Search results for: false EIV

Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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Authors: C. Bateira, J. Fernandes, A. Costa

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The Douro Demarked Region (DDR) is a production Porto wine region. On the NE of Portugal, the strong incision of the Douro valley developed very steep slopes, organized with agriculture terraces, have experienced an intense and deep transformation in order to implement the mechanization of the work. The old terrace system, based on stone vertical wall support structure, replaced by terraces with earth embankments experienced a huge terrace instability. This terrace instability has important economic and financial consequences on the agriculture enterprises. This paper presents and develops cartographic tools to access the embankment instability and identify the area prone to instability. The priority on this evaluation is related to the use of physically based mathematical models and develop a validation process based on an inventory of the past embankment instability. We used the shallow landslide stability model (SHALSTAB) based on physical parameters such us cohesion (c’), friction angle(ф), hydraulic conductivity, soil depth, soil specific weight (ϱ), slope angle (α) and contributing areas by Multiple Flow Direction Method (MFD). A terraced area can be analysed by this models unless we have very detailed information representative of the terrain morphology. The slope angle and the contributing areas depend on that. We can achieve that propose using digital elevation models (DEM) with great resolution (pixel with 40cm side), resulting from a set of photographs taken by a flight at 100m high with pixel resolution of 12cm. The slope angle results from this DEM. In the other hand, the MFD contributing area models the internal flow and is an important element to define the spatial variation of the soil saturation. That internal flow is based on the DEM. That is supported by the statement that the interflow, although not coincident with the superficial flow, have important similitude with it. Electrical resistivity monitoring values which related with the MFD contributing areas build from a DEM of 1m resolution and revealed a consistent correlation. That analysis, performed on the area, showed a good correlation with R2 of 0,72 and 0,76 at 1,5m and 2m depth, respectively. Considering that, a DEM with 1m resolution was the base to model the real internal flow. Thus, we assumed that the contributing area of 1m resolution modelled by MFD is representative of the internal flow of the area. In order to solve this problem we used a set of generalized DEMs to build the contributing areas used in the SHALSTAB. Those DEMs, with several resolutions (1m and 5m), were built from a set of photographs with 50cm resolution taken by a flight with 5km high. Using this maps combination, we modelled several final maps of terrace instability and performed a validation process with the contingency matrix. The best final instability map resembles the slope map from a DEM of 40cm resolution and a MFD map from a DEM of 1m resolution with a True Positive Rate (TPR) of 0,97, a False Positive Rate of 0,47, Accuracy (ACC) of 0,53, Precision (PVC) of 0,0004 and a TPR/FPR ratio of 2,06.

Keywords: agricultural terraces, cartography, landslides, SHALSTAB, vineyards

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Authors: Antonia Egli, Theo Lynn, Pierangelo Rosati, Gary Sinclair

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The World Health Organization (WHO) defines vaccine hesitancy as the postponement or complete denial of vaccines and estimates a direct linkage to approximately 1.5 million avoidable deaths annually. This figure is not immune to public health developments, as has become evident since the global spread of COVID-19 from Wuhan, China in early 2020. Since then, the proliferation of influential, but oftentimes inaccurate, outdated, incomplete, or false vaccine-related information on social media has impacted hesitancy levels to a degree described by the WHO as an infodemic. The COVID-19 pandemic and related vaccine hesitancy levels have in 2022 resulted in the largest drop in childhood vaccinations of the 21st century, while the prevalence of online stigma towards vaccine hesitant consumers continues to grow. Simultaneously, a second disease has risen to global importance: Monkeypox is an infection originating from west and central Africa and, due to racially motivated online hate, was in August 2022 set to be renamed by the WHO. To better understand public reactions towards two viral infections that became global threats to public health no two years apart, this research examines user replies to threads published by the WHO on Twitter. Replies to two Tweets from the @WHO account declaring COVID-19 and Monkeypox as ‘public health emergencies of international concern’ on January 30, 2020, and July 23, 2022, are gathered using the Twitter application programming interface and user mention timeline endpoint. Research methodology is unique in its analysis of stigmatizing, racist, and hateful content shared on social media within the vaccine discourse over the course of two disease outbreaks. Three distinct analyses are conducted to provide insight into (i) the most prevalent topics and sub-topics among user reactions, (ii) changes in sentiment towards the spread of the two diseases, and (iii) the presence of stigma, racism, and online hate. Findings indicate an increase in hesitancy to accept further vaccines and social distancing measures, the presence of stigmatizing content aimed primarily at anti-vaccine cohorts and racially motivated abusive messages, and a prevalent fatigue towards disease-related news overall. This research provides value to non-profit organizations or government agencies associated with vaccines and vaccination programs in emphasizing the need for public health communication fitted to consumers' vaccine sentiments, levels of health information literacy, and degrees of trust towards public health institutions. Considering the importance of addressing fears among the vaccine hesitant, findings also illustrate the risk of alienation through stigmatization, lead future research in probing the relatively underexamined field of online, vaccine-related stigma, and discuss the potential effects of stigma towards vaccine hesitant Twitter users in their decisions to vaccinate.

Keywords: social marketing, social media, public health communication, vaccines

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Authors: Michela Del Vicario, Walter Quattrociocchi, Antonio Scala, Ana Lucía Schmidt, Fabiana Zollo

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Information, rumors, and debates may shape and impact public opinion heavily. In the latest years, several concerns have been expressed about social influence on the Internet and the outcome that online debates might have on real-world processes. Indeed, on online social networks users tend to select information that is coherent to their system of beliefs and to form groups of like-minded people –i.e., echo chambers– where they reinforce and polarize their opinions. In this way, the potential benefits coming from the exposure to different points of view may be reduced dramatically, and individuals' views may become more and more extreme. Such a context fosters misinformation spreading, which has always represented a socio-political and economic risk. The persistence of unsubstantiated rumors –e.g., the hypothetical and hazardous link between vaccines and autism– suggests that social media do have the power to misinform, manipulate, or control public opinion. As an example, current approaches such as debunking efforts or algorithmic-driven solutions based on the reputation of the source seem to prove ineffective against collective superstition. Indeed, experimental evidence shows that confirmatory information gets accepted even when containing deliberately false claims while dissenting information is mainly ignored, influences users’ emotions negatively and may even increase group polarization. Moreover, confirmation bias has been shown to play a pivotal role in information cascades, posing serious warnings about the efficacy of current debunking efforts. Nevertheless, mitigation strategies have to be adopted. To generalize the problem and to better understand social dynamics behind information spreading, in this work we rely on a tight quantitative analysis to investigate the behavior of more than 300M users w.r.t. news consumption on Facebook over a time span of six years (2010-2015). Through a massive analysis on 920 news outlets pages, we are able to characterize the anatomy of news consumption on a global and international scale. We show that users tend to focus on a limited set of pages (selective exposure) eliciting a sharp and polarized community structure among news outlets. Moreover, we find similar patterns around the Brexit –the British referendum to leave the European Union– debate, where we observe the spontaneous emergence of two well segregated and polarized groups of users around news outlets. Our findings provide interesting insights into the determinants of polarization and the evolution of core narratives on online debating. Our main aim is to understand and map the information space on online social media by identifying non-trivial proxies for the early detection of massive informational cascades. Furthermore, by combining users traces, we are finally able to draft the main concepts and beliefs of the core narrative of an echo chamber and its related perceptions.

Keywords: information spreading, misinformation, narratives, online social networks, polarization

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Authors: Z. Giagkazoglou, D. Loukovitis, C. Gubili, A. Imsiridou

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Along with the increase in human population, demand for seafood has increased. Despite the strict labeling regulations that exist for most marketed species in the European Union, seafood substitution remains a persistent global issue. Food fraud occurs when food products are traded in a false or misleading way. Mislabeling occurs when one species is substituted and traded under the name of another, and it can be intentional or unintentional. Crustaceans are one of the most regularly consumed seafood in Greece. Shrimps, prawns, lobsters, crayfish, and crabs are considered a delicacy and can be encountered in a variety of market presentations (fresh, frozen, pre-cooked, peeled, etc.). With most of the external traits removed, products as such are susceptible to species substitution. DNA barcoding has proven to be the most accurate method for the detection of fraudulent seafood products. To our best knowledge, the DNA barcoding methodology is used for the first time in Greece, in order to investigate the labeling practices for crustacean products available in the market. A total of 100 tissue samples were collected from various retailers and markets across four Greek cities. In an effort to cover the highest range of products possible, different market presentations were targeted (fresh, frozen and cooked). Genomic DNA was extracted using the DNeasy Blood & Tissue Kit, according to the manufacturer's instructions. The mitochondrial gene selected as the target region of the analysis was the cytochrome c oxidase subunit I (COI). PCR products were purified and sequenced using an ABI 3500 Genetic Analyzer. Sequences were manually checked and edited using BioEdit software and compared against the ones available in GenBank and BOLD databases. Statistical analyses were conducted in R and PAST software. For most samples, COI amplification was successful, and species-level identification was possible. The preliminary results estimate moderate mislabeling rates (25%) in the identified samples. Mislabeling was most commonly detected in fresh products, with 50% of the samples in this category labeled incorrectly. Overall, the mislabeling rates detected by our study probably relate to some degree of unintentional misidentification, and lack of knowledge surrounding the legal designations by both retailers and consumers. For some species of crustaceans (i.e. Squila mantis) the mislabeling appears to be also affected by the local labeling practices. Across Greece, S. mantis is sold in the market under two common names, but only one is recognized by the country's legislation, and therefore any mislabeling is probably not profit-motivated. However, the substitution of the speckled shrimp (Metapenaus monoceros) for the distinct, giant river prawn (Macrobranchium rosenbergii), is a clear example of deliberate fraudulent substitution, aiming for profit. To our best knowledge, no scientific study investigating substitution and mislabeling rates in crustaceans has been conducted in Greece. For a better understanding of Greece's seafood market, similar DNA barcoding studies in other regions with increased touristic importance (e.g., the Greek islands) should be conducted. Regardless, the expansion of the list of species-specific designations for crustaceans in the country is advised.

Keywords: COI gene, food fraud, labelling control, molecular identification

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Authors: Carmen W. H. Chan, Helen Y. L. Chan, Kai Chow Choi, Ka Ming Chow, Cecilia W. M. Kwan, Nancy H. Y. Ng, Jackie Robinson

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Introduction: In Hong Kong, a significant number of deaths occur in acute care wards, which requires nurses in these settings to provide end-of-life care and lead ACP implementation. However, nurses in these settings, in fact, have very low-level involvement in ACP discussions because of limited training in ACP conversations. Objective: This study aims to assess the impact of a multi-media experiential ACP (MEACP) training program, which is guided by the experiential learning model and theory of planned behaviour, on nurses' knowledge and confidence in assisting patients with ACP. Methodology: The study utilizes a cluster randomized controlled trial with a 12-week follow-up. Eligible nurses working in acute care hospital wards are randomly assigned at the ward level, in a 1:1 ratio, to either the control group (no ACP education) or the intervention group (4-week MEACP training program). The training programme includes training through a webpage and mobile application, as well as a face-to-face training workshop with enhanced lectures and role play, which is based on the Theory of Planned Behavior and Kolb's Experiential Learning Model. Questionnaires were distributed to assess nurses' knowledge (a 10-item true/false questionnaire) and level of confidence (five-point Likert scale) in ACP at baseline (T0), four weeks after the baseline assessment (T1), and 12 weeks after T1 (T2). In this interim report, data analysis was mainly descriptive in nature. Result: The interim report focuses on the preliminary results of 165 nurses at T0 (Control: 74, Intervention: 91) over a 5-month period, 69 nurses from the control group who completed the 4-week follow-up and 65 nurses from the intervention group who completed the 4-week MEACP training program at T1. The preliminary attrition rate is 6.8% and 28.6% for the control and intervention groups, respectively, as some nurses did not complete the whole set of online modules. At baseline, the two groups were generally homogeneous in terms of their years of nursing practice, weekly working hours, working title, and level of education, as well as ACP knowledge and confidence levels. The proportion of nurses who answered all ten knowledge questions correctly increased from 13.8% (T0) to 66.2% (T1) for the intervention group and from 13% (T0) to 20.3% (T1) for the control group. The nurses in the intervention group answered an average of 7.57 and 9.43 questions correctly at T0 and T1, respectively. They showed a greater improvement in the knowledge assessment at T1 with respect to T0 when compared with their counterparts in the control group (mean difference of change score, Δ=1.22). They also exhibited a greater gain in level of confidence at T1 compared to their colleagues in the control group (Δ=0.91). T2 data is yet available. Conclusion: The prevalence of nurses engaging in ACP and their level of knowledge about ACP in Hong Kong is low. The MEACP training program can enrich nurses by providing them with more knowledge about ACP and increasing their confidence in conducting ACP.

Keywords: advance directive, advance care planning, confidence, knowledge, multi-media experiential, randomised control trial

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

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Authors: Petra Hypšová, Martin Seitl

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One of the challenges of forensic psychology is to detect deception during a face-to-face interview. In addition to the classical approaches of monitoring the utterance and its components, detection is also sought by observing behavioral and physiological changes that occur as a result of the increased emotional and cognitive load caused by the production of distorted information. Typical are changes in facial temperature, eye movements and their fixation, pupil dilation, emotional micro-expression, heart rate and its variability. Expanding technological capabilities have opened the space to detect these psychophysiological changes and behavioral manifestations through non-contact technologies that do not interfere with face-to-face interaction. Non-contact deception detection methodology is still in development, and there is a lack of studies that combine multiple non-contact technologies to investigate their accuracy, as well as studies that show how different types of lies produced by different interviewers affect physiological and behavioral changes. The main objective of this study is to apply a specific non-contact technology for deception detection. The next objective is to investigate scenarios in which non-contact deception detection is possible. A series of psychophysiological experiments using infrared thermal imaging, eye tracking and behavioral analysis with FaceReader 9.0 software was used to achieve our goals. In the laboratory experiment, 16 adults (12 women, 4 men) between 18 and 35 years of age (SD = 4.42) were instructed to produce alternating prepared and spontaneous truths and lies. The baseline of each proband was also measured, and its results were compared to the experimental conditions. Because the personality of the examiner (particularly gender and facial appearance) to whom the subject is lying can influence physiological and behavioral changes, the experiment included four different interviewers. The interviewer was represented by a photograph of a face that met the required parameters in terms of gender and facial appearance (i.e., interviewer likability/antipathy) to follow standardized procedures. The subject provided all information to the simulated interviewer. During follow-up analyzes, facial temperature (main ROIs: forehead, cheeks, the tip of the nose, chin, and corners of the eyes), heart rate, emotional expression, intensity and fixation of eye movements and pupil dilation were observed. The results showed that the variables studied varied with respect to the production of prepared truths and lies versus the production of spontaneous truths and lies, as well as the variability of the simulated interviewer. The results also supported the assumption of variability in physiological and behavioural values during the subject's resting state, the so-called baseline, and the production of prepared and spontaneous truths and lies. A series of psychophysiological experiments provided evidence of variability in the areas of interest in the production of truths and lies to different interviewers. The combination of technologies used also led to a comprehensive assessment of the physiological and behavioral changes associated with false and true statements. The study presented here opens the space for further research in the field of lie detection with non-contact technologies.

Keywords: emotional expression decoding, eye-tracking, functional infrared thermal imaging, non-contact deception detection, psychophysiological experiment

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Authors: Marcelo Lopes Dias Kolling, Felipe Ferreira Laranjeira, Guilherme Augusto Hettwer, Pedro Salomão Piccinini, Marwan Masri, Carlos Oscar Uebel

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Introduction: Injectable fillers are among the most common nonsurgical cosmetic procedures, with significant growth yearly. Knowledge of rheological and mechanical characteristics of fillers, facial anatomy, and injection technique is essential for safety. Concepts such as the use of cannula versus needle, aspiration before injection, and facial danger zones have been well discussed. In case of an accidental intravascular puncture, the pressure inside the vessel may not be sufficient to push blood into the syringe due to the characteristics of the filler product; this is especially true for calcium hydroxyapatite (CaHA) or hyaluronic acid (HA) fillers with high G’. Since viscoelastic properties of normal saline are much lower than those of fillers, aspiration with saline prior to filler injection may decrease the risk of a false negative aspiration and subsequent catastrophic effects. We discuss a technique to add an additional safety step to the procedure with saline aspiration prior to injection, a ‘’reverse Seldinger’’ technique for intravascular access, which we term SANIT: Saline Aspiration Negative Intravascular Test. Objectives: To demonstrate the author’s (PSP) technique which adds an additional safety step to the process of filler injection, with both CaHA and HA, in order to decrease the risk of intravascular injection. Materials and Methods: Normal skin cleansing and topical anesthesia with prilocaine/lidocaine cream are performed; the facial subunits to be treated are marked. A 3mL Luer lock syringe is filled with 2mL of 0.9% normal saline and a 27G needle, which is turned one half rotation. When a cannula is to be used, the Luer lock syringe is attached to a 27G 4cm single hole disposable cannula. After skin puncture, the 3mL syringe is advanced with the plunger pulled back (negative pressure). Progress is made to the desired depth, all the while aspirating. Once the desired location of filler injection is reached, the syringe is exchanged for the syringe containing a filler, securely grabbing the hub of the needle and taking care to not dislodge the needle tip. Prior to this, we remove 0.1mL of filler to allow for space inside the syringe for aspiration. We again aspirate and inject retrograde. SANIT is especially useful for CaHA, since the G’ is much higher than HA, and thus reflux of blood into the syringe is less likely to occur. Results: The technique has been used safely for the past two years with no adverse events; the increase in cost is negligible (only the cost of 2mL of normal saline). Over 100 patients (over 300 syringes) have been treated with this technique. The risk of accidental intravascular puncture has been calculated to be between 1:6410 to 1:40882 syringes among expert injectors; however, the consequences of intravascular injection can be catastrophic even with board-certified physicians. Conclusions: While the risk of intravascular filler injection is low, the consequences can be disastrous. We believe that adding the SANIT technique can help further mitigate risk with no significant untoward effects and could be considered by all performing injectable fillers. Further follow-up is ongoing.

Keywords: injectable fillers, safety, saline aspiration, injectable filler complications, hyaluronic acid, calcium hydroxyapatite

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Li Jiuen Ong, Magdalin Cheong, Sri Rahayu, Lek Alexander, Pei Ting Tan

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Background: Previous programme evaluations from the diabetes training programme conducted in Changi General Hospital revealed that healthcare professionals (HCPs) are keen to receive advance diabetes training and education, specifically in medical, nutritional therapy. HCPs also expressed a preference for interactive activities over didactic teaching methods to enhance their learning. Since the War on Diabetes was initiated by MOH in 2016, HCPs are challenged to be actively involved in continuous education to be better equipped to reduce the growing burden of diabetes. Hence, streamlining training to incorporate an element of fun is of utmost importance. Aim: The nutrition programme incorporates game play using an interactive board game that aims to provide a more conducive and less stressful environment for learning. The board game could be adapted for training of community HCPs, health ambassadors or caregivers to cope with the increasing demand of diabetes care in the hospital and community setting. Methodology: Stages for game’s conception (Jaffe, 2001) were adopted in the development of the interactive board game ‘Sweet Score™ ’ Nutrition concepts and topics in diabetes self-management are embedded into the game elements of varying levels of difficulty (‘Easy,’ ‘Medium,’ ‘Hard’) including activities such as a) Drawing/ sculpting (Pictionary-like) b)Facts/ Knowledge (MCQs/ True or False) Word definition) c) Performing/ Charades To study the effects of game play on knowledge acquisition and perceived experiences, participants were randomised into two groups, i.e., lecture group (control) and game group (intervention), to test the difference. Results: Participants in both groups (control group, n= 14; intervention group, n= 13) attempted a pre and post workshop quiz to assess the effectiveness of knowledge acquisition. The scores were analysed using paired T-test. There was an improvement of quiz scores after attending the game play (mean difference: 4.3, SD: 2.0, P<0.001) and the lecture (mean difference: 3.4, SD: 2.1, P<0.001). However, there was no significance difference in the improvement of quiz scores between gameplay and lecture (mean difference: 0.9, 95%CI: -0.8 to 2.5, P=0.280). This suggests that gameplay may be as effective as a lecture in terms of knowledge transfer. All the13 HCPs who participated in the game rated 4 out of 5 on the likert scale for the favourable learning experience and relevance of learning to their job, whereas only 8 out of 14 HCPs in the lecture reported a high rating in both aspects. 16. Conclusion: There is no known board game currently designed for diabetes training for HCPs.Evaluative data from future training can provide insights and direction to improve the game format and cover other aspects of diabetes management such as self-care, exercise, medications and insulin management. Further testing of the board game to ensure learning objectives are met is important and can assist in the development of awell-designed digital game as an alternative training approach during the COVID-19 pandemic. Learning through gameplay increases opportunities for HCPs to bond, interact and learn through games in a relaxed social setting and potentially brings more joy to the workplace.

Keywords: active learning, game, diabetes, nutrition

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Henry Okechukwu Onyeiwu

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In today’s Nigerian digital age, the landscape of public diplomacy has been significantly altered by the rise of social media platforms like YouTube, Facebook, Twitter, and Instagram. In recent years, social media platforms have emerged as powerful tools for public diplomacy, transforming how countries communicate with both domestic and global audiences. International broadcasting as a tool of public diplomacy has undergone a significant transformation. Traditional methods of state-run media and controlled broadcasting have evolved to incorporate the dynamic, interactive, and decentralized nature of digital platforms. Understanding how Nigerian governments engages in international broadcasting of public diplomacy, the influence of social media on broadcasting public diplomacy, focusing on the advantages and disadvantages of controlling media outlets for diplomatic purposes and also covers the changing nature of global communication in this digital era. As countries navigate the complexities of international relations, the effectiveness of controlled media in shaping public perception and engagement raises significant questions worth exploring. The vast amount of content available can make it challenging to capture and retain audience attention. The ease of spreading false information on social media requires international broadcasters to maintain credibility and counteract misleading narratives. Addressing these challenges requires a comprehensive research that integrates digital communication tools, cultural sensitivity, cybersecurity measures and ongoing evaluation to enhance Nigeria’s international broadcasting of public diplomacy. This study employed a mixed-methods approach, combining qualitative and quantitative research methods. A content analysis of Nigeria’s international broadcasting content was conducted to assess its themes, narratives, and engagement strategies. Additionally, surveys and interviews with communications professionals, diplomats, and social media users were carried out to gather insights on perceptions and effectiveness of public diplomacy initiatives. It has highlighted some of the present trends in technology and the international environmental in which public diplomacy must work, and show how the past can illuminate the road for those navigating this new world. The rise of the social network creates more opportunities than it closes for public diplomacy. This evolution highlights the increasing importance of engagement, mutual understanding, and cooperation in international relations. By Adopting a more inclusive and participatory approach, public diplomacy can more effectively address global challenges and build stronger, more resilient relationships between nations. As Nigeria navigates the complexities of its international relations, this abstract will provide a vital examination of how it can better utilize the dual platforms of international broadcasting and social media in its public diplomacy efforts. The outcome will bear significance not only for Nigeria but also for other nations grappling with similar challenges in the digital age. As social media continues to play a crucial role in public diplomacy, understanding the dynamics of controlled media outlets becomes ever more critical. This abstract shed light on the advantages and disadvantages of such control, ultimately contributing valuable insights to practitioners in the field of diplomacy as they adapt to the rapidly changing communication landscape.

Keywords: international broadcasting, public diplomacy, social media, international relation, polities

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Authors: Stephen Tullis, M. David DiDonato, Hong Sung Park

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Rowing boats (shells) are often steered by a small rudder operated by one of the backward-facing rowers; the attention required of that athlete then slightly decreases the power that that athlete can provide. Reducing the steering distraction would then increase the overall boat speed. Races are straight 2000 m courses with each boat in a 13.5 m wide lane marked by small (~15 cm) widely-spaced (~10 m) buoys, and the boat trajectory is affected by both cross-currents and winds. An optical buoy recognition and tracking system has been developed that provides the boat’s location and orientation with respect to the lane edges. This information is provided to the steering athlete as either: a simple overlay on a video display, or fed to a simplified autopilot system giving steering directions to the athlete or directly controlling the rudder. The system is then effectively a “lane-assist” device but with small, widely-spaced lane markers viewed from a very shallow angle due to constraints on camera height. The image is captured with a lightweight 1080p webcam, and most of the image analysis is done in OpenCV. The colour RGB-image is converted to a grayscale using the difference of the red and blue channels, which provides good contrast between the red/yellow buoys and the water, sky, land background and white reflections and noise. Buoy detection is done with thresholding within a tight mask applied to the image. Robust linear regression using Tukey’s biweight estimator of the previously detected buoy locations is used to develop the mask; this avoids the false detection of noise such as waves (reflections) and, in particular, buoys in other lanes. The robust regression also provides the current lane edges in the camera frame that are used to calculate the displacement of the boat from the lane centre (lane location), and its yaw angle. The interception of the detected lane edges provides a lane vanishing point, and yaw angle can be calculated simply based on the displacement of this vanishing point from the camera axis and the image plane distance. Lane location is simply based on the lateral displacement of the vanishing point from any horizontal cut through the lane edges. The boat lane position and yaw are currently fed what is essentially a stripped down marine auto-pilot system. Currently, only the lane location is used in a PID controller of a rudder actuator with integrator anti-windup to deal with saturation of the rudder angle. Low Kp and Kd values decrease unnecessarily fast return to lane centrelines and response to noise, and limiters can be used to avoid lane departure and disqualification. Yaw is not used as a control input, as cross-winds and currents can cause a straight course with considerable yaw or crab angle. Mapping of the controller with rudder angle “overall effectiveness” has not been finalized - very large rudder angles stall and have decreased turning moments, but at less extreme angles the increased rudder drag slows the boat and upsets boat balance. The full system has many features similar to automotive lane-assist systems, but with the added constraints of the lane markers, camera positioning, control response and noise increasing the challenge.

Keywords: auto-pilot, lane-assist, marine, optical, rowing

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Authors: Bianca Peterson, Tomasz J. Sańko, Carlos C. Bezuidenhout, Johnnie Van Den Berg

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Busseola fusca (Fuller) (Lepidoptera: Noctuidae), the maize stem borer, is a major pest in sub-Saharan Africa. It causes economic damage to maize and sorghum crops and has evolved non-recessive resistance to genetically modified (GM) maize expressing the Cry1Ab insecticidal toxin. Since B. fusca is a non-model organism, very little genomic information is publicly available, and is limited to some cytochrome c oxidase I, cytochrome b, and microsatellite data. The biology of B. fusca is well-described, but still poorly understood. This, in combination with its larval-specific behavior, may pose problems for limiting the spread of current resistant B. fusca populations or preventing resistance evolution in other susceptible populations. As part of on-going research into resistance evolution, B. fusca larvae were collected from Bt and non-Bt maize in South Africa, followed by RNA isolation (15 specimens) and sequencing on the Illumina HiSeq 2500 platform. Quality of reads was assessed with FastQC, after which Trimmomatic was used to trim adapters and remove low quality, short reads. Trinity was used for the de novo assembly, whereas TransRate was used for assembly quality assessment. Transcript identification employed BLAST (BLASTn, BLASTp, and tBLASTx comparisons), for which two libraries (nucleotide and protein) were created from 3.27 million lepidopteran sequences. Several transcripts that have previously been implicated in Cry toxin resistance was identified for B. fusca. These included aminopeptidase N, cadherin, alkaline phosphatase, ATP-binding cassette transporter proteins, and mitogen-activated protein kinase. MEGA7 was used to align these transcripts to reference sequences from Lepidoptera to detect mutations that might potentially be contributing to Cry toxin resistance in this pest. RSEM and Bioconductor were used to perform differential gene expression analysis on groups of B. fusca larvae challenged and unchallenged with the Cry1Ab toxin. Pairwise expression comparisons of transcripts that were at least 16-fold expressed at a false-discovery corrected statistical significance (p) ≤ 0.001 were extracted and visualized in a hierarchically clustered heatmap using R. A total of 329,194 transcripts with an N50 of 1,019 bp were generated from the over 167.5 million high-quality paired-end reads. Furthermore, 110 transcripts were over 10 kbp long, of which the largest one was 29,395 bp. BLAST comparisons resulted in identification of 157,099 (47.72%) transcripts, among which only 3,718 (2.37%) were identified as Cry toxin receptors from lepidopteran insects. According to transcript expression profiles, transcripts were grouped into three subclusters according to the similarity of their expression patterns. Several immune-related transcripts (pathogen recognition receptors, antimicrobial peptides, and inhibitors) were up-regulated in the larvae feeding on Bt maize, indicating an enhanced immune status in response to toxin exposure. Above all, extremely up-regulated arylphorin genes suggest that enhanced epithelial healing is one of the resistance mechanisms employed by B. fusca larvae against the Cry1Ab toxin. This study is the first to provide a resource base and some insights into a potential mechanism of Cry1Ab toxin resistance in B. fusca. Transcriptomic data generated in this study allows identification of genes that can be targeted by biotechnological improvements of GM crops.

Keywords: epithelial healing, Lepidoptera, resistance, transcriptome

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Authors: Dina Farran, Mark Ashworth, Fiona Gaughran

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Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, is associated with an increased risk of stroke, contributing to heart failure and death. In this project, we aim to improve patient safety by screening for stroke risk among people with AF and co-morbid mental illness. To do so, we started by conducting a systematic review and meta-analysis on prevalence, management, and outcomes of AF in people with Serious Mental Illness (SMI) versus the general population. We then evaluated oral anticoagulation (OAC) prescription trends in people with AF and co-morbid SMI in King’s College Hospital. We also evaluated the association between mental illness severity and OAC prescription in eligible patients in South London and Maudsley (SLaM) NHS Foundation Trust. Next, we implemented an electronic clinical decision support system (eCDSS) consisting of a visual prompt on patient electronic Personal Health Records to screen for AF-related stroke risk in three Mental Health of Older Adults wards at SLaM. Finally, we assessed the feasibility and acceptability of the eCDSS by qualitatively investigating clinicians’ perspectives of the potential usefulness of the eCDSS (pre-intervention) and their experiences and their views regarding its impact on clinicians and patients (post-intervention). The systematic review showed that people with SMI had low reported rates of AF. AF patients with SMI were less likely to receive OAC than the general population. When receiving warfarin, people with SMI, particularly bipolar disorder, experienced poor anticoagulation control compared to the general population. Meta-analysis showed that SMI was not significantly associated with an increased risk of stroke or major bleeding when adjusting for underlying risk factors. The main findings of the first observational study were that among AF patients having a high stroke risk, those with co-morbid SMI were less likely than non-SMI to be prescribed any OAC, particularly warfarin. After 2019, there was no significant difference between the two groups. In the second observational study, patients with AF and co-morbid SMI were less likely to be prescribed any OAC compared to those with dementia, substance use disorders, or common mental disorders, adjusting for age, sex, stroke, and bleeding risk scores. Among AF patients with co-morbid SMI, warfarin was less likely to be prescribed to those having alcohol or substance dependency, serious self-injury, hallucinations or delusions, and activities of daily living impairment. In the intervention, clinicians were asked to confirm the presence of AF, clinically assess stroke and bleeding risks, record risk scores in clinical notes, and refer patients at high risk of stroke to OAC clinics. Clinicians reported many potential benefits for the eCDSS, including improving clinical effectiveness, better identification of patients at risk, safer and more comprehensive care, consistency in decision making and saving time. Identified potential risks included rigidity in decision-making, overreliance, reduced critical thinking, false positive recommendations, annoyance, and increased workload. This study presents a unique opportunity to quantify AF patients with mental illness who are at high risk of severe outcomes using electronic health records. This has the potential to improve health outcomes and, therefore patients' quality of life.

Keywords: atrial fibrillation, stroke, mental health conditions, electronic clinical decision support systems

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Authors: Nurlan Smagulov, Aiman Konkabayeva, Akerke Sadykova, Arailym Serik

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Introduction. Adverse environmental factors do not immediately lead to pathological changes in the body. They can exert the growth of pre-pathology characterized by shifts in physiological, biochemical, immunological and other indicators of the body state. These disorders are unstable, reversible and indicative of body reactions. There is an opportunity to objectively judge the internal structure of the adaptive body reactions at the level of individual organs and systems. In order to obtain a stable response of the body to the chronic effects of unfavorable environmental factors of low intensity (compared to production environment factors), a time called the «lag time» is needed. The obtained results without considering this factor distort reality and, for the most part, cannot be a reliable statement of the main conclusions in any work. A technique is needed to reduce methodological errors and combine mathematical logic using statistical methods and a medical point of view, which ultimately will affect the obtained results and avoid a false correlation. Objective. Development of a methodology for assessing and predicting the environmental factors impact on the population health considering the «lag time.» Methods. Research objects: environmental and population morbidity indicators. The database on the environmental state was compiled from the monthly newsletters of Kazhydromet. Data on population morbidity were obtained from regional statistical yearbooks. When processing static data, a time interval (lag) was determined for each «argument-function» pair. That is the required interval, after which the harmful factor effect (argument) will fully manifest itself in the indicators of the organism's state (function). The lag value was determined by cross-correlation functions of arguments (environmental indicators) with functions (morbidity). Correlation coefficients (r) and their reliability (t), Fisher's criterion (F) and the influence share (R2) of the main factor (argument) per indicator (function) were calculated as a percentage. Results. The ecological situation of an industrially developed region has an impact on health indicators, but it has some nuances. Fundamentally opposite results were obtained in the mathematical data processing, considering the «lag time». Namely, an expressed correlation was revealed after two databases (ecology-morbidity) shifted. For example, the lag period was 4 years for dust concentration, general morbidity, and 3 years – for childhood morbidity. These periods accounted for the maximum values of the correlation coefficients and the largest percentage of the influencing factor. Similar results were observed in relation to the concentration of soot, dioxide, etc. The comprehensive statistical processing using multiple correlation-regression variance analysis confirms the correctness of the above statement. This method provided the integrated approach to predicting the degree of pollution of the main environmental components to identify the most dangerous combinations of concentrations of leading negative environmental factors. Conclusion. The method of assessing the «environment-public health» system (considering the «lag time») is qualitatively different from the traditional (without considering the «lag time»). The results significantly differ and are more amenable to a logical explanation of the obtained dependencies. The method allows presenting the quantitative and qualitative dependence in a different way within the «environment-public health» system.

Keywords: ecology, morbidity, population, lag time

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Authors: Raham El-Kahlout, Hadi Yassin, Asmaa Athani, Marwan Abou Madi, Gheyath Nasrallah

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Background: Since its first isolation in September 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) has diffused across 27 countries infecting more than two thousand individuals with a high case fatality rate. MERS-CoV–specific antibodies are widely found in Dromedary camel along with viral shedding of similar viruses detected in human at same region, suggesting that MERS epidemiology may be central role by camel. Interestingly, MERS-CoV has also been also reported to be asymptomatic or to cause influenza-like mild illnesses. Therefore, in a country like Qatar (bordered Saudi Arabia), where camels are widely spread, serological surveys are important to explore the role of camels in MERS-CoV transmission. However, widespread strategic serological surveillances of MERS-CoV among populations, particularly in endemic country, are infrequent. In the absence of clear epidemiological view, cross-sectional MERS antibody surveillances in human populations are of global concern. Method: We performed a comparative serological screening of 4719 healthy blood donors, 135 baseline case contacts (high risk individual), and four MERS confirmed patients (by PCR) for the presence of anti-MERS IgG. Initially, samples were screened using Euroimmune anti- MERS-CoV IgG ELISA kit, the only commercial kit available in the market and recommended by the CDC as a screening kit. To confirm ELISA test results, farther serological testing was performed for all borderline and positive samples using two assays; the anti MERS-CoV IgG and IgM Euroimmune indirect immunofluorescent test (IIFT) and pseudoviral particle neutralizing assay (PPNA). Additionally, to test cross reactivity of anti-MERS-CoV antibody with other family members of coronavirus, borderline and positive samples were tested for the presence of the of IgG antibody of the following viruses; SARS, HCoV-229E, HKU1 using the Euroimmune IIFT for SARS and HCoV-229E and ELISA for HKU1. Results: In all of 4858 screened 15 samples [10 donors (0.21%, 10/4719), 1 case contact (0.77 %, 1/130), 3 patients (75%, 3/4)] anti-MERS IgG reactive/borderline samples were seen in ELISA. However, only 7 (0.14%) of them gave positive with in IIFT and only 3 (0.06%) was confirmed by the specific anti-MERS PPNA. One of the interesting findings was, a donor, who was selected in the control group as a negative anti-MERS IgG ELISA, yield reactive for anti-MERS IgM IIFT and was confirmed with the PPNA. Further, our preliminary results showed that there was a strong cross reactivity between anti- MERS-COV IgG with both HCoV-229E or anti-HKU1 IgG, yet, no cross reactivity of SARS were found. Conclusions: Our findings suggest that MERS-CoV is not heavily circulated among the population of Qatar and this is also indicated by low number of confirmed cases (only 18) since 2012. Additionally, the presence of antibody of other pathogenic human coronavirus may cause false positive results of both ELISA and IIFT, which stress the need for more evaluation studies for the available serological assays. Conclusion: this study provides an insight about the epidemiological view for MERS-CoV in Qatar population. It also provides a performance evaluation for the available serologic tests for MERS-CoV in a view of serologic status to other human coronaviruses.

Keywords: seroprevalence, MERS-CoV, healthy individuals, Qatar

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Authors: Shuo Li, Yannick Coffinier, Chann Lagadec, Fabrizio Cleri, Katsuhiko Nishiguchi, Akira Fujiwara, Soo Hyeon Kim, Nicolas Clément

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The need for single cell detection and analysis techniques has increased in the past decades because of the heterogeneity of individual living cells, which increases the complexity of the pathogenesis of malignant tumors. In the search for early cancer detection, high-precision medicine and therapy, the technologies most used today for sensitive detection of target analytes and monitoring the variation of these species are mainly including two types. One is based on the identification of molecular differences at the single-cell level, such as flow cytometry, fluorescence-activated cell sorting, next generation proteomics, lipidomic studies, another is based on capturing or detecting single tumor cells from fresh or fixed primary tumors and metastatic tissues, and rare circulating tumors cells (CTCs) from blood or bone marrow, for example, dielectrophoresis technique, microfluidic based microposts chip, electrochemical (EC) approach. Compared to other methods, EC sensors have the merits of easy operation, high sensitivity, and portability. However, despite various demonstrations of low limits of detection (LOD), including aptamer sensors, arrayed EC sensors for detecting single-cell have not been demonstrated. In this work, a new technique based on 20-nm-thick nanopillars array to support cells and keep them at ideal recognition distance for redox-labeled aptamers grafted on the surface. The key advantages of this technology are not only to suppress the false positive signal arising from the pressure exerted by all (including non-target) cells pushing on the aptamers by downward force but also to stabilize the aptamer at the ideal hairpin configuration thanks to a confinement effect. With the first implementation of this technique, a LOD of 13 cells (with5.4 μL of cell suspension) was estimated. In further, the nanosupported cell technology using redox-labeled aptasensors has been pushed forward and fully integrated into a single-cell electrochemical aptasensor array. To reach this goal, the LOD has been reduced by more than one order of magnitude by suppressing parasitic capacitive electrochemical signals by minimizing the sensor area and localizing the cells. Statistical analysis at the single-cell level is demonstrated for the recognition of cancer cells. The future of this technology is discussed, and the potential for scaling over millions of electrodes, thus pushing further integration at sub-cellular level, is highlighted. Despite several demonstrations of electrochemical devices with LOD of 1 cell/mL, the implementation of single-cell bioelectrochemical sensor arrays has remained elusive due to their challenging implementation at a large scale. Here, the introduced nanopillar array technology combined with redox-labeled aptamers targeting epithelial cell adhesion molecule (EpCAM) is perfectly suited for such implementation. Combining nanopillar arrays with microwells determined for single cell trapping directly on the sensor surface, single target cells are successfully detected and analyzed. This first implementation of a single-cell electrochemical aptasensor array based on Brownian-fluctuating redox species opens new opportunities for large-scale implementation and statistical analysis of early cancer diagnosis and cancer therapy in clinical settings.

Keywords: bioelectrochemistry, aptasensors, single-cell, nanopillars

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Authors: Charalabos Antonatos, Mariza Panoutsopoulou, Georgios K. Georgakilas, Evangelos Evangelou, Yiannis Vasilopoulos

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The extended tissue damage and severe clinical outcomes of autoimmune diseases, accompanied by the high annual costs to the overall health care system, highlight the need for an efficient therapy. Increasing knowledge over the pathophysiology of specific chronic inflammatory diseases, namely Psoriasis (PsO), Inflammatory Bowel Diseases (IBD) consisting of Crohn’s disease (CD) and Ulcerative colitis (UC), and Rheumatoid Arthritis (RA), has provided insights into the underlying mechanisms that lead to the maintenance of the inflammation, such as Tumor Necrosis Factor alpha (TNF-α). Hence, the anti-TNFα biological agents pose as an ideal therapeutic approach. Despite the efficacy of anti-TNFα agents, several clinical trials have shown that 20-40% of patients do not respond to treatment. Nowadays, high-throughput technologies have been recruited in order to elucidate the complex interactions in multifactorial phenotypes, with the most ubiquitous ones referring to transcriptome quantification analyses. In this context, a random effects meta-analysis of available gene expression cDNA microarray datasets was performed between responders and non-responders to anti-TNFα therapy in patients with IBD, PsO, and RA. Publicly available datasets were systematically searched from inception to 10th of November 2020 and selected for further analysis if they assessed the response to anti-TNFα therapy with clinical score indexes from inflamed biopsies. Specifically, 4 IBD (79 responders/72 non-responders), 3 PsO (40 responders/11 non-responders) and 2 RA (16 responders/6 non-responders) datasetswere selected. After the separate pre-processing of each dataset, 4 separate meta-analyses were conducted; three disease-specific and a single combined meta-analysis on the disease-specific results. The MetaVolcano R package (v.1.8.0) was utilized for a random-effects meta-analysis through theRestricted Maximum Likelihood (RELM) method. The top 1% of the most consistently perturbed genes in the included datasets was highlighted through the TopConfects approach while maintaining a 5% False Discovery Rate (FDR). Genes were considered as Differentialy Expressed (DEGs) as those with P ≤ 0.05, |log2(FC)| ≥ log2(1.25) and perturbed in at least 75% of the included datasets. Over-representation analysis was performed using Gene Ontology and Reactome Pathways for both up- and down-regulated genes in all 4 performed meta-analyses. Protein-Protein interaction networks were also incorporated in the subsequentanalyses with STRING v11.5 and Cytoscape v3.9. Disease-specific meta-analyses detected multiple distinct pro-inflammatory and immune-related down-regulated genes for each disease, such asNFKBIA, IL36, and IRAK1, respectively. Pathway analyses revealed unique and shared pathways between each disease, such as Neutrophil Degranulation and Signaling by Interleukins. The combined meta-analysis unveiled 436 DEGs, 86 out of which were up- and 350 down-regulated, confirming the aforementioned shared pathways and genes, as well as uncovering genes that participate in anti-inflammatory pathways, namely IL-10 signaling. The identification of key biological pathways and regulatory elements is imperative for the accurate prediction of the patient’s response to biological drugs. Meta-analysis of such gene expression data could aid the challenging approach to unravel the complex interactions implicated in the response to anti-TNFα therapy in patients with PsO, IBD, and RA, as well as distinguish gene clusters and pathways that are altered through this heterogeneous phenotype.

Keywords: anti-TNFα, autoimmune, meta-analysis, microarrays

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Authors: K. O'Malley

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Background: The emergence and rapid evolution of large language models (LLMs) (i.e., models of generative artificial intelligence, or AI) has been unprecedented. ChatGPT is one of the most widely used LLM platforms. Using natural language processing technology, it generates customized responses to user prompts, enabling it to mimic human conversation. Responses are generated using predictive modeling of vast internet text and data swathes and are further refined and reinforced through user feedback. The popularity of LLMs is increasing, with a growing number of students utilizing these platforms for study and revision purposes. Notwithstanding its many novel applications, LLM technology is inherently susceptible to bias and error. This poses a significant challenge in the educational setting, where academic integrity may be undermined. This study aims to evaluate the performance of the latest iteration of ChatGPT (ChatGPT4.0) in standardized state medical licensing examinations. Methods: A considered search strategy was used to interrogate the PubMed electronic database. The keywords ‘ChatGPT’ AND ‘medical education’ OR ‘medical school’ OR ‘medical licensing exam’ were used to identify relevant literature. The search included all peer-reviewed literature published in the past five years. The search was limited to publications in the English language only. Eligibility was ascertained based on the study title and abstract and confirmed by consulting the full-text document. Data was extracted into a Microsoft Excel document for analysis. Results: The search yielded 345 publications that were screened. 225 original articles were identified, of which 11 met the pre-determined criteria for inclusion in a narrative synthesis. These studies included performance assessments in national medical licensing examinations from the United States, United Kingdom, Saudi Arabia, Poland, Taiwan, Japan and Germany. ChatGPT 4.0 achieved scores ranging from 67.1 to 88.6 percent. The mean score across all studies was 82.49 percent (SD= 5.95). In all studies, ChatGPT exceeded the threshold for a passing grade in the corresponding exam. Conclusion: The capabilities of ChatGPT in standardized academic assessment in medicine are robust. While this technology can potentially revolutionize higher education, it also presents several challenges with which educators have not had to contend before. The overall strong performance of ChatGPT, as outlined above, may lend itself to unfair use (such as the plagiarism of deliverable coursework) and pose unforeseen ethical challenges (arising from algorithmic bias). Conversely, it highlights potential pitfalls if users assume LLM-generated content to be entirely accurate. In the aforementioned studies, ChatGPT exhibits a margin of error between 11.4 and 32.9 percent, which resonates strongly with concerns regarding the quality and veracity of LLM-generated content. It is imperative to highlight these limitations, particularly to students in the early stages of their education who are less likely to possess the requisite insight or knowledge to recognize errors, inaccuracies or false information. Educators must inform themselves of these emerging challenges to effectively address them and mitigate potential disruption in academic fora.

Keywords: artificial intelligence, ChatGPT, generative ai, large language models, licensing exam, medical education, medicine, university

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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Authors: Merav Alush Levron, Sivan Rajuan Shtang

Abstract:

From as early as their emergence in Europe and the US, postmodern and post-colonial paradigm have formed the backbone of the visual culture field of study. The self-representation project of political minorities is studied, described and explained within the premises and perspectives drawn from these paradigms, addressing the key issues they had raised: modernism’s crisis of representation. The struggle for self-representation, agency and multicultural visibility sought to challenge the liberal pretense of universality and equality, hitting at its different blind spots, on issues such as class, gender, race, sex, and nationality. This struggle yielded subversive identity and hybrid performances, including reclaiming, mimicry and masquerading. These performances sought to defy the uniform, universal self, which forms the basis for the liberal, rational, enlightened subject. The argument of this research runs that this politics of representation itself is confined within liberal thought. Alongside post-colonialism and multiculturalism’s contribution in undermining oppressive structures of power, generating diversity in cultural visibility, and exposing the failure of liberal colorblindness, this subversion is constituted in the visual field by way of confrontation, flying in the face of the universal law and relying on its ongoing comparison and attribution to this law. Relying on Deleuze and Guattari, this research set out to draw theoretic and empiric attention to an alternative, post-liberal occurrence which has been taking place in the visual field in parallel to the contra-hegemonic phase and as a product of political reality in the aftermath of the crisis of representation. It is no longer a counter-representation; rather, it is a motion of organic minor desire, progressing in the form of flows and generating what Deleuze and Guattari termed deterritorialization of social structures. This discussion shall have its focus on current post-liberal performances of ‘Mizrahim’ (Jewish Israelis of Arab and Muslim extraction) in the visual field in Israel. In television, video art and photography, these performances challenge the issue of representation and generate concrete peripheral Mizrahiness, realized in the visual organization of the photographic frame. Mizrahiness then transforms from ‘confrontational’ representation into a 'presence', flooding the visual sphere in our plain sight, in a process of 'becoming'. The Mizrahi desire is exerted on the plains of sound, spoken language, the body and the space where they appear. It removes from these plains the coding and stratification engendered by European dominance and rational, liberal enlightenment. This stratification, adhering to the hegemonic surface, is flooded not by way of resisting false consciousness or employing hybridity, but by way of the Mizrahi identity’s own productive, material immanent yearning. The Mizrahi desire reverberates with Mizrahi peripheral 'worlds of meaning', where post-colonial interpretation almost invariably identifies a product of internalized oppression, and a recurrence thereof, rather than a source in itself - an ‘offshoot, never a wellspring’, as Nissim Mizrachi clarifies in his recent pioneering work. The peripheral Mizrahi performance ‘unhook itself’, in Deleuze and Guattari words, from the point of subjectification and interpretation and does not correspond with the partialness, absence, and split that mark post-colonial identities.

Keywords: desire, minority, Mizrahi Jews, post-colonialism, post-liberalism, visibility, Deleuze and Guattari

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Authors: Areti Okalidou, Paul D. Hatzigiannakoglou, Aikaterini Vatou, George Kyriafinis

Abstract:

Young children are nowadays adept at using technology. Hence, computer-based auditory training programs (CBATPs) have become increasingly popular in aural rehabilitation for children with hearing loss and/or with cochlear implants (CI). Yet, their clinical utility for prognostic, diagnostic, and monitoring purposes has not been explored. The purposes of the study were: a) to develop an updated version of the auditory rehabilitation tool for Greek-speaking children with cochlear implants, b) to develop a database for behavioral responses, and c) to compare accuracy rates and reaction times in children differing in hearing status and other medical and demographic characteristics, in order to assess the tool’s clinical utility in prognosis, diagnosis, and progress monitoring. The updated version of the auditory rehabilitation tool was developed on a tablet, retaining the User-Centered Design approach and the elements of the Virtual Reality (VR) serious game. The visual stimuli were farm animals acting in simple game scenarios designed to trigger children’s responses to animal sounds, names, and relevant sentences. Based on an extended version of Erber’s auditory development model, the VR game consisted of six stages, i.e., sound detection, sound discrimination, word discrimination, identification, comprehension of words in a carrier phrase, and comprehension of sentences. A familiarization stage (learning) was set prior to the game. Children’s tactile responses were recorded as correct, false, or impulsive, following a child-dependent set up of a valid delay time after stimulus offset for valid responses. Reaction times were also recorded, and the database was in Εxcel format. The tablet version of the auditory rehabilitation tool was piloted in 22 preschool children with Νormal Ηearing (ΝΗ), which led to improvements. The study took place in clinical settings or at children’s homes. Fifteen children with CI, aged 5;7-12;3 years with post-implantation 0;11-5;1 years used the auditory rehabilitation tool. Eight children with CI were monolingual, two were bilingual and five had additional disabilities. The control groups consisted of 13 children with ΝΗ, aged 2;6-9;11 years. A comparison of both accuracy rates, as percent correct, and reaction times (in sec) was made at each stage, across hearing status, age, and also, within the CI group, based on presence of additional disability and bilingualism. Both monolingual Greek-speaking children with CI with no additional disabilities and hearing peers showed high accuracy rates at all stages, with performances falling above the 3rd quartile. However, children with normal hearing scored higher than the children with CI, especially in the detection and word discrimination tasks. The reaction time differences between the two groups decreased in language-based tasks. Results for children with CI with additional disability or bilingualism varied. Finally, older children scored higher than younger ones in both groups (CI, NH), but larger differences occurred in children with CI. The interactions between familiarization of the software, age, hearing status and demographic characteristics are discussed. Overall, the VR game is a promising tool for tracking the development of auditory skills, as it provides multi-level longitudinal empirical data. Acknowledgment: This work is part of a project that has received funding from the Research Committee of the University of Macedonia under the Basic Research 2020-21 funding programme.

Keywords: VR serious games, auditory rehabilitation, auditory training, children with cochlear implants

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Authors: Roseanne Sharon Borromeo, Mariana Carvalho, Mariia Karizhenskaia

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Healthcare providers need to comprehend cultural diversity for optimal patient-centered care, especially near the end of life. Although a universal method for navigating cultural differences would be ideal, culture’s high complexity makes this strategy impossible. Adding cultural humility, a process of self-reflection to understand personal and systemic biases and humbly acknowledging oneself as a learner when it comes to understanding another's experience leads to a meaningful process in palliative care generating respectful, honest, and trustworthy relationships. This study is a systematic review of the literature on cultural humility in palliative care research and best practices. Race, religion, language, values, and beliefs can affect an individual’s access to palliative care, underscoring the importance of culture in palliative care. Cultural influences affect end-of-life care perceptions, impacting bereavement rituals, decision-making, and attitudes toward death. Cultural factors affecting the delivery of care identified in a scoping review of Canadian literature include cultural competency, cultural sensitivity, and cultural accessibility. As the different parts of the world become exponentially diverse and multicultural, healthcare providers have been encouraged to give culturally competent care at the bedside. Therefore, many organizations have made cultural competence training required to expose professionals to the special needs and vulnerability of diverse populations. Cultural competence is easily standardized, taught, and implemented; however, this theoretically finite form of knowledge can dangerously lead to false assumptions or stereotyping, generating poor communication, loss of bonds and trust, and poor healthcare provider-patient relationship. In contrast, Cultural humility is a dynamic process that includes self-reflection, personal critique, and growth, allowing healthcare providers to respond to these differences with an open mind, curiosity, and awareness that one is never truly a “cultural” expert and requires life-long learning to overcome common biases and ingrained societal influences. Cultural humility concepts include self-awareness and power imbalances. While being culturally competent requires being skilled and knowledgeable in one’s culture, being culturally humble involves the sometimes-uncomfortable position of healthcare providers as students of the patient. Incorporating cultural humility emphasizes the need to approach end-of-life care with openness and responsiveness to various cultural perspectives. Thus, healthcare workers need to embrace lifelong learning in individual beliefs and values on suffering, death, and dying. There have been different approaches to this as well. Some adopt strategies for cultural humility, addressing conflicts and challenges through relational and health system approaches. In practice and research, clinicians and researchers must embrace cultural humility to advance palliative care practices, using qualitative methods to capture culturally nuanced experiences. Cultural diversity significantly impacts patient-centered care, particularly in end-of-life contexts. Cultural factors also shape end-of-life perceptions, impacting rituals, decision-making, and attitudes toward death. Cultural humility encourages openness and acknowledges the limitations of expertise in one’s culture. A consistent self-awareness and a desire to understand patients’ beliefs drive the practice of cultural humility. This dynamic process requires practitioners to learn continuously, fostering empathy and understanding. Cultural humility enhances palliative care, ensuring it resonates genuinely across cultural backgrounds and enriches patient-provider interactions.

Keywords: cultural competency, cultural diversity, cultural humility, palliative care, self-awareness

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Authors: Waqas Abrar

Abstract:

Background: Pakistan is confronted with a formidable challenge to increase uptake of modern contraceptive methods. USAID, through its flagship Maternal and Child Survival Program (MCSP), in Pakistan is determined to support provincial Departments of Health and Population Welfare to increase the country's contraceptive prevalence rates (CPR) in Sindh, Punjab and Balochistan to achieve FP2020 goals. To inform program design and planning, a Rapid Situation Assessment (RSA) of family planning was carried out in Rawalpindi and Lahore districts in Punjab and Karachi district in Sindh. Methodology: The methodology consisted of comprehensive desk review of available literature and used a qualitative approach comprising of in-depth interviews (IDIs) and focus group discussions (FGDs). FGDs were conducted with community women, men, and mothers-in-law whereas IDIs were conducted with health facility in-charges/chiefs, healthcare providers, and community health workers. Results: Some of the oft-quoted reasons captured during desk review included poor quality of care at public sector facilities, affordability and accessibility in rural communities and providers' technical incompetence. Moreover, providers had inadequate knowledge of contraceptive methods and lacked counseling techniques; thereby, leading to dissatisfied clients and hence, discontinuation of contraceptive methods. These dissatisfied clients spread the myths and misconceptions about contraceptives in their respective communities which seriously damages community-level family planning efforts. Private providers were found reluctant to insert Intrauterine Contraceptive Devices (IUCDs) due to inadequate knowledge vis-à-vis post insertion issues/side effects. FGDs and IDIs unveiled multi-faceted reasons for poor contraceptives uptake. It was found that low education and socio-economic levels lead to low contraceptives uptake and mostly uneducated women rely on condoms provided by Lady Health Workers (LHWs). Providers had little or no knowledge about postpartum family planning or lactational amenorrhea. At community level family planning counseling sessions organized by LHWs and Male Mobilizers do not sensitize community men on permissibility of contraception in Islam. Many women attributed their physical ailments to the use of contraceptives. Lack of in-service training, job-aids and Information, Education and Communications (IEC) materials at facilities seriously comprise the quality of care in effective family planning service delivery. This is further compounded by frequent stock-outs of contraceptives at public healthcare facilities, poor data quality, false reporting, lack of data verification systems and follow-up. Conclusions: Some key conclusions from this assessment included capacity building of healthcare providers on long acting reversible contraceptives (LARCs) which give women contraception for a longer period. Secondly, capacity building of healthcare providers on postpartum family planning is an enormous challenge that can be best addressed through institutionalization. Thirdly, Providers should be equipped with counseling skills and techniques including inculcation of pros and cons of all contraceptive methods. Fourthly, printed materials such as job-aids and Information, Education and Communications (IEC) materials should be disseminated among healthcare providers and clients. These concluding statements helped MCSP to make informed decisions with regard to setting broad objectives of project and were duly approved by USAID.

Keywords: capacity building, contraceptive prevalence rate, family planning, Institutionalization, Pakistan, postpartum care, postpartum family planning services

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