Project Success Research Articles

Hyperspectral imaging coupled with deep learning model for visualization and detection of early bruises on apples

Early bruise on apples caused by external impacts during the transportation process is commonly difficult to be detected on the apple surface, limiting the application of traditional machine vision methods in determining fruit quality. In recent years, hyperspectral imaging (HSI) has emerged as a promising technology for identifying early bruise of fruits due to its efficient and nondestructive detection. In this study, HSI data in the shortwave infrared range were collected at 2-hour and 6-hour intervals after mechanical damage. The combination of the successive projections algorithm (SPA) and principal component analysis (PCA) was used to select three key feature bands, namely, 1074 nm, 1269 nm and 1441 nm. Pseudo color transformation and band ratio algorithm were then employed to improve the contrast between damaged and healthy apple tissues for image enhancement. The fast and precise YOLOv5 (FP-YOLOv5) model achieved effective identification of apple bruises, with a high recognition rate of 95 % and a fast detection speed at 130 fps. Overall, the proposed framework based on band selection and image enhancement exhibits better performance in the detection of early apple bruises, providing useful insights for HSI combined with a deep learning model in the grading evaluation of fruit quality.

Apple firmness detection method based on hyperspectral technology

Firmness is a key indicator of apple quality. Building a predictive model for apple firmness based on hyperspectral technology and regression algorithms can achieve rapid, non-destructive, and high-throughput detection of apple firmness. This paper adopts an Adaptive Window Length Savitzky-Golay Smoothing (AWL-SG smoothing) algorithm based on the Savitzky-Golay Smoothing (SG smoothing) algorithm, which can adaptively adjust the window length according to the change rate of spectral data at different wavelengths. SG smoothing, AWL-SG smoothing, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC) algorithms were used to preprocess the original spectral data, and Partial Least Squares (PLS), Ridge Regression (Ridge), and Kernel Ridge Regression (Kernel Ridge) predictive models were constructed to analyze the impact of different preprocessing methods on model prediction accuracy. The prediction models established with spectral data preprocessed by SG smoothing and AWL-SG smoothing algorithms showed significant improvement in predictive performance on the basis of the original spectral data, among which the AWL-SG smoothing algorithm performed the best. The Ridge model established with spectra data preprocessed by AWL-SG smoothing achieved an R2 of 0.8914 in the test set. Successive Projection Algorithm (SPA), Principal Component Analysis (PCA), and Independent Component Analysis (ICA) dimensionality reduction algorithms were used to reduce the dimensions of the full-band spectral data preprocessed by SG smoothing and AWL-SG smoothing algorithms, and Ridge and Kernel Ridge prediction models were constructed. The results showed that both SPA and PCA algorithms could improve the predictive performance of the models, with the PCA performing the best. The combination of AWL-SG + PCA + Ridge achieved the best predictive effect, with an R2 of 0.9146 in the test set.

Key aspects of successful science fair projects

ABSTRACT Students’ projects are a valuable way to gain experience with real science and motivate them towards a career in science. However, a description of a successful project is missing. To identify the key aspects of successful projects, we analysed biological projects presented in the national round of the Students’ Professional Activities. Particular projects presented in 10 years were evaluated based on, eg, branch of biology, number of authors, cooperation with experts, use of statistics or laboratory versus fieldwork, and county (region of origin within Czechia). Several different variables (eg, statistical analysis or additional work suggestions) pointed to the influential role of the supervisor, under whose guidance projects are more likely to be successful. Additionaly, projects carried out in the laboratory likely ranked better than those carried out in the field. Furthermore, we traced the influence of supportive teachers and show a high influence of the region on projects’ ranking revealing a potential socioeconomic inequity in access to science in Czechia. The trend of doing more biology work in the laboratory is reflected in increasing the percentage of laboratory-based projects over the ten years. We present a summary of how to conduct successful science projects.

Jenna's Project: Preventing Overdose and Improving Recovery Outcomes for Women Leaving Incarcerated Settings during Pregnancy and Postpartum Periods.

For people with opioid use disorder (OUD), transitioning from pregnancy to postpartum and from incarceration to the community is a time of increased risk for opioid overdose. This prospective project evaluated the extent of Jenna's Project's success in preventing overdoses and improving recovery outcomes by coordinating postrelease care in incarcerated OUD perinatal patients. Participants (N = 132) were pregnant or postpartum (1 year postdelivery) with OUD during incarceration and self-referred for postrelease services. From March 2020 to October 2021, participants could receive up to 6 months of postincarceration care coordination services (eg, regular communication, transportation, emergency housing, SUD treatment), medication to treat OUD (MOUD) and other treatment services. Outcomes included verified overdose (fatal), self-reported nonfatal overdose, reincarceration, active Medicaid, receipt of MOUD, presence of children living with participants, open Child Protective Services cases, and number of referrals for services. There were 0 nonfatal and 0 fatal overdoses at both 1 and 6 months postrelease, and 3 of 132 (2%) returned to incarceration. Significantly fewer participants had Medicaid at release (36%) and at 6 months postrelease (60%) than before incarceration (87%) (P < 0.001 for all 3 pairwise comparisons). At 6 months postrelease, significantly more participants reported MOUD receipt (51%) compared with before incarceration (39%) (P < 0.001). There was no significant change in the number of open Child Protective Services cases. Referrals for childcare or parenting services were the most common referrals provided. Immediate postrelease care coordination for pregnant and postpartum women with OUD was feasible and effective in preventing overdose, reincarceration, and promoting recovery outcomes.

The Relationship Between Knowledge Management, Dynamic Capability and Project Success in Indonesian Construction Industry

The Relationship Between Knowledge Management, Dynamic Capability and Project Success in Indonesian Construction Industry

The Influence of Contractors’ Selection Attributes. A Study of Public Construction Projects Performance in Kenya

This paper evaluated the influence of contractors’ selection attributes on public construction project performance in Kenya. This research contends that an investigation into contractors’ selection attributes at the tender evaluation stage is important to enhance the likelihood of public construction project success performance. The study used a survey method and administered questionnaires to 263 respondents selected through stratified sampling. One hundred and eighty-nine (189) practitioners and professionals in state agencies returned filed questionnaires representing a 71.86% return rate. The literature review and interview reveal thirteen contractor selection variables and six project performance criteria. SPSS was used to analyse the data set obtained for reliability, normality, correlation, and logit regression test. Logit regression analysis reveals the omnibus test of model coefficients converged (Chi-square=26.116, df=13, p=0.016). The model summary reveals Nagelkerke R^2 of 0.246 indicating a relationship of 24.6% between the thirteen predictor variables and project performance indicators. It was found that contractors quoted tender price (Exp (B) = 1.866), innovation (Exp (B) = 2.808), and occupation health and safety (Exp (B) = 0.358) significantly influence the performance of public construction projects when all other contractor's selection variables are controlled. This indicates that the contractor’s quoted tender price, innovation, and occupation health and safety explain well the success of the public construction project from thea six (performance by budget, time, procuring entity satisfaction, environmental sustainability, quality specifications, and fulfilling public need) project performance indicators

Supporting advanced practice: feedback from a mental health, community and primary care advanced practice support project in South Yorkshire, UK

Background: Despite the significant input from Heath Education England (HEE), now NHS England (NHSE), there are still significant variations around advanced practice programmes, support and training, particularly in less developed areas such as mental health, community posts and primary care. To help target these areas, and to support the NHS Long Term Plan (2023) to increase the number of advanced clinical practitioners (ACPs), the Faculty of Advanced Practice South Yorkshire recruited three ACP leads from mental health, community, and primary care backgrounds to establish a support project led by one professional lead. These leads were able to utilise their lived experience of completing the ACP MSc programme and working as an ACP to help trainee advanced clinical practitioners (tACPs) navigate their journey. The ACP leads also developed a variety of educational events, which further supported the trainees' needs. Aim: This article reports the feedback given via an online questionnaire from trainees that accessed the project, which ended in March 2024. Its aim is to highlight the often-undervalued concept of using clinicians personal experience regarding advanced training and practice, to mentor, support and ultimately retain the future ACP workforce. Methods: An email with a link to a questionnaire was sent to 128 tACPs or recently qualified ACPs that had applied for their training via the Faculty of Advanced Practice South Yorkshire. The questionnaire, created in Microsoft Forms, was completed anonymously, and produced both numerical and free-text data. The questionnaire solicited feedback around: the number of contacts they had and how they contacted; the ACP leads; types of support accessed; and the educational events available. Free text answers were analysed by the author (CJ) and a second reviewer (SF) to generate a consensus on themes. Results: A total of 64 clinicians responded, of which 43 were tACPs, 10 were ACPs, five had other job titles (community matron, pharmacist, practice nurse) and five did not state what their job title was. Responses suggest that the support accessed from the project was highly beneficial and could not be gained elsewhere. A key factor in the project's success was the ACP leads' lived experience of having previously completed advanced practice training, as well as their ability to tailor learning events to the trainees' needs. Conclusion: Meeting the demands of ACP training in areas where the role is less established remains an ongoing challenge. It seems some of these challenges relate to the lack of practical support being provided by employers or higher education institutions, such as lived experience of completing the task of ACP training. Support projects, like the one discussed in this article, appear aptly placed to help fulfil these needs and begs the question ‘why aren't there more?’, especially when advanced practitioners are fundamental to the future healthcare workforce.

Analysis of passenger transportation and the public transportation impact on the reduction in a smart city

The state of atmospheric pollution is determined by the growth of the population, the amount of transport and the generated volumes of emissions. The object is the process of analyzing passenger transportation in the city. The subject is passenger transport analysis methods. Purpose: analysis of passenger transportation and approaches to optimization of public transport based on the concept of a smart city. Tasks: analysis of passenger transportation, classification of existing conceptual approaches to optimization of public transport with low carbon emissions, systematization of existing methods, means and types of neural networks in smart cities, analysis of successful implementation projects. Methods of statistical analysis, linear and non-linear interpolation, logical generalization, comparison, grouping, analysis and synthesis. Results: the analysis of passenger transportation in the city revealed that statistical data sets indicate a decrease in the main indicators of passenger traffic and an increase in the volume of emissions of carbon-containing compounds. The classification of existing approaches to the optimization of public transport is carried out according to the priority of public transport, hybridization and electrification of vehicles and the implementation of IT monitoring. During the systematization of methods and means in smart cities, the following are highlighted: smart transport systems; electric vehicles; transport sharing networks; smart applications and information systems; innovative payment systems; unmanned vehicles; information boards and announcement systems; networks of bicycle paths and equipped sidewalks; environmental monitoring systems. Among neural networks, recurrent, convolutional, and deep neural networks have been proposed as those that contribute to route optimization and traffic prediction. Conclusions: the statistical analysis of passenger transportation established that reducing carbon dioxide emissions is an unresolved task for both public transport and the transportation system. It is proposed to include methods and means that optimize public transport, reducing the carbon footprint of the initiatives of implementing the concept of a smart city, which are successful all over the world. It is proposed to use recurrent, convolutional and deep neural networks to optimize passenger transportation in smart cities.

Measuring the impact of lean implementation on BIM and project success: case of construction firms

PurposeThe main purpose of this study is to reveal the degree of association between lean, building information modeling (BIM) and construction project success. The study further intends to provide strategies for high and low associations of the factors.Design/methodology/approachLean construction and BIM are two important applications that have recently gained popularity in terms of enhancing project success. Considering this impact, this study investigates the synergy between lean construction and BIM and determines to what extent these two contribute to the success of the projects. As a first step, lean, BIM and project success were examined based on an in-depth literature review. In the second stage, a structural equation model (SEM) was established to reflect the relationship among these three through hypotheses. Then, a questionnaire was designed and administered to the construction professionals experienced in both lean and BIM implementation. The SEM was tested using Analysis of Moment Structures (AMOS), an SPSS tool.FindingsThe results indicated that lean implementation has a significant and positive impact on BIM implementation and project success. On the other hand, BIM implementation had a lower significant impact on project success than lean implementation construct.Research limitations/implicationsThe results of this study can be used by both policymakers and industry practitioners in terms of developing strategies for effectively using both lean and BIM. The researchers can further develop other implementation models to investigate whether these concepts are more effective in increasing project success when used integratively.Originality/valueThis study considers both the impact of lean and BIM on project success through input from construction practitioners working on large projects. This way, the study fosters the use of lean, BIM or lean–BIM together in construction projects to enhance project success.

A framework for leadership practices and communication in the context of the construction sector

An ongoing debate in the leadership domain underscores the prevalence of project failures globally, prompting project managers to refine leadership strategies and create innovative practices to navigate project complexities, emerging technologies, artificial intelligence, evolving stakeholder expectations, shifting construction landscape, and brittle, anxious, nonlinear and incompressible environment challenges in the construction industry. This study aims to provide insights into the relationship mechanisms between leadership practices, communication, and project success, introducing a framework for behavioral practices to address challenges in the Australian construction industry, such as project complexities, inefficiencies, cash flow disruptions, supply chain issues, communication barriers, cultural diversity, digitalization, and regulatory protocols compliance. Exploratory factors/multivariate regression analysis on collected responses on 66 projects have identified four critical success factors: relationship management, communication effectiveness, leading by example, and self-management that impact and improve project success significantly using relationship and conflict management as mediators, help formulate creative practices using stakeholder analysis, conflict resolution strategies, and promoting collaboration enhancing the decision-making process to overcome challenges. Researchers validated and confirmed results through in-depth interviews with independent practitioners, encouraging project managers to modify behavioral practices using an explicit model for enhancing project success.

Steckman Ridge: A naturally fractured underground gas storage field

This paper presents a field development case study of a gas storage field in Pennsylvania within a fractured reservoir. Integration of multiple data sets was required for the project's success. During the study, the interpretation of 3D isofrequency seismic volumes, generated through spectral decomposition, was shown to be the key step in this data integration. The interpretation of these isofrequency volumes successfully identified previously unresolved subtle structural features, which controlled the natural fracture system. The existence of these subtle structural features was subsequently confirmed with eight horizontal gas storage wells. Accurate characterization of a fracture trend requires the integration of seismic data, available well data, surface geology, drilling, and production data. This study was performed to develop the Oriskany reservoir in the Steckman Ridge underground gas storage field located in Bedford County, Pennsylvania. This field had undergone rapid depletion during its primary production phase. During the early portion of the study, the reservoir was determined to be a type 1 fractured reservoir with little to no gas storage available in the sandstone matrix. Multiple data types were acquired and integrated to detect and characterize the structural features that compose the reservoir. Isofrequency volumes, developed through spectral decomposition of 3D seismic data over the field, helped identify and map subtle shear faults that proved to be controlling the location and orientation of the dominant fracture trend in the field. Horizontal boreholes, needed to convert the field from gas production to gas storage, were planned and drilled based on this seismic interpretation. The existence of these shear faults, their location, and their orientation were confirmed by image logs acquired along the length of six horizontal gas storage boreholes. Additionally, flow tests performed to determine gas deliverability supported this interpretation.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Distributed leadership and the shaping of infrastructure project portfolios

Important socio-technical changes are increasingly being implemented through major programmes or portfolios of projects (Maylor et al., 2006). These interventions, while necessary for modernisation and the green transition, cause short-term disruption which generates discord and opposition impacting stakeholder perception. The long-term sustainable transformation of rail transportation infrastructure toward electrification, greater speed, digital signalling, creates great benefit in terms of energy and carbon efficiency, reliability, and punctuality, but the short-term disruption caused by the projects can reduce the attractiveness of this mode of transport. This paper reports findings from an embedded case study with the Danish national railway infrastructure owner and analyses how the leadership team uses distributed leadership as a dynamic process of project and portfolio shaping which internalises diverse external perspectives and reconciles competing objectives. Using the “Tesseract” four-dimensional model of project success Ika and Pinto (2022a)to frame these competing interests, this paper presents a model of how distributed leadership shapes the railway project portfolio through the considerations of coherence, disruption, and benefit.

Investigating the Impact of Project Planning on Construction Project Success through the Mediating Role of Risk Management and Safety Climate

Investigating the Impact of Project Planning on Construction Project Success through the Mediating Role of Risk Management and Safety Climate

Identification of fermented soy sauce and blended soy sauce based on dielectric spectra

A new method based on dielectric properties has been developed to distinguish blended soy sauce from fermented soy sauce. Fifty samples of pure fermented soy sauce and blended soy sauce with different contents of hydrolyzed vegetable protein (HVP) were prepared. The dielectric constant and dielectric loss factor of all soy sauce samples in the 30 MHz–3000 MHz frequency range were measured using an impedance analyzer. The sample set was divided into a correction set and a prediction set using the joint x-y distances (SPXY) algorithm, and the partial least squares (PLS) and support vector machine (SVM) models were adopted to distinguish the different samples. The effects of selecting characteristic variables on model prediction using the full spectra (FS), principal component analysis (PCA), and successive projection algorithm (SPA) were compared. Results indicate that the discriminant effect of the PLS model was better than that of the SVM model overall. The SPA–PLS model had the best predictive performance among the six developmental models. The correlation coefficients of the correction set and prediction set were 0.9205 and 0.9096, respectively. The root mean square error of the calibration set was 1.3699 and that of the prediction set was 1.5950. The study demonstrated that the combination of dielectric spectra and stoichiometry can be utilized to determine whether soy sauce has been adulterated.

Project Risk Management Strategies, Project Quality and Project Success - Moderating Role of Financial Self-Efficacy

Project Risk Management Strategies, Project Quality and Project Success - Moderating Role of Financial Self-Efficacy

INTERNAL TEAMWORK PRACTICES AND PROJECT IMPLEMENTATION IN NON-PROFIT PROJECT IN RWANDA: A CASE OF UPHLS

Background:This research study aimed to assess the influence of internal teamwork practices on project implementation in non-profit organizations operating in Rwanda, with a specific focus on The Umbrella of Organizations of Persons with Disabilities in the Fight Against HIV&amp;AIDS and for Health Promotion (UPHLS). The study was structured around three specific objectives which were: To assess the effect of Team Collaboration on Project Implementation Success within the UPHLS project in Rwanda. To determine the influence of Resource Allocation on the Timeliness of project implementation within the UPHLS project in Rwanda. And finally, to evaluate the influence of Training and Development on the Quality of Deliverables in the UPHLS project in Rwanda. Materials and Methods:In order for the researcher to achieve the objectives of the research, a descriptive and correlation research design were adopted. Data was collected through a combination of both quantitative and qualitative methods, utilizing interview guides and questionnaires. The research population of this research included the broader staff population of 22 at UPHLS headquarters, these individuals were all considered as the sample size through the adoption of the census sampling technique where the entire population is inclusive in the study under investigation due to the small size of this population. Results:From the collected data the findings identified critical factors influencing success. Effective communication (mean=4.23, SD=1.45), clear task allocation (mean=4.27, SD=1.31), appropriate leadership (mean=4.36, SD=1.27), a culture of trust (mean=4.46, SD=1.18), and regular feedback (mean=4.32, SD=1.29) were key contributors to successful project implementation. Resource allocation significantly impacted timeliness, with financial resources (mean=4.68, SD=1.23), human resources (mean=4.89, SD=1.56), material resources (mean=4.45, SD=1.34), procurement practices (mean=4.76, SD=1.45), and budget oversight (mean=4.89, SD=1.23) playing crucial roles. Training and development initiatives positively influenced the quality of project deliverables, with mean scores ranging from 4.39 to 4.58 and corresponding percentages of agreement ranging from 44.46% to 50.87%. Correlation analysis demonstrated a strong positive correlation (r=0.736, p=0.001) between internal teamwork practices and project implementation. The research therefore concluded by emphasizing the significant role of cohesive teamwork in project success. The study went further to mention that these findings underscored the importance of effective communication, resource management, and training initiatives in enhancing project outcomes in the context of non-profit projects, providing actionable insights for organizations aiming to improve their project implementation processes. Conclusions: Based on the studys findings and conclusions, several recommendations were made by the research where the study suggested enhancing internal teamwork practices in non-profit projects, especially UPHLS in Rwanda, by fostering trust, allocating resources, investing in training, and recognizing the strong correlation between teamwork and project success. These recommendations aimed to improve collaboration, efficiency, and project outcomes. Building on the insights from the current study on internal teamwork practices in Rwandan non-profit projects, the study suggested further research, including cross-cultural analysis, longitudinal studies, qualitative leadership styles, external stakeholders, comparative analysis, and global events, to enhance project management and foster social development.

Project Success Criteria in Social Infrastructure

The main aim of this paper is to conceptualize success for social infrastructure projects, examining juxtapositions between project management- and overall project success , as well as prevalent definitions for success in the public realm. The text includes a review of relevant literature on the topic of project success frameworks, in combination with a case study illustrating the differences between several success criteria on the example of the Indiana Toll Road project. The findings of the case study suggest that despite the project resulting in the bankruptcy of the private entity, it could nonetheless be considered a public project success, due to its large approval ratings by Indiana’s citizens. Additionally, the study highlights the importance of safeguards against planning fallacy and optimism bias – issues which are amplified in social infrastructure procurement procedures. This topic discussed in this research paper concern qualifiers for success, in combination with a case study outlining the impact of improperly accounted for optimism bias. The obtained results may be of interest to representatives of science and practice, especially with regard to social infrastructure development.

MEDIATING FINANCIAL AND HSE ROLES IN RISK MANAGEMENT IMPACT ON MYANMAR CONSTRUCTION SUCCESS

This study seeks to examine the effects of risk management strategies on the success of construction projects in Myanmar, a country with a rapidly expanding construction industry that has received little scholarly attention. Using a combination of traditional paper-based surveys and online questionnaires, a comprehensive dataset has been compiled for this study. The participants, which included project managers, project engineers, safety engineers, financial experts, and project proprietors, played a crucial role in providing diverse perspectives. From the initial sample of 500 questionnaires, a response rate of 84.6 percent yielded 397 valid responses. Using Smart PLS as a data analysis instrument, the study deconstructed risk management into its fundamental components: identification, analysis, response, and monitoring. In addition to the immediate effects, the study reveals the intermediate roles played by financial performance and Health, Safety, and Environment (HSE) issues. The findings indicate a correlation between the implementation of effective risk management strategies and the overall success of construction projects. The intermediate effect of financial performance and HSE performance strengthens the relationship between these variables. This research offers significant academic and practical insights by providing evidence-based strategies to improve risk management practices in Myanmar's rapidly developing construction industry.

A MODEL FOR ASSESSING SHIPPING SAFETY WITHIN PROJECT-ORIENTATED RISK MANAGEMENT BASED ON HUMAN ELEMENT

Safety risk management in shipping projects is an extremely important aspect aimed at ensuring the success of the project and the safety of all participants in the maritime transportation process. This paper presents an approach to assessing safety risks that considers multiple factors including equipment condition, external circumstances, and human factors. The risk assessment utilizes the probability of accidents and their consequences, as well as the weighting factors of each factor. The results of the assessment are interpreted using a scale that defines the hazard level. The proposed methodology can effectively identify, analyse and manage safety risks, which can contribute to the success and safety of shipping projects. The study also discusses the importance of dividing the crew into functional groups based on the operations performed, which helps to better identify the safety risk for each group. Safety risk assessment is conducted for each operation individually as well as for the entire project or multiple operations to provide a comprehensive safety assessment. The results of the study have shown the feasibility of the proposed method for assessing the safety risks of shipping projects and its suitability to the initial data “safety” taking into account its separate sides, features, as well as the constituent aspects of the concept, systematization of the ship's safety structure in order to develop solutions to improve integral safety and optimize decision-making in emergency situations. Achievement of the general purpose of shipping safety thus means realization of ways of reduction of influence of the human factor on the number of accidents, and an estimation of the degree of influence of a set of factors on a ship during operation.