Effective Resource Utilization Research Articles

Leveraging the interplay of Internet of Things (IoT) capabilities for achieving customer satisfaction in a B2B2C context: an integrative perspective

PurposeDespite IoT’s huge potential, enterprises’ ability to leverage it is their competitive advantage. Thus, competitive differentiation is primarily predicated on leveraging IoT toward customer needs. To examine the research gap, this study aims to explore the drivers of customer satisfaction and how they are affected by the interaction between IoT capabilities.Design/methodology/approachA mixed-method research framework is applied to assess the impact of IoT capabilities on customer satisfaction. Based on the theoretical underpinning of the resource-based view and dynamic capability, the study highlights the importance of IoT capabilities in active resource allocation and effective resource utilization. First, DEMATEL is used capture the interrelationship between IoT capabilities. Further, the impact of each IoT capabilities on customer satisfaction is studied using CoCoSo method.FindingsThe study highlights the importance of IoT capabilities in active resource allocation and effective resource utilization. The findings are enriched through the complementarity of resources in a dynamic business-to-business-to-customer (B2B2C) scenario. The authors expand the IoT capabilities from conventional business-to-business (B2B) or business-to-customer (B2C) scenario to tri-nodal B2B2C relationship triangle.Practical implicationsBased on the findings, the authors offer a business transformation strategy for firms in key areas of customer satisfaction by leveraging IoT. The study can help management prioritize and develop key IoT capabilities to meaningfully increase customer satisfaction metrics.Originality/valueBuilding on the dynamic capabilities and resource-based view of the firm, an integrated decision-making research model is proposed. In addition, this study investigates the product and service capabilities unlocked using IoT capabilities. This work can be considered one of the leading attempts to improve customer satisfaction using IoT capabilities from traditional dyadic (B2B or B2C) structure to triadic (B2B2C) framework.

Effects of bicarbonate on antioxidant, immune, osmolytic and metabolic capabilities of Scylla paramamosain

The world is rich in saline-alkali water resources, and the effective utilization of saline-alkali water resources is of great significance to the promotion of human economic development. This study investigated the effects of carbonate saline water on the immune, antioxidant, osmotic regulatory, and metabolic capacities of S. paramamosain. Bicarbonate stress resulted in damage to the hepatopancreas, as well as significantly upregulating the activities of superoxide dismutase and catalase, indicating that the antioxidant capacity increased under short-term bicarbonate stress, but then decreased. Long-term bicarbonate stress caused further oxidative damage. The activities of alkaline phosphatase and acid phosphatase also significantly increased, indicating an initially enhanced immune response of S. paramamosain to carbonate salinity stress that then declined, suggesting that the ability to mount an immune response decreased following long-term exposure. High bicarbonate levels also significantly inhibited the osmotic regulation ability of S. paramamosain, and negatively impacted metabolic indexes (e.g., glucose, total cholesterol, triglycerides, urea nitrogen, and uric acid). Three groups of metabolites were differentially upregulated and 95 were differentially downregulated, including ‘organooxygen compounds’, ‘carboxylic acids and derivatives’, ‘fatty acyls’, ‘glycerophospholipids’ and ‘keto acids and derivatives’. Thus, long-term bicarbonate stress also decreased energy metabolism in S. paramamosain. Under long-term bicarbonate stress, related products of energy metabolism were significantly downregulated, and bicarbonate stress significantly inhibited energy uptake in S. paramamosain. This study analyzed the stress mechanism of bicarbonate on S. paramamosain, further enriched the culture theory of S. paramamosain, in order to provide a theoretical basis for the improvement of saline-alkaline water culture technology of S. paramamosain.

Study on the micro-rheological properties of fly ash-based cement mortar

Aiming at the shortcomings of poor rheological properties and low strength of tailings filling mortar, solid waste materials such as fly ash, steel slag, and desulfurization ash were introduced to improve the filling mortar. The Response Surface Methodology (RSM) was used to design the test ratios to determine the significance of the effect of fly ash, steel slag and desulphurization ash on the physical properties of the mortar. The rheological characterization law of filling mortar was studied based on macroscopic L-tube test. The mechanism of filling mortar improvement was analyzed by Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray Spectroscopy (EDS) and other microscopic tests. The results show that the response-factor model fit regression is significant, with the highest model satisfaction at 12 % fly ash dosing, 15 % steel slag dosing, and 10 % desulfurization ash dosing. The self-flowing extension diameter of the mortar is 15.34 cm, and the 3d and 28d strengths are 0.58 MPa and 3.24 MPa, respectively. Fly ash has effectively improved the phenomenon of mortar segregation and precipitation, and the yield stress of mortar is reduced by 35.1∼64.9 % when the dosage is 8∼12 %. The fractal dimension and mortar workability are positively correlated with fly ash admixture. Fly ash incorporation will gradually consume the calcium hydroxide (CH) to generate alumina ferrite (Aft) and calcium silicate hydrate (C-S-H), which reduces mortar porosity and promotes long-term strength growth. The research results not only realized the effective utilization of solid waste resources, but also significantly improved the performance of the filler and ensured the safe production of the mine.

Comprehensive Comparison of Three Different Medicinal Parts of Eupatorium lindleyanum DC. Using the RRLC-Q-TOF-MS-Based Metabolic Profile and In Vitro Anti-Inflammatory Activity.

Eupatorium lindleyanum DC. (EL) is a traditional Chinese herb known for its phlegm-reducing, cough-relieving and asthma-calming properties. It is widely used for treating cough and bronchitis. However, preliminary experiments have revealed wide variations in the composition of its different medicinal parts (flowers, leaves and stems), and the composition and efficacy of its different medicinal parts remain largely underexplored at present. In this study, non-targeted rapid resolution liquid chromatography coupled with a quadruple time-of-flight mass spectrometry (RRLC-Q-TOF-MS)-based metabolomics approach was developed to investigate the differences in the chemical composition of different medicinal parts of EL. We identified or tentatively identified 9 alkaloids, 11 flavonoids, 14 sesquiterpene lactones, 3 diterpenoids and 24 phenolic acids. In addition, heatmap visualization, quantitative analysis by high-performance liquid chromatography (HPLC-PDA) and ultra-high-performance liquid chromatography-triple quadrupole tandem mass spectrometry (UPLC-MS/MS) showed particularly high levels of sesquiterpene lactones, flavonoids and phenolic acids in the flowers, such as eupalinolide A and B and chlorogenic acid, among others. The leaves also contained some flavonoid sesquiterpene lactones and phenolic acids, while the stems were almost absent. The findings of in vitro activity studies indicated that the flowers exhibited a notable inhibitory effect on the release of the inflammatory factors TNF-α and IL-6, surpassing the anti-inflammatory efficacy observed in the leaves. Conversely, the stems demonstrated negligible anti-inflammatory activity. The variations in anti-inflammatory activity among the flowers, leaves and stems of EL can primarily be attributed to the presence of flavonoids, phenolic acids and sesquiterpene lactones in both the flowers and leaves. Additionally, the flowers contain a higher concentration of these active components compared to the leaves. These compounds mediate their anti-inflammatory effects through distinct biochemical pathways. The results of this study are anticipated to provide a scientific basis for the rational and effective utilization of EL resources.

Production forecast, comprehensive utilization, management measures and visualization analysis of construction waste

To achieve the national policy objectives of carbon peak and carbon neutrality, as outlined in the 14th Five-Year Plan, it is essential to enhance resource utilization efficiency, establish a resource recycling system, promote a green economy, and develop a green development policy framework. This thesis utilizes statistical data from the Chongqing municipality yearbook and employs the Time Series Forecasting Model (TSFM) and Gray Prediction Model (GM) to predict the generation of construction waste (CW) in Chongqing Municipality from 2021 to 2030.By 2030, engineering waste, demolition waste, renovation waste, and total CW generation are predicted to increase by 40.93%/34.30%, 37.3%/57.78%, 6.00%/9.55%, and 38.71%/57.85%, respectively, compared to 2020. This indicates significant pressure on the city's CW disposal system. The current situation of CW disposal and transfer in Chongqing City shows that the main methods are simple landfills, engineering backfills, and even private dumping. The actual resource utilization rate is still relatively low. Current CW disposal enterprises have some disposal and recycling capacity, but most are private, with insufficient distribution points and limited processing capacity. This results in a low overall comprehensive utilization rate of CW in central Chongqing. Since 2003, Chongqing Municipality has issued CW disposal-related policy documents. Although relevant policies have been introduced, they need further improvement. Recycled products are mainly bricks and aggregates, and there is a lack of local standards, which hinders effective resource utilization of CW in Chongqing. Finally, using the Citespace tool, a visual analysis of global CW research hotspots and trends over the past 14 years was conducted to accelerate CW resource utilization in Chongqing Municipality.

Optimizing Maintenance Resource Scheduling and Site Selection for Urban Metro Systems: A Multi-Objective Approach to Enhance System Resilience

This study developed an optimization model for the strategic location of maintenance resource supply sites and the scheduling of multiple resources following failures in urban metro systems, with the objective of enhancing system resilience. The model employs a multi-objective optimization framework, focusing primarily on minimizing resource scheduling time and reducing costs. It incorporates critical factors such as spatial location, network topology, station size, and passenger flow. A hybrid method, combining the non-dominated sorting genetic algorithm III and the technique for order of preference by similarity to ideal solution, is used to solve the model, with its effectiveness confirmed through a case study of the Nanjing Metro system. The simulation results yielded an optimal number of 21 maintenance resource supply stations and provided their placement. In the event of large-scale failures, the optimal resource scheduling strategy ensures demand satisfaction rates exceed 90% at critical stations, maintaining an overall rate of 87.09%, therefore significantly improving resource scheduling efficiency and the system’s emergency response capabilities and enhancing the physical resilience and recovery capabilities of the urban metro system. Moreover, the model accounts for economic factors, striving to balance emergency response capabilities with production continuity and cost efficiency through effective maintenance strategies and resource utilization. This approach provides a systematic framework for urban metro systems to manage sudden failures, ensuring rapid recovery to normal operations and minimizing operational disruptions in scenarios of limited resources.

FSBOA: feature selection using bat optimization algorithm for software fault detection

Feature selection (FS) plays a crucial role in software fault prediction (SFP), aiming to identify a subset of relevant and discriminative features from a large pool of software metrics. It serves as a critical preprocessing step in building accurate fault prediction models, enabling the identification of potential software faults early in the development life cycle and facilitating effective resource allocation for testing and maintenance activities. The study's objective is to determine how well the bat optimization algorithm (BOA) can extract the features that are most important for correctly predicting software flaws, improve the accuracy of fault prediction, reduce the dimensionality of the feature space, and mitigate the risk of overfitting, thereby enabling more effective resource utilization and better allocation of testing efforts. The forecasting models underwent testing and training utilizing a collection of software metrics, with the datasets undergoing evaluation using several different FS algorithms. An assessment was conducted by contrasting the effectiveness of multiple optimization algorithms, including evolutionary methods such as FS employing genetic algorithm (FSGA), FS employing differential evolution (FSDE), and swarm-based techniques such as FS employing ant colony optimization (FSACO), FS employing particle swarm optimization (FSPSO), FS employing firefly algorithm (FSFA), and FS employing binary grey wolf optimization algorithm (FSBGWO) in relation to FS employing bat optimization algorithm (FSBAO). The results obtained from FSBAO approach demonstrate the effectiveness in solving FS optimization problems with at most accuracy of 98.92%. Furthermore, the experimental results have been statistically validated for the greater efficiency of the proposed FSBAO algorithm. This study's findings have crucial implications for developing a software failure prediction models that is more accurate and efficient.

Sustainable (balanced) land use development is the basic foundation for the formation of Ukraine’s land system

The article examines the key aspects of sustainable land use as a foundation for the effective development of rural areas and communities. Balanced land use should be viewed through the lens of land management development and the implementation of integrated land resource management. The excessive intensification of agriculture and the rapid expansion of cities lead to the loss of beneficial land functions, causing land degradation. The study assessed the ecological stability of territories and revealed an unsatisfactory state of ecological stability in Ukraine, associated with the high level of land plowing (68.4%). It is noted that preserving and restoring land as a unique natural resource requires active intervention by government bodies to stimulate measures for the protection and restoration of land resources through the implementation of effective environmental initiatives. Due to progressing erosion, approximately 40–50 billion hryvnias are lost annually. It is proposed to develop a conceptual strategy for enhancing the ecological sustainability of land management at the legislative level, as well as during the reform of land relations, with a focus on the ecological reorganization of the land management system in the country, regions, and newly established territorial communities. Ecologically sustainable land use is possible only with the implementation of scientific and technological advancements and the orientation of the economy towards ecological principles at the national, regional, and local levels. Sustainable land use development means not only the effective utilization of land resources but also the creation of an appropriate land institutional structure and a favorable environment for land resource management, as well as the formation of social relations that promote the rational use of ecosystems while maintaining a balance between environmental, economic, and social components. In recent years, the practice of farming under market conditions has confirmed that the productivity and efficiency of agricultural activities primarily depend on the effective use of agricultural land. Modern research presents various approaches to identifying factors that influence the efficiency of agricultural land use. It should be noted that effective land use in agricultural enterprises must be combined with economic efficiency in land management. One of the important ways to achieve this is by enhancing the operation of agricultural producers, as well as improving the mechanism based on the integration of ecological and economic components, which ensure the quality use of agricultural land.

Evaluating the cost and operational context for national human papillomavirus (HPV) vaccine delivery in three regions of Ethiopia.

Cervical cancer is the second leading cause of cancer deaths among women in Ethiopia. Human papillomavirus (HPV) vaccination is a primary prevention method for cervical cancer and was introduced in Ethiopia in 2018. We conducted a cross-sectional, mixed-methods study to understand the operational context for the HPV vaccination program and the associated costs in three regions of Ethiopia. Operations research provided insights on the frequency and intensity with which HPV vaccination program activities were done, focusing on activities conducted to vaccinate the cohort receiving its first dose in October 2019 and second dose in January 2021. Microcosting was used to estimate the costs to the health system for these activities. Data collection using structured questionnaires was done at 60 health facilities, 17 woredas, and 9 zones/sub-cities that were randomly selected from three purposively selected regions and the national level. Financial costs (monetary outlays) and economic costs (financial costs plus opportunity costs of resource use) were estimated in 2019 US$. Health facilities delivered an average of 411 HPV vaccine doses during the reference period, ranging from a mean of 86 to 606 across the three regions. Aggregated across all levels of the health system, the estimated financial cost per dose across the pooled sample was $2.23, and the economic cost per dose was $7.19, excluding the cost of vaccines and supplies. There were regional variations in these estimates, with mean financial cost per dose ranging from $1.17 to $7.18 and mean economic cost per dose ranging from $5.80 to $18.13 across the three regions. Regional variations exist in the service volume, frequency, and intensity of conducting HPV vaccination activities, as reflected in the estimated costs of delivery. Data generated from this study can be used to inform program planning and budgeting, taking into account regional variations, for effective utilization of resources.

Impact of legal documentation requirements on physician practice using a regional specialty hospital as an example: an inventory

Doctors in German hospitals are critical of their working conditions. They complain about long working hours, inadequate remuneration for their work, poor training and development opportunities, and increasing time spent on administrative tasks. As these points of criticism are largely based on subjective perception, in the present study we documented in detail the workflows of physicians in a major regional hospital, determined the time taken for the workflows, and performed a statistical evaluation of the data. Nine doctors from the specialties of internal medicine, surgery, and anesthesia/intensive care medicine were observed during their shifts for a total period of 216 hours at an urban hospital in Germany. All of the tasks performed by the doctors were recorded in an observation protocol. The time spent daily on documentation by doctors of all specialties was on average 93.1 ± 23.4 minutes, accounting for 19.4 % of a doctor's working hours. The specialists who spent the longest period of time on documentation were internists (120.2 ± 15.0 minutes; 25 %). During an eight-hour working day, computers were used on average for 123.5 ± 44.4 minutes; surgeons spent the shortest period of time on computers (71.5 ± 16.6 minutes). The direct patient-related work time (excluding the time spent on operations) was considerably lower (33.8 + 22.7 minutes; 7 %) than thetime spent daily on documentation, increased to 80.7 ± 62.9 minutes when the time expended on actual surgical tasks was taken into account, and was then similar to the time spent on documentation (93.1 minutes). This pilot study was the first to determine, in real time, the work processes of doctors from different specialties at a German hospital. We noted a disparity between administrative and patient-related tasks in the in-patient setting. Legal and economic requirements exert a negative impact on medical care. We need to develop strategies for effective utilization of medical resources and for ensuring a high standard of medical care.

Improving Sustainable Financing for Universal Health Coverage in Bhutan: Exploring Policy Options and Financial Strategies

AbstractDeeply rooted in its developmental philosophy of gross national happiness (GNH), Bhutan's healthcare system strives towards achieving a shared goal of universal health coverage (UHC). Despite being primarily financed by the government, the health system faces a plethora of challenges. To overcome these hurdles and achieve UHC goals, expanding the fiscal space for health and improving operational efficiency are crucial. This article aims to address Bhutan's evolving healthcare landscape and advance the achievement of UHC through two policy options. The first policy option focuses on the dual objective of improving health outcomes and promoting financial sustainability by leveraging health taxes, whereas the second option emphasizes reinforcing a systematic health technology assessment (HTA) in the Bhutanese health system. First, drawing lessons from global experiences, the policy brief recommends leveraging health taxes to reduce societal and healthcare costs and enhance financial sustainability in the health sector. Considering Bhutan's high prevalence of tobacco and alcohol consumption, and taking opportunity from the ongoing Goods and Services Tax (GST) reform, continued advocacy on health taxes is essential, and soft earmarking the health taxes may be considered to finance a broader array of public health programmes, particularly focusing on the promotion of healthy lifestyle, health screening and outreach public health activities. Second, the integration of HTA into policymaking and decision‐making processes is essential for effective resource allocation in UHC. Nurturing and strengthening the existing HTA governance structure under the Ministry of Health (MoH) and establishing a dedicated multidisciplinary HTA Committee will ensure informed decision‐making and resource optimization. HTA evidence should inform the revision of health service standards, clinical guidelines development, procurement decisions and healthcare priorities. These policy options can assist the country in improving financial sustainability, enhancing effective resource allocation and utilization and improving healthcare delivery, aligning with its vision of GNH and ultimately accelerating progress towards achieving UHC.

Leadership Styles and School Performance: A Comparative Analysis of PEELI-Trained and Non-PEELI-Trained Head Teachers

This study explores the impact of PEELI training on head teachers in Multan, Punjab, Pakistan, focusing on their pivotal role in school performance. A sample of 416 participants, equally divided between trained and untrained head teachers, was selected through random sampling. Data was collected using questionnaires and checklists and analyzed using SPSS-11 software. This descriptive study found that PEELI training significantly enhances headteachers' awareness of children's right to education, barriers to accessing education, and effective resource utilization. Trained head teachers demonstratedbetter practices in using learning aids and maintaining hygienic school conditions, highlighting the importance of in-service training for educational leaders.

Relationship between intellectual capital and firm performance: evidence from the Indian sugar mill industry

PurposeThe aim of the study is to examine the influence of Intellectual Capital (IC) and its components on the financial performance of Indian sugar mill companies.Design/methodology/approachThe present study follows the quantitative research, and uses data from Indian sugar mill companies over the period of recent 10 years. The Modified Value- Added Intellectual Capital (MVAIC) method is employed to evaluate IC. Authors construct panel regression models to test the hypotheses where Return on Equity (RoE) and Return on Asset (RoA) were considered as a representation of financial performance (dependent variable) and IC has been considered as the independent variable along with control variables.FindingsThe findings reveal that IC components show greater explanatory power than aggregate IC and MVAIC has a positive relationship with firm performance. It is evident that Capital Employed Efficiency (CEE) and Relational Capital Efficiency (RCE) have a positive effect on the RoA, while Human Capital Efficiency (HCE) and CEE have a positive impact on RoE. CEE is found to be a highly significant component to explain the financial performance of Indian sugar mill firms.Practical implicationsThe study has practical implications for the policymakers for effective utilization of IC resources for worth enhancement which is essential for the improvement of financial performance.Originality/valueThe research extends the literature of IC by linking it to the financial performance of Indian sugar mill industry.

THE RELATIONSHIP BETWEEN INNOVATION ACTIVITIES AND PROFITABILITY OF BIST MANUFACTURING COMPANIES

This study's purpose is to analyze the connection between innovation activites and profitability of companies trading in the manufacturing industry on the Istanbul Stock Exchange. For this purpose, data from independently audited financial and income statements of 45 manufacturing companies with R&D expenditures during 2008-2021 are analyzed using the panel data. According to the examination's findings, there is an important and advantageous connection between business innovation activities and firm profitability. To conclude, the results of research examining the connection between innovation activity and profitability have both micro and macro implications. On a micro level, the solution assists firms in gaining a competitive advantage by ensuring that existing production systems operate effectively and increasing their share. At the macro level, they improve the welfare of society and help promote long-term economic growth and the accumulation of knowledge, especially the effective and cost-effective utilization of national resources.

Automated Shape Correction for Wood Composites in Continuous Pressing

The effective and comprehensive utilization of forest resources has become the theme of the global “dual-carbon strategy”. Forestry restructured wood is a kind of wood-based panel made of wood-based fiber composite material by high-temperature and high-pressure restructuring–molding, and has become an important material in the field of construction, furniture manufacturing, as well as derivative processing for its excellent physical and mechanical properties, decorative properties, and processing performance. Taking Medium Density Fiberboard (MDF) as the recombinant material as the research object, an event-triggered synergetic control mechanism based on interventional three-way decision making is proposed for the viscoelastic multi-field coupling-distributed agile control of the “fixed thickness section” in the MDF continuous flat-pressing process, where some typical quality control problems of complex plate shape deviations including thickness, slope, depression, and bump tend to occur. Firstly, the idea of constructing the industrial event information of continuous hot pressing based on information granulation is proposed, and the information granulation model of the viscoelastic plate shape process mechanism is established by combining the multi-field coupling effect. Secondly, an FMEA-based cyber granular method for diagnosing and controlling the plate thickness diagnosis and control failure information expression of continuous flat pressing is proposed for the problems of plate thickness control failure and plate thickness deviation defect elimination that are prone to occur in the continuous flat-pressing process. The precise control of the plate thickness in the production process is realized based on event-triggered control to achieve the intelligent identification and processing of the various types of faults. The application test is conducted in the international mainstream production line of a certain type of continuous hot-pressing equipment for the production of 18 mm plate thickness; the synergistic effect is basically synchronized after 3 s, the control accuracy reaches 30%, and the average value of the internal bond strength is 1.40, which ensures the integrity of the slab. Practical tests show that the method in the actual production is feasible and effective, with detection and control accuracy of up to ±0.05 mm, indicating that in the production of E0- and E1-level products, the rate of superior products can reach more than 95%.

Latency-Sensitive Function Placement among Heterogeneous Nodes in Serverless Computing.

Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance.

Resources policy from extraction to innovation: The interplay of minerals, geothermal energy, technological advancements, and ecological footprint in high-ecological footprint economies

The present study aims to examine the impact of minerals, geothermal energy, and technology on the ecological footprint within the top five countries exhibiting the highest ecological footprint. In particular, the study utilizes a panel dataset covering China, the US, India, Brazil, and Japan, spanning the years 1998–2022. The empirical findings indicate that the minerals increase the ecological footprint in the long run. Contrarily, both geothermal energy and technology have the potential to mitigate the ecological footprint in the long run. The aforementioned findings indicate the necessity of reconfiguring the utilization of mineral rent in a manner that enables the revenue derived from minerals to effectively alleviate the ecological footprint. Moreover, it is advisable to formulate strategies aimed at augmenting the utilization of geothermal energy. To plunge the ecological footprint, it is imperative to implement policies that incentivize and facilitate the development and adoption of technological innovations. Overall, the current research suggests that the governments in these sample countries with the highest ecological footprint should formulate policies to effectively manage minerals, promote geothermal energy, and enhance technology innovation for effective utilization of resources.

Improving Performance of Data Science Applications in Python

Objectives: The objective of this paper is to focus on the need of performance optimization of Data Science applications with an aim to enhance the speed, responsiveness, and effective resource utilization. Methods: This paper identifies the factors which effect the performance of an application and then classifies them into internal and external factors. This study mainly focuses on internal factors such as execution speed and space requirement. It uses code developed in Python as use cases to explore and examine features which improve the performance and effectiveness of Data Science (DS) applications. Findings: This paper provides comprehensive way of improving performance of applications by implementing features of Python like generator, vectorization, profiling, concurrency & parallelism, and caching. It is observed that space requirement reduces with the use of generators in code, leading to better space management. The conventional way of writing code in Python required additional 5.2 MiB (Mebibyte) space, while there was no additional space requirement with the use of generators in the same code. It is further observed that vectorization feature of Python helps in reducing execution time in comparison to the code written without vectorization. This paper further uses parallelism in the code and gets approximately 68% improvement in execution time. The implementation of profiling feature helps in identifying the time and space requirement which can be improved using features like generator, vectorization, concurrency, and parallelism. Other features like handling I/O operations, use of caching and choice between flat file data and database are also deliberated to improve the performance of code. Novelty: This paper highlights the potential of improving performance of DS applications using generator, vectorization, concurrency and parallelism features of Python and examines the effectiveness of the claim through its implementation. Keywords: Data Science, Generator, Vectorization, Parallelism, Profiling

Ensemble empirical mode decomposition based deep learning models for forecasting river flow time series

River flow forecasting is important for flood prediction and effective utilization of water resources. This study proposed a comprehensive methodology that simultaneously enables the creation of multiple datasets and a comparison of multiple learning models for forecasting river flows. First, the data characteristics of the river flow time series were assessed based on its nature and pattern characteristics. Second, data decomposition was made for simplifying the complex data into decomposed modes. Ensemble Empirical Mode Decomposition (EEMD) was used for this purpose that provides intrinsic mode functions (IMFs). In addition, as benchmark, traditional time series decomposition was also applied over the complex data. Third, based on their data characteristics, the IMFs were combined into different components. These components along with the traditional time series decomposition create multiple datasets. Then, the components from the different datasets were forecasted using deep learning models, mainly BPNN (Back-Propagation Neural Network), CNN (Convoluted Neural Network) and LSTM (Long-Short Term Memory). Then, ensemble prediction was used to get the final output. For verification and benchmark purpose, we have also used statistical models like SARIMA over the datasets. A comprehensive evaluation and model comparisons were made using Diebold-Mariano (DM) test, k-fold cross validation and with the results of Prophet. In addition, the temporal convolution network (TCN) and the gated recurrent unit (GRU) were also modelled. Finally, all the DL modelling was performed for two data situations: with missing data deleted and missing data imputed. An empirical case study was done considering the average of daily time series data of Neeleswaram Hydrological Observation (HO) station over the Periyar river of South India for the period 2017–2021. The empirical assessment was made using several tests for all the different datasets and different learning models for further insights. Our results indicate that the deep learning-based models offer better predictions for complex time series in comparison to traditional statistical models. The DL models applied on the original time series data provided robust predictions with good performance such as low RMSE and high correlation values. However, the DL models’ performances are not enhanced through integration with time series decomposition and EEMD. Furthermore, the empirical results and the k-fold cross validation-based test indicate that all the DL models show equivalent performances. Interestingly, the DL models show superior performance over Prophet.

Duration and resource constraint prediction models for construction projects using regression machine learning method

PurposeThe construction projects are highly subjected to uncertainties, which result in overruns in time and cost. Realistic estimates of workforce and duration are imperative for construction projects to attain their intended objectives. The aim of this study is to provide accurate labor and duration estimates for the construction projects, considering actual uncertainties.Design/methodology/approachThe dataset was formulated from the information collected from 186 construction projects through direct interviews, group discussions and questionnaire methods. The actual uncertainties and exposure conditions of construction activities were recorded. The data were verified with the standard guideline to remove the outliers. The prediction model was developed using support vector regression (SVR), a machine learning (ML) method. The performance was evaluated using the widely adopted regression metrics. Further, the cross validation was made with the visualization of residuals and predicted errors, ridge regression with transformed target distribution and a Gaussian Naive Bayes (NB) regressor.FindingsThe prediction models predicted the duration and labor requirements with the consideration of actual uncertainties. The residual plot indicated the appropriate use of SVR to develop the prediction model. The duration (DC) and resource constraint (RC) prediction models obtained 80 and 82% accuracy, respectively. Besides, the developed model obtained better accuracy for the training and test scores than the Gaussian NB regressor. Further, the range of the explained variance score and R2 was from 0.95 to 0.97, indicating better efficiency compared with other prediction models.Research limitations/implicationsThe researchers will utilize the research findings to estimate the duration and labor requirements under uncertain conditions and further improve the construction project management practices.Practical implicationsThe research findings will enable industry practitioners to accurately estimate the duration and labor requirements, considering historical uncertain conditions. A precise estimation of resources will ensure the attainment of the intended project outcomes.Social implicationsDelays in construction projects will be reduced by implementing the research findings, which significantly ensures the effective utilization of resources and attainment of other economic benefits. The policymakers will develop a guideline to develop a database to collect the uncertainties of the construction projects and relatively estimate the resource requirements.Originality/valueThis is the first study to consider the actual uncertainties of construction projects to develop RC and DC prediction models. The developed prediction models accurately estimate the duration and labor requirements with minimal computational time. The industry practitioners will be able to accurately estimate the duration and labor requirements using the developed models.