Case Study Research Articles

Validation of satellite-derived solar irradiance datasets: a case study in Saudi Arabia

A robust dataset of Surface Solar Irradiance is essential for secure competitive financing for solar energy projects. Rating agencies and lenders alike require verification of the solar-resource dataset for utilizing each solar energy project, as this can be translated directly into expected electrical energy and revenues. The accuracy of the dataset and the variability of solar radiation, as recorded by historical solar data, play a significant role in estimating the future performance of the project and its budget. The historical observed solar irradiance datasets by local stations are the best and most reliable for a specific site, but they are not always available for long and continuous periods in any location, especially in arid areas. So, the importance of historical solar radiation datasets derived from satellite-based models arises here. This paper validates the historical modeled datasets of the three most famous satellite-based commercial prediction models (SolarGIS, SUNY, and Solcast) against the observed dataset by six ground stations in Saudi Arabia under different climatic zones. The validation method has been implemented using the standard error metrics: Maximum Absolute Error (MAE) and relative Maximum Bias Error (rMBE). The validation process showed that, in the case of GHI, the discrepancy between observed and predicted values is narrow, while in the case of DNI, the discrepancy is wide. Also, the predicted GHI values are more accurate than predicted DNI values, and -in general- the values predicted by the SUNY model are less accurate than those predicted by SolarGIS and Solcast models for both GHI and DNI. The resultant of this validation process could be accepted not for the six locations under study only but, also for deserts and arid areas across Saudi Arabia and might be extended to similar arid areas around the world.

Interpreting causee in a ‘permissive’ causative: A case study on Teochew

The verbal domain has been long argued to be a strong phase, of which the origin can be traced back to the discussions on theta role assignment in Chomsky (2000). This study contributes to the phasehood issue at the understudied LF by exploring the argument thematic interpretation in the verbal domain. I argue that in Teochew permissive bun-causative, the interpretation of causee as an understudied external argument is not listed as syntactic primitives, but rather as post-syntactic derivatives derived from syntactically-oriented causal event structure. Such a causal event structure is first affected by the eventuality of the embedded predicate and then influenced by two modalities sublexically encoded in the embedding causative verb, namely a volitional modality in the at-issue meaning and a deontic modality in the presupposition. Such a two-step contextualization mechanism constrains the final interpretation of causee, and, for the first time, sheds light on discussions on the domain sensitivity at LF.

An Investigation of the Impact of Precipitation Temperature and Filter Cake Thickness on the Physical Stability of Amorphous Solids: A Case Study

The purpose of this study was to resolve the issue of physical instability in amorphous solid drugs, which can result in unwanted crystallization, affecting solubility and dissolution rates. The focus was on precipitating physically stable amorphous forms of the nilotinib free base, an anticancer drug, by monitoring preparation conditions such as precipitation temperature and filter cake thickness. A comprehensive set of characterization techniques, including powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and focused beam reflectance measurement (FBRM), were used. These were supplemented by advanced data analysis methods that incorporated pair distribution function (PDF), reduced crystallization temperature (Rc), and principal component analysis (PCA) to evaluate the physical stability of the amorphous samples. Results emphasized that optimal physical stability was achieved when amorphous solids were prepared at a precipitation temperature of 10 °C and a filter cake thickness of 4 cm. Moreover, the integration of PDF analysis with Rc values was confirmed as an innovative approach for assessing physical stability, thus offering enhanced efficiency and accuracy over conventional accelerated stability testing methods.

Deep learning vulnerability analysis against adversarial attacks

In the age of artificial intelligence advancements, deep learning models are essential for applications ranging from image recognition to natural language processing. Despite their capabilities, they're vulnerable to adversarial examplesdeliberately modified inputs to cause errors. This paper explores these vulnerabilities, attributing them to the complexity of neural networks, the diversity of training data, and the training methodologies. It demonstrates how these aspects contribute to the models' susceptibility to adversarial attacks. Through case studies and empirical evidence, the paper highlights instances where advanced models were misled, showcasing the challenges in defending against these threats. It also critically evaluates mitigation strategies, including adversarial training and regularization, assessing their efficacy and limitations. The study underlines the importance of developing AI systems that are not only intelligent but also robust against adversarial tactics, aiming to enhance future deep learning models' resilience to such vulnerabilities.

Optimization of Blasting Parameters Considering Both Vibration Reduction and Profile Control: A Case Study in a Mountain Hard Rock Tunnel

The number of excavated tunnels is increasing day by day, and the corresponding engineering scale is also getting increasing. Safe, efficient, and economically beneficial tunnel construction methods are indispensable in the process of crossing mountains and steep ridges in the southwest region. However, behind the improvement of transportation infrastructure in Southwest China is the support provided by the rapid development of blasting industry engineering technology in China. In the process of tunnel construction using the drilling and blasting method, in addition to blasting vibration disasters the phenomenon of overbreak and underbreak caused by blasting construction is a prominent problem. This phenomenon not only affects the safety and stability of the tunnel excavation but also seriously increases the construction cost. Based on a short mountain hard rock tunnel project in southwest China, this paper studies the effect of blasting construction on the blasting vibration of adjacent structures and the influence of tunnel contour forming quality. Through the monitoring and analysis of in situ blasting vibration, the Sadowski formula is used to study the attenuation law of blasting vibration velocity in different tunnel sites, which provides a theoretical basis for tunnel blasting vibration control. This article compares the use of overbreak and underbreak value with the traditional method to determine the degree of overbreak and underbreak. It introduces the analysis of contour section fractal dimension value and uses fractal theory in the Python image processing module to accurately and quantitatively describe the problems of tunnel overbreak and underbreak. The feasibility and accuracy of this method have been verified, by combining the total station and 3D laser scanner results of overbreak and underbreak measurements of the Brenner Base Tunnel and a short hard rock tunnel in a mountainous area of southwestern China. The blasting scheme was optimized from the aspects of cut hole form, detonator interval time, and peripheral hole charge structure, and the rationality of the optimized scheme was verified according to the on-site blasting experiments. It has a profound influence on strengthening the protection of adjacent tunnel structures and improving the economic benefit of mountain highway projects.

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system.

Review and Case Study on Control of Induction Motor Using High-Level Converter

Review and Case Study on Control of Induction Motor Using High-Level Converter

An Empirical Exploration of Factors Contributing to Malaysian Men Becoming Involved in a Feminine Career: A Case Study of Male Nurse and Pak Andam

An Empirical Exploration of Factors Contributing to Malaysian Men Becoming Involved in a Feminine Career: A Case Study of Male Nurse and Pak Andam

Integrating portable X-ray fluorescence site survey and ArcGIS models for rapid risk assessment and mitigation strategies at an abandoned arsenic mine site: a case study

ABSTRACT Australia’s metalliferous abandoned mine sites (MAMSs), pose tangible threats to the environment and human health. To address these concerns, our study utilised state-of-the-art handheld XRF technology to conduct a real-time assessment of the Mole River arsenic mine site. The data revealed notably elevated levels of arsenic and manganese, with the southeast corner of the site identified as a contaminant hotspot. We used a tiered risk assessment approach to compare the detected contaminant concentrations to the Australian health investigation levels (tier 1). This led us to a broader examination of erosion vulnerabilities and the potential migration of contaminants (tier 2). Further, a hydrological assessment (tier 3) identified significant erosion in southern regions, indicating the potential for contaminants to be transported off-site through surface water runoff to Sam’s Creek and Mole River. The proximity of a reservoir to these runoff pathways brought forth additional challenges, especially during heavy rainfall events. Subsequent laboratory analysis of water samples reinforced our findings, as they confirmed heightened arsenic concentrations in Mole River downstream, accentuating the potential risks to ecosystems and human health. By integrating the XRF contour map and erosion assessment with the RUSLE model, valuable insights are gained into critical hotspots with high contamination and erosion potential. By directing rehabilitation efforts towards critical hotspots, resources can be allocated more efficiently and cost-effectively.

Risk‐based student performance prediction model for engineering courses

AbstractHigh academic failure and dropout rates in engineering courses are significant worldwide concerns attributed to various factors, with academic performance being a critical variable. This article provides a methodology to estimate the performance risk of students in engineering schools. Risk analysis is a strategy to evaluate academic success, which provides a set of methods to analyze, understand, and predict student outcomes before enrolling in specific majors or challenging college courses. This article develops a methodology to estimate fragility curves for students entering an engineering course. The fragility function concept, borrowed from the earthquake engineering field, estimates the likelihood of success in a course, given relevant student metadata, such as the grade point average, thus comprehensively addressing student performance variability. A student academic success prediction model enables instructional designers to make informed decisions. For example, fragility curves can help achieve two goals: (i) assessing the population at risk for a course to take actions to improve student success rates and (ii) assessing a course's relative difficulty based on its fragility function parameters. We demonstrate this methodology through a case study comparing the relative difficulty of two engineering courses, Statics and Solid Mechanics, at a university in Colombia. Given that Statics serves as a prerequisite for Solid Mechanics, deficiencies in the former can significantly impact student performance in the latter. The case study results reveal that Solid Mechanics poses a higher risk of academic failure than Statics, underscoring the importance of a strong foundation in prerequisite courses.

The occurrence of and factors associated with mental ill-health amongst humanitarian aid workers: A systematic review and meta-analysis.

Humanitarian crises and disasters affect millions of people worldwide. Humanitarian aid workers are civilians or professionals who respond to disasters and provide humanitarian assistance. In doing so, they face several stressors and traumatic exposures. Humanitarian aid workers also face unique challenges associated with working in unfamiliar settings. To determine the occurrence of and factors associated with mental ill-health among humanitarian aid workers. CINAHL plus, Cochrane library, Global Health, Medline, PubMed, Web of Science were searched from 2005-2020. Grey literature was searched on Google Scholar. PRISMA guidelines were followed and after double screening, studies reporting occurrence of mental ill-health were included. Individual narratives and case studies were excluded, as were studies that reported outcomes in non-humanitarian aid workers. Data on occurrence of mental ill-health and associated factors were independently extracted and combined in a narrative summary. A random effects logistic regression model was used for the meta-analysis. Nine studies were included with a total of 3619 participants, reporting on five types of mental ill-health (% occurrence) including psychological distress (6.5%-52.8%); burnout (8.5%-32%); anxiety (3.8%-38.5%); depression (10.4%-39.0%) and post-traumatic stress disorder (0% to 25%). Hazardous drinking of alcohol ranged from 16.2%-50.0%. Meta-analysis reporting OR (95% CI) among humanitarian aid workers, for psychological distress was 0.45 (0.12-1.64); burnout 0.34 (0.27-0.44); anxiety 0.22 (0.10-0.51); depression 0.32 (0.18-0.57) and PTSD 0.11 (0.03-0.39). Associated factors included young age, being female and pre-existing mental ill-health. Mental ill-health is common among humanitarian aid workers, has a negative impact on personal well-being, and on a larger scale reduces the efficacy of humanitarian organisations with delivery of aid and retention of staff. It is imperative that mental ill-health is screened for, detected and treated in humanitarian aid workers, before, during and after their placements. It is essential to implement psychologically protective measures for individuals working in stressful and traumatic crises.

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging.

In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

“We all see things through a different lens based on our life experiences”: co-production of a web-based implementation toolkit with stakeholders across the health and social care system

BackgroundImplementing new innovations across the health and social care system is complex, involving many factors that in recent years have been compounded by Covid-19. While a plethora of implementation tools and frameworks are available, there are limitations in terms of their design and accessibility. Co-production is a valuable mechanism for developing tools that have utility and accessibility for those tasked with using them in health and social care organisations and there is growing acknowledgement of increasing the role of co-production in implementation science. This paper provides novel insight into co-production practices and relevance to implementation science by reporting findings from a study to co-produce a web-based implementation toolkit (WIT) that is accessible, usable and designed to support adaptive implementation across health and social care systems. Key themes relating to the process of co-production are outlined and the value of using co-production in implementation processes are discussed.MethodsA web-based survey (n = 36) was conducted with a range of stakeholders across health and social care. Findings identified a need for WIT. Survey respondents were invited to express interest in becoming part of a co-production group and to take part in three online interactive workshops to co-produce WIT. Workshops took place with the group (n = 12) and focused on key developmental stages of WIT.ResultsOnline co-production workshops were integral to the development and refinement of WIT. Benefits of using this process identified three interrelated themes: (i) Co-designing key features of the toolkit, (ii) Co-producing a toolkit with utility for users across health and social care settings, (iii) Co-producing a toolkit to support the implementation journey. Our approach of undertaking co-production as a dialogic process enabled generation of these themes. To illuminate discussion of these themes we draw upon iterative co-development of the “active ingredients” of key components (e.g., interactive Implementation Wheel) and functions (e.g., interactive “pop-up” definitions of keyword) and features (e.g., case studies) of WIT.ConclusionUsing a co-production approach with a range of end-users across health and social care systems, highlights the benefits of understanding implementation processes for users in these settings. User-centred design and processes for ensuring accessibility readily support the translation of implementation into rapidly changing health and social care systems to benefit outcomes for patients, their families, carers, service users and practitioners.

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

Floods pose a significant threat to road networks globally, disrupting transportation, isolating communities, and causing economic losses. This study proposes a four-stage methodology (avoidance, endurance, recovery, and adaptability) to enhance the resilience of road networks. We combine analysis of constructed assets and asset system performance with multiple disaster scenarios (Reactive Flood Response, Proactive Resilience Planning, and Early Warning Systems). Advanced flood Geospatial-AI models and open data sources pinpoint high-risk zones affecting crucial routes. The study investigates how resilient assets and infrastructure scenarios improve outcomes within Urban Resilience Index (CRI) planning, integrating performance metrics with cost–benefit analysis to identify effective and economically viable solutions. A case study on the Lisbon Road network subjected to flood risk analyzes the effectiveness and efficiency of these scenarios, through loss and gain cost analysis. Scenario 2, Proactive Resilience Planning, demonstrates a 7.6% increase compared to Scenario 1, Reactive Flood Response, and a 3.5% increase compared to Scenario 3, Early Warning Systems Implementation. By considering asset performance, risk optimization, and cost, the study supports resilient infrastructure strategies that minimize economic impacts, while enabling communities to withstand and recover from flood events. Integrating performance and cost–benefit analysis ensures the sustainability and feasibility of risk reduction measures.

Exploring Multi-Armed Bandit (MAB) as an AI Tool for Optimising GMA-WAAM Path Planning

Conventional path-planning strategies for GMA-WAAM may encounter challenges related to geometrical features when printing complex-shaped builds. One alternative to mitigate geometry-related flaws is to use algorithms that optimise trajectory choices—for instance, using heuristics to find the most efficient trajectory. The algorithm can assess several trajectory strategies, such as contour, zigzag, raster, and even space-filling, to search for the best strategy according to the case. However, handling complex geometries by this means poses computational efficiency concerns. This research aimed to explore the potential of machine learning techniques as a solution to increase the computational efficiency of such algorithms. First, reinforcement learning (RL) concepts are introduced and compared with supervised machining learning concepts. The Multi-Armed Bandit (MAB) problem is explained and justified as a choice within the RL techniques. As a case study, a space-filling strategy was chosen to have this machining learning optimisation artifice in its algorithm for GMA-AM printing. Computational and experimental validations were conducted, demonstrating that adding MAB in the algorithm helped to achieve shorter trajectories, using fewer iterations than the original algorithm, potentially reducing printing time. These findings position the RL techniques, particularly MAB, as a promising machining learning solution to address setbacks in the space-filling strategy applied.

Preservation of sentiment in machine translation of low-resource languages: a case study on Slovak movie subtitles

AbstractThis research explores the effectiveness of machine translation from Slovak to English for sentiment analysis, specifically focusing on the translation of movie subtitles. The study employs a parallel corpus of segmented movie subtitles in both languages and utilizes IBM Watson™ Natural Language Understanding service and Google Translate. The research aims to assess the correlation between human-generated text and machine-translated text in sentiment analysis. A comparative analysis was also conducted using OpenAI to evaluate the sentiment of the Slovak text directly, without translation into English. The findings reveal a strong correlation between human text and machine translation, with a Pearson correlation coefficient of 0.86, and a correlation with OpenAI’s GPT model evaluation at 0.72. Despite the relatively high accuracy of the end-to-end solution using OpenAI, the methodology comprising machine translation followed by sentiment analysis in English was found to be significantly more precise. The research further investigates the challenges in translating specific language nuances, such as humor and vulgarism, and their impact on sentiment analysis. The study concludes that machine translation can be effectively used for sentiment analysis in Slovak, a flective language, and highlights the potential of advanced language models in low-resource languages. Future research directions include expanding the study to other text types and comparable languages beyond Slovak.

Phytoremediation of Grey Water

Greywater, generated from domestic activities excluding toilet waste, presents a significant challenge for water management due to its diverse contaminants and potential environmental impact. Conventional treatment methods often fall short in addressing the complex composition of greywater while maintaining sustainability. Phytoremediation, a green technology that utilizes plants and their associated microorganisms, offers a promising and ecofriendly alternative for greywater treatment. This paper reviews the principles, mechanisms, and applications of phytoremediation in the context of greywater treatment. Various phytoremediation techniques such as rhizofiltration, phytoextraction, and phytodegradation are discussed, highlighting their efficacy in removing pollutants such as organic matter, nutrients, pathogens, and heavy metals from greywater. The role of specific plant species with remediation potential and their physiological mechanisms in contaminant uptake and transformation are examined. Case studies and experimental findings from greywater phytoremediation projects around the world are presented to demonstrate the practicality and effectiveness of this approach. Factors influencing phytoremediation performance, including plant selection, hydraulic conditions, nutrient availability, and system design, are analysed to optimize greywater treatment efficiency.

Assessment of particulate Zn and Pb sources in the Orne watershed (Northeast France) using geochemical tools.

The Orne River, a tributary of the Moselle River, was highly impacted by industrial activities for more than one century. Land use along the Orne River is highly contrasted, with local specificity from its source to its junction with the Moselle River. The intense industrial activity left behind tons of steelmaking wastes (SMW) on the land surface and within the Orne riverbed. To assess the sources of particulate Zn and Pb transported as suspended sediment in the Orne River, different sets of samples from likely Zn- and Pb-bearing particle sources within the Orne watershed were collected. Three sets of samples were taken from potential sources representing detrital, urban, and inherited industrial particles. Mineralogy, element contents, and Zn and Pb isotope compositions were obtained to characterize and reveal the fingerprint of each set of samples. Soil samples were collected on distinct geomorphological areas characterized by different soil types and land uses. They all display detrital minerals assigned to the geological background. Urban dusts and steelmaking residues display specific mineral phases (sulfates and iron oxides, respectively). Element compositions present strong discrepancies between the distinct sets of samples. SMWs are particularly enriched in Fe, Zn, and Pb. Concerning isotopic composition, SMWs exhibit δ66Zn values ranging from - 0.67 to 1.66‰. Urban samples display δ66Zn values between - 0.11 and 0.13‰, and soils present δ66Zn values between - 0.24 and 0.47‰. The 206Pb/204Pb ratio was estimated to range from 17.550 to 18.807 for soils, from 17.973 to 18.219 for urban samples, and from 18.313 to 18.826 for SMWs. For each of the three sets of samples (soils, urban, industrial), variations of geochemical fingerprint were observed. For soils, the relatively large variations of Zn and Pb isotopic compositions were attributed to distinct land use and the contribution of atmospheric deposition. For industrial samples, the variations were more intense and may be attributed either to distinct industrial processes in the production of pig iron or to distinct furnace-flume treatment modes. The three sets of samples (urban, industrial, and detrital) could be distinguished based on Zn and Pb contents and isotopes. Finally, this study not only highlighted the sources that released particulate Zn and Pb into the Orne River system, it also demonstrated that urban particles are well defined in terms of Zn and Pb isotopic signatures, and those isotopic signatures could be extrapolated to other case studies.

Hydraulic Analysis study and redesign of the water distribution system simulation using GIS- EPANET, case study: Laylan sub-district, Kirkuk city, Iraq

Hydraulic Analysis study and redesign of the water distribution system simulation using GIS- EPANET, case study: Laylan sub-district, Kirkuk city, Iraq

Geometrical Variation Analysis of Landslides in Different Geological Settings Using Satellite Images: Case Studies in Japan and Sri Lanka

Over the past three decades, Sri Lanka has observed a substantial rise in landslide occurrences linked to intensified rainfall. However, the lack of comprehensive landslide inventories has hampered the development of effective risk analysis and simulation systems, requiring Sri Lanka to rely heavily on foreign-developed models, despite the difficulty of fully examining the similarities between the characteristics of landslides in Sri Lanka and the areas where the model has been developed. Satellite images have become readily available in recent years and have provided information about the Earth’s surface conditions over the past few decades. Thus, this study verifies the utility of satellite images as a cost-effective remote-sensing method to clarify the commonalities and differences in the characteristics of landslides in two regions Ikawa, Japan, and Sabaragamuwa, Sri Lanka, which exhibit different geological formations despite similar annual rainfall. Using Google Earth satellite images from 2013 to 2023, we evaluated land-slide density, types, and geometry. The findings reveal that Ikawa exhibits a higher landslide density and experiences multiple-type landslides. In contrast, both areas have similar initiation areas; however, Sabaragamuwa predominantly experiences single landslides that are widespread and mobile. The findings also reveal that various characteristics of landslides are mainly influenced by varied topography. Here, we confirmed that even in areas where comprehensive information on landslides is conventionally lacking, we can understand the characteristics of landslides by comparing landslide geometry between sites using satellite imagery.