Search results

Some studies and reports have recently suggested using blockchain technology to improve transparency and trust in humanitarian supply chains (HSCs). However, evidence-based studies to display the utility and applicability of blockchains in HSCs are missing in the literature. This paper aims to investigate the key drivers and barriers of blockchain application to HSCs and explore whether evidence could support that the application of blockchain improves transparency and trust in HSCs.

This paper puts forward a two-stage approach to explore the blockchain application in HSCs: an initial exploration of humanitarian practitioners and academicians interested in blockchain through focus group discussions; semi-structured interviews with practitioners involved at the UK Department for International Development's Humanitarian Supply Blockchain pilot project.

First, we found that main drivers include accountability, visibility, traceability, trust, collaboration, time efficiency, reducing administrative work and cross-sector partnership. Main barriers, however, are composed of engagement issues, lack of technical skills and training, lack of resources, privacy concerns, regulatory problems, pilot scalability issues and governance challenges. Second, evidence from our case study revealed the blockchain application could have added value to improve visibility and traceability, thus contributing to improve transparency. Concerning trust, evidence supports that blockchain could enhance both commitment and swift trust in the pilot study.

Our study contributes to a more understanding of added values and challenges of blockchain application to HSCs and creates a perspective for humanitarian decision-makers.

This study provides the first evidence from the actual application of blockchain technology in HSCs. The study discovered that it is still less evident in many humanitarian organizations, including medium- and small-sized nongovernmental organizations, that they engage in a direct deployment of in-house or customized blockchain-based HSC. Instead, these actors are more likely to indirectly use blockchain in HSCs through a private commercial partner.

This study aims to describe the compounding factors in a complex emergency, which exacerbate a cholera epidemic among vulnerable populations due to supply chain disruptions. Basic needs such as food, medicine, water, sanitation and hygiene commodities are critical to reduce the incidence rate of cholera and control the spread of infection. Conflicts cause damage to infrastructure, displace vulnerable populations and restrict the flow of goods from both commercial and humanitarian organizations. This study assesses the underlying internal and external factors that either aggravate or mitigate the risk of a cholera outbreak in such settings, using Yemen as a case study.

This study adopts a system dynamics methodology to analyze factors that influence cholera outbreaks in the context of the Yemeni Civil War. A causal loop diagram with multiple components was constructed to represent the complexities of humanitarian situations that require critical decision-making. The model was built using data from humanitarian organizations, non-governmental organizations and practitioners, along with literature from academic sources. Variables in the model were confirmed through semi-structured interviews with a field expert.

Compounding factors that influenced the cholera outbreak in Yemen are visualized in a causal loop diagram, which can improve the understanding of relationships where numerous uncertainties exist. A strong link exists between humanitarian response and the level of infrastructure development in a country. Supply chains are affected by constraints deriving from the Yemeni conflict, further inhibiting the use of infrastructure, which limits access to basic goods and services. Aligning long-term development objectives with short-term humanitarian response efforts can create more flexible modes of assistance to prevent and control future outbreaks.

The model focuses on the qualitative aspects of system dynamics to visualize the logistics and supply chain-related constraints that impact cholera prevention, treatment and control through humanitarian interventions. The resulting causal loop diagram is bounded by the Yemen context; thus, an extension of the model adapted for other contexts is recommended for further study.

This study presents a systematic view of dynamic factors existing in complex emergencies that have cause-and-effect relationships. Several models of cholera outbreaks have been used in previous studies, primarily focusing on the modes and mechanisms of transmission throughout a population. However, such models typically do not include other internal and external factors that influence the population and context at the site of an outbreak. This model incorporates those factors from a logistics perspective to address the distribution of in-kind goods and cash and voucher assistance.

This study has been aligned with six of the United Nations Sustainable Development Goals (SDGs), using their associated targets in the model as variables that influence the cholera incidence rate. Recognizing that the SDGs are interlinked, as are the dynamic factors in complex humanitarian emergencies, the authors have chosen to take an interdisciplinary approach to consider social, economic and environmental factors that may be impacted by this research.

This paper provides an insight into the underlying inter-relations of internal and external factors present in the context of a cholera outbreak in a complex crisis. Supply chains for food; water, sanitation and hygiene; and health products are crucial to help prevent, control and treat an outbreak. The model exposes vulnerabilities in the supply chain, which may offer guidance for decision makers to improve resilience, reduce disruptions and decrease the severity of cholera outbreaks.

This paper identifies the challenges during a recent disaster relief operation in a developing country where the humanitarian response is dominated by national actors, with international actors having a minor role.

A case study design is used; the main data sources are semi-structured interviews with 43 informants involved in the 2017 Kermanshah earthquake relief operation.

The findings suggest that humanitarian practitioners deal with multiple challenges during disaster relief operations. One group of challenges relates to humanitarian logistics (HL) like needs assessment, procurement, warehousing, transportation and distribution, all widely discussed in the literature. Another involves the growing use of social media, legitimacy regulations and the engagement of new humanitarian actors (HAs) like social media activists and celebrities. These factors have not been extensively studied in the literature; given their growing influence, they require more scholarly attention.

The findings will help humanitarian practitioners and policymakers better understand the challenges involved in disaster relief operations conducted by multiple actors and thus help them improve their practices, including the creation of proper regulations, policies and logistics strategies.

The study uses primary data on a recent disaster to assess and extend the findings of previous studies regarding HL challenges. It also elaborates on the critical non-logistical challenges that influence aid delivery in emergency responses, including the growth of social media, regulations and the engagement of new HAs. The results may motivate future empirical and modelling studies to investigate the identified challenges and identify practices to mitigate them.

This paper aims to, by connecting to the ongoing conversation on the importance of supply chain visibility, empirically examine the impact of visibility in supply chain relationships, on resource sharing among and on the performance of humanitarian organizations.

Survey data were collected from 101 humanitarian organizations in Southeast Asia. The organizations all experienced being interconnected within the supply chain relationships formed in humanitarian response settings. Data are used to test the conceptually developed model, using the structural equation modeling-partial least square (SEM-PLS) approach.

Results show that visibility has a significant impact on resource sharing and the performance of the organizations, especially in terms of the willingness to share resources, resources used and flexibility of organizations. The results also show that, in situations of high uncertainty, the association between resource sharing and performance becomes weaker.

The study is limited to the method used.

Findings of this research provide insights for humanitarian practitioners on the need to increase visibility of the scarce resources available within the relationships formed during a disaster relief operation to improve overall disaster response. The level of uncertainty in terms of needs assessment, number of affected people, location of a disaster and so forth, is also taken into account in the recommendations made.

This study is among the first to empirically test the link between visibility, resource sharing and performance, specifically in a humanitarian context, which is among the critical success factors for better interorganizational coordination and better aid delivery.

The main objective of this study is to consider how to benefit efficiently from the significant potential of humanitarian operations by individuals. For this purpose, this study aims to assess failure factors in humanitarian supply chain operations after the Kermanshah earthquake considering the role of all parties, focusing on individuals who did not wish to work with formal organisations on the whole. In the aftermath of the Kermanshah earthquake, which occurred on 12 November 2017, improvised groups of Iranian civilians from all over the country played an important role in humanitarian supply chain operations as individuals. Although most of these groups sincerely intended to help the affected society, victims could not benefit properly from these significant potential humanitarian actions. On the contrary, these potential actions caused some issues during humanitarian operations, such as blocking roads, inappropriate last-mile distribution, wasting resources and so on.

This research study considers mixed methods, including an on-site survey, semi-structured interviewing and a questionnaire designed for statistical analyses. The analysis included 140 responses to the questionnaire, semi-structured interviews with 32 affected families, interviews with 5 emergency managers from the Housing Foundation of the Islamic Republic of Iran and on-site survey reports.

This study presents a framework for humanitarian supply chain management to deal with future disasters in the same area or areas with similar characteristics to the case study. In general, the results of this study demonstrate that the nature of humanitarian supply chain operations makes it impossible to consider that these operations are free of challenges. However, several influential factors, such as training humanitarian actors and integrated management, might considerably increase the efficiency of humanitarian operations by individuals.

This study highlights the influential factors of inappropriate humanitarian operations by individuals, derived from an analysis of the Kermanshah case and literature review. The authors suggest a framework to benefit from the significant potential of individuals with wide-ranging experiences and proficiency, for future cases similar to the case study.

The authors originally explore the factors for blockchain technology (BCT) adoption in agricultural supply chains (ASCs) to enhance circularity and understand the dependencies, hierarchical structure and causalities between these factors.

Based on an extant literature review and expert opinion, the present study identified ten enablers for adopting BCT to leverage the circular economy (CE) practices in the ASCs. Then, using an integrated interpretive structural modeling and decision-making trial and evaluation laboratory (ISM-DEMATEL) approach, hierarchical and cause–effect relationships are established.

It was observed that traceability is the most prominent enabler from the CE perspective in ASCs. However, traceability, being a net effect enabler, will be realized through the achievement of other cause enablers, such as seamless connectivity and information flow and decentralized and distributed ledger technology. The authors also propose a 12 Rs framework for enhancing circularity in ASC operations.

The paper identifies enablers to BCT adoption that will enhance circularity in ASC operations. The ISM hierarchical model is based on the driving and dependence powers of the enablers, and DEMATEL aids in identifying causal relationships among the enablers.

The study's findings and proposed 12 Rs framework may help the practitioners and policymakers devise effective BCT implementation strategies in ASCs, thereby empowering sustainability and circularity.

This study enriches the literature by identifying and modeling enablers for BCT adoption in ASCs. The study also proposes a new 12 Rs framework to help enhance ASC circularity.

Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO₂) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO₂ consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO₂ consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019.

This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model.

After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process.

The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

The purpose of this study is to contribute with experimental study of the effects of binary and ternary combinations of river sand (RS), crushed sand (CS) and dune sand (DS) on the physical and mechanical performances of self-compacting concrete (SCC) subjected to acidic curing environments, HCl and H₂SO₄ solutions.

Five SCCs were prepared with the combinations 100% RS, 0.8RS + 0.2CS, 0.6RS + 0.2CS + 0.2DS, 0.6RS + 0.4DS and 0.6CS + 0.4DS. The porosity of sand, fluidity, deformability, stability, compressive strength and sorptivity coefficient were tested. SCCs cubic specimens with a side length of 10 cm were submerged in HCl and H₂SO₄ acids, wherein the concentration was 5%, for periods of 28, 90 and 180 days. The resistance to acid attack was evaluated by visual examination, mass loss and compressive strength loss.

The results showed that it is possible to partially substitute the RS with CS and DS in the SCC, without strongly affecting the fluidity, deformability, stability, compressive strength and durability against HCl and H₂SO₄ attack. The two combinations, 0.8RS + 0.2CS and 0.6RS + 0.2CS + 0.2DS, improved the compactness and the resistance to acid attacks of SCC. Consequently, the improvement in SCC compactness, by the combination of RS, CS and DS, decreased the sorptivity coefficient of SCC and increased its resistance to acid attacks, in comparison with that made only by RS.

The use of RS is experiencing a considerable increase in line with the development of the country. To satisfy this demand, it is necessary to substitute this sand with other materials more abundant. The use of locally available materials is a very effective way to protect the environment, improve the physico-mechanical properties and durability of SCC and it can be a beneficial economical alternative. Few studies have addressed the effect of the binary and ternary combination of RS, CS and DS on the resistance to acid attacks of SCC.