Project Success Research Articles

SINTOMATOLOGIA E MÉTODOS ALTERNATIVOS DE CONTROLE DE PRAGAS NO VIVEIRO FLORESTAL DA UFAM

Pest management in forest nurseries is crucial for ensuring the health of seedlings and the success of reforestation projects. At the forest nursery of the Federal University of Amazonas (UFAM), the pursuit of sustainable methods led to the evaluation of six alternative control techniques and one chemical treatment. The methods investigated included the insecticide FORTH (deltamethrin), mineral oil, onion extract, neem oil (Azadirachta indica), garlic extract, and garlic macerate. The study aimed to compare the effectiveness of these methods in reducing the infestation of scale insects (Dactylopius coccus) on Cenostigma tocantinum seedlings. A 7 x 3 factorial design was used, with 42 seedlings divided among treatments and infestation levels (high, medium, and low). Weekly observations and infestation analyses revealed that treatments with FORTH, mineral oil, and onion extract were the most effective, reducing infestation to low levels in 28.57% of the seedlings. In contrast, neem oil and garlic extract showed low effectiveness, with high infestation intensity persisting. This study demonstrates that alternative methods, such as plant extracts and mineral oils, can offer viable and sustainable solutions for pest control in forest nurseries, contributing to more ecological management practices.

Non-invasive diagnostic test for lung cancer using biospectroscopy and variable selection techniques in saliva samples.

Lung cancer is one of the most commonly occurring malignant tumours worldwide. Although some reference methods such as X-ray, computed tomography or bronchoscope are widely used for clinical diagnosis of lung cancer, there is still a need to develop new methods for early detection of lung cancer. Especially needed are approaches that might be non-invasive and fast with high analytical precision and statistically reliable. Herein, we developed a swab "dip" test in saliva whereby swabs were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy harnessed to principal component analysis-quadratic discriminant analysis (QDA) and variable selection techniques employing successive projections algorithm (SPA) and genetic algorithm (GA) for feature selection/extraction combined with QDA. A total of 1944 saliva samples (56 designated as lung-cancer positive and 1888 designed as controls) were obtained in a lung cancer-screening programme being undertaken in North-West England. GA-QDA models achieved, for the test set, sensitivity and specificity values of 100.0% and 99.1%, respectively. Three wavenumbers (1422 cm-1, 1546 cm-1 and 1578 cm-1) were identified using the GA-QDA model to distinguish between lung cancer and controls, including ring C-C stretching, CN adenine, Amide II [δ(NH), ν(CN)] and νs(COO-) (polysaccharides, pectin). These findings highlight the potential of using biospectroscopy associated with multivariate classification algorithms to discriminate between benign saliva samples and those with underlying lung cancer.

Innovative Reward-Based Crowdfunding Decision Model

Due to the risky nature of newly creative projects for entrepreneurs, reward-based crowdfunding is currently an alternative fundraising channel for those who need seed funding to finance the creation of their prototype. The objectives of this research are to explore the success factors, including entrepreneurial, project and campaign factors, in project fundraising under a reward-based crowdfunding platform. We propose to develop a model for predicting the success of crowdfunding projects by machine learning. The datasets have been retrospectively gathered from historical records of campaigns in the Kickstarter website. The study's findings show that the logistic regression and decision tree models, respectively, had accuracy rates of 88.2% and 88.8%. The highest accuracy percentage of 94.1% originates from new testing data that has been externally validated for the technology industry. The practical implication of our research is that entrepreneurs can apply the proposed prediction model to identify the most influential topical features embedded in campaigns.

Research on Refined Management and Project Risk Control Strategies in Whole Process Engineering Consultation

Whole Process Engineering Consultation (WPEC) plays an increasingly vital role in modern engineering project management. It not only encompasses the entire project lifecycle from planning and design to implementation and operation but also significantly enhances project efficiency, reduces costs, and mitigates risks. As the complexity of engineering projects grows, refined management emerges as a crucial method for achieving efficient project management. This paper explores the application of refined management in WPEC and analyzes its role in project risk control through real-case studies. By delving into the principles of refined management, management tools, and risk control strategies, this study proposes an integrated approach to optimize project management processes and improve project success rates. Additionally, it discusses the impact of emerging technologies on refined management and risk control, providing insights into future development trends. The aim is to offer theoretical support and practical guidance for engineering project management, ensuring efficient management and risk control.

Analyzing Critical Factors Influencing the Quality Management in Smart Construction Site: A DEMATEL-ISM-MICMAC Based Approach

The swift integration of advanced technologies in the construction sector has significantly propelled the adoption of smart construction sites (SCSs). Quality management (QM), a critical endeavor within the construction domain, is central to the operational success of construction projects. The establishment of quality management in smart construction sites (SCS-QM) specifically seeks to delineate the principal factors influencing quality management in the context of SCS, with the objective of enhancing overall project quality. This study has identified 19 pivotal factors impacting SCS-QM by drawing upon the 4M1E quality management framework and an extensive review of the literature. Utilizing the hybrid DEMATEL-ISM-MICMAC analytical framework, the research evaluates these factors in terms of significance, hierarchical structure, and interdependencies, thereby formulating targeted strategies for the advancement of SCS-QM. Through a systematic evaluation by nine experts, this study categorizes the influencing factors into nine levels, three layers, and four areas, further classifying them into four distinct impact typologies. The results underscore that those technologies, such as automation and intelligence, along with regulatory frameworks, comprehensive quality management standards, transparency of critical technologies, training of construction personnel, and effective process management, constitute the foundational elements crucial for enhancing SCS-QM.

Servant leadership and project success: the mediating roles of team learning orientation and team agility

Servant leadership and project success: the mediating roles of team learning orientation and team agility

Green Orientation & Crowdfunding Success of Green Projects: Role of Project Characteristics

Green Orientation & Crowdfunding Success of Green Projects: Role of Project Characteristics

Predicting the effects of microcredit on women’s empowerment in rural Bangladesh: using machine learning algorithms

This study aimed to predict the impact of microcredit on women’s empowerment in Bangladesh using machine learning (ML) algorithms. In rural Bangladesh, where microcredit programs are not significantly employed, data for the study was gathered through a survey. The study gathered data on a range of socioeconomic, demographic, and women’s empowerment indicators. The Naive Bayes (NB), sequential minimal optimization (SMO), k-nearest neighbor (k-NN), decision tree (DT), and random forest (RF) ML techniques were used in the investigation. In terms of the prediction of women’s empowerment, the findings indicated that all five algorithms performed well, with the DT having the highest level of accuracy (83.72%). The results of this study have significant consequences for Bangladesh’s microcredit programs and those in nations that are developing. Microcredit programs can focus their efforts on women who, based on their socioeconomic and demographic features, are most likely to benefit from the program by employing ML algorithms. This may result in more successful microcredit projects that support the empowerment of women and general socioeconomic growth.

The Importance of Dynamic Project Leadership in Influencing Project Success

The Importance of Dynamic Project Leadership in Influencing Project Success

The Importance of Dynamic Project Leadership in Influencing Project Success

The Importance of Dynamic Project Leadership in Influencing Project Success

Heritage and identity: The vernacular transformation of de tjolomadoe

Background: This research paper focuses on the adaptive reuse of the De Tjolomadoe (Colomadu) heritage sugar factory within the field of vernacular architecture. Method: Utilizing qualitative research methods such as in-depth interviews, site observations, and archival analysis, it examines the preservation of industrial heritage through its integration into vernacular architectural practices, yielding insights for sustainable development. Findings: The De Tjolomadoe factory, a prominent Indonesian historical and cultural landmark, serves as a compelling case study in adaptive reuse, offering significant findings. Firstly, it illustrates how adaptive reuse effectively preserves cultural identity by seamlessly blending vernacular design elements and traditional building techniques, thereby maintaining a strong connection to its historical and cultural heritage. Secondly, the De Tjolomadoe case exemplifies adaptive reuse as a sustainable development strategy. By utilizing local resources and adhering to vernacular construction methods, the project minimizes environmental impact while generating economic and social benefits for the community. Community engagement emerges as a crucial factor in the success of adaptive reuse projects, as actively involving local communities ensures the authenticity and relevance of such initiatives. Moreover, the transformation of the De Tjolomadoe factory into a cultural destination underscores its substantial economic impact on the region. Similar adaptive reuse projects have the potential to boost tourism, stimulate local economies, and preserve cultural heritage. Conclusion: The lessons drawn from the De Tjolomadoe case offer practical insights and principles guiding future adaptive reuse endeavours, emphasizing cultural preservation, sustainability, community participation, and economic viability. Novelty/Originality of this article: These findings contribute to the growing knowledge base on the adaptive reuse of industrial heritage sites within the context of vernacular architecture, inspiring and informing initiatives aimed at revitalizing our industrial heritage for future generations.

A comparison on the critical success factors of MSW and RDF power plant development in Thailand: Using an interpretive structural modelling and cross-impact matrix multiplication applied to classification analysis

Waste-to-energy (WtE) power plants, supplied mainly with municipal solid waste (MSW) and refuse-derived fuel (RDF), which convert waste into electricity, have emerged as a solution to Thailand's waste management problems. This study focused on identifying and studying the critical success factors (CSFs) that influence the success of MSW and RDF power plants in Thailand. This study employed interpretive structural Modelling and cross-impact matrix multiplication applied to a classification analysis to evaluate the impact of these CSFs on the development of WtE projects. The results showed that, for MSW, most CSFs were related to energy and waste management policies, followed by waste quality for electricity generation. In addition, strong financial resources and appropriate power plant locations are important for MSW management success. Conversely, for RDF, most CSFs were sufficient waste quality for electricity generation and performed well according to licensing conditions. In this study, high-level CSFs indicated that these factors were crucial for MSW and RDF development. CSFs differ based on specific technologies and regulations. However, sufficient waste quality (heating value and moisture content) is a common CSF in the MSW and RDF technologies. This study provides valuable insights into the CSFs that affect the development of WtE. Understanding and addressing these CSFs is essential for the development and operation of WtE power plants in Thailand and other countries with similar conditions. Thus, policy-makers and other stakeholders can make informed decisions to ensure the success of WtE projects.

An Open Approach to Autonomous Vehicles

The evolution of autonomous vehicle (AV) technology is rapidly transforming the automotive industry, driving innovations in safety, efficiency, and user experience. This research paper explores the impact of open approaches on the development and deployment of autonomous vehicles. By leveraging open-source software and hardware platforms, such as OpenPilot and Apollo, the study highlights how collaborative, transparent methodologies foster innovation and accelerate technological advancements. The paper examines the technical, regulatory, and ethical challenges associated with open-source AV development, including data management, sensor integration, and compliance with safety standards. Through a comprehensive analysis of successful open-source projects and emerging trends, the research identifies the benefits of open approaches in promoting cross-disciplinary collaboration and addressing complex challenges. The findings underscore the potential of open-source solutions to drive the future of autonomous vehicle technology, offering insights into their role in shaping industry standards and regulatory frameworks

Economic and Institutional Factors Influencing the Selection of Project Delivery Models for Built Heritage Conservation in India

ABSTRACT Selecting a suitable project delivery method is one of the most critical decisions responsible for project success in heritage conservation. This is an area where projects have greater uncertainties and governance issues in comparison to other building projects. India has a huge inventory of built heritage sites, but the heritage conservation sector is highly fragmented, with a wide range of project delivery methods. This paper discusses the selection of project delivery in the Indian context, using the theoretical lenses of agency theory, transaction cost economics (TCE), and institution theory. A conceptual framework has been proposed to explain the selection based on economical and institutional constructs. Our conclusion is that when institutional factors are weak, projects are executed departmentally or through non-priced contracts. In contrast, strong institutional forces discourage non-priced contracts and replace them with priced contracts.

Symposium 2: DIGIFLEX - from flexible and digital courses to an overall networked learning perspective on digital course design

The digital two-way communication that society expects today can be said to blur the boundaries between the universities and the surrounding society. As the campus becomes more of an open network, the gap between formal and informal learning narrows. One can see a shift towards a perspective of lifelong learning with a more social and global perspective. Seen in that perspective, higher education has a greater value for people, than just specific skills or a specific degree. How people in society interact with various groups and organizations on social media also demonstrates this development and it challenges how we traditionally view learning. It also affects how the university conducts course design. Students and educators have come to expect the use of new digital tools. Many of these tools contribute to interaction between educators and students in collaborative learning. In a societal perspective we see a two-way exchange of meanings and needs, which makes it possible to create solutions that neither the university nor organizations in society could have adopted on their own. The focus of this paper is the course design process of a commissioned education course, DIGIFLEX. The practice meanings from students, colleagues and a reference group are referred to as feedback loops. These feedback loops continuously drove the course design and development forward and clearly indicated the need for an adaption to a more societal context. The design process is described via three stages and two feedback loops. The first stage focused on finding a modern digital design with lots of audiovisual elements for short and flexible courses. In the first feedback loop, we gained inspiration via a learning technology conference, which led to sessions with reverse brainstorming where the project group further challenged the standard norm for course design. In step two, the pilot course was implemented with the new elements generated from the first feedback loop. We received support from a reference group with expertise in networked learning and the students evaluated the course. Our findings show a need for more dialogue and informal structure as complement to the existing digital course design. This insight was confirmed by the experiences and lessons learned from distance education during the pandemic. There is a need for a networked learning perspective in this kind of digital course design. More frequent reconciliations, evaluations of needs and expectations from the surrounding society is important for the success of similar projects in the future.

Optimizing construction supply chains through AI: Streamlining material procurement and logistics for project success

Optimizing construction supply chains through AI: Streamlining material procurement and logistics for project success

Scale dependency of taxonomic and functional diversity in pristine and recovered loess steppic grasslands

Widespread campaigns on forest restoration and various tree planting actions lower the awareness of the importance of grasslands for carbon sequestration and biodiversity conservation. Even lower attention is given to the conservation of biodiversity and ecosystem functioning in remnants of ancient, so-called pristine grasslands. Pristine grasslands generally harbour high biodiversity, and even small patches can act as important refuges for many plant and animal species in urbanised or agricultural landscapes. Spontaneous succession of grassland is frequently viewed as a cost-effective tool for grassland restoration, but its applicability is strongly dependent on many local to landscape-scale factors, and the recovery is often slow. It is therefore essential to monitor the success of grassland restoration projects that rely on spontaneous succession. We compared the species diversity and functional attributes of pristine and recovered grasslands by studying the taxonomic and functional diversity in thirteen (8 pristine and 5 recovered) loess steppic grasslands using differently sized sampling plots from 0.01 to 100 m2. Our results indicate that there are remarkable differences in taxonomic and functional diversity between pristine and recovered grasslands. We also found that during secondary succession there is a likely functional saturation of the species assembly in the first few decades of recovery, and while patterns and structure of recovered grasslands became quite similar to those of pristine grasslands, species richness and diversity still remained much lower. Pristine grasslands support considerable plant diversity, and species composition is slow to recover if destroyed by agricultural land use. This underlines the importance of preserving existing pristine grassland remnants, which might serve as sources of species for future restoration measures.

Race, Gender, Sexuality, and the Pursuit of Modernity: British Biopower and Female Sexuality in Domestic and Colonial Practice

This paper explores how female sexuality became a primary site for the exercise of British biopolitical regulation as illustrated both in colonial Hong Kong and Singapore, and in domestic practice within the metropole. The application of biopolitical regulation on the subject of female sexuality was based on a discursive production making indissociable the success of the imperial project and the survival of the imperial race and the control of the female body. This discursive production mobilized intersections of race, class, gender, and sexuality through the Victorian cult of domesticity, resulting in a racialization of female sexuality with implications transcending the permeable frontier between the metropole and the colonies. Making “the prostitute” the exemplar of deviant, dangerous, and immoral sexuality had discursive repercussions for female sexuality more broadly; explained in contrast to “the prostitute,” the construction of sexual deviance had implications for understandings of sexual conformity and acceptability both in Victorian society and abroad.

A CNN model for early detection of pepper Phytophthora blight using multispectral imaging, integrating spectral and textural information

BackgroundPepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images.ResultsThe results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%.ConclusionsThe research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.

Mapping tidal restrictions to support blue carbon restoration

Blue carbon ecosystems (BCEs), encompassing mangroves, saltmarshes, and seagrasses, are vital ecosystems that deliver valuable services such as carbon sequestration, biodiversity support, and coastal protection. However, these ecosystems are threatened by various anthropogenic factors, including tidal restrictions like levees, barriers, and embankments. These structures alter the natural seawater flow, often converting coastal ecosystems into freshwater environments. Identifying tidal restrictions and assessing their suitability for tidal restoration in areas amenable for coastal management is a crucial first step to successfully restore BCEs and the associated ecosystem services they provide, i.e., managed realignment. This study presents a novel approach for detecting tidal restrictions in the state of Victoria, Australia, using high-resolution LiDAR data, geospatial analysis techniques, and a multi-criteria scoring system. Our model successfully identified 90 % of known tidal restrictions from an existing dataset, while also detecting an additional 118 potential tidal restrictions, representing a 35 % increase. The model performance analysis revealed trade-offs between precision, recall, and noise ratio when using different noise reduction thresholds, highlighting the importance of selecting an appropriate threshold based on project objectives. The multi-criteria scoring system, which considered factors such as proximity to BCEs and current land use, enabled the selection of tidal restrictions based on their hydrological suitability for restoration. The results of this study have significant implications for BCE restoration efforts not only in Victoria, but more broadly across Australia and globally, providing a systematic approach to identifying and targeting areas with the greatest potential for successful restoration projects. While the approach is low-cost and user-friendly, it is dependent on the availability of LiDAR data for the study area. This can make it accessible to researchers and practitioners worldwide, allowing for its adaptation and application in diverse regions to support global efforts in restoring BCEs through tidal restoration.