Mining globally contributes to the growth of many economies of the world. Since its inception, the Zambian mining industry has contributed largely to the country’s economy. The various developments both in technology...Mining globally contributes to the growth of many economies of the world. Since its inception, the Zambian mining industry has contributed largely to the country’s economy. The various developments both in technology and knowledge have contributed to the scale at which mining is being done. Challenges in such a setting arise due to the socio-economic and environmental impacts of mining, which create multidimensional problems. The study investigated the importance of engaging stakeholders in progressive rehabilitation programs for large-scale open pit mines, using a case study of the Lumwana Mine and its host community, Manyama. A qualitative approach was used, and data was collected through one-on-one interviews. A combination of convenient and quota sampling was used to engage with host community leaders, professionals and academicians from various fields and institutions. Results showed that most participants had agreed that stakeholder engagement is important for progressive rehabilitation, but the challenge was that the host community and municipal council representatives were not aware of any progressive rehabilitation efforts at Lumwana Mine. This was attributed to a lack of stakeholder engagement and communication of mitigation progress activities by the Lumwana Mine. Results also revealed that the lack of environmental impact assessment regulations to compel companies to involve stakeholders throughout the entire life of the mine other than just at the pre-mining stage led to a lack of compliance and accountability in rehabilitation.展开更多
Solid waste management is one of the major concerns of the authorities in the town of Koudougou. The town’s dynamic is reflected in relative demographic growth and consumption patterns that are conducive to the forma...Solid waste management is one of the major concerns of the authorities in the town of Koudougou. The town’s dynamic is reflected in relative demographic growth and consumption patterns that are conducive to the formation of landfill sites. These landfills are the source of numerous environmental consequences and risk factors for local residents. The aim of this article is to analyze the dysfunctions in the solid waste sector caused by the interplay of actors. It draws on secondary data from the state of the art on the subject and primary data collected from 305 households and 89 actors in the sector between September 2022 and March 2023, as part of an ongoing thesis. These data show that the interplay of actors contributes to the malfunctioning of pre-collection and secondary collection, and remains a factor in the proliferation of illegal dumpsites.展开更多
This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a co...This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a comprehensive literature review with case studies of five multinational corporations, allows us to investigate the applications, challenges, and ethical implications of leveraging predictive models in communication strategies. While our findings reveal significant potential for enhancing personalized content delivery, real-time sentiment analysis, and proactive crisis management, we stress the need for careful consideration of challenges such as data privacy concerns and algorithmic bias. This emphasis on ethical implementation is crucial in navigating the complex landscape of predictive analytics in corporate communications. To address these issues, we propose a framework that prioritizes ethical considerations. Furthermore, we identify key areas for future research, thereby contributing to the evolving field of data-driven communication management.展开更多
The worldwide prevalence of anxiety disorders among college students is high,which negatively affects countries,schools,families,and individual students to varying degrees.This paper reviews the relevant literature re...The worldwide prevalence of anxiety disorders among college students is high,which negatively affects countries,schools,families,and individual students to varying degrees.This paper reviews the relevant literature regarding risk factors and digital interventions for anxiety disorders among college students from the perspectives of different stakeholders.Risk factors at the national and societal levels include class differences and the coronavirus disease 2019 pandemic.College-level risk factors include the indoor environment design of the college environment,peer relationships,student satisfaction with college culture,and school functional levels.Family-level risk factors include parenting style,family relationship,and parental level of education.Individual-level risk factors include biological factors,lifestyle,and personality.Among the intervention options for college students'anxiety disorders,in addition to traditional cognitive behavioral therapy,mindfulness-based interventions,psychological counseling,and group counseling,digital mental health interventions are increasingly popular due to their low cost,positive effect,and convenient diagnostics and treatment.To better apply digital intervention to the prevention and treatment of college students'anxiety,this paper suggests that the different stakeholders form a synergy among themselves.The nation and society should provide necessary policy guarantees,financial support,and moral and ethical supervision for the prevention and treatment of college students'anxiety disorders.Colleges should actively participate in the screening and intervention of college students'anxiety disorders.Families should increase their awareness of college students'anxiety disorders and take the initiative to study and understand various digital intervention methods.College students with anxiety disorders should actively seek psychological assistance and actively accept and participate in digital intervention projects and services.We believe that in the future,the application of methods such as big data and artificial intelligence to improve digital interventions and provide individualized treatment plans will become the primary means of preventing and treating anxiety disorders among college students.展开更多
Background: Waste generation and its disposal is an essential issue in the sustainability of the environment and the planet’s future. Waste management is essential across sectors, likewise the health sector. Therefor...Background: Waste generation and its disposal is an essential issue in the sustainability of the environment and the planet’s future. Waste management is essential across sectors, likewise the health sector. Therefore, there is a need to employ extra care and attention to handling waste generated from healthcare facilities to avoid the dangers of poor biomedical waste management. We carried out this study to examine the waste management practice in healthcare facilities in Lagos State. Methods: The study was a descriptive survey carried out in one-thousand two hundred and fifty-six (1256) healthcare facilities in Lagos State. Nine hundred sixty-nine (969) of these facilities are located in urban areas, while two hundred and eighty-seven (287) are rural. The facilities studied are government/public health facilities (15.45%), private-for-profit facilities (82.88%), NGOs, Mission/Faith-Based medical facilities (1.67%). The data collected were analyzed using descriptive statistics. Specifically, we utilized bar charts, frequency, and percentage. Result: The result shows that 98.4% (1236) of the studied facilities are registered with the Lagos State Waste Management Authority (LAWMA), while 1.6% (20) are not registered. 98.5% (191) of the 194 government-owned facilities, 98.5% (1025) of the 1041 private-for-profit facilities, and 98.2% (20) of the 21 NGOs/faith-based health facilities are registered with Lagos State Waste Management Authority. The result also shows that 94% of the healthcare facilities studied in Lagos State use color-coded waste bags to segregate waste at the point of origin. 58.7% of the facilities use red-colored bags, 33.3% use yellow-colored bags, 10.7% use black-colored bags, and 1.3% use brown biohazard bags for segregating Infectious waste. Also, 34.2% of the health facilities in Lagos state use red-colored bags, 36.9% use yellow-colored bags, 11% use black-colored bags, and 4.1% use brown-colored bags to segregate their hazardous waste. Conclusion: Some healthcare facilities in Lagos State do not follow the recommended guidelines for medical waste segregation. Waste generated is not appropriately segregated at the point of origin into the recommended colored bags/bins in some facilities. Thus, a policy and procedure regulating healthcare waste are mandatory. It is important to regularly train healthcare workers on proper waste management practices and encourage staff to read and apply WHO rules in managing healthcare waste. Healthcare personnel should realize that hazardous material is a potential cause of a public disaster.展开更多
[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer bloc...[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer block),用于不同成熟度苹果检测。首先改进YOLOv5s的多尺度目标检测层,在Prediction中构建检测160×160特征图的检测头,提高小尺寸的不同成熟度苹果的检测精度;其次在Backbone结构中融合Swin Transformer Block,加强同级成熟度的苹果纹理特征融合,弱化纹理特征分布差异带来的消极影响,提高模型泛化能力;最后将Neck结构的Conv模块替换为动态卷积模块ODConv,细化局部特征映射,实现局部苹果细粒度特征的充分提取。基于不同成熟度苹果数据集进行试验,验证改进模型的性能。[结果]改进模型SODSTR-YOLOv5s检测的精确率、召回率、平均精度均值分别为89.1%、95.5%、93.6%,高、中、低成熟度苹果平均精度均值分别为94.1%、93.1%、93.7%,平均检测时间为16 ms,参数量为7.34 M。相比于YOLOv5s模型,改进模型SODSTR-YOLOv5s精确率、召回率、平均精度均值分别提高了3.8%、5.0%、2.9%,参数量和平均检测时间分别增加了0.32 M和5 ms。[结论]改进模型SODSTR-YOLOv5s提升了在自然环境下对不同成熟度苹果的检测能力,能较好地满足实际采摘苹果的检测要求。展开更多
针对煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂工况环境因素导致煤矸识别存在误检、漏检以及检测精度低的问题,提出一种基于CFS-YOLO算法的煤矸智能识别模型。采用ConvNeXt V2(Convolutional Neural Network with NeXt Units...针对煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂工况环境因素导致煤矸识别存在误检、漏检以及检测精度低的问题,提出一种基于CFS-YOLO算法的煤矸智能识别模型。采用ConvNeXt V2(Convolutional Neural Network with NeXt Units Version 2)特征提取模块替换主干网络末端的2个C3(Cross Stage Partial Bottle Neck Mudule)模块,通过将掩码自动编码器(Masked Autoencoders,MAE)和全局响应归一化(Global Response Normalization,GRN)层添加到ConvNeXt架构中,有效缓解特征崩溃问题以及保持特征在网络传递过程中的多样性;采用Focal-EIOU(Focal and Efficient Intersection Over Union)损失函数替换原CIOU(Computer Intersection Over Union)损失函数,通过其Focal-Loss机制和调整样本权重的方式优化边界框回归任务中的样本不平衡问题,提高模型的收敛速度和定位精度;添加无参注意力机制(Simple Attention Mechanism,SimAM)于主干网络每个C3模块的后端,凭借其注意力权重自适应调整策略,提升模型对尺度变化较大或低分辨率煤矸目标关键特征的提取能力。通过消融试验和对比试验验证所提CFS-YOLO模型的有效性与优越性。试验结果表明:CFS-YOLO模型对于煤矸在煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂环境下的检测效果均得到有效提高,模型的平均精度均值达到90.2%,相较于原YOLOv5s模型的平均精度均值提高了3.7%,平均检测速度达到90.09 FPS,可充分满足煤矸实时检测的需求。同时与YOLOv5s、YOLOv7-tiny与YOLOv8n等6种YOLO系列算法相比,CFS-YOLO模型对煤矿复杂环境的适应性最强且综合检测性能最佳,可为煤矸的智能高效分选提供技术支持。展开更多
文摘Mining globally contributes to the growth of many economies of the world. Since its inception, the Zambian mining industry has contributed largely to the country’s economy. The various developments both in technology and knowledge have contributed to the scale at which mining is being done. Challenges in such a setting arise due to the socio-economic and environmental impacts of mining, which create multidimensional problems. The study investigated the importance of engaging stakeholders in progressive rehabilitation programs for large-scale open pit mines, using a case study of the Lumwana Mine and its host community, Manyama. A qualitative approach was used, and data was collected through one-on-one interviews. A combination of convenient and quota sampling was used to engage with host community leaders, professionals and academicians from various fields and institutions. Results showed that most participants had agreed that stakeholder engagement is important for progressive rehabilitation, but the challenge was that the host community and municipal council representatives were not aware of any progressive rehabilitation efforts at Lumwana Mine. This was attributed to a lack of stakeholder engagement and communication of mitigation progress activities by the Lumwana Mine. Results also revealed that the lack of environmental impact assessment regulations to compel companies to involve stakeholders throughout the entire life of the mine other than just at the pre-mining stage led to a lack of compliance and accountability in rehabilitation.
文摘Solid waste management is one of the major concerns of the authorities in the town of Koudougou. The town’s dynamic is reflected in relative demographic growth and consumption patterns that are conducive to the formation of landfill sites. These landfills are the source of numerous environmental consequences and risk factors for local residents. The aim of this article is to analyze the dysfunctions in the solid waste sector caused by the interplay of actors. It draws on secondary data from the state of the art on the subject and primary data collected from 305 households and 89 actors in the sector between September 2022 and March 2023, as part of an ongoing thesis. These data show that the interplay of actors contributes to the malfunctioning of pre-collection and secondary collection, and remains a factor in the proliferation of illegal dumpsites.
文摘This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a comprehensive literature review with case studies of five multinational corporations, allows us to investigate the applications, challenges, and ethical implications of leveraging predictive models in communication strategies. While our findings reveal significant potential for enhancing personalized content delivery, real-time sentiment analysis, and proactive crisis management, we stress the need for careful consideration of challenges such as data privacy concerns and algorithmic bias. This emphasis on ethical implementation is crucial in navigating the complex landscape of predictive analytics in corporate communications. To address these issues, we propose a framework that prioritizes ethical considerations. Furthermore, we identify key areas for future research, thereby contributing to the evolving field of data-driven communication management.
文摘The worldwide prevalence of anxiety disorders among college students is high,which negatively affects countries,schools,families,and individual students to varying degrees.This paper reviews the relevant literature regarding risk factors and digital interventions for anxiety disorders among college students from the perspectives of different stakeholders.Risk factors at the national and societal levels include class differences and the coronavirus disease 2019 pandemic.College-level risk factors include the indoor environment design of the college environment,peer relationships,student satisfaction with college culture,and school functional levels.Family-level risk factors include parenting style,family relationship,and parental level of education.Individual-level risk factors include biological factors,lifestyle,and personality.Among the intervention options for college students'anxiety disorders,in addition to traditional cognitive behavioral therapy,mindfulness-based interventions,psychological counseling,and group counseling,digital mental health interventions are increasingly popular due to their low cost,positive effect,and convenient diagnostics and treatment.To better apply digital intervention to the prevention and treatment of college students'anxiety,this paper suggests that the different stakeholders form a synergy among themselves.The nation and society should provide necessary policy guarantees,financial support,and moral and ethical supervision for the prevention and treatment of college students'anxiety disorders.Colleges should actively participate in the screening and intervention of college students'anxiety disorders.Families should increase their awareness of college students'anxiety disorders and take the initiative to study and understand various digital intervention methods.College students with anxiety disorders should actively seek psychological assistance and actively accept and participate in digital intervention projects and services.We believe that in the future,the application of methods such as big data and artificial intelligence to improve digital interventions and provide individualized treatment plans will become the primary means of preventing and treating anxiety disorders among college students.
文摘Background: Waste generation and its disposal is an essential issue in the sustainability of the environment and the planet’s future. Waste management is essential across sectors, likewise the health sector. Therefore, there is a need to employ extra care and attention to handling waste generated from healthcare facilities to avoid the dangers of poor biomedical waste management. We carried out this study to examine the waste management practice in healthcare facilities in Lagos State. Methods: The study was a descriptive survey carried out in one-thousand two hundred and fifty-six (1256) healthcare facilities in Lagos State. Nine hundred sixty-nine (969) of these facilities are located in urban areas, while two hundred and eighty-seven (287) are rural. The facilities studied are government/public health facilities (15.45%), private-for-profit facilities (82.88%), NGOs, Mission/Faith-Based medical facilities (1.67%). The data collected were analyzed using descriptive statistics. Specifically, we utilized bar charts, frequency, and percentage. Result: The result shows that 98.4% (1236) of the studied facilities are registered with the Lagos State Waste Management Authority (LAWMA), while 1.6% (20) are not registered. 98.5% (191) of the 194 government-owned facilities, 98.5% (1025) of the 1041 private-for-profit facilities, and 98.2% (20) of the 21 NGOs/faith-based health facilities are registered with Lagos State Waste Management Authority. The result also shows that 94% of the healthcare facilities studied in Lagos State use color-coded waste bags to segregate waste at the point of origin. 58.7% of the facilities use red-colored bags, 33.3% use yellow-colored bags, 10.7% use black-colored bags, and 1.3% use brown biohazard bags for segregating Infectious waste. Also, 34.2% of the health facilities in Lagos state use red-colored bags, 36.9% use yellow-colored bags, 11% use black-colored bags, and 4.1% use brown-colored bags to segregate their hazardous waste. Conclusion: Some healthcare facilities in Lagos State do not follow the recommended guidelines for medical waste segregation. Waste generated is not appropriately segregated at the point of origin into the recommended colored bags/bins in some facilities. Thus, a policy and procedure regulating healthcare waste are mandatory. It is important to regularly train healthcare workers on proper waste management practices and encourage staff to read and apply WHO rules in managing healthcare waste. Healthcare personnel should realize that hazardous material is a potential cause of a public disaster.
文摘[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer block),用于不同成熟度苹果检测。首先改进YOLOv5s的多尺度目标检测层,在Prediction中构建检测160×160特征图的检测头,提高小尺寸的不同成熟度苹果的检测精度;其次在Backbone结构中融合Swin Transformer Block,加强同级成熟度的苹果纹理特征融合,弱化纹理特征分布差异带来的消极影响,提高模型泛化能力;最后将Neck结构的Conv模块替换为动态卷积模块ODConv,细化局部特征映射,实现局部苹果细粒度特征的充分提取。基于不同成熟度苹果数据集进行试验,验证改进模型的性能。[结果]改进模型SODSTR-YOLOv5s检测的精确率、召回率、平均精度均值分别为89.1%、95.5%、93.6%,高、中、低成熟度苹果平均精度均值分别为94.1%、93.1%、93.7%,平均检测时间为16 ms,参数量为7.34 M。相比于YOLOv5s模型,改进模型SODSTR-YOLOv5s精确率、召回率、平均精度均值分别提高了3.8%、5.0%、2.9%,参数量和平均检测时间分别增加了0.32 M和5 ms。[结论]改进模型SODSTR-YOLOv5s提升了在自然环境下对不同成熟度苹果的检测能力,能较好地满足实际采摘苹果的检测要求。
文摘针对煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂工况环境因素导致煤矸识别存在误检、漏检以及检测精度低的问题,提出一种基于CFS-YOLO算法的煤矸智能识别模型。采用ConvNeXt V2(Convolutional Neural Network with NeXt Units Version 2)特征提取模块替换主干网络末端的2个C3(Cross Stage Partial Bottle Neck Mudule)模块,通过将掩码自动编码器(Masked Autoencoders,MAE)和全局响应归一化(Global Response Normalization,GRN)层添加到ConvNeXt架构中,有效缓解特征崩溃问题以及保持特征在网络传递过程中的多样性;采用Focal-EIOU(Focal and Efficient Intersection Over Union)损失函数替换原CIOU(Computer Intersection Over Union)损失函数,通过其Focal-Loss机制和调整样本权重的方式优化边界框回归任务中的样本不平衡问题,提高模型的收敛速度和定位精度;添加无参注意力机制(Simple Attention Mechanism,SimAM)于主干网络每个C3模块的后端,凭借其注意力权重自适应调整策略,提升模型对尺度变化较大或低分辨率煤矸目标关键特征的提取能力。通过消融试验和对比试验验证所提CFS-YOLO模型的有效性与优越性。试验结果表明:CFS-YOLO模型对于煤矸在煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂环境下的检测效果均得到有效提高,模型的平均精度均值达到90.2%,相较于原YOLOv5s模型的平均精度均值提高了3.7%,平均检测速度达到90.09 FPS,可充分满足煤矸实时检测的需求。同时与YOLOv5s、YOLOv7-tiny与YOLOv8n等6种YOLO系列算法相比,CFS-YOLO模型对煤矿复杂环境的适应性最强且综合检测性能最佳,可为煤矸的智能高效分选提供技术支持。
