Search results for: painful vision loss

Authors: Emi Govorčin Bajsića, Vesna Ocelić Bulatović, Miroslav Slouf, Ana Šitum

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Composites based on a biodegradable polycaprolactone (PCL) containing 0.5, 1.0 and 2.0 wt % of titanium dioxide (TiO2) micro and nanoparticles were prepared by melt mixing and the effect of filler type and contents on the thermal properties, dynamic-mechanical behaviour and morphology were investigated. Measurements of storage modulus and loss modulus by dynamic mechanical analysis (DMA) showed better results for microfilled PCL/TiO2 composites than nanofilled composites, with the same filler content. DSC analysis showed that the Tg and Tc of micro and nanocomposites were slightly lower than those of neat PCL. The crystallinity of the PCL increased with the addition of TiO2 micro and nanoparticles; however, the c for the PCL was unchanged with micro TiO2 content. The thermal stability of PCL/TiO2 composites were characterized using thermogravimetric analysis (TGA). The initial weight loss (5 wt %) occurs at slightly higher temperature with micro and nano TiO2 addition and with increasing TiO2 content.

Keywords: polycaprolactone, titanium dioxide, thermal properties, morphology

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Mujtaba Roshan, John A. Schormans

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Quality of Experience (QoE) drives churn in the broadband networks industry, and good QoE plays a large part in the retention of customers. QoE is known to be affected by the Quality of Service (QoS) factors packet loss probability (PLP), delay and delay jitter caused by the network. Earlier results have shown that the relationship between these QoS factors and QoE is non-linear, and may vary from application to application. We use the network emulator Netem as the basis for experimentation, and evaluate how QoE varies as we change the emulated QoS metrics. Focusing on Video-on-Demand, we discovered that the reported QoE may differ widely for users of different age groups, and that the most demanding age group (the youngest) can require an order of magnitude lower PLP to achieve the same QoE than is required by the most widely studied age group of users. We then used a bottleneck TCP model to evaluate the capacity cost of achieving an order of magnitude decrease in PLP, and found it be (almost always) a 3-fold increase in link capacity that was required.

Keywords: network capacity, packet loss probability, quality of experience, quality of service

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Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

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Vision is considered as the most important sense in humans, without which leading a normal can be often difficult. There are many existing smart canes for visually impaired with obstacle detection using ultrasonic transducer to help them navigate. Though the basic smart cane increases the safety of the users, it does not help in filling the void of visual loss. This paper introduces the concept of Digi-Buddy which is an evolved smart cane for visually impaired. The cane consists for several modules, apart from the basic obstacle detection features; the Digi-Buddy assists the user by capturing video/images and streams them to the server using a wide-angled camera, which then detects the objects using Deep Convolutional Neural Network. In addition to determining what the particular image/object is, the distance of the object is assessed by the ultrasonic transducer. The sound generation application, modelled with the help of Natural Language Processing is used to convert the processed images/object into audio. The object detected is signified by its name which is transmitted to the user with the help of Bluetooth hear phones. The object detection is extended to facial recognition which maps the faces of the person the user meets in the database of face images and alerts the user about the person. One of other crucial function consists of an automatic-intimation-alarm which is triggered when the user is in an emergency. If the user recovers within a set time, a button is provisioned in the cane to stop the alarm. Else an automatic intimation is sent to friends and family about the whereabouts of the user using GPS. In addition to safety and security by the existing smart canes, the proposed concept devices to be implemented as a prototype helping visually-impaired visualize their surroundings through audio more in an amicable way.

Keywords: artificial intelligence, facial recognition, natural language processing, internet of things

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Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

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Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

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Authors: Kasirga Yildirak, Ismail Gur

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We propose a model to measure hail risk of an Agricultural Insurance portfolio. Hail is one of the major catastrophic event that causes big amount of loss to an insurer. Moreover, it is very hard to predict due to its strange atmospheric characteristics. We make use of parcel based claims data on apricot damage collected by the Turkish Agricultural Insurance Pool (TARSIM). As our ultimate aim is to compute the loadings assigned to specific parcels, we build a portfolio risk model that makes use of PD and the severity of the exposures. PD is computed by Spherical-Linear and Circular –Linear regression models as the data carries coordinate information and seasonality. Severity is mapped into integer brackets so that Probability Generation Function could be employed. Individual regressions are run on each clusters estimated on different criteria. Loss distribution is constructed by Panjer Recursion technique. We also show that one risk-one crop model can easily be extended to the multi risk–multi crop model by assuming conditional independency.

Keywords: hail insurance, spherical regression, circular regression, spherical clustering

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Authors: Jiheon Park, Taekwang Kim, Kwang Ryel Ryu

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Electric automated guided vehicles (AGVs) are becoming popular in many automated container terminals nowadays because they are pollution-free and environmentally friendly vehicles for transporting the containers within the terminal. Since efficient operation of AGVs is critical for the productivity of the container terminal, the replacement of batteries of the AGVs must be conducted in a strategic way to minimize undesirable transportation interruptions. While a too frequent replacement may lead to a loss of terminal productivity by delaying container deliveries, missing the right timing of battery replacement can result in a dead AGV that causes a severer productivity loss due to the extra efforts required to finish post treatment. In this paper, we propose a strategy for battery replacement based on a scoring function of multiple criteria taking into account the current battery level, the distances to different battery stations, and the progress of the terminal job operations. The strategy is optimized using a genetic algorithm with the objectives of minimizing the total time spent for battery replacement as well as maximizing the terminal productivity.

Keywords: AGV operation, automated container terminal, battery replacement, electric AGV, strategy optimization

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: B. Lee, B. H. Choi

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The loss of safety for reservoirs brought about by climate change and facility aging leads to reservoir failures, which results in the loss of lives and property damage in downstream areas. Therefore, it is necessary to provide a reservoir failure alert system for downstream residents to detect the early signs of failure (with sensors) in real-time and perform safety management to prevent and minimize possible damage. 10 case studies were carried out to verify the water level management criteria of four levels (attention, caution, alert, serious). Peak changes in water level data were analysed. The results showed that ‘Caution’ and ‘Alert’ were closed to 33% and 66% of difference in level between flood water level and full water level. Therefore, it is adequate to use initial water level management criteria of reservoir failure alert system for the first year. Acknowledgment: This research was supported by a grant (2017-MPSS31-002) from 'Supporting Technology Development Program for Disaster Management' funded by the Ministry of the Interior and Safety(MOIS)

Keywords: alert system, management criteria, reservoir failure, sensor

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Authors: Divya Raghunandanan, Bhavesh D. Gajbhiye, C. S. Sona, Channamallikarjun S. Mathpati

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Molten fluoride salts are considered as potential coolants for next generation nuclear plants where the heat can be utilized for production of hydrogen and electricity. Among molten fluoride salts, FLiNaK (LiF-NaF-KF: 46.5-11.5-42 mol %) is a potential candidate for the coolant due to its superior thermophysical properties such as high temperature stability, boiling point, volumetric heat capacity and thermal conductivity. Major technical challenge in implementation is the selection of structural material which can withstand corrosive nature of FLiNaK. Corrosion study of alloys SS 316L, Hastelloy B, Ni-201 was performed in molten FLiNaK at 650°C. Nickel was found to be more resistant to corrosive attack in molten fluoride medium. Corrosion experiments were performed to study the effect of nickel coating on corrosion of alloys SS 316L and Hastelloy B. Weight loss of the alloys due to corrosion was measured and corrosion rate was estimated. The surface morphology of the alloys was analyzed by Scanning Electron Microscopy.

Keywords: corrosion, FLiNaK, hastelloy, weight loss

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Authors: Jenny Lee

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Fat Studies is emerging as an interdisciplinary area of study, intersecting with Gender Studies, Sociology, Human Development and the Creative Arts. A focus on weight loss, and, therefore, fat hatred, has resulted in a form of discriminatory institutional practice that impacts women in the Western world. This focus is sanctioned by a large dieting industry, medical associations, the media, and at times, government initiatives. This paper will discuss the emergence of the so-called ‘Obesity Epidemic’ in Australia and the Western world and the stereotypes that thin equals healthy and fat equals unhealthy. This paper will argue that, for those with a health focus, ‘Health at every size’ is a more effective principle, which involves striving for healthy living, without a focus on weight loss. This discussion will contextualise an autoethnographic exploration of how fat acceptance and Health at Every Size can be encouraged through fat activism and fat political art. As part of this paper, a selection of the recent performance, writing and art in Australia will be presented, including Aquaporko, the fat femme synchronised swim team and VaVaBoomBah, the Melbourne fat burlesque performances.

Keywords: activism, fat, health, obesity, performance

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Authors: Sidharth Talan, Sanjay Kumar Sharma, Eoin Joseph Wallace, Nikita Agrawal

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Underground mining is at the core of rapidly evolving metals and minerals sector due to the increasing mineral consumption globally. Even though the surface mines are still more abundant on earth, the scales of industry are slowly tipping towards underground mining due to rising depth and complexities of orebodies. Thus, the efficient and productive functioning of underground operations depends significantly on the synchronized performance of key elements such as operating site, mining equipment, manpower and mine services. Production drilling is the process of conducting long hole drilling for the purpose of charging and blasting these holes for the production of ore in underground metal mines. Thus, production drilling is the crucial segment in the underground metal mining value chain. This paper presents the process mapping tool to evaluate the production drilling process in the underground metal mining operation by dividing the given process into three segments namely Input, Process and Output. The three segments are further segregated into factors and sub-factors. As per the study, the major input factors crucial for the efficient functioning of production drilling process are power, drilling water, geotechnical support of the drilling site, skilled drilling operators, services installation crew, oils and drill accessories for drilling machine, survey markings at drill site, proper housekeeping, regular maintenance of drill machine, suitable transportation for reaching the drilling site and finally proper ventilation. The major outputs for the production drilling process are ore, waste as a result of dilution, timely reporting and investigation of unsafe practices, optimized process time and finally well fragmented blasted material within specifications set by the mining company. The paper also exhibits the drilling loss matrix, which is utilized to appraise the loss in planned production meters per day in a mine on account of availability loss in the machine due to breakdowns, underutilization of the machine and productivity loss in the machine measured in drilling meters per unit of percussion hour with respect to its planned productivity for the day. The given three losses would be essential to detect the bottlenecks in the process map of production drilling operation so as to instigate the action plan to suppress or prevent the causes leading to the operational performance deficiency. The given tool is beneficial to mine management to focus on the critical factors negatively impacting the production drilling operation and design necessary operational and maintenance strategies to mitigate them. 

Keywords: process map, drilling loss matrix, SIPOC, productivity, percussion rate

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Authors: M. A. Zaki Ewiss, S. Afifi

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Nahda University (NUB) believes that internationalisation of higher educational is able to provide global society with an education that meets current needs and that can respond efficiently to contemporary demands and challenges, which are characterized by globalisation, interdependence, and multiculturalism. In this paper, we will discuss the the challenges of the Egyptian Higher Education system and the future vision to improve this system> In this report, the following issues will be considered: Increasing knowledge on the development of specialized programs of study at the university. Developing international cooperation programs, which focus on the development of the students and staff skills, and providing academic culture and learning opportunities. Increasing the opportunities for student mobility, and research projects for faculty members. Increased opportunities for staff, faculty and students to continue to learn foreign universities, and to benefit from scholarships in various disciplines. Taking the advantage of the educational experience and modern teaching methods; Providing the opportunities to study abroad without increasing the period of time required for graduation, and through greater integration in the curricula and programs; More cultural interaction through student exchanges.Improving and providing job opportunities for graduates through participation in the global labor market. This document sets out NUB strategy to move towards that vision. We are confident that greater explicit differentiation, greater freedom and greater collaboration are the keys to delivering the further improvement in quality we shall need to retain and strengthen our position as one of the world’s leading higher education systems.

Keywords: technology transfer higher education, knowledge transfer, internationalisation, mobility

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Authors: Ahmad Hamidi Mohamed, Jamaluddin Othman, Mashitah Suid, Mohd Zaim Mohd Shukri

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Floods are considered an annual natural disaster in Kelantan. However, the record-setting flood of 2014 was a 'tsunami-like disaster'. A study has been conducted with the objectives to assess the economic impact of the flood to the resident of Dabong area in Kelantan Darul Naim, Malaysia. This area was selected due to the severity during the flood. The impacts of flood on local people were done by conducting structured interviews with the use of questionnaires. The questionnaire was intended to acquire information on losses faced by Dabong residence. Questionnaires covered various areas of inconveniences suffered with respect to health effects, including illnesses suffered, their intensities, duration and their associated costs. Loss of productivity and quality of life was also assessed. Inquiries were made to Government agencies to obtain relevant statistical data regarding the loss due to the flood tragedy. The data collected by giving formal request to the governmental agencies and formal meetings were done. From the study a staggering amount of losses were calculated. This figure comes from losses of property, Farmers/Agriculture, Traders/Business, Health, Insurance and Governmental losses. Flood brings hardship to the people of Dabong and these losses of home will cause inconveniences to the society. The huge amount of economic loss extracted from this study shows that federal and state government of Kelantan need to find out the cause of the major flood in 2014. Fast and effective measures have to be planned and implemented in flood prone area to prevent same tragedy happens in the future.

Keywords: economic impact, flood tragedy, Malaysia, property losses

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Authors: Vijaya K. Narne, Gerard Loquet, Tobias Piechowiak, Dorte Hammershoi, Jesper H. Schmidt

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BEAR, which stands for better hearing rehabilitation is a large-scale project in Denmark designed and executed by three national universities, three hospitals, and the hearing aid industry with the aim to improve hearing aid fitting. A total of 1963 hearing impaired people were included and were segmented into subgroups based on hearing-loss, demographics, audiological and questionnaires data (i.e., the speech, spatial and qualities of hearing scale [SSQ-12] and the International Outcome Inventory for Hearing-Aids [IOI-HA]). With the aim to provide a better hearing-aid fit to individual patients, we applied modern machine learning techniques with traditional audiograms rule-based systems. Results show that age, speech discrimination scores, and audiogram configurations were evolved as important parameters in characterizing sub-population from the data-set. The attempt to characterize sub-population reveal a clearer picture about the individual hearing difficulties encountered and the benefits derived from more individualized hearing aids.

Keywords: hearing loss, audiological data, machine learning, hearing aids

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Authors: Mona Hamdy, Iman Shaheen, Hadeel Seif Eldin, Basma Ali, Omnia Abdeldayem

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Summary: Bone involvement of sickle cell disease (SCD) patients varies from acute clinical manifestations of painful vaso-occlusive crises or osteomyelitis to more chronic affection of bone mineral density (BMD) and debilitating osteonecrosis and osteoporosis. Secreted klotho protein is involved in calcium (Ca) reabsorption in the kidney. This study aimed to measure serum klotho levels in children with SCD to determine the possibility of using it as a marker of low BMD in children with SCD in correlation with a dual-energy radiograph absorptiometry scan. This study included 60 sickle disease patients and 30 age-matched and sex-matched control participants without SCD. A highly statistically significant difference was found between patients with normal BMD and those with low BMD, with serum Ca and klotho levels being lower in the latter group. Klotho serum level correlated positively with both serum Ca and BMD. Serum klotho level showed 94.9% sensitivity and 95.2% specificity in the detection of low BMD. Both serum Ca and klotho serum levels may be useful markers for detection of low BMD related to SCD with high sensitivity and specificity; however, klotho may be a better indicator as it is less affected by the nutritional and endocrinal status of patients or by intake of Ca supplements.

Keywords: sickle cell disease, BMD, osteoporosis, DEXA, klotho

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Authors: Yogendra Sisodia

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Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results.

Keywords: legal contracts, multiple negative ranking loss, natural language inference, sentence transformers, semantic textual similarity

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Authors: L. N. Shubha, M. Kalpana, P. Madhusudana Rao

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Polyaniline/gold (PANI/Au) nanocomposite was prepared by in-situ chemical oxidation polymerization method. The synthesis involved the formation of polyaniline-gold nanocomposite, by in-situ redox reaction and the dispersion of gold nano particles throughout the polyaniline matrix. The nanocomposites were characterized by XRD, FTIR, TEM and UV-visible spectroscopy. The characteristic peaks in FTIR and UV-visible spectra confirmed the expected structure of polymer as reported in the literature. Further, transmission electron microscopy (TEM) confirmed the formation of gold nano particles. The crystallite size of 30 nm for nanoAu was supported by the XRD pattern. Further, the A.C. conductivity, dielectric constant (€’(w)) and dielectric loss (€’’(w)) of PANI/Au nano composite was measured using impedance analyzer. The effect of doping on the conductivity was investigated. The antibacterial activity was examined for this nano composite and it was observed that PANI/Au nanocomposite could be used as an antibacterial agent.

Keywords: AC-conductivity, anti-microbial activity, dielectric constant, dielectric loss, polyaniline/gold (PANI/AU) nanocomposite

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Authors: M. E. Razipoor, T. Masunaga, K. Sato, M. S. Saboory

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Estimation of soil erosion is economically and environmentally important in Herat, Afghanistan. Degradation of soil has negative impact (decreased soil fertility, destroyed soil structure, and consequently soil sealing and crusting) on life of Herat residents. Water and wind are the main erosive factors causing soil erosion in Herat. Furthermore, scarce vegetation cover, exacerbated by socioeconomic constraint, and steep slopes accelerate soil erosion. To sustain soil productivity and reduce soil erosion impact on human life, due to sustaining agricultural production and auditing the environment, it is needed to quantify the magnitude and extent of soil erosion in a spatial domain. Thus, this study aims to estimate soil loss potential and its spatial distribution in Herat, Afghanistan by applying RUSLE in GIS environment. The rainfall erosivity factor ranged between values of 125 and 612 (MJ mm ha-1 h-1 year-1). Soil erodibility factor varied from 0.036 to 0.073 (Mg h MJ-1 mm-1). Slope length and steepness factor (LS) values were between 0.03 and 31.4. The vegetation cover factor (C), derived from NDVI analysis of Landsat-8 OLI scenes, resulting in range of 0.03 to 1. Support practice factor (P) were assigned to a value of 1, since there is not significant mitigation practices in the study area. Soil erosion potential map was the product of these factors. Mean soil erosion rate of Herat Province was 29 Mg ha-1 year-1 that ranged from 0.024 Mg ha-1 year-1 in flat areas with dense vegetation cover to 778 Mg ha-1 year-1 in sharp slopes with high rainfall but least vegetation cover. Based on land cover map of Afghanistan, areas with soil loss rate higher than soil loss tolerance (8 Mg ha-1 year-1) occupies 98% of Forests, 81% rangelands, 64% barren lands, 60% rainfed lands, 28% urban area and 18% irrigated Lands.

Keywords: Afghanistan, erosion, GIS, Herat, RUSLE

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Authors: Enyia James Diwa, Dodeye Ina Igbong, Archibong Archibong Eso

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Gas turbine operators have been faced with the dramatic financial setback resulting from compressor fouling. In a highly deregulated power industry where there is stiffness in the market competition, has made it imperative to improvise means of reducing maintenance cost in other to yield maximum profit. Compressor fouling results from the deposition of contaminants in the presence of oil and moisture on the compressor blade or annulus surfaces, which leads to a loss in flow capacity and compressor efficiency. These combined effects reduce power output, increase heat rate and cause creep life reduction. This paper also contains a model of two gas turbine engines via Cranfield University software known as TURBOMATCH, which is simulation software for detecting engine fouling rate. The model engines are of different configurations and capacities, and are operating in two different modes of constant output power and turbine inlet temperature for a two and three stage filter system. The idea is to investigate the more economically viable filtration systems by gas turbine users based on performance only. It has been demonstrated in the results that the two spool engine is a little more beneficial compared to the single spool. This is as a result of a higher pressure ratio of the two spools as well as the deceleration of the high-pressure compressor and high-pressure turbine speed in a constant TET. Meanwhile, the inlet filtration system was properly designed and balanced with a well-timed and economical compressor washing regime/scheme to control compressor fouling. The different technologies of inlet air filtration and compressor washing are considered and an attempt at optimization with respect to the cost of a combination of both control measures are made.

Keywords: inlet filtration, pressure loss, single spool, two spool

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Authors: Korpapa Srisamai, Pawee Siriruk

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The purpose of this study is to forecast product demands and develop appropriate and adequate procurement plans to meet customer needs and reduce costs. When the product exceeds customer demands or does not move, it requires the company to support insufficient storage spaces. Moreover, some items, when stored for a long period of time, cause deterioration to dead stock. A case study of the wholesale company of electronic equipment and components, which has uncertain customer demands, is considered. The actual purchasing orders of customers are not equal to the forecast provided by the customers. In some cases, customers have higher product demands, resulting in the product being insufficient to meet the customer's needs. However, some customers have lower demands for products than estimates, causing insufficient storage spaces and dead stock. This study aims to reduce the loss of sales opportunities and the number of remaining goods in the warehouse, citing 30 product samples of the company's most popular products. The data were collected during the duration of the study from January to October 2022. The methods used to forecast are simple moving averages, weighted moving average, and exponential smoothing methods. The economic ordering quantity and reorder point are used to calculate to meet customer needs and track results. The research results are very beneficial to the company. The company can reduce the loss of sales opportunities by 20% so that the company has enough products to meet customer needs and can reduce unused products by up to 10% dead stock. This enables the company to order products more accurately, increasing profits and storage space.

Keywords: demand forecast, reorder point, lost sale, dead stock

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Authors: Ercan Eker, Muhammer Karaman, Akif Aslan, Hakan Tanrikuluoglu

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Deficiency of institutional memory and knowledge management can result in information security breaches, loss of prestige and trustworthiness and the worst the loss of know-how and institutional knowledge. Traditional learning management within organizations is generally handled by personal efforts. That kind of struggle mostly depends on personal desire, motivation and institutional belonging. Even if an organization has highly motivated employees at a certain time, the institutional knowledge and memory life cycle will generally remain limited to these employees’ spending time in this organization. Having a learning management system in an organization can sustain the institutional memory, knowledge and know-how in the organization. Learning management systems are much more needed especially in public organizations where the job rotation is frequently seen and managers are appointed periodically. However, a learning management system should not be seen as an organizations’ website. It is a more comprehensive, interactive and user-friendly knowledge management tool for organizations. In this study, the importance of using learning management systems in the process of emerging tacit knowledge is underlined.

Keywords: knowledge management, learning management systems, tacit knowledge, institutional memory

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Authors: Seung Hun Lee, Sang Yeoup Lee

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Objectives: To investigate associations between degrees of obesity using correlations between obesity indices and cardiometabolic risk factors. Methods: BMI, waist circumference (WC), fasting insulin, fasting glucose, lipids, and visceral adipose tissue (VAT) area using computed tomographic images were measured in 113 obese female without cardiovascular disease (CVD). Correlations between obesity indices and cardiometabolic risk factors were analyzed in obese subgroups defined using sequential obesity indices. Results: Mean BMI and WC were 29.6 kg/m2 and 92.8 cm. BMI showed significant correlations with all five cardiometabolic risk factors until the BMI cut-off point reached 27 kg/m2, but when it exceeded 30 kg/m2, correlations no longer existed. WC was significantly correlated with all five cardiometabolic risk factors up to a value of 85 cm, but when WC exceeded 90 cm, correlations no longer existed. Conclusions: Our data suggest that moderate weight-loss goals may not be enough to ameliorate cardiometabolic markers in severely obese patients. Therefore, individualized weight-loss goals should be recommended to such patients to improve health benefits.

Keywords: correlation, cardiovascular disease, risk factors, obesity

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Authors: Xi Liu, Fan Yuan

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In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

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Authors: Lucas Kurth de Azambuja, Loise Silveira da Silva, Gean Vitor Salmoria, Darlan Dallacosta, Carlos Rodrigo de Mello Roesler

Abstract:

This work provides a physicochemical and thermal characterization assessment of three different hyaluronic acid (HA)-based injections used for rejuvenation skin treatments. The three products analyzed are manufactured by the same manufacturer and commercialized for application on different skin levels. According to the manufacturer, all three HA-based injections are crosslinked and have a concentration of 23 mg/mL of HA, and 0.3% of lidocaine. Samples were characterized by Fourier-transformed infrared (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and scanning electron microscope (SEM) techniques. FTIR analysis resulted in a similar spectrum when comparing the different products. DSC analysis demonstrated that the fusion points differ in each product, with a higher fusion temperature observed in specimen A, which is used for subcutaneous applications, when compared with B and C, which are used for the middle dermis and deep dermis, respectively. TGA data demonstrated a considerable mass loss at 100°C, which means that the product has more than 50% of water in its composition. TGA analysis also showed that Specimen A had a lower mass loss at 100°C when compared to Specimen C. A mass loss of around 220°C was observed on all samples, characterizing the presence of hyaluronic acid. SEM images displayed a similar structure on all samples analyzed, with a thicker layer for Specimen A when compared with B and C. This series of analyses demonstrated that, as expected, the physicochemical and thermal properties of the products differ according to their application. Furthermore, to better characterize the crosslinking degree of each product and their mechanical properties, a set of different techniques should be applied in parallel to correlate the results and, thereby, relate injection application with material properties.

Keywords: hyaluronic acid, characterization, soft-tissue fillers, injectable gels

Procedia PDF Downloads 75

Authors: Olfat E. El-Azabawy, Enas M. Attia, Nadia Shawky, Amira M. Hypa

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In this work, an attempt is made to study the effects of orange peel extract (OPE) as an environment-friendly corrosion inhibitor for carbon steel (CS) within a formation water solution (FW). The study was performed in different concentrations (0.5-2.5% (v/v)) of peel extracts at ambient temperatures (25oC) and (2.5% (v/v)) at temperature range (25- 55 oC) by weight loss measurements, open circuit potential, potentiodynamic polarization and electrochemical impedance. The inhibition efficiency was calculated from all measurements and confirmed by energy-dispersive X-ray spectroscopy (EDS). Inhibition was found to increase with increasing inhibitors concentration and decrease with increasing temperature. It was seen that IE% is about 92.84% in the presence of 2.5% (v/v) of orange peel inhibitor by using weight loss method. The adsorption process was of physical type and obey Langmuir adsorption isotherm. Also, adsorption, as well as the inhibition process, followed first-order kinetics at all concentrations.

Keywords: eco-friendly corrosion inhibitor, OPE, oilfield water, electrochemical impedance

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Authors: Muhammad Atif Randhawa, Adnan Amjad, Muhammad Nadeem

Abstract:

Potato (Solanum tuberosum) popularly known as ‘the king of vegetables’, has emerged as fourth most important food crop after rice, wheat and maize. Potato contains carbohydrates, minerals, vitamins and antioxidants. The antioxidants of potatoes especially vitamin C helps in reducing cancer, cardiovascular diseases and high blood pressure by binding free radicals. Physical characteristics and some major chemical properties of potato tubers at fresh and stored stages were investigated. Two varieties of potatoes, Sante (V1) having white colour and Lal moti (V2) with red colour were stored for 3 months and analysis were performed after each month interval. Physical and chemical attributes including weight loss, sprouting, specific gravity, pH, total sugars (reducing and non-reducing sugars) and vitamin C were analyzed before and after storage. Value of weight loss at zero day was null but it increased to 6.45% after 90 days on average in both cultivars and sprouting increased gradually at the end of 90 days. Moreover total sugars were 3.10% at zero day but increased to 9.30% after 90 days. Ascorbic acid was decreased during storage from 17.49(mg/100g) to 3.79. Both varieties of potato were stored at 60C and 120C temperatures with 85% relative humidity in order to prolong their acceptability in the market. The storage conditions influence the potatoes quality and consequently their acceptability to consumer. The data was analyzed statistically and clarifies that total sugars, weight loss, sprouting and specific gravity increase during the storage period while ascorbic acid (Vit-C) and pH decreased. Among both varieties that were stored at 60C and 120C, Sante (V1) was better than Lal moti (V2) due to less physicochemical and quality changes at 60C as compared to store at 120C.

Keywords: physicochemical, potato, quality attributes, storage

Procedia PDF Downloads 412

Authors: Hsin-Yun Lee

Abstract:

In this study, the pedestrian simulation VISWALK integration and application platform ant algorithms written program made to construct a renovation engineering schedule planning mode. The use of simulation analysis platform construction site when the user running the simulation, after calculating the user walks in the case of construction delays, the ant algorithm to find out the minimum delay time schedule plan, and add volume and unit area deactivated loss of business computing, and finally to the owners and users of two different positions cut considerations pick out the best schedule planning. To assess and validate its effectiveness, this study constructed the model imported floor of a shopping mall floor renovation engineering cases. Verify that the case can be found from the mode of the proposed project schedule planning program can effectively reduce the delay time and the user's walking mall loss of business, the impact of the operation on the renovation engineering facilities in the building to a minimum.

Keywords: pedestrian, renovation, schedule, simulation

Procedia PDF Downloads 396

Authors: M. Kashfi, K. Jahani

Abstract:

Sound attenuation in absorptive silencers has been analyzed in this paper. The structure of such devices is as follows. When the rigid duct of an expansion chamber has been lined by a packed absorptive material under a perforated membrane, incident sound waves will be dissipated by the absorptive liners. This kind of silencer, usually are applicable for medium to high frequency ranges. Several conditions for different absorptive materials, variety in their thicknesses, and different shapes of the expansion chambers have been studied in this paper. Also, graphs of sound attenuation have been compared between empty expansion chamber and duct of silencer with applying liner. Plane waves have been assumed in inlet and outlet regions of the silencer. Presented results that have been achieved by applying finite element method (FEM), have shown the dependence of the sound attenuation spectrum to flow resistivity and the thicknesses of the absorptive materials, and geometries of the cross section (configuration of the silencer). As flow resistivity and thickness of absorptive materials increase, sound attenuation improves. In this paper, diagrams of the transmission loss (TL) for absorptive silencers in five different cross sections (rectangle, circle, ellipse, square, and rounded rectangle as the main geometry) have been presented. Also, TL graphs for silencers using different absorptive material (glass wool, wood fiber, and kind of spongy materials) as liner with three different thicknesses of 5 mm, 15 mm, and 30 mm for glass wool liner have been exhibited. At first, the effect of substances of the absorptive materials with the specific flow resistivity and densities on the TL spectrum, then the effect of the thicknesses of the glass wool, and at last the efficacy of the shape of the cross section of the silencer have been investigated.

Keywords: transmission loss, absorptive material, flow resistivity, thickness, frequency

Procedia PDF Downloads 237

Authors: Charles Owenda Omulo

Abstract:

This study examines Occupational Health and Safety implications in flower farms in Central Uganda. An exploratory sequential mixed method design and methodology was employed, with multiple data collection methods, including interviews, focus group discussions, and surveys. The findings show that occupational health and safety issues remain a major problem in flower farms. While the majority of workers agreed that the farms provided them with protective equipment, the data collected from the workers point to either the improper use or ineffectiveness of this equipment. A number of workers reported skin irritations, sore and painful eyes, stiff necks, back pains, and occasional headaches that were presumably argued to have arisen from their work environment. The study also found that farms have been adjusting in an attempt to correct some of these anomalies. These included the use of biological approaches to control pests and diseases and restricting the use of some chemical formulations that are deemed to be harmful to applicators and the environment. Alongside these efforts, the study recommends increased vigilance by the flower farm owners in the provision of personal protective equipment to workers.

Keywords: flower farms, personal protective equipment, agrochemicals, rural communities, occupational health and safety

Procedia PDF Downloads 39