Search results for: healthcare networks

Authors: Moustafa Ahmed, Fumio Koyama

Abstract:

Directly-modulated semiconductor lasers, including edge-emitting and vertical-cavity surface-emitting lasers, have received considerable interest recently for use in data transmitters in cost-effective high-speed data centers, metro, and access networks. Optical feedback has been proved as an efficient technique to boost the modulation bandwidth and enhance the speed of the semiconductor laser. However, both the laser linewidth and frequency chirping in directly-modulated lasers are sensitive to both intensity modulation and optical feedback. These effects along width fiber dispersion affect the transmission bit rate and distance in single-mode fiber links. In this work, we continue our recent research on directly-modulated semiconductor lasers with modulation bandwidth in the millimeter-wave band by introducing simultaneous modeling and simulations on both the frequency chirping and lineshape broadening. The lasers are operating under strong optical feedback. The model takes into account the multiple reflections of laser reflections of laser radiation in the external cavity. The analyses are given in terms of the chirp-to-modulated power ratio, and the results are shown for the possible dynamic states of continuous wave, period-1 oscillation, and chaos.

Keywords: chirp, linewidth, optical feedback, semiconductor laser

Procedia PDF Downloads 476

Authors: Hashem Dadashpoor, Sadegh Saeidi Shirvan

Abstract:

While with the evolution of the economy towards a knowledge-based economy, innovation is a requirement for metropolitan regions, the adoption of an open innovation strategy is an option and a requirement for many industrial firms in these regions. Studies show that investing in research and development units cannot alone increase innovation. Within the framework of the theory of learning regions, this gap, which scholars call it the ‘innovation gap’, is filled with regional features of firms. This paper attempts to investigate the factors affecting the open innovation of firms in metropolitan regions, and it searches for these in territorial innovation models and, in particular, the theory of learning regions. In the next step, the effect of identified factors which is considered as regional learning factors in this research is analyzed on the innovation of sample firms by SPSS software using multiple linear regression. The case study of this research is constituted of industrial enterprises from two groups of food industry and auto parts in Toos industrial town in Mashhad metropolitan region. For data gathering of this research, interviews were conducted with managers of industrial firms using structured questionnaires. Based on this study, the effect of factors such as size of firms, inter-firm competition, the use of local labor force and institutional infrastructures were significant in the innovation of the firms studied, and 44% of the changes in the firms’ innovation occurred as a result of the change in these factors.

Keywords: regional knowledge networks, learning regions, interactive learning, innovation

Procedia PDF Downloads 174

Authors: Gurmail Singh

Abstract:

Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

Procedia PDF Downloads 124

Authors: Abdela Zeinu Yasin

Abstract:

The current research aimed to assess the status of IMC in providing treatment for malnourished children and programs in water, sanitation, and hygiene (WASH), food and livelihood security, and comprehensive healthcare through Mobile health and nutrition programs during the last 5 years period. We have conducted 60 in-depth interviews with women during the period from conception to a child’s birthday, health facility staff, and female community health volunteers (FCHVs), as well as 12 focus group discussions with health facility staff and other household decision-makers. We employed thematic analysis using framework matrices and analytical memorandums. The study revealed that 78% of the respondents, of whom 97% were women, have benefited from the selected vulnerable areas. The use of the clear water and sanitization program has reached the 81% of selected households. The use of a modern baby delivery system among the respondent has been 68% of the women and health facilities among the decision-makers/focal person. More than 8 in 10 participants (84%) could read and understand the health facility instructions, and the majority (82%) of women, health facility staff, and male decision-makers can also read and write bulletins and instructions. We found that decision-maker women preferred participative education, whereas health facilities and the IMC desired educational and motivational bulletins. A Mobile Health and Nutrition program intervention by the IMC is acceptable in the conditions of the Ethiopian community and has the potential to improve community health and nutrition service utilization, particularly by providing clean water and sanitization; women’s birth control, and health improvement in the vulnerable regions of the country. The current research findings shall contribute to text IMC Mobile Health and Nutritional intervention design in under-resourced settings.

Keywords: clean water, health and nutrition services, hygiene, IMC, mobile health, sanitation

Procedia PDF Downloads 90

Authors: Kalimah Ibrahiim, Israa Elmousa

Abstract:

This study employed the world café method alongside semi-structured interviews within a 'conversation café' setting to engage stakeholders from the public health and primary care sectors. The objective was to collaboratively explore strategies to improve outcomes for young people not in education, employment, or training (NEET). The discussions were aimed at identifying the underlying causes of disparities faced by NEET individuals, exchanging experiences, and formulating community-driven solutions to bolster preventive efforts and shape policy initiatives. A thematic analysis of the qualitative data gathered emphasized the importance of community problem-solving through the exchange of ideas and reflective discussions. Healthcare professionals reflected on their potential roles, pinpointing a significant gap in understanding the specific needs of the NEET population and the unclear distribution of responsibilities among stakeholders. The results underscore the necessity for a unified approach in primary care and the fostering of multi-agency collaborations that focus on addressing social determinants of health. Such strategies are critical not only for the immediate improvement of health outcomes for NEET individuals but also for informing broader policy decisions that can have long-term benefits. Further research is ongoing, delving deeper into the unique challenges faced by this demographic and striving to develop more effective interventions. The study advocates for continued efforts to integrate insights from various sectors to create a more holistic and effective response to the needs of the NEET population, ensuring that future strategies are informed by a comprehensive understanding of their circumstances and challenges.

Keywords: multi-agency working, primary care, public health, social inequalities

Procedia PDF Downloads 30

Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

Abstract:

Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

Procedia PDF Downloads 186

Authors: Robert Joseph M. Licup

Abstract:

The deployment of the fifth-generation (5G) mobile system through mmWave frequencies is the new solution in the requirement to provide higher bandwidth readily available for all users. The usage pattern of the mobile users has moved towards either the work from home or online classes set-up because of the pandemic. Previous mobile technologies can no longer meet the high speed, and bandwidth requirement needed, given the drastic shift of transactions to the home. The millimeter-wave (mmWave) underutilized frequency is utilized by the fifth-generation (5G) cellular networks that support multi-gigabit-per-second (Gbps) transmission. However, due to its short wavelengths, high path loss, directivity, blockage sensitivity, and narrow beamwidth are some of the technical challenges that need to be addressed. Different tools, technologies, and scenarios are explored to support network design, accurate channel modeling, implementation, and deployment effectively. However, there is a big challenge on how the consumer will adopt this solution and maximize the benefits offered by the 5G Technology. This research proposes to study the intricacies of technology diffusion, individual attitude, behaviors, and how technology adoption will be attained. The agent based simulation model shaped by the actual applications, technology solution, and related literature was used to arrive at a computational model. The research examines the different attributes, factors, and intricacies that can affect each identified agent towards technology adoption.

Keywords: agent-based model, AnyLogic, 5G O21, 5G mmWave solutions, technology adoption

Procedia PDF Downloads 104

Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

Abstract:

Crowd-sourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowd-sourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is -to our best knowledge- the first to develop safety performance functions using crowd-sourced data by adopting a negative binomial structure model and the Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

Procedia PDF Downloads 41

Authors: Giuffrida Graziella, Pennisi Stefania, Coppa Federica, Iannello Giulia, Cartelli Simone, Lo Faro Riccardo, Ferruggia Greta, Brundo Maria Violetta

Abstract:

Cladode chemical composition may vary according to soil factors, cultivation season, and plant age. The primary metabolites of cladodes are water, carbohydrates, and proteins. The carbohydrates in cladodes are divided into two types: structural and storage. Polysaccharides from Opuntia ficus‐indica (L.) Mill plants build molecular networks with the capacity to retain water; thus, they act as mucoprotective agents. Mucilage is the main polysaccharide of cladodes; it contains polymers of β‐d‐galacturonic acid bound in positions (1–4) and traces of R‐linked l‐rhamnose (1-2). Mucilage regulates both the cell water content during prolonged drought and the calcium flux in the plant cells. The in vitro analysis of keratinocytes in monolayer, through the scratch-wound-healing assay, provided promising results. After 48 hours of exposure, the wound scratch was almost completely closed in cells treated with cladode extract. After 72 hours, the treated cells reached complete confluence, while in the untreated cells (negative control) the confluence was reached after 96 hours. We also added a positive control group of cells treated with colchicine, which inhibited wound closure for a more comprehensive analysis.

Keywords: cladodes, metabolites, polysaccharide, scratch-wound-healing assay

Procedia PDF Downloads 44

Authors: Gregory Sullivan

Abstract:

The evolution of global warming has escalated the number and complexity of forest fires around the world. As an example, the United States and Brazil combined generated more than 30,000 forest fires last year. The impact to our environment, structures and individuals is incalculable. The world has learned to try to take this in stride, trying multiple ways to contain fires. Some countries are trying to use cameras in limited areas. There are discussions of using hundreds of low earth orbit satellites and linking them together, and, interfacing them through ground networks. These are all truly noble attempts to defeat the forest fire phenomenon. But there is a better, simpler answer. A bigger piece of the solutions puzzle is to see the fires while they are small, soon after initiation. The approach is to see the fires while they are very small and report their location (latitude and longitude) to local first responders. This is done by placing a sensor at geostationary orbit (GEO: 26,000 miles above the earth). By placing this small satellite in GEO, we can “stare” at the earth, and sense temperature changes. We do not “see” fires, but “measure” temperature changes. This has already been demonstrated on an experimental scale. Fires were seen at close to initiation, and info forwarded to first responders. it were the first to identify the fires 7 out of 8 times. The goal is to have a small independent satellite at GEO orbit focused only on forest fire initiation. Thus, with one small satellite, focused only on forest fire initiation, we hope to greatly decrease the impact to persons, property and the environment.

Keywords: space detection, wildfire early warning, demonstration wildfire detection and action from space, space detection to first responders

Procedia PDF Downloads 65

Authors: Colin Smith, Linsey S Passarella

Abstract:

Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

Procedia PDF Downloads 128

Authors: Muhammed Ordu, Eren Demir, Chris Tofallis

Abstract:

The number of inpatient admissions in the UK has been significantly increasing over the past decade. These increases cause bed occupancy rates to exceed the target level (85%) set by the Department of Health in England. Therefore, hospital service managers are struggling to better manage key resource such as beds. On the other hand, this severe demand pressure might lead to confusion in wards. For example, patients can be admitted to the ward of another inpatient specialty due to lack of resources (i.e., bed). This study aims to develop a simulation-optimization model to reallocate the available number of beds in a mid-sized hospital in the UK. A hospital simulation model was developed to capture the stochastic behaviours of the hospital by taking into account the accident and emergency department, all outpatient and inpatient services, and the interactions between each other. A couple of outputs of the simulation model (e.g., average length of stay and revenue) were generated as inputs to be used in the optimization model. An integer linear programming was developed under a number of constraints (financial, demand, target level of bed occupancy rate and staffing level) with the aims of maximizing number of admitted patients. In addition, a sensitivity analysis was carried out by taking into account unexpected increases on inpatient demand over the next 12 months. As a result, the major findings of the approach proposed in this study optimally reallocate the available number of beds for each inpatient speciality and reveal that 74 beds are idle. In addition, the findings of the study indicate that the hospital wards will be able to cope with 14% demand increase at most in the projected year. In conclusion, this paper sheds a new light on how best to reallocate beds in order to cope with current and future demand for healthcare services.

Keywords: bed occupancy rate, bed reallocation, discrete event simulation, inpatient admissions, integer linear programming, projected usage

Procedia PDF Downloads 139

Authors: Lorenza Abbracciavento, Valerio De Biagi

Abstract:

Infrastructures are key elements to ensure the operational functionality of the transport system. The collapse of a single bridge or, equivalently, a tunnel can leads an entire motorway to be considered completely inaccessible. As a consequence, the paralysis of the communications network determines several important drawbacks for the community. Recent chronicle events have demonstrated that ensuring the functional continuity of the strategic infrastructures during and after a catastrophic event makes a significant difference in terms of life and economical losses. Moreover, it has been observed that RC structures located in mountain environments show a worst state of conservation compared to the same typology and aging structures located in temperate climates. Because of its morphology, in fact, the mountain environment is particularly exposed to severe collapse and deterioration phenomena, generally: natural hazards, e.g. rock falls, and meteorological hazards, e.g. freeze-thaw cycles or heavy snows. For these reasons, deep investigation on the characteristics of these processes becomes of fundamental importance to provide smart and sustainable solutions and make the infrastructure system more resilient. In this paper, the design of a monitoring system in mountainous environments is presented and analyzed in its parts. The method not only takes into account the peculiar climatic conditions, but it is integrated and interacts with the environment surrounding.

Keywords: structural health monitoring, resilience of bridges, mountain infrastructures, infrastructural network, maintenance

Procedia PDF Downloads 67

Authors: Nithra Kitreerawutiwong, Sunsanee Mekrungrongwong, Artitaya Wongwonsin, Chakkraphan Phetphoom, Buaploy Phromjang

Abstract:

Background: Thailand has implemented a district health system based on the concept of primary health care. Since 2014, Family Care Team (FCT) was launched to improve the quality of care through a multidisciplinary team include not only the health sector but also social sector work together. FCT classified into 3 levels: district, sub-district, and community. This system now consists of 66,353 teams, including 3,890 teams at district level, 12,237 teams at the sub-district level, and 50,326 teams at the community level. There is a report regarding assessment the situation and perception on FCT, however, relatively few examined the operationality of this policy. This study aimed to explore the perception of district manager on the process of the implementation of FCT policy and the factors associating to implement FCT in the district health system. Methods/Results: Forty in-depth interviews were performed: 5 of primary care manager at the provincial medical health office, 5 of community hospital director, 5 of district administrative health office, 10 of sub-district health promoting hospital, and 10 of local organization. Semi-structure interview guidelines were used in the discussions. The data was analyzed by thematic analysis. This policy was formulated based on the demographic change and epidemiology transition to serve a long term care for elderly. Facilitator factors are social capital in district health systems such as family health leader and multidisciplinary team. Barrier factors are communication to the frontline provider and local organization. The output of this policy in relation to the structure of FCT is well-defined. Unanticipated effects include training of FCT in community level. Conclusion: Early feedback from healthcare manager is valuable information for the improvement of FCT to function optimally. Moreover, in the long term, health outcome need to be evaluated.

Keywords: family care team, district health system, primary care, qualitative study

Procedia PDF Downloads 403

Authors: David Guillermo Piedrahita Marquez, Hector Suarez Mahecha, Jairo Humberto Lopez

Abstract:

It was necessary to establish which formulation is suitable for the preservation of aquaculture products, that why edible films were made. These were to a characterization in order to meet their morphology physicochemical and mechanical properties, optical. Six Formulations of chitosan and propolis ethanolic extract encapsulated were developed because of their activity against pathogens and due to their properties, which allows the creation waterproof polymer networks against gasses, vapor, and physical damage. In the six Formulations, the concentration of comparison material (1% w/v, 2% pv) and the bioactive concentrations (0.5% w/v, 1% w/v, 1.5% pv) were changed and the results obtained were compared with statistical and multivariate analysis methods. It was observed that the matrices showed a mayor impermeability and thickness control samples and the samples reported in the literature. Also, these films showed a notorious uniformity of the films and a bigger resistance to the physical damage compared with other edible films made of other biopolymers. However the action of some compounds had a negative effect on the mechanical properties and changed drastically the optical properties, the bioactive has an effect on Polymer Matrix and it was determined that the films with 2% w / v of chitosan and 1.5% w/v encapsulated, exhibited the best properties and suffered to a lesser extent the negative impact of immiscible substances.

Keywords: chitosan, edible films, ethanolic extract of propolis, mechanical properties, optical properties, physical characterization, scanning electron microscopy (SEM)

Procedia PDF Downloads 441

Authors: Kunal K. S., Shwetashri K. R., Keerthan G., Ajinkya R.

Abstract:

Background: Normal pressure hydrocephalus is a condition caused by abnormal accumulation of cerebrospinal fluid (CSF) within the brain resulting in enlarged cerebral ventricles due to a disruption of CSF formation, absorption, or flow. Over the course of time, ventriculoperitoneal shunt under general anesthesia has become a standard of care. Yet only a finite number of centers have started the inclusion of regional anesthesia techniques for the such patient cohort. Stem Case: We report a case of a 75-year-old male with underlying aortic sclerosis and cardiomyopathy who presented with complaints of confusion, forgetfulness, and difficulty in walking. Neuro-imaging studies revealed disproportionally enlarged subarachnoid space hydrocephalus (DESH). The baseline blood pressure was 116/67 mmHg with a heart rate of 106 beats/min and SpO2 of 96% on room air. The patient underwent smooth induction followed by sonographically guided superficial cervical plexus block and transverse abdominis plane block. Intraoperative pain indices were monitored with Analgesia nociceptive index monitor (ANI, MdolorisTM) and surgical plethysmographic index (SPI, GE Healthcare, Helsinki, FinlandTM). These remained stable during the application of the block and the entire surgical duration. No significant hemodynamic response was observed during the tunneling of the skin by the surgeon. The patient underwent a smooth recovery and emergence. Conclusion: Our decision to incorporate peripheral nerve blockade in conjunction with general anesthesia resulted in opioid-sparing anesthesia and decreased post-operative analgesic requirement by the patient. This blockade was successful in suppressing intraoperative stress responses. Our patient recovered adequately and underwent an uncomplicated post-operative stay.

Keywords: desh, NPH, VP shunt, cervical plexus block, transversus abdominis plane block

Procedia PDF Downloads 76

Authors: Geeta, Richa, M. L. Sharma

Abstract:

Himachal Pradesh, a hilly State of India located in the Western Himalayas, with varied altitudinal gradients and climatic conditions, is a repository of plant diversity and the traditional knowledge associated with plants. The State is inhabited by various tribal communities who usually depend upon local plants for curing various ailments. Utilization of plant resources in their day-to-day life has been an age old practice of the people inhabiting this State. The present study pertains to the tribal district Kinnaur of Himachal Pradesh, located between 77°45’ and 79°00’35” east longitudes and between 31°05’50” and 32°05’15” north altitudes. Being a remote area with only very basic medical facilities, local people mostly use traditional herbal medicines for primary healthcare needs. Traditional healers called “Amji” are usually very secretive in revealing their medicinal knowledge to novice and pass on their knowledge to next generation orally. As a result, no written records of healing herbs are available. The aim of present study was to collect and consolidate the ethno-medicinal knowledge of local people of the district about the use of plants for treating gastrointestinal ailments. The ethnobotanical information was collected from the local practitioners, herbal healers and elderly people having rich knowledge about the medicinal herbs through semi-structured questionnaire and key informant discussions. A total 46 plant species belonging to 40 genera and 24 families have been identified which are used as cure for gastrointestinal ailments. Among the parts used for gastointestinal ailments, aerial parts (14%) were followed by the whole plant (13%), root (8%), leaves (6%), flower (5%), fruit and seed (3%) and tuber (1%). These plant species could be prioritized for conservation and subject to further studies related to phytochemical screening for their authenticity. Most of the medicinal plants of the region are collected from the wild and are often harvested for trade. Sustainable harvesting and domestication of the highly traded species from the study area is needed.

Keywords: Amji, gastrointestinal, Kinnaur, medicinal plants, traditional knowledge

Procedia PDF Downloads 391

Authors: Hassen Hamouda, Mohamed Ouwais Kabaou, Med Salim Bouhlel

Abstract:

The growth of wireless technology made opportunistic scheduling a widespread theme in recent research. Providing high system throughput without reducing fairness allocation is becoming a very challenging task. A suitable policy for resource allocation among users is of crucial importance. This study focuses on scheduling multiple streaming flows on the downlink of a WiMAX system based on orthogonal frequency division multiple access (OFDMA). In this paper, we take the first step in formulating and analyzing this problem scrupulously. As a result, we proposed a new scheduling scheme based on Round Robin (RR) Algorithm. Because of its non-opportunistic process, RR does not take in account radio conditions and consequently it affect both system throughput and multi-users diversity. Our contribution called MORRA (Modified Round Robin Opportunistic Algorithm) consists to propose a solution to this issue. MORRA not only exploits the concept of opportunistic scheduler but also takes into account other parameters in the allocation process. The first parameter is called courtesy coefficient (CC) and the second is called Buffer Occupancy (BO). Performance evaluation shows that this well-balanced scheme outperforms both RR and MaxSNR schedulers and demonstrate that choosing between system throughput and fairness is not required.

Keywords: OFDMA, opportunistic scheduling, fairness hierarchy, courtesy coefficient, buffer occupancy

Procedia PDF Downloads 296

Authors: Shogo Nagano, Yusuke Tanigaki, Hirokazu Fukuda

Abstract:

Vegetables harvested early in the morning or late in the afternoon are valued in plant production, and so the time of harvest is important. The biological functions known as circadian clocks have a significant effect on this harvest timing. The purpose of this study was to non-destructively estimate the circadian clock and so construct a method for determining a suitable harvest time. We took eight samples of green busil (Perilla frutescens var. crispa) every 4 hours, six times for 1 day and analyzed all samples at the same time. A hyperspectral camera was used to collect spectrum intensities at 141 different wavelengths (350–1050 nm). Calculation of correlations between spectrum intensity of each wavelength and harvest time suggested the suitability of the hyperspectral camera for non-destructive estimation. However, even the highest correlated wavelength had a weak correlation, so we used machine learning to raise the accuracy of estimation and constructed a machine learning model to estimate the internal time of the circadian clock. Artificial neural networks (ANN) were used for machine learning because this is an effective analysis method for large amounts of data. Using the estimation model resulted in an error between estimated and real times of 3 min. The estimations were made in less than 2 hours. Thus, we successfully demonstrated this method of non-destructively estimating internal time.

Keywords: artificial neural network (ANN), circadian clock, green busil, hyperspectral camera, non-destructive evaluation

Procedia PDF Downloads 296

Authors: Debalke Dale, Bezabh Geneta, Yohannes Amene, Yordanos Bergene, Mohammed Yimam

Abstract:

Background: A drug therapy problem (DTP) is an event or circumstance that involves drug therapies that actually or potentially interfere with the desired outcome and requires professional judgment to resolve. Heart failure is an emerging worldwide threat whose prevalence and health loss burden constantly increase, especially in the young and in low-to-middle-income countries. There is a lack of population-based incidence and prevalence of heart failure (HF) studies in sub-Saharan African countries, including Ethiopia. Objective: The aim of this study was designed to assess drug therapy problems and associated factors among patients with HF in the medical ward of Arba Minch General Hospital(AGH), Ethiopia, from June 5 to August 20, 2022. Methods: A retrospective cross-sectional study was conducted among 180 patients with HF who were admitted to the medical ward of AGH. Data were collected from patients' cards by using questionnaires. The data were categorized and analyzed by using SPSS version 25.0 software, and data were presented in tables and words based on the nature of the data. Result: Out of the total, 85 (57.6%) were females, and 113 (75.3%) patients were aged over fifty years. Of the 150 study participants, 86 (57.3%) patients had at least one DTP identified, and a total of 116 DTPs were identified, which is 0.77 DTPs per patient. The most common types of DTP were unnecessary drug therapy (32%), followed by the need for additional drug therapy (36%), and dose too low (15%). Patients who used polypharmacy were 5.86 (AOR) times more likely to develop DTPs than those who did not (95% CI = 1.625–16.536, P = 0.005), and patients with more co-morbid conditions developed 3.68 (AOR) times more DTPs than those who had fewer co-morbidities (95% CI = 1.28–10.5, P = 0.015). Conclusion: The results of this study indicated that drug therapy problems were common among medical ward patients with heart failure. These problems are adversely affecting the treatment outcomes of patients, so it requires the special attention of healthcare professionals to optimize them.

Keywords: heart failure, drug therapy problems, Arba Minch general hospital, Ethiopia

Procedia PDF Downloads 100

Authors: Sisay Tiroro Salato

Abstract:

Background: Ethiopia has been implementing the maternal death surveillance and response system to provide real-time actionable information, including causes of death and contributing factors. Analysis of maternal mortality surveillance data was conducted to identify the causes and underlying factors in Addis Ababa, Ethiopia. Methods: We carried out a retrospective surveillance data analysis of 324 maternal deaths reported in Addis Ababa, Ethiopia, from 2017 to 2021. The data were extracted from the national maternal death surveillance and response database, including information from case investigation, verbal autopsy, and facility extraction forms. The data were analyzed by computing frequency and presented in numbers, proportions, and ratios. Results: Of 324 maternal deaths, 92% died in the health facilities, 6.2% in transit, and 1.5% at home. The mean age at death was 28 years, ranging from 17 to 45. The maternal mortality ratio per 100,000 live births was 77for the five years, ranging from 126 in 2017 to 21 in 2021. The direct and indirect causes of death were responsible for 87% and 13%, respectively. The direct causes included obstetric haemorrhage, hypertensive disorders in pregnancy, puerperal sepsis, embolism, obstructed labour, and abortion. The third delay (delay in receiving care after reaching health facilities) accounted for 57% of deaths, while the first delay (delay in deciding to seek health care) and the second delay (delay in reaching health facilities) and accounted for 34% and 24%, respectively. Late arrival to the referral facility, delayed management after admission, andnon-recognition of danger signs were underlying factors. Conclusion: Over 86% of maternal deaths were attributed by avoidable direct causes. The majority of women do try to reach health services when an emergency occurs, but the third delays present a major problem. Improving the quality of care at the healthcare facility level will help to reduce maternal death.

Keywords: maternal death, surveillance, delays, factors

Procedia PDF Downloads 103

Authors: Magdalena Barbara Kaziuk, Waldemar Kosiba

Abstract:

Introduction: Despite the development of technologies and improvements in the interior of dialysis stations, dialysis remains an unpleasant procedure, difficult to accept by the patients (who undergo it 2 to 3 times a week, a single treatment lasting several hours). Haemodialysis is one of the renal replacement therapies, in Poland most commonly used in patients with chronic or acute kidney failure. Purpose: An attempt was made to evaluate the quality of life in haemodialysed patients using the WHOQOL-BREF questionnaire. Material and methods: The study covered 422 patients (200 women and 222 men, aged 60.5 ± 12.9 years) undergoing dialysis at three selected stations in Poland. The patients were divided into 2 groups, depending on the duration of their dialysis treatment. The evaluation was conducted with the WHOQOL-BREF questionnaire containing 26 questions analysing 4 areas of life, as well as the perception of the quality of life and health self-assessment. A 5-point scale is used to answer them. The maximum score in each area is 20 points. The results in individual areas have a positive direction. Results: In patients undergoing dialysis for more than 3 years, a reduction in the quality of life was found in the physical area and in their environment versus a group of patients undergoing dialysis for less than 3 years, where a reduced quality of life was found in the areas of social relations and mental well-being (p < 0.05). A significant correlation (p < 0.01) between the two groups was found in self-perceived general health, while no significant differences were observed in the general perception of the quality of life (p > 0.05). Conclusions: The study confirmed that in patients undergoing dialysis for more than three years, the quality of life is especially reduced in their environment (access to and quality of healthcare, financial resources, and mental and physical safety). The assessment of the quality of life should form a part of the therapeutic process, in which the role of the patient in chronic renal care should be emphasised, reflected in the quality of services provided by dialysis stations.

Keywords: haemodialysis, perception of quality of life, quality of services provided, dialysis station

Procedia PDF Downloads 258

Authors: Biniam Bahiru Tufa, Hana Delil Tesfaye, Seife Demisse Legesse, Manaye Tamire

Abstract:

The healthcare industry is witnessing a digital transformation, with artificial intelligence (AI) offering potential solutions for challenges in health supply chain management (HSCM). However, the adoption of AI in this field remains limited. This research aimed to assess the knowledge, attitude, and practice of AI among students and employees in the health supply chain sector in Ethiopia. Using an explanatory case study research design with a concurrent mixed approach, quantitative and qualitative data were collected simultaneously. The study included 153 participants comprising students and employed health supply chain professionals working in various sectors. The majority had a pharmacy background, and one-third of the participants were male. Most respondents were under 35 years old, and around 68.6% had less than 10 years of experience. The findings revealed that 94.1% of participants had prior knowledge of AI, but only 35.3% were aware of its application in the supply chain. Moreover, the majority indicated that their training curriculum did not cover AI in health supply chain management. Participants generally held positive attitudes toward the necessity of AI for improving efficiency, effectiveness, and cost savings in the supply chain. However, many expressed concerns about its impact on job security and satisfaction, considering it as a burden Graduate students demonstrated higher knowledge of AI compared to employed staff, while graduate students also exhibited a more positive attitude toward AI. The study indicated low previous utilization and potential future utilization of AI in the health supply chain, suggesting untapped opportunities for improvement. Overall, while supply chain experts and graduate students lacked sufficient understanding of AI and its significance, they expressed favorable views regarding its implementation in the sector. The study recommends that the Ethiopian government and international organizations consider introducing AI in the undergraduate pharmacy curriculum and promote its integration into the health supply chain field.

Keywords: knowledge, attitude, practice, supply chain, articifial intellegence

Procedia PDF Downloads 86

Authors: Feng Tao, Han Ye, Shaoyi Liao

Abstract:

City constructions collapse after earthquake and people will be buried under ruins. Search and rescue should be conducted as soon as possible to save them. Therefore, according to the complicated environment, irregular aftershocks and rescue allow of no delay, a kind of target localization method based on RSSI (Received Signal Strength Indication) is proposed in this article. The target localization technology based on RSSI with the features of low cost and low complexity has been widely applied to nodes localization in WSN (Wireless Sensor Networks). Based on the theory of RSSI transmission and the environment impact to RSSI, this article conducts the experiments in five scenes, and multiple filtering algorithms are applied to original RSSI value in order to establish the signal propagation model with minimum test error respectively. Target location can be calculated from the distance, which can be estimated from signal propagation model, through improved centroid algorithm. Result shows that the localization technology based on RSSI is suitable for large-scale nodes localization. Among filtering algorithms, mixed filtering algorithm (average of average, median and Gaussian filtering) performs better than any other single filtering algorithm, and by using the signal propagation model, the minimum error of distance between known nodes and target node in the five scene is about 3.06m.

Keywords: signal propagation model, centroid algorithm, localization, mixed filtering, RSSI

Procedia PDF Downloads 296

Authors: Wardatutthoyyibah, Satyawan Pudyatmoko, Sena Adi Subrata, Muhammad Ali Imron

Abstract:

The proboscis monkey is an endemic species to the island of Borneo with conservation status IUCN (The International Union for Conservation of Nature) of endangered. The population of the monkey has a specific habitat and sensitive to habitat disturbances. As a consequence of increasing rates of land-use change in the last four decades, its population was reported significantly decreased. We quantified the effect of land use change on the proboscis monkey’s habitat through the species distribution modeling (SDM) approach with Maxent Software. We collected presence data and environmental variables, i.e., land cover, topography, bioclimate, distance to the river, distance to the road, and distance to the anthropogenic disturbance to generate predictive distribution maps of the monkeys. We compared two prediction maps for 2000 and 2015 data to represent the current habitat of the monkey. We overlaid the monkey’s predictive distribution map with the existing protected areas to investigate whether the habitat of the monkey is protected under the protected areas networks. The results showed that almost 50% of the monkey’s habitat reduced as the effect of land use change. And only 9% of the current proboscis monkey’s habitat within protected areas. These results are important for the master plan of conservation of the endangered proboscis monkey and provide scientific guidance for the future development incorporating biodiversity issue.

Keywords: endemic species, land use change, maximum entropy, spatial distribution

Procedia PDF Downloads 150

Authors: Bruno Di Pace, Roxanne H. Padley

Abstract:

Aims: The recent increase in remote video interaction has increased the number of requests for teleconsultations with plastic surgeons in private practice (70% in the UK and 64% in the USA). This study investigated the motivations for such an increase and the underlying psychological impact on patients. Method: An anonymous web-based poll of 8 questions was designed and distributed to patients seeking cosmetic surgery through social networks in both Italy and the UK. The questions gathered responses regarding 1. Reasons for pursuing cosmetic surgery; 2. The effects of delays caused by the SARS-COV-2 pandemic; 3. The effects on mood; 4. The influence of video conferencing on body-image perception. Results: 85 respondents completed the online poll. Overall, 68% of respondents stated that seeing themselves more frequently online had influenced their decision to seek cosmetic surgery. The types of surgeries indicated were predominantly to the upper body and face (82%). Delays and access to surgeons during the pandemic were perceived as negatively impacting patients' moods (95%). Body-image perception and self-esteem were lower than in the pre-pandemic, particularly during lockdown (72%). Patients were more inclined to undergo cosmetic surgery during the pandemic, both due to the wish to improve their “lockdown face” for video conferencing (77%) and also due to the benefits of home recovery while in smart working (58%). Conclusions: Overall, findings suggest that video conferencing has led to a significant increase in requests for cosmetic surgery and the so-called “Zoom Boom” effect.

Keywords: cosmetic surgery, remote communication, telehealth, zoom boom

Procedia PDF Downloads 177

Authors: Amirhossein Chambari

Abstract:

This article proposes a bi-objective model for the facility location problem subject to congestion (overcrowding). Motivated by implementations to locate servers in internet mirror sites, communication networks, one-server-systems, so on. This model consider for situations in which immobile (or fixed) service facilities are congested (or queued) by stochastic demand to behave as M/M/1/K queues. We consider for this problem two simultaneous perspectives; (1) Customers (desire to limit times of accessing and waiting for service) and (2) Service provider (desire to limit average facility idle-time). A bi-objective model is setup for facility location problem with two objective functions; (1) Minimizing sum of expected total traveling and waiting time (customers) and (2) Minimizing the average facility idle-time percentage (service provider). The proposed model belongs to the class of mixed-integer nonlinear programming models and the class of NP-hard problems. In addition, to solve the model, controlled elitist non-dominated sorting genetic algorithms (Controlled NSGA-II) and controlled elitist non-dominated ranking genetic algorithms (NRGA-I) are proposed. Furthermore, the two proposed metaheuristics algorithms are evaluated by establishing standard multiobjective metrics. Finally, the results are analyzed and some conclusions are given.

Keywords: bi-objective, facility location, queueing, controlled NSGA-II, NRGA-I

Procedia PDF Downloads 579

Authors: Grzegorz Kubinski

Abstract:

The proposed paper is focused on forms of identity expression in people with disabilities (PWD) in the social networks like Instagram. Theoretical analysis widely proposes using the new media as an assistive tool for improving wellbeing and labour activities of PWD. This kind of use is definitely important and plays a key role in all social inclusion processes. However, Instagram is not a place where PWD only express their own problems, but in the opposite, allows them to construct a new definition of disability. In the paper, the problem how this different than a classical approach to disability is created by PWD will be discussed. This issue will be scrutinized mainly in two points. Firstly, the question of how disability is changed by other everyday activities, like fashion or sport, will be described. Secondly, and this could be seen as more important, the point how PWD redefining their bodies creating a different form of aesthetic will be presented. The paper is based on content analysis of Instagram accounts. About 20 accounts created by PWD were analyzed for 6 month period, taking into account elements like photos, comments and discussions. All those information were studied in relation to 'everyday life' category and 'aesthetic' category. Works by T. Siebers, L. J. Davis or R. McRuer were used as theoretical background. Conclusions and interpretations presented in the proposed paper show that the Internet can be used by PWD not only as prosthetic and assistive tools. PWD willingly use them as modes of expression their independence, agency and identity. The paper proposes that in further research this way of using the Internet communication by PWD should be taken into account as an important part of the understanding of disability.

Keywords: body, disability, identity, new media

Procedia PDF Downloads 134

Authors: A. Degale Desta, Cheng Jian

Abstract:

Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

Procedia PDF Downloads 156

Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

Abstract:

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

Procedia PDF Downloads 186