Search results for: data driven diagnosis

Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

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Authors: Riyas Peringattuthodiyil, Mark Taylor, Ian Wallace, Ailish Nugent, Mike Mitchell, Judith Thompson, Allison McKee, Philip C. Johnston

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Aim of Study: The purpose of the study was to screen for diabetes through HbA1c in patients with chronic pancreatitis (CP) within the Belfast Trust. Background: Patients with chronic pancreatitis are at risk of developing diabetes, earlier diagnosis with subsequent multi-disciplinary input has the potential to improve clinical outcomes. Methods: Clinical and laboratory data of patients with chronic pancreatitis were obtained through the Northern Ireland Electronic Healthcare Record (NIECR), specialist hepatobiliary, and gastrointestinal clinics. Patients were invited to have a blood test for HbA1c. Newly diagnosed patients with diabetes were then invited to attend a dedicated Belfast City Hospital (BCH) specialist chronic pancreatitis and diabetes clinic for follow up. Results: A total of 89 chronic pancreatitis patients were identified; Male54; Female:35, mean age 52 years, range 12-90 years. Aetiology of CP included alcohol 52/89 (58%), gallstones 18/89 (20%), idiopathic 10/89 11%, 2 were genetic, 1: post ECRP, 1: IgG autoimmune, 1: medication induced, 1: lipoprotein lipase deficiency 1: mumps, 1: IVDU and 1: pancreatic divisum. No patients had pancreatic carcinoma. Mean duration of CP was nine years, range 3-30 years. 15/89 (16%) of patients underwent previous pancreatic surgery/resections. Recent mean BMI was 25.1 range 14-40 kg/m². 62/89 (70%) patients had HbA1c performed. Mean HbA1c was 42 mmol/mol, range 27-97mmol/mol, 42/62 (68%) had normal HbA1c (< 42 mmol/mol) 13/62 (21%) had pre-diabetes (42-47mmol/mol) and 7/62 (11%) had diabetes (≥ 48 mmol/mol). Conclusions: Of those that participated in the screening program around one-third of patients with CP had glycaemic control in the pre and diabetic range. Potential opportunities for improving screening rates for diabetes in this cohort could include regular yearly testing at gastrointestinal and hepatobiliary clinics.

Keywords: pancreatogenic diabetes, screening, chronic pancreatitis, trust experience

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Authors: Thomas Chong, Irene Victor

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This paper discusses the history and plausible future of assistive technology and what it means for students with disabilities in Singapore, a country known for its high quality of education in the world. Over more than a century, students with disabilities have benefitted from relatively low-tech assistive technology (like eye-glasses, Braille, magnifiers and wheelchairs) to high-tech assistive technology including electronic mobility switches, alternative keyboards, computer-screen enlargers, text-to-speech readers, electronic sign-language dictionaries and signing avatars for individuals with hearing impairments. Driven by legislation, the use of assistive technology in many countries is becoming so ubiquitous that more and more students with disabilities are able to perform as well as if not better than their counterparts. Yet in many other learning environments where assistive technology is not affordable or mandated, the learning gaps can be quite significant. Without stronger legislation, Singapore may still have a long way to go in levelling the playing field for its students with disabilities.

Keywords: assistive technology, students with disabilities, disability laws in Singapore, inclusiveness

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Authors: Irina Khutsishvili

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The presented study develops a decision support methodology for multi-criteria group decision-making problem. The proposed methodology is based on the TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) approach in the hesitant fuzzy environment. The main idea of decision-making problem is a selection of one best alternative or several ranking alternatives among a set of feasible alternatives. Typically, the process of decision-making is based on an evaluation of certain criteria. In many MCDM problems (such as medical diagnosis, project management, business and financial management, etc.), the process of decision-making involves experts' assessments. These assessments frequently are expressed in fuzzy numbers, confidence intervals, intuitionistic fuzzy values, hesitant fuzzy elements and so on. However, a more realistic approach is using linguistic expert assessments (linguistic variables). In the proposed methodology both the values and weights of the criteria take the form of linguistic variables, given by all decision makers. Then, these assessments are expressed in triangular fuzzy numbers. Consequently, proposed approach is based on triangular hesitant fuzzy TOPSIS decision-making model. Following the TOPSIS algorithm, first, the fuzzy positive ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS) are defined. Then the ranking of alternatives is performed in accordance with the proximity of their distances to the both FPIS and FNIS. Based on proposed approach the software package has been developed, which was used to rank investment projects in the real investment decision-making problem. The application and testing of the software were carried out based on the data provided by the ‘Bank of Georgia’.

Keywords: fuzzy TOPSIS approach, investment project, linguistic variable, multi-criteria decision making, triangular hesitant fuzzy set

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Authors: Fakhrosadat Sajjadian

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The majority of deaths in cancer patients are caused by distant metastasis. Breast cancer shows a unique spread pattern, often affecting bone, liver, lung, and brain. Breast cancer can be categorized into various subtypes according to gene expression patterns, and these subtypes exhibit specific preferences for organs where metastasis occurs. Breast tumors with luminal characteristics have a preference for spreading to the bone, whereas basal-like breast cancer (BLBC) shows a tendency to metastasize to the lungs. Still, the mechanisms behind this particular pattern of metastasis in organs have yet to be fully understood. In this evaluation, we will outline the latest progress in molecular signaling pathways and treatment methods for breast cancer lung metastasis.

Keywords: lung cancer, liver cancer, diagnosis, BLBC, metastasis

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Authors: Fajar Ajie Setiawan, Ratu Azima Mayangsari, Bunga Aprilia

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The emergence of creative hubs around the world, including in Bandung, was primarily driven by the needs of collaborative-innovative spaces for creative industry activities such as the Maker Movement and the Coworking Movement. These activities pose challenges for identification and formulation of sets of indicators for modeling creative hubs in Bandung to help stakeholders in formulating strategies. This study intends to identify their characteristics. This research was conducted using a qualitative approach comparing three concepts of creative hub categorization and integrating them into a single instrument to analyze 12 selected creative hubs. Our results showed three new functions of creative hubs in Bandung: (1) cultural, (2) retail business, and (3) community network. Results also suggest that creative hubs in Bandung are commonly established for networking and community activities. Another result shows that there was a shifting pattern of creative hubs before the 2000s and after the 2000s, which also creates a hybrid group of creative hubs.

Keywords: creative industry, creative hubs, Ngariung, Bandung

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Authors: Olakanmi Oladayo Olufemi, Bamifewe Olusegun James, Badmus Yaya Opeyemi, Adegoke Kayode

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The wireless sensor network (WSN) has made a significant contribution to the emergence of various intelligent services or cloud-based applications. Most of the time, these data are stored on a cloud platform for efficient management and sharing among different services or users. However, the sensitivity of the data makes them prone to various confidentiality and performance-related attacks during and after harvesting. Various security schemes have been developed to ensure the integrity and confidentiality of the WSNs' data. However, their specificity towards particular attacks and the resource constraint and heterogeneity of WSNs make most of these schemes imperfect. In this paper, we propose a secure variance-aware routing and authentication scheme with two-tier verification to collect, share, and manage WSN data. The scheme is capable of classifying WSN into different subnets, detecting any attempt of wormhole and black hole attack during harvesting, and enforcing access control on the harvested data stored in the cloud. The results of the analysis showed that the proposed scheme has more security functionalities than other related schemes, solves most of the WSNs and cloud security issues, prevents wormhole and black hole attacks, identifies the attackers during data harvesting, and enforces access control on the harvested data stored in the cloud at low computational, storage, and communication overheads.

Keywords: data block, heterogeneous IoT network, data harvesting, wormhole attack, blackhole attack access control

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Authors: A. Sathiya Susuman, Hamisi F. Hamisi

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Background: Many socio-economic and demographic data are age-sex attributed. However, a variety of irregularities and misstatement are noted with respect to age-related data and less to sex data because of its biological differences between the genders. Noting the misstatement/misreporting of age data regardless of its significance importance in demographics and epidemiological studies, this study aims at assessing the quality of 2012 Tanzania Population and Housing Census Results. Methods: Data for the analysis are downloaded from Tanzania National Bureau of Statistics. Age heaping and digit preference were measured using summary indices viz., Whipple’s index, Myers’ blended index, and Age-Sex Accuracy index. Results: The recorded Whipple’s index for both sexes was 154.43; male has the lowest index of about 152.65 while female has the highest index of about 156.07. For Myers’ blended index, the preferences were at digits ‘0’ and ‘5’ while avoidance were at digits ‘1’ and ‘3’ for both sexes. Finally, Age-sex index stood at 59.8 where sex ratio score was 5.82 and age ratio scores were 20.89 and 21.4 for males and female respectively. Conclusion: The evaluation of the 2012 PHC data using the demographic techniques has qualified the data inaccurate as the results of systematic heaping and digit preferences/avoidances. Thus, innovative methods in data collection along with measuring and minimizing errors using statistical techniques should be used to ensure accuracy of age data.

Keywords: age heaping, digit preference/avoidance, summary indices, Whipple’s index, Myer’s index, age-sex accuracy index

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Authors: Saeedeh Shahbazkhany

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Cancer is a terrible disease that, if not diagnosed early, therapy can be difficult while it is easily medicable if it is diagnosed in early stages. So it is very important for cancer diagnosis that medical procedures are performed. In this paper we introduce a new method. In this method, we only need pictures of healthy cells and cancer cells. In fact, where we suspect cancer, we take a picture of cells or tissue in that area, and then take some pictures of the surrounding tissues. Then, fractal dimension of images are calculated and compared. Cancer can be easily detected by comparing the fractal dimension of images. In this method, we use Matlab software.

Keywords: Matlab software, fractal dimension, cancer, surrounding tissues, cells or tissue, new method

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Authors: Olakunle Felix Adekunle

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Innovation has become the key to firm success, and entrepreneurial leadership displayed by the top management team fundamentally drives innovation in firms. This paper discusses findings from an empirical study of factors influencing the success of firms operating in developing economies in an industry where science drives the pace of change. We find that success of firms in such industries depends on thriving innovation activity that in turn is primarily driven by effective entrepreneurial leadership of the top management team. The paper presents the dimensions of entrepreneurial leadership and its linkage to innovation and firm success in the form of testable propositions. Finally, a preliminary theory of firm success in industries where science drives the pace of change is also inducted from this empirical study and presented.

Keywords: leadership, innovation, firms, economic, efficiency, industry, resources

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Authors: Shubham Gogri, Lucia Florescu

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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

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Authors: Nasim Hashemi, Abbas Sheikhtaheri

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Telemedicine is the use of telecommunication and information technologies to provide health care services that would often not be consistently available in distant rural communities to people at these remote areas. Teleophthalmology is a branch of telemedicine that delivers eye care through digital medical equipment and telecommunications technology. Thus, teleophthalmology can overcome geographical barriers and improve quality, access, and affordability of eye health care services. Since teleophthalmology has been widespread applied in recent years, the aim of this study was to determine the different applications of teleophthalmology in the world. To this end, three bibliographic databases (Medline, ScienceDirect, Scopus) were comprehensively searched with these keywords: eye care, eye health care, primary eye care, diagnosis, detection, and screening of different eye diseases in conjunction with telemedicine, telehealth, teleophthalmology, e-services, and information technology. All types of papers were included in the study with no time restriction. We conducted the search strategies until 2015. Finally 70 articles were surveyed. We classified the results based on the’type of eye problems covered’ and ‘the type of telemedicine services’. Based on the review, from the ‘perspective of health care levels’, there are three level for eye health care as primary, secondary and tertiary eye care. From the ‘perspective of eye care services’, the main application of teleophthalmology in primary eye care was related to the diagnosis of different eye diseases such as diabetic retinopathy, macular edema, strabismus and aged related macular degeneration. The main application of teleophthalmology in secondary and tertiary eye care was related to the screening of eye problems i.e. diabetic retinopathy, astigmatism, glaucoma screening. Teleconsultation between health care providers and ophthalmologists and also education and training sessions for patients were other types of teleophthalmology in world. Real time, store–forward and hybrid methods were the main forms of the communication from the perspective of ‘teleophthalmology mode’ which is used based on IT infrastructure between sending and receiving centers. In aspect of specialists, early detection of serious aged-related ophthalmic disease in population, screening of eye disease processes, consultation in an emergency cases and comprehensive eye examination were the most important benefits of teleophthalmology. Cost-effectiveness of teleophthalmology projects resulted from reducing transportation and accommodation cost, access to affordable eye care services and receiving specialist opinions were also the main advantages of teleophthalmology for patients. Teleophthalmology brings valuable secondary and tertiary care to remote areas. So, applying teleophthalmology for detection, treatment and screening purposes and expanding its use in new applications such as eye surgery will be a key tool to promote public health and integrating eye care to primary health care.

Keywords: applications, telehealth, telemedicine, teleophthalmology

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Authors: Komol Phaisarn, Anuphan Suttimarn, Vitchanan Keawtong, Kittisak Thongyoun, Chaiyos Jamsawang

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This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible.

Keywords: decision tree, data mining, customers, life insurance pay package

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Authors: Ji Su Kim, Bo Ram Choi, Ju Yeon Cho, Hyukjin Lee

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Epidermal growth factor receptor (EGFR) 19 deletion mutation gene is over-expressed in carcinoma patient. EGFR 19 deletion mutation is known as typical biomarker of non-small cell lung cancer (NSCLC), which one section in the coding exon 19 of EGFR is deleted. Therefore, there have been many attempts over the years to detect EGFR 19 deletion mutation for replacing conventional diagnostic method such as PCR and tissue biopsy. We developed a simple and facile detection platform based on Rolling Circle Amplification (RCA), which provides highly amplified products in isothermal amplification of the ligated DNA template. Limit of detection (~50 nM) and a faster detection time (~30 min) could be achieved by introducing RCA.

Keywords: EGFR19, cancer, diagnosis, rolling circle amplification (RCA), hydrogel

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Authors: Tarek Aly

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The shoulder is the most mobile joint whose stability requires the interaction of both dynamic and static stabilizers. Its wide range of movement predisposes to a high susceptibility to dislocation, accounting for nearly 50% of all dislocations. This trauma typically results in ligament injury (e.g., labral tear, capsular strain) or bony fracture (e.g., loss of glenoid or humeral head bone), which frequently causes recurrent instability. Patients with significant glenoid defects may require Latarjet procedure, which involves transferring the coracoid to the antero-inferior glenoid rim. In spite of outstanding results, 15 to 30% of cases suffer complications. In this article, we discuss the diagnosis of recurrent shoulder instability, the surgical technique and various complications of Latarjet procedure.

Keywords: recurrent, anterior gleno-humeral instability, latarjet, unstable shoulder

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Authors: Brenda Thean I. Lim, Safurah Jaafar

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Augmented reality (AR) in the healthcare industry has been gaining popularity in recent years, principally in areas of medical education, patient care and digital health solutions. One of the drivers in deciding to invest in AR technology is the potential economic benefits it could bring for patients and healthcare providers, including the pharmaceutical and medical technology sectors. Works of literature have shown that the benefits and impact of AR technologies have left trails of achievements in improving medical education and patient health outcomes. However, little has been published on the economic impact of AR in healthcare, a very resource-intensive industry. This systematic review was performed on studies focused on the benefits and impact of AR in healthcare to appraise if they meet the founded quality criteria so as to identify relevant publications for an in-depth analysis of the economic impact assessment. The literature search was conducted using multiple databases such as PubMed, Cochrane, Science Direct and Nature. Inclusion criteria include research papers on AR implementation in healthcare, from education to diagnosis and treatment. Only papers written in English language were selected. Studies on AR prototypes were excluded. Although there were many articles that have addressed the benefits of AR in the healthcare industry in the area of medical education, treatment and diagnosis and dental medicine, there were very few publications that identified the specific economic impact of technology within the healthcare industry. There were 13 publications included in the analysis based on the inclusion criteria. Out of the 13 studies, none comprised a systematically comprehensive cost impact evaluation. An outline of the cost-effectiveness and cost-benefit framework was made based on an AR article from another industry as a reference. This systematic review found that while the advancements of AR technology is growing rapidly and industries are starting to adopt them into respective sectors, the technology and its advancements in healthcare were still in their early stages. There are still plenty of room for further advancements and integration of AR into different sectors within the healthcare industry. Future studies will require more comprehensive economic analyses and costing evaluations to enable economic decisions for or against implementing AR technology in healthcare. This systematic review concluded that the current literature lacked detailed examination and conduct of economic impact and benefit analyses. Recommendations for future research would be to include details of the initial investment and operational costs for the AR infrastructure in healthcare settings while comparing the intervention to its conventional counterparts or alternatives so as to provide a comprehensive comparison on impact, benefit and cost differences.

Keywords: augmented reality, benefit, economic impact, healthcare, patient care

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Authors: I. K. Sharma

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Over the years world over system are being developed by research labs continuously monitor under water parameters in the coastal waters of sea such as conductivity, salinity, pressure, temperature, chlorophyll and biological blooms at different levels of water column. The research institutions have developed profilers which are launched by ship connected through cable, glider type profilers following underwater trajectory, buoy any driven profilers, wire guided profilers etc. In all these years, the effect was to design autonomous profilers with no cable quality connection, simple operation and on line date transfer in terms accuracy, repeatability, reliability and consistency. Hence for the Ministry of Communication and Information Technology, India sponsored research project to National Institute of Oceanography, GOA, India to design and develop autonomous vertical profilers, it has taken system and AVP has been successfully developed and tested.

Keywords: oceanography, water column, autonomous profiler, buoyancy

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Authors: Andreea Campu, Ilinica Muresan, Simona Cainap, Simion Astilean, Monica Focsan

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Acute myocardial infarction (AMI) is the most severe cardiovascular disease, which has threatened human lives for decades, thus a continuous interest is directed towards the detection of cardiac biomarkers such as cardiac troponin I (cTnI) in order to predict risk and, implicitly, fulfill the early diagnosis requirements in AMI settings. Microfluidics is a major technology involved in the development of efficient sensing devices with real-time fast responses and on-site applicability. Microfluidic devices have gathered a lot of attention recently due to their advantageous features such as high sensitivity and specificity, miniaturization and portability, ease-of-use, low-cost, facile fabrication, and reduced sample manipulation. The integration of gold nanoparticles into the structure of microfluidic sensors has led to the development of highly effective detection systems, considering the unique properties of the metallic nanostructures, specifically the Localized Surface Plasmon Resonance (LSPR), which makes them highly sensitive to their microenvironment. In this scientific context, herein, we propose the implementation of a novel detection device, which successfully combines the efficiency of gold bipyramids (AuBPs) as signal transducers and thermal generators with the sample-driven advantages of the microfluidic channels into a miniaturized, portable, low-cost, specific, and sensitive test for the dual LSPR-thermographic cTnI detection. Specifically, AuBPs with longitudinal LSPR response at 830 nm were chemically synthesized using the seed-mediated growth approach and characterized in terms of optical and morphological properties. Further, the colloidal AuBPs were deposited onto pre-treated silanized glass substrates thus, a uniform nanoparticle coverage of the substrate was obtained and confirmed by extinction measurements showing a 43 nm blue-shift of the LSPR response as a consequence of the refractive index change. The as-obtained plasmonic substrate was then integrated into a microfluidic “Y”-shaped polydimethylsiloxane (PDMS) channel, fabricated using a Laser Cutter system. Both plasmonic and microfluidic elements were plasma treated in order to achieve a permanent bond. The as-developed microfluidic plasmonic chip was further coupled to an automated syringe pump system. The proposed biosensing protocol implicates the successive injection inside the microfluidic channel as follows: p-aminothiophenol and glutaraldehyde, to achieve a covalent bond between the metallic surface and cTnI antibody, anti-cTnI, as a recognition element, and target cTnI biomarker. The successful functionalization and capture of cTnI was monitored by LSPR detection thus, after each step, a red-shift of the optical response was recorded. Furthermore, as an innovative detection technique, thermal determinations were made after each injection by exposing the microfluidic plasmonic chip to 785 nm laser excitation, considering that the AuBPs exhibit high light-to-heat conversion performances. By the analysis of the thermographic images, thermal curves were obtained, showing a decrease in the thermal efficiency after the anti-cTnI-cTnI reaction was realized. Thus, we developed a microfluidic plasmonic chip able to operate as both LSPR and thermal sensor for the detection of the cardiac troponin I biomarker, leading thus to the progress of diagnostic devices.

Keywords: gold nanobipyramids, microfluidic device, localized surface plasmon resonance detection, thermographic detection

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Authors: Warjito, Kevin Geraldo, Budiarso, Muhammad Mizan, Rafi Adhi Pranata, Farhan Rizqi Syahnakri

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According to data from 2021, the number of households in Indonesia that have access to on-grid electricity is claimed to have reached 99.28%, which means that around 0.7% of Indonesia's population (1.95 million people) still have no proper access to electricity and 38.1% of it comes from remote areas in Nusa Tenggara Timur. Remote areas are classified as areas with a small population of 30 to 60 families, have limited infrastructure, have scarce access to electricity and clean water, have a relatively weak economy, are behind in access to technological innovation, and earn a living mostly as farmers or fishermen. These people still need electricity but can’t afford the high cost of electricity from national on-grid sources. To overcome this, it is proposed that a hydroelectric power plant driven by a pico-hydro turbine with an undershot water wheel will be a suitable pico-hydro turbine technology because of the design, materials and installation of the turbine that is believed to be easier (i.e., operational and maintenance) and cheaper (i.e., investment and operating costs) than any other type. The comparative study of the angle of the undershot water wheel blades will be discussed comprehensively. This study will look into the best variation of curved blades on an undershot water wheel that produces maximum hydraulic efficiency. In this study, the blade angles were varied by 180 ̊, 160 ̊, and 140 ̊. Two methods of analysis will be used, which are analytical and numerical methods. The analytical method will be based on calculations of the amount of torque and rotational speed of the turbine, which is used to obtain the input and output power of the turbine. Whereas the numerical method will use the ANSYS application to simulate the flow during the collision with the designed turbine blades. It can be concluded, based on the analytical and numerical methods, that the best angle for the blade is 140 ̊, with an efficiency of 43.52% for the analytical method and 37.15% for the numerical method.

Keywords: pico hydro, undershot waterwheel, blade angle, computational fluid dynamics

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Authors: Edward Poluyi, Eghosa Morgan, Charles Poluyi, Chibuikem Ikwuegbuenyi, Grace Imaguezegie

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Background: Current epidemiological studies have examined the associations between moderate and severe traumatic brain injury (TBI) and their risks of developing neurodegenerative diseases. Concussion, also known as mild TBI (mTBI), is however quite distinct from moderate or severe TBIs. Only few studies in this burgeoning area have examined concussion—especially repetitive episodes—and neurodegenerative diseases. Thus, no definite relationship has been established between them. Objectives : This review will discuss the available literature linking concussion and amyotrophic lateral sclerosis (ALS) and Alzheimer’s disease (AD). Materials and Methods: Given the complexity of this subject, a realistic review methodology was selected which includes clarifying the scope and developing a theoretical framework, developing a search strategy, selection and appraisal, data extraction, and synthesis. A detailed literature matrix was set out in order to get relevant and recent findings on this topic. Results: Presently, there is no objective clinical test for the diagnosis of concussion because the features are less obvious on physical examination. Absence of an objective test in diagnosing concussion sometimes leads to skepticism when confirming the presence or absence of concussion. Intriguingly, several possible explanations have been proposed in the pathological mechanisms that lead to the development of some neurodegenerative disorders (such as ALS and AD) and concussion but the two major events are deposition of tau proteins (abnormal microtubule proteins) and neuroinflammation, which ranges from glutamate excitotoxicity pathways and inflammatory pathways (which leads to a rise in the metabolic demands of microglia cells and neurons), to mitochondrial function via the oxidative pathways.

Keywords: amyotrophic lateral sclerosis, Alzheimer's disease, mild traumatic brain injury, neurodegeneration

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: A. Bouchakour, L. Zaghba, M. Brahami, A. Borni

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The work presented in this paper present the design and the simulation of a centrifugal pump coupled to a photovoltaic (PV) generator via a MPPT controller. The PV system operating is just done in sunny period by using water storage instead of electric energy storage. The process concerns the modelling, identification and simulation of a photovoltaic pumping system, the centrifugal pump is driven by an asynchronous three-phase voltage inverter sine triangle PWM motor through. Two configurations were simulated. For the first, it is about the alimentation of the motor pump group from electrical power supply. For the second, the pump unit is connected directly to the photovoltaic panels by integration of a MPPT control. A code of simulation of the solar pumping system was initiated under the Matlab-Simulink environment. Very convivial and flexible graphic interfaces allow an easy use of the code and knowledge of the effects of change of the sunning and temperature on the pumping system.

Keywords: photovoltaic generator, chopper, electrical motor, centrifugal pump

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Authors: Tamas Garamhegyi, Jozsef Kovacs, Rita Pongracz, Peter Tanos, Balazs Trasy, Norbert Magyar, Istvan G. Hatvani

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Like most environmental processes, shallow groundwater fluctuation under natural circumstances also behaves periodically. With the statistical tools at hand, it can easily be determined if a period exists in the data or not. Thus, the question may be raised: Does the estimated average period time characterize the whole time period, or not? This is especially important in the case of such complex phenomena as shallow groundwater fluctuation, driven by numerous factors. Because of the continuous changes in the oscillating components of shallow groundwater time series, the most appropriate method should be used to investigate its periodicity, this is wavelet spectrum analysis. The aims of the research were to investigate the periodic behavior of the shallow groundwater time series of an agriculturally important and drought sensitive region in Central-Eastern Europe and its relationship to the European pressure action centers. During the research ~216 shallow groundwater observation wells located in the eastern part of the Great Hungarian Plain with a temporal coverage of 50 years were scanned for periodicity. By taking the full-time interval as 100%, the presence of any period could be determined in percentages. With the complex hydrogeological/meteorological model developed in this study, non-periodic time intervals were found in the shallow groundwater levels. On the local scale, this phenomenon linked to drought conditions, and on a regional scale linked to the maxima of the regional air pressures in the Gulf of Genoa. The study documented an important link between shallow groundwater levels and climate variables/indices facilitating the necessary adaptation strategies on national and/or regional scales, which have to take into account the predictions of drought-related climatic conditions.

Keywords: climate change, drought, groundwater periodicity, wavelet spectrum and coherence analyses

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Authors: Maria Ahmad, Alexandra Valetopoulou, Michael D. Christian

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Background: London’s Air Ambulance (LAA) provides advanced pre-hospital trauma care across London, bringing specialist resources and expert trauma teams to patients. Since its inception 32 years ago, LAA has treated over 40,000 pre-hospital patients and significantly contributed to pre-hospital patient care in London. To the authors’ best knowledge, this is the first analysis to quantify the magnitude of the publication impact of LAA on the international field of pre-hospital medicine. Method: We searched the Scopus, Web of Science, Google Scholar and PubMed databases to identify LAA focused articles. These were defined as articles on the topic of pre-hospital medicine which either utilised data from LAA, or focused on LAA patients, or were authored by LAA clinicians. A bibliometric analysis was conducted and the impact of each eligible article was classified as either: high (article directly influenced the change or creation of clinical guidelines); medium (the article was referenced in clinical guidelines or had >20 Google Scholar citations or >10 PubMed citations); or low impact (article had <20 Google Scholar citations or <10 PubMed citations). Results: The literature search yielded 1,120 articles in total. 198 articles met our inclusion criteria, and their full text was analysed to determine the level of impact. 19 articles were classified as high-impact, 76 as medium-impact, and 103 as low-impact. 20 of the 76 medium-impact articles were referenced in clinical guidelines but had not prompted changes to the guidelines. Conclusion: To our knowledge, this review is the first to quantify the significant publication impact of LAA within the field of pre-hospital medicine over the last 32 years. LAA publications have focused on and driven clinical innovations in trauma care, particularly in pre-hospital anaesthesia, haemorrhage control, and major incidents, with many impacting national and international guidelines. We recommend a greater emphasis on multidisciplinary pre-hospital collaboration in publications in future research and quality improvement projects across all pre-hospital services.

Keywords: air ambulance, pre-hospital medicine, London’s Air Ambulance, London HEMS

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

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Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

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Authors: Anwaar Anwar Tayel

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Background: Rheumatic arthritis (RA) affect all aspects of patients' life, lead to various degrees of disability, and ultimately has a profound impact on the social, economic, psychological, and sexual aspects of the patient's life. Aim of the study: Identify physical, psychological, and sexual implications of rheumatoid arthritis among women in reproductive age. In addition to investigating the correlations between physical functional disability, psychological problems, and sexual dysfunction.Settings: The study was conducted at Rheumatology Clinic at the Main University Hospital of Alexandria. Subjects: Purposive sample was chosen from women patients with rheumatoid arthritis to be subjects of this study (n=250). Tools: Four tools were used to collect data. Tool I: Socio-demographic questionnaire. Tool II: Stanford Health Assessment Questionnaire Disability Index (HAQ- DI). Tool III: Depression Anxiety Stress Scale (DASS). Tool IV: The Sexual Dysfunction Questionnaire (SDQ) Results: The majority of the studied women suffer from severe physical disability, extreme level of depression, anxiety, and about half of them had an extreme level of stress. Also, the majority of the studied women had a severe level of sexual dysfunction. Also, statistically significant correlations between women's physical disability index, psychological problems, and sexual dysfunction were detected. Conclusion: The findings from this study confirm that women patients with RA suffer from multiple negative implications on the physical, psychological and sexual functions. Recommendations: Provide ongoing support to the patients from the time of diagnosis throughout their care and management. To help them to manage their pain and disabilities, improve their sexual function, promote their mental health, and optimize psychosocial functioning

Keywords: pysical, spycholgical, sexual, implication, rheumatic arthritis

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Authors: Abi Miller, Eleanor Wilson, Claire Diver

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Background: UK Head and Neck Cancer incidence and prevalence were rising related to better treatment outcomes and changed demographics. More people of working-age now survive Head and Neck Cancer. For individuals, work provides income, purpose, and social connection. For society, work increases economic productivity and reduces welfare spending. In the UK, a cancer diagnosis is classed as a disability and more disabled people leave the workplace than non-disabled people. Limited evidence exists on return-to-work after Head and Neck Cancer, with no UK qualitative studies. Head and Neck Cancer survivors appear to return to work less when compared to other cancer survivors. This study aimed to explore the effects of Head and Neck Cancer disability on survivors’ return-to-work experience. Methodologies: This was an exploratory qualitative study using a critical realist approach to carry out semi-structured one-off interviews with Head and Neck Cancer survivors who had returned to work. Interviews were informed by an interview guide and carried out remotely by Microsoft Teams or telephone. Interviews were transcribed verbatim, pseudonyms allocated, and transcripts anonymized. Data were interpreted using Reflexive Thematic Analysis. Findings: Thirteen Head and Neck Cancer survivors aged between 41 -63 years participated in interviews. Three major themes were derived from the data: changed identity and meaning of work after Head and Neck Cancer, challenging and supportive work experiences and impact of healthcare professionals on return-to-work. Participants described visible physical appearance changes, speech and eating challenges, mental health difficulties and psycho-social shifts following Head and Neck Cancer. These factors affected workplace re-integration, ability to carry out work duties, and work relationships. Most participants experienced challenging work experiences, including stigmatizing workplace interactions and poor communication from managers or colleagues, which further affected participant confidence and mental health. Many participants experienced job change or loss, related both to Head and Neck Cancer and living through a pandemic. A minority of participants experienced strategies like phased return, which supported workplace re-integration. All participants, bar one, wanted conversations with healthcare professionals about return-to-work but perceived these conversations as absent. Conclusion: All participants found returning to work after Head and Neck Cancer to be a challenging experience. This appears to be impacted by participant physical, psychological, and functional disability following Head and Neck Cancer, work interaction and work context.

Keywords: disability, experience, head and neck cancer, qualitative, return-to-work

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Authors: Hadeer M. Sayed, Hesham E. El Deeb, Shereen A. Taie

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In the big data era, we are facing a diversity of datasets from different sources in different domains that describe a single life event. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. Multimodal fusion is the concept of integrating information from multiple modalities in a joint representation with the goal of predicting an outcome through a classification task or regression task. In this paper, multimodal fusion techniques are classified into two main classes: model-agnostic techniques and model-based approaches. It provides a comprehensive study of recent research in each class and outlines the benefits and limitations of each of them. Furthermore, the audiovisual speech recognition task is expressed as a case study of multimodal data fusion approaches, and the open issues through the limitations of the current studies are presented. This paper can be considered a powerful guide for interested researchers in the field of multimodal data fusion and audiovisual speech recognition particularly.

Keywords: multimodal data, data fusion, audio-visual speech recognition, neural networks

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Authors: Faiza Marrium, Masood Rabbani, Ali Ahmad Sheikh, Muhammad Yasin Tipu Javed Muhammad, Sohail Raza

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More than 75% diseases spreading in the past 10 years in human population globally are linked to veterinary sector. Veterinary diagnostic labs are the powerful ally for diagnosis, prevention and monitoring of animal diseases in any country. In order to avoid detrimental effects of errors in disease diagnostic and biorisk management, there is a dire need to establish quality control system. In current study, 3 private and 6 public sectors veterinary diagnostic labs were selected for survey. A questionnaire survey in biorisk management guidelines of CWA 15793 was designed to find quality control breaches in lab design, personal, equipment and consumable, quality control measures adopted in lab, waste management, environmental monitoring and customer care. The data was analyzed through frequency distribution statistically by using (SPSS) version 18.0. A non-significant difference was found in all parameters of lab design, personal, equipment and consumable, quality control measures adopted in lab, waste management, environmental monitoring and customer care with an average percentage of 46.6, 57.77, 52.7, 55.5, 54.44, 48.88 and 60, respectively. A non-significant difference among all nine labs were found, with highest average compliance percentage of all parameters are lab 2 (78.13), Lab 3 (70.56), Lab 5 (57.51), Lab 6 (56.37), Lab 4 (55.02), Lab 9 (49.58), Lab 7 (47.76), Lab 1 (41.01) and Lab 8 (36.09). This study shows that in Lahore district veterinary diagnostic labs are not giving proper attention to quality of their system and there is no significant difference between setups of private and public sector laboratories. These results show that most of parameters are between 50 and 80 percent, which needs some work and improvement as per WHO criteria.

Keywords: veterinary lab, quality management system, accreditation, regulatory body, disease identification

Procedia PDF Downloads 140