Performance Index Research Articles

Advancing rice breeding for drought tolerance: a comprehensive study of traditional and mutant lines through agronomic performance and drought tolerance indices

Advancing rice breeding for drought tolerance: a comprehensive study of traditional and mutant lines through agronomic performance and drought tolerance indices

Optimum solution of power flow problem based on search and rescue algorithm

AbstractIn order to solve the optimal power flow (OPF) problem, a unique algorithm based on a search and rescue method is applied in this study. For the OPF problem under three objective functions, the SAR offers a straightforward and reliable solution. The three objective functions are used to minimize the fuel cost, power loss and voltage deviation as a single objective function. The OPF problem for benchmark test system, including the IEEE-14 bus, IEEE-30 bus, and IEEE-57 bus, are solved by the Search and Rescue algorithm (SAR) under specific objective functions that are determined by the operational and economic performance indices of the power system. To demonstrate the efficacy and possibilities of the SAR algorithm, SAR is contrasted with alternative optimization techniques such as harmony search algorithm, gradient method, adaptive genetic algorithm, biogeography-based optimization, Artificial bee colony, gravitational search algorithm, particle swarm optimization, Jaya algorithm, enhanced genetic algorithm, modified shuffle frog leaping algorithm, practical swarm optimizer, Moth flam optimizer, whale and moth flam optimizer, grey wolf optimizer, cheap optimization algorithm and differential evolution algorithm. The value of minimum power losses based on SAR technique is equal to 0.459733441487247 MW for IEEE-14 bus. The value of minimum total fuel cost based on SAR technique is equal to 8051.12225602148 $/h for IEEE-14 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0357680148269292 for IEEE-14 bus. The value of minimum power losses based on SAR technique is equal to 2.71286428848434 MW for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 798.197578585806 $/h for IEEE-30 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0978069572088536 for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 38017.7691758245 $/h for IEEE-57 bus. The acquired results for the OPF compared to all competitor algorithms in every case of fitness function demonstrate the superiority of the SAR method.

How to arrange the robotic environment? Leveraging experience in both motion planning and environment optimization

This study presents an experience-based hierarchical-structure optimization algorithm to address the robotic system environment design problem, which combines motion planning and environment arrangement problems together. The motion planning problem, which could be defined as a multiple-degree-of-freedom (m-DOF) problem, together with the environment arrangement problem, which could be defined as a free DOF problem, is a high-dimensional optimization problem. Therefore, the hierarchical structure was established, with the higher layer solving the environment arrangement problem and lower layer solving the problem of motion planning. Previously planned trajectories and past results for this design problem were first constructed as datasets; however, they cannot be seen as optimal. Therefore, this study proposed an experience-reuse manner, which selected the most “useful” experience from the datasets and reused it to query new problems, optimize the results in the datasets, and provide better environment arrangement with shorter path lengths within the same time. Therefore, a hierarchical structural caseGA-ERTC algorithm was proposed. In the higher layer, a novel approach employing the case-injected genetic algorithm (GA) was implemented to tackle optimization challenges in robot environment design, leveraging experiential insights. Performance indices in the arrangement of the robot system’s environment were determined by the robotic arm’s motion and path length calculated using an experience-driven random tree (ERT) algorithm. Moreover, the effectiveness of the proposed method is illustrated with the 12.59% decrease in path lengths by solving different settings of hard problems and 5.05% decrease in easy problems compared with other state-of-the-art methods in three small robots.

Photosynthetic efficiency and water retention in okra (Abelmoschus esculentus) contribute to tolerance to single and combined effects of drought and heat stress.

The co-occurrence of drought and heat significantly hampers plant productivity. Although their impacts are well studied, these studies have been based on the effects of individual stressors rather than their combined influence. Okra is crucial for food and nutritional security and livelihoods in many regions, yet it remains under-researched and unimproved. Okra has been proven to be sensitive to both drought and heat stress. This study employed a cost-effective phenotyping method to assess key traits characterising the diversity of okra morphophysiological responses to independent and interactive heat-drought stresses. This study aimed to understand okra responses to stress, identify stress-resilient traits, and characterise okra genotypes. We also addressed the need to examine interactive stress effects, which mirror real-world scenarios more accurately than single-stress studies. Sixty-three okra genotypes were subjected to heat, drought, or concurrent heat-drought stress at the seedling stage in improvised climate-controlled chambers. The germplasm exhibited significant variations in response to the various stresses. The broad-sense heritability was high (> 0.60) for traits such as chlorophyll content, plant biomass, performance indices, electrolyte leakage, and total leaf area. Drought stress alone had a more pronounced effect than heat stress alone, and the adverse impact was worsened under combined heat and drought stress. The interactive impact of drought and heat was more likely additive than antagonistic or synergistic. A positive and strong relationship was observed between photosynthetic efficiency parameters such as the Fv/Fm ratio, chlorophyll content, relative water index, and biomass parameters such as dry shoot weight. The 63 okra genotypes were classified into three distinct clusters, suggesting potential for future breeding efforts. Okra genotype considered to be tolerant or climate resilient (such as GH170, V1060831, GH174, V1060874, and GH106) to drought and heat, maintained enhanced photosynthetic efficiency and high internal water potential, possibly reducing osmotic and oxidative damage. This study revealed some mechanisms underlying the adaptation of okra genotypes to independent and combined heat and drought stress. The results provide a basis for breeding efforts to develop climate-resilient okra varieties.

The triple-helix model as foundation of innovative entrepreneurial ecosystems

Purpose Recommend ways to increase the entrepreneurial performance score for city-centered entrepreneurial ecosystems (EES) and the possible role within the triplex helix concept. The former applies to lower-ranked EESs. For the latter, the authors hypothesize that the standard triple helix model of counting on some existing, well known multi-party interest in bringing together businesses and other stakeholders (especially universities and government) is insufficient. Including more players and alternative cooperation models may truly enable a long lasting productive interplay of cooperative and competitive forces leading to genuinely innovative (non-marginal, non-opportunistic) businesses and also to more regional well-being. This study aims at leveraging the city-based view on entrepreneurial ecosystems, to enable new ways of boosting the functionality of triple-helix minded programs, especially so for emerging economies and their cross-border regions. Design/methodology/approach Methodology is dual: (1) paying tribute to the history of entrepreneurial behavior and reviewing the diverse sources of support (institutional and otherwise), eventually leading to today’s concentrations of successful innovative business, and (2) analyzing modern data on performance of city-centered EESs. Putting emphasis on path-dependence (meaning that history matters) the authors analyze data distributions of measures concerning city-centered EESs and also nation-wide entrepreneurial aggregates, searching signs of long-tail statistic, an indicator of path dependence. Furthermore the authors statistically analyze if and to what extend, key university performance indices translate into successful EESs. From these results, recommendation follow. Findings Historical developments and cultural backgrounds of different countries lead to different ways of support for new ideas and approaches. Entrepreneurship success is path-dependent and there are entrepreneurial approaches more easy – but others way more difficult – to imitate (e.g. “Hidden Champions”). Statistical analysis of entrepreneurial performance data confirms power law decay, which confirms the action of path-depend processes. Statistical models show that university performance is just weakly influencing entrepreneurial success. This leads to proposing a new, polycentric approach for organizing robust cooperation between the stakeholders, potentially capable of lifting low-score EESs into a more productive regime. Research limitations/implications Research limitation primarily concern data availability, as potentially useful information is not publicly available. The findings lead to further questions, and to questioning too optimistic expectations about cooperation readiness of the implied triple helix stakeholders. As pointed out repeatedly, other important players are non-university research organizations with more focused goals (national labs, etc.), which should be explicitly accounted for. Another directions is distinguishing between support for (a) short-term high-tech, and for (b) longer term, deep-tech entrepreneurs. The latter may require new evaluation techniques and specially tailored private-public partnerships. Finally, polycentric and polycentric cross-border cooperation requires more research into networked organizations. Practical implications Triple-helix based, cooperative agreements should rethink the effective functionality of EESs. City-centered, EESs tend to be most expressive in their projects but also subject to a stronger long tail-effect. This implies “success-breads-success” for the front-runners, enjoying very low ranking mobility. Lower-positioned EESs may profit from the mobility within this score region. However, the still lower positioned ones face strong downward rank mobility, implying that special effort (support) is needed. The authors’ proposal of polycentric cooperation, especially the cross-border variant, would credibly enable such support by bringing in both, more players and more levels of play, helping robust networking. Social implications This research has potentially profound social implications. Acknowledging the path-dependence of intellectual capital formation and emergence of entrepreneurial spirit in the modern sense helps to understand which techno-entrepreneurial endeavors would have increased chance of success of being brought up in the respective EES. It is also useful to identify the oversold (over-hyped) entrepreneurial projects. Concerning the cross-border polycentric organizations harboring EESs one may have to concede that national borders are sub-optimal, at least with regard to lifting up the entrepreneurial reputation of border-adjacent regions. Implicitly, opportunistic and marginal entrepreneurship (e.g. in Eastern Europe) would reduce. Originality/value This research claims originality for combining a qualitative-historical approach with a statistical modeling approach, which can explain the dynamic nature of the involved entrepreneurial processes (long-tails). While the historical approach is deemed to be necessary for understanding the reasons for the large differences in entrepreneurial success across nations and regions, the statistical modeling is helping to identify lower-score intervals within the entrepreneurial score hierarchy which are more easy, or, alternatively, very hard to escape. This in turn leads to adapted recommendations and mobilizing special efforts for creating new, potentially robust, cooperative arrangements beyond the triple-helix concept.

Quantum Control Design by Lyapunov Trajectory Tracking and Optimal Control

In this paper, we investigate a Lyapunov trajectory tracking design method that incorporates a Schrödinger equation with a dipole subterm and polarizability. Our findings suggest that the proposed control law can overcome the limitations of certain existing control laws that do not converge. By integrating a quadratic performance index, we introduce an optimal control law, which we subsequently analyze for stability and optimality. We also simulate the spin-1/2 particle system to illustrate our results. These findings are further validated through numerical illustrations involving a 3D, 5D system, and a spin-1/2 particle system.

Hydrological Modeling of Krishna Upper Catchment Area of India Using Multisite Calibration and Validation of SWAT Model

Multisite calibration & validation of model reduces the uncertainty present in the model parameters and improve the SWAT model output. In the present study, an effort was made to set up SWAT model along with multisite calibration and validation for Kishna upper basin of India. A total of six gauging stations maintained by Central water commission across the area were selected for SWAT model calibration and validation. Sequential Uncertainty Fitting (SUFI-2) programme of SWAT-CUP (Calibration and Uncertainty Programme) was employed to calibrate the SWAT model. During sensitivity analysis of SWAT model, parameters which are highly sensitive that influences flow characteristics of different gauging station were identified. These parameters along with parameter range were used to calibration and validation of SWAT model each of the sub basins. Observed flow and predicted stream flows were compared with certain model performance indices. These indices indicated that for all six gauging stations, the SWAT model found to be very good. The performance indices like R2, NSE, RSR and PBIAS were ranged from 0.70 to 0.84, 0.67 to 0.76, 0.49 to 0.57 and -11.7 to 24.0% respectively during model calibration period and 0.70 to 0.93, 0.67 to 0.83, 0.41 to 0.58 and 8.5 to 22.9% respectively during model validation period. A good agreement between observed and simulated flows at all the gauging stations were observed. It can be concluded that SWAT model output was improved with multisite calibration and validation of the model.

Impact of Corporate Governance on Firms’ Sustainability Performance: Case Study of BIST 50 Index Companies

Purpose: the purpose of this study is to investigate whether corporate governance mechanisms and attributes influence the sustainability performance of companies included in the BIST 50 Index. Results and contributions: Regression analysis showed that there was a significant positive influence of board tenure on sustainability performance and all its types; board size on environmental performance; and a dummy variable for board evaluation externally facilitated and company size on sustainability, environmental, and social performance. A significant negative impact of director attendance at board meetings on social performance was also revealed. This study contributes to the literature on the role of corporate governance in achieving the SDGs for BIST 50 Index companies, highlighting the significant impact of its individual indicators on the achievement of sustainability performance. Methodology: The authors reviewed 45 sustainability reports of BIST 50 Index companies for 2023. Four indices—Sustainability Performance, Environmental Performance, Social Performance, and Corporate Governance Performance Indexes—were developed to characterize sustainability performance and its types based on a content analysis of sustainability disclosures. To analyze the influence of mechanisms and characteristics of the corporate governance system on sustainability performance, eight independent variables were used: board size, number of board meetings, director attendance at board meetings, board independence, board tenure, a dummy variable for board evaluation externally facilitated, a dummy variable for internal auditors present, and a dummy variable for CEO and Chair functions combined. Two control variables, company size and leverage, were used as well. Gap: Today, the scientific literature has no universal approach and understanding of how the corporate governance system should be developed to improve sustainability performance or its individual components. Relevance: Development of a corporate governance system is one of the ways to increase the level of sustainability performance of companies. Impact: The results of the study made it possible to produce several recommendations (expand the number of board members, develop an effective procedure for regular changes of general directors in company boards, introduce independent external control tools in the corporate governance systems of companies) that will lead to the achievement of SDGs 5, 8, 16.

Assessing Project Resilience Through Reference Class Forecasting and Radial Basis Function Neural Network

A project needs to be able to anticipate potential threats, respond effectively to adverse events, and adapt to environmental changes. This overall capability is known as project resilience. In order to make efficient project decisions when the project is subjected to disruption, such as adjusting the project budget, reformulating the work plan, and rationalizing the allocation of resources, it is necessary to quantitatively understand the level of project resilience. Therefore, this paper develops a novel approach for forecasting project performance, illustrating the changes in performance levels during the disruption and recovery phases of a project and thus quantitatively assessing project resilience. While there are several methods for assessing project resilience in existing research, the majority of assessment approaches originate from within projects and are highly subjective, which makes it difficult to objectively reflect the level of project resilience. Moreover, the availability of project samples is limited, which makes it difficult to forecast the level of project performance. In view of the fact that the Reference Class Forecasting (RCF) technique avoids subjectivity and the Radial Basis Function (RBF) neural network is known to be better at forecasting small sample datasets, this paper therefore combines the RCF technique and the RBF neural network to construct a model that forecasts the project performance of the current project after experiencing a disruption, further assessing the level of the project resilience. Specifically, this paper first presents a conceptual model of project resilience assessment; subsequently, an RBF neural network model that takes into account project budget, duration, risk level of disruption, and performance before disruption based on the RCF technique is developed to forecast project performance after experiencing disruption; and finally, the level of project resilience is assessed through calculating the ratio of recovery to loss of project performance. The model is trained and validated using 64 completed construction projects with disruptions as the datasets. The results show that the average relative errors between the forecast results of schedule performance index (SPI) and the real values are less than 5%, and the R2 of the training set and the testing set is 0.991 and 0.964, respectively, and the discrepancy between the forecasted and real values of project resilience is less than 10%. These illustrate that the model performs well and is feasible for quantifying the level of project resilience, clarifying its impact on project disruption and recovery situations, and facilitating the decision-makers of the project to make reasonable decisions.

Autistic adults exhibit holistic face processing: evidence from inversion and composite face effects

Holistic processing is commonly measured by the face inversion effect (FIE) and the composite face effect (CFE). Previous studies examining whether individuals with autism spectrum disorder (ASD) employ holistic processing using either FIE or CFE have reported inconclusive results. By adopting a customized composite face paradigm, the present study aims to simultaneously assess both the inversion and the composite effects of holistic processing in autistic and neurotypical adults. We tested 24 adults with ASD and 24 neurotypical (NT) adults matched in age, gender, and years of education. Participants viewed sequentially presented composite faces in three Presentation Modes (aligned, inverted, and misaligned) with three Stimuli Conditions (same, composite, and different) and judged whether the top half was the same. For the dependent variables, we calculated a “performance index” in the form of the accuracy/response time of each stimuli condition in each presentation mode. The FIE and CFE were computed to index the magnitude of holistic processing. Our results showed that the NT group responded more accurately in less time than the ASD group across task conditions. Notably, both the NT and the ASD groups exhibited a significant FIE with similar magnitude. Likewise, both the NT and the ASD groups showed a greater-than-zero CFE. Moreover, individuals’ CFE positively correlated with FIE and negatively correlated with the AQ scores for all participants. In summary, individuals with ASD exhibit holistic processing when viewing faces, evidenced by the presence of both FIE and CFE and the positive correlations between the two effects.

Heart ultrasound and biomechanical evaluation of radiation‐induced heart toxicity using transthoracic echocardiogram (TTE) and dynamic mechanical analysis (DMA)

AbstractRadiation‐induced heart disease (RIHD) is a serious complication but difficult to assess in patients undergoing thoracic radiotherapy (RT). We aim to analyze RIHD using heart ultrasound and elastic modulus, exploring relationships between functional, anatomical or biomechanical changes of the heart and radiation dose.Twenty BALB/c mice were divided into four groups (control, 10 Gy, 20 Gy and 25 Gy) with a single fraction of image‐guided volumetric modulated arc radiotherapy (VMAT) to murine heart on a linear accelerator. Transthoracic echocardiography (TTE) was performed on a small‐animal ultrasound imaging system with a handheld microscan transducer. E‐wave/A‐wave ratio (E/A) and myocardial performance index (MPI) for diastolic performance were noninvasively evaluated weekly, as well as ejection fraction (EF%), fractional shortening (FS%), left ventricle (LV) mass and heart wall thickening for systolic performance. At the end of the fifth week, all mice were sacrificed for elastic modulus measurement on a dynamic mechanical analyzer (DMA) and for histopathological staining. All experiments were conducted in accordance with the local institution's animal research committee guideline.Significant difference was observed in E/A ratio between the control and 25 Gy irradiated groups (1.8±0.5 and 0.7±0.9, respectively; p<0.05), indicating reduced diastolic performance and increased stiffness in left ventricle after high‐dose heart radiation. Diastolic dysfunction in irradiated groups was also observed with significantly increased MPI. In contrast, posterior wall thickness, aortic peak velocity, heart rate, EF and FS were not significantly different after RT. Heart elasticity was reduced substantially with the increased radiation dose. HE and Masson Trichrome staining confirmed more fibrosis deposition in irradiated hearts.RIHD evaluation with ultrasound imaging noninvasively and biomechanical modulus measurement invasively in the image guided, precision dose‐escalated murine heart irradiation is feasible. Increased myocardial stiffness, abnormal diastolic relaxation, more collagen deposition, and reduced tissue elasticity are observed in irradiated heart tissue. This study may facilitate our understanding of RIHD and facilitate improving patients’ quality of life in the future.

Comparison of the Short Time Effect of an Oral Hygiene Education in Four Sessions via Quantitative Light-Induced Fluorescence Technology Versus Disclosing Agents in Children: A Randomized, Crossover Clinical Trial

Objectives: The aim of this study is to compare the effectiveness of Qscan plus™ (AIOBIO, Seoul, Korea) based on quantitative light-induced fluorescence (QLF) technology and disclosing agents in oral health programs in children. Methods: A randomized crossover study was conducted for Korean children aged 6–11 years. Fifty-eight participants (29 to use Qscan plus™ first and 29 to use the disclosing agent first) were enrolled in this study. The participants were randomly divided into two groups. One group was assigned to brush with Qscan plus™, while the other group brushed with disclosed plaque visualization. One month later, the groups switched procedures. A total of 39 participants were analyzed, excluding those lost during the trial. There was no adverse event during the trial. The patient hygiene performance (PHP) index was used to assess oral hygiene status, and questionnaires about oral health behavior and attitude were completed. The data were analyzed using repeated-measure analysis of variance, with a significance level of p < 0.05. Results: The PHP score decreased significantly on post-brushing and follow-up compared to baseline in both methods (p < 0.001), but there was no significant difference between the two methods. After oral hygiene education, participants’ brushing time increased, and their oral care attitudes improved. More participants preferred the Qscan device to the disclosed plaque visualization because it is more easily noticeable. Conclusions: The Qscan device has a similar educational effect as disclosing agents, and can be used as a supplementary tool to encourage children in oral hygiene education.

Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks.

Automatic screening of retinal lesions for detecting diabetic retinopathy using adaptive multiscale MobileNet with abnormality segmentation from public dataset

ABSTRACT Owing to the epidemic growth of diabetes, ophthalmologists need to examine the huge fundus images for diagnosing the disease of Diabetic Retinopathy (DR). Without proper knowledge, people are too lethargic to detect the DR. Therefore, the early diagnosis system is requisite for treating ailments in the medical industry. Therefore, a novel deep model-based DR detection structure is recommended to fix the aforementioned difficulties. The developed deep model-based diabetic retinopathy detection process is performed adaptively. The DR detection process is imitated by garnering the images from benchmark sources. The gathered images are further preceded by the abnormality segmentation phase. Here, the Residual TransUNet with Enhanced loss function is used to employ the abnormality segmentation, and the loss function in this structure may be helpful to lessen the error in the segmentation procedure. Further, the segmented images are passed to the final phase of retinopathy detection. At this phase, the detection is carried out through the Adaptive Multiscale MobileNet. The variables in the AMMNet are optimized by the Adaptive Puzzle Optimization to obtain better detection performance. Finally, the effectiveness of the offered approach is confirmed by the experimentation procedure over various performance indices.

NIMG-06. AUTOMATED VOLUMETRY OF MENINGIOMAS IN CONTRAST-ENHANCED T1-WEIGHTED MRI USING DEEP LEARNING

Abstract BACKGROUND With the widespread use of MRI equipment and brain docks, there are more opportunities to perform follow-up observation for meningiomas. In recent years, attempts have been made to predict prognosis by using tumor volume as a highly objective imaging biomarker. On the other hand, manual lesion extraction by a reader with anatomical knowledge is indispensable for volume measurement, which requires a certain amount of labor and time. This is a major barrier to the generation of evidence and clinical application in this field. OBJECTIVE To establish the clinical significance of tumor volume as an imaging biomarker in meningiomas, and to develop a simple and highly reproducible application for automated volume measurement using artificial intelligence, and to investigate its accuracy and potential for clinical application. METHODS 178 patients with meningiomas who underwent MRI at Osaka University Hospital were included in the study. Contrast-enhanced T1-weighted images were used for manual lesion extraction by two neurosurgeons. These annotated image data were randomly divided into training (90%) and testing (10%). The deeplabV3 model was used to train the artificial intelligence, and leave-one-out cross-validation was used to train and validate the automated model. The test data were used for comparison, and the agreement rate (Dice index) on the images of the automatically extracted regions and the error rate of the calculated tumor volume (mean square error rate: RMSPE) were used as indices of measurement performance. RESULTS The average agreement rate between the automatically extracted area and the test data was 92.3±5.1%. The error rate of tumor volume was 9.5±1.2%. CONCLUSION This application using artificial intelligence was considered to have a certain validity in the accuracy of automatic lesion extraction. This method, which enables simple tumor volume measurement, is expected to create new imaging biomarkers that will contribute to the optimization and personalization of meningioma treatment.

Environmental Threat Assessment Framework for Mining Activities in Guinea: An Integrated Approach for Sustainable Development.

The present study aimed to investigate the environmental consequences of mining activities in boke bauxite mining areas and the Kerouane iron mining project in Guinea using a mixed-methods approach that combines quantitative and qualitative data. A reference matrix was used to evaluate the impact of the mining activities, classifying them as negligible, moderate, or significant. Data were collected from October 2022 to January 2023 by assessing their impact on water pollution, soil, noise, air quality, vegetation, fauna, and flora. These findings indicate concerns regarding the water pH, electrical conductivity, and turbidity in both the Boke and Kerouane regions. The soil composition analysis revealed the presence of Cr, Cu, Ni, and Zn in both the Boke and Kerouane project areas. Noise levels exceeded the guideline levels and substantial amounts of particulate matter (PM) were detected, with high levels of PM10 in mining operations. The Kerouane Iron Project resulted in the direct loss of 2929 ha of natural habitats and 466 ha of modified habitats, with the extraction of approximately 1.291 billion tons of ore anticipated over a 22-year mine lifespan. This study proposes an environmental threat assessment framework that integrates technical and human activity data to evaluate the environmental impacts of mining activities comprehensively. To promote sustainable development and minimize the negative impacts of mining, an integrated index of economic and environmental performance in the mining sector is recommended, along with collaboration between researchers and policymakers to develop effective climate-change mitigation strategies.

The macroeconomic determinants of trade openness in Latin American countries: A panel data analysis

Background Trade openness shows a positive impact on economic growth, supported by economic theory, and export diversification and economic complexity show a positive dynamic in trade openness in the world; however, a specificity is generated in South American countries. Therefore, the objective of the research is to analyse the macroeconomic determinants of trade openness in Latin American countries. Methods The research approach was quantitative and explanatory using panel data methodology from the databases of the World Bank, Harvard University and the Economic Commission for Latin America and the Caribbean for the period 2000-2020. Results The fixed effects panel data model showed that the variables that had a negative impact on trade openness were GDP, the economic complexity index and the logistic performance index, while the variables that had a positive impact were exports of high-tech products (a proxy for innovation), exports, imports, research and development expenditure and interregional trade in goods. Conclusions Therefore, during the analysis period of 2000-2020 in South America, based on the panel data analysis under fixed effects, a total of 8 countries had a negative impact on trade openness, and only the economies of Chile, French Guiana, and Brazil had a positive impact on trade openness; these economies are characterized by their better performance in the economic complexity index, their higher percentage of budget for research and development expenses, and their trade policies oriented towards the industrialization of their value-added products.

Development of a Smart Lock System using QR Code Technology

A smart lock system refers to a modern security solution that enables users to remotely control access to their homes or businesses using a smart device such as smartphone, tablet, or computer. This paper presents the development of a smart lock system using Quick Response (QR) code technology to address the problem of insecurity, inconvenience, and inflexibility in access control. The proposed system eliminates the need for physical keys or complex authentication and verification methods. The system integrates an ESP8266 microcontroller, barcode scanner, 16x2 liquid-crystal display (LCD), solenoid lock, relay, and a 12V power supply. The system allows authorised users to generate unique QR codes representing their access credentials through a user-friendly interface. These QR codes are scanned by the system, which processes the codes, verifies access permissions via a central server and provides real-time feedback through the LCD. Upon successful verification, the system triggers a relay to control the solenoid lock, to grant or deny access. The implementation and testing of the smart lock system demonstrated successful operation. The performance indices include authentication speed, system reliability, accuracy and response time, which were evaluated and found to be satisfactory. The obtained results showed that the proposed system outperformed traditional methods such as keys, and fingerprint authentication, with an access speed of 2.00 seconds, which is significantly faster than the alternatives. Comparative analysis with existing access control techniques demonstrated the developed system's enhanced security features and user-friendly operation, making it applicable for residential, commercial, and industrial use. Additionally, the system's scalability and adaptability allow it to be customised for various environments, offering a versatile solution to diverse security challenges. This work contributes to the field of security technology by providing an efficient and scalable smart lock system with novel algorithms, which enhances the convenience, robustness of access control and seamless user experience.

Addressing the Global Logistics Performance Index Rankings with Methodological Insights and an Innovative Decision Support Framework

This study examines the Logistics Performance Index (LPI) rankings developed by the World Bank from a methodological perspective and proposes an alternative decision support framework. LPI serves as an interactive tool that helps countries identify challenges, innovative solutions, and opportunities in their trade and logistics sectors. In this study, the efficiency of logistics operations in 118 countries was evaluated using an integrated multi-criteria decision-making (MCDM) model objectively weighted by the Entropy method. Countries were ranked using the MCRAT, SAW, TOPSIS, and FUCA methods. According to the findings, large datasets provide more robust insights for sensitivity analyses, and wider weighting coefficient combinations make the data more meaningful. In addition, it is suggested to use low-compensation methods instead of classical additive methods for LPI. Unlike other studies in literature, this research applied an innovative sensitivity analysis to test the robustness of the model and comprehensively examined the effects of weighting techniques based on over 2500 different MCDM results. The findings suggest that the FUCA method should be recommended to decision-makers for calculating LPI rankings due to its simplicity, practicality, low compensatory power, and low sensitivity. This study offers methodological improvements when evaluating logistics performance and provides significant contributions to decision-making processes. The findings are expected to provide a valuable resource for policymakers and businesses in understanding a country’s position in global competition, as well as serving as a reference for researchers evaluating the logistics performance of countries.

Life Cycle Assessment and Experimental Mechanical Investigation of Test Samples for High-Performance Racing Boats

This paper investigates the environmental impact and mechanical performance of two composite sandwich structures, named Series 1 and Series 2, used in high-performance racing boats. Mechanical tests, including four-point bending and drop impact tests, were performed. It was found on a general basis that Series 2 has higher load-bearing capacity and limited deflection. Series 1, which has a higher density, was able to absorb more impact energy but was more susceptible to damage. A Life Cycle Assessment (LCA) was conducted to evaluate the environmental impact associated with the materials, considering also the testing phase, which plays an important role in the life cycle of materials and structures for advanced marine applications. In addition, two performance indexes were introduced to correlate the mechanical and environmental properties of the analyzed materials. This study emphasizes the importance of considering the testing phase in LCA, as the energy-intensive nature of mechanical testing contributes significantly to the overall environmental impact. The introduced indexes allow for a more comprehensive understanding of the balance between mechanical performance and environmental sustainability. The findings suggest a trade-off between mechanical performance and sustainability, calling for further research into recyclable composites and greener manufacturing processes to balance these competing priorities.