The performance of bridge projects in Kenya is poor in terms of completion by schedule, cost, and quality (scope). Yet, there is less evidence of empirical research on what factors contribute to this outcome. This stu...The performance of bridge projects in Kenya is poor in terms of completion by schedule, cost, and quality (scope). Yet, there is less evidence of empirical research on what factors contribute to this outcome. This study aimed to bridge this gap by examining the effects of contractor-related factors on the performance of bridge construction projects in Kenya through a case study of the Bridge projects Implemented by the Kenya National Highway Agency (KeNHA). The theory of constraints (TOC) was adopted as its theoretical framework. Descriptive research was used, and the target population was 18 bridge construction projects, which were the units of analysis from 2012 to 2022. In each of these projects, 18 respondents, namely clients, consultants, contractors, engineers, environment and social guards, project managers, stakeholders, subcontractors, technical advisors, and inspectors, were included in a target population of 144 respondents. A census was conducted and a structured questionnaire was administered from which a response rate of 68% was achieved. The information was analyzed using descriptive, correlation, and multiple linear regression analysis. The contractor-related factors considered in the study were staff and management factors. The findings indicated that staff and management factors had a positive and significant outcome on performance of bridge construction projects. The study recommends continuous training and a safe learning environment for staff to improve their skills and performance in future projects. The study also recommends that a special category for bridge contractors be created in Kenya’s National Construction Authority rankings to ensure that only qualified contractors implement the Bridge projects.展开更多
This study comprehensively examines the patterns and regional variation of severe rainfall across the African continent, employing a suite of eight extreme precipitation indices. The analysis extends to the assessment...This study comprehensively examines the patterns and regional variation of severe rainfall across the African continent, employing a suite of eight extreme precipitation indices. The analysis extends to the assessment of projected changes in precipitation extremes using five General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs) scenarios at the long-term period (2081-2100) of the 21<sup>st</sup> century. Furthermore, the study investigates potential mechanisms influencing precipitation extremes by correlating extreme precipitation indices with oceanic system indices, specifically Ni?o 3.4 for El Ni?o-Southern Oscillation (ENSO) and Dipole Mode Index (DMI) for the Indian Ocean Dipole (IOD). The findings revealed distinct spatial distributions in mean trends of extreme precipitation indices, indicating a tendency toward decreased extreme precipitation in North Africa, Sahel region, Central Africa and the Western part of South Africa. Conversely, West Africa, East Africa and the Eastern part of South Africa exhibit an inclination toward increased extreme precipitation. The changes in precipitation extreme indices indicate a general rise in both the severity and occurrence of extreme precipitation events under all scenarios by the end of the 21<sup>st</sup> century. Notably, our analysis projects a decrease in consecutive wet days (CWD) in the far-future. Additionally, correlation analysis highlights significant correlation between above or below threshold rainfall fluctuation in East Africa and South Africa with oceanic systems, particularly ENSO and the IOD. Central Africa abnormal precipitation variability is also linked to ENSO with a significant negative correlation. These insights contribute valuable information for understanding and projecting the dynamics of precipitation extreme in Africa, providing a foundation for climate adaptation and mitigation efforts in the region.展开更多
Maps, essential tools for portraying the Earth’s surface, inherently introduce distortions to geographical features. While various quantification methods exist for assessing these distortions, they often fall short w...Maps, essential tools for portraying the Earth’s surface, inherently introduce distortions to geographical features. While various quantification methods exist for assessing these distortions, they often fall short when evaluating actual geographic features. In our study, we took a novel approach by analyzing map projection distortion from a geometric perspective. We computed the fractal dimensions of different stretches of coastline before and after projection using the divide-and-conquer algorithm and image processing. Our findings revealed that map projections, even when preserving basic shapes, inevitably stretch and compress coastlines in diverse directions. This analysis method provides a more realistic and practical way to measure map-induced distortions, with significant implications for cartography, geographic information systems (GIS), and geomorphology. By bridging the gap between theoretical analysis and real-world features, this method greatly enhances accuracy and practicality when evaluating map projections.展开更多
It is alarming for the fact that Wildfires number, severity and consequently impact have significantly increased during the last years, an aftermath of the Climate Change. One of the most affected areas worldwide is M...It is alarming for the fact that Wildfires number, severity and consequently impact have significantly increased during the last years, an aftermath of the Climate Change. One of the most affected areas worldwide is Mediterranean, due to the unique combination of its type of vegetation and demanding climatic conditions. This research is focused on the Region of Epirus in Greece, an area with significant natural vegetation and a range of geomorphological aspects. In order to estimate the Wildfire Risk Hazard, several factors have been used: geomorphological (slope, aspect, elevation, TWI, Hydrographic network), social (Settlements and landfils, roads, overhead lines and substations), environmental (land cover) and climatic (Fire Weather Index). Through a multi-criteria decision analysis (MCDA) and an analytic hierarchy process (AHP) in a GIS environment, the Wildfire Risk Hazard has been estimated not only for current conditions but also for future projections for the near future (2031-2060) and the far future (2071-2100). The selected case study includes the potential impact of the Wildfires to the installed (or targeted to be installed) RES projects in the studied region.展开更多
This study assesses the projected changes in the climate zoning of Côte d’Ivoire using the hierarchical classification of principal components (HCPC) method applied to the daily precipitation data of an ensemble...This study assesses the projected changes in the climate zoning of Côte d’Ivoire using the hierarchical classification of principal components (HCPC) method applied to the daily precipitation data of an ensemble of 14 CORDEX-AFRICA simulations under RCP4.5 and RCP8.5 scenarios. The results indicate the existence of three climate zones in Côte d’Ivoire (the coastal, the centre and the north) over the historical period (1981-2005). Moreover, CORDEX simulations project an extension of the surface area of drier climatic zones while a reduction of wetter zones, associated with the appearance of an intermediate climate zone with surface area varying from 77,560 km<sup>2</sup> to 134,960 km<sup>2</sup> depending on the period and the scenario. These results highlight the potential impacts of climate change on the delimitation of the climate zones of Côte d’Ivoire under the greenhouse gas emission scenarios. Thus, there is a reduction in the surface areas suitable for the production of cash crops such as cocoa and coffee. This could hinder the country’s economy and development, mainly based on these cash crops.展开更多
In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing custome...In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.展开更多
文摘The performance of bridge projects in Kenya is poor in terms of completion by schedule, cost, and quality (scope). Yet, there is less evidence of empirical research on what factors contribute to this outcome. This study aimed to bridge this gap by examining the effects of contractor-related factors on the performance of bridge construction projects in Kenya through a case study of the Bridge projects Implemented by the Kenya National Highway Agency (KeNHA). The theory of constraints (TOC) was adopted as its theoretical framework. Descriptive research was used, and the target population was 18 bridge construction projects, which were the units of analysis from 2012 to 2022. In each of these projects, 18 respondents, namely clients, consultants, contractors, engineers, environment and social guards, project managers, stakeholders, subcontractors, technical advisors, and inspectors, were included in a target population of 144 respondents. A census was conducted and a structured questionnaire was administered from which a response rate of 68% was achieved. The information was analyzed using descriptive, correlation, and multiple linear regression analysis. The contractor-related factors considered in the study were staff and management factors. The findings indicated that staff and management factors had a positive and significant outcome on performance of bridge construction projects. The study recommends continuous training and a safe learning environment for staff to improve their skills and performance in future projects. The study also recommends that a special category for bridge contractors be created in Kenya’s National Construction Authority rankings to ensure that only qualified contractors implement the Bridge projects.
文摘This study comprehensively examines the patterns and regional variation of severe rainfall across the African continent, employing a suite of eight extreme precipitation indices. The analysis extends to the assessment of projected changes in precipitation extremes using five General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs) scenarios at the long-term period (2081-2100) of the 21<sup>st</sup> century. Furthermore, the study investigates potential mechanisms influencing precipitation extremes by correlating extreme precipitation indices with oceanic system indices, specifically Ni?o 3.4 for El Ni?o-Southern Oscillation (ENSO) and Dipole Mode Index (DMI) for the Indian Ocean Dipole (IOD). The findings revealed distinct spatial distributions in mean trends of extreme precipitation indices, indicating a tendency toward decreased extreme precipitation in North Africa, Sahel region, Central Africa and the Western part of South Africa. Conversely, West Africa, East Africa and the Eastern part of South Africa exhibit an inclination toward increased extreme precipitation. The changes in precipitation extreme indices indicate a general rise in both the severity and occurrence of extreme precipitation events under all scenarios by the end of the 21<sup>st</sup> century. Notably, our analysis projects a decrease in consecutive wet days (CWD) in the far-future. Additionally, correlation analysis highlights significant correlation between above or below threshold rainfall fluctuation in East Africa and South Africa with oceanic systems, particularly ENSO and the IOD. Central Africa abnormal precipitation variability is also linked to ENSO with a significant negative correlation. These insights contribute valuable information for understanding and projecting the dynamics of precipitation extreme in Africa, providing a foundation for climate adaptation and mitigation efforts in the region.
文摘Maps, essential tools for portraying the Earth’s surface, inherently introduce distortions to geographical features. While various quantification methods exist for assessing these distortions, they often fall short when evaluating actual geographic features. In our study, we took a novel approach by analyzing map projection distortion from a geometric perspective. We computed the fractal dimensions of different stretches of coastline before and after projection using the divide-and-conquer algorithm and image processing. Our findings revealed that map projections, even when preserving basic shapes, inevitably stretch and compress coastlines in diverse directions. This analysis method provides a more realistic and practical way to measure map-induced distortions, with significant implications for cartography, geographic information systems (GIS), and geomorphology. By bridging the gap between theoretical analysis and real-world features, this method greatly enhances accuracy and practicality when evaluating map projections.
文摘It is alarming for the fact that Wildfires number, severity and consequently impact have significantly increased during the last years, an aftermath of the Climate Change. One of the most affected areas worldwide is Mediterranean, due to the unique combination of its type of vegetation and demanding climatic conditions. This research is focused on the Region of Epirus in Greece, an area with significant natural vegetation and a range of geomorphological aspects. In order to estimate the Wildfire Risk Hazard, several factors have been used: geomorphological (slope, aspect, elevation, TWI, Hydrographic network), social (Settlements and landfils, roads, overhead lines and substations), environmental (land cover) and climatic (Fire Weather Index). Through a multi-criteria decision analysis (MCDA) and an analytic hierarchy process (AHP) in a GIS environment, the Wildfire Risk Hazard has been estimated not only for current conditions but also for future projections for the near future (2031-2060) and the far future (2071-2100). The selected case study includes the potential impact of the Wildfires to the installed (or targeted to be installed) RES projects in the studied region.
文摘This study assesses the projected changes in the climate zoning of Côte d’Ivoire using the hierarchical classification of principal components (HCPC) method applied to the daily precipitation data of an ensemble of 14 CORDEX-AFRICA simulations under RCP4.5 and RCP8.5 scenarios. The results indicate the existence of three climate zones in Côte d’Ivoire (the coastal, the centre and the north) over the historical period (1981-2005). Moreover, CORDEX simulations project an extension of the surface area of drier climatic zones while a reduction of wetter zones, associated with the appearance of an intermediate climate zone with surface area varying from 77,560 km<sup>2</sup> to 134,960 km<sup>2</sup> depending on the period and the scenario. These results highlight the potential impacts of climate change on the delimitation of the climate zones of Côte d’Ivoire under the greenhouse gas emission scenarios. Thus, there is a reduction in the surface areas suitable for the production of cash crops such as cocoa and coffee. This could hinder the country’s economy and development, mainly based on these cash crops.
文摘In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.
基金supported by the Natural Science Foundation of Hubei Province of China[grant number 2020CFB331]the National Key Research and Development Program of China[grant number2018YFA0605602]the Strategic Project of the Chinese Academy of Sciences[grant number XDA19070402]。
基金the National Natural Science Foundation of China[grant number 42275022]the Second Tibetan Plateau Scientific Expedition and Research Program[grant number 2019QZKK010203]+1 种基金the Sichuan Natural Science Foundation[grant number 2022NSFSC1092]the Scientific and Technological Innovation Capacity Improvement Project of Chengdu University of Information Technology[KYQN202202].
