Search results for: anterior crucial ligament reconstruction

Authors: Chenyi Ma

Abstract:

Background: The second costliest hurricane in U.S. history, Sandy landed in southern New Jersey on October 29, 2012, and struck the entire state with high winds and torrential rains. The disaster killed more than 100 people, left more than 8.5 million households without power, and damaged or destroyed more than 200,000 homes across the state. Immediately after the disaster, public policy support was provided in nine coastal counties that constituted 98% of the major and severely damaged housing units in NJ overall. The programs include Individuals and Households Assistance Program, Small Business Loan Program, National Flood Insurance Program, and the Federal Emergency Management Administration (FEMA) Public Assistance Grant Program. In the most severely affected counties, additional funding was provided through Community Development Block Grant: Reconstruction, Rehabilitation, Elevation, and Mitigation Program, and Homeowner Resettlement Program. How these policies individually and as a whole impacted housing recovery across communities with different socioeconomic and demographic profiles has not yet been studied, particularly in relation to damage levels. The concept of community social vulnerability has been widely used to explain many aspects of natural disasters. Nevertheless, how communities are vulnerable has been less fully examined. Community resilience has been conceptualized as a protective factor against negative impacts from disasters, however, how community resilience buffers the effects of vulnerability is not yet known. Because housing recovery is a dynamic social and economic process that varies according to context, this study examined the path from community vulnerability and resilience to housing recovery looking at both community characteristics and policy interventions. Sample/Methods: This retrospective longitudinal case study compared a literature-identified set of pre-disaster community characteristics, the effects of multiple public policy programs, and a set of time-variant community resilience indicators to changes in housing stock (operationally defined by percent of building permits to total occupied housing units/households) between 2010 and 2014, two years before and after Hurricane Sandy. The sample consisted of 51 municipalities in the nine counties in which between 4% and 58% of housing units suffered either major or severe damage. Structural equation modeling (SEM) was used to determine the path from vulnerability to the housing recovery, via multiple public programs, separately and as a whole, and via the community resilience indicators. The spatial analytical tool ArcGIS 10.2 was used to show the spatial relations between housing recovery patterns and community vulnerability and resilience. Findings: Holding damage levels constant, communities with higher proportions of Hispanic households had significantly lower levels of housing recovery while communities with households with an adult >age 65 had significantly higher levels of the housing recovery. The contrast was partly due to the different levels of total public support the two types of the community received. Further, while the public policy programs individually mediated the negative associations between African American and female-headed households and housing recovery, communities with larger proportions of African American, female-headed and Hispanic households were “vulnerable” to lower levels of housing recovery because they lacked sufficient public program support. Even so, higher employment rates and incomes buffered vulnerability to lower housing recovery. Because housing is the "wobbly pillar" of the welfare state, the housing needs of these particular groups should be more fully addressed by disaster policy.

Keywords: community social vulnerability, community resilience, hurricane, public policy

Procedia PDF Downloads 357

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 102

Authors: S. N. Susenburger, D. Boecker

Abstract:

In the age of Volatility, Uncertainty, Complexity, and Ambiguity (VUCA), where digital transformation is challenging long standing traditional hardware and manufacturing companies, innovation needs a different methodology, strategy, mindset, and culture. What used to be a mindset of scaling per quantity is now shifting to orchestrating ecosystems, platform business models and service bundles. While large corporations are trying to mimic the nimbleness and versatile mindset of startups in the core of their digital strategies, they’re at the frontier of facing one of the largest organizational and cultural changes in history. This paper elaborates on how a manufacturing giant transformed its Corporate Information Technology (IT) to enable digital and Internet of Things (IoT) business while establishing the mindset and the approaches of the Innovation Inside-Out/Outside-In Strategy. It gives insights into the core elements of an innovation culture and the tactics and methodologies leveraged to support the cultural shift and transformation into an IoT company. This paper also outlines the core elements for an innovation culture and how the persona 'Connected Engineer' thrives in the digital innovation environment. Further, it explores how tapping domain-focused ecosystems in vibrant innovative cities can be used as a part of the strategy to facilitate partner co-innovation. Therefore, findings from several use cases, observations and surveys led to conclusion for the strategy tandem of Innovation Inside-Out/Outside-In. The findings indicate that it's crucial in which phases and maturity level the Innovation Inside-Out/Outside-In Strategy is activated: cultural aspects of the business and the regional ecosystem need to be considered, as well as cultural readiness from management and active contributors. The 'not invented here syndrome' is a barrier of large corporations that need to be addressed and managed to successfully drive partnerships, as well as embracing co-innovation and a mindset shifting away from physical products toward new business models, services, and IoT platforms. This paper elaborates on various methodologies and approaches tested in different countries and cultures, including the U.S., Brazil, Mexico, and Germany.

Keywords: innovation management, innovation culture, innovation methodologies, digital transformation

Procedia PDF Downloads 123

Authors: Donna Erika E. De Jesus

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Introduction: Cardiac arrest occurs when abrupt cessation of cardiac function results in loss of effective circulation and complete cardiovascular collapse. For every minute of cardiac arrest without early intervention (cardiopulmonary resuscitation [CPR], defibrillation), chances of survival drop by 7-10%. It is crucial that CPR be initiated within 4-6 minutes to avoid brain death. Most out-of-hospital cardiac arrests (OHCA) occur in a residential setting where access to trained personnel and equipment is not readily available, resulting in poor victim outcomes. Methods: This is a descriptive study done from August to November 2021 using a prospective cohort design. Participants of the study include adult patients aged 18 years and above brought to the emergency room who suffered from out-of-hospital cardiac arrest. Out of the total 102 cases of OHCA, 63 participants were included in the study. Descriptive statistics were used to summarize the demographic and clinical characteristics of the patients. Results: 43 were male patients, comprising the majority at 73.02%. Hypertension was identified as the top co-morbidity, followed by diabetes mellitus, heart failure, and chronic kidney disease (CKD). Medical causes of arrest were identified in 96.83% of the cases. 90.48% of cardiac arrests occurred at home. Only 26 patients (41.27%) received pre-hospital intervention prior to ER arrival, which comprised only hands-only CPR. Twenty-three of which were performed by individuals with background knowledge of CPR. 60.32% were brought via self-conduction, the remainder by ambulances, which were noted to have no available equipment necessary to provide proper resuscitation. The average travel time from dispatch to ER arrival is 20 minutes. Conclusion: Overall survival of OHCA in our local setting remains dismal, as a return of spontaneous circulation was not achieved in any of the patients. The small number of patients having pre-hospital CPR indicates the need for emphasis on training and community education.

Keywords: out-of-hospital cardiac arrest, cardiopulmonary resuscitation, basic life support, emergency medical services

Procedia PDF Downloads 92

Authors: Kurias George, Ildefonso Cuesta Romeo, Clara Salueña Pérez, Jordi Sole Olle

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With the growing prevalence of wind energy there is a need, for modeling techniques to evaluate the impact of wind farms on meteorology and oceanography. This study presents an approach that utilizes the WRF (Weather Research and Forecasting )with that include a Wind Farm Parametrization model to simulate the dynamics around Parc Tramuntana project, a offshore wind farm to be located near the Gulf of Roses off the coast of Barcelona, Catalonia. The model incorporates parameterizations for wind turbines enabling a representation of the wind field and how it interacts with the infrastructure of the wind farm. Current results demonstrate that the model effectively captures variations in temeperature, pressure and in both wind speed and direction over time along with their resulting effects on power output from the wind farm. These findings are crucial for optimizing turbine placement and operation thus improving efficiency and sustainability of the wind farm. In addition to focusing on atmospheric interactions, this study delves into the wake effects within the turbines in the farm. A range of meteorological parameters were also considered to offer a comprehensive understanding of the farm's microclimate. The model was tested under different horizontal resolutions and farm layouts to scrutinize the wind farm's effects more closely. These experimental configurations allow for a nuanced understanding of how turbine wakes interact with each other and with the broader atmospheric and oceanic conditions. This modified approach serves as a potent tool for stakeholders in renewable energy, environmental protection, and marine spatial planning. environmental protection and marine spatial planning. It provides a range of information regarding the environmental and socio economic impacts of offshore wind energy projects.

Keywords: weather research and forecasting, wind turbine wake effects, environmental impact, wind farm parametrization, sustainability analysis

Procedia PDF Downloads 57

Authors: Dawit Assefa, Kassaye Tekie, Gebrie Alebachew, Degefu Beyene, Bikila Alemu, Naji Mohammed, Asnakech Agegnehu, Seble Tsehay, Geremew Tasew

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Background: Implementation of a Laboratory Quality Management System (LQMS) is critical to ensure accurate, reliable, and efficient laboratory testing of antimicrobial resistance (AMR). However, limited LQMS implementation and progress toward accreditation in the AMR surveillance laboratory testing setting exist in Ethiopia. By addressing non-conformances (NCs) and working towards accreditation, microbiology laboratories can improve the quality of their services, increase staff competence, and contribute to mitigate the spread of AMR. Methods: Using standard ISO 15189 horizontal and vertical assessment checklists, certified assessors identified NCs at Hawassa and Jimma Hospital microbiology laboratories. The Ethiopian Public Health Institute AMR mentors and IDDS staff prioritized closing the NCs through the implementation of an intensified mentorship program that included ISO 15189 orientation training, resource allocation, and action plan development. Results: For the two facilities to clear their NCs, an intensified mentorship approach was adopted by providing ISO 15189 orientation training, provision of buffer reagents, controls, standards, and axillary equipment, and facilitating equipment maintenance and calibration. Method verification and competency assessment were also conducted along with the implementation of standard operating procedures and recommended corrective actions. This approach enhanced the laboratory's readiness for accreditation. After addressing their NCs, the two laboratories applied to Ethiopian Accreditation Services for ISO 15189 accreditation. Conclusions: Clearing NCs through the implementation of intensified mentorship was crucial in preparing the two laboratories for accreditation and improving quality laboratory test results. This approach can guide other microbiology laboratories’ accreditation attainment efforts.

Keywords: non-conformance clearance, intensified mentorship, accreditation, ISO 15189

Procedia PDF Downloads 55

Authors: Mircan Kaya

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A multi-disciplinary approach is required in any intervention project for historical monuments. Due to the complexity of their geometry, the variable and unpredictable characteristics of original materials used in their creation, heritage structures are peculiar. Their histories are often complex, and they require correct diagnoses to decide on the techniques of intervention. This approach should not only combine technical aspects but also historical research that may help discover phenomena involving structural issues, and acquire a knowledge of the structure on its concept, method of construction, previous interventions, process of damage, and its current state. In addition to the traditional techniques like bed joint reinforcement, the repairing, strengthening and restoration of historical buildings may require several other modern methods which may be described as innovative techniques like pre-stressing and post-tensioning, use of shape memory alloy devices and shock transmission units, shoring, drilling, and the use of stainless steel or titanium. Regardless of the method to be incorporated in the strengthening process, which can be traditional or innovative, it is crucial to recognize that structural strengthening is the process of upgrading the structural system of the existing building with the aim of improving its performance under existing and additional loads like seismic loads. This process is much more complex than dealing with a new construction, owing to the fact that there are several unknown factors associated with the structural system. Material properties, load paths, previous interventions, existing reinforcement are especially important matters to be considered. There are several examples of seismic strengthening with traditional and innovative techniques around the world, which will be discussed in this paper in detail, including their pros and cons. Ultimately, however, the main idea underlying the philosophy of a successful intervention with the most appropriate techniques of strengthening a historic monument should be decided by a proper assessment of the specific needs of the building.

Keywords: bed joint reinforcement, historical monuments, post-tensioning, pre-stressing, seismic strengthening, shape memory alloy devices, shock transmitters, tie rods

Procedia PDF Downloads 242

Authors: Trianasari, Gede Adi Yuniarta

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The tourism industry has been widely recognized as one of the world’s largest industries and is expected to have continuous growth. While it has positive impacts especially on the job markets and economic aspect, this industry also brings serious environmental impacts that may not be neglected. As such, the tourism industry is faced with increasing demands and challenges to deal with the environmental issues. Corporate Social Responsibility (CSR) is a way to show the firms’ concern on the societal and environmental aspects. In line with the increasing pressure on such responsibilities, a growing number of firms have involved in CSR activities. In Bali, the majority of both chained and locally owned hotels have shown their efforts on CSR practices. However, little is known about what and how they perform or implement such program especially within the environmental aspect. The importance of understanding what they focus on lays in the identification of areas that have received sufficient treatment and those that require more attention. Furthermore, also, it is especially essential considering that Bali is one of the worldly known destinations that have been facing numerous crucial issues on environment that may threaten the sustainability of the island and its people. This paper reports on the results of a study exploring the practices of CSR in hotels in Bali. Data were collected from 49 hotel managers and human resource managers in Bali across four major tourist areas, using semi structured interview method. The analysis was conducted qualitatively. The results showed that all hotels under study have implemented CSR activities in which environment was found to be the second key aspect, following the activities directly related to community aspect. Moreover, there were five major types of environmental action identified: beach cleaning, replantation, marine conservation, turtle conservation, mangrove, and garbage management. These findings suggest that hotels in Bali under study have shown their concern on the environment, however, less attention was given on attempt to reduce the environmental impacts of their operations. Mapping the types of environmental related CSR activities enhances the knowledge of and gives lights into the CSR literature especially from the perspective of Eastern practice.

Keywords: CSR, exploratory study, sustainable tourism, tourist object

Procedia PDF Downloads 152

Authors: Konstantinos Tolidis

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The mountains are amongst the most fragile environments in the world. The world’s mountain areas cover 24% of the Earth’s land surface and are home to 12% of the global population. A further 14% of the global population is estimated to live in the vicinity of their surrounding areas. As urbanization continues to increase in the world, the mountains are also key centers for recreation and tourism; their attraction is often heightened by their remarkably high levels of biodiversity. Due to the fact that the features in mountain areas vary spatially (development degree, human geography, socio-economic reality, relations of dependency and interaction with other areas-regions), the spatial planning on these areas consists of a crucial process for preserving the natural, cultural and human environment and consists of one of the major processes of an integrated spatial policy. This research has been focused on the spatial decision problem of land use allocation optimization which is an ordinary planning problem on the mountain areas. It is a matter of fact that such decisions must be made not only on what to do, how much to do, but also on where to do, adding a whole extra class of decision variables to the problem when combined with the consideration of spatial optimization. The utility of optimization as a normative tool for spatial problem is widely recognized. However, it is very difficult for planners to quantify the weights of the objectives especially when these are related to mountain areas. Furthermore, the land use allocation optimization problems at mountain areas must be addressed not only by taking into account the general development objectives but also the spatial objectives (e.g. compactness, compatibility and accessibility, etc). Therefore, the main research’s objective was to approach the land use allocation problem by utilizing a hybrid meta-heuristic optimization technique tailored to the mountain areas’ spatial characteristics. The results indicates that the proposed methodological approach is very promising and useful for both generating land use alternatives for further consideration in land use allocation decision-making and supporting spatial management plans at mountain areas.

Keywords: multiobjective land use allocation, mountain areas, spatial planning, spatial decision making, meta-heuristic methods

Procedia PDF Downloads 315

Authors: M. G. Cattaneo

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The non-effectiveness of certain codified human rights is particularly apparent with reference to the lack of access to essential drugs in developing countries, which represents a breach of the human right to receive adequate health assistance. This paper underlines the conflict and the legal contradictions between human rights, namely health rights, international Intellectual Property Rights, in particular patent law, as well as international trade law. The paper discusses the crucial links between R&D costs for innovation, patents and new medical drugs, with the goal of reformulating the hierarchies of priorities and of interests at stake in the international intellectual property (IP) law system. Different from what happens today, International patent law should be a legal instrument apt at rebalancing an axiological asymmetry between the (conflicting) needs at stake The core argument in the paper is the proposal of an alternative pathway, namely a feasible proposal for a patent law reform. IP laws tend to balance the benefits deriving from innovation with the costs of the provided monopoly, but since developing countries and industrialized countries are in completely different political and economic situations, it is necessary to (re)modulate such exchange according to the different needs. Based on this critical analysis, the paper puts forward a proposal, called Trading Time for Space (TTS), whereby a longer time for patent exclusive life in western countries (Time) is offered to the patent holder company, in exchange for the latter selling the medical drug at cost price in developing countries (Space). Accordingly, pharmaceutical companies should sell drugs in developing countries at the cost price, or alternatively grant a free license for the sale in such countries, without any royalties or fees. However, such social service shall be duly compensated. Therefore, the consideration for such a service shall be an extension of the temporal duration of the patent’s exclusive in the country of origin that will compensate the reduced profits caused by the supply at the price cost in developing countries.

Keywords: global health, global justice, patent law reform, access to drugs

Procedia PDF Downloads 236

Authors: Joanna Cabaj, Sylwia Baluta, Karol Malecha, Kamila Drzozga

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Catecholamines are the principal neurotransmitters that mediate a variety of the central nervous system functions, such as motor control, cognition, emotion, memory processing, and endocrine modulation. Dysfunctions in catecholamine neurotransmission are induced in some neurologic and neuropsychiatric diseases. Changeable neurotransmitters level in biological fluids can be a marker of several neurological disorders. Because of its significance in analytical techniques and diagnostics, sensitive and selective detection of neurotransmitters is increasingly attracting a lot of attention in different areas of bio-analysis or biomedical research. Recently, fluorescent techniques for detection of catecholamines have attracted interests due to their reasonable cost, convenient control, as well as maneuverability in biological environments. Nevertheless, with the observed need for a sensitive and selective catecholamines sensor, the development of a convenient method for this neurotransmitter is still at its basic level. The manipulation of nanostructured materials in conjunction with biological molecules has led to the development of a new class of hybrid modified biosensors in which both enhancement of charge transport and biological activity preservation may be obtained. Immobilization of biomaterials on electrode surfaces is the crucial step in fabricating electrochemical as well as optical biosensors and bioelectronic devices. Continuing systematic investigation in the manufacturing of enzyme–conducting sensitive systems, here is presented a convenient fluorescence sensing strategy for catecholamines detection based on FRET (fluorescence resonance energy transfer) phenomena observed for, i.e., complexes of Fe²⁺ and epinephrine. The biosensor was constructed using low temperature co-fired ceramics technology (LTCC). This sensing system used the catalytical oxidation of catecholamines and quench of the strong luminescence of obtained complexes due to FRET. The detection process was based on the oxidation of substrate in the presence of the enzyme–laccase/tyrosinase.

Keywords: biosensor, conducting polymer, enzyme, FRET, LTCC

Procedia PDF Downloads 243

Authors: Abdelmoniem A. Attaelmanan, Ahmed A. H. Siddig

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This study was conducted in light of continual global climate changes that projected increasing aridity, changes in soil fertility, and pollution. Plant growth and development largely depend on the combination of availing water and nutrients in the soil. Changes in the climate and atmospheric chemistry can cause serious effects on these growth factors. Plant carbon (C), nitrogen (N), and hydrogen (H) play a fundamental role in the maintenance of ecosystem structure and function. Hashab (Acacia senegal), which produces gum Arabic, supports dryland ecosystems in tropical zones by its potentiality to restore degraded soils; hence it is ecologically and economically important for the dry areas of sub-Saharan Africa. The study aims at investigating the effects of water stress (simulated drought) and poor soil type on Acacia senegal C, N, and H contents. Seven days old seedlings were assigned to the treatments in Split- plot design for four weeks. The main plot is irrigation interval (well-watered and water-stressed), and the subplot is soil types (silt and sand soils). Seedling's C%, N%, and H% were measured using CHNS-O Analyzer and applying Standard Test Method. Irrigation intervals and soil types had no effects on seedlings and leaves C%, N%, and H%, irrigation interval had affected stem C and H%, both irrigation intervals and soil types had affected root N% and interaction effect of water and soil was found on leaves and root's N%. Synthesis application of well-watered irrigation with soil that is rich in N and other nutrients would result in the greatest seedling C, N, and H content which will enhance growth and biomass accumulation and can play a crucial role in ecosystem productivity and services in the dryland regions.

Keywords: Acacia senegal, Africa, climate change, drylands, nutrients biomass, Sub-Saharan, Sudan

Procedia PDF Downloads 96

Authors: Galih Imaduddin

Abstract:

Knowledge has been viewed as one of the most important resources in organizations, including those that operate in the healthcare sector. On that basis, Knowledge Management (KM) is crucial for healthcare organizations to improve their productivity and ensure effective utilization of their resources. Despite the growing interests to understand how KM might work for healthcare organizations, there is only a modest amount of empirical inquiries which have specifically focused on the tools and initiatives to share knowledge. Hence, the main purpose of this paper is to investigate the way healthcare organizations, particularly public sector ones, utilize knowledge sharing tools and initiatives for the benefit of patient-care. Employing a qualitative method, 13 (thirteen) Community Health Centers (CHCs) from a high-performing district health setting in Indonesia were observed. Data collection and analysis involved a repetition of document retrievals and interviews (n=41) with multidisciplinary health professionals who work in these CHCs. A single case study was cultivated reflecting on the means that were used to share knowledge, along with the factors that inhibited the exchange of knowledge among those health professionals. The study discovers that all of the thirteen CHCs exhibited and applied knowledge sharing means which included knowledge documents, virtual communication channels (i.e. emails and chatting applications), and social learning forums such as staff meetings, morning briefings, and communities of practices. However, the intensity of utilization was different among these CHCs, in which organizational culture, leadership, professional boundaries, and employees’ technological aptitude were presumed to be the factors that inhibit knowledge sharing processes. Making a distance with the KM literature of other sectors, this study denounces the primacy of technology-based tools, suggesting that socially-based initiatives could be more reliable for sharing knowledge. This suggestion is largely due to the nature of healthcare work which is still predominantly based on the tacit form of knowledge.

Keywords: knowledge management, knowledge sharing, knowledge sharing tools and initiatives, knowledge sharing inhibitors, primary health care organizations

Procedia PDF Downloads 227

Authors: Pratap K. Chhotaray, Jitendriya Swain, Ashok Mishra, Ramesh L. Gardas

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Ionic liquids (ILs) are molten salts, consist predominantly of ions and found to be liquid below 100°C. The unparalleled growing interest in ILs is based upon their never ending design flexibility. The use of ILs as a co-solvent in binary as well as a ternary mixture with molecular solvents multifold it’s utility. Since polarity is one of the most widely applied solvent concepts which represents simple and straightforward means for characterizing and ranking the solvent media, its study for a binary mixture of ILs is crucial for its widespread application and development. The primary approach to the assessment of solution phase intermolecular interactions, which generally occurs on the picosecond to nanosecond time scales, is to exploit the optical response of photophysical probe. Pyrene butyric acid (PBA) is used as fluorescence probe due to its high quantum yield, longer lifetime and high solvent polarity dependence of fluorescence spectra. Propylammonium formate (PAF) is the IL used for this study. Both the UV-absorbance spectra and steady state fluorescence intensity study of PBA in different concentration of aqueous PAF, reveals that with an increase in PAF concentration, both the absorbance and fluorescence intensity increases which indicate the progressive solubilisation of PBA. Whereas, near about 50% of IL concentration, all of the PBA molecules get solubilised as there are no changes in the absorbance and fluorescence intensity. Furthermore, the ratio II/IV, where the band II corresponds to the transition from S1 (ν = 0) to S0 (ν = 0), and the band IV corresponds to transition from S1 (ν = 0) to S0 (ν = 2) of PBA, indicates that the addition of water into PAF increases the polarity of the medium. Time domain lifetime study shows an increase in lifetime of PBA towards the higher concentration of PAF. It can be attributed to the decrease in non-radiative rate constant at higher PAF concentration as the viscosity is higher. The monoexponential decay suggests that homogeneity of solvation environment whereas the uneven width at full width at half maximum (FWHM) indicates there might exist some heterogeneity around the fluorophores even in the water-IL mixed solvents.

Keywords: fluorescence, ionic liquid, lifetime, polarity, pyrene butyric acid

Procedia PDF Downloads 442

Authors: J. Cadby, K. Miyazawa

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Information and Communication Technology (ICT) has been widely adopted into the agricultural landscape with advancements of mobile connectivity and data accessibility. In developed nations, mobile-technology is well integrated into marketing transactions, and also plays a crucial role in making data-driven decisions on-farm. In developing nations, mobile banking and access to agricultural extension services allow for informed decision-making and smoother transactions. In addition, the availability of updated and readily available market and climate data provides a negotiation platform, reducing economic risks for farmers worldwide. The total usage of mobile technology has risen over the past 20 years, and almost three-quarters of the world’s population subscribes to mobile technology. This study reviewed mobile technology integration into agricultural systems in developing and developed nations. Data from secondary sources were collected and investigated. The objectives of the study include a review of the success of mobile banking transactions in developing nations, and a review of application and SMS based services for direct marketing in both developed and developing nations. Rural farmers in developing countries with access to diverse m-banking options experienced increased access to farm investment resources with the use of mobile banking technology. Rural farmers involved in perishable crop production were also more likely to benefit from mobile platform sales participation. ICT programs reached through mobile application and SMS increased access to agricultural extension materials and marketing tools for demographics that faced literacy-challenges and isolated markets. As mobile technology becomes more ubiquitous in the global agricultural system, training and market opportunities to facilitate mobile usage in developing agricultural systems are necessary. Digital skills training programs are necessary in order to improve equal global adoption of ICT in agriculture.

Keywords: market participation, mobile banking, mobile technology, rural farming

Procedia PDF Downloads 229

Authors: Sevgi Özlem Bulu, Arif Furkan Mendi, Tolga Erol, İzzet Gökhan Özbilgin

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The rapid progress of technology in today's competitive conditions has also accelerated companies' technology development activities. As a result, companies are paying more attention to R&D studies and are beginning to allocate a larger share to R&D projects. A more systematic, comprehensive, target-oriented implementation of R&D studies is crucial for the company to achieve successful results. As a consequence, Technology Roadmap (TRM) is gaining importance as a management tool. It has critical prospects for achieving medium and long term success as it contains decisions about past business, future plans, technological infrastructure. When studies on TRM are examined, projects to be placed on the roadmap are selected by many different methods. Generally preferred methods are based on multi-criteria decision making methods. Management of selected projects becomes an important point after the selection phase of the projects. At this stage, TRM are used. TRM can be created in many different ways so that each institution can prepare its own Technology Roadmap according to their strategic plan. Depending on the intended use, there can be TRM with different layers at different sizes. In the evaluation phase of the R&D projects and in the creation of the TRM, HAVELSAN, Turkey's largest defense company in the software field, carries out this process with great care and diligence. At the beginning, suggested R&D projects are evaluated by the Technology Management Board (TMB) of HAVELSAN in accordance with the company's resources, objectives, and targets. These projects are presented to the TMB periodically for evaluation within the framework of certain criteria by board members. After the necessary steps have been passed, the approved projects are added to the time-based TRM, which is composed of four layers as market, product, project and technology. The use of a four-layered roadmap provides a clearer understanding and visualization of company strategy and objectives. This study demonstrates the benefits of using TRM, four-layered Technology Roadmapping and the possibilities for the institutions in the defense industry.

Keywords: technology roadmap, research and development project, project selection, research development in defense industry

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Authors: Kira Toxopeus, Kamran Siddiqui

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Channel flows are common in a wide range of engineering applications. In some types of channel flows, particularly the ones involving chemical or biological processes, the control of the flow temperature is crucial to maintain the optimal conditions for the chemical reaction or to control the growth of biological species. This often becomes an issue when the flow experiences temperature fluctuations due to external conditions. While active heating and cooling could regulate the channel temperature, it may not be feasible logistically or economically and is also regarded as a non-sustainable option. Thermal energy storage utilizing phase change material (PCM) could provide the required thermal regulation sustainably by storing the excess heat from the channel and releasing it back as required, thus regulating the channel temperature within a range in the proximity of the PCM melting temperature. However, in designing such systems, the configuration of the PCM storage within the channel is critical as it could influence the channel flow dynamics, which would, in turn, affect the heat exchange between the channel fluid and the PCM. The present research is focused on the investigation of the flow dynamical behavior in the channel during heat transfer from the channel flow to the PCM thermal energy storage. Offset vertical columns in a narrow channel were used that contained the PCM. Two different column shapes, square and circular, were considered. Water was used as the channel fluid that entered the channel at a temperature higher than that of the PCM melting temperature. Hence, as the water was passing through the channel, the heat was being transferred from the water to the PCM, causing the PCM to store the heat through a phase transition from solid to liquid. Particle image velocimetry (PIV) was used to measure the two-dimensional velocity field of the channel flow as it flows between the PCM columns. Thermocouples were also attached to the PCM columns to measure the PCM temperature at three different heights. Three different water flow rates (0.5, 0.75 and 1.2 liters/min) were considered. At each flow rate, experiments were conducted at three different inlet water temperatures (28ᵒC, 33ᵒC and 38ᵒC). The results show that the flow rate and the inlet temperature influenced the flow behavior inside the channel.

Keywords: channel flow, phase change material, thermal energy storage, thermal regulation

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Authors: Sarchil H. Qader, Andrew Harfoot, Mathias Kuepie, Sabrina Juran, Attila Lazar, Andrew J. Tatem

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The need for high-quality, reliable, and timely population data, including demographic information, to support the achievement of the sustainable development goals (SDGs) in all countries was recognized by the United Nations' 2030 Agenda for sustainable development. However, many low and middle-income countries lack reliable and recent census data. To achieve reliable and accurate census and survey outputs, up-to-date census enumeration areas and digital national sampling frames are critical. Census enumeration areas (EAs) are the smallest geographic units for collection, disseminating, and analyzing census data and are often used as a national sampling frame to serve various socio-economic surveys. Even for countries that are wealthy and stable, creating and updating EAs is a difficult yet crucial step in preparing for a national census. Such a process is commonly done manually, either by digitizing small geographic units on high-resolution satellite imagery or walking the boundaries of units, both of which are extremely expensive. We have developed a user-friendly tool that could be employed to generate draft EA boundaries automatically. The tool is based on high-resolution gridded population and settlement datasets, GPS household locations, building footprints and uses publicly available natural, man-made and administrative boundaries. Initial outputs were produced in Burkina Faso, Paraguay, Somalia, Togo, Niger, Guinea, and Zimbabwe. The results indicate that the EAs are in line with international standards, including boundaries that are easily identifiable and follow ground features, have no overlaps, are compact and free of pockets and disjoints, and the boundaries are nested within administrative boundaries.

Keywords: enumeration areas, national sampling frame, gridded population data, preEA tool

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Authors: Lee A. Browne, K. Rumbold

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The first fluorinating enzyme, 5'-fluoro-5'-deoxyadenosine synthase (fluorinase) was isolated from the soil bacterium Streptomyces cattleya. Such an enzyme, with the ability to catalyze a C-F bond, presents great potential as a biocatalyst. Naturally fluorinated compounds are extremely rare in nature. As a result, the number of fluorinases identified remains relatively few. The field of fluorination is almost completely synthetic. However, with the increasing demand for fluorinated organic compounds of commercial value in the agrochemical, pharmaceutical and materials industries, it has become necessary to utilize biologically based methods such as biocatalysts. A key step in this crucial process is the large-scale production of the fluorinase enzyme in considerable quantities for industrial applications. Thus, this study aimed to optimize expression of the fluorinase enzyme in both prokaryotic and eukaryotic expression systems in order to obtain high protein yields. The fluorinase gene was cloned into the pET 41b(+) and pPinkα-HC vectors and used to transform the expression hosts, E.coli BL21(DE3) and Pichia pastoris (PichiaPink™ strains) respectively. Expression trials were conducted to select optimal conditions for expression in both expression systems. Fluorinase catalyses a reaction between S-adenosyl-L-Methionine (SAM) and fluoride ion to produce 5'-fluorodeoxyadenosine (5'FDA) and L-Methionine. The activity of the enzyme was determined using HPLC by measuring the product of the reaction 5'FDA. A gradient mobile phase of 95:5 v/v 50mM potassium phosphate buffer to a final mobile phase containing 80:20 v/v 50mM potassium phosphate buffer and acetonitrile were used. This resulted in the complete separation of SAM and 5’-FDA which eluted at 1.3 minutes and 3.4 minutes respectively. This proved that the fluorinase enzyme was active. Optimising expression of the fluorinase enzyme was successful in both E.coli and PichiaPink™ where high expression levels in both expression systems were achieved. Protein production will be scaled up in PichiaPink™ using fermentation to achieve large-scale protein production. High level expression of protein is essential in biocatalysis for the availability of enzymes for industrial applications.

Keywords: biocatalyst, expression, fluorinase, PichiaPink™

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Authors: Ana C. Sousa, Beatriz C. Santos, Fátima N. Serralha

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The Tagus estuary is heavily affected by industrial and urban activities, making bioremediation studies crucial for environmental preservation. Fuel contamination in the area can arise from various anthropogenic sources, such as oil spills from shipping, fuel storage and transfer operations, and industrial discharges. These pollutants can cause severe harm to the ecosystem and the organisms, including humans, that inhabit it. Nonetheless, there are always natural organisms with the ability to resist these pollutants and transform them into non-toxic or harmless substances, which defines the process of bioremediation. Exploring the microbial communities existing in soil and their capacity to break down hydrocarbons has the potential to enhance the development of more efficient bioremediation approaches. The aim of this investigation was to explore the existence of hydrocarbonoclastic microorganisms in six locations within the Tagus estuary, three on the north bank: Trancão River, Praia Fluvial do Cais das Colinas and Praia de Algés, and three on the south bank: Praia Fluvial de Alcochete, Praia Fluvial de Alburrica, and Praia da Trafaria. In all studied locations, native microorganisms of the genus Pseudomonas were identified. The bioremediation rate of common hydrocarbons like gasoline, hexane, and toluene was assessed using the redox indicator 2,6-dichlorophenolindophenol (DCPIP). Effective hydrocarbon-degrading bacterial strains were identified in all analyzed areas, despite adverse environmental conditions. The highest bioremediation rates were achieved for gasoline (68%) in Alburrica, hexane (65%) in Algés, and toluene (79%) in Algés. Generally, the bacteria demonstrated efficient degradation of hydrocarbons added to the culture medium, with higher rates of aerobic biodegradation of hydrocarbons observed. These findings underscore the necessity for further in situ studies to better comprehend the relationship between native microbial communities and the potential for pollutant degradation in soil.

Keywords: biodegradability rate, hydrocarbonoclastic microorganisms, soil bioremediation, tagus estuary

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Authors: Anka Gorgiev, Chris Martin, Nikolaos Dimitriadis, Dimitrios V. Nikolaidis

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This paper is part of a doctoral research study aimed to identify behavioral indicators for the existence of the new marketing paradigm. Neuromarketing is becoming a growing trend in the marketing industry worldwide and it is capturing a lot of interest among the members of academia and the practitioner community. However, it is still not very clear how big of an impact neuromarketing might have in the following years. In an effort to get closer to an answer, this study investigates behavioral intentions and willingness to adopt neuromarketing and its practices by the marketing professionals, including academics, practitioners, students, researchers, experts and journal editors. The participants in the study include marketing professionals at different levels of neuromarketing fluency with residency in the United States of America and the South East Europe. The total of 19 participants participated in the interviews, all of whom belong to more than one group of marketing professionals. The authors use qualitative research approach and open-ended interview questions specifically developed to assess ideas, beliefs and opinions that marketing professionals hold towards neuromarketing. In constructing the interview questions, the authors have used the theory of planned behavior, the prototype willingness model and the technology acceptance model as a theoretical framework. Previous studies have not explicitly investigated the behavioral intentions of marketing professionals to engage in neuromarketing behavior, which is described here as a tendency to apply neuromarketing assumptions and tools in usual marketing practices. This study suggests that the marketing professionals believe that neuromarketing can contribute to the business in a positive way and outlines the main advantages and disadvantages of adopting neuromarketing as identified by the participants. In addition, the study reveals an emerging image of an exemplar company that is perceived to be using neuromarketing, including the most common characteristics and attributes. These findings are believed to be crucial in facilitating a way for neuromarketing field to have a broader impact than it currently does by recognizing and understanding the limitations that such exemplars imply and how that has an effect on the decision-making of marketing professionals.

Keywords: behavioral intentions, marketing paradigm, neuromarketing adoption, theory of planned behavior

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Authors: Nazanin Reza Nejad, Kamil Guley

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Shopping is one of the most entertaining activities of daily life. In parallel to this, the successful settings of the stores impress the customers and made it more appealing for the users. The design of the atmosphere is the language of the interior space, and this design directly affects users’ emotions and perceptions. One of the goals of interior design is to increase the quality of the designed space. A well-designed venue satisfies the user and ensures happiness and safety. Thus, customers are turned into frequent users of the store. Spaces without the right designs negatively influence the user. The accurate interior design of the stores becomes crucial at this point. This study aims to act as a guideline for the betterment of the interior design of a newly designed or already existing clothing store located on the shopping streets of the cities. In light of the relevant literature review, the most important point in interior store design is the design and ambiance factors and how these factors are used in the interior space of the stores. Within the scope of this study, 27 clothing stores located on Dereboyu, the largest shopping street in Nicosia, the capital of North Cyprus, were examined. The examined stores were grouped as brand stores and non-brand stores which sell products from different production sites. The observation regarding the interiors of the selected stores was analyzed through qualitative and quantitative research methods. The arrangements of the sub-functions in the stores were analyzed through various reading methods over the plan schemes and recorded images. The sub-functions of all examined stores are compared against the ambiance and design factors in the literature, and results were interpreted accordingly. At the end of the study, the differences among stores that belong to a brand with an identity and stores which have not yet established an identity were identified and compared. The results of the comparisons were used to offer implications for the betterment of the interior design on a future or already existing store on the street. Thus, the study was concluded to be a guideline for people interested in interior store design.

Keywords: atmosphere, ambiance factors, clothing store, identity, interior design

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Authors: Surya Prakash Gajagouni, Akram Alfantazi, Imad Barsoum

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Austenitic stainless steels are widely recognized for their exceptional corrosion resistance and mechanical properties, rendering them indispensable materials across various industries from construction to biomedical applications. However, in chloride and high temperature atmosphere it to further enhance their surface properties, anodization has emerged as a promising surface treatment technique. Anodization modifies the surface of stainless steels by creating a protective oxide layer, improving corrosion resistance and imparting additional functional characteristics. This paper explores the structural and corrosion characteristics of anodized austenitic stainless steels (AISI 304) using a two-step anodic technique. We utilized a perchloric acid-based electrolyte followed by an ammonium fluoride-based electrolyte. This sequential approach aimed to cultivate deeper and intricately self-ordered nanopore oxide arrays on a substrate made of 304 stainless steel. Electron Microscopic (SEM and TEM) images revealed nanoporous layered structures with increased length and crack development correlating with higher voltage and anodization time. Surface composition and chemical oxidation state of surface-treated SS were determined using X-ray photoelectron spectroscopy (XPS) techniques, revealing a surface layer rich in Ni and suppressed Cr, resulting in a thin film composed of Ni and Fe oxide compared to untreated SS. Electrochemical studies demonstrated enhanced corrosion resistance in a strong alkaline medium compared to untreated SS. Understanding the intricate relationship between the structural features of anodized stainless steels and their corrosion resistance is crucial for optimizing the performance of these materials in diverse applications. This study aims to contribute to the advancement of surface engineering strategies for enhancing the durability and functionality of austenitic stainless steels in aggressive environments.

Keywords: austenitic stainless steel, anodization, nanoporous oxides, marine corrosion

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Authors: Vistorina Amputu, Katja Tielboerger, Nichola Knox

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Primary productivity in dry savannahs is constraint by moisture availability and under increasing anthropogenic pressure. Thus, considering climate change and the unprecedented pace and scale of rangeland deterioration, methods for assessing the status of such rangelands should be easy to apply, yield reliable and repeatable results that can be applied over large spatial scales. Global and local scale monitoring of rangelands through satellite data and labor-intensive field measurements respectively, are limited in accurately assessing the spatiotemporal heterogeneity of vegetation dynamics to provide crucial information that detects degradation in its early stages. Fortunately, newly emerging techniques such as unmanned aerial vehicles (UAVs), associated miniaturized sensors and improving digital photogrammetric software provide an opportunity to transcend these limitations. Yet, they have not been extensively calibrated in natural systems to encompass their complexities if they are to be integrated for long-term monitoring. Limited research using drone technology has been conducted in arid savannas, for example to assess the health status of this dynamic two-layer vegetation ecosystem. In our study, we fill this gap by testing the relationship between UAV-estimated cover of rangeland functional attributes and field data collected in discrete sample plots in a Namibian dryland savannah along a degradation gradient. The first results are based on a supervised classification performed on the ultra-high resolution multispectral imagery to distinguish between rangeland functional attributes (bare, non-woody, and woody), with a relatively good match to the field observations. Integrating UAV-based observations to improve rangeland monitoring could greatly assist in climate-adapted rangeland management.

Keywords: arid savannah, degradation gradient, field observations, narrow-band sensor, supervised classification

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Authors: Alok Kumar Yadav, Saurabh Saraswat, Preeti Sirohi, Manjoo Rani, Sameer Srivastava, Manish Pratap Singh, Nand K. Singh

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The development of antibiotic resistance in bacteria is increasing at an alarming rate, and this is considered as one of the most serious threats in the history of medicine, and an alternative solution should be derived so as to tackle this problem. In many countries, people use the medicinal plants for the treatment of various diseases as these are cheaper, easily available and least toxic. Syzygium cumini is used for the treatment of various kinds of diseases but their mechanism of action is not reported. The antimicrobial activity of Syzygium cumini was tested by the well diffusion assay and zone of inhibition was reported to be 20.06 mm as compared to control with MIC of 0.3 mg/ml. Genomic DNA fragmentation of Bacillus subtilis revealed apoptosis and FE-SEM indicate cell wall cracking on several intervals of time. Propidium iodide staining results showed that few bacterial cells were stained in the control and population of stained cells increase after exposing them for various period of time. Flow cytometric kinetic data analysis on the membrane permeabilization in bacterial cell showed the significant contribution of antimicrobial potential of the seed extract on antimicrobial-induced permeabilization. Two components of Syzygium cumini methanolic seed extract was found to be quite active against four enzymes like PDB ID- 1W5D, 4OX3, 3MFD and 5E2F which have a very crucial role in membrane synthesis in Bacillus subtilis by in silico analysis. Through in silico analysis, lupeol showed highest binding energy for macromolecule 1W5D and 4OX3 whereas stigmasterol showed the highest binding energy for macromolecule 3MFD and 5E2F respectively. It showed that methanolic seed extract of Syzygium cumini can be used for the inhibition of foodborne infections caused by Bacillus subtilis and also as an alternative of prevalent antibiotics.

Keywords: antibiotics, Bacillus subtilis, inhibition, Syzygium cumini

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Anna Rhodora A. Solar, Jan Emil N. Langomez

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Social Media plays a crucial role in influencing Philippine public opinion with regard to a variety of socio-political issues. This became evident in the massacre of 44 members of the Special Action Force (SAF 44) tasked by the Philippine government to capture one of the US Federal Bureau of Investigation’s most wanted terrorists. The incident was said to be perpetrated by members of the Moro Islamic Liberation Front and the Bangsamoro Islamic Freedom Fighters. Part of the online discourse within Philippine cyberspace sparked intense debates on Filipino Muslim identity, with several Facebook viral posts linking Islam as a factor to the tragic event. Facebook is considered to be the most popular social media platform in the Philippines. As such, this begs the question of the extent to which social media, specifically Facebook, shape the perceptions of Filipinos on Filipino Muslims. This study utilizes Habermas’ theory of communicative action as it offers an explanation on how public sphere such as social media could be a network for communicating information and points of view through free and open dialogue among equal citizens to come to an understanding or common perception. However, the paper argues that communicative action which is aimed at reaching understanding free from force, and strategic action which is aimed at convincing someone through argumentation may not necessarily be mutually exclusive since reaching an understanding can also be considered as a result of convincing someone through argumentation. Moreover, actors may clash one another in their ideas before reaching common understanding, hence the presence of force. Utilizing content analysis on the Facebook posts with Islamic component that went viral after the massacre of the SAF 44, this paper argues that framing the image of Filipino Muslims through Facebook reflects both communicative and strategic actions. Moreover, comment threads on viral posts manifest force albeit implicit.

Keywords: communication, Muslim, Philippines, social media

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Authors: Beatrice Adeoye

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Background: One of the health challenges developing countries face is air pollution from indoor and outdoor activities. Smoke as a result of cooking, burning wastes and power generators litter the air space on a continual basis due to poverty and governance challenges. The short and long term implications of these actions are enormous and studies have attributed smoke as one of the leading preventable risk factors contributing to global burden of respiratory infections. Design/Methods: The issue at hand therefore includes an exploration of the existing policy frameworks regarding smoke, adherence to international conventions and practices, and more importantly the activities of the government in addressing these issues. Aside this, an understanding of the implications of smoke on peoples’ health and well-being also become crucial. Consequently, this article seeks to interrogate the effect of smoke on the health and well-being of Nigerians and the activities of the policy makers in addressing these challenges. Results (Main Argument): This study reviewed both primary and secondary data on poverty, smoke emission and attendant health risks coupled with existing policies on smoke and air pollution in the country. For instance, over 69% of Nigerians are poor, ranking third in the world; 2.9 billion people live in homes using wood, coal or dung as their primary cooking fuel; equally, 50.6% of Nigerians has no access to regular electricity supply. Further, sustainable policy regarding smoke emission is lacking in the country. This work further submitted that continued low standard of living as a result of governance challenges coupled with a lack of sustainable policy have aggravated the health risks related to smoke in the country. Conclusions: The implication on the health of the children, mothers and vulnerable groups for the future of the country is enormous and may continue if not addressed. Urgent attention therefore needs to be focused on this area considering what this portends for the nation now and in the future.

Keywords: air pollution, indoor, outdoor, respiratory infections

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Authors: Michael A. Sprayberry, Vincent C. Paquit

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Process parameter optimization in metal powder bed electron beam melting (MPBEBM) is crucial to ensure the technology's repeatability, control, and industry-continued adoption. Despite continued efforts to address the challenges via the traditional design of experiments and process mapping techniques, there needs to be more successful in an on-the-fly optimization framework that can be adapted to MPBEBM systems. Additionally, data-intensive physics-based modeling and simulation methods are difficult to support by a metal AM alloy or system due to cost restrictions. To mitigate the challenge of resource-intensive experiments and models, this paper introduces a Multi-Objective Reinforcement Learning (MORL) methodology defined as an optimization problem for MPBEBM. An off-policy MORL framework based on policy gradient is proposed to discover optimal sets of beam power (P) – beam velocity (v) combinations to maintain a steady-state melt pool depth and phase transformation. For this, an experimentally validated Eagar-Tsai melt pool model is used to simulate the MPBEBM environment, where the beam acts as the agent across the P – v space to maximize returns for the uncertain powder bed environment producing a melt pool and phase transformation closer to the optimum. The culmination of the training process yields a set of process parameters {power, speed, hatch spacing, layer depth, and preheat} where the state (P,v) with the highest returns corresponds to a refined process parameter mapping. The resultant objects and mapping of returns to the P-v space show convergence with experimental observations. The framework, therefore, provides a model-free multi-objective approach to discovery without the need for trial-and-error experiments.

Keywords: additive manufacturing, metal powder bed fusion, reinforcement learning, process parameter optimization

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Authors: Ignatius Madu

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The study aims at assessing the environmental and socioeconomic determinants of climate change resilience in rural Nigeria. This is necessary because researches and development efforts on building climate change resilience of rural areas in developing countries are usually made without the knowledge of the impacts of the inherent rural characteristics that determine resilient capacities of the households. This has, in many cases, led to costly mistakes, delayed responses, inaccurate outcomes, and other difficulties. Consequently, this assessment becomes crucial not only to policymakers and people living in risk-prone environments in rural areas but also to fill the research gap. To achieve the aim, secondary data were obtained from the Annual Abstract of Statistics 2017, LSMS-Integrated Surveys on Agriculture and General Household Survey Panel 2015/2016, and National Agriculture Sample Survey (NASS), 2010/2011.Resilience was calculated by weighting and adding the adaptive, absorptive and anticipatory measures of households variables aggregated at state levels and then regressed against rural environmental and socioeconomic characteristics influencing it. From the regression, the coefficients of the variables were used to compute the impacts of the variables using the Stochastic Regression of Impacts on Population, Affluence and Technology (STIRPAT) Model. The results showed that the northern States are generally low in resilient indices and are impacted less by the development indicators. The major determining factors are percentage of non-poor, environmental protection, road transport development, landholding, agricultural input, population density, dependency ratio (inverse), household asserts, education and maternal care. The paper concludes that any effort to a successful resilient building in rural areas of the country should first address these key factors that enhance rural development and wellbeing since it is better to take action before shocks take place.

Keywords: climate change resilience; spatial impacts; STIRPAT model; Nigeria

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