Important Criteria Research Articles

Immunological reactivity and natural resistance in patients with tuberculosis, toxocariasis and tuberculosis associated with toxocariasis

Introduction. Parasitic diseases continue to affect the population of many countries of the world. About 2 billion people on the planet are infected with helminths, including Toxocara canis. Invasion with Toxocara canis represents a strong endogenous factor in the formation of the regulatory imbalance of the immune system. The integral evaluation of these indices serves as an important criterion in the diagnosis of toxocarosis and the effectiveness of the applied therapy. In the context of the above, information about research on immune reactivity and natural resistance of tuberculosis patients in association with toxocariasis is of interest. Material and methods. This article is a systematic review of the relevant literature between the year 2001 and 2022. The material for the study was electronic sources published through the web Medline, EMBASE, Global Health, Scopus and Web of science; PubMed; crossref; Google Scholar and others. The following keywords were used “immunological reactivity”, “natural resistance”, “parasitic diseases”, “toxocara canis”, “tuberculosis and toxocariasis”, “immunity”, “helminthiasis”, “toxocariasis”. Bibliographic resources have been systematized by subject and content. The results were grouped and systematized according to the relevant criteria and the level of scientific evidence. Results. Parasites, in particular, Toxocara canis and M. tuberculosis use different mechanisms to alter the immune response, but these mechanisms can interact with significant consequences for the immunological reactivity of each infection. Chronic helminthic infection causes a wide range of changes in the immune system. Conclusion. The immune reactivity and natural resistance of tuberculosis patients in association with toxocarosis is insufficiently elucidated.

Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging.

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disorder with insidious onset. Accurate prediction of the disease progression has received increasing attention. Cognitive scores that reflect patients' cognitive status have become important criteria for predicting AD. Most existing methods consider the relationship between neuroimages and cognitive scores to improve prediction results. However, the inherent structure information in interrelated cognitive scores is rarely considered. In this article, we propose a relation-aware tensor completion multitask learning method (RATC-MTL), in which the cognitive scores are represented as a third-order tensor to preserve the global structure information in clinical scores. We combine both tensor completion and linear regression into a unified framework, which allows us to capture both inter and intra modes correlations in cognitive tensor with a low-rank constraint, as well as incorporate the relationship between biological features and cognitive status by imposing a regression model on multiple cognitive scores. Compared to the single-task and state-of-the-art multi-task algorithms, our proposed method obtains the best results for predicting cognitive scores in terms of four commonly used metrics. Furthermore, the overall performance of our method in classifying AD progress is also the best. Our results demonstrate the effectiveness of the proposed framework in fully exploring the global structure information in cognitive scores. This study introduces a novel concept of leveraging tensor completion to assist in disease diagnoses, potentially offering a solution to the issue of data scarcity encountered in prolonged monitoring scenarios.

Mount Erciyes and sports tourism: A study to evaluate the current situation

In the changing and developing world, people's needs and requests have also begun to vary, and this situation has given the opportunity for the emergence of alternative tourism types on the basis of the tourism sector. Sports tourism, on the other hand, has emerged as an alternative tourism type that includes the active or passive participation of individual or group participants in different branches as a niche market. In this research, it is aimed to evaluate the potential of Mount Erciyes in terms of sports tourism and to present guiding knowledge for sector and public representatives. In this direction, prioritization was made with the SWOT analysis through interviews with fourteen stakeholders in the region, and then with the Analytical Hierarchy Process (AHP) method in line with the findings obtained. Accordingly, it has been determined that Mount Erciyes has strengths and opportunities such as accessibility and being in the Cappadocia Region, as well as weaknesses and threats such as insufficient infrastructure and increased competition. In addition, it has been seen that global competition is a threat to destination, but accessibility is the most important criteria to gain a competitive advantage.

Rapid Bioassay for Detection of Insecticide Resistance in Aphis gossypii Glover, 1877 (Hemiptera: Aphididae)

Cotton aphid, Aphis gossypii Glover, 1877 (Hemiptera: Aphididae) is one of the most economically important polyphagous pests that shows rapid resistance to chemical control. Determination of resistance levels is important within resistance management. The insecticide activities of Acetamiprid, Dimethoate, Imidacloprid, L-cyalothrin were investigated on different aphid populations to develop a faster and more economical bioassay method. Resistance levels were compared by making bioassays with insecticides using the leaf dipping method for 72 hours, 120 minutes and different doses. Resistance rates varying between 1-10 times were observed in both methods. The rapid application of 120 minutes is more advantageous in terms of speed, application time and economy of the method in the detection of resistance against Acetamiprid, Dimethoate, Imidacloprid, L-cyalothrin insecticides, and will contribute to the detection of resistance and the development of bioassay method.

COVID-19 and the Heart: Lessons Learned and Future Research Directions

It has become evident that acute COVID-19 infection can lead to cardiovascular complications. While the exact mechanisms by which COVID-19 affects the cardiovascular system have yet to be fully elucidated, several mechanisms have been proposed, including direct myocardial effects on the virus and systemic inflammation as an indirect result. The cardiovascular complications of COVID-19 have been characterized and described using noninvasive cardiac imaging. The impact of COVID-19 on the cardiovascular system extends beyond the acute phase of the infection and well beyond recovery or the convalescent period. However, the underlying mechanisms of post-viral long-COVID symptoms have yet to be elucidated. It is evident that COVID-19 has become endemic and is here to stay. Future studies are needed (1) to understand the long-term effects of the cardiovascular complications of COVID-19, future cardiovascular events and the impact of mutating variants on cardiovascular complications through data collection and analysis, (2) to identify the most important diagnostic criteria for prognosis of COVID-19 and to understand the disease mechanism through biomarkers and advanced cardiac imaging, including echocardiography and (3) to develop novel strategies to manage and treat these cardiovascular complications using the knowledge gained.

The effect of ESG on enterprise value under the dual carbon goals: From the perspectives of financing constraints and green innovation

Environmental, Social, and Governance (ESG) is an important criterion for evaluating non-financial indicators of firm performance, offering substantial support for advancing green transformation. This paper examines the impact of ESG performance on enterprise value using data from 3139 Chinese A-listed companies spanning from 2010 to 2021. The results demonstrate that enhancing ESG performance significantly boosts enterprise value. Additionally, ESG initiatives can alleviate corporate financing constraints and stimulate green innovation, consequently driving enterprise value. Furthermore, the positive effect of ESG on enterprise value is more pronounced for non-state-owned enterprises, low-polluting enterprises, and small-scale enterprises. These findings deepen our understanding of how ESG influences enterprise value within the context of the dual-carbon goal, offering theoretical backing for companies and investors to bolster ESG management practices and aiding government efforts to enhance ESG macro guidance.

Hybrid multi-criteria decision making approach proposal for evaluating workloads of aircraft maintenance technicians.

Aircraft maintenance technicians (AMTs) have the most difficulty in terms of total workload criteria working in line maintenance. This is a very important problem for the Aircraft Maintenance Organization. A systematic and scientific approach is required for its solution. This study proposes a new Multi-Criteria Decision Making (MCDM) based approach to evaluating the total workloads of AMTs to identify the most challenging AMT tasks in the aircraft maintenance organization. A new hybrid MCDM approach is proposed by integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity (TOPSIS) methods to compare AMTs on the basis of workloads according to license categories. The hybrid method proposed in this study evaluates the total workload under three main titles: mental, physical, and environmental workload. Focusing on AMTs working in line maintenance of an aircraft maintenance organization, this study revealed that the most important workload criteria determined by the DEMATEL method are lower back strain, upper back strain, time pressure, and air temperature criteria. The results of the TOPSIS method showed that the license categories of AMTs are sorted according to the workloads as follows: A, B2, B1, and B1 + B2. The AMTs holding a "Category A" license have fewer workloads than the other categories. The findings of the study reveal some measures that might allow authorities to minimize the workload of AMTs. In addition, the study contributes to the literature because there are few studies that systematically analyze total workloads by using MCDM methods.

Molecular factors determining brightness in fluorescence-encoded infrared vibrational spectroscopy.

Fluorescence-encoded infrared (FEIR) spectroscopy is a recently developed technique for solution-phase vibrational spectroscopy with detection sensitivity at the single-molecule level. While its spectroscopic information content and important criteria for its practical experimental optimization have been identified, a general understanding of the electronic and nuclear properties required for highly sensitive detection, i.e., what makes a molecule a "good FEIR chromophore," is lacking. This work explores the molecular factors that determine FEIR vibrational activity and assesses computational approaches for its prediction. We employ density functional theory (DFT) and its time-dependent version (TD-DFT) to compute vibrational and electronic transition dipole moments, their relative orientation, and the Franck-Condon factors involved in FEIR activity. We apply these methods to compute the FEIR activities of normal modes of chromophores from the coumarin family and compare these predictions with experimental FEIR cross sections. We discuss the extent to which we can use computational models to predict the FEIR activity of individual vibrations in a candidate molecule. The results discussed in this work provide the groundwork for computational strategies for choosing FEIR vibrational probes or informing the structure of designer chromophores for single-molecule spectroscopic applications.

Bridging comic art and research: lessons from an interdisciplinary collaboration project in a palliative care context

The Dutch graphic novel Naasten, about palliative family caregiving, is the product of an interdisciplinary collaboration between researchers and two comic artists. This paper aims to present lessons, reflections and...

Multi-criteria structural optimization of a hood inner structure using machine learning technique

This article presents an efficient multi-criteria structural optimization process based on machine learning for shape and size optimization of the hood inner. For this purpose, a low dimensional parameterization based on two-dimensional mapping and dimensionality reduction is used to decrease the number of design variables. There are many design parameters to be considered for designing this structure. In this study, the effect of three important criteria consisting of head injury, eigenfrequency, and static load were analyzed. A full vehicle finite element model is prepared for a newly developed product because head injury analysis requires a full vehicle model due to contact with the inner components of the engine compartment. Then, an experimental study is performed based on the real test scenario for model verification. A new rating method for analyzing the head injury criteria evaluated in different points is proposed and different machine learning techniques are compared in the approximation of the objective function. Finally, the problem is solved as both single and multi-objective optimization problems. Results present considerable improvement in accuracy and efficiency in the identified structure while the method has a low computational cost and the implementation is not difficult.

Analyzing the Shelter Site Selection Criteria for Disaster Preparedness Using Best–Worst Method under Interval Type-2 Fuzzy Sets

Shelters are vital for affected people after a disaster because of the accommodation, safety, and security. In this paper, we analyze the shelter site selection criteria for disaster preparedness applying the best–worst method under interval type-2 fuzzy sets. The proposed approach utilizes the advantages of fewer pairwise comparisons with the best–worst method and better reflection of uncertainty with interval type-2 fuzzy sets. For this reason, the criteria are determined based on a literature review and the opinion of nine disaster experts. The experts have worked as disaster officers in a variety of locations, including Sivrice (Elazığ), Pazarcık, and Elbistan (Kahramanmaras) and Syrian refugee camps such as Nizip container city. In this step, 6 main criteria and 25 sub-criteria are evaluated using the proposed approach. According to the nine experts’ opinions, the most important main criterion is determined as proximity. Distribution center capacity, adequate distribution logistics personnel, available electricity, distance to settlement, and landslides and flooding are also determined as the five most important sub-criteria. For disaster preparedness, responsible organizations and managers should consider these important criteria for temporary shelter site selection.

Complexity in Construction Projects: A Literature Review

Improper understanding of complexity can be a leading factor in the failure of construction projects. This study aims to provide a better understanding of the complexity of construction projects. For this purpose, this study uses the systematic literature review (SLR) approach to review the related literature and propose a definition for complexity and the criteria that affect the degree of complexity in construction. The results of analyzing 49 studies from the literature showed that, generally, complexity is understood in three ways: the meaning of the word “complexity”, system and organizational complexity, and project complexity. Within these three types of definitions, it was found that “interdependency” and “multiple parts/parties” are the most frequently used keywords. The results also showed that another look at the current lingual definition of complexity is needed. Regarding the criteria, the results showed that the “number of stakeholders”, “scope and project objectives”, and “management structure” are the most important criteria to assess construction project complexity. Accordingly, this study provides a set of recommendations and strategies to help manage complexity in construction projects.

Optimizing Cross-Dock Terminal Location Selection: A Multi-Step Approach Based on CI-DEA–IDOCRIW–MABAC for Enhanced Supply Chain Efficiency—A Case Study

Thedistribution of products stands out as one of the pivotal activities for logistics companies in recent years, particularly in the aftermath of the COVID-19 pandemic and other geopolitical events. Intense competition compels companies to efficiently execute their logistical processes, with cross-docking emerging as a frequently applied solution. However, the location of cross-dock terminals in urban areas remains a problem insufficiently addressed in the literature, with a dearth of studies and models tackling this issue. This paper introduces a novel and innovative model for locating cross-dock terminals based on the CI-DEA–IDOCRIW–MABAC (Composite Indicators–Data Envelopment Analysis-Integrated Determination of Objective Criteria Weights–Multi-Attributive Border Approximation Area Comparison) methods. In the process of defining input indicators, the following three sources were utilized: relevant literature, practical insights from logistics experts, and the knowledge and experience of the authors. Eight inputs and three outputs were considered (the number of users in the observed channel; the area served by the channel; the average distance a vehicle travels in one delivery; the required number of vehicles; labor availability; competition; construction, and expansion possibilities; proximity to the main infrastructure and traffic facilities; the average number of deliveries; average delivered quantity; and service level). The model underwent testing in a case study analyzing nine distribution channels (areas within the observed urban zone). The results indicated that alternative A4 (in the southwest area) ranked the highest since it was the best-ranked in accordance with the most important criteria, suggesting that the terminal is best located in the southwest zone. The accuracy of the results was confirmed by company management. By developing a completely new model and addressing the identified gap in the literature, this paper provides unequivocal scientific contributions.

Cold molecules in H I 21 cm absorbers across redshifts ∼0.1–4

Absorption lines at high-redshift in front of quasars are quite rare in the millimeter (mm) domain. Only five associated and five intervening systems have been reported in the literature. Nevertheless, these discoveries provide very useful information that is complementary to emission lines, allowing, for instance, to distinguish between inflows and outflows. These lines are also good candidates for studying the variations of the fundamental constants of physics. Here we report the findings of our search for CO and other molecules in emission and absorption in front of a sample of 30 targets, comprising 16 associated and 14 intervening H I 21 cm absorbers. The observations were made with the IRAM-30 m telescope simultaneously at 3 mm and 2 mm, exploring several lines of the CO ladder and HCO+, depending on the redshift. We detected eight targets in emission, of which five are new. The derived molecular gas masses range from 109 to 7 × 1011 M⊙ and the highest redshift detection (z = 3.387) corresponds to a relatively average-metallicity damped Lyman-α absorber for this redshift. We also report four new detections in absorption. Two of the associated CO absorption line detections at high-redshift (z = 1.211 and 1.275) result from high-spatial-resolution follow-up observations with NOEMA. The disparity between the mm molecular and H I 21 cm absorption lines for these and another intervening system detected in HNC at z = 1.275 is attributable to radio and mm sight lines tracing different media. We compare the atomic and molecular column densities of 14 known high-redshift (z > 0.1) molecular absorption line systems. The associated H I absorption lines are broad and exhibit multiple components, and the molecular absorption generally corresponds to the broader and weaker 21 cm absorption component. This indicates two distinct phases: one near galaxy centers with a larger CO-to-H I abundance ratio, and another with lower molecular abundance in the outer regions of the galaxy. In comparison, intervening absorption profiles correspond primarily to H I-dominated gas structure in galaxy outskirts, except for gas at low impact parameters in gravitationally lensed systems. The comparison of interferometric and single-dish observations presented here shows that the detection of absorption requires sufficient spatial resolution to overcome the dilution by emission and will be an important criterion for mm follow-up of 21 cm absorbers from ongoing large-scale surveys.

Reversible Deacidification and Preventive Conservation of Paper-Based Cultural Relics by Mineralized Bacterial Cellulose.

Paper-based cultural relics experience irreversible aging and deterioration during long-term preservation. The most common process of paper degradation is the acid-catalyzed hydrolysis of cellulose. Nowadays, deacidification has been considered as a practical way to protect acidified literature; however, two important criteria of minimal intervention and reversibility should be considered. Inspired by the superior properties of bacterial cellulose (BC) and its structural similarity to paper, herein, the mineralized BC membranes are applied to deacidification and conservation of paper-based materials for the first time. Based on the enzyme-induced mineralization process, the homogeneous and high-loaded calcifications of hydroxyapatite (HAP) and calcium carbonate (CaCO3) nanoparticles onto the nanofibers of BC networks have been achieved, respectively. The size, morphology, structure of minerals, as well as the alkalinity and alkali reserve of BC membranes are well controlled by regulating enzyme concentration and mineralization time. Compared with HAP/CaCO3-immersed method, HAP/CaCO3-BC membranes show more efficient and sustained deacidification performance on paper. The weak alkalinity of mineralized BC membranes avoids the negative effect of alkali on paper, and the high alkali reserve implies a good sustained-release effect of alkali to neutralize the future generated acid. The multiscale nanochannels of the BC membrane provide ion exchange and acid/alkali neutralization channels between paper and the BC membrane, and the final pH of protected paper can be well stabilized in a certain range. Most importantly, this BC-deacidified method is reversible since the BC membrane can be removed without causing any damage to paper and the original structure and fiber morphology of paper are well preserved. In addition, the mineralized BC membrane provides excellent flame-retardant performance on paper thanks to its unique organic-inorganic composite structure. All of these advantages of the mineralized BC membrane indicate its potential use as an effective protection material for the reversible deacidification and preventive conservation of paper-based cultural relics.

Bag-Based Feature-Class Correlation Analysis for Multi-Instance Learning Application

Multi-instance Learning (MIL) is widely applied in image classification. In MIL, an image is presented as a bag. A bag consists of multi-instance which is known as patches. Irrelevant features of the image presented to the classifier affects the classification performance. Feature selection is one of the essential phases to select relevant. However, limited studies discuss the feature selection phase in MIL. Correlation between feature-class (FC) relationship is one important criterion to analyse features’ relevance. However, it cannot be performed directly in MIL. To address this gap, this study proposed the MultiBag-FCCorr feature selection technique. It consists of three steps: transformation, evaluation and fusion. The bags of feature information are acquired from summarization from different statistical central tendency measures of trimmed mean, mode and median. In feature evaluation step, extended point biserial correlation has been used to measure FC correlation and then the FC score has been analysed. The selected features are validated via two prominent classifiers (Support Vector Machine (SVM) and K-Nearest Neighbour (KNN)) on benchmark MI image datasets: UCSB Breast Cancer, Tiger, Elephant and Fox datasets. The selected features of UCSB Breast Cancer dataset were reduced to 92% number of features from the proposed technique giving the best result of average accuracy with 86.8.% using SVM and 84.5% using KNN. The average accuracy improved 6.3% using SVM and 16.4% using KNN compared without implementing the proposed feature selection. The results proved that the selected feature set improved the performance of MI image classification.

A Viscoplasticity Model for Shale Creep Behavior and Its Application on Fracture Closure and Conductivity

Hydraulic fracturing is the main means for developing low-permeability shale reservoirs. Whether to produce artificial fractures with sufficient conductivity is an important criterion for hydraulic fracturing evaluation. The presence of clay and organic matter in the shale gives the shale creep, which makes the shale reservoir deform with time and reduces the conductivity of the fracture. In the past, the influence of shale creep was ignored in the study of artificial fracture conductivity, or the viscoelastic model was used to predict the conductivity, which represents an inaccuracy compared to the actual situation. Based on the classical Perzyna viscoplastic model, the elasto-viscoplastic constitutive model was obtained by introducing isotropic hardening, and the model parameters were obtained by fitting the triaxial compression creep experimental data under different differential stresses. Then, the constitutive model was programmed in a software platform using the return mapping algorithm, and the model was verified through the numerical simulation of the triaxial creep experiment. Then, the creep calculation results of the viscoplastic constitutive model and the power law model were compared. Finally, the viscoplastic constitutive model was applied to the simulation of the long-term conductivity of the fracture to study the influence of creep on the fracture width, and sensitivity analysis of the influencing factors of the fracture width was carried out. The results show that the numerical calculation results of the viscoplastic model were in agreement with the experimental data. The decrease in fracture width caused by pore pressure dissipation and reservoir creep after 72 h accounts for 32.07% of the total fracture width decrease.

Assessment of Service Quality in Aviation Using the CRITIC-Supported TOPSIS Method: A Case Study of Central Airports in Asia

In this study, the service quality performance of 14 central airports that are members of SKYTRAX in the Asian continent are analyzed. The data of 2022 are taken as basis in the analysis process. The aim of the study is to evaluate the service quality of the airports included in the analysis. In order to compare the service quality performances of the selected airports, The Criteria Importance Trough Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods from Multi-Criteria Decision-Making Methods (MCDM) are used. The data of the airports included in the analysis are obtained through SKYTRAX. While the importance degrees of the criteria of the alternatives are calculated by the CRITIC method, the ranking of the alternatives is obtained by the TOPSIS method. The criteria used in the analysis are security screening, arrival, departure, terminal comfort, terminal facilities, food and beverage services. According to the findings of the CRITIC method; it is concluded that the most important criterion is departure and the least important criterion is terminal comfort. According to the results of the TOPSIS method; Singapore Changi Airport ranks first in terms of service quality performance. Seoul Incheon and Tokyo Haneda airports follow this airport in the ranking. Mumbai Chhatrapati Airport takes the last place.

Proposal of Industry 5.0-Enabled Sustainability of Product–Service Systems and Its Quantitative Multi-Criteria Decision-Making Method

In the wake of Industry 4.0, the ubiquitous internet of things provides big data to potentially quantify the environmental footprint of green products. Further, as the concept of Industry 5.0 emphasizes, the increasing mass customization production makes the product configurations full of individuation and diversification. Driven by these fundamental changes, the design for sustainability of a high-mix low-volume product–service system faces the increasingly deep coupling of technology-driven product solutions and value-driven human-centric goals. The multi-criteria decision making of sustainability issues is prone to fall into the complex, contradictory, fragmented, and opaque flood of information. To this end, this work presents a data-driven quantitative method for the sustainability assessment of product–service systems by integrating analytic hierarchy process (AHP) and data envelopment analysis (DEA) methods to measure the sustainability of customized products and promote the Industry 5.0-enabled sustainable product–service system practice. This method translates the sustainability assessment into a multi-criteria decision-making problem, to find the solution that meets the most important criteria while minimizing trade-offs between conflicting criteria, such as individual preferences or needs and the life cycle sustainability of bespoke products. In the future, the presented method can extend to cover more concerns of Industry 5.0, such as digital-twin-driven recyclability and disassembly of customized products, and the overall sustainability and resilience of the supply chain.

Multicriteria Decision Making in Site Selection for Nuclear Power Plants

Nuclear energy is an important alternative energy source. However, construction of nuclear power plants (NPPs) requires the consideration of environmental, economic, socioeconomic, health, and safety factors since using such an energy source involves some risks during the operation lifetime of a NPP. Accordingly, the selection of the most suitable site for a NPP yields a multicriteria decision making (MCDM) problem. Natural hazards, health hazards, environmental conditions, population density, and the availability of water resources are among the main factors that should be considered. To demonstrate the application of the proposed methodology, two previously identified NPP sites in Turkey, specifically, Mersin-Akkuyu and Sinop-Abalı, are compared utilizing the multicriteria utility functions. In the comparison of these two sites, seismic hazard, tsunami hazard; extreme wind hazard; distance to facilities; population density; proximity to the city center; existence of forests, natural parks, nature conservation areas, and natural monuments; biodiversity; and immovable cultural heritage have been identified as the most important critical criteria and are evaluated concerning the utility functions developed for each criterion. The optimal site is selected within the framework of the MCDM rules, and is recommended as the output of the study.