Search results for: Address Malata

Authors: Asfaw Gezae Daful, Mohsen Alimandagari, Kathleen Haigh, Somayeh Farzad, Eugene Van Rensburg, Johann F. Görgens

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The sugar industry has to 're-invent' itself to ensure long-term economic survival and opportunities for job creation and enhanced community-level impacts, given increasing pressure from fluctuating and low global sugar prices, increasing energy prices and sustainability demands. We propose biorefineries for re-vitalisation of the sugar industry using low value lignocellulosic biomass (sugarcane bagasse, leaves, and tops) annexed to existing sugar mills, producing a spectrum of high value platform chemicals along with biofuel, bioenergy, and electricity. Opportunity is presented for greener products, to mitigate climate change and overcome economic challenges. Xylose from labile hemicellulose remains largely underutilized and the conversion to value-add products a major challenge. Insight is required on pretreatment and/or extraction to optimize production of cellulosic ethanol together with lactic acid, furfural or biopolymers from sugarcane bagasse, leaves, and tops. Experimental conditions for alkaline and pressurized hot water extraction dilute acid and steam explosion pretreatment of sugarcane bagasse and harvest residues were investigated to serve as a basis for developing various process scenarios under a sugarcane biorefinery scheme. Dilute acid and steam explosion pretreatment were optimized for maximum hemicellulose recovery, combined sugar yield and solids digestibility. An optimal range of conditions for alkaline and liquid hot water extraction of hemicellulosic biopolymers, as well as conditions for acceptable enzymatic digestibility of the solid residue, after such extraction was established. Using data from the above, a series of energy efficient biorefinery scenarios are under development and modeled using Aspen Plus® software, to simulate potential factories to better understand the biorefinery processes and estimate the CAPEX and OPEX, environmental impacts, and overall viability. Rigorous and detailed sustainability assessment methodology was formulated to address all pillars of sustainability. This work is ongoing and to date, models have been developed for some of the processes which can ultimately be combined into biorefinery scenarios. This will allow systematic comparison of a series of biorefinery scenarios to assess the potential to reduce negative impacts on and maximize the benefits of social, economic, and environmental factors on a lifecycle basis.

Keywords: biomass, biorefinery, green economy, sustainability

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Authors: Ezinne K. Okoro, Takahiko Katoh, Yoko Kawamura, Stanley C. Meribe

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HIV infection in children is largely as a result of vertical transmission (mother to child transmission [MTCT]). Thus, elimination of mother to child transmission of HIV/AIDS is critical in eliminating HIV infection in children. In Nigeria, drawbacks such as; limited pediatric screening, limited human capital, insufficient advocacy and poor understanding of ART guidelines, have impacted efforts at combating the disease, even as treatment services are free. Prevention of Mother to Child Transmission (PMTCT) program relies on health workers who not only counsel pregnant women on first contact but can competently provide HIV-positive pregnant women with accurate information about the PMTCT program such as feeding techniques and drug adherence. In developing regions like Nigeria where health care delivery faces a lot of drawbacks, it becomes paramount to address these issues of poor PMTCT coverage by conducting a baseline assessment of the knowledge, practices and perceptions related to HIV prevention amongst healthcare workers in Nigeria. A descriptive cross-sectional study was conducted amongst 250 health workers currently employed in health facilities in Abuja, Nigeria where PMTCT services were offered with the capacity to carry out early infant diagnosis testing (EID). Data was collected using a self-administered, pretested, structured questionnaire. This study showed that the knowledge of PMTCT of HIV was poor (30%) among healthcare workers who offer this service day-to-day to pregnant women. When PMTCT practices were analyzed in keeping with National PMTCT guidelines, over 61% of the respondents reported observing standard practices and the majority (58%) had good attitudes towards caring for patients with HIV/AIDS. Although 61% of the respondents reported being satisfied with the quality of service being rendered, 63% reported not being satisfied with their level of knowledge. Predictors of good knowledge were job designation and level of educational attainment. Health workers who were more satisfied with their working conditions and those who had worked for a longer time in the PMTCT service were more likely to observe standard PMTCT practices. With over 62% of the healthcare workers suggesting that more training would improve the quality of service being rendered, this is a strong pointer to stakeholders to consider a ‘healthcare worker-oriented approach’ when planning and conducting PMTCT training for healthcare workers. This in turn will increase pediatric ARV coverage, the knowledge and effectiveness of the healthcare workers in carrying out appropriate PMTCT interventions and culminating in the reduction/elimination of HIV transmission to newborns.

Keywords: attitudes, HIV/AIDS, healthcare workers, knowledge, mother to child transmission, Nigeria, perceptions

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Authors: Shiva Shakori Poshteh

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Lung cancer is a highly prevalent and devastating disease, representing a significant global health concern with profound implications for healthcare systems and society. Its high incidence, mortality rates, and late-stage diagnosis contribute to its formidable nature. To address these challenges, nanoparticle-based drug delivery has emerged as a promising therapeutic strategy. Curcumin (CUR), a natural compound derived from turmeric, has garnered attention as a potential nanomedicine for lung cancer treatment. Nanoparticle formulations of CUR offer several advantages, including improved drug delivery efficiency, enhanced stability, controlled release kinetics, and targeted delivery to lung cancer cells. CUR exhibits a diverse array of effects on cancer cells. It induces apoptosis by upregulating pro-apoptotic proteins, such as Bax and Bak, and downregulating anti-apoptotic proteins, such as Bcl-2. Additionally, CUR inhibits cell proliferation by modulating key signaling pathways involved in cancer progression. It suppresses the PI3K/Akt pathway, crucial for cell survival and growth, and attenuates the mTOR pathway, which regulates protein synthesis and cell proliferation. CUR also interferes with the MAPK pathway, which controls cell proliferation and survival, and modulates the Wnt/β-catenin pathway, which plays a role in cell proliferation and tumor development. Moreover, CUR exhibits potent antioxidant activity, reducing oxidative stress and protecting cells from DNA damage. Utilizing CUR as a standalone treatment is limited by poor bioavailability, lack of targeting, and degradation susceptibility. Nanoparticle-based delivery systems can overcome these challenges. They enhance CUR’s bioavailability, protect it from degradation, and improve absorption. Further, Nanoparticles enable targeted delivery to lung cancer cells through surface modifications or ligand-based targeting, ensuring sustained release of CUR to prolong therapeutic effects, reduce administration frequency, and facilitate penetration through the tumor microenvironment, thereby enhancing CUR’s access to cancer cells. Thus, nanoparticle-based CUR delivery systems promise to improve lung cancer treatment outcomes. This article provides an overview of lung cancer, explores CUR nanoparticles as a treatment approach, discusses the benefits and challenges of nanoparticle-based drug delivery, and highlights prospects for CUR nanoparticles in lung cancer treatment. Future research aims to optimize these delivery systems for improved efficacy and patient prognosis in lung cancer.

Keywords: lung cancer, curcumin, nanomedicine, nanoparticle-based drug delivery

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Authors: Mahmoud Reza Hosseini

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The universe is in a continuous expansion process, resulting in the reduction of its density and temperature. Also, by extrapolating back from its current state, the universe at its early times is studied known as the big bang theory. According to this theory, moments after creation, the universe was an extremely hot and dense environment. However, its rapid expansion due to nuclear fusion led to a reduction in its temperature and density. This is evidenced through the cosmic microwave background and the universe structure at a large scale. However, extrapolating back further from this early state reaches singularity which cannot be explained by modern physics and the big bang theory is no longer valid. In addition, one can expect a nonuniform energy distribution across the universe from a sudden expansion. However, highly accurate measurements reveal an equal temperature mapping across the universe which is contradictory to the big bang principles. To resolve this issue, it is believed that cosmic inflation occurred at the very early stages of the birth of the universe. According to the cosmic inflation theory, the elements which formed the universe underwent a phase of exponential growth due to the existence of a large cosmological constant. The inflation phase allows the uniform distribution of energy so that an equal maximum temperature could be achieved across the early universe. Also, the evidence of quantum fluctuations of this stage provides a means for studying the types of imperfections the universe would begin with. Although well-established theories such as cosmic inflation and the big bang together provide a comprehensive picture of the early universe and how it evolved into its current state, they are unable to address the singularity paradox at the time of universe creation. Therefore, a practical model capable of describing how the universe was initiated is needed. This research series aims at addressing the singularity issue by introducing a state of energy called a “neutral state” possessing an energy level which is referred to as the “base energy”. The governing principles of base energy are discussed in detail in our second paper in the series “A Conceptual Study for Addressing the Singularity of the Emerging Universe” which is discussed in detail. To establish a complete picture, the origin of the base energy should be identified and studied. In this research paper, the mechanism which led to the emergence of this natural state and its corresponding base energy is proposed. In addition, the effect of the base energy in the space-time fabric is discussed. Finally, the possible role of the base energy in quantization and energy exchange is investigated. Therefore, the proposed concept in this research series provides a road map for enhancing our understating of the universe's creation from nothing and its evolution and discusses the possibility of base energy as one of the main building blocks of this universe.

Keywords: big bang, cosmic inflation, birth of universe, energy creation, universe evolution

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Authors: Amiza Surmi, Azmi Shariff, Sow Mun Serene Lock

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In recent decades, there has been a significant emphasis on the pivotal role of Rotating Packed Beds (RPBs) in absorption processes, encompassing the removal of Volatile Organic Compounds (VOCs) from groundwater, deaeration, CO2 absorption, desulfurization, and similar critical applications. The primary focus is elevating mass transfer rates, enhancing separation efficiency, curbing power consumption, and mitigating pressure drops. Additionally, substantial efforts have been invested in exploring the adaptation of RPB technology for offshore deployment. This comprehensive study delves into the intricacies of nitrogen removal under low temperature and high-pressure conditions, employing the high gravity principle via innovative RPB distillation concept with a specific emphasis on optimizing mass transfer. Based on the author's knowledge and comprehensive research, no cryogenic experimental testing was conducted to remove nitrogen via RPB. The research identifies pivotal process control factors through meticulous experimental testing, with pressure, reflux ratio, and reboil ratio emerging as critical determinants in achieving the desired separation performance. The results are remarkable, with nitrogen removal reaching less than one mole% in the Liquefied Natural Gas (LNG) product and less than three moles% methane in the nitrogen-rich gas stream. The study further unveils the mass transfer coefficient, revealing a noteworthy trend of decreasing Number of Transfer Units (NTU) and Area of Transfer Units (ATU) as the rotational speed escalates. Notably, the condenser and reboiler impose varying demands based on the operating pressure, with lower pressures at 12 bar requiring a more substantial duty than the 15-bar operation of the RPB. In pursuit of optimal energy efficiency, a meticulous sensitivity analysis is conducted, pinpointing the ideal combination of pressure and rotating speed that minimizes overall energy consumption. These findings underscore the efficiency of the RPB distillation approach in effecting efficient separation, even when operating under the challenging conditions of low temperature and high pressure. This achievement is attributed to a rigorous process control framework that diligently manages the operational pressure and temperature profile of the RPB. Nonetheless, the study's conclusions point towards the need for further research to address potential scaling challenges and associated risks, paving the way for the industrial implementation of this transformative technology.

Keywords: mass transfer coefficient, nitrogen removal, liquefaction, rotating packed bed

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Authors: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: Rui Brito

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This contribution regards a literature review of inventory management that is a relevant topic for the firms, due to its important use of capital with implications in firm’s profitability within the complexity of a more competitive and globalized world. Firms look for small inventories in order to reduce holding costs, namely opportunity cost, warehousing and handling costs, deterioration and being out of style, but larger inventories are required by some reasons, such as customer service, ordering cost, transportation cost, supplier’s payment to reduce unit costs or to take advantage of price increase in the near future, and equipment setup cost. Thus, management shall address a trade-off between small inventories and larger inventories. This literature review concerns three types of inventory (spare parts, safety stock, and vendor) whose management usually is beyond the scope of logistics. The applied methodology consisted of an online search of databases regarding scientific documents in English, namely Elsevier, Springer, Emerald, Wiley, and Taylor & Francis, but excluding books except if edited, using search engines, such as Google Scholar and B-on. The search was based on three keywords/strings (themes) which had to be included just as in the article title, suggesting themes were very relevant to the researchers. The whole search period was between 2009 and 2018 with the aim of collecting between twenty and forty studies considered relevant within each of the key words/strings specified. Documents were sorted by relevance and to prevent the exclusion of the more recent articles, based on lower quantity of citations partially due to less time to be cited in new research articles, the search period was divided into two sub-periods (2009-2015 and 2016-2018). The number of surveyed articles by theme showed a variation from 40 to 200 and the number of citations of those articles showed a wider variation from 3 to 216. Selected articles from the three themes were analyzed and the first seven of the first sub-period and the first three of the second sub-period with more citations were read in full to make a synopsis of each article. Overall, the findings show that the majority of article types were models, namely mathematical, although with different sub-types for each theme. Almost all articles suggest further studies, with some mentioning it for their own author(s), which widen the diversity of the previous research. Identified research gaps concern the use of surveys to know which are the models more used by firms, the reasons for not using the models with more performance and accuracy, and which are the satisfaction levels with the outcomes of the inventories management and its effect on the improvement of the firm’s overall performance. The review ends with the limitations and contributions of the study.

Keywords: inventory management, safety stock, spare parts inventory, vendor managed inventory

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Yun-Shan Tsai, Si-Ping Lin, En-Chieh Lin, Xin-Hong Chen, Shin-Yun Ho, Shin-Hong Huang, Ching-ju Hsieh

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Background and significance: Uncorrected refractive errors and cataracts are the main visually debilitating ophthalmological abnormalities in adult participants with intellectual disability (ID). However, not all adult participants with ID may receive a regular and timely ophthalmological assessment. Consequently, some of the ocular diseases may not be diagnosed until late, thereby causing unnecessary ocular morbidity. In addition, recent clinical practice and researches have also suggested that eye-care services for this group are neglected. Purpose: To investigate the unmet need for eye care services, clinical characteristics of cataract, visual function, surgical outcome and prognostic predictors in adult participants with ID at Taipei City Hospital in Taiwan. Methods: This is a one-year prospective clinical study. We recruited about 120 eyes of 60 adult participants with ID who were received cataract surgery. Caregivers of all participants received a questionnaire on current eye care services. Clinical demographic data, such as age, gender, and associated systemic diseases or syndromes, were collected. All complete ophthalmologic examinations were performed 1 month preoperatively and 3 months postoperatively, including ocular biometry, visual function, refractive status, morphology of cataract, associated ocular features, anesthesia methods, surgical types, and complications. Morphology of cataract, visual and surgical outcome was analyzed. Results: A total of 60 participants with mean age 43.66 ± 13.94 years, including 59.02% male and 40.98% female, took part in comprehensive eye-care services. The prevalence of unmet need for eye care services was high (about 70%). About 50% of adult participants with ID have bilateral cataracts at the time of diagnosis. White cataracts were noted in about 30% of all adult participants with ID at the time of presentation. Associated ocular disorders were included myopic maculopathy (4.54%), corneal disorders (11.36%), nystagmus (20.45%), strabismus (38.64%) and glaucoma (2.27%). About 26.7% of adult participants with ID underwent extracapsular cataract extraction whereas a phacoemulsification was performed in 100% of eyes. Intraocular lens implantation was performed in all eyes. The most common postoperative complication was posterior capsular opacification (30%). The mean best-corrected visual acuity was significantly improved from preoperatively (mean log MAR 0.48 ± 0.22) to at 3 months postoperatively (mean log MAR 0.045 ± 0.22) (p < .05). Conclusions: Regular follow up will help address the need for eye-care services in participants with ID. A high incidence of bilateral cataracts, as well as white cataracts, was observed in adult participants with ID. Because of early diagnosis and early intervention of cataract, the visual and surgical outcomes of cataract are good, but the visual outcomes are suboptimal due to associated ocular comorbidities.

Keywords: adult participants with intellectual disability, cataract, cataract surgery

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Authors: Benjamin Blat Belmonte, Stephan Rinderknecht

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The transition to electric vehicles (EVs) stands at the forefront of innovative strategies designed to address environmental concerns and reduce fossil fuel dependency. As the number of EVs on the roads increases, so too does the potential for their integration into energy markets. This research dives deep into the transformative possibilities of using electric vehicle fleets, specifically electric bus fleets, not just as consumers but as active participants in the energy market. This paper investigates the feasibility and grid effects of electric vehicle fleet operators in the energy market. Our objective centers around a comprehensive exploration of the sector coupling domain, with an emphasis on the economic potential in both electricity and balancing markets. Methodologically, our approach combines data mining techniques with thorough pre-processing, pulling from a rich repository of electricity and balancing market data. Our findings are grounded in the actual operational realities of the bus fleet operator in Darmstadt, Germany. We employ a Mixed Integer Linear Programming (MILP) approach, with the bulk of the computations being processed on the High-Performance Computing (HPC) platform ‘Lichtenbergcluster’. Our findings underscore the compelling economic potential of EV fleets in the energy market. With electric buses becoming more prevalent, the considerable size of these fleets, paired with their substantial battery capacity, opens up new horizons for energy market participation. Notably, our research reveals that economic viability is not the sole advantage. Participating actively in the energy market also translates into pronounced positive effects on grid stabilization. Essentially, EV fleet operators can serve a dual purpose: facilitating transport while simultaneously playing an instrumental role in enhancing grid reliability and resilience. This research highlights the symbiotic relationship between the growth of EV fleets and the stabilization of the energy grid. Such systems could lead to both commercial and ecological advantages, reinforcing the value of electric bus fleets in the broader landscape of sustainable energy solutions. In conclusion, the electrification of transport offers more than just a means to reduce local greenhouse gas emissions. By positioning electric vehicle fleet operators as active participants in the energy market, there lies a powerful opportunity to drive forward the energy transition. This study serves as a testament to the synergistic potential of EV fleets in bolstering both economic viability and grid stabilization, signaling a promising trajectory for future sector coupling endeavors.

Keywords: electric vehicle fleet, sector coupling, optimization, electricity market, balancing market

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Authors: Mohammad Bagher Anvari, Arman Shojaei

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Incremental launching, a widely adopted bridge erection technique, offers numerous advantages for bridge designers. However, accurately simulating and modeling the dynamic behavior of the bridge during each step of the launching process proves to be tedious and time-consuming. The perpetual variation of internal forces within the deck during construction stages adds complexity, exacerbated further by considerations of other load cases, such as support settlements and temperature effects. As a result, there is an urgent need for a reliable, simple, economical, and fast algorithmic solution to model bridge construction stages effectively. This paper presents a novel Finite Element (FE) model that focuses on studying the static behavior of bridges during the launching process. Additionally, a simple method is introduced to normalize all quantities in the problem. The new FE model overcomes the limitations of previous models, enabling the simulation of all stages of launching, which conventional models fail to achieve due to underlying assumptions. By leveraging the results obtained from the new FE model, this study proposes solutions to improve the accuracy of conventional models, particularly for the initial stages of bridge construction that have been neglected in previous research. The research highlights the critical role played by the first span of the bridge during the initial stages, a factor often overlooked in existing studies. Furthermore, a new and simplified model termed the "semi-infinite beam" model, is developed to address this oversight. By utilizing this model alongside a simple optimization approach, optimal values for launching nose specifications are derived. The practical applications of this study extend to optimizing the nose-deck system of incrementally launched bridges, providing valuable insights for practical usage. In conclusion, this paper introduces a comprehensive Finite Element model for studying the static behavior of bridges during incremental launching. The proposed model addresses limitations found in previous approaches and offers practical solutions to enhance accuracy. The study emphasizes the importance of considering the initial stages and introduces the "semi-infinite beam" model. Through the developed model and optimization approach, optimal specifications for launching nose configurations are determined. This research holds significant practical implications and contributes to the optimization of incrementally launched bridges, benefiting both the construction industry and bridge designers.

Keywords: incremental launching, bridge construction, finite element model, optimization

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Authors: Manisha Singh, Padam Simkhada

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Background: Although mental health policy has been adapted in Nepal since 1997, the implementation of the policy framework is yet to happen. The fact that mental health services are largely concentrated in urban areas more specific to treatment only provides a clear picture of the scarcity of mental health services in the country. The shreds of evidence from around the world, along with WHO’s (World Health Organization) Mental Health Gap Action Program (mhGAP) suggest that effective mental health services can be provided from Primary Health Care (PHC) centers through community-based programs without having to place a specialized health worker. However, the country is still facing the same challenges to date with very few psychiatrists and psychologists, but they are largely based in cities. Objectives: The main objectives of this study are; (a) to understand the perception of health workers at PHC on maternal mental health, and (b) to assess the availability of the mental health services at PHC to address maternal mental health. Methods: This study used a qualitative approach where an in-depth interview was conducted with the health workers at the primary level. “Mayadevi” rural municipality in Rupendehi District that comprised of 13 small villages, was chosen as the study site. A total 8 health institutions which covered all 13 sites were included where either the health post in- charge or health worker working in maternal and child health care was interviewed for the study. All the health posts in the study area were included in the study. The interviews were conducted in Nepali; later, they were translated in English, transcribed, and triangulated. NViVO was used for the analysis. Results: The findings show that most of the health workers understood what maternal mental health was and deemed it as a public health issue. They could explain the symptoms and knew what medication to prescribe if need be. However, the majority of them failed to name the screening tools in place for maternal mental health. Moreover, they hadn’t even seen one. None of the health care centers had any provision for screening mental health status. However, one of the centers prescribed medication when the patients displayed symptoms of depression. But they believed there were a significant number of hidden cases in the community due to the stigma around mental health and being a woman with mental health problem makes the situation even difficult. Nonetheless, the health workers understood the importance of having screening tools and acknowledged the need of training and support in order to provide the services from PHC. Conclusion: Community health workers can identify cases with mental health problems and prevent them from deteriorating further. But there is a need for robust training and support to build the capacity of the health workers. The screening tools on mental health needs to be encouraged to be used in the PHC levels. Furthermore, community-based culture-sensitive programs need to be initiated and implemented to mitigate the stigma related issues around mental health.

Keywords: maternal mental health, health care providers, screening, Nepal

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Authors: Shabnam Najafi, Metin Turkay

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Network design problem (NDP) is used to determine the set of optimal values for certain pre-specified decision variables such as capacity expansion of nodes and links by optimizing various system performance measures including safety, congestion, and accessibility. The designed transportation network should improve objective functions defined for the system by considering the route choice behaviors of network users at the same time. The NDP studies mostly investigated the random demand and route selection constraints separately due to computational challenges. In this work, we consider both random demand and route selection constraints simultaneously. This work presents a nonlinear stochastic model for land use and road network design problem to address the development of different functional zones in urban areas by considering both cost function and air pollution. This model minimizes cost function and air pollution simultaneously with random demand and stochastic route selection constraint that aims to optimize network performance via road capacity expansion. The Bureau of Public Roads (BPR) link impedance function is used to determine the travel time function in each link. We consider a city with origin and destination nodes which can be residential or employment or both. There are set of existing paths between origin-destination (O-D) pairs. Case of increasing employed population is analyzed to determine amount of roads and origin zones simultaneously. Minimizing travel and expansion cost of routes and origin zones in one side and minimizing CO emission in the other side is considered in this analysis at the same time. In this work demand between O-D pairs is random and also the network flow pattern is subject to stochastic user equilibrium, specifically logit route choice model. Considering both demand and route choice, random is more applicable to design urban network programs. Epsilon-constraint is one of the methods to solve both linear and nonlinear multi-objective problems. In this work epsilon-constraint method is used to solve the problem. The problem was solved by keeping first objective (cost function) as the objective function of the problem and second objective as a constraint that should be less than an epsilon, where epsilon is an upper bound of the emission function. The value of epsilon should change from the worst to the best value of the emission function to generate the family of solutions representing Pareto set. A numerical example with 2 origin zones and 2 destination zones and 7 links is solved by GAMS and the set of Pareto points is obtained. There are 15 efficient solutions. According to these solutions as cost function value increases, emission function value decreases and vice versa.

Keywords: epsilon-constraint, multi-objective, network design, stochastic

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Authors: Shaukat Ali Shahee, Usha Ananthakumar

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A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets.

Keywords: classification, imbalanced dataset, Lowner-John ellipsoid, model based clustering, oversampling

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Authors: Emily Warren

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Despite the growing body of research on Japanese food history, little has been written about the attitudes and perspectives premodern Japanese people held about their food, even on special celebratory days. In fact, the overall image that arises from the literature is one of ambivalence: that the medieval nobility of the Heian and Kamakura periods (795-1333) did not much care about what they ate and for that reason, food seems relatively scarce in certain historical records. This study challenges this perspective by analyzing the manuals written to guide palace management and feast preparation for royals, introducing two of the sources into English for the first time. This research is primarily based on three manuals that address different aspects of royal food culture and preparation. The Chujiruiki, or Record of the Palace Kitchens (1295), is a fragmentary manual written by a bureaucrat in charge of the main palace kitchen office. This document collection details the utensils, furnishing, and courses that officials organized for the royals’ two daily meals in the morning (asagarei gozen) and in the afternoon (hiru gozen) when they enjoyed seven courses, each one carefully cooked and plated. The orchestration of daily meals and frequent banquets would have been complicated affairs for those preparing the tableware and food, thus requiring texts like the Chûjiruiki, as well as another manual, the Nicchûgyôji (11th c.), or The Daily Functions. Because of the complex coordination between various kitchen-related bureaucratic offices, kitchen officials endeavored to standardize the menus and place settings depending on the time of year, religious abstinence days, and available ingredients flowing into the capital as taxes. For the most important annual banquets and rites celebrating deities and the royal family, kitchen officials would likely refer to the Engi Shiki (927), or Protocols of the Engi Era, for details on offerings, servant payments, and menus. This study proposes that many of the great feast events, and indeed even daily meals at the palace, were so standardized and carefully planned for repetition that there would have been little need for the contents of such feasts to be detailed in diaries or novels—places where historians have noted a lack of the mention of food descriptions. These descriptions were not included for lack of interest on the part of the nobility, but rather because knowledge of what would be served at banquets and feasts would be considered a matter-of-course in the same way that a modern American would likely not need to state the menu of a traditional Thanksgiving meal to an American audience. Where food was concerned, novelty more so than tradition prompted a response in personal records, like diaries.

Keywords: banquets, bureaucracy, Engi shiki, Japanese food

Procedia PDF Downloads 92

Authors: Edison Ramos, John Peter V. Dacanay, Milwida Josefa Villanueva

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Soil-Transmitted Helminth (STH) infections caused by helminths are the most prevalent neglected tropical diseases (NTDs). They are commonly found in warm, humid regions and developing countries, particularly in rural areas with poor hygiene. Occasionally, human hosts exposed to pig manure may harbor Ascaris suum parasites without experiencing any symptoms. To address the significant issue of helminth infections, an effective anthelmintic is necessary. However, the effectiveness of various medications as anthelmintics can be reduced due to mutations. In recent years, there has been a growing interest in using plants as a source of medicine due to their natural origin, accessibility, affordability, and potential lack of complications. Herbal medicine has been advocated as an alternative treatment for helminth infections, especially in underdeveloped countries, considering the numerous adverse effects and drug resistance associated with commercially available anthelmintics. Medicinal plants are considered suitable replacements for current anthelmintics due to their historical usage in treating helminth infections. The objective of this research was to investigate the effects of aqueous extraction of pomegranate peel (Punica granatum L.) as an anthelmintic on female Ascaris suum in vitro. The in vitro assay involved observing the motility of Ascaris suum in different concentrations (25%, 50%, 75%, and 100%) of pomegranate peel aqueous extraction, along with mebendazole as a positive control. The results indicated that as the concentration of the extract increased, the time required to paralyze the worms decreased. At 25% concentration, the average time for paralysis was 362.0 minutes, which decreased to 181.0 minutes at 50% concentration, 122.7 minutes at 75% concentration, and 90.0 minutes at 100% concentration. The time of death for the worms was directly proportional to the concentration of the pomegranate peel extract. Death was observed at an average time of 240.7 minutes at 75% concentration and 147.7 minutes at 100% concentration. The findings suggest that as the concentration of pomegranate peel extract increases, the time required for paralysis and death of Ascaris suum decreases. This indicates a concentration-dependent relationship, where higher concentrations of the extract exhibit greater effectiveness in inducing paralysis and causing the death of the worms. These results emphasize the potential anthelmintic properties of pomegranate peel extract and its ability to effectively combat Ascaris suum infestations. There was no significant difference in the anthelmintic effectiveness between the pomegranate peel extract and Mebendazole. These findings highlight the potential of pomegranate peel extract as an alternative anthelmintic treatment for Ascaris suum infections. The researchers recommend determining the optimal dose and administration route to maximize the effectiveness of pomegranate peel as an anthelmintic therapeutic against Ascaris suum.

Keywords: pomegranate peel, aqueous extract, anthelmintic, in vitro

Procedia PDF Downloads 73

Authors: Aude Gounelle, Gloria Senfaute, Ludivine Saint-Mard, Thomas Chartier

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The long-term deformation rates of faults are not fully captured by Probabilistic Seismic Hazard Assessment (PSHA). PSHA that use catalogues to develop area or smoothed-seismicity sources is limited by the data available to constraint future earthquakes activity rates. The integration of faults in PSHA can at least partially address the long-term deformation. However, careful treatment of fault sources is required, particularly, in low strain rate regions, where estimated seismic hazard levels are highly sensitive to assumptions concerning fault geometry, segmentation and slip rate. When integrating faults in PSHA various constraints on earthquake rates from geologic and seismologic data have to be satisfied. For low strain rate regions where such data is scarce it would be especially challenging. Faults in PSHA requires conversion of the geologic and seismologic data into fault geometries, slip rates and then into earthquake activity rates. Several approaches exist for translating slip rates into earthquake activity rates. In the most frequently used approach, the background earthquakes are handled using a truncated approach, in which earthquakes with a magnitude lower or equal to a threshold magnitude (Mw) occur in the background zone, with a rate defined by the rate in the earthquake catalogue. Although magnitudes higher than the threshold are located on the fault with a rate defined using the average slip rate of the fault. As high-lighted by several research, seismic events with magnitudes stronger than the selected magnitude threshold may potentially occur in the background and not only at the fault, especially in regions of slow tectonic deformation. It also has been known that several sections of a fault or several faults could rupture during a single fault-to-fault rupture. It is then essential to apply a consistent modelling procedure to allow for a large set of possible fault-to-fault ruptures to occur aleatory in the hazard model while reflecting the individual slip rate of each section of the fault. In 2019, a tool named SHERIFS (Seismic Hazard and Earthquake Rates in Fault Systems) was published. The tool is using a methodology to calculate the earthquake rates in a fault system where the slip-rate budget of each fault is conversed into rupture rates for all possible single faults and faultto-fault ruptures. The objective of this paper is to compare the SHERIFS method with one other frequently used model to analyse the impact on the seismic hazard and through sensibility studies better understand the influence of key parameters and assumptions. For this application, a simplified but realistic case study was selected, which is in an area of moderate to hight seismicity (South Est of France) and where the fault is supposed to have a low strain.

Keywords: deformation rates, faults, probabilistic seismic hazard, PSHA

Procedia PDF Downloads 38

Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

Procedia PDF Downloads 128

Authors: Mokhlisur Rahman

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The growing consensus among scientists and world leaders indicates that immediate action should be considered regarding the climate change phenomenon. However, climate change is no more a global issue but a personal one. Thus, individual participation is necessary to address such a significant issue. Studies show that individuals who perceive climate change as a personal issue are more likely to act toward it. This abstract presents augmented reality (A.R.) technology in the social media platform Facebook video advertising. The idea involves creating a video advertisement that enables users to interact with the video by navigating its features and experiencing the result uniquely and engagingly. This advertisement uses A.R. to bring changes, such as people making changes in real-life scenarios by simple clicks on the video and hearing an instant rewarding fact about their choices. The video shows three options: room, lawn, and driveway. Users select one option and engage in interaction based on while holding the camera in their personal spaces: Suppose users select the first option, room, and hold their camera toward spots such as by the windows, balcony, corners, and even walls. In that case, the A.R. offers users different plants appropriate for those unoccupied spaces in the room. Users can change the options of the plants and see which space at their house deserves a plant that makes it more natural. When a user adds a natural element to the video, the video content explains a piece of beneficiary information about how the user contributes to the world more to be livable and why it is necessary. With the help of A.R., if users select the second option, lawn, and hold their camera toward their lawn, the options are various small trees for their lawn to make it more environmentally friendly and decorative. The video plays a beneficiary explanation here too. Suppose users select the third option, driveway, and hold their camera toward their driveway. In that case, the A.R. video option offers unique recycle bin designs using A.I. measurement of spaces. The video plays audio information on anthropogenic contribution to greenhouse gas emission. IoT embeds tracking code in the video ad on Facebook, which stores the exact number of views in the cloud for data analysis. An online survey at the end collects short qualitative answers. This study helps understand the number of users involved and willing to change their behavior; It makes personalized advertising in social media. Considering the current state of climate change, the urgency for action is increasing. This ad increases the chance to make direct connections with individuals and gives a sense of personal responsibility for climate change to act

Keywords: motivations, climate, iot, personalized-advertising, action

Procedia PDF Downloads 54

Authors: Fouad Mrad, Viviane Zaccour

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The accelerated developments in the political, economic, environmental, security, health, and social folders are exhausting planners across the world, especially in Arab countries. The impact of the tension is multifaceted and has resulted in conflicts, wars, migration, and human insecurity. The potential cross-cutting role that science, innovation and technology can play in supporting Arab societies to address these pressing challenges is a serious, unique chance for the people of the region. This opportunity is based on the existing capacity of educated youth and inaccessible talents in the local universities and research centers. It has been accepted that Arab countries have achieved major advancements in the economy, education and social wellbeing since the 70s of the 20th Century. Mainly direct outcome of the oil and other natural resources. The UN Secretary-General, during the Education Summit in Sep 2022, stressed that “Learning continues to underplay skills, including problem-solving, critical thinking and empathy.” Stars of Science by Qatar Foundation was launched in 2009 and has been sustained through 2023. Consistent mission from the start: To mobilize a new generation of Pan-Arab innovators and problem solvers by encouraging youth participation and interest in Science, Technology and Entrepreneurship throughout the Arab world via the program and its social media activities. To make science accessible and attractive to mass audiences by de-mystifying the process of innovation. Harnessing best practices within reality TV to show that science, engineering, and innovation are important in everyday life and can be fun.” Thousands of Participants learned unforgettable lessons; winners changed their lives forever as they learned and earned seed capital; they became drivers of change in their countries and families; millions of viewers were exposed to an innovative experimental process, and culturally, several relevant national institutions adopted the SOS track in their national initiatives. The program exhibited experientially youth self-efficacy as the most distinct core property of human agency, which is an individual's belief in his or her capacity to execute behaviors necessary to produce specific performance attainments. In addition, the program proved that innovations are performed by networks of people with different sets of technological, useful knowledge, skills and competencies introduced by socially shared technological knowledge as a main determinant of economic activities in any economy.

Keywords: science, invention, innovation, Qatar foundation, QSTP, prototyping

Procedia PDF Downloads 58

Authors: Doaa Alhaboby

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Behavior change is a complex process that often requires ongoing support and guidance. Telecoaching programs have emerged as effective tools in facilitating behavior change by providing personalized support remotely. This abstract explores the lessons learned from a randomized controlled trial (RCT) evaluation of a telecoaching program focused on behavior change for Diabetics and discusses strategies for implementing these lessons to overcome the challenge of making physical activity a habit. The telecoaching program involved participants engaging in regular coaching sessions delivered via phone calls. These sessions aimed to address various aspects of behavior change, including goal setting, self-monitoring, problem-solving, and social support. Over the course of the program, participants received personalized guidance tailored to their unique needs and preferences. One of the key lessons learned from the RCT was the importance of engagement, readiness to change and the use of technology. Participants who set specific, measurable, attainable, relevant, and time-bound (SMART) goals were more likely to make sustained progress toward behavior change. Additionally, regular self-monitoring of behavior and progress was found to be instrumental in promoting accountability and motivation. Moving forward, implementing the lessons learned from the RCT can help individuals overcome the hardest part of behavior change: making physical activity a habit. One strategy is to prioritize consistency and establish a regular routine for physical activity. This may involve scheduling workouts at the same time each day or week and treating them as non-negotiable appointments. Additionally, integrating physical activity into daily life routines and taking into consideration the main challenges that can stop the process of integrating physical activity routines into the daily schedule can help make it more habitual. Furthermore, leveraging technology and digital tools can enhance adherence to physical activity goals. Mobile apps, wearable activity trackers, and online fitness communities can provide ongoing support, motivation, and accountability. These tools can also facilitate self-monitoring of behavior and progress, allowing individuals to track their activity levels and adjust their goals as needed. In conclusion, telecoaching programs offer valuable insights into behavior change and provide strategies for overcoming challenges, such as making physical activity a habit. By applying the lessons learned from these programs and incorporating them into daily life, individuals can cultivate sustainable habits that support their long-term health and well-being.

Keywords: lifestyle, behavior change, physical activity, chronic conditions

Procedia PDF Downloads 36

Authors: Ilona Buchem, Carolin Gellner

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Over the last few years, the call for the support of senior learners in the development of their digital literacy has become prevalent, mainly due to the progression towards ageing societies paired with advances in digitalisation in all spheres of life, including e-health and electronic patient record (EPA). While major research efforts in supporting senior learners in developing digital literacy have been invested so far in e-learning focusing on knowledge acquisition and cognitive tasks, little research exists in learning models which target virtual mentoring and coaching with the help of pedagogical agents and address the social dimensions of learning. Research from studies with students in the context of formal education has already provided methods for designing intelligent virtual agents in support of personalised learning. However, this research has mostly focused on cognitive skills and has not yet been applied to the context of mentoring/coaching of senior learners, who have different characteristics and learn in different contexts. In this paper, we describe how insights from previous research can be used to develop an intelligent virtual learning coach (agent) for senior learners with a focus on building the social relationship between the agent and the learner and the key task of the agent to socialize learners to the larger context of digital literacy with a focus on electronic health records. Following current approaches to mentoring and coaching, the agent is designed not to enhance and monitor the cognitive performance of the learner but to serve as a trusted friend and advisor, whose role is to provide one-to-one guidance and support sharing of experiences among learners (peers). Based on literature review and synopsis of research on virtual agents and current coaching/mentoring models under consideration of the specific characteristics and requirements of senior learners, we describe the design framework which was applied to design an intelligent virtual learning coach as part of the e-learning system for digital literacy of senior learners in the ePA-Coach project founded by the German Ministry of Education and Research. This paper also presents the results from the evaluation study, which compared the use of the first prototype of the virtual learning coach designed according to the design framework with a voice narration in a multimedia learning environment with senior learners. The focus of the study was to validate the agent design in the context of the persona effect (Lester et al., 1997). Since the persona effect is related to the hypothesis that animated agents are perceived as more socially engaging, the study evaluated possible impacts of agent coaching in comparison with voice coaching on motivation, engagement, experience, and digital literacy.

Keywords: virtual learning coach, virtual mentor, pedagogical agent, senior learners, digital literacy, electronic health records

Procedia PDF Downloads 99

Authors: Maryam Mohammadi, Mohammadjavad Mahdavinejad, Mojtaba Ansari

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The main driving force for increasing movement towards the design of High-Performance Buildings (HPB) are building codes and rating systems that address the various components of the building and their impact on the environment and energy conservation through various methods like prescriptive methods or simulation-based approaches. The methods and tools developed to meet these needs, which are often based on building performance simulation tools (BPST), have limitations in terms of compatibility with the integrated design process (IDP) and HPB design, as well as use by architects in the early stages of design (when the most important decisions are made). To overcome these limitations in recent years, efforts have been made to develop Design Decision Support Systems, which are often based on artificial intelligence. Numerous needs and steps for designing and developing a Decision Support System (DSS), which complies with the early stages of energy-efficient architecture design -consisting of combinations of different methods in an integrated package- have been listed in the literature. While various review studies have been conducted in connection with each of these techniques (such as optimizations, sensitivity and uncertainty analysis, etc.) and their integration of them with specific targets; this article is a critical and holistic review of the researches which leads to the development of applicable systems or introduction of a comprehensive framework for developing models complies with the IDP. Information resources such as Science Direct and Google Scholar are searched using specific keywords and the results are divided into two main categories: Simulation-based DSSs and Meta-simulation-based DSSs. The strengths and limitations of different models are highlighted, two general conceptual models are introduced for each category and the degree of compliance of these models with the IDP Framework is discussed. The research shows movement towards Multi-Level of Development (MOD) models, well combined with early stages of integrated design (schematic design stage and design development stage), which are heuristic, hybrid and Meta-simulation-based, relies on Big-real Data (like Building Energy Management Systems Data or Web data). Obtaining, using and combining of these data with simulation data to create models with higher uncertainty, more dynamic and more sensitive to context and culture models, as well as models that can generate economy-energy-efficient design scenarios using local data (to be more harmonized with circular economy principles), are important research areas in this field. The results of this study are a roadmap for researchers and developers of these tools.

Keywords: integrated design process, design decision support system, meta-simulation based, early stage, big data, energy efficiency

Procedia PDF Downloads 143

Authors: Kedar J. Mahadeshwar

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Concept introduction: This paper talks about how an innovative cloud based analytics enabled solution that could address a major industry challenge that is approaching all of us globally faster than what one would think. The world of supply chain for drugs and devices is changing today at a rapid speed. In the US, the Drug Supply Chain Security Act (DSCSA) is a new law for Tracing, Verification and Serialization phasing in starting Jan 1, 2015 for manufacturers, repackagers, wholesalers and pharmacies / clinics. Similarly we are seeing pressures building up in Europe, China and many countries that would require an absolute traceability of every drug and device end to end. Companies (both manufacturers and distributors) can use this opportunity not only to be compliant but to differentiate themselves over competition. And moreover a country such as UAE can be the leader in coming up with a global solution that brings innovation in this industry. Problem definition and timing: The problem of counterfeit drug market, recognized by FDA, causes billions of dollars loss every year. Even in UAE, the concerns over prevalence of counterfeit drugs, which enter through ports such as Dubai remains a big concern, as per UAE pharma and healthcare report, Q1 2015. Distribution of drugs and devices involves multiple processes and systems that do not talk to each other. Consumer confidence is at risk due to this lack of traceability and any leading provider is at risk of losing its reputation. Globally there is an increasing pressure by government and regulatory bodies to trace serial numbers and lot numbers of every drug and medical devices throughout a supply chain. Though many of large corporations use some form of ERP (enterprise resource planning) software, it is far from having a capability to trace a lot and serial number beyond the enterprise and making this information easily available real time. Solution: The solution here talks about a service provider that allows all subscribers to take advantage of this service. The solution allows a service provider regardless of its physical location, to host this cloud based traceability and analytics solution of millions of distribution transactions that capture lots of each drug and device. The solution platform will capture a movement of every medical device and drug end to end from its manufacturer to a hospital or a doctor through a series of distributor or retail network. The platform also provides advanced analytics solution to do some intelligent reporting online. Why Dubai? Opportunity exists with huge investment done in Dubai healthcare city also with using technology and infrastructure to attract more FDI to provide such a service. UAE and countries similar will be facing this pressure from regulators globally in near future. But more interestingly, Dubai can attract such innovators/companies to run and host such a cloud based solution and become a hub of such traceability globally.

Keywords: cloud, pharmaceutical, supply chain, tracking

Procedia PDF Downloads 509

Authors: Celine Davis

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The novel coronavirus 2019 (COVID-19) is an infectious disease caused by the virus SARS-CoV-2, which has detrimental respiratory, cardiovascular, and neurological effects impacting over one million lives in the United States. New researches has emerged indicating long-term neurologic consequences in those who survive COVID-19 infections, including more than seven million Americans and another 27 million people worldwide. These consequences include attentional deficits, memory impairments, executive function deficits and aphasia-like symptoms which fall within the purview of speech-language pathology. The National Health Interview Survey (NHIS) is a comprehensive annual survey conducted by the National Center for Health Statistics (NCHS), a branch of the Centers for Disease Control and Prevention (CDC) in the United States. The NHIS is one of the most significant sources of health-related data in the country and has been conducted since 1957. The longitudinal nature of the study allows for analysis of trends in various variables over the years, which can be essential for understanding societal changes and making treatment recommendations. This current study will utilize NHIS data from 2020-2022 which contained interview questions specifically related to COVID-19. Adult cases of individuals between the ages of 18-50 diagnosed with COVID-19 in the United States during 2020-2022 will be identified using the National Health Interview Survey (NHIS). Multiple regression analysis of self-reported data confirming COVID-19 infection status and challenges with concentration, communication, and memory will be performed. Latent class analysis will be utilized to identify subgroups in the population to indicate whether certain demographic groups have higher susceptibility to cognitive-linguistic deficits associated with COVID-19. Completion of this study will reveal whether there is an association between confirmed COVID-19 diagnosis and heightened incidence of cognitive deficits and subsequent implications, if any, on activities of daily living. This study is distinct in its aim to utilize national survey data to explore the relationship between confirmed COVID-19 diagnosis and the prevalence of cognitive-communication deficits with a secondary focus on resulting activity limitations. To the best of the author’s knowledge, this will be the first large-scale epidemiological study investigating the associations between cognitive-linguistic deficits, COVID-19 and implications on activities of daily living in the United States population. These findings will highlight the need for targeted interventions and support services to address the cognitive-communication needs of individuals recovering from COVID-19, thereby enhancing their overall well-being and functional outcomes.

Keywords: cognition, COVID-19, language, limitations, memory, NHIS

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Authors: George Hanna Abdelmelek Henien

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Quality and safety issues are common in Ethiopia's food processing industry, which can negatively impact consumers' health and livelihoods. The country is known for its various agricultural products that are important to the economy. However, food quality and safety policies and management practices in the food processing industry have led to many health problems, foodborne illnesses and economic losses. This article aims to show the causes and consequences of food safety and quality problems in the food processing industry in Ethiopia and discuss possible solutions to solve them. One of the main reasons for food quality and safety in Ethiopia's food processing industry is the lack of adequate regulation and enforcement mechanisms. Inadequate food safety and quality policies have led to inefficiencies in food production. Additionally, the failure to monitor and enforce existing regulations has created a good opportunity for unscrupulous companies to engage in harmful practices that endanger the lives of citizens. The impact on food quality and safety is significant due to loss of life, high medical costs, and loss of consumer confidence in the food processing industry. Foodborne diseases such as diarrhoea, typhoid and cholera are common in Ethiopia, and food quality and safety play an important role in . Additionally, food recalls due to contamination or contamination often cause significant economic losses in the food processing industry. To solve these problems, the Ethiopian government began taking measures to improve food quality and safety in the food processing industry. One of the most prominent initiatives is the Ethiopian Food and Drug Administration (EFDA), which was established in 2010 to monitor and control the quality and safety of food and beverage products in the country. EFDA has implemented many measures to improve food safety, such as carrying out routine inspections, monitoring the import of food products and implementing labeling requirements. Another solution that can improve food quality and safety in the food processing industry in Ethiopia is the implementation of food safety management system (FSMS). FSMS is a set of procedures and policies designed to identify, assess and control food safety risks during food processing. Implementing a FSMS can help companies in the food processing industry identify and address potential risks before they harm consumers. Additionally, implementing an FSMS can help companies comply with current safety and security regulations. Consequently, improving food safety policy and management system in Ethiopia's food processing industry is important to protect people's health and improve the country's economy. . Addressing the root causes of food quality and safety and implementing practical solutions that can help improve the overall food safety and quality in the country, such as establishing regulatory bodies and implementing food management systems.

Keywords: food quality, food safety, policy, management system, food processing industry food traceability, industry 4.0, internet of things, block chain, best worst method, marcos

Procedia PDF Downloads 25

Authors: Birgit Rasmussen, Thomas Maribo, Bente S. Toft

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BACKGROUND: Cardiac rehabilitation is the standard treatment for ischemic heart disease. Cardiac rehabilitation improves quality of life, reduces mortality and the risk of readmission, and provides patients with valuable knowledge and encouragement from peers and staff. Still, less than half of eligible patients enroll. Face-to-face interventions have the potential to support patients' decision-making and increase enrolment in cardiac rehabilitation. However, we lack knowledge of the content and characteristics of interventions. AIM: The aim was to outline and evaluate the content and characteristics of studies that have reported on face-to-face interventions to encourage enrolment in cardiac rehabilitation in patients with ischemic heart disease. METHOD: This scoping review followed the Joanne Briggs Institute methodology. Based on an a-priori protocol that defined the systematic search criteria, six databases were searched for studies published between 2001 and 2023. Two reviewers independently screened and selected studies. All authors discussed the summarized data prior to the narrative presentation. RESULTS: After screening and full text review of 5583 records, 20 studies of heterogeneous design and content were included. Four studies described the key contents in face-to-face interventions to be education, support of autonomy, addressing reasons for change, and emotional and cognitive support while showing understanding. Two studies used motivational interviewing to target patients' experiences and address worries and anticipated difficulties. Four quantitative studies found associations between enrolment and intention to attend, cardiac rehabilitation barriers, exercise self-efficacy, and perceived control. When patients asked questions, enrolment rates were higher, while providing reassurance and optimism could lead to non-attendance if patients had a high degree of worry. In qualitative studies, support to overcome barriers and knowledge about health benefits from participation in cardiac rehabilitation facilitated enrolment. Feeling reassured that the cardiac condition was good could lead to non-attendance. DISCUSSION AND CONCLUSION: To support patients' enrolment in cardiac rehabilitation, it is recommended that interventions integrate a person-centered dialogue. Individual worries and barriers to cardiac rehabilitation should be jointly explored. When talking with patients for whom worries predominate, the recommendation is to focus on the patients' perspectives and avoid too much focus on reassurance and problem-solving. The patients' perspectives, the mechanisms of change, and the process evaluation of the intervention including person-centeredness are relevant to include in future studies.

Keywords: ischemic heart disease, cardiac rehabilitation, enrolment, person-centered, in-hospital interventions

Procedia PDF Downloads 46

Authors: Sara E. Baumann, Megan A. Rabin, Mary Hawk, Bhimsen Devkota, Kajol Upadhyaya, Guna Raj Shrestha, Brigit Joseph, Annika Agarwal, Jessica G. Burke

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In Nepal, prevailing sociocultural norms associated with menstruation prompt adherence to stringent rules that limit participation in daily activities. Chhaupadi is a specific menstrual tradition in Nepal in which women and girls segregate themselves and follow a series of restrictions during menstruation. Despite having numerous physical and mental health implications, extant interventions have yet to sustainably address the harms associated with chhaupadi. In this study, the authors describe insights garnered from a collaboration with community members in Dailekh district, who formulated their own approaches to mitigate the adverse facets of chhaupadi. Envisaged as an entry point to improve women’s menstrual health experiences, this investigation employed an approach that uses Human-centered Design and a community-engaged approach. The authors conducted a four-day design workshop which unfolded in two phases: The Discovery Phase, to uncover chhaupadi context and key stakeholders, and the Design Phase, to design contextually relevant interventions. Diverse community-members, including those with lived experience practicing chhaupadi, developed five intervention concepts: 1) harnessing Female Community Health Volunteers as role models, for counseling, and raising awareness; 2) focusing on mothers and mother’s groups to instigate behavioral shifts; 3) engaging the broader community in behavior change efforts; 4) empowering fathers to effect change in their homes through counseling and education; and 5) training and emboldening youth to advocate for positive change through advocacy in their schools and homes. This research underscores the importance of employing multi-level approaches tailored to specific stakeholder groups, given Nepal’s rich cultural diversity. The engagement of Female Community Health Volunteers emerged as a promising yet underexplored intervention concept for chhaupadi, warranting broader implementation. Crucially, it is also imperative for interventions to prioritize tackling deleterious aspects of the chhaupadi tradition, emphasizing safety considerations, all while acknowledging chhaupadi’s entrenched cultural history; for some, there are positive aspects of the tradition that women and girls wish to preserve.

Keywords: human-centered design, menstrual health, Nepal, community-engagement, intervention development, women's health, rural health

Procedia PDF Downloads 36

Authors: M. Roberts

Abstract:

We Are Undefeatable - is a mass marketing behaviour change campaign that aims to support the least active people living with long term health conditions to be more active. This is an important issue to address because people with long term conditions are an historically underserved community for the sport and physical activity sector and the least active of those with long term conditions have the most to gain in health and wellbeing benefits. The campaign has generated a significant change in the way physical activity is communicated and people with long term conditions are represented in the media and marketing. The goal is to create a social norm around being active. The campaign is led by a unique partnership of organisations: the Richmond Group of Charities (made up of Age UK, Alzheimer’s Society, Asthma + Lung UK, Breast Cancer Now, British Heart Foundation, British Red Cross, Diabetes UK, Macmillan Cancer Support, Rethink Mental Illness, Royal Voluntary Service, Stroke Association, Versus Arthritis) along with Mind, MS Society, Parkinson’s UK and Sport England, with National Lottery Funding. It is underpinned by the COM-B model of behaviour change. It draws on the lived experience of people with multiple long term conditions to shape the look and feel of the campaign and all the resources available. People with long term conditions are the campaign messengers, central to the ethos of the campaign by telling their individual stories of overcoming barriers to be active with their health conditions. The central messaging is about finding a way to be active that works for the individual. We Are Undefeatable is evaluated through a multi-modal approach, including regular qualitative focus groups and a quantitative evaluation tracker undertaken three times a year. The campaign has highlighted the significant barriers to physical activity for people with long term conditions. This has changed the way our partnership talks about physical activity but has also had an impact on the wider sport and physical activity sector, prompting an increasing departure from traditional messaging and marketing approaches for this audience of people with long term conditions. The campaign has reached millions of people since its launch in 2019, through multiple marketing and partnership channels including primetime TV advertising and promotion through health professionals and in health settings. Its diverse storytellers make it relatable to its target audience and the achievable activities highlighted and inclusive messaging inspire our audience to take action as a result of seeing the campaign. The We Are Undefeatable campaign is a blueprint for physical activity campaigns; it not only addresses individual behaviour change but plays a role in addressing systemic barriers to physical activity by sharing the lived experience insight to shape policy and professional practice.

Keywords: behaviour change, long term conditions, partnership, relatable

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Authors: Edna Negron-Martinez

Abstract:

Our time's global challenges and trends —like those associated with climate change, demographics displacements, growing health inequalities, and increasing burden of diseases— have complex connections to the determinants of health. Information on the burden of disease causes and prevention is fundamental for public health actions, like preparedness and responses for disasters, and recovery resources after the event. For instance, there is an increasing consensus about key findings of the effects and connections of the global burden of disease, as it generates substantial healthcare costs, consumes essential resources and prevents the attainment of optimal health and well-being. The goal of this research endeavor is to promote a comprehensive understanding of the connections between social, environmental, and economic influences on health. These connections are illustrated by pulling from clearly the core curriculum of multidisciplinary areas —as urban design, energy, housing, and economy— as well as in the health system itself. A systematic review of primary and secondary data included a variety of issues as global health, natural disasters, and critical pollution impacts on people's health and the ecosystems. Environmental health is challenged by the unsustainable consumption patterns and the resulting contaminants that abound in many cities and urban settings around the world. Poverty, inadequate housing, and poor health are usually linked. The house is a primary environmental health context for any individual and especially for more vulnerable groups; such as children, older adults and those who are sick. Nevertheless, very few countries show strong decoupling of environmental degradation from economic growth, as indicated by a recent 2017 Report of the World Bank. Worth noting, the environmental fraction of the global burden of disease in a 2016 World Health Organization (WHO) report estimated that 12.6 million global deaths, accounting for 23% (95% CI: 13-34%) of all deaths were attributable to the environment. Among the environmental contaminants include heavy metals, noise pollution, light pollution, and urban sprawl. Those key findings make a call to the significance to urgently adopt in a global scale the United Nations post-2015 Sustainable Development Goals (SDGs). The SDGs address the social, environmental, and economic factors that influence health and health inequalities, advising how these sectors, in turn, benefit from a healthy population. Consequently, more actions are necessary from an inter-sectoral and systemic paradigm to enforce an integrated sustainability policy implementation aimed at the environmental, social, and economic determinants of health.

Keywords: building capacity for workforce development, ecological and environmental health effects of pollution, public health education, sustainability

Procedia PDF Downloads 87