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To contribute to the identification of the parameters influencing the behavior of textile-reinforced concrete (TRC), the purpose of this paper is to investigate the flexural behavior of TRC-based plates under four-point bending notably designed in the context of sustainable development and the substitution of mortar components with natural and abundant materials.

An extensive experimental campaign was focused about two main parameters. The first one emphases the textile reinforcements, such as the number of layers, the nature and the textile mesh size. In the second step, the composition of the mortar matrix was explored through the use of dune sand as a substitute of the river one.

Test results in terms of load-displacement response and failure patterns were highlighted, discussed and confronted to literature ones. As key findings, an increase of the load-bearing capacity and ductility, comparable to the use of an industrially produced second textile layer was recorded with the use of dune sand in the mortar mix design. The designed ecofriendly samples with economic concerns denote the significance of obtained outcomes in this research study.

The novelty of the present work was to valorize the use of natural dune sand to design new TRC samples to respond to the environmental and economical requirements. The obtained values provide an improved textiles–matrix interface performance compared to classical TRC samples issued from the literature.

This paper aims to characterize and develop a new ecological lightweight concrete reinforced by addition of palm plant fibers (from vegetal waste) to be used in the thermal and acoustical insulation of local constructions. The date palm plant fibers are characterized by their low sensitivity to chemical reactions, low cost and large availability in local regions. Therefore, the newly obtained lightweight concrete may suggest a great interest, as it seems to be able to achieve good solutions for local construction problems, technically, economically and ecologically.

The experimental program focused on developing the composition of palm-fiber-reinforced concrete, by studying the effect of the length of the fibers (10, 20, 30 and 40 mm) and their mass percentage (0.5%, 1%, 1.5% and 2%), on the mechanical and acoustical properties of the composite. The main measured parameters were the compressive strength and flexural strength, sound absorption coefficient, noise reduction coefficient (NRC), etc. These tests were also borne out by the measure of density and water absorption, as well as microstructure analyses. To fully appreciate the behavior of the material, visualizations under optical microscope and scanning electron microscope analyses were carried out.

The addition of plant fibers to concrete made it possible to formulate a new lightweight concrete having interesting properties. The addition of date palm fibers significantly decreased the density of the concrete and consequently reduced its mechanical strength, particularly in compression. Acceptable compressive strength values were possible, according to the fibers content, while better values have been obtained in flexion. On the other hand, good acoustical performances were obtained: a considerable increase in the sound absorption coefficient and the NRC was recorded, according to the content and length of fibers. Even the rheological behavior has been improved with the addition of fibers, but with short fibers only.

Over the recent decades, many studies have attempted to search for more sustainable and environmentally friendly building materials. Therefore, this work aims to study the possibility of using waste from date palm trees as fibers in concrete instead of the conventionally used fibers. Although many researches have already been conducted on the effect of palm plant fibers on the mechanical/physical properties of concrete, no information is available neither on the formulation of this type of concrete nor on its acoustical properties. Indeed, due to the scarcity of raw materials and the excessive consumption of energy, the trend of plant fibers as resources, which are natural and renewable, is very attractive. It is therefore a major recycling project of waste and recovery of local materials.

The purpose of this paper is to make a contribution to understanding the influence of factors such as the water/cement (W/C) ratio and the granular class on the mechanical and physical properties of high-strength concretes (HSCs). In the formulations of HSC, aggregates by their high mass and volume proportion play an important role. When selecting aggregates, it is necessary to know their intrinsic properties. These properties influence the performance of concrete, in particular the quality of the granulate cimentary adhesion.

This experimental study focused on the effect of W/C ratio (0.25, 0.30, 0.35), the effect of replacing a part of cement by silica fume (SF) (8%), the effect of fraction of aggregate on properties of fresh and hardened concrete, the effect of different environment conversation like drinking water and sea water on compressive strength and the study of absorption of water and softening using the mix design method of the University of Sherbrooke combined with the Dreux-Gorisse method which gives good results.

At the end of our work, the examination of the results obtained made it possible to establish the correlations between the formulations studied and the physicomechanical characteristics of the concrete compositions (HSC25, HSC16, HSC8). The results of this study show that the use of three granular classifications (DMAX8, DMAX16 and DMAX25) and three report W/C (0.25, 0.30 and 0.35) in two different conservation environment (drinking water and sea water) give HSCs, HSC25 with an W/C = 0.25 ratio has reached the largest mechanical strength of 90 MPa for different environments of conservation. For selecting aggregates, it is necessary to know their intrinsic properties, these properties influence the strength of concrete. In general, there is a slight decrease in the compressive resistance of the specimens stored in seawater, it can be said that the conservation life has not had effect on the resistance (28 days). The effect of aggressive environment can appear in the long term.

Mixed design and concrete fabrication with a 28-day compressive strength of up to 68 MPa or more of 90 MPa can now be possible used in Jiel (Algeria), and it should no longer be considered to be used only in an experimental domain. Addition of SF in concrete showed good development of strength between 7 and 28 days, depending on the design of the mix.

Concrete containing 8% SF with W/B of 0.25 has higher compressive strength than the other concretes, and concretes with SF are more resistant than concretes without SF, so it is possible to have concrete with a compressive strength of 82 MPa for W/C 0.25 without SF. Like as a result, we can avoid the use of SF to affect the strength of concrete at compressive strength of 68 MPa, and a slump of 21 cm, because the SF is the most expensive ingredient used in the composition of concrete and is therefore very important economically. One of the main factors of production of HSC above 90 MPa is use of aggregate DMAX25, which is stronger with W/B of 0.25 and 0.30.

This mixtures leads to a very dense microstructure and low porosity and produces increased permeability of HSC and is able to resist the penetration of aggressive agents. This combination has a positive effect on the economy of concrete.

The combination of the Dreux-Gorisse method with the Sherbrook method is very beneficial for determining the percentage of aggregates used, and the use of coarse aggregates of Jijel to obtain HSC with 90 MPa and 16 cm of workability.

This case teaches students the importance of maintaining a strong FICO score by illustrating the consequences of paying bills late or not at all. The protagonist is David Molina, a waiter at a struggling Italian restaurant located down the block from where he lives. Money is tight for Molina right now—his limited income means he lives paycheck to paycheck. However, Molina knows things will be looking up for him soon because he recently accepted a job as a bank teller across town—his first desk job.

Molina has been putting off paying two of his bills: a cable bill and his Bank of America credit card bill, both of which are late and have been issued, this time, in the form of threats to impact Molina's credit score if he doesn't pay them. He has just enough money to afford the minimum payments on each overdue bill. But then he receives a phone call from his friend, Jim Lindsey, reminding him about an invitation to go to Myrtle Beach for the upcoming weekend. Molina knows he cannot afford it, but a woman he's attracted to, Jessica, will be there too. Should Molina put off the bills yet again, and if so, how exactly will being late on them hurt his credit score?

The aim of the paper is to determine the asymmetric impact of tourism on the deficit in the balance of payments (BOPs).

The research uses the non-linear autoregressive distributed lag (ARDL) model to investigate the asymmetric impact of tourism on Pakistan's BOPs deficit using quarterly data from 1995 to 2019.

The finding reveals that due to the positive change in tourism, the BOPs deficit decreases by 27%, although due to the negative change in tourism, the BOPs deficit rises by 2.3%. In addition, the significance of F-statistics (10.609) confirms the existence of co-integration between tourism and the deficit in the BOPs. The Wald test confirms the asymmetric association between tourism and the deficit in the BOPs over the long term.

In order to improve tourism in Pakistan, policymakers must consider the following implications. First, there is a need for an adequate infrastructure that can help the tourist. Second, the Government must maintain a stable law and order situation as a whole and particularly at tourist destinations. Finally, the Government should develop tourism-friendly policies in order to boost tourism in Pakistan.

The research provides new evidence of the impact of tourism on the BOPs using the novel non-linear ARDL (NARDL) technique. The evidence will help policymakers to develop policies to improve tourism in order to reduce the BOPs deficit.

This study aims on a broad review of Concrete's Rheological Properties. The Concrete is a commonly used engineering material because of its exquisite mechanical interpretation, but the addition of constituent amounts has significant effects on the concrete’s fresh properties. The workability of the concrete mixture is a short-term property, but it is anticipated to affect the concrete’s long-term property.

In this review, the concrete and workability definition; concrete’s rheology models like Bingham model, thixotropy model, H-B model and modified Bingham model; obtained rheological parameters of concrete; the effect of constituent’s rheological properties, which includes cement and aggregates; and the concrete’s rheological properties such as consistency, mobility, compatibility, workability and stability were studied in detail.

Also, this review study has detailed the constituents and concrete’s rheological properties effects. Moreover, it exhibits the relationship between yield stress and plastic viscosity in concrete’s rheological behavior. Hence, several methods have been reviewed, and performance has been noted. In that, the abrasion resistance concrete has attained the maximum compressive strength of 73.6 Mpa; the thixotropy approach has gained the lowest plastic viscosity at 22 Pa.s; and the model coaxial cylinder has recorded the lowest stress rate at 8 Pa.

This paper especially describes the possible strategies to constrain improper prediction of concrete’s rheological properties that make the workability and rheological behavior prediction simpler and more accurate. From this, future guidelines can afford for prediction of concrete rheological behavior by implementing novel enhancing numerical techniques and exploring the finest process to evaluate the workability.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

A comprehensive understanding of the determining factors and implications of the frameworks for appreciating the relationships between climate risks and cultural heritage remains deficient. To address the gap, the review analysed literature on the management of climate risk in cultural heritage. The review examines the strengths and weaknesses of climate risk management (CRM) frameworks and attendant implications for the conservation of cultural heritage.

The study adopted a two-phased systematic review procedure. In the first phase, the authors reviewed related publications published between 2017 and 2021 in Scopus and Google Scholar. Key reports published by organisations such as the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and International Council on Monuments and Sites (ICOMOS) were identified and included in Phase Two to further understand approaches to CRM in cultural heritage.

Results established the changes in trend and interactions between factors influencing the adoption of CRM frameworks, including methods and tools for CRM. There is also increasing interest in adopting quantitative and qualitative methods using highly technical equipment and software to assess climate risks to cultural heritage assets. However, climate risk information is largely collected at the national and regional levels rather than at the cultural heritage asset.

The review establishes increasing implementation of CRM frameworks across national boundaries at place level using high-level technical skills and knowledge, which are rare amongst local organisations and professionals involved in cultural heritage management.

The review established the need for multi-sectoral, bottom-up and place-based approaches to improve the identification of climate risks and decision-making processes for climate change adaptation.

After the completion of this case study, students will be able to understand the importance of being close to local people when embarking on social business; understand that clear purpose and good decision-making can lead to great outcomes; and learn that innovation is crucial to ensure sustainability of both business and impact.

The case highlights the journey of Laiterie du Berger (LDB), a social enterprise in the agribusiness industry and the challenges faced as it expands and innovates. LDB’s roots lie in its commitment to social impact, aiming to uplift the Fulani livestock farmers and address socioeconomic issues. The company’s business model prioritizes people over profits, focusing on sustainable development and poverty alleviation. The LDB case showcases the challenges and opportunities in the agribusiness industry. LDB’s commitment to social impact, demonstrated through its support for farmers and sustainable farming practices, has been integral to its success. As the company expands and innovates, it faces critical decisions that require balancing financial growth with social responsibility. By embracing development, innovation and collaboration, LDB can continue to be a catalyst for positive change in the agribusiness industry while staying true to its roots and the principles that have defined its journey.

This case study is designed for bachelor’s and master’s degree students in the field of entrepreneurship and innovation, as well as MBA students. The case focuses on social entrepreneurship with the example of an agribusiness company located in Senegal, prioritizing social impact and quality of life. The case study explores the dynamics of the sector, including expansion strategy, innovation initiatives and the dilemma of balancing social mission and profit that social entrepreneurs may be facing. By analyzing this real-world situation of LDB, students will have the opportunity to enhance their decision-making skills.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship