Search results for: biological insights

Authors: Yemane Tadesse Gebreslassie, Henok Gidey Gebretnsae

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Nanotechnology has emerged as a highly promising field of research with wide-ranging applications across various scientific disciplines. In recent years, tin oxide has garnered significant attention due to its intriguing properties, particularly when synthesized in the nanoscale range. While numerous physical and chemical methods exist for producing tin oxide nanoparticles, these approaches tend to be costly, energy-intensive, and involve the use of toxic chemicals. Given the growing concerns regarding human health and environmental impact, there has been a shift towards developing cost-effective and environmentally friendly processes for tin oxide nanoparticle synthesis. Green synthesis methods utilizing biological entities such as plant extracts, bacteria, and natural biomolecules have shown promise in successfully producing tin oxide nanoparticles. However, scaling up the production to an industrial level using green synthesis approaches remains challenging due to the complexity of biological substrates, which hinders the elucidation of reaction mechanisms and formation processes. Thus, this review aims to provide an overview of the various sources of biological entities and methodologies employed in the green synthesis of tin oxide nanoparticles, as well as their impact on nanoparticle properties. Furthermore, this research delves into the strides made in comprehending the mechanisms behind the formation of nanoparticles as documented in existing literature. It also sheds light on the array of analytical techniques employed to investigate and elucidate the characteristics of these minuscule particles.

Keywords: nanotechnology, tin oxide, green synthesis, formation mechanisms

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Authors: Vishnu Pratap Singh Kirar, Madhuri Saxena

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Advancement in sequence data generation technologies is churning out voluminous omics data and posing a massive challenge to annotate the biological functional features. With so much data available, the use of machine learning methods and tools to make novel inferences has become obvious. Machine learning methods have been successfully applied to a lot of disciplines, including computational biology and bioinformatics. Researchers in computational biology are interested to develop novel machine learning frameworks to classify the huge amounts of biological data. In this proposal, it plan to employ novel machine learning approaches to aid the understanding of how apparently innocuous mutations (in intergenic DNA and at synonymous sites) cause diseases. We are also interested in discovering novel functional sites in the genome and mutations in which can affect a phenotype of interest.

Keywords: genome wide association studies (GWAS), next generation sequencing (NGS), deep learning, omics

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Authors: Kaouther Selmi, Alaeddine Sridi, Mohamed Bouallegue, Kais Bouallegue

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Neurons are the fundamental units of the brain and the nervous system. The variable structure model of neurons consists of a system of differential equations with various parameters. By optimizing these parameters, we can create a unique model that describes the dynamic behavior of a single neuron. We introduce a neural network based on neurons with multiple dendrites employing an activation function with a variable structure. In this paper, we present a model for heart behavior. Finally, we showcase our successful simulation of the heart's ECG diagram using our Variable Structure Neuron Model (VSMN). This result could provide valuable insights into cardiology.

Keywords: neural networks, neuron, dendrites, heart behavior, ECG

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Authors: Bernasconi Matteo, Grothkopp Mark, Friedli Thomas

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The objective of this paper is to examine the relationships between technical and social lean bundles as well as operational performance in the context of the pharmaceutical industry. We investigate the direct and mediating effects of the lean bundles total productive maintenance (TPM), total quality management (TQM), Just-In-Time (JIT), and human resource management (HRM) on operational performance. Our analysis relies on 113 manufacturing facilities from the St.Gallen OPEX benchmarking database. The results show that HRM has a positive indirect effect on operational performance mediated by the technical lean bundles.

Keywords: human resource management, operational performance, pharmaceutical industry, technical lean practices

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Authors: Khaled Alshammari

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Ciprofloxacin (CIP) is a common antibiotic drug used as a strudy electron donor that interacts with dynamic π -acceptors such as 2,3-dinitrosalsylic acid (HDNS) and Tetracyanoethylene (TCNE) for synthesizing a new model of charge transfer (CT) complexes. The synthesized complexes were identified using diverse analytical methods such as UV–vis spectra, photometric titration measurements, FT-IR, HNMR Spectroscopy, and thermogravimetric analysis techniques (TGA/DTA). The stoichiometries for all the formed complexes were found to be a 1:1 M ratio between the reactants. The characteristic spectroscopic properties such as transition dipole moment (µ), oscillator strength (f), formation constant (KCT), ionization potential (ID), standard free energy (∆G), and energy of interaction (ECT) for the CT-complexes were collected. The developed CT complexes were tested for their toxicity on main organs, antimicrobial activity, antioxidant activity, and biofilm formation.

Keywords: biological, biofilm, toxicity, thermal analysis, charge transfer, spectroscopy

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Authors: Ali Alzeyadi, Edward Loffill, Rafid Alkhaddar Ali Alattabi

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Phosphate, ammonium, and nitrates are forms of nutrients; they are released from different sources. High nutrient levels contribute to the eutrophication of water bodies by accelerating the extraordinary growth of algae. Recently, many filtration and treatment systems were developed and used for different removal processes. Due to enhanced operational aspects for the up-flow, continuous, granular Media filter researchers became more interested in further developing this technology and its performance for nutrient removal from wastewater. Environmental factors significantly affect the filtration process performance, and understanding their impact will help to maintain the nutrient removal process. Phosphate removal by phosphate sorption materials PSMs and nitrogen removal biologically are the methods of nutrient removal that have been discussed in this paper. Hence, the focus on the factors that influence these processes is the scope of this work. The finding showed the presence of factors affecting both removal processes; the size, shape, and roughness of the filter media particles play a crucial role in supporting biofilm formation. On the other hand, all of which are effected on the reactivity of surface between the media and phosphate. Many studies alluded to factors that have significant influence on the biological removal for nitrogen such as dissolved oxygen, temperature, and pH; this is due to the sensitivity of biological processes while the phosphate removal by PSMs showed less affected by these factors. This review work provides help to the researchers in create a comprehensive approach in regards study the nutrient removal in up flow filtration systems.

Keywords: nitrogen biological treatment, nutrients, psms, upflow filter, wastewater treatment

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Authors: Slimane Mokrani, Nabti El hafid

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Description of the subject: Currently, several efforts focus on the study of biodiversity, microbial biotechnology, and the use of ecological strategies. Objectives: The aim of this present work is to determine the functional diversity of bacteria in rhizospheric and non-rhizospheric soils of different plants. Methods: Bacteria were isolated from soil and identified based on physiological and biochemical characters and genotypic taxonomy performed by 16S rDNA and BOX-PCR. As well as the characterization of various PGPR traits. Then, they are tested for their effects on the stimulation of seed germination and the growth of Phaseolus vulgaris L. As well as their biological control activities with regard to the phytopathogenic bacterial isolate Xapf. Results and Discussion: The biochemical and physiological identification of 75 bacterial isolates made it possible to associate them with the two groups of fluorescent Pseudomonas (74.67%) and non-fluorescent Pseudomonas (25.33%). The identification by 16S rDNA of 27 strains made it possible to attribute the majority of the strains to the genus Pseudomonas (81.48%), Serratia (7.41%) and Bacillus (11.11%). The bacterial strains showed a high capacity to produce IAA, siderophores, HCN and to solubilize phosphate. A significant stimulation of germination and growth was observed by applying the Pseudomonas strains. Furthermore, significant reductions in the severity and intensity of the disease caused caused by Xapf were observed. Conclusion: The bacteria described in this present study endowed with different PGPR activities seem to be very promising for their uses as biological control agents and bio-fertilization.

Keywords: biofertilization, biological control, phaseolus vulgaris L, pseudomonas, Xanthomonas axonopodis pv. phaseoli var. fuscans and common blight

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Authors: Iris Lavalle Acejo, Helen Sampson

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Drawing from the perspectives of female seafarers, this article offers an analysis of an empirical investigation of the mechanisms by which women are excluded from working on board cargo ships. Female seafarers currently make up about 1% of seafarers in the global cargo shipping industry. As female presence onboard continues to constitute a small proportion of the total crew, we consider their professional advancement in comparison with their male counterparts and the ways in which they are discouraged from entering, or remining within, the industry. With interactions at work severely constrained by the traditionally masculine working culture of the ship, the study hopes to provide insights on the mechanisms in which they face and navigate barriers as they both accommodate and resist gendered differences onboard.

Keywords: female seafarers, glasswalls, genderbarriers, genderedworkplaces

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Authors: Miftah Hassen Jemal, Solomon Negash

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This study focuses on undertaking a qualitative meta-analysis of qualitative studies conducted on ICT4D implementations. The interpretive approach of synthesis of the interpretation of qualitative studies is adopted to guide the whole process of the study. The traditional criteria of trustworthiness of qualitative studies in terms of transferability, consistency, and credibility are used as quality metrics of the output of the interpretive synthesis process. The findings of the study are anticipated to be of value for policymakers in providing guidance to decisions related to ICT4D implementations. The study is also anticipated to have contributions to research by extracting valuable insights from extant literature and identifying potential areas that warrant further investigation.

Keywords: ICT4D implementations, interpretive synthesis, qualitative meta-analysis, qualitative studies

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Authors: Kamran Siddique, Yangwoo Kim, Zahid Akhtar

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In recent years, the technological advancements have led to a deluge of data from distinctive domains and the need for development of solutions based on parallel and distributed computing has still long way to go. That is why, the research and development of massive computing frameworks is continuously growing. At this particular stage, highlighting a potential research area along with key insights could be an asset for researchers in the field. Therefore, this paper explores one of the emerging distributed computing frameworks, Apache Hama. It is a Top Level Project under the Apache Software Foundation, based on Bulk Synchronous Processing (BSP). We present an unbiased and critical interrogation session about Apache Hama and conclude research directions in order to assist interested researchers.

Keywords: apache hama, bulk synchronous parallel, BSP, distributed computing

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Authors: Jonas Kahlert

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Blockchain technology has gained importance and momentum in the energy sector. Yet, there is no structured analysis of how specific features of the blockchain technology can create value in the energy sector. We employ a qualitative analysis on insights gained from the current literature and expert interviews. Along the four most prevalent use cases of blockchain technology in the energy sector, we discuss the potential of blockchain technology to support a transition to a more affordable, sustainable and reliable energy system. We show that in its current state, blockchain and adjacent technologies are not a necessity but a sufficiency towards this transition. We also show how current limitations of the blockchain and adjacent technologies can be even counterproductive. Finally, we discuss implications for policy makers and managers.

Keywords: blockchain technology, affordance theory, energy trilemma, sustainability

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Authors: Jung-Hoon Han, Jooyoung Kwak

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Distances between cultures, institutions are gaining academic attention once again since the classical debate on the validity of globalization. Despite the incessant efforts to define international segments with various concepts, no significant attempts have been made considering the institutional dimensions. Resource-based theory and institutional theory provides useful insights in assessing market environment and understanding when and how MNEs loose or gain advantages. This study consists of two parts: identifying institutional clusters and predicting the effect of MNEs’ origin on the applicability of competitive advantages. MNEs in one country cluster are expected to use similar management systems.

Keywords: institutional theory, resource-based theory, institutional environment, cultural dimensions, cluster analysis, standardized management

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Authors: Ayda Maadani Mallak, Amir lakzian, Elham Khodaverdi, Gholam Hossein Haghnia

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The use of pharmaceuticals and personal care products such as antibiotics and antibacterials has been increased in recent years. Since the major part of consumed compounds remains unchanged in the wastewater treatment plant, they will easily find their way into the human food chain following the land use of sewage sludge (SS). Biological treatment of SS is one the most effective methods for expunging contaminants. White rot fungi, due to their ligninolytic enzymes, are extensively used to degrade organic compounds. Among all three different morphological forms and growth patterns of filamentous fungi (mycelia, clumps, and pellets), fungal pellet formation has been the subject of interest in industrial bioprocesses. Therefore this study was aimed to investigate the uptake of tetracycline (TC) and triclosan (TCS) by radish plant (Raphanus sativus) from soil amended with untreated and pretreated SS by P. ostreatus fungal pellets under greenhouse conditions. The experimental soil was amended with 1) Contaminated SS with TC at a concentration of 100 mgkg-1 and pretreated by fungal pellets, 2) Contaminated SS with TC at 100 mgkg-1 and untreated with fungal pellets, 3) Contaminated SS with TCS at a concentration of 50 mgkg-1 and pretreated by fungal pellets, 4) contaminated SS with TCS at 50 mgkg-1 and untreated with fungal pellets. An uncontaminated and untreated SS-amended soil also was considered as control treatment. An AB SCIEX 3200 QTRAP LC-MS/MS system was used in order to analyze the concentration of TC and TCS in plant tissues and soil medium. Results of this study revealed that the presence of TC and TCS in SS-amended soil decreased the radish biomass significantly. The reduction effect of TCS on dry biomass of shoot and root was 39 and 45% compared to controls, whereas for TC, the reduction percentage for shoot and root was 27 and 40.6%, respectively. However, fungal treatment of SS by P. ostreatus pellets reduced the negative effect of both compounds on plant biomass remarkably, as no significant difference was observed compared to control treatments. Pretreatment of SS with P. ostreatus also caused a significant reduction in translocation factor (concentration in shoot/root), especially for TC compound up to 32.3%, whereas this reduction for TCS was less (8%) compared to untreated SS. Generally, the results of this study confirmed the positive effect of using fungal pellets in SS amendment to decrease TC and TCS uptake by radish plants. In conclusion, P. ostreatus fungal pellets might provide future insights into bioaugmentation to remove antibiotics from environmental matrices.

Keywords: antibiotic, fungal pellet, sewage sludge, white-rot fungi

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Authors: Samy M. Shaban, Ismail Aiad, Mohamed M. El-Sukkary, E. A. Soliman, Moshira Y. El-Awady

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In situ and green synthesis of cubic and spherical silver nanoparticles were developed using sun light as reducing agent in the presence of newly prepared cationic surfactant which acting as capping agents. The morphology of prepared silver nanoparticle was estimated by transmission electron microscope (TEM) and the size distribution determined by dynamic light scattering (DLS). The hydrophobic chain length of the prepared surfactant effect on the stability of the prepared silver nanoparticles as clear from zeta-potential values. Also by increasing chain length of the used capping agent the amount of formed nanoparticle increase as indicated by increasing the absorbance. Both prepared surfactants and surfactants capping silver nanoparticles showed high antimicrobial activity against gram positive and gram-negative bacteria.

Keywords: photosynthesis, hexaonal shapes, zetapotential, biological activity

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Authors: Ofosuhene O. Apenteng, Noor Azina Ismail

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Recently tourism is a major foreign exchange earner in the World. In this paper, we propose the mathematical model to study the impact of tourists on the spread of HIV incidences using compartmental differential equation models. Simulation studies of reproduction number are used to demonstrate new insights on the spread of HIV disease. The periodogram analysis of a time series was used to determine the speed at which the disease is spread. The results indicate that with the persistent flow of tourism into a country, the disease status has increased the epidemic rate. The result suggests that the government must put more control on illegal prostitution, unprotected sexual activity as well as to emphasis on prevention policies that include the safe sexual activity through the campaign by the tourism board.

Keywords: HIV/AIDS, mathematical transmission modeling, tourists, stability, simulation

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Authors: Anyapak Prapannetivuth

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The purpose of this research is to investigate the marketing mix that is perceived to be important for the small sized hotels in Pattaya. Unlike previous studies, this research provides insights through a review of the marketing activities performed by the small sized hotels. Nine owners and marketing manager of small sized hotels and resorts, all local Chonburi people, were selected for an in-depth interview. A snowball sampling process was employed. The research suggests that seven marketing mixes (e.g. Product, Price, Place, Promotion, People, Physical Evidence and Process) were commonly used by these hotels, however, three types – People, price and physical evidence were considered most important by the owners.

Keywords: marketing mix, marketing tools, small sized hotels, pattaya

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Authors: Sukrat Sinha, Abhay Kumar, Shanthy Sundaram

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The Leishmania homologue of activated C kinase (LACK) is known T cell epitope from soluble Leishmania antigens (SLA) that confers protection against Leishmania challenge. This antigen has been found to be highly conserved among Leishmania strains. LACK has been shown to be protective against L. donovani challenge. A comprehensive analysis of several LACK sequences was completed. The analysis shows a high level of conservation, lower variability and higher antigenicity in specific portions of the LACK protein. This information provides insights for the potential consideration of LACK as a putative candidate in the context of visceral Leishmaniasis vaccine target.

Keywords: bioinformatics, genome assembly, leishmania activated protein kinase c (lack), next-generation sequencing

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Authors: Victor Iorungwa Gwa, Ebenezer Jonathan Ekefan

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Potential of Trichoderma harzianum for biological control of postharvest fungal rot of white yam (Dioscorea rotundata Poir) tubers in storage was studied. Pathogenicity test revealed the susceptibility of healthy looking yam tubers to Aspergillus niger, Botryodiplodia theobromae, and Fusarium oxysporum f. sp. melonganae after fourteen days of inoculation. Treatments comprising A. niger, B. theobromae, and F. oxysporum each paired with T. harzianum and were arranged in completely randomized design and stored for five months. Experiments were conducted between December 2015 and April 2016 and December 2016 and April 2017. Results showed that tubers treated with the pathogenic fungi alone caused mean percentage rot of between 6.67 % (F. oxysporum) and 22.22 % (A. niger) while the paired treatments produced only between 2.22 % (T. harzianum by F. oxysporum) and 6.67 % (T. harzianum by A. niger). In the second year of storage, mean percentage rot was found to be between 13.33 % (F. oxysporum) and 28.89 % (A. niger) while in the paired treatment rot was only between 6.67 % (F. oxysporum) and 8.89% (A. niger). Tubers treated with antagonist alone produced 0.00 % and 2.22 % in the first and second year, respectively. Result revealed that there was a significant difference (P ≤ 0.05) in mean percentage rot between the first year and the second year except where B. theobromae was inoculated alone, A. niger and T. harzianum paired and B. theobromae and T. harzianum paired. The most antagonised fungus in paired treatment for both years was F. oxysporum f. sp. melonganae, while the least antagonised, was A. niger and B. theobromae. It is, therefore, concluded that T. harzianum has potentials to control rot causing pathogens of yam tubers in storage. This can compliment or provide better alternative ways of reducing rot in yam tubers than by the use of chemical fungicides which are not environmentally friendly.

Keywords: biological control, fungal rot, postharvest, Trichoderma harzianum, white yam

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Authors: R. Kaleeswari

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An investigation was carried out to assess the soil biological quality parameters in rice soils under rainfed and to compare soil quality indexing methods viz., Principal component analysis, Minimum data set and Indicator scoring method and to develop soil quality indices for formulating soil and crop management strategies.Soil samples were collected and analyzed for soil biological properties by adopting standard procedure. Biological indicators were determined for soil quality assessment, viz., microbial biomass carbon and nitrogen (MBC and MBN), potentially mineralizable nitrogen (PMN) and soil respiration and dehydrogenease activity. Among the methods of rice cultivation, Organic nutrition, Integrated Nutrient Management (INM) and System of Rice Intensification (SRI ), rice cultivation registered higher values of MBC, MBN and PMN. Mechanical and conventional rice cultivation registered lower values of biological quality indicators. Organic nutrient management and INM enhanced the soil respiration rate. SRI and aerobic rice cultivation methods increased the rate of soil respiration, while conventional and mechanical rice farming lowered the soil respiration rate. Dehydrogenase activity (DHA) was registered to be higher in soils under organic nutrition and Integrated Nutrient Management INM. System of Rice Intensification SRI and aerobic rice cultivation enhanced the DHA; while conventional and mechanical rice cultivation methods reduced DHA. The microbial biomass carbon (MBC) of the rice soils varied from 65 to 244 mg kg-1. Among the nutrient management practices, INM registered the highest available microbial biomass carbon of 285 mg kg-1.Potentially mineralizable N content of the rice soils varied from 20.3 to 56.8 mg kg-1. Aerobic rice farming registered the highest potentially mineralizable N of 78.9 mg kg-1..The soil respiration rate of the rice soils varied from 60 to 125 µgCO2 g-1. Nutrient management practices ofINM practice registered the highest. soil respiration rate of 129 µgCO2 g-1.The dehydrogenase activity of the rice soils varied from 38.3 to 135.3µgTPFg-1 day-1. SRI method of rice cultivation registered the highest dehydrogenase activity of 160.2 µgTPFg-1 day-1. Soil variables from each PC were considered for minimum soil data set (MDS). Principal component analysis (PCA) was used to select the representative soil quality indicators. In intensive rice cultivating regions, soil quality indicators were selected based on factor loading value and contribution percentage value using principal component analysis (PCA).Variables having significant difference within production systems were used for the preparation of minimum data set (MDS).

Keywords: soil quality, rice, biological properties, PCA analysis

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Authors: Godian Asami Mabindah

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The convergence of knowledge management (KM) and competitive intelligence (CI) has gained significant attention in recent years as organizations seek to enhance their competitive advantage in an increasingly complex and dynamic business environment. This research study aims to explore and understand the synergistic relationship between KM and CI and its impact on organizational performance. By investigating how the integration of KM and CI practices can contribute to decision-making, innovation, and competitive advantage, this study seeks to unlock the potential benefits and challenges associated with this integration. The research employs a mixed-methods approach to gather comprehensive data. A quantitative analysis is conducted using survey data collected from a diverse sample of organizations across different industries. The survey measures the extent of integration between KM and CI practices and examines the perceived benefits and challenges associated with this integration. Additionally, qualitative interviews are conducted with key organizational stakeholders to gain deeper insights into their experiences, perspectives, and best practices regarding the synergistic relationship. The findings of this study are expected to reveal several significant outcomes. Firstly, it is anticipated that organizations that effectively integrate KM and CI practices will outperform those that treat them as independent functions. The study aims to highlight the positive impact of this integration on decision-making, innovation, organizational learning, and competitive advantage. Furthermore, the research aims to identify critical success factors and enablers for achieving constructive interaction between KM and CI, such as leadership support, culture, technology infrastructure, and knowledge-sharing mechanisms. The implications of this research are far-reaching. Organizations can leverage the findings to develop strategies and practices that facilitate the integration of KM and CI, leading to enhanced competitive intelligence capabilities and improved knowledge management processes. Additionally, the research contributes to the academic literature by providing a comprehensive understanding of the synergistic relationship between KM and CI and proposing a conceptual framework that can guide future research in this area. By exploring the synergies between KM and CI, this study seeks to help organizations harness their collective power to gain a competitive edge in today's dynamic business landscape. The research provides practical insights and guidelines for organizations to effectively integrate KM and CI practices, leading to improved decision-making, innovation, and overall organizational performance.

Keywords: Competitive Intelligence, Knowledge Management, Organizational Performance, Incusivity, Optimum Performance

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Authors: Richard L. Summers

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The advanced computational analysis of biological systems is becoming increasingly dependent upon an understanding of the information-theoretic structure of the materials, energy and interactive processes that comprise those systems. The stability and survival of these living systems are fundamentally contingent upon their ability to acquire and process the meaning of information concerning the physical state of its biological continuum (biocontinuum). The drive for adaptive system reconciliation of a divergence from steady-state within this biocontinuum can be described by an information metric-based formulation of the process for actionable knowledge acquisition that incorporates the axiomatic inference of Kullback-Leibler information minimization driven by survival replicator dynamics. If the mathematical expression of this process is the Lagrangian integrand for any change within the biocontinuum then it can also be considered as an action functional for the living system. In the direct method of Lyapunov, such a summarizing mathematical formulation of global system behavior based on the driving forces of energy currents and constraints within the system can serve as a platform for the analysis of stability. As the system evolves in time in response to biocontinuum perturbations, the summarizing function then conveys information about its overall stability. This stability information portends survival and therefore has absolute existential meaning for the living system. The first derivative of the Lyapunov energy information function will have a negative trajectory toward a system's steady state if the driving force is dissipating. By contrast, system instability leading to system dissolution will have a positive trajectory. The direction and magnitude of the vector for the trajectory then serves as a quantifiable signature of the meaning associated with the living system’s stability information, homeostasis and survival potential.

Keywords: meaning, information, Lyapunov, living systems

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Authors: Aron Witkowski, Andrzej Wodecki

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Purpose: This study aims to explore the feasibility and effectiveness of utilizing Generative Artificial Intelligence (GenAI) agents as product managers within the manufacturing sector. It seeks to evaluate whether current GenAI capabilities can fulfill the complex requirements of product management and deliver comparable outcomes to human counterparts. Study Design/Methodology/Approach: This research involved the creation of a support application for product managers, utilizing high-quality sources on product management and generative AI technologies. The application was designed to assist in various aspects of product management tasks. To evaluate its effectiveness, a study was conducted involving 10 experienced product managers from the manufacturing sector. These professionals were tasked with using the application and providing feedback on the tool's responses to common questions and challenges they encounter in their daily work. The study employed a mixed-methods approach, combining quantitative assessments of the tool's performance with qualitative interviews to gather detailed insights into the user experience and perceived value of the application. Findings: The findings reveal that GenAI-based product management agents exhibit significant potential in handling routine tasks, data analysis, and predictive modeling. However, there are notable limitations in areas requiring nuanced decision-making, creativity, and complex stakeholder interactions. The case study demonstrates that while GenAI can augment human capabilities, it is not yet fully equipped to independently manage the holistic responsibilities of a product manager in the manufacturing sector. Originality/Value: This research provides an analysis of GenAI's role in product management within the manufacturing industry, contributing to the limited body of literature on the application of GenAI agents in this domain. It offers practical insights into the current capabilities and limitations of GenAI, helping organizations make informed decisions about integrating AI into their product management strategies. Implications for Academic and Practical Fields: For academia, the study suggests new avenues for research in AI-human collaboration and the development of advanced AI systems capable of higher-level managerial functions. Practically, it provides industry professionals with a nuanced understanding of how GenAI can be leveraged to enhance product management, guiding investments in AI technologies and training programs to bridge identified gaps.

Keywords: generative artificial intelligence, GenAI, NPD, new product development, product management, manufacturing

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Alhaji Khuzaima Mohammed Osman, Zaeem Sheikh Abdul Wadudi Haruna

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This article aims to explore the crucial link between counter-terrorism efforts and the preservation of human security. As acts of terrorism continue to pose significant threats to societies worldwide, it is imperative to develop effective strategies that mitigate risks while safeguarding the rights and well-being of individuals. This paper discusses key aspects of counter-terrorism and human security, emphasizing the need for a comprehensive approach that integrates intelligence, prevention, response, and resilience-building measures. By highlighting successful case studies and lessons learned, this article provides valuable insights for policymakers, law enforcement agencies, and practitioners in their quest to address terrorism and foster human security.

Keywords: human security, risk mitigation, terrorist activities, civil liberties

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Authors: Ellen Patricia, Marilou Hyson

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Coaching has been showing great potential to strengthen the impact of brief group trainings and help early childhood teachers solve specific problems at work with the goal of raising the quality of early childhood services. However, there have been some doubts about the benefits that village teachers can receive from coaching. It is perceived that village teachers may struggle with the thinking skills needed to make coaching beneficial. Furthermore, there are reservations about whether principals and supervisors in villages are open to coaching’s facilitative approach, as opposed to the directive approach they have been using. As such, the use of coaching to develop the professionalism of early childhood teachers in the villages needs to be examined. The Coaching Pilot for early childhood teachers in Indonesia villages provides insights for the above issues. The Coaching Pilot is part of the ECED Frontline Pilot, which is a collaboration project between the Government of Indonesia and the World Bank with the support from the Australian Government (DFAT). The Pilot started with coordinated efforts with the local government in two districts to select principals and supervisors who have been equipped with basic knowledge about early childhood education to take part in 2-days coaching training. Afterwards, the participants were asked to collect 25 hours of coaching early childhood teachers who have participated in the Enhanced Basic Training for village teachers. The participants who completed this requirement were then invited to come for an assessment of their coaching skills. Following that, a qualitative evaluation was conducted using in-depth interviews and Focus Group Discussion techniques. The evaluation focuses on the impact of the coaching pilot in helping the village teachers to develop in their professionalism, as well as on the sustainability of the intervention. Results from the evaluation indicated that although their low education may limit their thinking skills, village teachers benefited from the coaching that they received. Moreover, the evaluation results also suggested that with enough training and support, principals and supervisors in the villages were able to provide an adequate coaching service for the teachers. On top of that, beyond this small start, interest is growing, both within the pilot districts and even beyond, due to word of mouth of the benefits that the Coaching Pilot has created. The districts where coaching was piloted have planned to continue the coaching program, since a number of early childhood teachers have requested to be coached, and a number of principals and supervisors have also requested to be trained as a coach. Furthermore, the Association for Early Childhood Educators in Indonesia has started to adopt coaching into their program. Although further research is needed, the Coaching Pilot suggests that coaching can positively impact early childhood teachers in villages, and village principals and supervisors can become a promising source of future coaches. As such, coaching has a significant potential to become a sustainable model for a continuous professional development program for early childhood teachers in villages.

Keywords: coaching, coaching pilot, early childhood teachers, principals and supervisors, village teachers

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Authors: B. Bricklin Zeff

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This research explores the development and application of a specialized chatbot tailored for a nursing English course, with a primary objective of augmenting student engagement through situational content and responsiveness to key expressions and vocabulary. Introducing the chatbot, elucidating its purpose, and outlining its functionality are crucial initial steps in the research study, as they provide a comprehensive foundation for understanding the design and objectives of the specialized chatbot developed for the nursing English course. These elements establish the context for subsequent evaluations and analyses, enabling a nuanced exploration of the chatbot's impact on student engagement and language learning within the nursing education domain. The subsequent exploration of the intricate language model development process underscores the fusion of scientific methodologies and artistic considerations in this application of artificial intelligence (AI). Tailored for educators and curriculum developers in nursing, practical principles extending beyond AI and education are considered. Some insights into leveraging technology for enhanced language learning in specialized fields are addressed, with potential applications of similar chatbots in other professional English courses. The overarching vision is to illuminate how AI can transform language learning, rendering it more interactive and contextually relevant. The presented chatbot is a tangible example, equipping educators with a practical tool to enhance their teaching practices. Methodologies employed in this research encompass surveys and discussions to gather feedback on the chatbot's usability, effectiveness, and potential improvements. The chatbot system was integrated into a nursing English course, facilitating the collection of valuable feedback from participants. Significant findings from the study underscore the chatbot's effectiveness in encouraging more verbal practice of target expressions and vocabulary necessary for performance in role-play assessment strategies. This outcome emphasizes the practical implications of integrating AI into language education in specialized fields. This research holds significance for educators and curriculum developers in the nursing field, offering insights into integrating technology for enhanced English language learning. The study's major findings contribute valuable perspectives on the practical impact of the chatbot on student interaction and verbal practice. Ultimately, the research sheds light on the transformative potential of AI in making language learning more interactive and contextually relevant, particularly within specialized domains like nursing.

Keywords: chatbot, nursing, pragmatics, role-play, AI

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Authors: Danna Jia, Bin Li

Abstract:

Background: Atopic dermatitis (AD) is a chronic and refractory inflammatory skin disease characterized by relapsing eczematous and pruritic skin lesions. The global prevalence of AD ranges from 1~ 20%, and its incidence rates are increasing. It affects individuals from infancy to adulthood, significantly impacting their daily lives and social activities. Despite its major health burden, the precise mechanisms underlying AD remain unknown. Understanding the genetic differences associated with AD is crucial for advancing diagnosis and targeted treatment development. This study aims to identify candidate genes of AD by using bioinformatics analysis. Methods: We conducted a comprehensive analysis of four pooled transcriptomic datasets (GSE16161, GSE32924, GSE130588, and GSE120721) obtained from the Gene Expression Omnibus (GEO) database. Differential gene expression analysis was performed using the R statistical language. The differentially expressed genes (DEGs) between AD patients and normal individuals were functionally analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Furthermore, a protein-protein interaction (PPI) network was constructed to identify candidate genes. Results: Among the patient-level gene expression datasets, we identified 114 shared DEGs, consisting of 53 upregulated genes and 61 downregulated genes. Functional analysis using GO and KEGG revealed that the DEGs were mainly associated with the negative regulation of transcription from RNA polymerase II promoter, membrane-related functions, protein binding, and the Human papillomavirus infection pathway. Through the PPI network analysis, we identified eight core genes: CD44, STAT1, HMMR, AURKA, MKI67, and SMARCA4. Conclusion: This study elucidates key genes associated with AD, providing potential targets for diagnosis and treatment. The identified genes have the potential to contribute to the understanding and management of AD. The bioinformatics analysis conducted in this study offers new insights and directions for further research on AD. Future studies can focus on validating the functional roles of these genes and exploring their therapeutic potential in AD. While these findings will require further verification as achieved with experiments involving in vivo and in vitro models, these results provided some initial insights into dysfunctional inflammatory and immune responses associated with AD. Such information offers the potential to develop novel therapeutic targets for use in preventing and treating AD.

Keywords: atopic dermatitis, bioinformatics, biomarkers, genes

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Authors: Abdullah Faqihi, John Hedley

Abstract:

Biosensors play a significant role in the healthcare sectors, scientific and technological progress. Developing electrodes that are easy to manufacture and deliver better electrochemical performance is advantageous for diagnostics and biosensing. They can be implemented extensively in various analytical tasks such as drug discovery, food safety, medical diagnostics, process controls, security and defence, in addition to environmental monitoring. Development of biosensors aims to create high-performance electrochemical electrodes for diagnostics and biosensing. A biosensor is a device that inspects the biological and chemical reactions generated by the biological sample. A biosensor carries out biological detection via a linked transducer and transmits the biological response into an electrical signal; stability, selectivity, and sensitivity are the dynamic and static characteristics that affect and dictate the quality and performance of biosensors. In this research, a developed experimental study for laser scribing technique for graphene oxide inside a vacuum chamber for processing of graphene oxide is presented. The processing of graphene oxide (GO) was achieved using the laser scribing technique. The effect of the laser scribing on the reduction of GO was investigated under two conditions: atmosphere and vacuum. GO solvent was coated onto a LightScribe DVD. The laser scribing technique was applied to reduce GO layers to generate rGO. The micro-details for the morphological structures of rGO and GO were visualised using scanning electron microscopy (SEM) and Raman spectroscopy so that they could be examined. The first electrode was a traditional graphene-based electrode model, made under normal atmospheric conditions, whereas the second model was a developed graphene electrode fabricated under a vacuum state using a vacuum chamber. The purpose was to control the vacuum conditions, such as the air pressure and the temperature during the fabrication process. The parameters to be assessed include the layer thickness and the continuous environment. Results presented show high accuracy and repeatability achieving low cost productivity.

Keywords: laser scribing, lightscribe DVD, graphene oxide, scanning electron microscopy

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Authors: Randa Alharbi, Vladislav Vyshemirsky

Abstract:

Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost.

Keywords: Approximate Bayesian computation(ABC), Continuous-Time Markov Chains, Sequential Monte Carlo, Particle Markov chain Monte Carlo (PMCMC)

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Authors: Makkia Ahmad Ali Al-Falahi, Abdullah Abdelaziz Muharram

Abstract:

Background: Healthcare workers (HCWs) in Yemen are exposed to a myriad of occupational health hazards, including biological, physical, ergonomic, chemical and psychosocial hazards. HCWs operate in an environment that is considered to be one of the most hazardous occupational settings. Objective: To assess the prevalence of occupational health hazards among healthcare workers and associated risk factors in public hospitals in Sana'a City, Yemen. Method: Descriptive cross-sectional design was utilized; out of 5443 totals of HCWs 396 were selected by multistage sampling technique was carried out in the public hospitals in Sana'a city, Yemen. Results: More the half (60.6%) of HCWs aged between 20-30 years, (50.8%) were males, (56.3%) were married, and (45.5%) had a diploma qualification, while (65.2%) of HCWs had less than 6 years of experience. The result showed that the highest prevalence of occupational hazards was (99%), (ergonomic hazards (93.4%), biological hazards (87.6%), psychosocial (86.65%), physical hazards (83.3%), and chemical hazards (73.5%). There were no statistically significant differences between demographic characteristics and the prevalence of occupational hazards (p >0.05). Conclusion and recommendations: The study showed the highest prevalence of occupational hazards; regarding the prevalence of biological hazards exposure to sharp-related injury, the most prevalent physical hazards were slip/trip/and fall. Ergonomic hazards had back or neck pain during work. Chemical hazards were allergic to medical gloves powder. On psychosocial hazards was suffered from verbal and physical harassment. The study concluded by raising awareness among HCWs by conducting training courses to prevent occupational hazards.

Keywords: health workers, occupational hazards, risk factors, the prevalence

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