination scheme of n-type and p-type MOS for forming four MEMS gas sensors, exhibiting differentiated response characteristics. Subsequently, a wireless supply module and a wireless signal processing module are designed on a flexible porous circuit board. Then, a waterproof and breathable membrane by electrospinning was adopted to encapsulate the flexible circuit board, endowing the e-nose with high safety. Finally, accurate identification and low-error concentration prediction of BTEXS are achieved with the help of a 1D Convolutional Neural Network (1DCNN) algorithm. We believe the e-nose reported here will shed new light on the design of new devices for detecting BTEXS.

Passivation of Cadmium and Arsenic in Acidified Paddy Soil by Calcium Fertilizer with Biochar-ferromanganese Composites

Due to the aggravation of atmospheric nitrogen and sulfur deposition and the unreasonable application of fertilizer, soil acidification is becoming increasingly serious. In heavy metal-contaminated soils, acidification not only seriously affects fertility but also the effectiveness and sustainability of conventional passivation remediation materials such as biochar. The application of calcium fertilizer may improve soil acidification, alleviate the aging of biochar materials in soil, and improve its remediation ability to composite polluted soil. However, the mechanism of its effect is still unclear. Based on this, this study selected iron-manganese oxide （FM） and hickory cattails biochar （BC） to prepare biochar-ferromanganese composites （BFM） and conducted simulated acidification on it. Through characterization and an aqueous adsorption test, the changes in physicochemical properties and adsorption properties of the material after acidification were explored. Then, orthogonal tests were carried out to explore the effects of the combination of BFM and calcium fertilizer on soil pH and the availability of Cd and As under acidification conditions and to obtain the best combination scheme. The results showed that the optimal ratio of BC and FM in BFM was 7∶3 （quality ratio）, the removal rates of Cd（Ⅱ） and As（Ⅲ） were as high as 94.58% and 97.14%, respectively, and the adsorption capacities were 120.74 mg·g-1 and 129.29 mg·g-1 （solid-liquid ratio 1∶500）. After acidification treatment, the pore structure of BFM surface decreased, and the types and quantities of functional groups changed, resulting in the removal rates of Cd（Ⅱ） and As（Ⅲ） in aqueous solution decreasing by 73.97%-92.84% and 73.56%-93.61%, respectively. The combined application of calcium fertilizer and BFM could significantly increase soil pH, with an increase range of 3.06%-37.84%. The effect of increasing pH decreased with the increase in culture time and acidification degree. Compared with that in the blank control, the content of available Cd in soil was significantly reduced by 22.67%-97.78%. The content of available As in soil was generally stable. According to the effect curve analysis of the orthogonal test results, under the condition of weak acidification degree （pH=5.6）, the application of 2% supplemental amount with 2% silica-calcium fertilizer and 2% calcium-magnesium phosphate fertilizer had a good passivation effect on soil Cd. Under the condition of strong acidification degree （pH=4.0）, the application of 2% supplemental amount and 2% silica-calcium fertilizer had a good passivation effect on soil As. In summary, simulated acidification will affect the adsorption performance of BFM, and calcium fertilizer combined with it can increase soil pH, improve soil acidification, alleviate the aging of BFM in acidified soil, and improve its repair ability to heavy metal-polluted soil.

Analysis of the effects of combined suction on hypersonic inlet starting performance

Abstract Taking a two-dimensional inlet (Case 1) as the original inlet, the combined suction schemes are designed and their influences on the self-starting ability of the inlet are studied. The flow fields at the design point and the self-starting progress of the inlets are calculated by the numerical simulation method. The results show that the three-slot combination scheme (Case 2) leads to the reduction of the mass flow rate of the throat at the design point by 1.15% and the increase of the total pressure recovery coefficient by 6.73%. The self-starting Mach number of the inlet decreases from 4.6 to 3.2, which indicates that the self-starting ability is improved significantly by the three-slot combination scheme. In order to illustrate the role of each slot in improving the self-starting ability, three two-slot combination schemes, which are called Case 3, Case 4, and Case 5, are set up by closing Slot 1, Slot 2, and Slot 3 in turn and their self-starting processes are simulated. The results indicate that the self-starting Mach numbers of Case 3, Case 4, and Case 5 are 3.6, 4.1, and 4.5 respectively, which are 0.4, 0.9, and 1.3 higher than that of Case 2 respectively. Slot 3 has the greatest influence on the self-starting ability. When the incoming Mach number rises to 4.2, a closed separation zone is induced between Slot 2 and the throat by the reflected shock wave on the cowl side of Case 5. Due to the lack of lip overflow, which is an important flow regulation mechanism, the separation zone has a strong self-sustaining ability, and the inlet keeps unstart until the incoming Mach number rises to 4.5. In addition, in the absence of Slot 1 or Slot 2, the large-scale separation zone will not disappear until the incoming flow pressure increases to a certain value.

An optimal multivariate-stratification geographical detector model for revealing the impact of multi-factor combinations on the dependent variable

ABSTRACT Spatial heterogeneity (SH), known as the second law of geography, has been a topic of extensive research. One common approach to analyzing SH involves comparing variances between and within strata to assess the impact of independent variables on the dependent variable. This method, known as spatial stratified heterogeneity (SSH) analysis, is often performed using the geographical detector model. Over time, several optimized versions of geographical detectors have emerged, focusing on discretizing single or dual variables. However, methods for discretizing three or more variables are still limited to the interaction detector, with research on spatial scale effects mainly focused on single factors. To overcome these limitations, an optimal multivariate-stratification geographical detector (OMGD) model has been developed. This model includes two additional modules: factor discretization optimization and scale detector. Fine-tuning factor discretization involves using five univariate and five cluster-based stratification methods to automatically explore the optimal discretization scheme for single factors or multi-factor combinations based on the Geodetector q statistics. The scale detector can then iterate through various spatial scales to identify the optimal spatial scale for SSH analysis. Furthermore, the developed OMGD model has been tested with multiple case datasets to validate its applicability and robustness. The findings demonstrate that the OMGD model can effectively extract the main attributes of single factors and multi-factor combinations, providing a better explanation for geographical phenomena. It can also automatically determine the best spatial scale for SSH analysis, thereby enhancing the overall capability of conducting SSH analysis with the geographical detector.

Combined high order compact schemes for non-self-adjoint nonlinear Schrödinger equations

Some combined high order compact (CHOC) schemes are proposed for non-self-adjoint and nonlinear Schrödinger equation (NSANLSE). There are first order and second order spatial derivatives ux‾, uxx in the NSANLSE. If one uses classical high order compact schemes to approximate uxx and ux‾ separately, it will widen the bandwidth in practical coding due to matrix multiplication. This will partly counteract the advantages of high order compact. To overcome the deficiency, one solves the spatial derivatives simultaneously by combining them. In other words, it solves uxjn and uxxjn simultaneously in terms of uj. The idea is applied to discretize NSANLSE in space. Two efficient numerical schemes are proposed for NSANLSE. The stability and convergence of the new schemes are analyzed theoretically. Numerical experiments are reported to verify the new schemes.

Blending gauge, multi-satellite and atmospheric reanalysis precipitation products to facilitate drought monitoring

Long-term, continuous, and highly accurate precipitation estimation is crucial for reliable drought monitoring. Precipitation estimation based on gauge measurements, satellite retrieval, and reanalysis datasets exhibits heterogeneous uncertainties across different regions of mainland China. This study introduces two modifiable weighting schemes (Cheng-Kling-Gupta Efficiency (CKGE) weighted-ensemble model (CWEM) and Bayesian Model Averaging (BMA)), utilizing posterior probabilities generated by BMA and the performance of CKGE, to merge 7 monthly precipitation datasets into new weighted precipitation (BMA Ensemble Precipitation (BMAEP) and CKGE Weighted-Ensemble Precipitation (CWEP)) using various quantity combination schemes. Subsequently, the precision and drought monitoring utility of the weighted precipitation are evaluated and compared with the representative fused precipitation product Multi-Source Weighted-Ensemble Precipitation (MSWEP) in mainland China using gauged data. The amalgamated results demonstrate that, compared to individual datasets, certain weighted precipitation schemes exhibit superiority over MSWEP. In particular, BMAEP-2P demonstrates superior performance in the composite index CKGE, achieving a value of 0.828. CWEP-4P exhibits a higher correlation coefficient (CC), reaching 0.905. CWEP-2P excels in terms of relative bias (BIAS) and root mean square error (RMSE), with values of 0.579 % and 20.755 mm, respectively. Furthermore, BMAEP or CWEP performs optimally in drought monitoring applications across all sub-regions of mainland China at different time scales (1, 3, 6, 12 and 24 months), with the average of the highest values of CC and probability of detection (POD) reached 0.919 and 0.844, respectively. Further contribution analysis reveals CPC as the dominant factor contributing to the greatest improvement in the performance (excluding CKGE, CC_SPEI1, CC_SPEI24 and POD_SPEI24) of the fusion models, among which it boosted MERRA2′s performance on POD_SPEI6 by 9.41 %. In conclusion, the merging schemes based on CWEM and BMA methods effectively generate a new precipitation dataset, integrating information from multiple products into drought monitoring applications.

Research on the Arrangement Scheme of Spirally Twisted Tape Inserts in a Grate Furnace

To eliminate the flow dead zone and homogenize the asymmetric flow field of a grate furnace, spirally twisted tape inserts (STTIs) with a pitch ratio of 1.5 were installed in the vertical flues of an SCL1000-13.5/450 grate boiler. The arrangement schemes found to be present inside the chosen 1000 t/d grate furnace, determined using the orthogonal experimental method, included separate installation in chamber II, separate placement in chamber III, and simultaneous arrangement in both chamber II and chamber III. The effects of row spacing H, column spacing W, and mounting angle φ were investigated by means of the practicable and feasible numerical simulation method. With a focus on the uniformity degree of the flue gas, the results showed that temperature distribution is directly correlated with the velocity field. When it comes to the uniformity of the flow field, the exclusive use of STTIs in chamber II was better than that in chamber III. Under the optimal combination scheme of STTIs in both chamber II and chamber III (scheme N323), the exhaust gas temperature reached the minimum value and the uniformity index of temperature increased to the range of 0.994~0.997. The findings in this work could provide a reference for the optimization of the flow field in a grate furnace.

How to combine different types of prefabricated components in a building to reduce construction costs and carbon emissions?

In the context of industrialized construction and carbon emission reduction globally, prefabrication has gained considerable attention. However, most studies cared the carbon emissions and construction costs from the perspective of a whole prefabricated building rather than the prefabricated components. It makes it difficult to understand the role of prefabricated components play and make improvements from the prefabricated components level. To fill this gap, this study proposes a model to optimize the selection and combination of prefabricated components in a building project to simultaneously reduce the construction costs and carbon emissions. Six common component types were considered: shear wall, beam, floor slab, stair, balcony, and air-conditioning slab. Their costs and carbon emissions were quantified when adopting prefabrication and cast-in-situ technologies. To obtain the optimal solution, we developed an improved multi-objective mayfly algorithm (IMOMA), which incorporates enhanced circle chaos mapping and a global best guiding strategy to generate Pareto solution sets. The performance of IMOMA was evaluated using the Zitzler-Deb-Thiele series test functions, demonstrating its advantages in convergence, diversity, and comprehensive performance through metrics such as generation distance, diversity metric Δ, and inverse generational distance. A case study validated the effectiveness of this proposed method, demonstrating an average reduction of 9.05 % in total construction costs and 8.04 % in carbon emissions when compared to the initial scheme. Further study revealed that different prefabrication rates correspond to different optimal components combination schemes, with different trends in the costs and carbon emission curves as the prefabrication rate increases. Additionally, carbon trading policies influence the components combination, with construction costs reduced by 10.88 % at a carbon trading price of 42.80 CNY/t CO2eq. This study provides valuable guidance for general contractors to design optimal component combinations and for governments to promote the sustainable development of prefabricated construction.

Assessing the practice of total neoadjuvant therapy for rectal cancer: an online survey among radiation oncology departments in Germany and German-speaking regions of Austria and Switzerland

Total neoadjuvant therapy (TNT) of rectal cancer improves rates of pathological complete remission and progression-free survival. With improved clinical response rates, interest grew in a non-operative approach/watch and wait (WaW) for this disease. In 2020, the working groups of ACO/AIO/ARO published a consensus statement on the use of TNT, including a non-operative approach. However, the best combination scheme remains unclear. Despite the increasing use of TNT, there is a lack of comprehensive data on its current implementation and practices. To address this knowledge gap, a multicenter survey was conducted to capture the use of TNT protocols in German-speaking radiotherapy departments. At the beginning of 2023, a GDPR-compliant online survey was conducted in Germany, Austria, and German-speaking Switzerland. The questionnaire comprised 43 questions covering various aspects of TNT, including chemotherapy and WaW concepts. Most respondents (98.4%) confirmed awareness of the consensus on TNT for rectal cancer. Institutions treated an average of 30.22 rectal cancer patients annually. Most respondents (76.2%) reported treating over 80% of patients neoadjuvantly. Regarding TNT, 33.3% treated 21–50% with such a protocol. No significant association was found between the institution type and TNT application. In 62/63 cases, tumor board discussion was standard before offering TNT. VMAT was the predominant technique (82.5%). For rectal cancer dosing, the 50/50.4Gy scheme was most common, followed by 45Gy with a boost and the 5 × 5Gy scheme. Dosing schemes for TNT varied slightly, with more participants reporting the use of 5 × 5Gy compared to radiation therapy for rectal cancer in general. CBCT was the primary IGRT method (88.9%). Larger hospitals typically administered chemotherapy themselves, while private practices collaborated with medical oncologists (p < 0.0001). The most common concurrent chemotherapy drugs were 5-fluorouracil/capecitabine (64.4%) and oxaliplatin (37.3%). A WaW strategy was reported to be institutional implemented by 63.8%. The timing of offering WaW was split, with 50% offering it after radiochemotherapy and 47% during the informed consent talk. For planned WaW, 62% prefer normofractionated TNT. TNT appears to be widely implemented in the German-speaking radio-oncological community, regardless of the type of institution. Image-guided therapy, multidisciplinary team decisions, and internal guidelines play an important role. TNT seems to have already altered treatment protocols for rectal cancer toward an organ-preserving approach in selected cases. In these WaW cases, normofractionation appears to be preferred over hypofractionation.

Combining style generative adversarial networks with particle swarm optimisation-support vector regression to design affective social robot for public health intervention

In order to improve the attractiveness of social robot serving health interventions in public workspace, we propose a product design method with the combination of Style-generative adversarial network (StyleGAN) model and particle swarm optimisation-support vector regression (PSO-SVR). This paper aims to explore the modelling generation method of social robots for health intervention based on artificial intelligence generated content (AIGC) and a mapping model between the shape characteristics and users’ visual perception based on Kansei Engineering (KE). Firstly, to address the defects of typical KE over-reliance on existing samples, we introduce the StyleGAN model in AIGC to learn and train existing robots’ shape samples and generate new robots’ shape sample images. Secondly, the morphological deconstruction method is used to deconstruct the shape features of the new robot sample. Factor analysis (FA) is also used to reduce dimension and cluster emotional words and establish a Likert scale between the shape features of robots and users’ emotional vocabulary. Finally, particle swarm optimisation-support vector regression (PSO-SVR) is used to establish a mapping model between the shape features of social robots and the emotional images of users, thus obtaining the most attractive shape combination scheme. The research results showed that the StyleGAN model in AIGC can be used to assist industrial designers in creative expression and provide rich sample sources for KE; and the PSO-SVR of machine learning can be used to build a mapping model among users’ visual feelings, emotional images and shape features. In the end, we designed an attractive social robot for public health intervention.

Seismic experiment and performance analysis on embedded optimized steel plate-reinforced concrete composite shear wall under multi-dimensional loading

In order to investigate the seismic performance of reinforced concrete shear walls under multi-dimensional loading mode and its effect on performance improvement, an embedded optimized steel plate-reinforced concrete composite shear wall is proposed. Based on the design principle that the shear wall could adequately bear multi-dimensional loading and the embedded steel plate could reach the full stress state, the X-shaped optimized steel plate (for in-plane loading mode) and the triangular optimized steel plate (for out-of-plane loading mode) are determined using different optimization methods. The combination scheme of these two plates is utilized in the oblique loading mode. Quasi-static loading tests are conducted on eight typical shear wall specimens, and performance parameters such as hysteresis curve, skeleton curve, ductility, stiffness degradation, strain evolution, and damage evaluation of the specimens are compared and analyzed. In addition, variable parameter analysis is performed using finite element software to compare the strain distribution state of each steel plate. The results indicate that the embedded optimized steel plate-reinforced concrete composite shear wall structure exhibits higher bearing capacity, greater deformation capacity, and superior energy dissipation capacity under different loading angles compared to the tradition reinforced concrete shear walls. This composite structure can provide greater lateral stiffness, and the optimized steel plate can reach the full stress state at all loading angles, effectively reducing the damage of steel bars and concrete. These findings offer a foundation for the study of seismic performance and performance improvement methods for shear wall structures under multi-dimensional earthquake action.

A Hybrid Ensemble Approach for Greek Text Classification Based on Multilingual Models

The present study explores the field of text classification in the Greek language. A novel ensemble classification scheme based on generated embeddings from Greek text made by the multilingual capabilities of the E5 model is presented. Our approach incorporates partial transfer learning by using pre-trained models to extract embeddings, enabling the evaluation of classical classifiers on Greek data. Additionally, we enhance the predictive capability while maintaining the costs low by employing a soft voting combination scheme that exploits the strengths of XGBoost, K-nearest neighbors, and logistic regression. This method significantly improves all classification metrics, demonstrating the superiority of ensemble techniques in handling the complexity of Greek textual data. Our study contributes to the field of natural language processing by proposing an effective ensemble framework for the categorization of Greek texts, leveraging the advantages of both traditional and modern machine learning techniques. This framework has the potential to be applied to other less-resourced languages, thereby broadening the impact of our research beyond Greek language processing.

An optimal scheduling method for heavy haul trains with virtual coupling technology

ABSTRACT Heavy haul transportation enjoys the benefits of cost-efficiency by using long trains, but suffers from high cost in combination process and low efficiency in bottleneck areas. Virtual coupling technology (VCT) is helpful in resolving the dilemma. In this paper, we propose an optimal method for scheduling the route, timetable, and combination scheme of heavy haul trains with VCT in an integrated way. Specifically, six groups of constraints are first established, in which the sequence and train length problems incorporated by virtual coupling are properly handled. Then, a combined objective function comprised makespan and total flowtime indicators is proposed to optimize the throughput and turnover rate and further speeds up the optimization convergence. Finally, the optimization problem equipped with the proposed constraints and the combined objective function is transformed into an integer linear programming problem and solved by GUROBI. Simulation results demonstrate the effectiveness and advantages of the proposed method in leveraging VCT, and the benefits of virtual coupling over traditional physical coupling for heavy haul transportation.

Optimizing kitchen ventilation with an integrated stove air supply-exhaust system for reducing PM2.5 intake fraction and enhancing energy efficiency

Ensuring good ventilation is crucial for reducing the pollution caused by cooking activities in the indoor environment. Among them, fume exhaust devices as a vital component of kitchen ventilation, and their performance is particularly critical. Merely increasing the air volume of exhaust devices to remove fumes not only leads to the higher energy consumption of exhaust fans, but also has limited practical effect on reducing pollution. For improving the ventilation condition and reducing the energy consumption of supply and exhaust fan, a new ventilation system for kitchen with integrated stove air supply-exhaust (ISASE) was developed in this study. Firstly, the orthogonal experiment was utilized for arranging different combination schemes of five influencing factors, including the emission rate, exhaust flow, upper air supply angle, upper and lower air supply velocities. Then, the effect of ISASE in reducing the intake fraction of PM2.5 under each scheme was studied by using the computational fluid dynamics method. The energy consumption of this system under different ventilation schemes was investigated with on-site testing. Finally, performance-gaining rate was proposed by introducing intake fraction reduction rate and energy consumption growth rate to quantitatively evaluate the performance advantage of ISASE. Polynomial fitting was also used to explore the energy-saving effect of ISASE at high emission rate. The results showed that at low, medium, and high emission rates, the intake fraction of PM2.5 was reduced by 65.0 %–84.2 %. However, the energy consumption of ISASE merely increased by 7.4 %–16.8 % compared with traditional integrated stove without air supply conditions. Its maximum performance-gaining rate reached 0.49–0.66. The energy-saving rate of ISASE was 46.7 % compared with traditional integrated stove without air supply when the same intake fraction was achieved at high emission rate. The practical schemes of ISASE at different emission rates were given in this study, which optimized the working performance of integrated stove and provided a useful reference for its innovative design.

Comparative evaluation of the therapeutic effect of combined schemes for therapy and rehabilitation in acute respiratory infections with the inclusion of immunocorrective and sedative agents in children from 6 months to 6 years on the background of stress disorders in the conditions of war in Ukraine

Background. Acute respiratory infections (ARIs) are one of the most common groups of diseases in the practice of pediatricians. Stressful factors affect the immune system, reducing its effectiveness and leading to direct impact on the nervous system and the occurrence of its disorders: sleep changes, the development of a post-traumatic stress disorder, a decrease in the quality of life, especially during the last years in wartime conditions in Ukraine. The latter requires improving the implementation of combined therapeutic approaches to increase a short- and long-term effect on the health of the youngest children. Aim of the study: to increase the effectiveness of treatment and rehabilitation of children from the youngest age group (6 months to 6 years) with ARIs against the background of stress disorders of the war in Ukraine by using comprehensive schemes with the inclusion of immunocorrective (the drug with immunocorrective properties contained a complex of ultra-low-dose dilutions of gamma interferon antibodies, histamine antibodies, CD4 antibodies) and sedative (the drug with sedative properties contained antibodies to the brain-specific protein S100) agents. Material and methods. The study involved 119 children aged 6 months to 6 years who had ≥ 5 episodes of ARIs (55 %) during the previous year and permanently lived in almost the entire territory of Ukraine (Kyiv, Kharkiv, Lviv, Dnipro, Zaporizhzhia, Odesa, Kropyvnytskyi, Vinnytsia, Kryvyi Rih, Zhytomyr, Cherkasy, Poltava, Sumy, Berdychiv, Romny, Bila Tserkva). The study was carried out as part of the program for outpatient observation of children with various manifestations of ARIs against the background of stress damage to the nervous system who took drugs with immunocorrective properties (scheme (1)) alone and in combination with a sedative agent (scheme (1+2)). The drug with immunocorrective pro­perties contained a complex of ultra-low-dose dilutions of gamma interferon antibodies, histamine antibodies, CD4 antibodies, and the drug with sedative properties — antibodies to the brain-specific protein S100. The observation period was October-December 2023. Statistical processing of the results was carried out using GraphPad Prism 9.0 Software for Windows (USA, San Diego, CA). Results. The combined therapeutic approach of the scheme (1+2) showed a significantly better effect on fever, duration of low fever, sore throat, runny nose, bronchitis symptoms, manifestations of respiratory infection, assessed by the Wisconsin Questionnaire, as well as stress, sleep disorders, and changes in the quality of life on the 5th day of treatment and after 1 month of observation. The comparative evaluation of scheme (1) and scheme (1+2) showed a significant diffe­rence in favor of the effectiveness of the latter, which is ensured by its additional sedative and anti-anxiety action, resulting in a direct positive effect on stress-induced disturbances of the nervous system and an indirect — on the immune response, which in general increases the effectiveness of solving the problem of ARI therapy in children aged 6 months to 6 years, who for 2.5 years were constantly in psycho-traumatizing and socially oppressed conditions of the war in Ukraine. The analysis of satisfaction with the treatment effect according to the international IMOS scale during the observation period showed positive results and a high level of evaluation by both parents and doctors. Conclusions. The use of a combination of reme­dies with immunocorrective and sedative effects showed their high mutual enhancing effectiveness in the treatment and rehabilitation of children aged 6 months to 6 years, suffering from ARIs, and in the correction of stress disorders caused by the war in Ukraine.

Collaborative graph neural networks for augmented graphs: A local-to-global perspective

In the field of graph neural networks (GNNs) for representation learning, a noteworthy highlight is the potential of embedding fusion architectures for augmented graphs. However, prevalent GNN embedding fusion architectures mainly focus on handling graph combinations from a global perspective, often ignoring their collaboration with the information of local graph combinations. This inherent limitation constrains the ability of the constructed models to handle multiple input graphs, particularly when dealing with noisy input graphs collected from error-prone sources or those resulting from deficiencies in graph augmentation methods. In this paper, we propose an effective and robust embedding fusion architecture from a local-to-global perspective termed collaborative graph neural networks for augmented graphs (LoGo-GNN). Essentially, LoGo-GNN leverages a pairwise graph combination scheme to generate local perspective inputs. Together with the global graph combination, this serves as the basis to generate a local-to-global perspective. Specifically, LoGo-GNN employs a perturbation augmentation strategy to generate multiple augmentation graphs, thereby facilitating collaboration and embedding fusion from a local-to-global perspective through the use of graph combinations. In addition, LoGo-GNN incorporates a novel loss function for learning complementary information between different perspectives. We also conduct theoretical analysis to assess its expressive power under ideal conditions, demonstrating the effectiveness of LoGo-GNN. Our experiments, focusing on node classification and clustering tasks, highlight the superior performance of LoGo-GNN compared to state-of-the-art methods. Additionally, robustness analysis further confirms its effectiveness in addressing uncertainty challenges.