Search results for: energy performance assessment

Authors: Jigg Pelayo, Ricardo Villar, Einstine Opiso

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The rampant and unintended spread of urban areas resulted in increasing artificial component features in the land cover types of the countryside and bringing forth the urban heat island (UHI). This paved the way to wide range of negative influences on the human health and environment which commonly relates to air pollution, drought, higher energy demand, and water shortage. Land cover type also plays a relevant role in the process of understanding the interaction between ground surfaces with the local temperature. At the moment, the depiction of the land surface temperature (LST) at city/municipality scale particularly in certain areas of Misamis Oriental, Philippines is inadequate as support to efficient mitigations and adaptations of the surface urban heat island (SUHI). Thus, this study purposely attempts to provide application on the Landsat 8 satellite data and low density Light Detection and Ranging (LiDAR) products in mapping out quality automated LST model and crop-level land cover classification in a local scale, through theoretical and algorithm based approach utilizing the principle of data analysis subjected to multi-dimensional image object model. The paper also aims to explore the relationship between the derived LST and land cover classification. The results of the presented model showed the ability of comprehensive data analysis and GIS functionalities with the integration of object-based image analysis (OBIA) approach on automating complex maps production processes with considerable efficiency and high accuracy. The findings may potentially lead to expanded investigation of temporal dynamics of land surface UHI. It is worthwhile to note that the environmental significance of these interactions through combined application of remote sensing, geographic information tools, mathematical morphology and data analysis can provide microclimate perception, awareness and improved decision-making for land use planning and characterization at local and neighborhood scale. As a result, it can aid in facilitating problem identification, support mitigations and adaptations more efficiently.

Keywords: LiDAR, OBIA, remote sensing, local scale

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: Mustafa Metin Donma, Orkide Donma

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Obesity is a disease with an ever-increasing prevalence throughout the world. The metabolic network associated with obesity is very complicated. In metabolic syndrome (MetS), it becomes even more difficult to understand. Within this context, hormones, cytokines, and many others participate in this complex matrix. The collaboration among all of these parameters is a matter of great wonder. Cortisol, as a stress hormone, is closely associated with obesity. Thyroid hormones are involved in the regulation of energy as well as glucose metabolism with all of its associates. Fetuin A is known for years; however, the involvement of this parameter in obesity discussions is rather new. Recently, it has been defined as one of the new generation markers of obesity. In this study, the aim was to introduce complex interactions among all to be able to make clear comparisons, at least for a part of this complicated matter. Morbid obese (MO) children participated in the study. Two groups with 46 MO children and 43 with MetS were constituted. All children included in the study were above 99th age- and sex-adjusted body mass index (BMI) percentiles according to World Health Organization criteria. Forty-three morbid obese children in the second group had also MetS components. Informed consent forms were filled by the parents of the participants. The institutional ethics committee has given approval for the study protocol. Data as well as the findings of the study were evaluated from a statistical point of view. Two groups were matched for their age and gender compositions. Significantly higher body mass index (BMI), waist circumference, thyrotropin, and insulin values were observed in the MetS group. Triiodothyronine concentrations did not differ between the groups. Elevated levels for thyroxin, cortisol, and fetuin-A were detected in the MetS group compared to the first group (p > 0.05). In MO MetS- group, cortisol was correlated with thyroxin and fetuin-A (p < 0.05). In the MO MetS+ group, none of these correlations were present. Instead, a correlation between cortisol and thyrotropin was found (p < 0.05). In conclusion, findings have shown that cortisol was the key player in severely obese children. The association of this hormone with the participants of thyroid hormone metabolism was quite important. The lack of association with fetuin A in the morbid obese MetS+ group has suggested the possible interference of MetS components in the behavior of this new generation obesity marker. The most remarkable finding of the study was the unique correlation between cortisol and thyrotropin in the morbid obese MetS+ group, suggesting that thyrotropin may serve as a target along with cortisol in the morbid obese MetS+ group. This association may deserve specific attention during the development of remedies against MetS in the pediatric population.

Keywords: children, cortisol, fetuin A, morbid obesity, thyrotropin

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Authors: Morteza Malek Yarand, Hadi Saebi Monfared

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This study aimed at designing and developing a mechanical force gauge for the square watermelon mold for the first time. It also tried to introduce the square watermelon characteristics and its production limitations. The mechanical force gauge performance and the product itself were also described. There are three main designable gauge models: a. hydraulic gauge, b. strain gauge, and c. mechanical gauge. The advantage of the hydraulic model is that it instantly displays the pressure and thus the force exerted by the melon. However, considering the inability to measure forces at all directions, complicated development, high cost, possible hydraulic fluid leak into the fruit chamber and the possible influence of increased ambient temperature on the fluid pressure, the development of this gauge was overruled. The second choice was to calculate pressure using the direct force a strain gauge. The main advantage of these strain gauges over spring types is their high precision in measurements; but with regard to the lack of conformity of strain gauge working range with water melon growth, calculations were faced with problems. Finally the mechanical pressure gauge has advantages, including the ability to measured forces and pressures on the mold surface during melon growth; the ability to display the peak forces; the ability to produce melon growth graph thanks to its continuous force measurements; the conformity of its manufacturing materials with the required physical conditions of melon growth; high air conditioning capability; the ability to permit sunlight reaches the melon rind (no yellowish skin and quality loss); fast and straightforward calibration; no damages to the product during assembling and disassembling; visual check capability of the product within the mold; applicable to all growth environments (field, greenhouses, etc.); simple process; low costs and so forth.

Keywords: mechanical force gauge, mold, reshaped fruit, square watermelon

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Authors: David de Oliveira Costa, Miguel Ângelo Lellis Moreira, Carlos Francisco Simões Gomes, Daniel Augusto de Moura Pereira, Marcos dos Santos

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Strategic management is a fundamental process for global companies that intend to remain competitive in an increasingly dynamic and complex market. To do so, it is necessary to maintain alignment with their principles and values. The Balanced Scorecard (BSC) proposes to ensure that the overall business performance is based on different perspectives (financial, customer, internal processes, and learning and growth). However, relying solely on the BSC may not be enough to ensure the success of strategic management. It is essential that companies also evaluate and prioritize strategic projects that need to be implemented to ensure they are aligned with the business vision and contribute to achieving established goals and objectives. In this context, the proposition involves the incorporation of the SAPEVO-M multicriteria method to indicate the degree of relevance between different perspectives. Thus, the strategic objectives linked to these perspectives have greater weight in the classification of structural projects. Additionally, it is proposed to apply the concept of the Impact & Probability Matrix (I&PM) to structure and ensure that strategic projects are evaluated according to their relevance and impact on the business. By structuring the business's strategic management in this way, alignment and prioritization of projects and actions related to strategic planning are ensured. This ensures that resources are directed towards the most relevant and impactful initiatives. Therefore, the objective of this article is to present the proposal for integrating the BSC methodology, the SAPEVO-M multicriteria method, and the prioritization matrix to establish a concrete weighting of strategic planning and obtain coherence in defining strategic projects aligned with the business vision. This ensures a robust decision-making support process.

Keywords: MCDA process, prioritization problematic, corporate strategy, multicriteria method

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Authors: Suvalux Chaichuchote, Ratchadaporn Thonghem

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Pleurotus mushroom is one of popular edible mushrooms in Thailand. It is much favored by consumers due to its delicious taste and high nutrition. It is commonly used as an ingredient in several dishes. The commercially cultivated strain grown in most farms is the Pleurotus sp., Hed Bhutan, that is widely distributed to mushroom farms throughout the country and can be cultivated almost all year round. However, it demands different cultivated strains from mushroom growers, therefore, the improving mushroom strains should be done to their benefits. In this study, we used a di-mon mating method to hybrid production from Hed Bhutan (P-3) as dikaryon material and monokaryotic mycelium were isolated from basidiospores of other three Pleurotus sp. by single spore isolation. The 3 hybrids: P-3XSA-6, P-3XSB-24 and P-3XSE-5 were recognized from the 12 hybridized successfully. They were appropriate hybridized in terms of fruiting body performance in the three time cycles of cultivation such as the number of days until growing, time for pinning, color and shape of fruiting bodies and yield. For genetic study, genomic DNAs of both Hed Bhutan (P-3) and three hybrids were extracted. A couple of primer ITS1 and ITS4 were used to amplify the gene coding for ITS1, ITS2 and 5.8S rRNA. The similarities between these amplified genes and databases of DNA revealed that Hed Bhutan (P-3) was the Pleurotus pulmonarius as well as P-3XSA-6, P-3XSB-24 and P-3XSE-5 hybrids. Furthermore, Hed Bhutan (P3) and three hybrids were distributed to 3 small-scale farms, with mushroom farming experience, in the countryside. To address this, one hundred and twenty mushroom bags of each strain were supplied to them. The findings, by interview, indicated two mushroom farmers were satisfied with P-3XSA-6 hybrid and P-3XSB-24 hybrid, thanks to their simultaneous fruiting time and good yield. While the other was satisfied with P-3XSB-24 hybrid due to its good yield and P-3XSE-5 hybrids thanks to its gradually fruiting body, benefiting in frequent harvest. Overall, farmers adopted all hybrids to grow as commercially cultivated strains as well as Hed Bhutan (P-3) strain.

Keywords: dikaryon, monokaryon, pleurotus, strain improvement

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Authors: Salomão Manuel Francisco, Manuel António Salgueiro Da Silva, Bento Filipe Barreiras Pinto Cavadas, Teresa Monteiro Seixas

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In bachelor's degrees in Physics Education framework in Angola, and to a certain extent, within the community of Portuguese language countries (CPLP), teaching methodologies rely heavily on theoretical memorization and mathematical demonstrations. This approach often discourages students, particularly the female population, as the reliance on theoretical mathematical demonstrations generates the perception of Physics as an arduous, challenging discipline. To address this challenge and recognize the value of practical application as an evaluative criterion of material truth, we propose a practical activity in Environmental Physics that will be shared with Angolan higher education teachers, who will receive full scaffolding and support from the authors. These teachers, adopting and developing similar activities in a classroom setting, will contribute to the environmental education framework as well. Additionally, this work aligns with different goals of UNESCO's 2030 agenda, namely, specifically, goals 4, 5, 7, 11, 13, and 17. The experimental activity developed in this work is centered around the demonstration of the photovoltaic effect and its application for renewable energy production. The first objective of the activity is to study the variation of electrical power supplied by a photovoltaic system (PV) to an electrical circuit as the angle of light incidence changes. Students can observe that the power supplied to the circuit is greater when light rays fall perpendicularly on the PV. However, as the angle of incidence increases, resulting in a larger area covered by the light rays, the power supplied to the circuit decreases due to lower irradiance. The second objective is to demonstrate that the power output can be maximized by adjusting the circuit load resistance at each irradiance value. In these two parts of the activity, students can analyze experimental data taking into account the irradiance law and the equivalent circuit description of a PV cell. Through detailed data analysis, students are also expected to assess the effects of temperature on PV efficiency degradation and the efficiency enhancement provided by light concentration mechanisms. As a third objective, students can explore how the color of incident light affects the PV output power, considering the quantum nature of light and its interaction with the PV system.

Keywords: experiments, irradiation law, physic teaching, photovoltaic effect

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Authors: G. Henini, Y. Laidani, F. Souahi, A. Labbaci, S. Hanini

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Environmental contamination is a major problem being faced by the society today. Industrial, agricultural, and domestic wastes, due to the rapid development in the technology, are discharged in the several receivers. Generally, this discharge is directed to the nearest water sources such as rivers, lakes, and seas. While the rates of development and waste production are not likely to diminish, efforts to control and dispose of wastes are appropriately rising. Wastewaters from textile industries represent a serious problem all over the world. They contain different types of synthetic dyes which are known to be a major source of environmental pollution in terms of both the volume of dye discharged and the effluent composition. From an environmental point of view, the removal of synthetic dyes is of great concern. Among several chemical and physical methods, adsorption is a promising technique due to the ease of use and low cost compared to other applications in the process of discoloration, especially if the adsorbent is inexpensive and readily available. The focus of the present study was to assess the potentiality of Brahea edulis (BE) for the removal of synthetic dye Yellow bemacid (YB) from aqueous solutions. The results obtained here may transfer to other dyes with a similar chemical structure. Biosorption studies were carried out under various parameters such as mass adsorbent particle, pH, contact time, initial dye concentration, and temperature. The biosorption kinetic data of the material (BE) was tested by the pseudo first-order and the pseudo-second-order kinetic models. Thermodynamic parameters including the Gibbs free energy ΔG, enthalpy ΔH, and entropy ΔS have revealed that the adsorption of YB on the BE is feasible, spontaneous, and endothermic. The equilibrium data were analyzed by using Langmuir, Freundlich, Elovich, and Temkin isotherm models. The experimental results show that the percentage of biosorption increases with an increase in the biosorbent mass (0.25 g: 12 mg/g; 1.5 g: 47.44 mg/g). The maximum biosorption occurred at around pH value of 2 for the YB. The equilibrium uptake was increased with an increase in the initial dye concentration in solution (C_o = 120 mg/l; q = 35.97 mg/g). Biosorption kinetic data were properly fitted with the pseudo-second-order kinetic model. The best fit was obtained by the Langmuir model with high correlation coefficient (R² > 0.998) and a maximum monolayer adsorption capacity of 35.97 mg/g for YB.

Keywords: adsorption, Brahea edulis, isotherm, yellow Bemacid

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Authors: Syed Asif Hassan, Tabrej Khan

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Multiple sclerosis (MS) is a chronic demyelinating autoimmune disorder, of the central nervous system (CNS). In the present scenario, the current therapies either do not halt the progression of the disease or have side effects which limit the usage of current Disease Modifying Therapies (DMTs) for a longer period of time. Therefore, keeping the current treatment failure schema, we are focusing on screening novel analogues of the available DMTs that specifically bind and inhibit the Sphingosine1-phosphate receptor1 (S1PR1) thereby hindering the lymphocyte propagation toward CNS. The novel drug-like analogs molecule will decrease the frequency of relapses (recurrence of the symptoms associated with MS) with higher efficacy and lower toxicity to human system. In this study, an integrated approach involving ligand-based virtual screening protocol (Ultrafast Shape Recognition with CREDO Atom Types (USRCAT)) to identify the non-toxic drug like analogs of the approved DMTs were employed. The potency of the drug-like analog molecules to cross the Blood Brain Barrier (BBB) was estimated. Besides, molecular docking and simulation using Auto Dock Vina 1.1.2 and GOLD 3.01 were performed using the X-ray crystal structure of Mtb LprG protein to calculate the affinity and specificity of the analogs with the given LprG protein. The docking results were further confirmed by DSX (DrugScore eXtented), a robust program to evaluate the binding energy of ligands bound to the ligand binding domain of the Mtb LprG lipoprotein. The ligand, which has a higher hypothetical affinity, also has greater negative value. Further, the non-specific ligands were screened out using the structural filter proposed by Baell and Holloway. Based on the USRCAT, Lipinski’s values, toxicity and BBB analysis, the drug-like analogs of fingolimod and BG-12 showed that RTL and CHEMBL1771640, respectively are non-toxic and permeable to BBB. The successful docking and DSX analysis showed that RTL and CHEMBL1771640 could bind to the binding pocket of S1PR1 receptor protein of human with greater affinity than as compared to their parent compound (Fingolimod). In this study, we also found that all the drug-like analogs of the standard MS drugs passed the Bell and Holloway filter.

Keywords: antagonist, binding affinity, chemotherapeutics, drug-like, multiple sclerosis, S1PR1 receptor protein

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Authors: Kehabtimer Shiferaw Kotiso, Getachew Hailemariam, Abiy Seifu Estifanos

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Introduction: Despite the highest share of low birth weight (LBW) for neonatal mortality and morbidity, predicting births with LBW for better intervention preparation is challenging. This study aims to predict LBW using a dataset encompassing 53,637 birth cohorts collected from 36 primary hospitals across seven regions in Ethiopia from February 2022 to June 2024. Methods: We identified ten explanatory variables related to maternal and neonatal characteristics, including maternal education, age, residence, history of miscarriage or abortion, history of preterm birth, type of pregnancy, number of livebirths, number of stillbirths, antenatal care frequency, and sex of the fetus to predict LBW. Using WEKA 3.8.2, we developed and compared seven machine learning algorithms. Data preprocessing included handling missing values, outlier detection, and ensuring data integrity in birth weight records. Model performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic curve (ROC AUC) using 10-fold cross-validation. Results: The results demonstrated that the decision tree, J48, logistic regression, and gradient boosted trees model achieved the highest accuracy (94.5% to 94.6%) with a precision of 93.1% to 93.3%, F1-score of 92.7% to 93.1%, and ROC AUC of 71.8% to 76.6%. Conclusion: This study demonstrates the effectiveness of machine learning models in predicting LBW. The high accuracy and recall rates achieved indicate that these models can serve as valuable tools for healthcare policymakers and providers in identifying at-risk newborns and implementing timely interventions to achieve the sustainable developmental goal (SDG) related to neonatal mortality.

Keywords: low birth weight, machine learning, classification, neonatal mortality, Ethiopia

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Authors: D. Jagath Kumari, K. Srinivasa Rao

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Reinforced concrete column is an important structural element in a building. Concrete usually performs well in building fires. However, when it is subjected to prolonged fire exposure or unusually high temperatures, and then it will suffer significant distress. Because concrete pre-fire compressive strength generally exceeds design requirements, therefore an average strength reduction can be tolerated. However high temperature reduces the compressive strength of concrete so much that the concrete retains no useful structural strength. Therefore the residual strength and its performance of structure can be assed by NDT testing. In this paper, rebound hammer test and the ultrasonic pulse velocity (UPV) are used to evaluate the residual compressive strength and material integrity of post-fire-curing concrete subjected to elevated temperatures. Also considering the large availability of fly ash in most of the countries, an attempt was made to study the effect of high volume fly ash concrete exposed to elevated temperatures. 32 RC column specimens were made with a M20 grade concrete mix. Out of 32 column specimens 16 column specimens were made with OPC concrete and other 16 column specimens were made with HVFA concrete. All specimens having similar cross-section details. Columns were exposed to fire for temperatures from 100oC to 800o C with increments of 100o C for duration of 3 hours. Then the specimens allowed cooling to room temperature by two methods natural air cooling method and immediate water quenching method. All the specimens were tested identically, for the compressive strengths and material integrity by rebound hammer and ultrasonic pulse velocity meter respectively for study. These two tests were carried out on preheating and post heating of the column specimens. The percentage variation of compressive strengths of RCC columns with the increase in temperature has been studied and compared the results for both OPC and HVFA concretes. Physical observations of the heated columns were observed.

Keywords: HVFA concrete, NDT testing, residual strength

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Authors: George Kyriakopoulos, Photini Pazartzis, Anthi Koskina, Crystalie Bourcha

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The need for securing safe access to and return from outer space, as well as ensuring the viability of outer space operations, maintains vivid the debate over the promotion of organization of space traffic through a Space Traffic Management System (STM). The proliferation of outer space activities in recent years as well as the dynamic emergence of the private sector has gradually resulted in a diverse universe of actors operating in outer space. The said developments created an increased adverse impact on outer space sustainability as the case of the growing number of space debris clearly demonstrates. The above landscape sustains considerable threats to outer space environment and its operators that need to be addressed by a combination of scientific-technological measures and regulatory interventions. In this context, recourse to recent technological advancements and, in particular, to Artificial Intelligence (AI) and machine learning systems, could achieve exponential results in promoting space traffic management with respect to collision avoidance as well as launch and re-entry procedures/phases. New technologies can support the prospects of a successful space traffic management system at an international scale by enabling, inter alia, timely, accurate and analytical processing of large data sets and rapid decision-making, more precise space debris identification and tracking and overall minimization of collision risks and reduction of operational costs. What is more, a significant part of space activities (i.e. launch and/or re-entry phase) takes place in airspace rather than in outer space, hence the overall discussion also involves the highly developed, both technically and legally, international (and national) Air Traffic Management System (ATM). Nonetheless, from a regulatory perspective, the use of AI for the purposes of space traffic management puts forward implications that merit particular attention. Key issues in this regard include the delimitation of AI-based activities as space activities, the designation of the applicable legal regime (international space or air law, national law), the assessment of the nature and extent of international legal obligations regarding space traffic coordination, as well as the appropriate liability regime applicable to AI-based technologies when operating for space traffic coordination, taking into particular consideration the dense regulatory developments at EU level. In addition, the prospects of institutionalizing international cooperation and promoting an international governance system, together with the challenges of establishment of a comprehensive international STM regime are revisited in the light of intervention of AI technologies. This paper aims at examining regulatory implications advanced by the use of AI technology in the context of space traffic management operations and its key correlating concepts (SSA, space debris mitigation) drawing in particular on international and regional considerations in the field of STM (e.g. UNCOPUOS, International Academy of Astronautics, European Space Agency, among other actors), the promising advancements of the EU approach to AI regulation and, last but not least, national approaches regarding the use of AI in the context of space traffic management, in toto. Acknowledgment: The present work was co-funded by the European Union and Greek national funds through the Operational Program "Human Resources Development, Education and Lifelong Learning " (NSRF 2014-2020), under the call "Supporting Researchers with an Emphasis on Young Researchers – Cycle B" (MIS: 5048145).

Keywords: artificial intelligence, space traffic management, space situational awareness, space debris

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Authors: A. Martins, I. Martins, O. Pereira

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The purpose of this research is to focus on the importance of distributed leadership in universities and Higher Education Institutions (HEIs). The research question is whether there a significant finding in self-reported ratings of leadership styles of those respondents that are studying management. The study aims to further discover whether students are encouraged to become responsible and proactive citizens, to develop their skills set, specifically shared leadership and higher-level skills to inspire creation knowledge, sharing and distribution thereof. Contemporary organizations need active and responsible individuals who are capable to make decisions swiftly and responsibly. Leadership influences innovative results and education play a dynamic role in preparing graduates. Critical reflection of extant literature indicates a need for a culture of leadership and innovation to promote organizational sustainability in the globalised world. This study debates the need for HEIs to prepare the graduate for both organizations and society as a whole. This active collaboration should be the very essence of both universities and the industry in order for these to achieve responsible sustainability. Learning and innovation further depend on leadership efficacy. This study follows the pragmatic paradigm methodology. Primary data collection is currently being gathered via the web-based questionnaire link which was made available on the UKZN notice system. The questionnaire has 35 items with a Likert scale of five response options. The purposeful sample method was used, and the population entails the undergraduate and postgraduate students in the College of Law and Business, University of KwaZulu-Natal, South Africa. Limitations include the design of the study and the reliance on the quantitative data as the only method of primary data collection. This study is of added value for scholars and organizations in the innovation economy.

Keywords: knowledge creation, learning, performance, sustainability

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Leila Asadollahi

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Dry powder inhalers (DPIs) have come a long way since their creation, starting with the Spinhaler Fisons in 1967. For optimal performance, it is important to consider the interplay between formulation, device, and patient. DPIs have shown great potential in treating systemic disorders, as evidenced by their success in clinical practices. Ongoing clinical trials and market availability of DPI products for systemic disease treatment are also examined. Furthermore, the current COVID-19 pandemic has sparked increased interest in dry powder inhalation as a potential avenue for vaccines and antiviral drugs, prompting further exploration of its applications. To achieve optimal treatment outcomes for respiratory diseases, a thorough understanding of the various types of DPIs currently available is crucial. These include single-dose, multiple-unit dose, and multi-dose DPIs. This informative article delves into the administration of drugs via inhalation, examining its diverse routes of administration. Additionally, it illuminates the exciting advancements in inhalation delivery systems and investigates the latest therapeutic approaches for the treatment of respiratory ailments. Additionally, the article discusses the historical development of DPIs and the need for improved designs to enhance efficacy and patient adherence. The potential of DPIs in treating systemic diseases is also examined. Overall, this review provides valuable insights into the advancements, challenges, and future prospects of inhalation drug delivery systems, highlighting the potential they hold for respiratory and systemic disorders. The review aims to provide valuable insights into the advancements, challenges, and future prospects of inhalation drug delivery systems, highlighting the potential they hold for respiratory and systemic disorders.

Keywords: dry powder inhalers (DPIs), respiratory diseases, systemic disorders, pulmonary drug delivery

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Authors: S. J. Addinell, A. F. Grabsch, P. D. Fawell, B. Evans

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The Deep Exploration Technologies Cooperative Research Centre (DET CRC) is researching and developing a new coiled tubing based greenfields mineral exploration drilling system utilising down-hole water-powered percussive drill tooling. This new drilling system is aimed at significantly reducing the costs associated with identifying mineral resource deposits beneath deep, barren cover. This system has shown superior rates of penetration in water-rich, hard rock formations at depths exceeding 500 metres. With fluid flow rates of up to 120 litres per minute at 200 bar operating pressure to energise the bottom hole tooling, excessive quantities of high quality drilling fluid (water) would be required for a prolonged drilling campaign. As a result, drilling fluid recovery and recycling has been identified as a necessary option to minimise costs and logistical effort. While the majority of the cuttings report as coarse particles, a significant fines fraction will typically also be present. To maximise tool life longevity, the percussive bottom hole assembly requires high quality fluid with minimal solids loading and any recycled fluid needs to have a solids cut point below 40 microns and a concentration less than 400 ppm before it can be used to reenergise the system. This paper presents experimental results obtained from the research program during laboratory and field testing of the prototype drilling system. A study of the morphological aspects of the cuttings generated during the percussive drilling process shows a strong power law relationship for particle size distributions. This data is critical in optimising solids control strategies and cuttings dewatering techniques. Optimisation of deployable solids control equipment is discussed and how the required centrate clarity was achieved in the presence of pyrite-rich metasediment cuttings. Key results were the successful pre-aggregation of fines through the selection and use of high molecular weight anionic polyacrylamide flocculants and the techniques developed for optimal dosing prior to scroll decanter centrifugation, thus keeping sub 40 micron solids loading within prescribed limits. Experiments on maximising fines capture in the presence of thixotropic drilling fluid additives (e.g. Xanthan gum and other biopolymers) are also discussed. As no core is produced during the drilling process, it is intended that the particle laden returned drilling fluid is used for top-of-hole geochemical and mineralogical assessment. A discussion is therefore presented on the biasing and latency of cuttings representivity by dewatering techniques, as well as the resulting detrimental effects on depth fidelity and accuracy. Data pertaining to the sample biasing with respect to geochemical signatures due to particle size distributions is presented and shows that, depending on the solids control and dewatering techniques used, it can have unwanted influence on top-of-hole analysis. Strategies are proposed to overcome these effects, improving sample quality. Successful solids control and cuttings dewatering for water-powered percussive drilling is presented, contributing towards the successful advancement of coiled tubing based greenfields mineral exploration.

Keywords: cuttings, dewatering, flocculation, percussive drilling, solids control

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Authors: Yen Yen Tan, Lynn Lam, Cynthia Lam, Angela Koh, Edwin Seng

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Entrepreneurial education plays a crucial role in nurturing future innovators and change-makers. Over time, significant progress has been made in refining instructional approaches to develop the necessary skills among learners effectively. Two highly valuable frameworks, Kearney's "4 Principles of Entrepreneurial Pedagogy" and Bloom's "Three Domains of Learning," serve as guiding principles in entrepreneurial education. Kearney's principles align with experiential and student-centric learning, which are crucial for cultivating an entrepreneurial mindset. The potential synergies between these frameworks hold great promise for enhancing entrepreneurial acumen among students. However, despite this potential, their integration remains largely unexplored. This study aims to bridge this gap by building upon the Business Essentials through Action (BETA) module and investigating its contributions to nurturing the entrepreneurial mindset. This study employs a quasi-experimental mixed-methods approach, combining quantitative and qualitative elements to ensure comprehensive and insightful data. A cohort of 235 students participated, with 118 enrolled in the BETA module and 117 in a traditional curriculum. Their Personal Entrepreneurial Competencies (PECs) were assessed before admission (pre-Y1) and one year into the course (post-Y1) using a comprehensive 55-item PEC questionnaire, enabling measurement of critical traits such as opportunity-seeking, persistence, and risk-taking. Rigorous computations of individual entrepreneurial competencies and overall PEC scores were performed, including a correction factor to mitigate potential self-assessment bias. The orchestration of Kearney's principles and Bloom's domains within the BETA module necessitates a granular examination. Here, qualitative revelations surface, courtesy of structured interviews aligned with contemporary research methodologies. These interviews act as a portal, ushering us into the transformative journey undertaken by students. Meanwhile, the study pivots to explore the BETA module's influence on students' entrepreneurial competencies from the vantage point of faculty members. A symphony of insights emanates from intimate focus group discussions featuring six dedicated lecturers, who share their perceptions, experiences, and reflective narratives, illuminating the profound impact of pedagogical practices embedded within the BETA module. Preliminary findings from ongoing data analysis indicate promising results, showcasing a substantial improvement in entrepreneurial skills among students participating in the BETA module. This study promises not only to elevate students' entrepreneurial competencies but also to illuminate the broader canvas of applicability for Kearney's principles and Bloom's domains. The dynamic interplay of quantitative analyses, proffering precise competency metrics, and qualitative revelations, delving into the nuanced narratives of transformative journeys, engenders a holistic understanding of this educational endeavour. Through a rigorous quasi-experimental mixed-methods approach, this research aims to establish the BETA module's effectiveness in fostering entrepreneurial acumen among students at Singapore Polytechnic, thereby contributing valuable insights to the broader discourse on educational methodologies.

Keywords: entrepreneurial education, experiential learning, pedagogical frameworks, innovative competencies

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Authors: Athanasia Chatzifotiou, Karen Tait

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Eco-school is an international initiative that offers schools the opportunity to develop practices on education for sustainable development (EfSD). Such practices need to focus on nine areas, namely: energy, water, biodiversity, school grounds, healthy living, transport, litter, waste and global citizenship. Acquiring the green flag status is the ultimate stage (silver and bronze are the other two) that is awarded by a committee external to the school and it lasts for two years. Our project focused on two such primary schools that had acquired the green flag status. The aim of our project is to describe the schools’ approach of becoming an eco-school, the practitioners’ role in promoting the values and principles of such endeavors, thus identifying the impact of EfSD. We chose the eco-schools initiative as it gives a clear and straightforward way to identify a school with an interest in EfSD. The project is important because even though EfSD attracts high attention in rhetoric, there is evidence indicating that EfSD may be neglected in practice. This paper presents part of a bigger project that aims to compare how primary schools and early years settings have approached EfSD via the eco-school initiative in the North East of England. This is a qualitative project that used a case study design to focus on the practices of two particular primary schools to gain a green flag status. A semi-structured interview was used with the lead teachers/practitioners of the schools; an audit was also conducted as part of a tour of the schools’ premises highlighting the initiatives, curriculum work, projects undertaken as well as resources available to school. A content analysis of the interview transcripts was conducted with the creation of response categories and response narratives by the two researchers first working individually and then collaboratively; the findings of the project reflected issues that concerned: a) pupils’ cognitive, physical and socio-emotional development, b) the wider community and c) the lead practitioners’ role and status in school. In relation to EfSD, our findings indicated that its impact upon these two eco-schools was rather minimal; a mismatch was identified between the eco-school practices and a holistic understanding of issues that EfSD aims to achieve. This mismatch between eco-school practices and EfSD is discussed with regard to: a) pupils’ understanding of the sustainability dimension in the topics they addressed; and b) teachers’ knowledge of sustainability and willingness to keep on such work in schools.

Keywords: eco-schools, environment, primary schools, sustainability education

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Kinga Wieczorek, Elzbieta Kukier, Remigiusz Pomykala, Beata Lachtara, Renata Szewczyk, Krzysztof Kwiatek, Jacek Osek

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The MICRORISK project (Research cooperation in assessment of microbiological hazard and risk in the food chain) was funded by the European Commission under the FP7 PEOPLE 2012 IRSES call within the International Research Staff Exchange Scheme of Marie Curie Action and realized during years from 2014 to 2015. The main aim of the project was to establish a cooperation between the European Union (EU) and the third State in the area important from the public health point of view. The following organizations have been engaged in the activity: National Veterinary Research Institute (NVRI) in Pulawy, Poland (coordinator), French Agency for Food, Environmental and Occupational Health & Safety (ANSES) in Maisons Alfort, France, National Scientific Center Institute of Experimental and Clinical Veterinary Medicine (NSC IECVM), Kharkov and State Scientific and Research Institute of Laboratory Diagnostics and Veterinary and Sanitary Expertise (SSRILDVSE) Kijev Ukraine. The results of the project showed that Ukraine used microbiological criteria in accordance with Commission Regulation (EC) No 2073/2005 of 15 November 2005 on microbiological criteria for foodstuffs. Compliance concerns both the criteria applicable at the stage of food safety (retail trade), as well as evaluation criteria and process hygiene in food production. In this case, the Ukrainian legislation also provides application of the criteria that do not have counterparts in the food law of the European Union, and are based on the provisions of Ukrainian law. Partial coherence of the Ukrainian and EU legal requirements in terms of microbiological criteria for food and feed concerns microbiological parameters such as total plate count, coliforms, coagulase-positive Staphylococcus spp., including S. aureus. Analysis of laboratory methods used for microbiological hazards control in food production chain has shown that most methods used in the EU are well-known by Ukrainian partners, and many of them are routinely applied as the only standards in the laboratory practice or simultaneously used with Ukrainian methods. The area without any legislation, where the EU regulation and analytical methods should be implemented is the area of Shiga toxin producing E. coli, including E. coli O157 and staphylococcal enterotoxin detection. During the project, the analysis of the existing Ukrainian and EU data concerning the prevalence of the most important food-borne pathogens on different stages of food production chain was performed. Particularly, prevalence of Salmonella spp., Campylobacter spp., L. monocytogenes as well as clostridia was examined. The analysis showed that poultry meat still appears to be the most important food-borne source of Campylobacter and Salmonella in the UE. On the other hand, L. monocytogenes were seldom detected above the legal safety limit (100 cfu/g) among the EU countries. Moreover, the analysis revealed the lack of comprehensive data regarding the prevalence of the most important food-borne pathogens in Ukraine. The results of the MICRORISK project are networking activities among researches originations participating in the tasks will help with a better recognition of each other regarding very important, from the public health point of view areas such as microbiological hazards in the food production chain and finally will help to improve food quality and safety for consumers.

Keywords: cooperation, European Union, food chain safety, food law, microbiological risk, Microrisk, Poland, Ukraine

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Authors: Adamu S. Salawu, Ibrahim O. Isah

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Direct solution to several forms of fourth-order ordinary differential equations is not easily obtained without first reducing them to a system of first-order equations. Thus, numerical methods are being developed with the underlying techniques in the literature, which seeks to approximate some classes of fourth-order initial value problems with admissible error bounds. Multistep methods present a great advantage of the ease of implementation but with a setback of several functions evaluation for every stage of implementation. However, hybrid methods conventionally show a slightly higher order of truncation for any k-step linear multistep method, with the possibility of obtaining solutions at off mesh points within the interval of solution. In the light of the foregoing, we propose the continuous form of a hybrid multistep method with Chebyshev polynomial as a basis function for the numerical integration of fourth-order initial value problems of ordinary differential equations. The basis function is interpolated and collocated at some points on the interval [0, 2] to yield a system of equations, which is solved to obtain the unknowns of the approximating polynomial. The continuous form obtained, its first and second derivatives are evaluated at carefully chosen points to obtain the proposed block method needed to directly approximate fourth-order initial value problems. The method is analyzed for convergence. Implementation of the method is done by conducting numerical experiments on some test problems. The outcome of the implementation of the method suggests that the method performs well on problems with oscillatory or trigonometric terms since the approximations at several points on the solution domain did not deviate too far from the theoretical solutions. The method also shows better performance compared with an existing hybrid method when implemented on a larger interval of solution.

Keywords: Chebyshev polynomial, collocation, hybrid multistep method, initial value problems, interpolation

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Authors: Mark Salisbury

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The arrival of artificially intelligent avatars and the automation they bring is worrying many of us, not only for our livelihood but for the jobs that may be lost to our kids. We worry about what our place will be as human beings in this new economy where much of it will be conducted online in the metaverse – in a network of 3D virtual worlds – working with intelligent machines. The Future of Leadership was written to address these fears and show what our place will be – the right place – in this new economy of AI avatars, automation, and 3D virtual worlds. But to be successful in this new economy, our job will be to bring wisdom to our workplace and the marketplace. And we will use AI avatars and 3D virtual worlds to do it. However, this book is about more than AI and the avatars that we will work with in the metaverse. It’s about building Organizational intelligence (OI) -- the capability of an organization to comprehend and create knowledge relevant to its purpose; in other words, it is the intellectual capacity of the entire organization. To increase organizational intelligence requires a new kind of knowledge worker, a wisdom worker, that requires a new kind of leadership. This book begins your story for how to become a leader of wisdom workers and be successful in the emerging wisdom economy. After this presentation, conference participants will be able to do the following: Recognize the characteristics of the new generation of wisdom workers and how they differ from their predecessors. Recognize that new leadership methods and techniques are needed to lead this new generation of wisdom workers. Apply personal and professional values – personal integrity, belief in something larger than yourself, and keeping the best interest of others in mind – to improve your work performance and lead others. Exhibit an attitude of confidence, courage, and reciprocity of sharing knowledge to increase your productivity and influence others. Leverage artificial intelligence to accelerate your ability to learn, augment your decision-making, and influence others.Utilize new technologies to communicate with human colleagues and intelligent machines to develop better solutions more quickly.

Keywords: metaverse, generative artificial intelligence, automation, leadership, organizational intelligence, wisdom worker

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Authors: Mudassir Ghafoor, Irsalan Arif, Shuaib Salamat

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This paper presents modeling and analysis of different intake bump (compression surface) configurations and comparison with an existing supersonic aircraft having bump intake configuration. Many successful aircraft models have shown that Diverter less Supersonic Inlet (DSI) as compared to conventional intake can reduce weight, complexity and also maintenance cost. The research is divided into two parts. In the first part, four different intake bumps are modeled for comparative analysis keeping in view the consistency of outer perimeter dimensions of fighter aircraft and various characteristics such as flow behavior, boundary layer diversion and pressure recovery are analyzed. In the second part, modeled bumps are integrated with intake duct for performance analysis and comparison with existing supersonic aircraft data is carried out. The bumps are named as uniform large (Config 1), uniform small (Config 2), uniform sharp (Config 3), non-uniform (Config 4) based on their geometric features. Analysis is carried out at different Mach Numbers to analyze flow behavior in subsonic and supersonic regime. Flow behavior, boundary layer diversion and Pressure recovery are examined for each bump characteristics, and comparative study is carried out. The analysis reveals that at subsonic speed, Config 1 and Config 2 give similar pressure recoveries as diverterless supersonic intake, but difference in pressure recoveries becomes significant at supersonic speed. It was concluded from research that Config 1 gives better results as compared to Config 3. Also, higher amplitude (Config 1) is preferred over lower (Config 2 and 4). It was observed that maximum height of bump is preferred to be placed near cowl lip of intake duct.

Keywords: bump intake, boundary layer, computational fluid dynamics, diverter-less supersonic inlet

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Authors: Neslihan Ozman Say, Jim Gilmour, Pratik Desai, William Zimmerman

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Landfill leachate treatment has been attracting researchers recently for various environmental and economical reasons. Leachate discharge to receiving waterbodies without treatment causes serious detrimental effects including partial oxygen depletion due to high biological oxygen demand (BOD) and chemical oxygen demand (COD) concentrations besides toxicity of heavy metals it contains and high ammonia concentrations. In this study, it is aimed to show microalgal ammonia removal performances of a wild microalgae consortia as an alternative treatment method and determine the dominant leachate tolerant species for this consortia. For the microalgae species identification experiments a microalgal consortium which has been isolated from a local pond in Sheffield inoculated in %5 diluted raw landfill leachate and acclimated to the leachate by batch feeding for a month. In order to determine the most tolerant microalgal consortium, four different untreated landfill leachate samples have been used as diluted in four different ratios as 5%, 10%, 20%, and 40%. Microalgae cell samples have been collected from all experiment sets and have been examined by using 18S rDNA sequencing and specialised gel electrophoresis which are adapted molecular biodiversity methods. The best leachate tolerant algal consortium is being used in order to determine ammonia removal performances of the culture in a microbubble assisted photobioreactor (PBR). A porous microbubble diffuser which is supported by a fluidic oscillator is being used for dosing CO₂ and air mixture in the PBR. It is known that high mass transfer performance of microbubble technology provides a better removal efficiency and a better mixing in the photobioreactor. Ammonia concentrations and microalgal growth are being monitored for PBR currently. It is aimed to present all the results of the study in final paper submission.

Keywords: ammonia removal from leachate, landfill leachate treatment, microalgae species identification, microbubble assisted photobioreactors

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Authors: Tadewos Awugchew Wudineh

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Health and safety issues have always been a major problem and concern in the building construction industry. The health and safety regulations are stated to eliminate the potential hazards and to reduce the consequential risks. However, the importance of the regulations seems to be overlooked in building construction sites of Hawassa City. Accordingly, many companies don’t follow the regulations as construction workers are more likely to be injured and killed by construction accident than any other type of employment. This paper aimed to identify factors that affect the implementation of construction health and safety regulations, their effects and mitigation measures in building construction project sites of Hawassa City. To reach this objective, a review of literature as well as the Ethiopian construction health and safety regulations have been undertaken. Mainly a five-point Likert scale questionnaire was distributed, and statistical analysis was used to summarize, interpret the data, and to find the significances of the responses. In addition, interviews were carried out. Accordingly, the findings indicate that the top factors which affect the implementation of CHS regulations are, availability and development of a clear health and safety policy, health and safety inspections by top management, conducting health and safety training and orientation, provision of healthy and safe working environment and employment of trained safety officers. The study revealed that implementation or non-implementation of CHS regulations have effects on the worker’s productivity, job satisfaction, rate of accidents, and cost greatly. Thus, the suggestion to minimize the impact on worker’s job performance are, developing of a clear health and safety policy, management commitment towards implementation of health and safety regulations, health and safety education and training and conducting regular health and safety inspections. It was concluded from the study that good implementation of health and safety regulations are the results from administrative and management commitment which calls for more attention to be paid to improve the implementation of CHS regulations in building construction sites of Hawassa City.

Keywords: construction health and safety regulations, effects, factors, mitigation

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes.

Keywords: data analytics, mathematical innovations, knowledge extraction, decision-making

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Authors: Hozaifa Zaki, Ghada Soliman

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In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

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Authors: Dawid Sołoducha, Tomasz Borowski, Agata Markowska-Szczupak, Aneta Wesołowska, Marian Kordas, Rafał Rakoczy

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Many studies report varied effects of the magnetic field in medicine, but applications are still missing. Also, essential oils (EOs) were historically used in healing therapies, food preservation and the cosmetic industry due to their wound healing and antioxidant properties and antimicrobial activity. Unfortunately, the chemical characterization of EOs activates its antibacterial action only at a fairly high concentration. They can cause skin reactions, e.g., irritation (irritant contact dermatitis) or allergic contact dermatitis; therefore, they should always be used with caution. However, the administration of EOs to achieve the desired antimicrobial activity and stability with long-term medical usage in low concentration is challenging. The aim of this work was to investigate the antimicrobial activity of commercial Pinus sylvestris L. essential oil from Polish company Avicenna-Oil® under Rotating Magnetic Field (RMF) at f = 1 – 50 Hz. The novel construction of the magnetically assisted self-constructed reactor (MAP) was applied for this study. The chemical composition of essential pine oil was determined by gas chromatography coupled with mass spectrometry (GC-MS). Model bacteria Escherichia coli K12 (ATCC 25922) was used. Different concentrations of pine oil was prepared: 100% 50%, 25%, 12.5% and 6.25%. The disc diffusion and MIC test were done. To examine the effect of essential pine oil and rotating magnetic field RMF on antibacterial performance agar plate method was used. Pine oil consist of α-pinene (28.58%), β-pinene (17.79%), δ-3-carene (14.17%) and limonene (11.58%). The present study indicates the exposition to the RMF, as compared to the unexposed controls causing an increase in the efficacy of antibacterial properties of pine oil. We have shown that the rotating magnetic fields (RMF) at a frequency, f, between 25 Hz to 50 Hz, increase the antimicrobial efficiency of oil at lower than 50% concentration. The new method can be applied in many fields e.g. aromatherapy, medicine as a component of dressing, or as food preservatives.

Keywords: rotating magnetic field, pine oil, antimicrobial activity, Escherichia coli

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Authors: Raymond Dominic Uzoh

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Starch fillers were extracted from three plant sources namely amora tuber (a wild variety of Irish potato), sweet potato and yam starch and their particle size, pH, amylose, and amylopectin percentage decomposition determined accordingly by high performance liquid chromatography (HPLC). The starch was introduced into natural rubber in liquid phase (through gelatinization) by the latex compounding method and compounded according to standard method. The prepared starch/natural rubber composites was characterized by Instron Universal testing machine (UTM) for tensile mechanical properties. The composites was further characterized by x-ray diffraction and crosslink density analysis. The particle size determination showed that amora starch granules have the highest particle size (156 × 47 μm) followed by yam starch (155× 40 μm) and then the sweet potato starch (153 × 46 μm). The pH test also revealed that amora starch has a near neutral pH of 6.9, yam 6.8, and sweet potato 5.2 respectively. Amylose and amylopectin determination showed that yam starch has a higher percentage of amylose (29.68), followed by potato (22.34) and then amora starch with the lowest value (14.86) respectively. The tensile mechanical properties testing revealed that yam starch produced the best tensile mechanical properties followed by amora starch and then sweet potato starch. The structure, crystallinity/amorphous nature of the product composite was confirmed by x-ray diffraction, while the nature of crosslinking was confirmed by swelling test in toluene solvent using the Flory-Rehner approach. This research study has rendered a workable strategy for enhancing interfacial interaction between a hydrophilic filler (starch) and hydrophobic polymeric matrix (natural rubber) yielding moderately good tensile mechanical properties for further exploitation development and application in the rubber processing industry.

Keywords: natural rubber, fillers, starch, amylose, amylopectin, crosslink density

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Authors: Belahmadi M. S. O., Abdessemed A., Benchiheub M., Doukali H., Kaid Kasbah K. M.

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The aim of this study is to evaluate the quality of leachate generated by the Batna landfill site, and to verify the performance of various advanced oxidation processes, in particular the Fenton and Photo-Fenton systems combined with biological treatment to eliminate the recalcitrant organic matter contained in this effluent, and to preserve reverse osmosis membranes used for leachate treatment. The average values obtained are compared with national and international discharge standards. The results of physico-chemical analyses show that the leachate has an alkaline pH =8.26 and a high organic load with a low oxygen content. Mineral pollution is represented by high conductivity (38.3 mS/cm), high Kjeldahl nitrogen content (1266.504 mg/L) and ammoniacal nitrogen (1098.384 mg/L). The average pollution indicator parameters measured were: BOD5 = 1483.333 mg O2 /L, COD = 99790.244 mg O 2/L, TOC = 22400 mg C/L. These parameters exceed Algerian standards. Hence, there is a necessity to treat this effluent before discharging it into the environment. A comparative study was carried out to estimate the efficiency of two oxidation processes. Under optimum reaction conditions, TOC removal efficiencies of 63.43% and 73.4% were achieved for the Fenton and Photo-Fenton processes, respectively. COD removal rates estimated at 88% and 99.5% for the Fenton and Photo- Fenton processes, respectively. In addition, the Photo-Fenton + bacteria + micro- algae hybrid treatment gave removal efficiencies of around 92.24% for TOC and 99.9% for COD; -0.5 for AOS and 0.01 for CN. The results obtained during this study showed that a hybrid approach combining the PhotoFenton process and biological treatment appears to be a highly effective alternative for achieving satisfactory treatment, which aimed at exploiting the advantages of this method in terms of organic pollutant removal.

Keywords: leachate, landfill, advanced oxidation processes, Fenton and Photo-Fenton systems, biological treatment, organic pollutants

Procedia PDF Downloads 65