bluuebunny/crossref_metadata_2025 huggingface dataset:
Flight Attitude Estimation with Radar for Remote Sensing Applications
Authors: Weber Christoph , Eggert Marius , Udelhoven Thomas
Published: July 2024

Unmanned aerial vehicles (UAVs) and radar technology have benefitted from breakthroughs in recent decades. Both technologies have found applications independently of each other, but together, they also unlock new possibilities, especially for remote sensing applications. One of the key factors for a remote sensing system is the estimation of the flight attitude. Despite the advancements, accurate attitude estimation remains a significant challenge, particularly due to the limitations of a conventional Inertial Measurement Unit (IMU). Because these sensors may suffer from issues such as drifting, additional effort is required to obtain a stable attitude. Against that background, this study introduces a novel methodology for making an attitude estimation using radar data. Herein, we present a drone measurement system and detail its calculation process. We also demonstrate our results using three flight scenarios and outline the limitations of the approach. The results show that the roll and pitch angles can be calculated using the radar data, and we conclude that the findings of this research will help to improve the flight attitude estimation of remote sensing flights with a radar sensor.
Infrared Bilateral Polarity Ship Detection in Complex Maritime Scenarios
Authors: Lu Dongming , Teng Longyin , Tan Jiangyun , Wang Mengke , Tian Zechen , Wang Guihua
Published: July 2024

In complex maritime scenarios where the grayscale polarity of ships is unknown, existing infrared ship detection methods may struggle to accurately detect ships among significant interference. To address this issue, this paper first proposes an infrared image smoothing method composed of Grayscale Morphological Reconstruction (GMR) and a Relative Total Variation (RTV). Additionally, a detection method considering the grayscale uniformity of ships and integrating shape and spatiotemporal features is established for detecting bright and dark ships in complex maritime scenarios. Initially, the input infrared images undergo opening (closing)-based GMR to preserve dark (bright) blobs with the opposite suppressed, followed by smoothing the image with the relative total variation model to reduce clutter and enhance the contrast of the ship. Subsequently, Maximally Stable Extremal Regions (MSER) are extracted from the smoothed image as candidate targets, and the results from the bright and dark channels are merged. Shape features are then utilized to eliminate clutter interference, yielding single-frame detection results. Finally, leveraging the stability of ships and the fluctuation of clutter, true targets are preserved through a multi-frame matching strategy. Experimental results demonstrate that the proposed method outperforms ITDBE, MRMF, and TFMSER in seven image sequences, achieving accurate and effective detection of both bright and dark polarity ship targets.
A Comparative Study of Causality Detection Methods in Root Cause Diagnosis: From Industrial Processes to Brain Networks
Authors: Zhou Sun , Cai He , Chen Huazhen , Ye Lishan
Published: July 2024

Abstracting causal knowledge from process measurements has become an appealing topic for decades, especially for fault root cause analysis (RCA) based on signals recorded by multiple sensors in a complex system. Although many causality detection methods have been developed and applied in different fields, some research communities may have an idiosyncratic implementation of their preferred methods, with limited accessibility to the wider community. Targeting interested experimental researchers and engineers, this paper provides a comprehensive comparison of data-based causality detection methods in root cause diagnosis across two distinct domains. We provide a possible taxonomy of those methods followed by descriptions of the main motivations of those concepts. Of the two cases we investigated, one is a root cause diagnosis of plant-wide oscillations in an industrial process, while the other is the localization of the epileptogenic focus in a human brain network where the connectivity pattern is transient and even more complex. Considering the differences in various causality detection methods, we designed several sets of experiments so that for each case, a total of 11 methods could be appropriately compared under a unified and reasonable evaluation framework. In each case, these methods were implemented separately and in a standard way to infer causal interactions among multiple variables to thus establish the causal network for RCA. From the cross-domain investigation, several findings are presented along with insights into them, including an interpretative pitfall that warrants caution.
Effortless Totem-Pole Converter Control Using a Power Factor Correction Peak Current-Mode Controller
Authors: Alsalemi Abdulazeez , Massoud Ahmed
Published: July 2024

This paper expands a recently proposed peak current-mode (PCM) control method for a power factor correction (PFC) boost converter to include the totem-pole converter and solves the controller’s compatibility problem with the totem-pole converter by proposing three input current sensing methods. Using MATLAB/Simulink 2023b, simulation experiments on a 2 kW totem-pole converter utilizing the PFC PCM controller were carried out to assess the performance of the controller with the proposed sensing methods. The findings indicate that under steady-state conditions, all three proposed sensing methods performed input current shaping successfully and yielded nearly identical THD% of about 4.4% in the input current waveform. However, it is noteworthy that method 2, referred to as the memory method, exhibited a sluggish and less robust transient response in comparison to the swift and resilient responses observed with method 1 and method 3. Additionally, the third proposed method, which involves a single current sensor positioned across the input inductor, emerged as the optimal and cost-effective sensing solution. This method achieved the same desirable attributes of fast and robust control while utilizing only a single current sensor, a notable advantage over method 1, which employs two current sensors.
Overview of Radar Alignment Methods and Analysis of Radar Misalignment’s Impact on Active Safety and Autonomous Systems
Authors: Burza Rafał Michał
Published: July 2024

The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor’s mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment’s influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers.
Eye Tracking Post Processing to Detect Visual Artifacts and Quantify Visual Attention under Cognitive Task Activity during fMRI
Authors: Leharanger Maxime , Liu Pan , Vandromme Luc , Balédent Olivier
Published: July 2024

Determining visual attention during cognitive tasks using activation MRI remains challenging. This study aimed to develop a new eye-tracking (ET) post-processing platform to enhance data accuracy, validate the feasibility of subsequent ET-fMRI applications, and provide tool support. Sixteen volunteers aged 18 to 20 were exposed to a visual temporal paradigm with changing images of objects and faces in various locations while their eye movements were recorded using an MRI-compatible ET system. The results indicate that the accuracy of the data significantly improved after post-processing. Participants generally maintained their visual attention on the screen, with mean gaze positions ranging from 89.1% to 99.9%. In cognitive tasks, the gaze positions showed adherence to instructions, with means ranging from 46.2% to 50%. Temporal consistency assessments indicated prolonged visual tasks can lead to decreased attention during certain tasks. The proposed methodology effectively identified and quantified visual artifacts and losses, providing a precise measure of visual attention. This study offers a robust framework for future work integrating filtered eye-tracking data with fMRI analyses, supporting cognitive neuroscience research.
Center of Pressure Measurement Accuracy via Insoles with a Reduced Pressure Sensor Number during Gaits
Authors: Fuchs Philip X. , Chen Wei-Han , Shiang Tzyy-Yuang
Published: July 2024

The objective was to compare simplified pressure insoles integrating different sensor numbers and to identify a promising range of sensor numbers for accurate center of pressure (CoP) measurement. Twelve participants wore a 99-sensor Pedar-X insole (100 Hz) during walking, jogging, and running. Eight simplified layouts were simulated, integrating 3–17 sensors. Concordance correlation coefficients (CCC) and root mean square errors (RMSE) between the original and simplified layouts were calculated for time-series mediolateral (ML) and anteroposterior (AP) CoP. Differences between layouts and between gait types were assessed via ANOVA and Friedman test. Concordance between the original and simplified layouts varied across layouts and gaits (CCC: 0.43–0.98; χ(7)2 ≥ 34.94, p < 0.001). RMSEML and RMSEAP [mm], respectively, were smaller in jogging (5 ± 2, 15 ± 9) than in walking (8 ± 2, 22 ± 4) and running (7 ± 4, 20 ± 7) (ηp2: 0.70–0.83, p < 0.05). Only layouts with 11+ sensors achieved CCC ≥ 0.80 in all tests across gaits. The 13-sensor layout achieved CCC ≥ 0.95 with 95% confidence, representing the most promising compromise between sensor number and CoP accuracy. Future research may refine sensor placement, suggesting the use of 11–13 sensors. For coaches, therapists, and applied sports scientists, caution is recommended when using insoles with nine or fewer sensors. Consulting task-specific validation results for the intended products is advisable.
Latent Prototype-Based Clustering: A Novel Exploratory Electroencephalography Analysis Approach
Authors: Zhou Sun , Zhang Pengyi , Chen Huazhen
Published: July 2024

Electroencephalography (EEG)-based applications in brain–computer interfaces (BCIs), neurological disease diagnosis, rehabilitation, etc., rely on supervised approaches such as classification that requires given labels. However, with the ever-increasing amount of EEG data, incomplete or incorrectly labeled or unlabeled EEG data are increasing. It likely degrades the performance of supervised approaches. In this work, we put forward a novel unsupervised exploratory EEG analysis solution by clustering based on low-dimensional prototypes in latent space that are associated with the respective clusters. Having the prototype as a baseline of each cluster, a compositive similarity is defined to act as the critic function in clustering, which incorporates similarities on three levels. The approach is implemented with a Generative Adversarial Network (GAN), termed W-SLOGAN, by extending the Stein Latent Optimization for GANs (SLOGAN). The Gaussian Mixture Model (GMM) is utilized as the latent distribution to adapt to the diversity of EEG signal patterns. The W-SLOGAN ensures that images generated from each Gaussian component belong to the associated cluster. The adaptively learned Gaussian mixing coefficients make the model remain effective in dealing with an imbalanced dataset. By applying the proposed approach to two public EEG or intracranial EEG (iEEG) epilepsy datasets, our experiments demonstrate that the clustering results are close to the classification of the data. Moreover, we present several findings that were discovered by intra-class clustering and cross-analysis of clustering and classification. They show that the approach is attractive in practice in the diagnosis of the epileptic subtype, multiple labelling of EEG data, etc.
Determination of Volatilome Profile in Carbonated Beverages Using n-Hexane as an Extractant by GC-MS
Authors: Mu Li , Meng Xianglong , Luo Huihong , Liu Qianqian , Zhang Li , Li Xin , Xu Ying , Li Gang
Published: July 2024

A liquid–liquid extraction pretreatment method using n-hexane as the extractant was developed for the analysis of volatile aroma substances in three flavors (six cola samples, six lemon samples, and six orange samples) of carbonated beverages by gas chromatography–mass spectrometry (GC-MS). Quantitative analysis was conducted using the external standard method. The spiked recovery rate of α-terpineol was used as the evaluation criterion. Single-factor and response surface experiments were conducted to investigate the effects of extraction temperature, extraction time, and solvent-to-sample ratio. The results indicated that the maximum spiked recovery rate of α-terpineol, 81.00%, was achieved at an extraction temperature of 45 °C, extraction time of 30 min, and a solvent-to-sample ratio of 1 mL:15 mL. Thirty-four components were identified by GC-MS on the pretreated samples via the internal standard method. 1,4-Cineole, fenchyl alcohol, borneol, and α-terpineol are covered aroma substances in cola beverages. Two aromatic substances, D-limonene and α-terpineol, were detected in orange juices. α-Terpineol was detected in each lemon-flavor carbonated beverage sample. Going a step further, α-terpineol was detected in all 18 carbonated beverage samples and had high response values. The principal component analysis by functional group classification led to the conclusion that acids, phenols, hydrocarbons, alcohols, and ethers played a major contribution to the aroma of these 18 beverages. Increased separation of target compounds was found using the new pre-treatment methods, resulting in improved analytical resolution and selectivity.
Traditional In Situ Water Harvesting Practices and Agricultural Sustainability in Sub-Saharan Africa—A Meta-Analysis
Authors: Tefera Meron Lakew , Seddaiu Giovanna , Carletti Alberto
Published: July 2024

Climate change severely impacts sub-Saharan Africa, which relies heavily on rainfed agriculture for food production. Variable and insufficient rainfall exacerbates food insecurity across the region. Traditional in situ water harvesting (IS_WH) practices enhance soil water-holding capacity, improve infiltration, and promote soil conservation. This meta-analysis of the peer-reviewed literature examines IS_WH practices’ effects on crop yield, soil moisture, runoff, and soil loss reduction across various rainfall conditions in sub-Saharan Africa. The analysis reveals that IS_WH practices significantly boost agricultural productivity, with a combined effect size showing a 71% increase in total crop yield. IS_WH practices also improve soil moisture retention by 59% and effectively reduce runoff by 53% and soil loss by 58.66%, demonstrating their robust water and soil conservation benefits. Despite their proven benefits, the adoption of IS_WH practices in sub-Saharan Africa is hindered by socioeconomic and institutional barriers, including limited technical knowledge, resource constraints, and inadequate extension services. By addressing these barriers, there is significant potential to scale up IS_WH practices, enhancing agricultural productivity and sustainability across the region. Such efforts are crucial for mitigating the impacts of climate change, ensuring food security, and promoting sustainable development in sub-Saharan Africa.
Application of the Node–Place Model in Algiers (Algeria)
Authors: Nemroudi Rayane , Ortuño Armando , Flor Maria , Guirao Begoña
Published: July 2024

Achieving sustainable and balanced urban growth often hinges on the integration of efficient transportation systems into urban planning. In Algeria, however, historical complexities and inadequate urban planning and transit policies have increased reliance on personal vehicles. This paper addresses the postcolonial challenges in Algeria’s urban planning and transport policies and their impact on Algiers, the capital city. By applying the Node–Place Model to transit-oriented development in Algiers, this study analyzes transit stations (nodes) and the surrounding development and activity zones (places). The analysis reveals significant disparities in accessibility, highlighting the monocentric urban structure with central stations exhibiting higher place indices. It identifies potential for harmonious development in balanced stations and underscores the need for targeted investments in stations with low public transport accessibility. This study concludes that implementing the Node–Place Model can facilitate informed decision-making, promoting sustainable urban planning and improved quality of life. By providing an integrated view of Algiers’ urban evolution, this approach offers practical solutions to current challenges, aiming to create more resilient and livable urban areas in Algeria’s capital. The findings emphasize the necessity of enhancing public transport connectivity and supporting diverse economic activities to achieve balanced and equitable urban development.
Transfer Students’ Perceptions of Science and Engineering Internships
Authors: McAlexander Shana L. , Blanchard Margaret R. , Venditti Richard A.
Published: July 2024

Economic mobility and career success for undergraduate students is enhanced by understanding students’ experiences and supports and addressing the barriers they face. This study explores how U.S. undergraduate transfer students described their career journeys and how they perceived contextual barriers and supports for their career-oriented experiences. This longitudinal narrative case study was conducted with undergraduate students who were transferring from a community college to a four-year college. They participated in a professional development program, which funded science/engineering internships within bioproducts and bioenergy sectors. The study drew from social cognitive career theory and analyzed data collected over two years, including student interviews, biographical mapping, and reflective writings. Students most frequently described supports they received from their professors and program staff (institutional), family (social), and their internship hosts. The barriers they identified were more variable, such as inadequate advising and communication issues, and were often resolved by program staff (institutional) and financial supports from the program and internship sites. Internship participation led to all of the participating students feeling a sense of job readiness. Programmatic supports, such as clear communication between the internship host and the academic program, funding the internships, and addressing barriers by providing adequate mentoring and logistical supports, were found to enhance the experiences of science and engineering transfer students.
A Decision Support Evaluation Framework for Community-Based Collaborative Urban Regeneration Processes
Authors: Della Spina Lucia
Published: July 2024

The current debate on urban regeneration highlights the significance of social innovation and the reuse of cultural heritage to develop new economic and entrepreneurial models, creating value chains and benefits for communities. International and European directives, such as the European Agenda 2030 and the New European Bauhaus, advocate for inclusive and sustainable urbanization, encouraging participatory and circular urban regeneration processes. This study presents a decision support framework for community-based collaborative urban regeneration processes, focusing on the urban area of Reggio Calabria in Southern Italy. In this region, some cultural assets have been reactivated and made accessible, while others remain inactive, underutilized, or abandoned. The research aims to explore how to effectively enhance cultural heritage and promote urban regeneration through a collaborative network strategy. By employing multicriteria analysis, involving the selection and definition of specific criteria and indicators for each site, a priority map is created to facilitate the implementation of regeneration processes. The application of this approach in the urban center of Reggio Calabria demonstrated its effectiveness in revitalizing unused or abandoned cultural heritage, transforming it into a catalyst for urban vibrancy and vitality.
Scenario Analysis of Electricity Demand in the Residential Sector Based on the Diffusion of Energy-Efficient and Energy-Generating Products
Authors: Kishita Yusuke , Yamaguchi Yohei , Mizuno Yuji , Fukushige Shinichi , Umeda Yasushi , Shimoda Yoshiyuki
Published: July 2024

A variety of energy-efficient and energy-generating products, such as photovoltaics (PV) and electric vehicles, have diffused into the market to reduce greenhouse gas emissions in the residential sector. Understanding future changes in electricity demand and supply is complicated by uncertainties such as lifestyle shifts and national energy policies, and how such changes interact with the diffusion of products. To address this issue, this study adopts a scenario approach to analyze the impact of product diffusion on residential electricity demand under different social circumstances. Two simulation models are employed for the analysis: (i) a model for estimating the diffusion of products based on consumer preferences and (ii) a model to estimate electricity demand in residential sectors considering product diffusion. To demonstrate the proposed method, a scenario analysis case study was conducted, estimating the electricity demand in the residential sector of Toyonaka City, Osaka, Japan, for 2030. The results show that compared to 2012, the net electricity demand in the city in 2030 is projected to decrease by 20–39% depending on the scenarios considered, with changes in demographics and PV diffusion identified as among the most critical factors.
Solar Irradiance Database Comparison for PV System Design: A Case Study
Authors: AlFaraj Jamal , Popovici Emanuel , Leahy Paul
Published: July 2024

Effective design of solar photovoltaic (PV) systems requires accurate meteorological data for solar irradiance, ambient temperature, and wind speed. In this study, we aim to assess the reliability of satellite-based solar resource databases such as NASA, Solcast, and PVGIS by comparing them with ground-based measurements of global horizontal irradiance (GHI) from six locations in the Republic of Ireland. We compared satellite- and ground-based GHI data recorded between 2011 and 2012 and used Python-based packages to simulate solar power output for the six locations using both data types. The simulated outputs were then compared against metered power output from PV arrays at the sites. Ground-based GHI measurements demonstrate superior accuracy due to their acquisition at specific locations, offering increased spatial representativity. On the other hand, satellite GHI measurements, although reasonably accurate for many applications, cover broader regions with lower spatial resolution, leading to averaging effects that may not fully capture localized variations. This difference is reflected in the mean absolute percentage error (MAPE) values, with ground-simulated data showing low MAPE values, indicating strong alignment with reference observations, while satellite-simulated data exhibit a slightly higher MAPE, suggesting less precise estimates despite a strong correlation with ground-based measurements. This study demonstrates the relative reliability of satellite- and ground-based GHI data for accurate solar PV system design, emphasizing the practical implications for energy planners and engineers, and providing a strong enhancement for researchers working on forecasting solar energy yields using satellite databases. The Python-based PVLib package was utilized for the simulation, offering a robust framework for modeling and analyzing solar power systems, and its effectiveness in this context is discussed in detail.
Replacing Gray Hydrogen with Renewable Hydrogen at the Consumption Location Using the Example of the Existing Fertilizer Plant
Authors: Dumančić Andrea , Vlahinić Nela , Skok Minea
Published: July 2024

The production and use of hydrogen are encouraged by the European Union through Delegated Acts, especially in sectors that are difficult to decarbonize, such as the industrial and transport sectors. This study analyzes the possibility of partial decarbonization of the existing plant in the petrochemical industry, with a partial transition from natural gas to renewable hydrogen, as a precursor to the adoption of the hydrogen economy by 2050. This study was based on the example of a plant from the petrochemical industry, namely an existing fertilizer plant. Namely, in the petrochemical industry, hydrogen is produced by steam-reforming natural gas, which is needed in the process of producing ammonia, one of the basic raw materials for mineral fertilizers. By building an electrolyzer at the location of the existing fertilizer plant, it is possible to obtain renewable hydrogen, which enters the ammonia production process as a raw material. The electricity from which hydrogen is produced in the electrolyzer is provided through Power Purchase Agreement contracts concluded with electricity producers from 12 wind power plants. The results of this study show that the production of renewable hydrogen at the location of the analyzed plant is not profitable, but due to the specificity of the process of such an industry, the high consumption of natural gas, and large savings in CO2 emissions which can be achieved by the production of renewable hydrogen, investment is needed. With a 370 MW electrolyzer, about 31,000 tons of renewable hydrogen is produced, which represents about 50% of the hydrogen needs of the analyzed plant. By producing renewable hydrogen for part of the needs of the analyzed plant, a saving of about 300,000 tons of CO2 emissions is achieved in relation to the production of gray hydrogen, which contributes to the partial decarbonization of the analyzed plant. The authors are aware that the current market opportunities do not allow the profitability of the investment without subsidies, but with the advancement of technology and a different price ratio of electricity, natural gas, and CO2 emissions, they believe that such investments will be profitable even without subsidies.
New Interpretation of Human–Land Relations: Evidence from the Impact of Population Aging on Resource Utilization Efficiency in the Yellow River Basin
Authors: Wang Ping , Wang Zhibao
Published: July 2024

In the current context of global resource constraints and sustainable development, the efficient utilization of resources in the Yellow River basin, as one of China’s main economic regions, is particularly important. Based on the Super-SBM model with undesirable outputs, we analyze the spatial–temporal evolution of resource utilization efficiency in the Yellow River basin during 2000–2020 and investigate the impact of population aging on it using a fixed effects regression model and spatial Durbin model. The resource utilization efficiency has shown an overall upward trend in the Yellow River basin, which never realizes the DEA efficiency. There remains considerable room for efficiency improvement. There is an obvious spatial heterogeneity in resource utilization efficiency, presenting a spatial pattern of “leading downstream, intermediate midstream and lagging upstream”. Resource utilization efficiency has a high redundancy rate in terms of environmental pollution output, water resource input, science and education input in the Yellow River basin. Currently, excessive resource consumption and excessive pollutant emissions are the main causes of efficiency loss. Population aging not only promotes regional resource utilization efficiency but also has a significant positive spatial spillover effect in neighboring regions. Population aging’s impact shows heterogeneity, with a significant promoting impact on resource-based cities and other cities, while its promoting impact on provincial capital cities is not significant.
A Cost-Effective Fault Diagnosis and Localization Approach for Utility-Scale PV Systems Using Limited Number of Sensors
Authors: Alfaris Faris E. , Al-Ammar Essam A. , Ghazi Ghazi A. , AL-Katheri Ahmed A.
Published: July 2024

As a result of global efforts to combat the rise in global climate change and carbon dioxide emissions, there has been a substantial increase in renewable energy investment for both residential and utility power generation. Solar power facilities are estimated to be among the major contributors to global decarbonization in terms of capacity by 2050. Consequently, the majority of economically significant countries are progressively implementing utility-scale photovoltaic (U-PV) systems. Nevertheless, a major obstacle to the expansion of U-PV generation is the identification and assessment of direct current (DC) faults in the extensive array of PV panels. In order to address this obstacle, it is imperative to provide an evaluation method that can accurately and cost-effectively identify and locate potential DC faults in PV arrays. Therefore, many studies attempted to utilize thermal cameras, voltage and current sensors, power databases, and other detecting elements; however, some of these technologies provide extra hurdles in terms of the quantity and expense of the utilized hardware equipment. This work presents a sophisticated system that aims to diagnose and locate various types of PV faults, such as line-to-ground, line-to-line, inter-string, open-circuit, and partial shading events, within a PV array strings down to a module level. This study primarily depends on three crucial indicators: precise calculation of the PV array output power and current, optimal placement of a limited number of voltage sensors, and execution of specifically specified tests. The estimation of PV array power, along with selectively placed voltage sensors, minimizes the time and equipment required for fault detection and diagnosis. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate that the proposed approach can accurately diagnose and localize faults with only NS/2 voltage sensors, where NS is the number of PV array parallel strings.
Financial Sustainability: Exploring the Influence of the Triple Bottom Line Economic Dimension on Firm Performance
Authors: Nogueira Elisabete , Gomes Sofia , Lopes João M.
Published: July 2024

Triple Bottom Line (TBL) may be one of the best weapons in one of today’s biggest business challenges, achieving sustainability. Despite the importance of the economic dimension of TBL for companies’ competitiveness, financial sustainability remains undervalued in research and business management practises. Thus, there is a need to deepen the knowledge about the relationship between sustainable business practises and the economic performance of firms. This study aims to analyse the influence of the economic dimension of TBL on firm financial performance. Three multiple linear regression models were estimated by the generalised method of moments for a sample of 70,057 Portuguese companies grouped according to their size. The results reveal that the economic dimension of TBL influences the financial performance of companies in a positive way, both at the level of value creation and from the perspective of company continuity. The study uses accounting indicators as the representatives of the economic dimension of the TBL and its consideration in conjunction with the stakeholder theory. The use, monitoring, and evaluation of the financial indicators of value and continuity that translate the application of TBL in the financial performance of companies may lead them to achieve financial sustainability.
Key Performance Indicators (KPI) to Measure Effectiveness of Lean Construction in Indonesian Project
Authors: Bigwanto Arviga , Widayati Naniek , Wibowo Mochamad Agung , Sari Endah Murtiana
Published: July 2024

The implementation of lean construction is very important in the construction industry to reduce waste and increase productivity. To ensure its effective implementation, clear and measurable Key Performance Indicators (KPIs) are necessary. Therefore, this research aimed to develop SMART-based KPIs (Specific, Measurable, Attainable, Realistic, and Time-bound) for lean construction implementation, which measure indicators throughout the project life cycle. In this context, both qualitative and quantitative methods were used to collect data. Quantitative data were collected through surveys, assessing the perceptions of respondents concerning KPIs that had been developed. Meanwhile, qualitative data were collected through interviews and expert Focus Group Discussions (FGDs), which included in-depth analysis and conclusions regarding lean construction KPIs. The results produced were KPIs that could be used to measure effectiveness in implementing lean construction, particularly for building projects in Indonesia. Consequently, this research provided new views concerning effective lean construction, which could be explored in more depth and implemented for stakeholders in the construction industry. This development could eventually improve project performance by reducing waste and increasing productivity in construction projects.
Associations between Grand Challenges and Multinational Enterprises: A Bibliometric and Thematic Analysis
Authors: Uğur Sinem Burcu , Alan Hale , Onur Neslihan , Demirel Hüsne
Published: July 2024

Grand challenges are defined as wicked problems that affect a significant number of people on a global scale. To overcome these challenges and ensure the continued existence of humanity, a greater focus has been placed on addressing the underlying issues. This has led to an increase in research and literature on grand challenges affecting both international business and multinational enterprises. The aim of this study is to assess the status of grand challenge literature in all areas connected to multinational enterprises and grand challenges, which are global issues with significant implications. From 2013 to 2023, the Web of Science database was used to investigate eight search terms from the literature, and bibliometric and thematic analyses were carried out based on the principles of the systematic literature analysis. The most focused areas of the research are performance, foreign direct investment, management, firms’ innovation, knowledge, corporate social responsibility, international business, and impact. Asian, African, European, and other countries may emerge together in different clusters depending on whether the grand challenges they are struggling with are similar or identical. The terms ‘grand challenges’, ‘innovation’ (open and social), ‘technology transfer’, ‘R&D internalization’, ‘digital transformation’, ‘technology’ and ‘artificial intelligence’ in the literature of multinational enterprises indicate that these themes are used as tools to solve grand challenges.
Seismic Performance and Failure Mechanisms of Reinforced Concrete Structures Subject to the Earthquakes in Türkiye
Authors: Işık Ercan , Avcil Fatih , Hadzima-Nyarko Marijana , İzol Rabia , Büyüksaraç Aydın , Arkan Enes , Radu Dorin , Özcan Zeki
Published: July 2024

Many reinforced-concrete structures collapsed or were seriously damaged in the 7.7 and 7.6 magnitude earthquakes that occurred in southern Türkiye on 6 February 2023. The recorded peak ground accelerations were quite high (2.2 g) and the recorded motions’ elastic acceleration response spectra were significantly greater than the elastic design spectra given by the most recent Turkish seismic design code. A total of 518,000 houses were heavily damaged or collapsed in the eleven cities affected by the earthquake. More than 53,000 people lost their lives and over 100,000 people were injured, the majority of these injurits caused by the collapse of reinforced concrete structures. Post-earthquake damage assessments are important in the context of applying sustainability principles to building design and construction. In this study, post-earthquake damage assesments and evaluations were made for the reinforced-concrete structures that were exposed to destruction or various structural damage in Hatay, Kahramanmaraş and Adıyaman, which where most affected after the Kahramanmaraş earthquakes. The RC building damage and failure mechanisms resulting from field observations were evaluated in detail from a broad performance-based structural and earthquake engineering perspective. Information about Kahramanmaraş earthquakes is given briefly. Design spectra and spectral accelerations were compared for the earthquake stations in these three provinces. Soft/weak story, short column, insufficiently reinforced-concrete, and poor workmanship are the primary causes of structural damage, which cause earthquake weaknesses in these buildings.
An Ecological Resilience Assessment of a Resource-Based City Based on Morphological Spatial Pattern Analysis
Authors: Pan Yuqi , Jiao Sheng , Hu Jiaqi , Guo Qichen , Yang Yuchen
Published: July 2024

In the context of ecological civilization construction, resource-based cities (RBCs) are tasked with the dual responsibility of national energy supply and transformation amidst complex social contradictions. These cities face the resource curse dilemma, characterized by resource depletion, conflicts in spatial production, in living, and in ecological spaces, and intertwined ecological–economic–social issues. Enhancing their ecological resilience is a crucial indicator of successful transformation strategies. This study focuses on Jinzhong City in Shanxi Province, employing Morphological Spatial Pattern Analysis (MSPA) to assist in the spatial analysis of urban ecological resilience. Additionally, Conefor Sensinode is utilized to identify key ecological sources that significantly contribute to urban resilience. A novel Risk-Connectivity-Potential (RCP) model is developed to construct a framework of indicators affecting the resilience of RBCs, which is used to assess the ecological resilience of Jinzhong City, particularly in relation to the spatial distribution of mining areas. The results indicate the following: (1) important ecological source areas within the city constitute approximately 39.47% of the total city area, predominantly located in woodland regions; (2) the overall assessment of ecological resilience reveals a heterogeneous pattern, increasing from west to east, with lower resilience observed in low-lying terrains and higher resilience in mountainous plateaus; (3) mines within significant ecological source areas are primarily situated in low-resilience zones near built land and agriculture land, while other mining areas are mainly found between high-resilience zones, posing risks of increased ecological resistance, reduced ecological connectivity, and potential ecological issues. This study explores the application of the ecological resilience framework in RBCs, providing a scientific basis and reference for the rational utilization of resources and urban transformation and development.The methodology and findings can be applied to similar cities globally, offering valuable insights for balancing resource management and ecological protection in the context of sustainable urban development.
Analysis of Social Vulnerability to Earthquake Disasters in Mountainous Areas: A Case Study of Sixteen Cities along the Fault Zone in Sichuan Province, China
Authors: Yin Hao , Xiang Yong , Lei Yangjuan , Xu Jiaojiao
Published: July 2024

Given that most cities in Sichuan Province, China, are located in mountainous areas and are frequently affected by earthquakes, this study selected 16 mountainous cities in Sichuan Province. Based on the “exposure–sensitivity–coping capacity” framework, we constructed a social vulnerability assessment index system for earthquake disasters that aligns with the characteristics of mountainous regions. Weights were determined using the entropy weight–CRITIC method, and the improved TOPSIS method was used to calculate the social vulnerability index (SoVI) of each city for comparative analysis. Additionally, the social vulnerability maps were created using ArcGIS software to explore the spatial distribution characteristics. The study found that among the 16 mountainous cities, there is a noticeable spatial clustering of social vulnerability. Yajiang, Daofu, and Luhuo are identified as high–high clustering areas, while Jiulong, Luding, Shimian, and Hanyuan also exhibit high–high clustering. Kangding, Baoxing, and Wenchuan fall into low–low clustering areas. Additionally, coping capacity is the most significant factor influencing the social vulnerability of mountainous cities. After experiencing high-magnitude earthquakes, most mountainous cities have not improved their coping abilities and continue to exhibit high vulnerability, primarily due to high illiteracy rates, significant altitude variations, and poor economic conditions. This study provides a scientific basis for local governments to formulate disaster prevention and mitigation strategies, which help enhance the disaster resilience of mountainous cities and promote their sustainable development.
Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory
Authors: Huang Xin , Jiang Keteng , Luo Shuxin , Li Haibo , Lu Zongxiang
Published: July 2024

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints.
Optimization Model and Solution Algorithm for Space Station Cargo Supply Planning under Complex Constraints
Authors: Kang Zhijuan , Gao Ming , Dang Wei , Wang Jiajie
Published: July 2024

To enhance the efficient utilization of space resources, it is critical to integrate information from various systems of the space station and formulate scientific and effective methods for planning cargo supplies. Considering the large-scale, multi-objective, complex nonlinear, non-convex, non-differentiable, and mixed-integer characteristics, this study decomposes the space station cargo supply planning problem into a bi-level optimization problem involving cargo manifest and loading layout iterations. A new CILPSO algorithm is proposed to solve this by integrating particle coding, reliability priority, and random generation mechanisms of population initialization, global and local versions of particle updating, and a local search strategy. The experimental results show that the CILPSO algorithm outperforms other algorithms regarding search performance and convergence efficiency. The proposed approach can effectively reduce the cargo supply cost of the space station and improve the output of space science and application achievements. It provides a decision-making basis for the responsible department to develop cargo supply schemes, for the cargo supply systems to submit cargo demands, and for the cargo spaceship system to design loading schemes. This study advances the logistics sustainability of the space station.
Reliability, Availability, and Maintainability Assessment-Based Sustainability-Informed Maintenance Optimization in Power Transmission Networks
Authors: Sagharidooz Motahareh , Soltanali Hamzeh , Farinha José Torres , Raposo Hugo D. N. , de-Almeida-e-Pais José Edmundo
Published: July 2024

Reliable and resilient power transmission networks serve as vital for sustainable development and uninterrupted electricity supply. Effective maintenance programs are necessary to comply with reliability and sustainability requirements in the power sector. To that end, RAM (reliability, availability, and maintainability) assessments can provide efficient maintenance services that minimize adverse consequences and increase productivity at the lowest possible cost. We employ a statistical framework to evaluate RAM principles, including data acquisition, homogenization, trend hypothesis validation, and parameter estimation. The RAM evaluation of power transmission networks identifies primary bottlenecks in subsystems based on failure and repair behavior trends, which should be prioritized. To find the optimal maintenance policies for each subsystem, we adapt a Multi-Attribute Utility Theory (MAUT) approach, taking costs, availability, and dependability into account. The results of this approach can help improve the operational performance and sustainability of power transmission networks.