Search results for: communicative approach

Authors: Diksha Sapkota, Kathleen Baird, Amornrat Saito, Debra Anderson

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Background: Violence during pregnancy is global public health problem incurring huge amount of social, economic and human costs. It is of particular concern as it affects health of mother, neonates and also disrupt family functioning. Mental illness is one of its commonest consequences affecting both mother and baby and likely to be chronic if left unattended. Past decade has seen advances in knowledge about different forms of violence, its health impacts and intervention/s helping to confront the violence. However, limited range and lack of consistency in measurable outcomes undermine overall effect of interventions, and available evidence are largely slanted towards high-income countries. Despite recognition of integrating screening and counselling for abused pregnant women in health settings, there is a dearth of evidence on its effectiveness from developing countries limiting its applicability and feasibility. This study intends to summarise the high-quality evidence on intimate partner violence interventions in reducing violence and improving mental health and implement the promising intervention in our context. Methods: Quantitative systematic review will be done using PRISMA statement and based on its finding; randomised controlled intervention will be carried out. The study will be conducted among women attending ANC clinic of Dhulikhel Hospital, Nepal. Being the pilot study, samples just adequate to draw the inferences i.e. not less than 30 in each arm will be taken. Phenomological approach will be used to explore the strengths and weaknesses of tested intervention and recommendations for better planning in future. Conclusion: This study intends to provide concrete evidence on what works best in our context and will assist policymakers, programme planners, donors in informed decision making.

Keywords: intimate partner violence/prevention and control, mental health, Nepal, pregnant

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Authors: Suang K. Lau, Ismail Goolam, Rafid Al-Asady

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Background: The majority of patients with hepatocellular carcinoma (HCC) are not suitable for curative treatment, in the form of surgical resection or transplantation, due to tumour extent and underlying liver dysfunction. In these non-resectable cases, a variety of non-surgical therapies are available, including microwave ablation (MWA), which has shown increasing popularity due to its low morbidity, low reported complication rate, and the ability to perform multiple ablations simultaneously. Objective: The aim of this study was to evaluate the validity of MWA as a viable treatment option in the management of HCC and hepatic metastatic disease, by assessing its efficacy and complication rate at a tertiary hospital situated in Westmead (Australia). Methods: A retrospective observational study was performed evaluating patients that underwent MWA between 1/1/2017–31/12/2018 at Westmead Hospital, NSW, Australia. Outcome measures, including residual disease, recurrence rates, as well as major and minor complication rates, were retrospectively analysed over a 12-months period following MWA treatment. Excluded patients included those whose lesions were treated on the basis of residual or recurrent disease from previous treatment, which occurred prior to the study window (11 patients) and those who were lost to follow up (2 patients). Results: Following treatment of 106 new hepatic lesions, the complete response rate (CR) was 86% (91/106) at 12 months follow up. 10 patients had the residual disease at post-treatment follow up imaging, corresponding to an incomplete response (ICR) rate of 9.4% (10/106). The local recurrence rate (LRR) was 4.6% (5/106) with follow-up period up to 12 months. The minor complication rate was 9.4% (10/106) including asymptomatic pneumothorax (n=2), asymptomatic pleural effusions (n=2), right lower lobe pneumonia (n=3), pain requiring admission (n=1), hypotension (n=1), cellulitis (n=1) and intraparenchymal hematoma (n=1). There was 1 major complication reported, with pleuro-peritoneal fistula causing recurrent large pleural effusion necessitating repeated thoracocentesis (n=1). There was no statistically significant association between tumour size, location or ablation factors, and risk of recurrence or residual disease. A subset analysis identified 6 segment VIII lesions, which were treated via a trans-pleural approach. This cohort demonstrated an overall complication rate of 33% (2/6), including 1 minor complication of asymptomatic pneumothorax and 1 major complication of pleuro-peritoneal fistula. Conclusions: Microwave ablation therapy is an effective and safe treatment option in cases of non-resectable hepatocellular carcinoma and liver metastases, with good local tumour control and low complication rates. A trans-pleural approach for high segment VIII lesions is associated with a higher complication rate and warrants greater caution.

Keywords: hepatocellular carcinoma, liver metastases, microwave ablation, trans-pleural approach

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Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

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Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

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Authors: Rona Lee Suelo-Calanao, Mahmoud Loubani

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Objective: Sternal wound infection (SWI) following cardiac operation has a significant impact on patient morbidity and mortality. It also contributes to longer hospital stays and increased treatment costs. SWI management is mainly focused on treatment rather than prevention. This study looks at the effect of closed incision negative pressure therapy (ciNPT) dressing to help reduce the incidence of superficial SWI in high-risk patients after cardiac surgery. The ciNPT dressing was evaluated at 3 cardiac hospitals in the United Kingdom". Methods: All patients who had cardiac surgery from 2013 to 2021 were included in the study. The patients were classed as high risk if they have two or more of the recognised risk factors: obesity, age above 80 years old, diabetes, and chronic obstructive pulmonary disease. Patients receiving standard dressing (SD) and patients using ciNPT were propensity matched, and the Fisher’s exact test (two-tailed) and unpaired T-test were used to analyse categorical and continuous data, respectively. Results: There were 766 matched cases in each group. Total SWI incidences are lower in the ciNPT group compared to the SD group (43 (5.6%) vs 119 (15.5%), P=0.0001). There are fewer deep sternal wound infections (14(1.8%) vs. 31(4.04%), p=0.0149) and fewer superficial infections (29(3.7%) vs. 88 (11.4%), p=0.0001) in the ciNPT group compared to the SD group. However, the ciNPT group showed a longer average length of stay (11.23 ± 13 days versus 9.66 ± 10 days; p=0.0083) and higher mean logistic EuroSCORE (11.143 ± 13 versus 8.094 ± 11; p=0.0001). Conclusion: Utilization of ciNPT as an approach to help reduce the incidence of superficial and deep SWI may be effective in high-risk patients requiring cardiac surgery.

Keywords: closed incision negative pressure therapy, surgical wound infection, cardiac surgery complication, high risk cardiac patients

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Authors: Mukhtar Raban

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As part of Nelson Mandela University’s journey of repositioning its learning and teaching agenda, the university adopted and foregrounded a humanizing pedagogy-aligning with institutional goals of critically transforming the academic project. The university established the Humanizing Pedagogy Praxis and Research Niche (HPPRN) as a centralized hub for coordinating institutional work exploring and advancing humanizing pedagogies and tasked the unit with developing and enacting a model for humanizing pedagogy exploration. This investigation endeavored to report on the development and enactment of a model that sought to institutionalize a humanizing pedagogy at a South African university. Having followed a qualitative approach, the investigation presents the case study of Nelson Mandela University’s HPPRN and the model it subsequently established and enacted for the advancement towards a more common institutional understanding, interpretation and application of the humanizing pedagogy. The study adopted an interpretive lens for analysis, complementing the qualitative approach of the investigation. The primary challenge that confronted the HPPRN was the development of a ‘living model’ that had to complement existing institutional initiatives while accommodating a renewed spirit of critical reflection, innovation and research of continued and new humanizing pedagogical exploration and applications. The study found that the explicit consideration of tenets of humanizing and critical pedagogies in underpinning and framing the HPPRN Model contributed to the sense of ‘lived’ humanizing pedagogy experiences during enactment. The multi-leveled inclusion of critical reflection in the development and enactment stages was found to further the processes of praxis employed at the university, which is integral to the advancement of humanizing and critical pedagogies. The development and implementation of a model that seeks to institutionalize the humanizing pedagogy at a university rely not only on sound theoretical conceptualization but also on the ‘richness of becoming more human’ explicitly expressed and encountered in praxes and application.

Keywords: humanizing pedagogy, critical pedagogy, institutional implementation, praxis

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Authors: Mohammadbagher Asghariaghamashhadi

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Internet users normally are automatically linked to an advertisement sponsored by a bidder when Internet users enter any trademarked keyword on a search engine. This advertisement appears beside the search results. Through the process of keyword advertising, advertisers can connect with many Internet users and let them know about their goods and services. This concept has generated heated disagreements among legal scholars, trademark proprietors, advertisers, search engine owners, and consumers. Therefore, use of trademarks in keyword advertising has been one of the most debatable issues in trademark law for several years. This entirely new way of using trademarks over the Internet has provoked a discussion concerning the core concepts of trademark law. In respect to legal issues, European Union (EU) trademark law is mostly governed by the Trademark Directive and the Community Trademark Regulation. Article 5 of the directive and Article 9 of the trademark regulation determine the circumstances in which a trademark owner holds the right to prohibit a third party’s use of his/her registered sign. Harmonized EU trademark law proved to be ambiguous on whether using of a trademark is amounted to trademark infringement or not. The case law of the European Court of Justice (ECJ), with reference to this legislation, is mostly unfavorable to trademark owners. This ambivalence was also exhibited by the case law of EU Member States. European keyword advertisers simply could not tell which use of a competitor‘s trademark was lawful. In recent years, ECJ has continuously expanded the scope and reach of trademark protection in the EU. It is notable that Inconsistencies in the Court’s system of infringement criteria clearly come to the fore and this approach has been criticized by analysts who believe that the Court should have adopted a more traditional approach to the analysis of trademark infringement, which was suggested by its Advocate General, in order to arrive at the same conclusion. Regarding case law of keyword advertising within Europe, one of the most disputable cases is Interflora vs. Marks and Spencer, which is still on-going. This study examines and critically analyzes the decisions of the ECJ, the high court of England, and the Court of Appeals of England and address critically keyword advertising issue within European trademark legislation.

Keywords: ECJ, Google, Interflora, keyword advertising, Marks and Spencer, trademark infringement

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Authors: Fabrizio Ascione, Martina Borrelli, Rosa Francesca De Masi, Silvia Ruggiero, Giuseppe Peter Vanoli

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The Directive 2010/31/EC 'Directive of the European Parliament and of the Council of 19 may 2010 on the energy performance of buildings' moved the targets of the previous version toward more ambitious targets, for instance by establishing that, by 31 December 2020, all new buildings should demand nearly zero-energy. Moreover, the demonstrative role of public buildings is strongly affirmed so that also the target nearly zero-energy buildings is anticipated, in January 2019. On the other hand, given the very low turn-over rate of buildings (in Europe, it ranges between 1-3%/yearly), each policy that does not consider the renovation of the existing building stock cannot be effective in the short and medium periods. According to this proposal, the study provides a novel, holistic approach to design the refurbishment of educational buildings in colder cities of Mediterranean regions enabling stakeholders to understand the uncertainty to use numerical modelling and the real environmental and economic impacts of adopting some energy efficiency technologies. The case study is a university building of Molise region in the centre of Italy. The proposed approach is based on the application of the cost-optimal methodology as it is shown in the Delegate Regulation 244/2012 and Guidelines of the European Commission, for evaluating the cost-optimal level of energy performance with a macroeconomic approach. This means that the refurbishment scenario should correspond to the configuration that leads to lowest global cost during the estimated economic life-cycle, taking into account not only the investment cost but also the operational costs, linked to energy consumption and polluting emissions. The definition of the reference building has been supported by various in-situ surveys, investigations, evaluations of the indoor comfort. Data collection can be divided into five categories: 1) geometrical features; 2) building envelope audit; 3) technical system and equipment characterization; 4) building use and thermal zones definition; 5) energy building data. For each category, the required measures have been indicated with some suggestions for the identifications of spatial distribution and timing of the measurements. With reference to the case study, the collected data, together with a comparison with energy bills, allowed a proper calibration of a numerical model suitable for the hourly energy simulation by means of EnergyPlus. Around 30 measures/packages of energy, efficiency measure has been taken into account both on the envelope than regarding plant systems. Starting from results, two-point will be examined exhaustively: (i) the importance to use validated models to simulate the present performance of building under investigation; (ii) the environmental benefits and the economic implications of a deep energy refurbishment of the educational building in cold climates.

Keywords: energy simulation, modelling calibration, cost-optimal retrofit, university building

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Authors: J. X. Dong, C. J. An, Z. Chen, E. H. Owens, M. C. Boufadel, E. Taylor, K. Lee

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Shorelines are vulnerable to significant environmental impacts from oil spills. Stranded oil can cause potential short- to long-term detrimental effects along beaches that include injuries to the ecosystem, socio-economic and cultural resources. In this study, a three-dimensional (3D) numerical modeling approach is developed to evaluate the fate and transport of spilled oil for hypothetical oiled shoreline cases under various combinations of beach geomorphology and environmental conditions. The developed model estimates the spatial and temporal distribution of spilled oil for the various test conditions, using the finite volume method and considering the physical transport (dispersion and advection), sinks, and sorption processes. The model includes a user-friendly interface for data input on variables such as beach properties, environmental conditions, and physical-chemical properties of spilled oil. An experimental mesoscale tank design was used to test the developed model for dissolved petroleum hydrocarbon within shorelines. The simulated results for effects of different sediment substrates, oil types, and shoreline features for the transport of spilled oil are comparable to those obtained with a commercially available model. Results show that the properties of substrates and the oil removal by shoreline effects have significant impacts on oil transport in the beach area. Sensitivity analysis, through the application of the one-step-at-a-time method (OAT), for the 3D model identified hydraulic conductivity as the most sensitive parameter. The 3D numerical model allows users to examine the behavior of oil on and within beaches, assess potential environmental impacts, and provide technical support for decisions related to shoreline clean-up operations.

Keywords: dissolved petroleum hydrocarbons, environmental multimedia model, finite volume method, sensitivity analysis, total petroleum hydrocarbons

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Authors: Andreas Johansson

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Conceptual understanding needs to be grounded on observation of physical phenomena, experiences or metaphors. Observation of physical phenomena using demonstration experiments has a long tradition within physics education and students need to develop mental models to relate the observations to concepts from scientific theories. This study investigates how live and video experiments involving an acoustic trap to visualize particle-field interaction, field properties and particle properties can help develop students' mental models and how they can be used differently to realize their potential as teaching tools. Initially, they were treated as analogs and the lesson designs were kept identical. With a design-based approach, the experimental and video designs, as well as best practices for a respective teaching tool, were then developed in iterations. Variation theory was used as a theoretical framework to analyze the planned respective realized pattern of variation and invariance in order to explain learning outcomes as measured by a pre-posttest consisting of conceptual multiple-choice questions inspired by the Force Concept Inventory and the Force and Motion Conceptual Evaluation. Interviews with students and teachers were used to inform the design of experiments and videos in each iteration. The lesson designs and the live and video experiments has been developed to help teachers improve student learning and make school physics more interesting by involving experimental setups that usually are out of reach and to bridge the gap between what happens in classrooms and in science research. As students’ conceptual knowledge also rises their interest in physics the aim is to increase their chances of pursuing careers within science, technology, engineering or mathematics.

Keywords: acoustic trap, design-based research, experiments, variation theory

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Authors: Thomas Heffernan, Nick Neave, Colin Hamilton, Gill Case

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Hoarding is the acquisition of and failure to discard possessions, leading to excessive clutter and significant psychological/emotional distress. From a cognitive-behavioural approach, excessive hoarding arises from information-processing deficits, as well as from problems with emotional attachment to possessions and beliefs about the nature of possessions. In terms of information processing, hoarders have shown deficits in executive functions, including working memory, planning, inhibitory control, and cognitive flexibility. However, this previous research is often confounded by co-morbid factors such as anxiety, depression, or obsessive-compulsive disorder. The current study adopted a cognitive-behavioural approach, specifically assessing executive deficits and working memory in a non-clinical sample of hoarders, compared with non-hoarders. In this study, a non-clinical sample of 40 hoarders and 73 non-hoarders (defined by The Savings Inventory-Revised) completed the Adult Executive Functioning Inventory, which measures working memory and inhibition, Dysexecutive Questionnaire-Revised, which measures general executive function and the Hospital Anxiety and Depression Scale, which measures mood. The participant sample was made up of unpaid young adult volunteers who were undergraduate students and who completed the questionnaires on a university campus. The results revealed that, after observing no differences between hoarders and non-hoarders on age, sex, and mood, hoarders reported significantly more deficits in inhibitory control and general executive function when compared with non-hoarders. There was no between-group difference on general working memory. This suggests that non-clinical hoarders have a specific difficulty with inhibition-control, which enables you to resist repeated, unwanted urges. This might explain the hoarder’s inability to resist urges to buy and keep items that are no longer of any practical use. These deficits may be underpinned by general executive function deficiencies.

Keywords: hoarding, memory, executive, deficits

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Authors: Ramorola Mmankoko Ziphorah

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Videoconference technology represents a recent experiment of technology integration into teaching and learning in South Africa. Increasingly, videoconference technology is commonly used as a substitute for the traditional face-to-face approaches to teaching and learning in helping tutors to reshape and change their teaching practices. Interestingly, though, some studies point out that videoconference technology is commonly used for knowledge dissemination by tutors and not so much for the actual teaching of course content in Open and Distance Learning context. Though videoconference technology has become one of the dominating technologies available among Open and Distance Learning institutions, it is not clear that it has been used as effectively to bridge the learning distance in time, geography, and economy. While tutors are prepared theoretically, in most tutor preparation programs, on the use of videoconference technology, there are still no practical guidelines on how they should go about integrating this technology into their course teaching. Therefore, there is an urgent need to focus on tutor development, specifically on their capacities and skills to use videoconference technology. The assumption is that if tutors become competent in the use of the videoconference technology for course teaching, then their use in Open and Distance Learning environment will become more commonplace. This is the imperative of the 4th Industrial Revolution (4IR) on education generally. Against the current vacuum in the practice of using videoconference technology for course teaching, the current study proposes a qualitative phenomenological approach to investigate the efficacy of videoconferencing as an approach to student learning. Using interviews and observation data from ten participants in Open and Distance Learning institution, the author discusses how dialogue and structure interacted to provide the participating tutors with a rich set of opportunities to deliver course content. The findings to this study highlight various challenges experienced by tutors when using videoconference technology. The study suggests tutor development programs on their capacity and skills and on how to integrate this technology with various teaching strategies in order to enhance student learning. The author argues that it is not merely the existence of the structure, namely the videoconference technology, that provides the opportunity for effective teaching, but that is the interactions, namely, the dialogue amongst tutors and learners that make videoconference technology an attractive vehicle for challenging and changing tutors practice.

Keywords: open distance learning, transactional distance, tutor, videoconference

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Authors: Nishantha Giguruwa

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This research investigates the transformative potential of smart container farming, building upon the successful cultivation of Japanese mushrooms at Sakai Farms in Aichi Prefecture, Japan, under the strategic collaboration with the Daikei Group. Inspired by this success, the study focuses on establishing an advanced urban strawberry farming laboratory with the aim of understanding strawberry farming technologies, fostering collaboration, and strategizing marketing approaches for both local and global markets. Positioned within the business framework of Sakai Farms and the Daikei Group, the study underscores the sustainability and forward-looking solutions offered by smart container farming in agriculture. The global significance of strawberries is emphasized, acknowledging their economic and cultural importance. The detailed examination of strawberry farming intricacies informs the technological framework developed for smart containers, implemented at Sakai Farms. Integral to this research is the incorporation of controlled bee pollination, a groundbreaking addition to the smart container farming model. The study anticipates future trends, outlining avenues for continuing exploration, stakeholder collaborations, policy considerations, and expansion strategies. Notably, the author expresses a strategic intent to approach the global market, leveraging the foreign student/faculty base at Ritsumeikan Asia Pacific University, where the author is affiliated. This unique approach aims to disseminate the research findings globally, contributing to the broader landscape of agricultural innovation. The integration of controlled bee pollination within this innovative framework not only enhances sustainability but also marks a significant stride in the evolution of urban agriculture, aligning with global agricultural trends.

Keywords: smart container farming, urban agriculture, strawberry farming technologies, controlled bee pollination, agricultural innovation

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Authors: Abul B. M. M. K. Islam, Mandar Dave, Roderick V. Jensen, Ashok R. Amin

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A systems approach was applied to characterize differentially expressed transcripts, bioinformatics pathways, and proteins and prostaglandins (PGs) from lung fibroblasts procured from wild-type (WT), COX-1-/- and COX-2-/- mice to understand system level control mechanism. Bioinformatics analysis of COX-2 and COX-1 ablated cells induced COX-1 and COX-2 specific signature respectively, which significantly overlapped with an 'IL-1β induced inflammatory signature'. This defined novel cross-talk signals that orchestrated coordinated activation of pathways of sterile inflammation sensed by cellular stress. The overlapping signals showed significant over-representation of shared pathways for interferon y and immune responses, T cell functions, NOD, and toll-like receptor signaling. Gene Ontology Biological Process (GOBP) and pathway enrichment analysis specifically showed an increase in mRNA expression associated with: (a) organ development and homeostasis in COX-1-/- cells and (b) oxidative stress and response, spliceosomes and proteasomes activity, mTOR and p53 signaling in COX-2-/- cells. COX-1 and COX-2 showed signs of functional pathways committed to cell cycle and DNA replication at the genomics level. As compared to WT, metabolomics analysis revealed a significant increase in COX-1 mRNA and synthesis of basal levels of eicosanoids (PGE2, PGD2, TXB2, LTB4, PGF1α, and PGF2α) in COX-2 ablated cells and increase in synthesis of PGE2, and PGF1α in COX-1 null cells. There was a compensation of PGE2 and PGF1α in COX-1-/- and COX-2-/- cells. Collectively, these results support a broader, differential and collaborative regulation of both COX-1 and COX-2 pathways at the metabolic, signaling, and genomics levels in cellular homeostasis and sterile inflammation induced by cellular stress.

Keywords: cyclooxygenases, inflammation, lung fibroblasts, systemic

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Authors: Silvia Grassi, Stefano Schiavon, Ernestina Casiraghi, Cristina Alamprese

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Dry-cured ham is an uncooked meat product particularly appreciated for its peculiar sensory traits among which lipid component plays a key role in defining quality and, consequently, consumers’ acceptability. Usually, fat content and distribution are chemically determined by expensive, time-consuming, and destructive analyses. Moreover, different sensory techniques are applied to assess product conformity to desired standards. In this context, visual systems are getting a foothold in the meat market envisioning more reliable and time-saving assessment of food quality traits. The present work aims at developing a simple but systematic and objective visual methodology to assess the fat amount of dry-cured ham slices, in terms of total, intermuscular and intramuscular fractions. To the aim, 160 slices from 80 PDO dry-cured hams were evaluated by digital image analysis and Soxhlet extraction. RGB images were captured by a flatbed scanner, converted in grey-scale images, and segmented based on intensity histograms as well as on a multi-stage algorithm aimed at edge enhancement. The latter was performed applying the Canny algorithm, which consists of image noise reduction, calculation of the intensity gradient for each image, spurious response removal, actual thresholding on corrected images, and confirmation of strong edge boundaries. The approach allowed for the automatic calculation of total, intermuscular and intramuscular fat fractions as percentages of the total slice area. Linear regression models were run to estimate the relationships between the image analysis results and the chemical data, thus allowing for the prediction of the total, intermuscular and intramuscular fat content by the dry-cured ham images. The goodness of fit of the obtained models was confirmed in terms of coefficient of determination (R²), hypothesis testing and pattern of residuals. Good regression models have been found being 0.73, 0.82, and 0.73 the R2 values for the total fat, the sum of intermuscular and intramuscular fat and the intermuscular fraction, respectively. In conclusion, the edge enhancement visual procedure brought to a good fat segmentation making the simple visual approach for the quantification of the different fat fractions in dry-cured ham slices sufficiently simple, accurate and precise. The presented image analysis approach steers towards the development of instruments that can overcome destructive, tedious and time-consuming chemical determinations. As future perspectives, the results of the proposed image analysis methodology will be compared with those of sensory tests in order to develop a fast grading method of dry-cured hams based on fat distribution. Therefore, the system will be able not only to predict the actual fat content but it will also reflect the visual appearance of samples as perceived by consumers.

Keywords: dry-cured ham, edge detection algorithm, fat content, image analysis

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Authors: Naif A. Haddad

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Greek, Hellenistic and Roman theatre heritage are exposed to multiple risks at varied times or simultaneously. There is no single reason why a theatre building becomes ‘at risk’, as each case has different circumstances which have led to the theatre building decay. There are complicated processes of destruction and distress that show divergence in theatre building materials' decay. Theatre modern use for cultural performances causes much of the risks concerning the physical structure and authenticity of theatre sites. In addition, there are some deterioration and deformations due to previous poor quality restorations and interventions through related excavation and conservation programmes as also risks to authenticity due to new additions. For preventive conservation, theatre natural and anthropogenic risks management can provide a framework for decision making. These risks to ancient theatre heritage may stem from exposure to one or more risk or synergy of many factors. We, therefore, need to link the theatre natural risks to the risks that come from anthropogenic factors associated with social and economic development. However, this requires a holistic approach, and systematic methodology for understanding these risks from various sources while incorporating specific actions, planning and strategies for each specific risk. Elaborating on recent relevant studies, and ERATO and ATHENA EU projects for ancient theaters and odea and general surveys, this paper attempts to discuss the main aspects of the ancient Greek, Hellenistic and Roman theatres risk related issues. Relevant case studies shall also be discussed and investigated to examine frameworks for risk mitigation, and related guidelines and recommendations that provide a systematic approach for sustainable management and planning in relation mainly to ‘compatible use’ of theatre sites.

Keywords: cultural heritage management, European ancient theatres projects, Anthropogenic risks mitigation, sustainable management and planning, preventive conservation, modern use, compatible use

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Authors: G. Judakova, M. Bause

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The ability to model and simulate numerically natural gas flow in pipelines has become of high importance for the design of pipeline systems. The understanding of the formation of hydrate particles and their dynamical behavior is of particular interest, since these processes govern the operation properties of the systems and are responsible for system failures by clogging of the pipelines under certain conditions. Mathematically, natural gas flow can be described by multiphase flow models. Using the two-fluid modeling approach, the gas phase is modeled by the compressible Euler equations and the particle phase is modeled by the pressureless Euler equations. The numerical simulation of compressible multiphase flows is an important research topic. It is well known that for nonlinear fluxes, even for smooth initial data, discontinuities in the solution are likely to occur in finite time. They are called shock waves or contact discontinuities. For hyperbolic and singularly perturbed parabolic equations the standard application of the Galerkin finite element method (FEM) leads to spurious oscillations (e.g. Gibb's phenomenon). In our approach, we use stabilized FEM, the streamline upwind Petrov-Galerkin (SUPG) method, where artificial diffusion acting only in the direction of the streamlines and using a special treatment of the boundary conditions in inviscid convective terms, is added. Numerical experiments show that the numerical solution obtained and stabilized by SUPG captures discontinuities or steep gradients of the exact solution in layers. However, within this layer the approximate solution may still exhibit overshoots or undershoots. To suitably reduce these artifacts we add a discontinuity capturing or shock capturing term. The performance properties of our numerical scheme are illustrated for two-phase flow problem.

Keywords: two-phase flow, gas-particle mixture, inviscid two-fluid model, euler equation, finite element method, streamline upwind petrov-galerkin, shock capturing

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Authors: Maylen Villamanan Alba

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Latin America is marked by its history: conquest, colonialism, and slavery left deep footprints in most Latin American countries. Also, the past century has been affected by wars, military dictatorships, and political violence, which profoundly influenced Latin American popular culture. Consequently, reminiscences of historical crimes are frequently present in daily life, media, public opinion, and arts. This legacy is remembered in novels, paintings, songs, and films. In fact, Latin American cinema has a trend which refers to the verisimilitude with reality in fiction films. These films about historical violence are narrated as fictional characters, but their stories are based on real historical contexts. Therefore, cultural criminology has considered films as a significant field to understand social representations of violence related to historical crimes. The aim of the present contribution is to analyze the legacy of past and historical memory in social representations of violence in Latin American cinema as a critical approach to historical crimes. This qualitative research is based on content analysis. The sample is seven multi-award winning films of the International Festival of New Latin American Cinema of Havana. The films selected are Kamchatka, Argentina (2002); Carandiru, Brazil (2003); Enlightened by fire, Argentina (2005); Post-mortem, Chile (2010); No, Chile (2012) Wakolda; Argentina (2013) and The Clan, Argentina (2015). Cultural criminology highlights that cinema shapes meanings of social practices such as historical crimes. Critical criminology offers a critical theory framework to interpret Latin American cinema. This analysis reveals historical conditions deeply associated with power relationships, policy, and inequality issues. As indicated by this theory, violence is characterized as a structural process based on social asymmetries. These social asymmetries are crossed by social scopes, including institutional and personal dimensions. Thus, institutions of the states are depicted through personal stories of characters involved with human conflicts. Intimacy and social background are linked in the personages who simultaneously perform roles such as soldiers, policemen, professionals or inmates and they are at the same time depict as human beings with family, gender, racial, ideological or generational issues. Social representations of violence related to past legacy are a portrait of historical crimes perpetrated against Latin American citizens. Thereby, they have contributed to political positions, social behaviors, and public opinion. The legacy of these historical crimes suggests a path that should never be taken again. It means past legacy is a reminder, a warning, and a historic lesson for Latin American people. Social representations of violence are permeated by historical memory as denunciation under a critical approach.

Keywords: Latin American cinema, historical memory, social representation, violence

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Authors: Jiarong Zhang

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Digitizing intangible cultural heritage is a complex and comprehensive process from which sorts of intellectual property rights may be generated. Digitizing may be a repacking process of cultural heritage, which creates copyrights; recording folk songs and indigenous performances can create 'related rights'. At the same time, digitizing intangible cultural heritage may infringe the intellectual property rights of others unintentionally. Recording religious rituals of indigenous communities without authorization can violate the moral right of the ceremony participants of the community; making digital copies of rock paintings may infringe the right of reproduction. In addition, several parties are involved in the digitization process: indigenous peoples, museums, and archives can be holders of cultural heritage; companies and research institutions can be technology providers; internet platforms can be promoters and sellers; the public and groups above can be beneficiaries. When diverse intellectual property rights versus various parties, problems and disputes can arise easily. What are the types of intellectual property rights generated in the digitization process? What is the essence of these rights? Who should these rights belong to? How to use intellectual property to protect the digitalization of cultural heritage? How to avoid infringing on the intellectual property rights of others? While the digitization has been regarded as an effective approach to preserve intangible cultural heritage, related intellectual property issues have not received the attention and full discussion. Thus, parties involving in the digitization process may face intellectual property infringement lawsuits. The article will explore those problems from the intersection perspective of intellectual property law and cultural heritage. From a comparative approach, the paper will analysis related legal documents and cases, and shed some lights of those questions listed. The findings show, although there are no intellectual property laws targeting the cultural heritage in most countries, the involved stakeholders can seek protection from existing intellectual property rights following the suggestions of the article. The research will contribute to the digitization of intangible cultural heritage from a legal and policy aspect.

Keywords: copyright, digitization, intangible cultural heritage, intellectual property, Internet platforms

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Authors: Mona Almanasef, Angel Chater, Jane Portlock

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Introduction: A 'flipped classroom' uses technology to shift the traditional lecture outside the scheduled class time and uses the face-to-face time to engage students in interactive activities. Aim of the Study: Assess the feasibility, acceptability, and effectiveness of using the 'flipped classroom' teaching format with MPharm students in two pharmacy schools in the UK: UCL School of Pharmacy and the School of Pharmacy and Biomedical Sciences at University of Portsmouth. Methods: An experimental mixed methods design was employed, with final year MPharm students in two phases; 1) a qualitative study using focus groups, 2) a quasi-experiment measuring knowledge acquisition and satisfaction by delivering a session on rheumatoid arthritis, in two teaching formats: the flipped classroom and the traditional lecture. Results: The flipped classroom approach was preferred over the traditional lecture for delivering a pharmacy practice topic, and it was comparable or better than the traditional lecture with respect to knowledge acquisition. In addition, this teaching approach was found to overcome the perceived challenges of the traditional lecture method such as fast pace instructions, student disengagement and boredom due to lack of activities and/or social anxiety. However, high workload and difficult or new concepts could be barriers to pre-class preparation, and therefore successful flipped classroom. The flipped classroom encouraged learning scaffolding where students could benefit from application of knowledge, and interaction with peers and the lecturer, which might, in turn, facilitate learning consolidation and deep understanding. This research indicated that the flipped classroom was beneficial for all learning styles. Conclusion: Implementing the flipped classroom at both pharmacy institutions was successful and well received by final year MPharm students. Given the attention now being put on the Teaching Excellence Framework (TEF), understanding effective methods of teaching to enhance student achievement and satisfaction is now more valuable than ever.

Keywords: blended learning, flipped classroom, inverted classroom, pharmacy education

Procedia PDF Downloads 136

Authors: Maad Kamal Al-Anni

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This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Intrusion Detection System (IDS), Mobile Adhoc Networks (MANET), Back Propagation Algorithm (BPA), Neural Networks (NN)

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Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

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As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

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Authors: Kristyna Hrabova, Sabina Hüblova, Tomas Vymazal

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Structural design of concrete structure has the result in qualities of structural safety and serviceability, together with durability, robustness, sustainability and resilience. A sustainable approach is at the heart of the research agenda around the world, and the Fibrillation Commission is also working on a new model code 2020. Now it is clear that the effects of mechanical, environmental load and even social coherence need to be reflected and included in the designing and evaluating structures. This study aimed to present the methodology for the sustainability assessment of various concrete mixtures.

Keywords: concrete, cement, sustainability, Model Code 2020

Procedia PDF Downloads 178

Authors: Edward Holupka, John Rossman, Tye Morancy, Joseph Aronovitz, Irving Kaplan

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A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates.

Keywords: prostate, deep neural network, seed implant, ultrasound

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Authors: L. Della Ragione, G. Meccariello

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In recent years the progress of the automobile industry in Italy in the field of reduction of emissions values is very remarkable. Nevertheless, their evaluation and reduction is a key problem, especially in the cities, which account for more than 50% of world population. In this paper we dealt with the problem of describing a quantitative approach for the reconstruction of GPS coordinates and altitude, in the context of correlation study between driving cycles / emission / geographical location, during an experimental campaign realized with some instrumented cars.

Keywords: air pollution, driving cycles, GPS signal, vehicle location

Procedia PDF Downloads 428

Authors: Anetta Caplanova, Eva Sirakovova, Estera Szakadatova

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Non-payment of taxes and fees represents a problem, which occurs at national and local government levels in many countries. An effective tax collection is key for generating government and local government budget revenues to finance public services and infrastructure; thus, there is the need to address this problem. The standard approach considers as a solution raising taxes/fees to boost public revenues, which may be politically challenging and time-consuming to implement. An alternative approach is related to using behavioral interventions. These can be usually implemented relatively quickly, and in most cases, they are associated with low cost. In the paper, we present the results of the behavioral experiment focused on raising the level of compliance with the payment of garbage collection fees in a selected municipality in the Slovak Republic. The experiment was implemented using the leaflets sent to residential households together with the invoice for the garbage collection in the municipality Hlohovec, Western Slovakia, in Spring 2021. The sample of about 10000 households was divided into three random groups, a control group and two intervention groups. Households in intervention group 1 were sent a leaflet using the social norm nudge, while households in intervention group 2 were sent a leaflet using the deterrence nudge. The social norm framing leaflet pointed out that in the municipality, the prevailing majority of people paid the garbage collection fee and encouraged recipients to join this majority. The deterrent leaflet reminded the recipients that if they did not pay the fee on time, enforcement proceedings would follow. This was aimed to increase the subjective perception of citizens of the enforcement proceedings in case of noncompliance. In the paper, we present and discuss the results from the experiment and formulate relevant generalizations for other municipalities.

Keywords: municipal governments, garbage fee collection, behavioural intervention, social norm, deterrence nudge

Procedia PDF Downloads 191

Authors: Anjalika Fernando, Banuka Athuraliya

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This paper presents a comprehensive approach for converting Sinhala Sign Language (SSL) into grammatically correct sentences using Natural Language Processing (NLP) techniques in real-time. While previous studies have explored various aspects of SSL translation, the research gap lies in the absence of grammar checking for SSL. This work aims to bridge this gap by proposing a two-stage methodology that leverages deep learning models to detect signs and translate them into coherent sentences, ensuring grammatical accuracy. The first stage of the approach involves the utilization of a Long Short-Term Memory (LSTM) deep learning model to recognize and interpret SSL signs. By training the LSTM model on a dataset of SSL gestures, it learns to accurately classify and translate these signs into textual representations. The LSTM model achieves a commendable accuracy rate of 94%, demonstrating its effectiveness in accurately recognizing and translating SSL gestures. Building upon the successful recognition and translation of SSL signs, the second stage of the methodology focuses on improving the grammatical correctness of the translated sentences. The project employs a Neural Machine Translation (NMT) architecture, consisting of an encoder and decoder with LSTM components, to enhance the syntactical structure of the generated sentences. By training the NMT model on a parallel corpus of Sinhala wrong sentences and their corresponding grammatically correct translations, it learns to generate coherent and grammatically accurate sentences. The NMT model achieves an impressive accuracy rate of 98%, affirming its capability to produce linguistically sound translations. The proposed approach offers significant contributions to the field of SSL translation and grammar correction. Addressing the critical issue of grammar checking, it enhances the usability and reliability of SSL translation systems, facilitating effective communication between hearing-impaired and non-sign language users. Furthermore, the integration of deep learning techniques, such as LSTM and NMT, ensures the accuracy and robustness of the translation process. This research holds great potential for practical applications, including educational platforms, accessibility tools, and communication aids for the hearing-impaired. Furthermore, it lays the foundation for future advancements in SSL translation systems, fostering inclusive and equal opportunities for the deaf community. Future work includes expanding the existing datasets to further improve the accuracy and generalization of the SSL translation system. Additionally, the development of a dedicated mobile application would enhance the accessibility and convenience of SSL translation on handheld devices. Furthermore, efforts will be made to enhance the current application for educational purposes, enabling individuals to learn and practice SSL more effectively. Another area of future exploration involves enabling two-way communication, allowing seamless interaction between sign-language users and non-sign-language users.In conclusion, this paper presents a novel approach for converting Sinhala Sign Language gestures into grammatically correct sentences using NLP techniques in real time. The two-stage methodology, comprising an LSTM model for sign detection and translation and an NMT model for grammar correction, achieves high accuracy rates of 94% and 98%, respectively. By addressing the lack of grammar checking in existing SSL translation research, this work contributes significantly to the development of more accurate and reliable SSL translation systems, thereby fostering effective communication and inclusivity for the hearing-impaired community

Keywords: Sinhala sign language, sign Language, NLP, LSTM, NMT

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Authors: Vahid Ebrahimipour

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Word representation and context meaning of text-based documents play an essential role in knowledge modeling. Business procedures written in natural language are meant to store technical and engineering information, management decision and operation experience during the production system life cycle. Context meaning representation is highly dependent upon word sense, lexical relativity, and sematic features of the argument. This paper proposes a method for lexical semantic analysis and context meaning representation of maintenance activity in a mass production system. Our approach constructs a straightforward lexical semantic approach to analyze facilitates semantic and syntactic features of context structure of maintenance report to facilitate translation, interpretation, and conversion of human-readable interpretation into computer-readable representation and understandable with less heterogeneity and ambiguity. The methodology will enable users to obtain a representation format that maximizes shareability and accessibility for multi-purpose usage. It provides a contextualized structure to obtain a generic context model that can be utilized during the system life cycle. At first, it employs a co-occurrence-based clustering framework to recognize a group of highly frequent contextual features that correspond to a maintenance report text. Then the keywords are identified for syntactic and semantic extraction analysis. The analysis exercises causality-driven logic of keywords’ senses to divulge the structural and meaning dependency relationships between the words in a context. The output is a word contextualized representation of maintenance activity accommodating computer-based representation and inference using OWL/RDF.

Keywords: lexical semantic analysis, metadata modeling, contextual meaning extraction, ontology modeling, knowledge representation

Procedia PDF Downloads 105

Authors: Hadj Sahraoui Omar, Kebir Lahcen Wahib, Bennia Ahmed

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Interferometric Synthetic Aperture Radar (InSAR) coherence is a crucial parameter for accurately monitoring ground deformation and environmental changes. However, coherence can be degraded by various factors such as temporal decorrelation, atmospheric disturbances, and geometric misalignments, limiting the reliability of InSAR measurements (Omar Hadj‐Sahraoui and al. 2019). To address this challenge, we propose an innovative hybrid approach that combines artificial intelligence (AI) with advanced filtering techniques to optimize interferometric coherence in InSAR data. Specifically, we introduce a Convolutional Neural Network (CNN) integrated with the Lee filter to enhance the performance of radar interferometry. This hybrid method leverages the strength of CNNs to automatically identify and mitigate the primary sources of decorrelation, while the Lee filter effectively reduces speckle noise, improving the overall quality of interferograms. We develop a deep learning-based model trained on multi-temporal and multi-frequency SAR datasets, enabling it to predict coherence patterns and enhance low-coherence regions. This hybrid CNN-SAR with Lee filtering significantly reduces noise and phase unwrapping errors, leading to more precise deformation maps. Experimental results demonstrate that our approach improves coherence by up to 30% compared to traditional filtering techniques, making it a robust solution for challenging scenarios such as urban environments, vegetated areas, and rapidly changing landscapes. Our method has potential applications in geohazard monitoring, urban planning, and environmental studies, offering a new avenue for enhancing InSAR data reliability through AI-powered optimization combined with robust filtering techniques.

Keywords: CNN-SAR, Lee Filter, hybrid optimization, coherence, InSAR phase unwrapping, speckle noise reduction

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Authors: Niyongabo Elyse

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Prefabricated building construction is a pioneering approach that combines design, production, and assembly to attain energy efficiency, environmental sustainability, and economic feasibility. Despite continuous development in the industry in China, the low technical maturity of standardized design, factory production, and construction assembly introduces uncertainties affecting prefabricated component production and on-site assembly processes. This research focuses on enhancing project management performance under uncertainty to help enterprises navigate these challenges and optimize project resources. The study introduces a perspective on how uncertain factors influence the implementation of prefabricated building construction projects. It proposes a theoretical model considering project process management ability, adaptability to uncertain environments, and collaboration ability of project participants. The impact of uncertain factors is demonstrated through case studies and quantitative analysis, revealing constraints on implementation time, cost, quality, and safety. To address uncertainties in prefabricated component production scheduling, a fuzzy model is presented, expressing processing times in interval values. The model utilizes a cooperative co-evolution evolution algorithm (CCEA) to optimize scheduling, demonstrated through a real case study showcasing reduced project duration and minimized effects of processing time disturbances. Additionally, the research addresses on-site assembly construction scheduling, considering the relationship between task processing times and assigned resources. A multi-objective model with fuzzy activity durations is proposed, employing a hybrid cooperative co-evolution evolution algorithm (HCCEA) to optimize project scheduling. Results from real case studies indicate improved project performance in terms of duration, cost, and resilience to processing time delays and resource changes. The study also introduces a multistage dynamic process control model, utilizing IoT technology for real-time monitoring during component production and construction assembly. This approach dynamically adjusts schedules when constraints arise, leading to enhanced project management performance, as demonstrated in a real prefabricated housing project. Key contributions include a fuzzy prefabricated components production scheduling model, a multi-objective multi-mode resource-constrained construction project scheduling model with fuzzy activity durations, a multi-stage dynamic process control model, and a cooperative co-evolution evolution algorithm. The integrated mathematical model addresses the complexity of prefabricated building construction project management, providing a theoretical foundation for practical decision-making in the field.

Keywords: prefabricated construction, project management performance, uncertainty, fuzzy scheduling

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Authors: Hediyeh Talebi, Shokoofeh Ghiam, Changiz Eslahchi

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Traditionally, Alzheimer’s disease research has focused on genes with significant fold changes, potentially neglecting subtle but biologically important alterations. Our study introduces an integrative approach that highlights genes crucial to underlying biological processes, regardless of their fold change magnitude. Alzheimer's Single-cell RNA-seq data related to the peripheral blood mononuclear cells (PBMC) was extracted from the Gene Expression Omnibus (GEO). After quality control, normalization, scaling, batch effect correction, and clustering, differentially expressed genes (DEGs) were identified with adjusted p-values less than 0.05. These DEGs were categorized based on cell-type, resulting in four datasets, each corresponding to a distinct cell type. To distinguish between cells from healthy individuals and those with Alzheimer's, an adversarial autoencoder with a classifier was employed. This allowed for the separation of healthy and diseased samples. To identify the most influential genes in this classification, the weight matrices in the network, which includes the encoder and classifier components, were multiplied, and focused on the top 20 genes. The analysis revealed that while some of these genes exhibit a high fold change, others do not. These genes, which may be overlooked by previous methods due to their low fold change, were shown to be significant in our study. The findings highlight the critical role of genes with subtle alterations in diagnosing Alzheimer's disease, a facet frequently overlooked by conventional methods. These genes demonstrate remarkable discriminatory power, underscoring the need to integrate biological relevance with statistical measures in gene prioritization. This integrative approach enhances our understanding of the molecular mechanisms in Alzheimer’s disease and provides a promising direction for identifying potential therapeutic targets.

Keywords: alzheimer's disease, single-cell RNA-seq, neural networks, blood biomarkers

Procedia PDF Downloads 66