Contractor-Related Factors and Performance of Bridge Construction Projects Implemented by Kenya National Highways Authority (KeNHA) Kenya

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.

Multi-Criteria Wildfire Risk Hazard Assessment in GIS Environment: Projection for the Future and Impact on RES Projects Installation Planning

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.

A Review on Determinants Affecting Commercialization of Academic Institution Final Year Projects

Final year project is an important training and assessment component incorporated in many courses of academic institution.The ability to translate ideas,research,and creativity into actual products,services,or businesses shows how successful and impactful people or institutions can be in the wider economy.However,in most of the cases,the outcomes of the final year project usually remain as academic discussion without maximizing their potential to be converted into a commercialized product or services.In this review,theory and applications are discussed to understand how entrepreneurial skills and networking abilities,components of individuals’human capital can impact the commercialisation of final year projects.Furthermore,determinants affecting commercialization of final year projects will also be explored namely:entrepreneurial skills,networking ability,access to resources,institutional support as well as creativity and innovation.

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success.Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits,minimize unnecessary costs,and provide decision-makers with a robust financial foundation.Additionally,implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages.Developing a practical and feasible fundraising plan,along with stringent fund management practices,can prevent fund wastage and optimize fund utilization efficiency.These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects

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.

Research on Intelligent Cost Estimation of Engineering Foundation Projects Based on CSIs Theory

Against the backdrop of rapid development in China’s construction and infrastructure sectors,discrepancies between project budgets and actual costs have become pronounced,manifesting in project overruns and suspensions,posing significant challenges.To address inaccuracies in investment targets and operational complexities,this study focuses on a beam-bridge construction project in a district of Shijiazhuang city as a case study.Drawing upon historical analogs,the project employs a Work Breakdown Structure(WBS)to decompose the engineering works.Building on theories of Cost Significant(CS)and Whole Life Costing(WLC),the study constructs Cost Significant Items(CSIs)and develops a CNN-BiLSTM-Attention neural network for nonlinear prediction.By identifying significant cost drivers in engineering projects,this paper presents a streamlined cost estimation method that significantly reduces computational burdens,simplifies data collection processes,and optimizes data analysis and forecasting,thereby enhancing prediction accuracy.Finally,validation with real-world cost fluctuation data demonstrates minor errors,meeting predictive requirements across project execution phases.

Construction Safety Management of Mechanical and Electrical Installation Projects

Various industries today rely on the support of electromechanical equipment,expanding its scope of application and leading to an increase in electromechanical installation projects.However,due to the high level of expertise required and the potential risks involved,it is crucial to emphasize safety management during construction.This paper delves into the significance of construction safety management for electromechanical installation projects,identifies common problems encountered during construction,and proposes solutions.This analysis aims to provide relevant personnel with essential guidance and references for managing electromechanical installation projects safely.

价值体系导向的吉林省“156项工程”工业建筑遗产构成解析

中华人民共和国成立初期的“156项工程”是中国现代工业的开端,见证了中国工业建设的发展历程,其现存的工业遗产资源具有重要的价值。以吉林省“156项工程”工业建筑遗产为研究对象,通过文献资料梳理和调研考察成果,摸清发展历程、建设概况、空间布局和工业遗产的保存现状及使用情况,将工业建筑遗产构成划分为三大类型,探究吉林省“156项工程”工业遗产的构成特征,基于遗产载体的历史价值、社会价值、文化艺术价值和技术援助等,挖掘其核心价值导向,探寻工业建筑遗产升级更新方式,以期为“一五”时期工业建筑遗产的保护再利用和遗产构成研究提供借鉴。

跨越发展陷阱--156项工程对中国汽车工业的深远影响

汽车工业是现代工业的重要拼图,有赖于完整工业体系的加持。发展中国家在追求现代化的过程中,发展本国的汽车工业注定充满着危机与陷阱。从中国70多年的汽车工业发展过程来看,得益于156项工程奠定的完整工业体系的同时,中国汽车工业还相继跨越了低水平发展陷阱、合资合作相对比较优势陷阱以及新世纪发展新能源汽车的高水平约束陷阱,从而取得了令人瞩目的发展成就。在承接产业梯度转移以及有效参与国际分工过程中,中国汽车工业越过跟跑、并跑阶段并蓄势向领跑的角色变换,这种局面的出现离不开“一五”时期发展汽车工业时相关政策的影响。诸如政府主导、产业规划先行、集中力量办大事、合理利用外援以及注重自主创新,随后在融入国际市场中也实现了有为政府与有效市场的结合。中国汽车行业的成功经验和曲折发展历程能够为后发国家发展汽车工业提供镜鉴。

水果黄瓜新品种京研玉甜156的选育

京研玉甜156是以自交系LB1817为母本,以MSR-1为父本配制而成的迷你型水果黄瓜一代杂种。生长势强,全雌型,主蔓结瓜为主,早熟。商品瓜短筒形,白绿色,无瘤,光滑顺直,有不明显的小黑刺,瓜长约13 cm;瓜肉浅绿色,清香、甜脆,可溶性固形物含量4.2%,VC含量96.2 mg · kg^(-1)。抗白粉病和霜霉病,中抗CMV,低温、弱光耐受性强,越冬温室栽培条件下每667 m^(2)产量可达11 000 kg,适合我国北方地区越冬温室和春大棚种植。

Impact of estuarine reclamation projects on saltwater intrusion and freshwater resource

Estuarine projects can change local topography and influence water transport and saltwater intrusion.The Changjiang(Yangtze)River estuary is a multichannel estuary,and four major reclamation projects have been implemented in the Changjiang River estuary in recent years:the Xincun Shoal reclamation project(RP-XCS),the Qingcao Shoal reclamation project(RP-QCS),the Eastern Hengsha Shoal reclamation project(RP-EHS),and the Nanhui Shoal reclamation project(RP-NHS).The effects of the four reclamation projects and each project on the saltwater intrusion and water resources in the Changjiang River estuary were simulated in a 3D numerical model.Results show that for a multichannel estuary,local reclamation projects change the local topography and water diversion ratio(WDR)between channels and influence water and salt transport and freshwater utilization in the estuary.During spring tide,under the cumulative effect of the four reclamation projects,the salinity decreases by approximately 0.5in the upper reaches of the North Branch and increases by 0.5-1.0 in the middle and lower reaches of the North Branch.In the North Channel,the salinity decreases by approximately 0.5.In the North Passage,the salinity increases by 0.5-1.0.In the South Passage,the salinity increases by approximately 0.5 in the upper reaches and decreases by 0.2-0.5 on the north side of the middle and lower reaches.During neap tide,the cumulative effects of the four reclamation projects and the individual projects are similar to those during spring tide,but there are some differences.The effects of an individual reclamation project on WDR and saltwater intrusion during spring and neap tides are simulated and analyzed in detail.The cumulative effect of the four reclamation projects favors freshwater usage in the Changjiang River estuary.

工业大麻miR156基因家族在NaHCO_(3)胁迫下的表达模式及生物信息学分析

工业大麻miR156基因家族在碱性盐胁迫响应过程中发挥重要的调控作用,为探究工业大麻miR156基因家族响应NaHCO_(3)胁迫的分子机制,以盐碱耐受型工业大麻品种火麻一号为供试材料,利用生物信息学和qRT-PCR方法,对miR156基因家族进行分析。结果表明,工业大麻miR156家族基因与芦笋、野草莓、苹果和大豆的亲缘关系较近。2条工业大麻pre-miR156序列,产生一条相同的miR156成熟体,且成熟体的保守性较高。在NaHCO_(3)胁迫后的24 h内,工业大麻miR156基因表达量随胁迫时间的延长呈先升高后降低再升高的趋势。该结果为进一步研究工业大麻miR156基因家族及其靶基因的调控网络提供理论依据。

156项工程与新中国地区工业集聚——基于产业关联的视角与证据

本文从产业关联的角度实证考察新中国成立之初156项工程对地区工业集聚的影响。我们利用工业普查微观数据和地级市层面经济社会数据,研究了1949—1975年期间156项工程对所在地区的关联企业进入的影响。分析结果表明,156项工程建设地区的工业企业进入数显著多于其他地区;而且,在投入产出关系上距离156项工程行业较近行业的工业企业进入数显著多于较远行业,156项工程的产品分配端行业工业企业进入数要显著多于原料投入端行业。这些产业关联效应集中在156项工程建设完成之后而不是建设中。本文还发现,在改革开放之后,尤其是随着乡镇企业大发展和市场化改革加速,156项工程对工业企业进入仍然发挥产业关联和空间集聚的促进作用。为应对实证分析中可能的内生性问题,本文基于战争威胁构造了工具变量,进一步识别因果效应。本文还利用一些156项工程因战争威胁而改变项目建设地区,比较实际建设地区与计划建设地区在企业进入和产业关联上的差异,发现156项工程的工业扩张和产业关联效应更多发生在实际建设地区。这些结果说明,156项工程显著促进了地区工业的产业延伸与空间集聚,为新中国的工业体系建立和发展的具体路径提供了重要经验证据。

高产紧凑红皮小麦新品种农麦156的选育与应用

农麦156是江苏神农大丰种业科技有限公司选育的春性中熟红皮小麦品种,该品种的研发主要为了解决传统红皮小麦品种株型散、叶片披、穗数不足、产量低等问题。于2019年通过江苏省审定(苏审麦20190004),2020年通过安徽省引种备案(皖引麦2020033),2022年通过浙江省引种备案(浙引种[2022]第001号)。集高产、稳产、紧凑型于一体,具有分蘖力较强、抗寒性较好、株型较紧凑、抗倒性较好、抗病性较好、灌浆快、熟相好、商品性好等特点,适宜在长江中下游冬麦区的江苏和安徽两省淮河以南地区、湖北、浙江、上海、河南信阳地区种植。文章介绍了农麦156的特征特性,并对其选育过程、栽培要点及品种优势进行总结,以期为农麦156推广栽培提供参考,提升其应用价值。

Future changes in precipitation and water availability over the Tibetan Plateau projected by CMIP6 models constrained by climate sensitivity

Precipitation projections over the Tibetan Plateau(TP)show diversity among existing studies,partly due to model uncertainty.How to develop a reliable projection remains inconclusive.Here,based on the IPCC AR6–assessed likely range of equilibrium climate sensitivity(ECS)and the climatological precipitation performance,the authors constrain the CMIP6(phase 6 of the Coupled Model Intercomparison Project)model projection of summer precipitation and water availability over the TP.The best estimates of precipitation changes are 0.24,0.25,and 0.45 mm d^(−1)(5.9%,6.1%,and 11.2%)under the Shared Socioeconomic Pathway(SSP)scenarios of SSP1–2.6,SSP2–4.5,and SSP5–8.5 from 2050–2099 relative to 1965–2014,respectively.The corresponding constrained projections of water availability measured by precipitation minus evaporation(P–E)are 0.10,0.09,and 0.22 mm d^(−1)(5.7%,4.9%,and 13.2%),respectively.The increase of precipitation and P–E projected by the high-ECS models,whose ECS values are higher than the upper limit of the likely range,are about 1.7 times larger than those estimated by constrained projections.Spatially,there is a larger increase in precipitation and P–E over the eastern TP,while the western part shows a relatively weak difference in precipitation and a drier trend in P–E.The wetter TP projected by the high-ECS models resulted from both an approximately 1.2–1.4 times stronger hydrological sensitivity and additional warming of 0.6℃–1.2℃ under all three scenarios during 2050–2099.This study emphasizes that selecting climate models with climate sensitivity within the likely range is crucial to reducing the uncertainty in the projection of TP precipitation and water availability changes.

Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling

Spring consecutive rainfall events(CREs) are key triggers of geological hazards in the Three Gorges Reservoir area(TGR), China. However, previous projections of CREs based on the direct outputs of global climate models(GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF(Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6(Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6,indicating larger uncertainties in the CREs projected by MIROC6.

HSPM:A Better Model to Effectively Preventing Open-Source Projects from Dying

With the rapid development of Open-Source(OS),more and more software projects are maintained and developed in the form of OS.These Open-Source projects depend on and influence each other,gradually forming a huge OS project network,namely an Open-Source Software ECOsystem(OSSECO).Unfortunately,not all OS projects in the open-source ecosystem can be healthy and stable in the long term,and more projects will go from active to inactive and gradually die.In a tightly connected ecosystem,the death of one project can potentially cause the collapse of the entire ecosystem network.How can we effectively prevent such situations from happening?In this paper,we first identify the basic project characteristics that affect the survival of OS projects at both project and ecosystem levels through the proportional hazards model.Then,we utilize graph convolutional networks based on the ecosystem network to extract the ecosystem environment characteristics of OS projects.Finally,we fuse basic project characteristics and environmental project characteristics and construct a Hybrid Structured Prediction Model(HSPM)to predict the OS project survival state.The experimental results show that HSPM significantly improved compared to the traditional prediction model.Our work can substantially assist OS project managers in maintaining their projects’health.It can also provide an essential reference for developers when choosing the right open-source project for their production activities.

人工智能背景下Materials Project数据库在计算材料学课程教学中的应用

该文探讨了在人工智能背景下,Materials Project数据库在计算材料学课程教学中的应用和影响。Materials Project数据库是一个集成了AI和大数据技术的开放获取的材料库,能为学生提供海量的材料晶体结构和物性数据,使教学内容更为丰富,让学生能通过亲自操作获取和分析数据,深入理解微观结构与物性之间的关系。这一新兴的教学模式不仅提升了学生的科研能力和创新思维能力,还有助于培养具备计算材料专业知识和多学科交叉的复合型人才。总体来说,人工智能时代下,大数据的引入为计算材料学课程带来新的活力,并对未来教育改革和实践产生了积极影响。

Transcriptional regulation in the development and dysfunction of neocortical projection neurons

Glutamatergic projection neurons generate sophisticated excitatory circuits to integrate and transmit information among different cortical areas,and between the neocortex and other regions of the brain and spinal cord.Appropriate development of cortical projection neurons is regulated by certain essential events such as neural fate determination,proliferation,specification,differentiation,migration,survival,axonogenesis,and synaptogenesis.These processes are precisely regulated in a tempo-spatial manner by intrinsic factors,extrinsic signals,and neural activities.The generation of correct subtypes and precise connections of projection neurons is imperative not only to support the basic cortical functions(such as sensory information integration,motor coordination,and cognition)but also to prevent the onset and progression of neurodevelopmental disorders(such as intellectual disability,autism spectrum disorders,anxiety,and depression).This review mainly focuses on the recent progress of transcriptional regulations on the development and diversity of neocortical projection neurons and the clinical relevance of the failure of transcriptional modulations.

Developing Projects with Low-code Combined with ChatGPT

This paper aims to explore a simpler and more user-friendly way of generating software based on model-driven development.Previous studies have attempted to generate code from domain models,hoping to reduce coding time by increasing modeling time.However,as code tools become more advanced,it is challenging to improve efficiency because models are abstract while implementations are concrete.This paper proposes a novel approach that integrates ChatGPT as a plug-in into the whole R&D process and combines it with our code generation tool to enhance R&D efficiency.We have developed some demos to demonstrate the effectiveness of our approach.According to our evaluation,our approach can save more than 90%of the work in implementing the code generation tool,leaving only about 10%of the work for code review,code improvement,and unit testing.