Search results for: predictive mining

Authors: Jianghao Hu, Hongyu Wang

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The effective and efficient human pose estimation algorithm is an important task for real-time human pose estimation on mobile devices. This paper proposes a light-weight human key points detection algorithm, Light-Weight Network for Real-Time Pose Estimation (LWPE). LWPE uses light-weight backbone network and depthwise separable convolutions to reduce parameters and lower latency. LWPE uses the feature pyramid network (FPN) to fuse the high-resolution, semantically weak features with the low-resolution, semantically strong features. In the meantime, with multi-scale prediction, the predicted result by the low-resolution feature map is stacked to the adjacent higher-resolution feature map to intermediately monitor the network and continuously refine the results. At the last step, the key point coordinates predicted in the highest-resolution are used as the final output of the network. For the key-points that are difficult to predict, LWPE adopts the online hard key points mining strategy to focus on the key points that hard predicting. The proposed algorithm achieves excellent performance in the single-person dataset selected in the AI (artificial intelligence) challenge dataset. The algorithm maintains high-precision performance even though the model only contains 3.9M parameters, and it can run at 225 frames per second (FPS) on the generic graphics processing unit (GPU).

Keywords: depthwise separable convolutions, feature pyramid network, human pose estimation, light-weight backbone

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Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdellatif, Rabah Siham

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In the field of veterinary medicine, there is a growing application of artificial intelligence (AI) for diagnosing bovine mastitis, a prevalent inflammatory disease in dairy cattle. AI technologies, such as automated milking systems, have streamlined the assessment of key metrics crucial for managing cow health during milking and identifying prevalent diseases, including mastitis. These automated milking systems empower farmers to implement automatic mastitis detection by analyzing indicators like milk yield, electrical conductivity, fat, protein, lactose, blood content in the milk, and milk flow rate. Furthermore, reports highlight the integration of somatic cell count (SCC), thermal infrared thermography, and diverse systems utilizing statistical models and machine learning techniques, including artificial neural networks, to enhance the overall efficiency and accuracy of mastitis detection. According to a review of 15 publications, machine learning technology can predict the risk and detect mastitis in cattle with an accuracy ranging from 87.62% to 98.10% and sensitivity and specificity ranging from 84.62% to 99.4% and 81.25% to 98.8%, respectively. Additionally, machine learning algorithms and microarray meta-analysis are utilized to identify mastitis genes in dairy cattle, providing insights into the underlying functional modules of mastitis disease. Moreover, AI applications can assist in developing predictive models that anticipate the likelihood of mastitis outbreaks based on factors such as environmental conditions, herd management practices, and animal health history. This proactive approach supports farmers in implementing preventive measures and optimizing herd health. By harnessing the power of artificial intelligence, the diagnosis of bovine mastitis can be significantly improved, enabling more effective management strategies and ultimately enhancing the health and productivity of dairy cattle. The integration of artificial intelligence presents valuable opportunities for the precise and early detection of mastitis, providing substantial benefits to the dairy industry.

Keywords: artificial insemination, automatic milking system, cattle, machine learning, mastitis

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Authors: Medina O. Kadiri, John E. Gabriel

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Heavy metals are metallic elements with a relatively high density, at least five times greater compared to water. The sources of heavy metal pollution in the environment include industrial, medical, agricultural, pharmaceutical, domestic effluents, and atmospheric sources, mining, foundries, smelting, and any heavy metal-based operation. Although some heavy metals in trace quantities are required for biological metabolism, their higher concentrations elicit toxicities. Others are distinctly toxic and are of no biological functions. Microalgae are the primary producers of aquatic ecosystems and, therefore, the foundation of the aquatic food chain. A study investigating the effects of copper and zinc on the two chlorophytes-Chlorella vulgaris and Dictyosphaerium pulchellum was done in the laboratory, under different concentrations of 0mg/l, 2mg/l, 4mg/l, 6mg/l, 8mg/l, 10mg/l, and 20mg/l. The growth of the test microalgae was determined every two days for 14 days. The results showed that the effects of the test heavy metals were concentration-dependent. From the two microalgae species tested, Chlorella vulgaris showed appreciable growth up to 8mg/l concentration of zinc. Dictyoshphaerium pulchellum had only minimal growth at different copper concentrations except for 2mg/l, which seemed to have relatively higher growth. The growth of the control was remarkably higher than in other concentrations. Generally, the growth of both test algae was consistently inhibited by heavy metals. Comparatively, copper generally inhibited the growth of both algae than zinc. Chlorella vulgaris can be used for bioremediation of high concentrations of zinc. The potential of many microalgae in heavy metal bioremediation can be explored.

Keywords: heavy metals, green algae, microalgae, pollution

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Authors: Johannes Barlang

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Adhesive bonding plays a pivotal role in various industrial applications, ranging from automotive manufacturing to aerospace engineering. The peel strength of adhesives, a critical parameter reflecting the ability of an adhesive to withstand external forces, is crucial for ensuring the integrity and durability of bonded joints. This study provides a synopsis of the methodologies, influencing factors, and significance of peel testing in the evaluation of adhesive performance. Peel testing involves the measurement of the force required to separate two bonded substrates under controlled conditions. This study systematically reviews the different testing techniques commonly applied in peel testing, including the widely used 180-degree peel test and the T-peel test. Emphasis is placed on the importance of selecting an appropriate testing method based on the specific characteristics of the adhesive and the application requirements. The influencing factors on peel strength are multifaceted, encompassing adhesive properties, substrate characteristics, environmental conditions, and test parameters. Through an in-depth analysis, this study explores how factors such as adhesive formulation, surface preparation, temperature, and peel rate can significantly impact the peel strength of adhesively bonded joints. Understanding these factors is essential for optimizing adhesive selection and application processes in real-world scenarios. Furthermore, the study highlights the role of peel testing in quality control and assurance, aiding manufacturers in maintaining consistent adhesive performance and ensuring the reliability of bonded structures. The correlation between peel strength and long-term durability is discussed, shedding light on the predictive capabilities of peel testing in assessing the service life of adhesive bonds. In conclusion, this study underscores the significance of peel testing as a fundamental tool for characterizing adhesive performance. By delving into testing methodologies, influencing factors, and practical implications, this study contributes to the broader understanding of adhesive behavior and fosters advancements in adhesive technology across diverse industrial sectors.

Keywords: adhesively bonded joints, cleavage resistance, elastic adhesives, peel strength

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Authors: R. Suresh, Pavan Kumar Nimmagadda, Ming Zo Tan, Shane Hart, Sharp Ugwuocha

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Polycrystalline diamond compact (PDC) drill bits are extensively used in the oil and gas industry as well as the mining industry. Industry engineers continually improve upon PDC drill bit designs and hydraulic conditions. Optimized injection nozzles play a key role in improving the drilling performance and efficiency of these ever changing PDC drill bits. In the first part of this study, computational fluid dynamics (CFD) modelling is performed to investigate the hydrodynamic characteristics of drilling fluid flow around the PDC drill bit. An Open-source CFD software – OpenFOAM simulates the flow around the drill bit, based on the field input data. A specifically developed console application integrates the entire CFD process including, domain extraction, meshing, and solving governing equations and post-processing. The results from the OpenFOAM solver are then compared with that of the ANSYS Fluent software. The data from both software programs agree. The second part of the paper describes the parametric study of the PDC drill bit nozzle to determine the effect of parameters such as number of nozzles, nozzle velocity, nozzle radial position and orientations on the flow field characteristics and bit washing patterns. After analyzing a series of nozzle configurations, the best configuration is identified and recommendations are made for modifying the PDC bit design.

Keywords: ANSYS Fluent, computational fluid dynamics, nozzle configuration, OpenFOAM, PDC dill bit

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Authors: Lin Qing

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[Objective/Significance] The cultivation of high-value patents is the focus and difficulty of patent work, which is of great significance to the construction of a powerful country with intellectual property rights. This work should not only pay attention to the existing patent applications, but also start from the pre-application to explore the high-value technical solutions as the core of high-value patents. [Methods/processes] Comply with the principle of scientific and technological knowledge diffusion, this study studies the top academic conference papers and their cited patent applications, taking the power semiconductor field as an example, using facts date show the feasibility and rationality of mining technology solutions from high quality research results to foster high value patents, stating the actual benefits of these achievements to the industry, giving patent protection suggestions for Chinese applicants comparative with field situation. [Results/Conclusion] The research shows that the quality of citation applications of ISPSD papers is significantly higher than the field average level, and the ability of Chinese applicants to use patent protection related achievements needs to be improved. This study provides a practical and highly targeted reference idea for patent administrators and researchers, and also makes a positive exploration for the practice of the spirit of breaking the five rules.

Keywords: high-value patents cultivation, technical solutions, knowledge diffusion, top academic conference papers, intellectual property information analysis

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Authors: Sergio Zabala-Vargas, Miguel Jiménez-Barrera, Luz VArgas-Sánchez

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In recent years, information related to project management in organizations has been increasing exponentially. Performance data, management statistics, indicator results have forced the collection, analysis, traceability, and dissemination of project managers to be essential. In this sense, there are current trends to facilitate efficient decision-making in emerging technology projects, such as: Machine Learning, Data Analytics, Data Mining, and Big Data. The latter is the most interesting in this project. This research is part of the thematic line Construction methods and project management. Many authors present the relevance that the use of emerging technologies, such as Big Data, has taken in recent years in project management in the construction sector. The main focus is the optimization of time, scope, budget, and in general mitigating risks. This research was developed in the northeastern region of Colombia-South America. The first phase was aimed at diagnosing the use of emerging technologies (Big-Data) in the construction sector. In Colombia, the construction sector represents more than 50% of the productive system, and more than 2 million people participate in this economic segment. The quantitative approach was used. A survey was applied to a sample of 91 companies in the construction sector. Preliminary results indicate that the use of Big Data and other emerging technologies is very low and also that there is interest in modernizing project management. There is evidence of a correlation between the interest in using new data management technologies and the incorporation of Building Information Modeling BIM. The next phase of the research will allow the generation of guidelines and strategies for the incorporation of technological tools in the construction sector in Colombia.

Keywords: big data, building information modeling, tecnology, project manamegent

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Authors: Usama Shahid, Muhammad Zunnurain Hussain

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This research paper presents a comprehensive analysis of a sentiment analysis approach that employs valence and arousal as its foundational pillars, in comparison to traditional techniques. Sentiment analysis is an indispensable task in natural language processing that involves the extraction of opinions and emotions from textual data. The valence and arousal dimensions, representing the intensity and positivity/negativity of emotions, respectively, enable the creation of four quadrants, each representing a specific emotional state. The study seeks to determine the impact of utilizing these quadrants to identify distinct emotional states on the accuracy and efficiency of sentiment analysis, in comparison to traditional techniques. The results reveal that the valence and arousal-based approach outperforms other approaches, particularly in identifying nuanced emotions that may be missed by conventional methods. The study's findings are crucial for applications such as social media monitoring and market research, where the accurate classification of emotions and opinions is paramount. Overall, this research highlights the potential of using valence and arousal as a framework for sentiment analysis and offers invaluable insights into the benefits of incorporating specific types of emotions into the analysis. These findings have significant implications for researchers and practitioners in the field of natural language processing, as they provide a basis for the development of more accurate and effective sentiment analysis tools.

Keywords: sentiment analysis, valence and arousal, emotional states, natural language processing, machine learning, text analysis, sentiment classification, opinion mining

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Authors: Haoyu Zhao, Qianshu Ma, Min Xie, Yunqi Huang, Yunjia Liu, Huan Song, Hongsheng Gui, Mingli Li, Qiang Wang

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Rationale: Bipolar disorder (BD) and major depressive disorder (MDD), as severe chronic illnesses that restrict patients’ psychosocial functioning and reduce their quality of life, are both categorized into mood disorders. Emerging evidence has suggested that the reliability of self-rated health (SRH) was wellvalidated and that the risk of various health outcomes, including mortality and health care costs, could be predicted by SRH. Compared with other lengthy multi-item patient-reported outcomes (PRO) measures, SRH was proven to have a comparable predictive ability to predict mortality and healthcare utilization. However, to our knowledge, no study has been conducted to assess the association between SRH and hospitalization among people with mental disorders. Therefore, our study aims to determine the association between SRH and subsequent all-cause hospitalizations in patients with BD and MDD. Methods: We conducted a prospective cohort study on people with BD or MDD in the UK from 2006 to 2010 using UK Biobank touchscreen questionnaire data and linked administrative health databases. The association between SRH and 2-year all-cause hospitalizations was assessed using proportional hazard regression after adjustment for sociodemographics, lifestyle behaviors, previous hospitalization use, the Elixhauser comorbidity index, and environmental factors. Results: A total of 29,966 participants were identified, experiencing 10,279 hospitalization events. Among the cohort, the average age was 55.88 (SD 8.01) years, 64.02% were female, and 3,029 (10.11%), 15,972 (53.30%), 8,313 (27.74%), and 2,652 (8.85%) reported excellent, good, fair, and poor SRH, respectively. Among patients reporting poor SRH, 54.19% had a hospitalization event within 2 years compared with 22.65% for those having excellent SRH. In the adjusted analysis, patients with good, fair, and poor SRH had 1.31 (95% CI 1.21-1.42), 1.82 (95% CI 1.68-1.98), and 2.45 (95% CI 2.22, 2.70) higher hazards of hospitalization, respectively, than those with excellent SRH. Conclusion: SRH was independently associated with subsequent all-cause hospitalizations in patients with BD or MDD. This large study facilitates rapid interpretation of SRH values and underscores the need for proactive SRH screening in this population, which might inform resource allocation and enhance high-risk population detection.

Keywords: severe mental illnesses, hospitalization, risk prediction, patient-reported outcomes

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Authors: Faten Hatem

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This paper promotes the importance of focusing on spatial aspects and affective factors that impact smart urbanism. This helps to better inform city governance, spatial planning, and policymaking to focus on what Smart does and what it can achieve for cities in terms of performance rather than on using the notion for prestige in a worldwide trend towards becoming a smart city. By illustrating how this style of practice compromises the social aspects and related elements of space making through an interdisciplinary comparative approach, the paper clarifies the impact of this compromise on the overall smart city performance. In response, this paper recognizes the importance of establishing a new meaning for urban progress by moving beyond improving basic services of the city to enhance the actual human experience which is essential for the development of authentic smart cities. The topic is presented under five overlooked areas that discuss the relation between smart cities’ potential and efficiency paradox, the social aspect, connectedness with nature, the human factor, and untapped resources. However, these themes are not meant to be discussed in silos, instead, they are presented to collectively examine smart cities in performance, arguing there is more to the practical life of smart cities than software and hardware inventions. The study is based on a case study approach, presenting Milton Keynes as a living example to learn from while engaging with various methods for data collection including multi-disciplinary semi-structured interviews, field observations, and data mining.

Keywords: smart design, the human in the city, human needs and urban planning, sustainability, smart cities, smart

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Authors: Manutchanok Jongprasithporn, Rawiphorn Srivilai, Paweena Pongsopha

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Fall is the most serious accident leading to increased unintentional injuries and mortality. Falls are not only the cause of suffering and functional impairments to the individuals, but also the cause of increasing medical cost and days away from work. The early detection of falls could be an advantage to reduce fall-related injuries and consequences of falls. Smartphones, embedded accelerometer, have become a common device in everyday life due to decreasing technology cost. This paper explores a physical activity monitoring and fall detection application in smartphones which is a non-invasive biomedical device to determine physical activities and fall event. The combination of application and sensors could perform as a biomedical sensor to monitor physical activities and recognize a fall. We have chosen Android-based smartphone in this study since android operating system is an open-source and no cost. Moreover, android phone users become a majority of Thai’s smartphone users. We developed Thai 3 Axis (TH3AX) as a physical activities and fall detection application which included command, manual, results in Thai language. The smartphone was attached to right hip of 10 young, healthy adult subjects (5 males, 5 females; aged< 35y) to collect accelerometer and gyroscope data during performing physical activities (e.g., walking, running, sitting, and lying down) and falling to determine threshold for each activity. Dependent variables are including accelerometer data (acceleration, peak acceleration, average resultant acceleration, and time between peak acceleration). A repeated measures ANOVA was performed to test whether there are any differences between DVs’ means. Statistical analyses were considered significant at p<0.05. After finding threshold, the results were used as training data for a predictive model of activity recognition. In the future, accuracies of activity recognition will be performed to assess the overall performance of the classifier. Moreover, to help improve the quality of life, our system will be implemented with patients and elderly people who need intensive care in hospitals and nursing homes in Thailand.

Keywords: activity recognition, accelerometer, fall, gyroscope, smartphone

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: Enebe Christian Chukwudi

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Water bodies around the world notably underground water, ground water, rivers, streams, and seas, face degradation of their water quality as a result of activities associated with coal utilization including coal mining, coal processing, coal burning, waste storage and thermal pollution from coal plants which tend to contaminate these water bodies. This contamination results from heavy metals, presence of sulphate and iron, dissolved solids, mercury and other toxins contained in coal ash, sludge, and coal waste. These wastes sometimes find their way to sources of urban water supply and contaminate them. A major problem encountered in the supply of potable water to Enugu municipality is the contamination of Ajali River, the source of water supply to Enugu municipal by coal waste. Hydro geochemical analysis of Ajali water samples indicate high sulphate and iron content, high total dissolved solids(TDS), low pH (acidity values) and significant hardness in addition to presence of heavy metals, mercury, and other toxins. This is indicative of the following remedial measures: I. Proper disposal of mine wastes at designated disposal sites that are suitably prepared. II. Proper water treatment and III. Reduction of coal related contaminants taking advantage of clean coal technology.

Keywords: effects, coal, utilization, water quality, sources, waste, contamination, treatment

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Authors: Branka Milicic Stanic, Romana Mijovic

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In primary hyperparathyroidism, an increased risk of developing cardiovascular disease may exist due to increased activity of the renin-angiotensin-aldosterone system (RAAS). In adenomatous altered tissue of parathyroid gland, compared to normal tissue, there are two to fourfold increase in the expression of type 1 angiotensin II receptors. As there is a clear evidence of the independent role of aldosterone on the cardiovascular system, the aim of this study was to evaluate the existence of an association between aldosterone secretion and parathyroid hormone in patients with primary hyperparathyroidism. This study included 48 patients with elevated parathyroid hormone who had come to the Departement of Nuclear Medicine, Clinical Center of Vojvodina, for Parathyroid Scintigraphy. The control group consisted of 30 healthy subjects who matched age and gender to the study group. All the results were statistically processed by statistical package STATISTICA 14 (Statsoft Inc,Tulsa, OK, USA). The survey was conducted between February 2017 and April 2018 at the Departement of Nuclear Medicine and at the Departement for Endocinology Diagnoistics, in Clinical Center of Vojvodina, Novi Sad. Compared to the control group, the study group had statistically significantly higher values of aldosterone (p=0.028), total calcium (p=0.01), ionized calcium (p=0.003) and parathyroid hormone (N-TACT PTH) (p=0.00), while statistically a significant lower levels in the study group were for phosphorus (p=0.003) and vitamin D (p=0.04). A linear correlation analysis in the study group revealed a statistically significant degree of positive correlation between renin and N-TACT PTH (r=0.688, p<0.05); renin and calcium (r=0.673, p<0.05) and renin and ionized calcium (r=0.641, p<0.05). Serum aldosterone and parathyroid hormone levels (N-TACT) were correlated positively in patients with primary hyperparathyroidism (r=0.509, p<0.05). According to the linear correlation analysis in the control group, aldosterone showed no positive correlation with N-TACT PTH (r=-0.285, p>0.05), as well as total and ionized calcium (r=-0.200, p>0.05; r=-0.313, p>0.05). In multivariate regression analysis of the study group, the strongest predictive variable of aldosterone secretion was N-TACT PTH (p=0.011). Aldosterone correlated positively to PTH levels in patients with primary hyperparathyroidism, and the fact is that in these patients aldosterone might be a key mediator of cardiovascular symptoms. All this knowledge should help to find new treatments to prevent cardiovascular disease.

Keywords: aldosterone, hyperparathyroidism, parathyroid hormone, parathyroid gland

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Authors: Li-Ching Lin, Yu-Tzu Dai

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Recurrent foot ulcers or foot amputation have a major impact on patients with diabetes mellitus (DM), medical professionals, and society. A critical procedure for foot care is foot self-monitoring. Medical professionals’ understanding of patients’ foot self-monitoring knowledge, attitude, and practice is beneficial for raising patients’ disease awareness. This study investigated these and related factors among patients with DM through a descriptive study of the correlations. A scale for measuring the foot self-monitoring knowledge, attitude, and practice of patients with DM was used. Purposive sampling was adopted, and 100 samples were collected from the respondents’ self-reports or from interviews. The statistical methods employed were an independent-sample t-test, one-way analysis of variance, Pearson correlation coefficient, and multivariate regression analysis. The findings were as follows: the respondents scored an average of 12.97 on foot self-monitoring knowledge, and the correct answer rate was 68.26%. The respondents performed relatively lower in foot health screenings and recording, and awareness of neuropathy in the foot. The respondents held a positive attitude toward self-monitoring their feet and a negative attitude toward having others check the soles of their feet. The respondents scored an average of 12.64 on foot self-monitoring practice. Their scores were lower in their frequency of self-monitoring their feet, recording their self-monitoring results, checking their pedal pulse, and examining if their soles were red immediately after taking off their shoes. Significant positive correlations were observed among foot self-monitoring knowledge, attitude, and practice. The correlation coefficient between self-monitoring knowledge and self-monitoring practice was 0.20, and that between self-monitoring attitude and self-monitoring practice was 0.44. Stepwise regression analysis revealed that the main predictive factors of the foot self-monitoring practice in patients with DM were foot self-monitoring attitude, prior experience in foot care, and an educational attainment of college or higher. These factors predicted 33% of the variance. This study concludes that patients with DM lacked foot self-monitoring practice and advises that the patients’ self-monitoring abilities be evaluated first, including whether patients have poor eyesight, difficulties in bending forward due to obesity, and people who can assist them in self-monitoring. In addition, patient education should emphasize self-monitoring knowledge and practice, such as perceptions regarding the symptoms of foot neurovascular lesions, pulse monitoring methods, and new foot self-monitoring equipment. By doing so, new or recurring ulcers may be discovered in their early stages.

Keywords: diabetic foot, foot self-monitoring attitude, foot self-monitoring knowledge, foot self-monitoring practice

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Authors: Badawia Ibrahim, Iman Hussein, Samar El Sheikh, Fatma Abou Elkasem, Hazem Abo Ismael

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Background: Response of breast carcinoma to neoadjuvant chemotherapy (NAC) varies regarding many factors including hormonal receptor status. Breast cancer is a heterogenous disease with different outcomes, hence a need arises for new markers predicting the outcome of NAC especially for the triple negative group when estrogen, progesterone receptors and Her2/neu are negative. FOXP3 is a promising target with unclear role. Aim: To examine the value of FOXP3 expression in locally advanced triple negative breast cancer tumoral cells as well as tumor infiltrating lymphocytes (TILs) and to elucidate its relation to the extent of NAC response. Material and Methods: Forty five cases of immunohistochemically confirmed to be triple negative breast carcinoma were evaluated for NAC (Doxorubicin, Cyclophosphamide AC x 4 cycles + Paclitaxel x 12 weeks, patients with ejection fraction less than 60% received Taxotere or Cyclophosphamide, Methotrexate, Fluorouracil CMF) response in both tumour and lymph nodes status according to Miller & Payne's and Sataloff's systems. FOXP3 expression in tumor as well as TILs evaluated in the pretherapy biopsies was correlated with NAC response in breast tumor and lymph nodes as well as other clinicopathological factors. Results: Breast tumour cells showed FOXP3 positive cytoplasmic expression in (42%) of cases. High FOXP3 expression percentage was detected in (47%) of cases. High infiltration by FOXP3+TILs was detected in (49%) of cases. Positive FOXP3 expression was associated with negative lymph node metastasis. High FOXP3 expression percentage and high infiltration by FOXP3+TILs were significantly associated with complete therapy response in axillary lymph nodes. High FOXP3 expression in tumour cells was associated with high infiltration by FOXP3+TILs. Conclusion: This result may provide evidence that FOXP3 marker is a good prognostic and predictive marker for triple negative breast cancer (TNBC) indicated for neoadjuvant chemotherapy and can be used for stratifications of TNBC cases indicated for NAC. As well, this study confirmed the fact that the tumour cells and the surrounding microenvironment interact with each other and the tumour microenvironment can influence the treatment outcomes of TNBC.

Keywords: breast cancer, FOXP3 expression, prediction of neoadjuvant chemotherapy effect, triple negative

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Authors: Alina M. Udall, Judith de Groot, Simon de Jong, Avi Shankar

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There are many ways in which environmentally significant behavior can be explained. Dominant psychological theories, namely, the theory of planned behavior, the norm-activation theory, its extension, the value-belief-norm theory, and the theory of habit do not explain large parts of environmentally significant behaviors. A new and rapidly growing approach is to focus on how consumer’s identities predict environmentally significant behavior. Identity may be relevant because consumers have many identities that are assumed to guide their behavior. Therefore, we assume that many identities will guide environmentally significant behavior. Many identities can be relevant for environmentally significant behavior. In reviewing the literature, over 200 identities have been studied making it difficult to establish the key identities for explaining environmentally significant behavior. Therefore, this paper first aims to establish the key identities previously used for explaining environmentally significant behavior. Second, the aim is to test which key identities explain environmentally significant behavior. To address the aims, an online survey study (n = 578) is conducted. First, the exploratory factor analysis reveals 15 identity factors. The identity factors are namely, environmentally concerned identity, anti-environmental self-identity, environmental place identity, connectedness with nature identity, green space visitor identity, active ethical identity, carbon off-setter identity, thoughtful self-identity, close community identity, anti-carbon off-setter identity, environmental group member identity, national identity, identification with developed countries, cyclist identity, and thoughtful organisation identity. Furthermore, to help researchers understand and operationalize the identities, the article provides theoretical definitions for each of the identities, in line with identity theory, social identity theory, and place identity theory. Second, the hierarchical regression shows only 10 factors significantly uniquely explain the variance in environmentally significant behavior. In order of predictive power the identities are namely, environmentally concerned identity, anti-environmental self-identity, thoughtful self-identity, environmental group member identity, anti-carbon off-setter identity, carbon off-setter identity, connectedness with nature identity, national identity, and green space visitor identity. The identities explain over 60% of the variance in environmentally significant behavior, a large effect size. Based on this finding, the article reveals a new, theoretical framework showing the key identities explaining environmentally significant behavior, to help improve and align the field.

Keywords: environmentally significant behavior, factor analysis, place identity, social identity

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Kolela J Nyembwe, Darlington C. Ashiegbu, Herman J. Potgieter

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Over the past few decades, the demand for metals has increased significantly. This has led to a decrease and decline of high-grade ore over time and an increase in mineral complexity and matrix heterogeneity. In addition to that, there are rising concerns about greener processes and a sustainable environment. Due to these challenges, the mining and metal industry has been forced to develop new technologies that are able to economically process and recover metallic values from low-grade ores, materials having a metal content locked up in industrially processed residues (tailings and slag), and complex matrix mineral deposits. Several methods to address these issues have been developed, among which are ionic liquids (IL), heap leaching, and bioleaching. Recently, the gas phase extraction technique has been gaining interest because it eliminates many of the problems encountered in conventional mineral processing methods. The technique relies on the formation of volatile metal complexes, which can be removed from the residual solids by a carrier gas. The complexes can then be reduced using the appropriate method to obtain the metal and regenerate-recover the organic extractant. Laboratory work on the gas phase have been conducted for the extraction and recovery of aluminium (Al), iron (Fe), copper (Cu), chrome (Cr), nickel (Ni), lead (Pb), and vanadium V. In all cases the extraction revealed to depend of temperature and mineral surface area. The process technology appears very promising, offers the feasibility of recirculation, organic reagent regeneration, and has the potential to deliver on all promises of a “greener” process.

Keywords: gas-phase extraction, hydrometallurgy, low-grade ore, sustainable environment

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Authors: Lucie Côté, Martin Lauzier, Guy Beauchamp, France Guertin

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Stress causes deleterious effects to the physical, psychological and organizational levels, which highlight the need to use effective coping strategies to deal with it. Several coping models exist, but they don’t integrate the different strategies in a coherent way nor do they take into account the new research on the emotional coping and acceptance of the stressful situation. To fill these gaps, an integrative model incorporating the main coping strategies was developed. This model arises from the review of the scientific literature on coping and from a qualitative study carried out among workers with low or high levels of stress, as well as from an analysis of clinical cases. The model allows one to understand under what circumstances the strategies are effective or ineffective and to learn how one might use them more wisely. It includes Specific Strategies in controllable situations (the Modification of the Situation and the Resignation-Disempowerment), Specific Strategies in non-controllable situations (Acceptance and Stubborn Relentlessness) as well as so-called General Strategies (Wellbeing and Avoidance). This study is intended to undertake and present the process of development and validation of an instrument to measure coping strategies based on this model. An initial pool of items has been generated from the conceptual definitions and three expert judges have validated the content. Of these, 18 items have been selected for a short form questionnaire. A sample of 300 students and employees from a Quebec university was used for the validation of the questionnaire. Concerning the reliability of the instrument, the indices observed following the inter-rater agreement (Krippendorff’s alpha) and the calculation of the coefficients for internal consistency (Cronbach's alpha) are satisfactory. To evaluate the construct validity, a confirmatory factor analysis using MPlus supports the existence of a model with six factors. The results of this analysis suggest also that this configuration is superior to other alternative models. The correlations show that the factors are only loosely related to each other. Overall, the analyses carried out suggest that the instrument has good psychometric qualities and demonstrates the relevance of further work to establish predictive validity and reconfirm its structure. This instrument will help researchers and clinicians better understand and assess coping strategies to cope with stress and thus prevent mental health issues.

Keywords: acceptance, coping strategies, stress, validation process

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Authors: Nafiseh Noormohammadi, Ahmad Sobhani Najafabadi

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Hypericins (hypericin and pseudohypericin) are natural napthodianthrone derivatives produced by Hypericum perforatum (St. John’s Wort), which have many medicinal properties such as antitumor, antineoplastic, antiviral, and antidepressant activities. Production and accumulation of hypericin in the plant are influenced by both genetic and environmental conditions. Despite the existence of different high-throughput data on the plant, genetic dimensions of hypericin biosynthesis have not yet been completely understood. In this research, 21 high-quality RNA-seq data on different parts of the plant were integrated into metabolic data to reconstruct a coexpression network. Results showed that a cluster of 30 transcripts was correlated with total hypericin. The identified transcripts were divided into three main groups based on their functions, including hypericin biosynthesis genes, transporters, detoxification genes, and transcription factors (TFs). In the biosynthetic group, different isoforms of polyketide synthase (PKSs) and phenolic oxidative coupling proteins (POCPs) were identified. Phylogenetic analysis of protein sequences integrated into gene expression analysis showed that some of the POCPs seem to be very important in the biosynthetic pathway of hypericin. In the TFs group, six TFs were correlated with total hypericin. qPCR analysis of these six TFs confirmed that three of them were highly correlated. The identified genes in this research are a rich resource for further studies on the molecular breeding of H. perforatum in order to obtain varieties with high hypericin production.

Keywords: hypericin, St. John’s Wort, data mining, transcription factors, secondary metabolites

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Authors: Chung-Yuan Huang, Yu-Hsiang Fu, Chuen-Tsai Sun

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Community detection represents an important data-mining tool for analyzing and understanding real-world complex network structures and functions. We believe that at least four criteria determine the appropriateness of a community detection algorithm: (a) it produces useable normalized mutual information (NMI) and modularity results for social networks, (b) it overcomes resolution limitation problems associated with synthetic networks, (c) it produces good NMI results and performance efficiency for Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and (d) it produces good modularity and performance efficiency for large-scale real-world complex networks. To our knowledge, no existing community detection algorithm meets all four criteria. In this paper, we describe a simple hierarchical arc-merging (HAM) algorithm that uses network topologies and rule-based arc-merging strategies to identify community structures that satisfy the criteria. We used five well-studied social network datasets and eight sets of LFR benchmark networks to validate the ground-truth community correctness of HAM, eight large-scale real-world complex networks to measure its performance efficiency, and two synthetic networks to determine its susceptibility to resolution limitation problems. Our results indicate that the proposed HAM algorithm is capable of providing satisfactory performance efficiency and that HAM-identified communities were close to ground-truth communities in social and LFR benchmark networks while overcoming resolution limitation problems.

Keywords: complex network, social network, community detection, network hierarchy

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Authors: Shani Brathwaite, Deborah Villarroel-Lamb

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Wave run-up may be defined as the time-varying position of the landward extent of the water’s edge, measured vertically from the mean water level position. The hydrodynamics of the swash zone and the accurate prediction of maximum wave run-up, play a critical role in the study of coastal engineering. The understanding of these processes is necessary for the modeling of sediment transport, beach recovery and the design and maintenance of coastal engineering structures. However, due to the complex nature of the swash zone, there remains a lack of detailed knowledge in this area. Particularly, there has been found to be insufficient consideration of bed porosity and ultimately infiltration/exfiltration processes, in the development of wave run-up models. Theoretically, there should be an inverse relationship between maximum wave run-up and beach porosity. The greater the rate of infiltration during an event, associated with a larger bed porosity, the lower the magnitude of the maximum wave run-up. Additionally, most models have been developed using data collected on North American or Australian beaches and may have limitations when used for operational forecasting in Trinidad. This paper aims to assess the influence and significance of infiltration and exfiltration processes on wave run-up magnitudes within the swash zone. It also seeks to pay particular attention to how well various empirical formulae can predict maximum run-up on contrasting beaches in Trinidad. Traditional surveying techniques will be used to collect wave run-up and cross-sectional data on various beaches. Wave data from wave gauges and wave models will be used as well as porosity measurements collected using a double ring infiltrometer. The relationship between maximum wave run-up and differing physical parameters will be investigated using correlation analyses. These physical parameters comprise wave and beach characteristics such as wave height, wave direction, period, beach slope, the magnitude of wave setup, and beach porosity. Most parameterizations to determine the maximum wave run-up are described using differing parameters and do not always have a good predictive capability. This study seeks to improve the formulation of wave run-up by using the aforementioned parameters to generate a formulation with a special focus on the influence of infiltration/exfiltration processes. This will further contribute to the improvement of the prediction of sediment transport, beach recovery and design of coastal engineering structures in Trinidad.

Keywords: beach porosity, empirical models, infiltration, swash, wave run-up

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Authors: Maria Medeiros, Suren Surenthiran, Abitha Muralithar, Soushma Seeburuth, Mohammed Mohammed

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Introduction: Appendicitis is the most common surgical emergency globally and can have devastating consequences. Diagnostic imaging in acute appendicitis has become increasingly common in aiding the diagnosis of acute appendicitis. Computerized tomography (CT) and ultrasound (US) are the most commonly used imaging modalities for diagnosing acute appendicitis. Pre-operative imaging has contributed to a reduction of negative appendicectomy rates from between 10-29% to 5%. Literature report CT scan has a diagnostic sensitivity of 94% in acute appendicitis. This clinical audit was conducted to establish if the CT scan's diagnostic yield for acute appendicitis matches the literature. CT scan has a high sensitivity and specificity for diagnosing acute appendicitis and its use can result in a lower negative appendicectomy rate. The aim of this study is to compare the pre-operative imaging findings from CT scans to the histopathology results post-operatively and establish the accuracy of CT scans in aiding the diagnosis of acute appendicitis. Methods: This was a retrospective study focusing on adult presentations to the general surgery department in a district general hospital in central London with an impression of acute appendicitis. We analyzed all patients from July 2022 to December 2022 who underwent a CT scan preceding appendicectomy. Pre-operative CT findings and post-operative histopathology findings were compared to establish the efficacy of CT scans in diagnosing acute appendicitis. Our results were also cross-referenced with pre-existing literature. Data was collected and anonymized using CERNER and analyzed in Microsoft Excel. Exclusion criteria: Children, age <16. Results: 65 patients had CT scans in which the report stated acute appendicitis. Of those 65 patients, 62 patients underwent diagnostic laparoscopies. 100% of patients who underwent an appendicectomy with a pre-operative CT scan showing acute appendicitis had acute appendicitis in histopathology analysis. 3 of the 65 patients who had a CT scan showing appendicitis received conservative treatment. Conclusion: CT scans positive for acute appendicitis had 100% sensitivity and a positive predictive value, which matches published research studies (sensitivity of 94%). The use of CT scans in the diagnostic work-up for acute appendicitis can be extremely helpful in a) confirming the diagnosis and b) reducing the rates of negative appendicectomies and consequently reducing unnecessary operative-associated risks for patients, reducing costs and reducing pressure on emergency theatre lists.

Keywords: acute apendicitis, CT scan, general surgery, imaging

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Authors: Brennan Lodge

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Amidst a complex regulatory landscape, Retrieval Augmented Generation (RAG) emerges as a transformative tool for Governance Risk and Compliance (GRC) officers. This paper details the application of RAG in synthesizing Large Language Models (LLMs) with external knowledge bases, offering GRC professionals an advanced means to adapt to rapid changes in compliance requirements. While the development for standalone LLM’s (Large Language Models) is exciting, such models do have their downsides. LLM’s cannot easily expand or revise their memory, and they can’t straightforwardly provide insight into their predictions, and may produce “hallucinations.” Leveraging a pre-trained seq2seq transformer and a dense vector index of domain-specific data, this approach integrates real-time data retrieval into the generative process, enabling gap analysis and the dynamic generation of compliance and risk management content. We delve into the mechanics of RAG, focusing on its dual structure that pairs parametric knowledge contained within the transformer model with non-parametric data extracted from an updatable corpus. This hybrid model enhances decision-making through context-rich insights, drawing from the most current and relevant information, thereby enabling GRC officers to maintain a proactive compliance stance. Our methodology aligns with the latest advances in neural network fine-tuning, providing a granular, token-level application of retrieved information to inform and generate compliance narratives. By employing RAG, we exhibit a scalable solution that can adapt to novel regulatory challenges and cybersecurity threats, offering GRC officers a robust, predictive tool that augments their expertise. The granular application of RAG’s dual structure not only improves compliance and risk management protocols but also informs the development of compliance narratives with pinpoint accuracy. It underscores AI’s emerging role in strategic risk mitigation and proactive policy formation, positioning GRC officers to anticipate and navigate the complexities of regulatory evolution confidently.

Keywords: cybersecurity, gen AI, retrieval augmented generation, cybersecurity defense strategies

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Authors: Carine Cridelich, Pablo Juesas Cano, Emmanuel Ramasso, Noureddine Zerhouni, Bernd Weiler

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Safety is one of the most important considerations when buying a new car. While active safety aims at avoiding accidents, passive safety systems such as airbags and seat belts protect the occupant in case of an accident. In addition to legal regulations, organizations like Euro NCAP provide consumers with an independent assessment of the safety performance of cars and drive the development of safety systems in automobile industry. Those ratings are mainly based on injury assessment reference values derived from physical parameters measured in dummies during a car crash test. The components and sub-systems of a safety system are designed to achieve the required restraint performance. Sled tests and other types of tests are then carried out by car makers and their suppliers to confirm the protection level of the safety system. A Knowledge Discovery in Databases (KDD) process is proposed in order to minimize the number of tests. The KDD process is based on the data emerging from sled tests according to Euro NCAP specifications. About 30 parameters of the passive safety systems from different data sources (crash data, dummy protocol) are first analysed together with experts opinions. A procedure is proposed to manage missing data and validated on real data sets. Finally, a procedure is developed to estimate a set of rough initial parameters of the passive system before testing aiming at reducing the number of tests.

Keywords: KDD process, passive safety systems, sled test, dummy injury assessment reference values, frontal impact

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Authors: Beloulou Mohamed Lamine, Fedili Benamar, Meliani Walid, Chaid Dalila, Lamara Abdelhak

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Introduction: Post-surgical chronic pain (PSCP) is a pathological condition with a rather complex etiopathogenesis that extensively involves sensitization processes and neuronal damage. The neuropathic component of these pains is almost always present, with variable expression depending on the type of surgery. Objective: To assess the presumed beneficial effect of Regional Anesthesia-Analgesia Techniques (RAAT) on the development of post-surgical chronic neuropathic pain (PSCNP) in various surgical procedures. Patients and Methods: A comparative study involving 510 patients distributed across five surgical models (mastectomy, thoracotomy, hernioplasty, cholecystectomy, and major abdominal-pelvic surgery) and randomized into two groups: Group A (240) receiving conventional postoperative analgesia and Group B (270) receiving balanced analgesia, including the implementation of a Regional Anesthesia-Analgesia Technique (RAAT). These patients were longitudinally followed over a 6-month period, with postsurgical chronic neuropathic pain (PSCNP) defined by a Neuropathic Pain Score DN2≥ 3. Comparative measurements through univariate and multivariable analyses were performed to identify associations between the development of PSCNP and certain predictive factors, including the presumed preventive impact (protective effect) of RAAT. Results: At the 6th month post-surgery, 419 patients were analyzed (Group A= 196 and Group B= 223). The incidence of PSCNP was 32.2% (n=135). Among these patients with chronic pain, the prevalence of neuropathic pain was 37.8% (95% CI: [29.6; 46.5]), with n=51/135. It was significantly lower in Group B compared to Group A, with respective percentages of 31.4% vs. 48.8% (p-value = 0.035). The most significant differences were observed in breast and thoracopulmonary surgeries. In a multiple regression analysis, two predictors of PSCNP were identified: the presence of preoperative pain at the surgical site as a risk factor (OR: 3.198; 95% CI [1.326; 7.714]) and RAAT as a protective factor (OR: 0.408; 95% CI [0.173; 0.961]). Conclusion: The neuropathic component of PSCNP can be observed in different types of surgeries. Regional analgesia included in a multimodal approach to postoperative pain management has proven to be effective for acute pain and seems to have a preventive impact on the development of PSCNP and its neuropathic nature, particularly in surgeries that are more prone to chronicization.

Keywords: post-surgical chronic pain, post-surgical chronic neuropathic pain, regional anesthesia-analgesia techniques, neuropathic pain score DN2, preventive impact

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Authors: Daphne Sobolev, Niklas Voege

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During the last three decades, consumer ethics perceptions have attracted the attention of a large number of researchers. Nevertheless, little is known about the effect of the cognitive and situational contexts of the decision on ethics judgments. In this paper, the interrelationship between consumers’ ethics perceptions, legality evaluations and mind-sets are explored. Legality evaluations represent the cognitive context of the ethical judgments, whereas mind-sets represent their situational context. Drawing on moral development theories and priming theories, it is hypothesized that both factors are significantly related to consumer ethics perceptions. To test this hypothesis, 289 participants were allocated to three mind-set experimental conditions and a control group. Participants in the mind-set conditions were primed for aggressiveness, politeness or awareness to the negative legal consequences of breaking the law. Mind-sets were induced using a sentence-unscrambling task, in which target words were included. Ethics and legality judgments were assessed using consumer ethics and internet ethics questionnaires. All participants were asked to rate the ethicality and legality of consumer actions described in the questionnaires. The results showed that consumer ethics and legality perceptions were significantly correlated. Moreover, including legality evaluations as a variable in ethics judgment models increased the predictive power of the models. In addition, inducing aggressiveness in participants reduced their sensitivity to ethical issues; priming awareness to negative legal consequences increased their sensitivity to ethics when uncertainty about the legality of the judged scenario was high. Furthermore, the correlation between ethics and legality judgments was significant overall mind-set conditions. However, the results revealed conflicts between ethics and legality perceptions: consumers considered 10%-14% of the presented behaviors unethical and legal, or ethical and illegal. In 10-23% of the questions, participants indicated that they did not know whether the described action was legal or not. In addition, an asymmetry between the effects of aggressiveness and politeness priming was found. The results show that the legality judgments and mind-sets interact with consumer ethics perceptions. Thus, they portray consumer ethical judgments as dynamical processes which are inseparable from other cognitive processes and situational variables. They highlight that legal and ethical education, as well as adequate situational cues at the service place, could have a positive effect on consumer ethics perceptions. Theoretical contribution is discussed.

Keywords: consumer ethics, legality judgments, mind-set, priming, aggressiveness

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

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Authors: Maher Aburrous, Adel Khelifi, Manar Abu Talib

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Transactions performed by financial institutions on daily basis require XML encryption on large scale. Encrypting large volume of message fully will result both performance and resource issues. In this paper a novel approach is presented for securing financial XML transactions using classification data mining (DM) algorithms. Our strategy defines the complete process of classifying XML transactions by using set of classification algorithms, classified XML documents processed at later stage using element-wise encryption. Classification algorithms were used to identify the XML transaction rules and factors in order to classify the message content fetching important elements within. We have implemented four classification algorithms to fetch the importance level value within each XML document. Classified content is processed using element-wise encryption for selected parts with "High", "Medium" or “Low” importance level values. Element-wise encryption is performed using AES symmetric encryption algorithm and proposed modified algorithm for AES to overcome the problem of computational overhead, in which substitute byte, shift row will remain as in the original AES while mix column operation is replaced by 128 permutation operation followed by add round key operation. An implementation has been conducted using data set fetched from e-banking service to present system functionality and efficiency. Results from our implementation showed a clear improvement in processing time encrypting XML documents.

Keywords: XML transaction, encryption, Advanced Encryption Standard (AES), XML classification, e-banking security, fuzzy classification, cryptography, intelligent encryption

Procedia PDF Downloads 380