In general, a stable and strong system shouldn't have an overly sensitive/dependent response to inputs (unless consciously and planned desired), as this would reduce efficiency. As in other techniques, approaches, and methodologies, if the results are excessively affected when the input parameters change in MCDM methods, this situation is identified with sensitivity analyses. Oversensitivity is generally accepted as a problem in the MCDM (Multi-Criteria Decision Making) methodology family, which has more than 200 members according to the current literature. The MCDM family is not just a weight coefficient-sensitive methodology. MCDM types can also be sensitive to many different calculation parameters such as data type, normalization, fundamental equation, threshold value, preference function, etc. Many studies to understand the degree of sensitivity simply monitor whether the ranking position of the best alternative changes. However, this is incomplete for understanding the nature of sensitivity, and more evidence is undoubtedly needed to gain insight into this matter. Observing the holistic change of all alternatives compared to a single alternative provides the researcher with more reliable and generalizing evidence, information, or assumptions about the degree of sensitivity of the system. In this study, we assigned a fixed reference point to measure sensitivity with a more robust approach. Thus, we took the distance to the fixed point as a base reference while observing the changeable MCDM results. We calculated sensitivity to normalization, not just sensitivity to weight coefficients. In addition, past MCDM studies accept existing data as the only criterion in sensitivity analysis and make generalizations easily. To show that the model proposed in this study is not a coincidence, in addition to the graphics card selection problem, an exploratory validation was performed for another problem with a different set of data, alternatives, and criteria. We comparatively measured sensitivity using the relationship between MCDM-based performance and the static reference point. We statistically measured the sensitivity with four types of weighting methods and 7 types of normalization techniques with the PROBID method. The striking result, confirmed by 56 different MCDM ranking findings, was this: In general, if the sensitivity of an MCDM method is high, the relationship of that MCDM method to a fixed reference point is low. On the other hand, if the sensitivity is low, a high correlation with the reference point is produced. In short, uncontrolled hypersensitivity disrupts not only the ranking but also external relations, as expected.
In this study, the FLAC3D finite difference numerical software was employed to simulate a geotechnical engineering project, establishing scenarios with concrete and steel pipe piles for support simulation. The analysis focused on the reinforcement effects provided by different types of piles on the geotechnical project. It was found that the reinforcement effects on the soil varied significantly between the pile types. Under the support condition of concrete piles, the maximum soil settlement observed was 4.12 mm, with a differential settlement of 3.19 mm. For steel pipe piles, the maximum soil settlement was reduced to 2.38 mm, with a differential settlement of 2.19 mm, indicating a superior support effect compared to that of concrete piles. Stress concentration phenomena were observed in the piles, becoming more pronounced when pile-soil friction was considered. The substitution of concrete piles with steel pipe piles led to an intensified stress concentration phenomenon in the soil surrounding the piles. The soil undergoing support from concrete piles exhibited the largest plastic deformation, whereas soil supported by steel pipe piles showed less plastic deformation. Consequently, it is concluded that steel pipe piles provide a superior support effect over concrete piles in terms of geotechnical engineering reinforcement.
In the evolution of blockchain technology, the traditional single-chain structure has faced significant challenges, including low throughput, high latency, and limited scalability. This paper focuses on leveraging multichain sharding technology to overcome these constraints and introduces a high-performance carbon cycle supply data sharing method based on a blockchain multichain framework. The aim is to address the difficulties encountered in traditional carbon data processing. The proposed method involves partitioning a consortium chain into multiple subchains and constructing a unique “child/parent” chain architecture, enabling cross-chain data access and significantly increasing throughput. Furthermore, the scheme enhances the security and processing capacity of subchains by dynamically increasing the number of validator broadcasting nodes and implementing parallel node operations within subchains. This approach effectively solves the problems of low throughput in single-chain blockchain networks and the challenges of cross-chain data sharing, realizing more efficient and scalable blockchain applications.
Dam deformation monitoring is a critical technical measure to ensure the safe and stable operation of dams. It involves measuring the structural deformation response of engineering dams using monitoring instruments or technological means. By analyzing the regularity and trend of deformation monitoring data, potential safety anomalies can be forecasted and warned against, providing timely and reliable data for the formulation and implementation of risk removal measures. Horizontal displacement, as the most intuitive and effective reflection of the dam's state under the action of internal and external loads and foundation deformation, is an indispensable part of dam safety monitoring. Currently, the plumb line method and the tensioned wire method are mainly used for horizontal displacement monitoring of dams. A plumb line coordinate instrument measures the horizontal deformation in the upstream and downstream directions and the left and right bank directions through two axes, or the radial and tangential horizontal displacements for arch dams. Compared to other principles, optoelectronic plumb line coordinate instruments have better long-term stability and anti-interference ability and are widely used on engineering sites. However, the orthogonality of the two measuring directions of the instrument is often overlooked. This paper starts from the principle of the development of the plumb line coordinate instrument, analyzes the source of instrument orthogonal error, and combines data collection, structural analysis, and experimental verification. By applying methods such as least squares and regression analysis, an effective calibration calculation and error correction method is proposed. This method is then programmed into the developed plumb line coordinate instrument to meet the real-time correction and output of measured values, providing a reliable technical method for the accuracy and continuous real-time remote monitoring of dam horizontal displacement monitoring. It also offers a technical path for the orthogonality testing of plumb line coordinate instruments.
Named Entity Recognition (NER), a pivotal task in information extraction, is aimed at identifying named entities of various types within text. Traditional NER methods, however, often fall short in providing sufficient semantic representation of text and preserving word order information. Addressing these challenges, a novel approach is proposed, leveraging dual Graph Neural Networks (GNNs) based on multi-feature fusion. This approach constructs a co-occurrence graph and a dependency syntax graph from text sequences, capturing textual features from a dual-graph perspective to overcome the oversight of word interdependencies. Furthermore, Bidirectional Long Short-Term Memory Networks (BiLSTMs) are utilized to encode text, addressing the issues of neglecting word order features and the difficulty in capturing contextual semantic information. Additionally, to enable the model to learn features across different subspaces and the varying degrees of information significance, a multi-head self-attention mechanism is introduced for calculating internal dependency weights within feature vectors. The proposed model achieves F1-scores of 84.85% and 96.34% on the CCKS-2019 and Resume datasets, respectively, marking improvements of 1.13 and 0.67 percentage points over baseline models. The results affirm the effectiveness of the presented method in enhancing performance on the NER task.
The cold chain industry plays a pivotal role in ensuring the quality and safety of temperature-sensitive products throughout their journey from production to consumption. Central to this process is the effective monitoring of temperature fluctuations, which directly impacts product integrity. With an array of temperature monitoring devices available in the market, selecting the most suitable option becomes a critical task for organizations operating within the cold chain. This paper presents a comprehensive analysis of seven prominent temperature monitoring devices utilized in the cold chain industry. Through a systematic evaluation process, each device is rigorously assessed across six key criteria groups: price, accuracy, usability, monitoring and reporting capabilities, flexibility, and capability. A total of 23 independent metrics are considered within these criteria, providing a holistic view of each device's performance. Building upon this analysis, a robust decision support model is proposed to facilitate the selection process for organizations. The model integrates the findings from the evaluation, allowing stakeholders to make informed decisions based on their specific requirements and priorities. Notably, the Chemical Time Temperature Integrators (CTTI) emerge as the top-ranked device, demonstrating superior performance across multiple criteria. The implications of this research extend beyond device selection, offering valuable insights for enhancing cold chain efficiency and product quality. By leveraging the decision support model presented in this study, organizations can streamline their temperature monitoring processes, mitigate risks associated with temperature excursions, and ultimately optimize their cold chain operations. This study serves as a foundation for further research in the field of cold chain management, paving the way for advancements in temperature monitoring technology and strategies. Future studies may explore additional criteria or expand the analysis to include a broader range of devices, contributing to ongoing efforts aimed at improving cold chain sustainability and reliability.
This study presents a novel generation theory based on FSPV system together with grid integration in Chhattisgarh state. In this study four regions mainly two industries and two local loads have been chosen to evaluate the results. There is availability of nearby water sources at the selected sites. The technical, economical and environmental aspects of a proposed FSPV-grid tied system is analyzed in the same selected regions of Chhattisgarh and the results are compared with an existing grid-only system. The entire system of four major sites in the state are simulated using HOMER energy, powered by the National Renewable Energy Laboratory (NREL), United States. At the moment, all of the selected systems are powered by the grid-only system, and data has been collected for the same. The results of HOMER energy are further classified based on economic parameters such as NPC, LCOE, operating cost, system cost and paybacks. The second category includes technical parameters such as production proportion whereas, the third category includes environmental parameters of pollutants and water saving. The obtained results show that the FSPV-grid system as compared to grid-only system’s NPC is reduced to 27%, 7.03%, 10.76%, 12.13%, LCOE is reduced to 69%, 27.8%, 44%, 44.6%, with paybacks of 10.11 years, 12.28 years, 11.99 years, and 12.21 years and IRRs of 8.8%, 7.7%, 8%, 7.7% respectively by following the production proportions of 77.7% and 22.3%, 54.2% and 45.8%, 65.2% and 34.8%, and 65% and 35% from FSPV and grid system, Also, CO2 emissions were reduced by 44.6%, 38.06%, 40.62%, and 41.23% compared to the grid-only system for all four selected sites in this study, which can help the attached industries and local loads gain carbon credit points.
The escalating migration from rural to urban locales necessitates an augmented demand for the workforce, local utility services, and mechanization to sustain a balance conducive to public health. This investigation delineates the pivotal role of human resources in executing daily operations required for the upkeep of public green and asphalted areas within Doboj, Bosnia and Herzegovina. It is posited that teamwork and the requisite competencies of the workforce are integral to the utility company’s efficacy and the establishment of conditions requisite for addressing business tasks delineated on weekly and monthly schedules. A cohort of 20 personnel, tasked with the aforementioned responsibilities, was segmented into three categories, predicated upon their skills and capability to fulfil the designated tasks within specified temporal bounds. A novel hybrid Multi-Criteria Decision-Making (MCDM) model, integrating Improved fuzzy Stepwise Weight Assessment Ratio Analysis (IMF SWARA) with Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), was employed to appraise employees across the designated categories. Decision-makers articulated five criteria, which were quantified via the IMF SWARA methodology. Subsequently, the appraisal of worker categories through three discrete models was undertaken employing the MARCOS technique. Outcomes for each category were individually derived and subjected to verification tests, revealing that criterion significance markedly influences human resource ranking. This study underscores the crucial intersection between environmental stewardship and human resource management, advocating for a systematic approach to urban maintenance that leverages MCDM techniques to optimize workforce performance.
In the digital age, technological advancements have reshaped the global educational landscape, prompting governments and educational institutions to recognize the critical role of research and innovative talent in driving societal progress and economic growth. Undergraduate education, as a pivotal phase for cultivating future innovators, faces unprecedented opportunities for transformation. The rise of online teaching models has catalyzed a profound pedagogical revolution, offering both flexibility in learning and significant potential for educational innovation. This study investigates the current state and influencing factors of research quality among undergraduates at the Capital University of Economics and Business within the online teaching model. The analysis is structured around four key dimensions: research preparation, research motivation, research communication, and research organization and management. Targeted recommendations are proposed to enhance these aspects, providing valuable insights for the reform of undergraduate education in the context of online learning. The findings underscore the potential of educational transformation as a development opportunity, advocating for the integration of innovative educational models with technological advancements to better align with the talent cultivation needs of the contemporary era.
The significance of modeling the process of implementing state policy for energy security and environmental protection lies in its potential to guide effective decision-making. The purpose of the article is to determine the main objectives of the state policy to ensure the energy security and environmental protection. The object of the study is the system for ensuring the energy supply and energy security. The scientific task is to model the process of implementing an effective state policy to ensure the energy security and environmental protection and calculate the most optimal way to supply energy to cities in the EU. The research methodology involves the use of ERD (Entity Relationship Diagram) and Linear Programming methods. ERD is used to visually organize and structure the complex data associated with energy supply systems and environmental policies, highlighting the interrelationships and dependencies between various elements. As a result of using the above methodology, a scheme for implementing state policy in the field of energy security and environmental protection was formed, the most optimal method of energy use was calculated, and also, based on the generated calculation models, a recommendation list was proposed for optimizing modern state policy in the field of energy security and environmental protection. The results of the study fully fulfill the tasks and goals. The novelty of the article is revealed in the proposed methodological approach to the presentation of the system for optimizing modern state policy in the field of energy security and environmental protection. In the future, it is planned to expand the list of elements of the scheme for state policy in the field of energy security and environmental protection and unify existing mathematical calculations for other countries. The methodological approach’s novelty lies in its unique integration of ERD and Linear Programming to address a highly relevant and complex issue-balancing energy security with environmental protection. This approach differs from existing literature.
The atmospheric El Nino phenomenon, characterized by elevated sea surface temperatures in the eastern Pacific Ocean, leads to reduced precipitation and increased temperatures in Indonesia due to diminished influx of moist air. These conditions necessitate the development of drought-resistant rubber (Hevea brasiliensis) seedlings, particularly for regions susceptible to such climatic variations. This study focuses on the PB 260 clone, investigating the efficacy of burnt husk applications in enhancing drought resilience. Employing a non-factorial randomized block design (RBD), three treatments were administered to the seedlings: no burnt husk, burnt husk as mulch, and burnt husk as a planting medium, with each treatment replicated three times and utilizing 30g of burnt husk per polybag. It was observed that the application of burnt husk as mulch significantly promoted root growth compared to the other treatments. This was quantified by measurements showing an increase in root length (98.7m), surface area (45.54m²), and volume (30 mL). These results suggest that the use of burnt husk as mulch might offer a viable strategy for enhancing drought adaptation in Hevea brasiliensis, providing a foundation for earlier tapping maturity through improved root development under drought conditions.
In compliance with European Regulation (EC) 2018/848, organic farming practices necessitate the use of organically produced seeds when available. When suitable organic seeds cannot be sourced, farmers may seek an exemption permitting the use of non-organic seeds. This study examines data from the German national organic seed database to assess trends in the use of crop varieties originating from both organic and conventional seed production. Findings reveal that the diversity of cultivars produced organically is expanding across 15 of the 17 analyzed crops. Correlation analysis suggests that as the availability of organic cereal seed cultivars rises, recorded in the national database, the demand for derogations to use conventional cereal seed cultivars decreases, and vice versa. This positive development in the organic seed sector implies that farmers are adapting their practices in response to the expanding supply of organically multiplied cultivars available through the national database. Nevertheless, challenges such as crop diseases and production limitations in organic cereal seed multiplication can lead to supply deficits, which subsequently result in increased derogation requests for non-organic seeds in the following year. The findings underscore the importance of ensuring robust multiplication of organic cereal seeds to increase the supply, leading to higher adoption of organic seeds and a reduction in derogation requests for non-organic seeds. This study contributes to understanding the impact of supply trends on farmer behavior and highlights the need for further strengthening organic seed multiplication to facilitate full compliance with European organic farming regulations.
This investigation addresses the issuance of preference shares by companies listed on the Malta Stock Exchange (MSE), identifying key determinants and obstacles associated with these initiatives. Semi-structured interviews were conducted with 27 stakeholders, including representatives from 23 MSE-listed companies (MLCs), one MSE official, two stockbrokers, and an advisor from a leading global accounting firm. An evaluation of the financial distress faced by issuers prior to the issuance of preference shares was also undertaken. Despite the establishment of the MSE in 1992, preference shares have been issued by only two listed companies, indicating their minimal utilization as financial instruments within the Maltese market. The findings reveal that preference shares are primarily issued to meet financing needs, support corporate expansion, prevent control dilution, capitalize on favorable market conditions, maintain balanced capital structures, and enhance debt capacity. However, several barriers hinder the issuance of preference shares, including limitations inherent to the Maltese capital market, low investor interest, perceived complexity, and a general lack of understanding regarding this hybrid financial instrument. The study underscores the necessity for improved educational efforts concerning preference shares and elucidates the distinctive characteristics of the local market.
Water governance becomes essential to meet societal needs despite water scarcity. However, in Indonesia, disparities in water governance, especially in the area of water quality perception, continue to exist. Therefore, this study investigates how water quality perception is influenced by examining key factors such as satisfaction, trust, and socio-economic status. This study was conducted in Jakarta by surveying through a questionnaire. This study employed satisfaction, trust, and socio-economic status as independent variables and the perception of water quality as a dependent variable. Also, the chi-square test and ordinal logistic regression were employed to test the hypothesis and the significance between independent and dependent variables. Chi-square tests reveal correlations between trust, socioeconomic status, and perception of water quality. However, regression analysis highlights socio-economic status and trust as the primary influential factors. The study provides evidence of unequal access to water across different socio-economic statuses and demonstrates how the need for government transparency and trust in water institutions affects urban communities' perceptions of water quality.
Open Access
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