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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

Abstract:

Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

Procedia PDF Downloads 42

Authors: M. H. Pazandeh, M. Doudi, S. Barahimi, L. Rahimzadeh Torabi

Abstract:

The use of antibiotics is essential in reducing the occurrence of adverse effects and inhibiting the emergence of antibiotic resistance in microbial populations. The necessity for a novel methodology concerning local administration of antibiotics has arisen, with particular focus on dealing with localized infections prompted by bacterial colonization of medical devices or implant materials. Bioactive glasses (BG) are extensively employed in the field of regenerative medicine, encompassing a diverse range of materials utilized for drug delivery systems. In the present investigation, various drug carriers for imipenem and tetracycline, namely single systems BG/SnO2, BG/NiO with varying proportions of metal oxide, and nanocomposite BG/SnO2/NiO, were synthesized through the sol-gel technique. The antibacterial efficacy of the synthesized samples was assessed through the utilization of the disk diffusion method with the aim of neutralizing Staphylococcus aureus as the bacterial model. The current study involved the examination of the bioactivity of two samples, namely BG10SnO2/10NiO and BG20SnO2, which were chosen based on their heightened bacterial inactivation properties. This evaluation entailed the employment of two techniques: the measurement of the pH of simulated body fluid (SBF) solution and the analysis of the sample tablets through X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared (FTIR) spectroscopy. The sample tablets were submerged in SBF for varying durations of 7, 14, and 28 days. The bioactivity of the composite bioactive glass sample was assessed through characterization of alterations in its surface morphology, structure, and chemical composition. This evaluation was performed using scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy, and X-ray diffraction spectroscopy. Subsequently, the sample was immersed in simulated liquids to simulate its behavior in biological environments. The specific body fat percentage (SBF) was assessed over a 28-day period. The confirmation of the formation of a hydroxyapatite surface layer serves as a distinct indicator of bioactivity. The infusion of antibiotics into the composite bioactive glass specimen was done separately, and then the release kinetics of tetracycline and imipenem were tested in simulated body fluid (SBF). Antimicrobial effectiveness against various bacterial strains have been proven in numerous instances using both melt and sol-gel techniques to create multiple bioactive glass compositions. An elevated concentration of calcium ions within a solution has been observed to cause an increase in the pH level. In aqueous suspensions, bioactive glass particles manifest a significant antimicrobial impact. The composite bioactive glass specimen exhibits a gradual and uninterrupted release, which is highly desirable for a drug delivery system over a span of 72 hours. The reduction in absorption, which signals the loss of a portion of the antibiotic during the loading process from the initial phosphate-buffered saline solution, indicates the successful bonding of the two antibiotics to the surfaces of the bioactive glass samples. The sample denoted as BG/10SnO2/10NiO exhibits a higher loading of particles compared to the sample designated as BG/20SnO2 in the context of bioactive glass. The enriched sample demonstrates a heightened bactericidal impact on the bacteria under investigation while concurrently preserving its antibacterial characteristics. Tailored bioactive glass that incorporates hydroxyapatite, with a regulated and efficient release of drugs targeting bacterial infections, holds promise as a potential framework for bone implant scaffolds following rigorous clinical evaluation, thereby establishing potential future biomedical uses. During the modification process, the introduction of metal oxides into bioactive glass resulted in improved antibacterial characteristics, particularly in the composite bioactive glass sample that displayed the highest level of efficiency.

Keywords: antibacterial, bioactive glasses, implant infections, multi drug resistant

Procedia PDF Downloads 100

Authors: Humaira Maheen, Elizabeth Hoban, Catherine Bennette

Abstract:

Background: Globally, role of Community Health Workers (CHW) as front line disaster health work force is underutilized. Developing countries which are at risk of natural disasters or humanitarian emergencies should lay down effective strategies especially to ensure adequate access to maternity care during crisis situation by using CHW as they are local, trained, and most of them possess a good relationship with the community. The Minimum Initial Service Package (MISP) is a set of universal guidelines that addresses women’s reproductive health needs during the first phase of an emergency. According to the MISP, pregnant women should have access to a skilled birth attendant and adequate transportation arrangements so they can access a maternity care facility. Pakistan is one of the few countries which has been severely affected by a number of natural disaster as well as humanitarian emergencies in last decade. Pakistan has a young and structured National Disaster Management System in place, where District Authorities play a vital role in disaster management. The District Health Department develops the contingency health plan for an emergency situation and implements it under the existing district health human resources (health workers and medical staff at the health facility) and infrastructure (health care facilities). Methods: A mixed methods study was conducted in rural villages of Sindh adjacent to the river Indus, and included in-depth interviews with 15 women who gave birth during the floods, structured interviews with 668 women who were pregnant during 2010-2014, and in-depth interviews with 25 community health workers (CHW) and 30 key informants. Results: Women said that giving birth in the relief camps during the floods was one of the most challenging times of their life. The district health department didn’t make transportation arrangement for labouring women from relief camp to the nearest health care facility. As a result 91.2% women gave birth in temporary shelters with the help of a traditional birth attendant (Dai) with no clean physical space available to birth. Of the 332 women who were pregnant at the time of the floods, 26 had adverse birth outcomes; 10 had miscarriages, 14 had stillbirths and there were four neonatal deaths. Conclusion: The district health department was not able to provide access to adequate maternity care during according to the international standard during the floods in 2011. We propose a model where CHWs will be used as frontline maternity care providers during any emergency or disaster situations in Pakistan. A separate "birthing station" should be mandatory in all district relief camps, managed by CHWs. Community midwives (CMW) would and the Lady Health Workers (LHW) would provide antenatal and postnatal care alongside, vaccination for pregnant women, neonates and children under five. There must be an ambulance facility for emergency obstetric cases and all district health facilities should have at least two medical staff identified and trained for emergency obstetric management. The District Health Department must provide clean birthing kits and regular and emergency contraceptives in the relief camps. Methods: A mixed methods study was conducted in rural villages of Sindh adjacent to the river Indus, and included in-depth interviews with 15 women who gave birth during the floods, structured interviews with 668 women who were pregnant during 2010-2014, and in-depth interviews with 25 community health workers (CHW) and 30 key informants. Results: Women said that giving birth in the relief camps during the floods was one of the most challenging times of their life. Nearly 91.2% women gave birth in temporary shelters with the help of a traditional birth attendant (Dai) with no clean physical space available to birth, and the health camp was mostly accessed by men and always overcrowded. There was no obstetric trained medical staff in the health camps or transportation provided to take women with complications to the nearest health facility. The rate of adverse outcome following disaster was 22.2% (95% CI: 8.62% – 42.2%) amongst 27 women who did not evacuate as compare to 7.91% (95% CI: 5.03% – 11.8%) among 278 women who lived in relief camp study participants. There were 27 women who evacuated on pre-flood warning and had 0% rate of adverse outcome. Conclusion: We propose a model where CHWs will be used as frontline maternity care providers during any emergency or disaster situations in Pakistan. A separate "birthing station" should be mandatory in all district relief camps, managed by CHWs. Community midwives (CMW) would and the Lady Health Workers (LHW) would provide antenatal and postnatal care alongside, vaccination for pregnant women, neonates and children under five. There must be an ambulance facility for emergency obstetric cases and all district health facilities should have at least two medical staff identified and trained for emergency obstetric management. The District Health Department must provide clean birthing kits and regular and emergency contraceptives in the relief camps.

Keywords: natural disaster, maternity care model, rural, Pakistan, community health workers

Procedia PDF Downloads 262