Background: Fall is a devastating consequence for elderly people that affects their quality of life. Many stumbles in the elderly are frequently associated with intrinsic risk factors such as muscle strength and balance impairments. The purpose of this study was to determine the relationship between quadriceps muscle strength and postural balance as intrinsic risk factors of falls among healthy elders in two selected elders’ homes in Colombo district, Sri Lanka. Methods: A cross-sectional study was conducted under a non-probability convenient sampling method with 60 elders (34 females & 26 males) who dwelt in the two selected elders’ homes in Colombo district. Fall history was obtained through an interview-administered assessment sheet. A modified sphygmomanometer was used to measure left and right quadriceps muscle strength. The postural balance was assessed using a mini version of the balance evaluation system test (Mini-BEST). Results: Mean age of the study sample was 76.67±6.23 years. The mean and standard deviation values of left quadriceps strength and right quadriceps strength were 138.63 ± 24.35mmHg, 149.90 ± 28.53mmHg respectively. The mean and standard deviation of postural balance was 20.88 ± 2.70. According to Pearson correlation coefficient, a strong positive linear relationship was revealed between right and left quadriceps muscle strength and postural balance. Conclusions: The results of our study revealed a strong positive relationship between quadriceps muscle strength and postural balance among elderly people in two elders’ homes. Therefore, it is recommended that maintaining good quadriceps strength and postural balance are more important to prevent falls in the elderly.
{"title":"Quadriceps muscle strength and postural balance as intrinsic risk factors of falls in institutionalized healthy older adults: A cross-sectional study in two selected elders’ homes in Colombo, Sri Lanka","authors":"Gamage Amara, Damayanthi Perera, Osadini Pramodhya, Isuri Madushani Kandege","doi":"10.30574/gscarr.2024.18.2.0057","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0057","url":null,"abstract":"Background: Fall is a devastating consequence for elderly people that affects their quality of life. Many stumbles in the elderly are frequently associated with intrinsic risk factors such as muscle strength and balance impairments. The purpose of this study was to determine the relationship between quadriceps muscle strength and postural balance as intrinsic risk factors of falls among healthy elders in two selected elders’ homes in Colombo district, Sri Lanka. Methods: A cross-sectional study was conducted under a non-probability convenient sampling method with 60 elders (34 females & 26 males) who dwelt in the two selected elders’ homes in Colombo district. Fall history was obtained through an interview-administered assessment sheet. A modified sphygmomanometer was used to measure left and right quadriceps muscle strength. The postural balance was assessed using a mini version of the balance evaluation system test (Mini-BEST). Results: Mean age of the study sample was 76.67±6.23 years. The mean and standard deviation values of left quadriceps strength and right quadriceps strength were 138.63 ± 24.35mmHg, 149.90 ± 28.53mmHg respectively. The mean and standard deviation of postural balance was 20.88 ± 2.70. According to Pearson correlation coefficient, a strong positive linear relationship was revealed between right and left quadriceps muscle strength and postural balance. Conclusions: The results of our study revealed a strong positive relationship between quadriceps muscle strength and postural balance among elderly people in two elders’ homes. Therefore, it is recommended that maintaining good quadriceps strength and postural balance are more important to prevent falls in the elderly.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.30574/gscarr.2024.18.2.0046
Dayinta Grahitanindya, Ida Bagus Gede Hendra Kusuma, Ida Bagus Semara Putra, Nyoman Dian Permatasari
Sinonasal inverted papilloma (SNIP) is a relatively rare benign tumor that occurs in 0.2-1.5 per 100.000 persons each year, comprising 0.5% - 4% of all sinonasal neoplasms. Despite being benign in nature, SNIP exhibits local aggressiveness due to its distinctive proliferation of metaplastic surface epithelium that undergoes inversion into the underlying stroma. Also, it carries a risk of malignant transformation. These characteristics thus emphasize the necessity for complete surgical excision as the primary treatment. A 50-year-old woman presented with sinonasal inverted papilloma classified as stage T2 according to Krouse staging, suitable for a less invasive endoscopic approach. However, CT scan revealed maxillary sinusitis. Adhering to SNIP management principles, a surgical excision through Endoscopic Sinus Surgery (ESS) followed by Endoscopic Modified Medial Maxillectomy (EMMM) or prelacrimal approach was chosen to provide better visualization of the anterior, lateral, posterior, inferior, and medial walls of the maxillary sinus. This approach aimed to preserve the inferior turbinate and nasolacrimal, avoiding postoperative lacrimation.
{"title":"Endoscopic surgical approach of sinonasal inverted papilloma: A case report and mini review","authors":"Dayinta Grahitanindya, Ida Bagus Gede Hendra Kusuma, Ida Bagus Semara Putra, Nyoman Dian Permatasari","doi":"10.30574/gscarr.2024.18.2.0046","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0046","url":null,"abstract":"Sinonasal inverted papilloma (SNIP) is a relatively rare benign tumor that occurs in 0.2-1.5 per 100.000 persons each year, comprising 0.5% - 4% of all sinonasal neoplasms. Despite being benign in nature, SNIP exhibits local aggressiveness due to its distinctive proliferation of metaplastic surface epithelium that undergoes inversion into the underlying stroma. Also, it carries a risk of malignant transformation. These characteristics thus emphasize the necessity for complete surgical excision as the primary treatment. A 50-year-old woman presented with sinonasal inverted papilloma classified as stage T2 according to Krouse staging, suitable for a less invasive endoscopic approach. However, CT scan revealed maxillary sinusitis. Adhering to SNIP management principles, a surgical excision through Endoscopic Sinus Surgery (ESS) followed by Endoscopic Modified Medial Maxillectomy (EMMM) or prelacrimal approach was chosen to provide better visualization of the anterior, lateral, posterior, inferior, and medial walls of the maxillary sinus. This approach aimed to preserve the inferior turbinate and nasolacrimal, avoiding postoperative lacrimation.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"116 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140422341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.30574/gscarr.2024.18.2.0069
Andrew Ifesinachi, Enoch Oluwademilade Sodiya, Boma Sonimitiem Jacks, Ejike David Ugwuanyi, Mojisola Abimbola Adeyinka, Uchenna Joseph Umoga, Andrew Ifesinachi Daraojimba, Oluwaseun Augustine Lottu
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain analytics has emerged as a transformative force in reshaping traditional logistics and operations. This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands. AI and ML technologies have revolutionized data analytics by enabling the extraction of actionable insights from vast and complex datasets. The application of predictive analytics, powered by machine learning algorithms, allows supply chain professionals to forecast demand more accurately, identify potential disruptions, and optimize inventory levels. This not only improves overall efficiency but also reduces costs and minimizes the risk of stockouts or overstock situations. Furthermore, the integration of AI-driven automation in supply chain management has streamlined routine tasks, such as order processing, inventory replenishment, and route optimization. This automation not only accelerates processes but also mitigates the risk of human errors, enhancing overall reliability. The ability of AI to continuously learn from historical data and adapt to evolving market conditions contributes to a more agile and responsive supply chain ecosystem. In the context of supply chain risk management, AI and ML play a pivotal role in identifying vulnerabilities and providing proactive strategies to mitigate potential disruptions. Sentiment analysis and predictive modeling enable organizations to assess geopolitical, economic, and environmental factors, thereby enhancing the resilience of their supply chains. However, the adoption of AI and ML in supply chain analytics is not without challenges. This review explores the ethical considerations, data security concerns, and the need for skilled personnel in managing these advanced technologies. Additionally, it delves into the importance of explainability and transparency in AI-driven decision-making processes, emphasizing the need for a balance between automation and human oversight. This review underscores the transformative impact of AI and ML on supply chain analytics, emphasizing their potential to revolutionize traditional practices, enhance efficiency, and fortify resilience in an increasingly complex and dynamic business environment.
人工智能（AI）和机器学习（ML）在供应链分析中的整合已成为重塑传统物流和运营的变革力量。本综述批判性地研究了人工智能和 ML 在优化供应链流程、提高决策能力以及在动态市场需求时代促进灵活性方面的多方面作用。人工智能和 ML 技术能够从庞大而复杂的数据集中提取可行的见解，从而彻底改变了数据分析。在机器学习算法的支持下，预测分析的应用使供应链专业人员能够更准确地预测需求、识别潜在的干扰并优化库存水平。这不仅能提高整体效率，还能降低成本，最大限度地减少缺货或库存过剩的风险。此外，将人工智能驱动的自动化整合到供应链管理中，简化了订单处理、库存补充和路线优化等常规任务。这种自动化不仅加快了流程，还降低了人为错误的风险，提高了整体可靠性。人工智能能够不断从历史数据中学习，并适应不断变化的市场条件，有助于建立一个更加灵活、反应更快的供应链生态系统。在供应链风险管理方面，人工智能和 ML 在识别漏洞和提供前瞻性战略以减轻潜在干扰方面发挥着举足轻重的作用。情感分析和预测建模使企业能够评估地缘政治、经济和环境因素，从而提高供应链的复原力。然而，在供应链分析中采用人工智能和 ML 并非没有挑战。本综述探讨了管理这些先进技术的道德考虑因素、数据安全问题以及对熟练人员的需求。此外，它还深入探讨了人工智能驱动的决策过程中可解释性和透明度的重要性，强调了在自动化和人工监督之间保持平衡的必要性。本综述强调了人工智能和 ML 对供应链分析的变革性影响，强调了它们在日益复杂多变的商业环境中革新传统做法、提高效率和增强应变能力的潜力。
{"title":"Reviewing the role of AI and machine learning in supply chain analytics","authors":"Andrew Ifesinachi, Enoch Oluwademilade Sodiya, Boma Sonimitiem Jacks, Ejike David Ugwuanyi, Mojisola Abimbola Adeyinka, Uchenna Joseph Umoga, Andrew Ifesinachi Daraojimba, Oluwaseun Augustine Lottu","doi":"10.30574/gscarr.2024.18.2.0069","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0069","url":null,"abstract":"The integration of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain analytics has emerged as a transformative force in reshaping traditional logistics and operations. This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands. AI and ML technologies have revolutionized data analytics by enabling the extraction of actionable insights from vast and complex datasets. The application of predictive analytics, powered by machine learning algorithms, allows supply chain professionals to forecast demand more accurately, identify potential disruptions, and optimize inventory levels. This not only improves overall efficiency but also reduces costs and minimizes the risk of stockouts or overstock situations. Furthermore, the integration of AI-driven automation in supply chain management has streamlined routine tasks, such as order processing, inventory replenishment, and route optimization. This automation not only accelerates processes but also mitigates the risk of human errors, enhancing overall reliability. The ability of AI to continuously learn from historical data and adapt to evolving market conditions contributes to a more agile and responsive supply chain ecosystem. In the context of supply chain risk management, AI and ML play a pivotal role in identifying vulnerabilities and providing proactive strategies to mitigate potential disruptions. Sentiment analysis and predictive modeling enable organizations to assess geopolitical, economic, and environmental factors, thereby enhancing the resilience of their supply chains. However, the adoption of AI and ML in supply chain analytics is not without challenges. This review explores the ethical considerations, data security concerns, and the need for skilled personnel in managing these advanced technologies. Additionally, it delves into the importance of explainability and transparency in AI-driven decision-making processes, emphasizing the need for a balance between automation and human oversight. This review underscores the transformative impact of AI and ML on supply chain analytics, emphasizing their potential to revolutionize traditional practices, enhance efficiency, and fortify resilience in an increasingly complex and dynamic business environment.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"136 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: In Indonesia, the Human Immunodeficiency Virus (HIV) is a serious health concern. Since HIV/AIDS patients must adhere to anti-retroviral (ARV) treatment for the rest of their lives, treatment and care must have this as their main objective. Objective: This study was to identify the factors that influence People Living with HIV/ AIDS (PLWHA) to adhere their anti-retroviral (ARV) medication consumption and the determinants of this adherence. Method: This study uses a descriptive analytic design with cross-sectional approach, which provides an overview of adherence factors in taking ARV, such as gender, education, occupation, knowledge, family support, instructional perception, and motivation. This study was taken in Mejobo District, Kudus Regency, and Indonesia. This study's sample was taken using the total sampling technique involving 33 respondents. Result: This study showed that instructional perception is a determining factor in adherence to ARV medication use with OR of 2.746, followed by education (OR 2.24) and education (OR 2.197). Conclusion: The necessity of education on how to consume ARVs is critical, thus medical professionals must be more active in ensuring patient comprehension and motivation so that ARV therapy is sustained.
{"title":"Determinants of anti-retroviral consumption adherence in people living with HIV/AIDS in the sub-district of Mejobo, Kudus, Central Java, Indonesia","authors":"Yosefin Beni Purbo Utami, Baharika Suci Dwi Aningsih, Lorensia Panselina Widowati, Eviyani Margaretha Manungkalit","doi":"10.30574/gscarr.2024.18.2.0041","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0041","url":null,"abstract":"Introduction: In Indonesia, the Human Immunodeficiency Virus (HIV) is a serious health concern. Since HIV/AIDS patients must adhere to anti-retroviral (ARV) treatment for the rest of their lives, treatment and care must have this as their main objective. Objective: This study was to identify the factors that influence People Living with HIV/ AIDS (PLWHA) to adhere their anti-retroviral (ARV) medication consumption and the determinants of this adherence. Method: This study uses a descriptive analytic design with cross-sectional approach, which provides an overview of adherence factors in taking ARV, such as gender, education, occupation, knowledge, family support, instructional perception, and motivation. This study was taken in Mejobo District, Kudus Regency, and Indonesia. This study's sample was taken using the total sampling technique involving 33 respondents. Result: This study showed that instructional perception is a determining factor in adherence to ARV medication use with OR of 2.746, followed by education (OR 2.24) and education (OR 2.197). Conclusion: The necessity of education on how to consume ARVs is critical, thus medical professionals must be more active in ensuring patient comprehension and motivation so that ARV therapy is sustained.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"125 S2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predictive analytics is increasingly recognized as a pivotal tool in climate finance, offering investors invaluable insights into both the risks posed by climate change and the opportunities for sustainable investment. This Review delves into the burgeoning field of predictive analytics within climate finance, emphasizing its significance in aiding investors to navigate the multifaceted landscape of climate-related risks and opportunities. By leveraging advanced data analytics techniques, predictive analytics empowers investors to anticipate and mitigate climate-related risks, ranging from physical risks such as extreme weather events and sea-level rise to transition risks associated with regulatory changes and technological shifts. Moreover, predictive analytics enables investors to identify emerging opportunities in sectors poised for sustainable growth, such as renewable energy, clean technology, and climate resilient infrastructure. This Review also sheds light on the methodologies and data sources utilized in predictive analytics for climate finance, encompassing climate models, satellite imagery, socioeconomic indicators, and financial data. Through the analysis of historical trends and future projections, predictive analytics provides investors with actionable insights to inform their investment decisions and align their portfolios with climate-related goals and mandates. Despite its potential benefits, the adoption of predictive analytics in climate finance is not without challenges. This Review examines the hurdles associated with data quality, model uncertainty, regulatory complexities, and the integration of climate-related factors into financial decision-making processes. Addressing these challenges necessitates interdisciplinary collaboration, robust risk assessment frameworks, and ongoing innovation in predictive analytics methodologies. In conclusion, this Review underscores the critical role of predictive analytics in climate finance and its transformative potential in enhancing the resilience and sustainability of investment portfolios. By harnessing the power of data-driven insights, investors can proactively manage climate-related risks, capitalize on sustainable investment opportunities, and contribute to the transition towards a low-carbon economy. As climate change continues to exert profound impacts on financial markets, the integration of predictive analytics represents a strategic imperative for investors seeking to navigate the evolving landscape of climate finance effectively.
{"title":"Predictive analytics in climate finance: Assessing risks and opportunities for investors","authors":"Onyeka Chrisanctus Ofodile, Adedoyin Tolulope Oyewole, Chinonye Esther Ugochukwu, Wilhelmina Afua Addy, Omotayo Bukola Adeoye, Chinwe Chinazo Okoye","doi":"10.30574/gscarr.2024.18.2.0076","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0076","url":null,"abstract":"Predictive analytics is increasingly recognized as a pivotal tool in climate finance, offering investors invaluable insights into both the risks posed by climate change and the opportunities for sustainable investment. This Review delves into the burgeoning field of predictive analytics within climate finance, emphasizing its significance in aiding investors to navigate the multifaceted landscape of climate-related risks and opportunities. By leveraging advanced data analytics techniques, predictive analytics empowers investors to anticipate and mitigate climate-related risks, ranging from physical risks such as extreme weather events and sea-level rise to transition risks associated with regulatory changes and technological shifts. Moreover, predictive analytics enables investors to identify emerging opportunities in sectors poised for sustainable growth, such as renewable energy, clean technology, and climate resilient infrastructure. This Review also sheds light on the methodologies and data sources utilized in predictive analytics for climate finance, encompassing climate models, satellite imagery, socioeconomic indicators, and financial data. Through the analysis of historical trends and future projections, predictive analytics provides investors with actionable insights to inform their investment decisions and align their portfolios with climate-related goals and mandates. Despite its potential benefits, the adoption of predictive analytics in climate finance is not without challenges. This Review examines the hurdles associated with data quality, model uncertainty, regulatory complexities, and the integration of climate-related factors into financial decision-making processes. Addressing these challenges necessitates interdisciplinary collaboration, robust risk assessment frameworks, and ongoing innovation in predictive analytics methodologies. In conclusion, this Review underscores the critical role of predictive analytics in climate finance and its transformative potential in enhancing the resilience and sustainability of investment portfolios. By harnessing the power of data-driven insights, investors can proactively manage climate-related risks, capitalize on sustainable investment opportunities, and contribute to the transition towards a low-carbon economy. As climate change continues to exert profound impacts on financial markets, the integration of predictive analytics represents a strategic imperative for investors seeking to navigate the evolving landscape of climate finance effectively.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"366 1‐2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140417508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Entrepreneurial leadership plays a pivotal role in navigating the dynamic landscape of high-tech industries, where innovation, agility, and strategic vision are imperative for success. This paper provides a comprehensive review of key traits and success strategies associated with entrepreneurial leadership in the context of high-tech sectors. In high-tech industries, characterized by rapid technological advancements and fierce competition, the traditional leadership paradigm often falls short. Entrepreneurial leaders in this domain are distinguished by a set of traits that enable them to thrive in uncertainty and foster innovation. Proactivity, risk-taking propensity, and a keen sense of opportunity recognition emerge as fundamental traits that differentiate successful entrepreneurial leaders from their counterparts. This review explores how these traits contribute to creating an environment conducive to innovation and adaptation. Strategies employed by entrepreneurial leaders in high-tech industries are multifaceted, encompassing both organizational and individual levels. At the organizational level, fostering a culture of experimentation, encouraging cross-functional collaboration, and establishing flexible structures emerge as critical success factors. These strategies not only enhance the organization's adaptive capacity but also stimulate a culture of continuous learning and knowledge sharing. On an individual level, entrepreneurial leaders in high-tech industries often display a high degree of emotional intelligence, enabling them to navigate complex interpersonal relationships and foster a collaborative work environment. Additionally, effective communication and the ability to inspire and motivate diverse teams become crucial tools for leaders to drive innovation and ensure sustained success. Furthermore, the review delves into the role of strategic vision and adaptability in entrepreneurial leadership. Successful leaders in high-tech industries possess the foresight to anticipate market trends, coupled with the flexibility to adjust strategies swiftly in response to evolving technological landscapes. This paper encapsulates the essence of entrepreneurial leadership in high-tech industries by exploring key traits and success strategies. By understanding and incorporating these elements, leaders can cultivate an environment that fosters innovation, resilience, and sustainable growth in the ever-evolving high-tech landscape.
{"title":"Entrepreneurial leadership in high-tech industries: A review of key traits and success strategies","authors":"Wilhelmina Afua Addy, Adeola Olusola Ajayi-Nifise, Binaebi Gloria Bello, Sunday Tubokirifuruar Tula, Olubusola Odeyemi, Titilola Falaiye","doi":"10.30574/gscarr.2024.18.2.0071","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0071","url":null,"abstract":"Entrepreneurial leadership plays a pivotal role in navigating the dynamic landscape of high-tech industries, where innovation, agility, and strategic vision are imperative for success. This paper provides a comprehensive review of key traits and success strategies associated with entrepreneurial leadership in the context of high-tech sectors. In high-tech industries, characterized by rapid technological advancements and fierce competition, the traditional leadership paradigm often falls short. Entrepreneurial leaders in this domain are distinguished by a set of traits that enable them to thrive in uncertainty and foster innovation. Proactivity, risk-taking propensity, and a keen sense of opportunity recognition emerge as fundamental traits that differentiate successful entrepreneurial leaders from their counterparts. This review explores how these traits contribute to creating an environment conducive to innovation and adaptation. Strategies employed by entrepreneurial leaders in high-tech industries are multifaceted, encompassing both organizational and individual levels. At the organizational level, fostering a culture of experimentation, encouraging cross-functional collaboration, and establishing flexible structures emerge as critical success factors. These strategies not only enhance the organization's adaptive capacity but also stimulate a culture of continuous learning and knowledge sharing. On an individual level, entrepreneurial leaders in high-tech industries often display a high degree of emotional intelligence, enabling them to navigate complex interpersonal relationships and foster a collaborative work environment. Additionally, effective communication and the ability to inspire and motivate diverse teams become crucial tools for leaders to drive innovation and ensure sustained success. Furthermore, the review delves into the role of strategic vision and adaptability in entrepreneurial leadership. Successful leaders in high-tech industries possess the foresight to anticipate market trends, coupled with the flexibility to adjust strategies swiftly in response to evolving technological landscapes. This paper encapsulates the essence of entrepreneurial leadership in high-tech industries by exploring key traits and success strategies. By understanding and incorporating these elements, leaders can cultivate an environment that fosters innovation, resilience, and sustainable growth in the ever-evolving high-tech landscape.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"12 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140419291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.30574/gscarr.2024.18.2.0074
Mamadou Salif SOW, Khady Waly FAYE, Maurice Jean François Sylvestre LOPY, Abdou DIOUF
The aim of this study was to explore the possibilities of producing composite breads from blends of rice and wheat flours, with the addition of xanthan gum. The aim is to provide an effective and considerable substitute for wheat in bread making. Six formulations of rice (R) and wheat (B) flour blends were prepared in different proportions: 100%B, 50%R/50%B, 60%R/40%B, 65%R/35%B, 70%R/30%B, 85%R/15%B. The functional and rheological characteristics of the composite breads were evaluated and compared with those of 100% wheat bread, used as a reference. A constant percentage of xanthan gum (0.25%) was added to each mix. Samples containing 50%, 65% and 70% rice flour, in combination with xanthan gum, showed promising results in terms of volume, water loss, color and sensory acceptability. The 50%R/50%B bread gave the best rheological results, with a volume of 1.7933cm3/g, closest to that of the control bread with a specific volume of 3.448cm3/g of wheat; its water loss was 8.9%, lower than that of the control bread (10%); and its color and honeycomb structure were similar to those of wheat. Sensory evaluation revealed that the 50%R/50%B bread was the most appreciated by panellists, achieving an acceptability rate of 80%. This study demonstrates the feasibility of replacing wheat with other cereals, such as rice, using xanthan gum to maintain excellent technological and sensory properties. These results suggest that rice could be appropriately incorporated into wheat flour, up to a substitution rate of 70%.
{"title":"Improvement of the rate of substitution of wheat by rice in bread making by the use of xanthan gum: Technological and sensory properties","authors":"Mamadou Salif SOW, Khady Waly FAYE, Maurice Jean François Sylvestre LOPY, Abdou DIOUF","doi":"10.30574/gscarr.2024.18.2.0074","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0074","url":null,"abstract":"The aim of this study was to explore the possibilities of producing composite breads from blends of rice and wheat flours, with the addition of xanthan gum. The aim is to provide an effective and considerable substitute for wheat in bread making. Six formulations of rice (R) and wheat (B) flour blends were prepared in different proportions: 100%B, 50%R/50%B, 60%R/40%B, 65%R/35%B, 70%R/30%B, 85%R/15%B. The functional and rheological characteristics of the composite breads were evaluated and compared with those of 100% wheat bread, used as a reference. A constant percentage of xanthan gum (0.25%) was added to each mix. Samples containing 50%, 65% and 70% rice flour, in combination with xanthan gum, showed promising results in terms of volume, water loss, color and sensory acceptability. The 50%R/50%B bread gave the best rheological results, with a volume of 1.7933cm3/g, closest to that of the control bread with a specific volume of 3.448cm3/g of wheat; its water loss was 8.9%, lower than that of the control bread (10%); and its color and honeycomb structure were similar to those of wheat. Sensory evaluation revealed that the 50%R/50%B bread was the most appreciated by panellists, achieving an acceptability rate of 80%. This study demonstrates the feasibility of replacing wheat with other cereals, such as rice, using xanthan gum to maintain excellent technological and sensory properties. These results suggest that rice could be appropriately incorporated into wheat flour, up to a substitution rate of 70%.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"61 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140420677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This comprehensive review explores the profound impact of artificial intelligence (AI) on warehouse automation, providing an in-depth examination of various AI-driven systems. As industries increasingly embrace automation to enhance efficiency and streamline operations, the integration of AI technologies into warehouse management systems has become pivotal, reshaping the landscape of logistics and supply chain management. AI-driven warehouse automation systems leverage advanced algorithms to optimize various aspects of warehouse operations, from inventory management to order fulfillment. Machine learning algorithms play a key role in demand forecasting, allowing warehouses to predict and adapt to changing customer needs. Computer vision technologies enhance robotic vision, facilitating tasks such as item recognition, pick-and-place operations, and quality control. These advancements significantly contribute to increased accuracy, speed, and cost-effectiveness in warehouse processes. The review provides a detailed examination of the applications of AI in warehouse automation, encompassing autonomous mobile robots (AMRs), robotic arms, and automated guided vehicles (AGVs). AMRs equipped with AI algorithms navigate warehouse environments autonomously, optimizing pick routes and adapting to changes in the warehouse layout. Robotic arms, enhanced by AI, enable precise and adaptable material handling, contributing to the efficiency of tasks like packing and palletizing. AGVs, guided by AI, ensure seamless material transport within warehouses, enhancing overall operational agility. Recent trends in AI-driven warehouse automation systems underscore the dynamic evolution of this field. Edge computing solutions empower these systems to process data locally, reducing latency and enhancing real-time decision-making. Reinforcement learning algorithms enable robotic systems to learn and adapt their behavior based on changing environmental conditions, contributing to continuous improvement and efficiency gains. In conclusion, this review illuminates the pivotal role of AI in transforming warehouse automation systems, revolutionizing the way logistics and supply chain operations are conducted. The collaborative synergy between AI and warehouse automation promises to drive unprecedented advancements in efficiency, accuracy, and adaptability within the evolving landscape of modern warehouses.
{"title":"AI-driven warehouse automation: A comprehensive review of systems","authors":"Enoch Oluwademilade Sodiya, Uchenna Joseph Umoga, Olukunle Oladipupo Amoo, Akoh Atadoga","doi":"10.30574/gscarr.2024.18.2.0063","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0063","url":null,"abstract":"This comprehensive review explores the profound impact of artificial intelligence (AI) on warehouse automation, providing an in-depth examination of various AI-driven systems. As industries increasingly embrace automation to enhance efficiency and streamline operations, the integration of AI technologies into warehouse management systems has become pivotal, reshaping the landscape of logistics and supply chain management. AI-driven warehouse automation systems leverage advanced algorithms to optimize various aspects of warehouse operations, from inventory management to order fulfillment. Machine learning algorithms play a key role in demand forecasting, allowing warehouses to predict and adapt to changing customer needs. Computer vision technologies enhance robotic vision, facilitating tasks such as item recognition, pick-and-place operations, and quality control. These advancements significantly contribute to increased accuracy, speed, and cost-effectiveness in warehouse processes. The review provides a detailed examination of the applications of AI in warehouse automation, encompassing autonomous mobile robots (AMRs), robotic arms, and automated guided vehicles (AGVs). AMRs equipped with AI algorithms navigate warehouse environments autonomously, optimizing pick routes and adapting to changes in the warehouse layout. Robotic arms, enhanced by AI, enable precise and adaptable material handling, contributing to the efficiency of tasks like packing and palletizing. AGVs, guided by AI, ensure seamless material transport within warehouses, enhancing overall operational agility. Recent trends in AI-driven warehouse automation systems underscore the dynamic evolution of this field. Edge computing solutions empower these systems to process data locally, reducing latency and enhancing real-time decision-making. Reinforcement learning algorithms enable robotic systems to learn and adapt their behavior based on changing environmental conditions, contributing to continuous improvement and efficiency gains. In conclusion, this review illuminates the pivotal role of AI in transforming warehouse automation systems, revolutionizing the way logistics and supply chain operations are conducted. The collaborative synergy between AI and warehouse automation promises to drive unprecedented advancements in efficiency, accuracy, and adaptability within the evolving landscape of modern warehouses.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"133 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.30574/gscarr.2024.18.2.0053
Chibuzo Emeruwa
This work attempts to effectively compare Call Setup Success Rate CSSR against industry benchmarks and competitor networks to identify areas for improvement and sets performance targets. Four mobile telecommunication networks operational in Yenagoa – Southern Nigeria were considered. Results obtained shows that MTN and Airtel performed above the regulator’s benchmark of 98% in all cases, while Globacom and 9moblie had instances where their performance fell below the benchmark. The maximum values obtained within the period in view was 99.79% and it was gotten from 9mobile while the minimum value obtained was 94.07% and it was from Globacom. In the years 2017, 2019 and 2020 all the networks had values exceeding the NCC’s benchmark, this affirms that it is possible for the networks to perform optimally if adequate measures are put in place for improved QoS.
{"title":"Statistical analysis of call setup success rate for 4 g networks in yenagoa-southern Nigeria","authors":"Chibuzo Emeruwa","doi":"10.30574/gscarr.2024.18.2.0053","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0053","url":null,"abstract":"This work attempts to effectively compare Call Setup Success Rate CSSR against industry benchmarks and competitor networks to identify areas for improvement and sets performance targets. Four mobile telecommunication networks operational in Yenagoa – Southern Nigeria were considered. Results obtained shows that MTN and Airtel performed above the regulator’s benchmark of 98% in all cases, while Globacom and 9moblie had instances where their performance fell below the benchmark. The maximum values obtained within the period in view was 99.79% and it was gotten from 9mobile while the minimum value obtained was 94.07% and it was from Globacom. In the years 2017, 2019 and 2020 all the networks had values exceeding the NCC’s benchmark, this affirms that it is possible for the networks to perform optimally if adequate measures are put in place for improved QoS.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"19 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.30574/gscarr.2024.18.2.0067
Oluwaseun Adekoya Adelaja
This study investigated the potential of a biocomposite adsorbent made of low-density polyethylene-chitosan nanoparticles (LDPE/CHNP) for removing Congo red and Crystal violet dyes from wastewater. Batch experiments were conducted at room temperature to study the effects of pH, contact time, initial concentration, adsorbent dosage, and temperature on the adsorption process. The results showed that the maximum sorption of Congo red and Crystal violet occurred at pH 8. The adsorption process was rapid in the first 30 minutes of contact, with more than 90% uptake, and equilibrium was achieved with 150 rpm agitation. The biosorption of Congo red and Crystal violet dyes was described using Langmuir, Freundlich, and isotherm models. The Langmuir model was found to fit the equilibrium data better, with a correlation coefficient of 0.99 and a maximal adsorption capacity of 27.1 mg/g. Based on these results, it can be concluded that the biocomposite adsorbent made of low density polyethylene-chitosan nanoparticles (LDPE/CHNP) has the potential to be an effective and abundant alternative biomass for removing these dyes from wastewater. The LDPE/CHNP biocomposite adsorbent could be applied as a remediation method for color contamination in wastewater.
{"title":"Bio-composite of chitosan and polyethylene for the biosorption of dye in wastewater","authors":"Oluwaseun Adekoya Adelaja","doi":"10.30574/gscarr.2024.18.2.0067","DOIUrl":"https://doi.org/10.30574/gscarr.2024.18.2.0067","url":null,"abstract":"This study investigated the potential of a biocomposite adsorbent made of low-density polyethylene-chitosan nanoparticles (LDPE/CHNP) for removing Congo red and Crystal violet dyes from wastewater. Batch experiments were conducted at room temperature to study the effects of pH, contact time, initial concentration, adsorbent dosage, and temperature on the adsorption process. The results showed that the maximum sorption of Congo red and Crystal violet occurred at pH 8. The adsorption process was rapid in the first 30 minutes of contact, with more than 90% uptake, and equilibrium was achieved with 150 rpm agitation. The biosorption of Congo red and Crystal violet dyes was described using Langmuir, Freundlich, and isotherm models. The Langmuir model was found to fit the equilibrium data better, with a correlation coefficient of 0.99 and a maximal adsorption capacity of 27.1 mg/g. Based on these results, it can be concluded that the biocomposite adsorbent made of low density polyethylene-chitosan nanoparticles (LDPE/CHNP) has the potential to be an effective and abundant alternative biomass for removing these dyes from wastewater. The LDPE/CHNP biocomposite adsorbent could be applied as a remediation method for color contamination in wastewater.","PeriodicalId":12791,"journal":{"name":"GSC Advanced Research and Reviews","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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