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Aleppo city in Syria has witnessed severe bombardment since the 2011 war affecting its landscape heritage, causing explicit geomorphological changes with anthropogenic qualities. The research aims to log observations on the patterns of bombardment craters. It investigates their key role in guiding post-war recovery plans. Currently, the interpretation of war scars is not considered in the reconstruction plans proposed by local administrations and here lies the importance of the research.

The study investigates the geomorphological transformations along the southern citadel perimeter in old Aleppo. Currently, digital tools facilitated data prediction in conflict areas. The research employs an empirical method for inhabiting war craters based on both qualitative and quantitative approaches. The former utilizes satellite images to define the geographical changes of landscape heritage. The latter applies geostatistical data analysis, validation, interpolation and simulation for multi-temporal Google Earth maps. The study exploits Surfer 13 software to localize and measure the preserved craters.

The research employs the generated models in a landscape design proposal examining the method's applicability. Finally, it offers a methodological toolkit guiding post-war landscape recovery toward the interpretation of conflict geography.

The paper enables a practical understanding of the contemporaneity of landscape heritage recovery as an action between sustainable development and conservation.

The paper integrates the conflict geographies to the people's commemoration of places and events.

The article offers an insight into the rehabilitation of war landscapes focusing on land craters, exploiting geostatistical data prediction methods.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.

The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.

Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.

The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.

Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might significantly degrade the overall performance of engineering systems and change the feasibility of the obtained solutions. This paper aims to propose a multi-objective robust optimization approach based on Kriging metamodel (K-MORO) to obtain the robust Pareto set under the interval uncertainty.

In K-MORO, the nested optimization structure is reduced into a single loop optimization structure to ease the computational burden. Considering the interpolation uncertainty from the Kriging metamodel may affect the robustness of the Pareto optima, an objective switching and sequential updating strategy is introduced in K-MORO to determine (1) whether the robust analysis or the Kriging metamodel should be used to evaluate the robustness of design alternatives, and (2) which design alternatives are selected to improve the prediction accuracy of the Kriging metamodel during the robust optimization process.

Five numerical and engineering cases are used to demonstrate the applicability of the proposed approach. The results illustrate that K-MORO is able to obtain robust Pareto frontier, while significantly reducing computational cost.

The proposed approach exhibits great capability for practical engineering design optimization problems that are multi-objective and constrained and have uncertainties.

A K-MORO approach is proposed, which can obtain the robust Pareto set under the interval uncertainty and ease the computational burden of the robust optimization process.

Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper.

In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed.

For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error.

This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.

Metamodeling is an effective method to approximate the relations between input and output parameters when significant efforts of experiments and simulations are required to collect the data to build the relations. This paper aims to develop a new sequential sampling method for adaptive metamodeling by using the data with highly nonlinear relation between input and output parameters.

In this method, the Latin hypercube sampling method is used to sample the initial data, and kriging method is used to construct the metamodel. In this work, input parameter values for collecting the next output data to update the currently achieved metamodel are determined based on qualities of data in both the input and output parameter spaces. Uniformity is used to evaluate data in the input parameter space. Leave-one-out errors and sensitivities are considered to evaluate data in the output parameter space.

This new method has been compared with the existing methods to demonstrate its effectiveness in approximation. This new method has also been compared with the existing methods in solving global optimization problems. An engineering case is used at last to verify the method further.

This paper provides an effective sequential sampling method for adaptive metamodeling to approximate highly nonlinear relations between input and output parameters.

This study aims to explore the role that artificial intelligence (AI) systems could play in configuring and enhancing the omnichannel customer experience (OCE).This paper aims to pave the way to better understand the intersection between these two novel topics through perspectives and associated interpretations from managers', consultants' and consumers' beliefs, experiences and thoughts.

The study adopts an explorative inductive design. Data from 41 in-depth interviews with high-level retail managers (12), AI consultants (3) and omnichannel consumers (26) was analyzed using grounded theory methodology.

The study's results revealed that, when AI systems are implemented in the omnichannel experience, some dimensions of the OCE change in relevance. The findings show that some OCE dimensions are easier to relate with experiential elements of the omnichannel experience, such as personalization, consistency and flexibility. In contrast, integration and connectivity are perceived as internal retailer capabilities that enable the omnichannel strategy. Consumers' data also show differences in the omnichannel customer journeys for the product categories of clothes, electronics and furniture.

This study presents insights on the impact of AI on OCE from top-retail managers', consultants' and consumers' perspectives. This choice allowed researchers to explore and uncover interesting intersecting points and examine issues related to omnichannel experience and AI systems implementation, providing guidance for future research.

The purpose of this paper is to improve the accuracy and signal-to-noise ratio (SN) of a crop nitrogen sensor.

The accuracy and wide adaptability of two spectral calibration methods for a crop nitrogen sensor based on standard reflectivity gray plates and standard detector, respectively, were compared.

The calibration method based on standard detector could significantly improve the measurement accuracy and the SN of this crop nitrogen sensor. When compared with the method based on standard gray plates, the measurement accuracy and the SN of the crop nitrogen sensor calibrated based on the standard detector method improved by 50 and 10 per cent, respectively.

This research analysed the calibration problems faced by the crop nitrogen sensor (type CGMD302) based on standard gray plates, and proposed a sensor calibration method based on a standard detector. Finally, the results of the two calibration methods were compared in terms of measurement accuracy and the SN of the crop nitrogen sensor.