In the realm of modern education, the integration of technology has emerged as a powerful catalyst for transforming traditional classrooms into dynamic and engaging learning environments. This paper provides a concise overview of the multifaceted ways in which technology contributes to enhanced classroom engagement.

The world has complex mega-cities and interdependent infrastructures. This complication in infrastructure relations makes it sensitive to disasters and failures. Cascading failure causes blackouts for the whole system of infrastructures during disasters and the lack of performance of the emergency management stakeholders is clear during a disaster due to the complexity of the system. This research aimed to develop a new concurrent engineering model following the total recovery effort. The objectives of this research were to identify the clustered intervention utilized in the field of resilience and developing a cross-functional intervention network to enhance the resilience of societies during a disaster. Content analysis was employed to classify and categorize the intervention in the main divisions and sub-divisions and the grouping of stakeholders. The transposing system was employed to develop an integrated model. The result of this research showed that the operations division achieved the highest weight of information interchange during the response to improve the resilience of the system. The committee of logistics and the committee of rescue and relief needed the widest bandwidth of information flow in the concurrent engineering (CE) model. The contributed CE model helped the stakeholders provide a resilient response system. The final model and the relative share value of exchanging information for each workgroup can speed up recovery actions. This research found that concurrent engineering (CE) is a viable concept to be implemented as a strategy for emergency management. The result of this research can help policymakers achieve a collaborative teamwork environment and to improve resilience factors during emergency circumstances for critical infrastructures.

This study introduces a novel Groundwater Flooding Risk Assessment (GFRA) model to evaluate risks associated with groundwater flooding (GF), a globally significant hazard often overshadowed by surface water flooding. GFRA utilizes a conditional probability function considering critical factors, including topography, ground slope, and land use-recharge to generate a risk assessment map. Additionally, the study evaluates the return period of GF events (GFRP) by fitting annual maxima of groundwater levels to probability distribution functions (PDFs). Approximately 57% of the pilot area falls within high and critical GF risk categories, encompassing residential and recreational areas. Urban sectors in the north and east, containing private buildings, public centers, and industrial structures, exhibit high risk, while developing areas and agricultural lands show low to moderate risk. This serves as an early warning for urban development policies. The Generalized Extreme Value (GEV) distribution effectively captures groundwater level fluctuations. According to the GFRP model, about 21% of the area, predominantly in the city's northeast, has over 50% probability of GF exceedance (1 to 2-year return period). Urban outskirts show higher return values (> 10 years). The model's predictions align with recorded flood events (90% correspondence). This approach offers valuable insights into GF threats for vulnerable locations and aids proactive planning and management to enhance urban resilience and sustainability.

The semiclassical boron–boron interatomic pair potential is constructed in an integral form allowing its converting into the analytical one. It is an ab initio B–B potential free of any semiempirical adjusting parameters, which would serve as an effective tool for the theoretical characterization of all-boron and boron-rich nanomaterials.

This study examines conditions that impact PPP delivery success or failure in the roadways sector in India using Qualitative Comparative Analysis. QCA is well-suited for problems where multiple factors combine to create pathways leading to an outcome. Past investigations have compared PPP and non-PPP project delivery performance, but this study examines performance within PPPs by uncovering a set of conditions that combine to influence the success or failure road PPP project delivery in India. Based on data from 21 cases, pathways explaining project delivery success or failure were identified. Specifically, PPPs with high concessionaire equity investment and low regional industrial activity led to project delivery success. Projects with lower concessionaire equity investment and low reliance on toll revenue and with either: (a) high project technical complexity or (b) high regional industrial activity, led to project delivery failure. The pathways identified did not have coverage values that they were extremely strong. Coverage strength was hindered by lack of access to information on additional conditions that could be configurationally important. Further, certain characteristics of the Indian market limit generalization. Identification of combinations of conditions leading to PPP project delivery success or failure improves knowledge of the impacts of structure and characteristics of these complex arrangements. This study is one of the first to use fuzzy QCA to understand project delivery success/failure in road PPP projects. Moreover, this study takes into account factors specific to a sector and delivery mode to explain project delivery performance.

The characteristics of agricultural products are influenced by the ecosystem, from the perspective of biotic and abiotic factors, which produce in the plant physiological responses and in turn in the fruit unique physicochemical properties, which are the basis for designations of origin and strategies to add value to the product in the current market. In the present work, ten cocoa materials (Theobroma cacao L.) were selected for their outstanding productivity (FSV41, FLE3, FEAR5, FSA12, FEC2, SCC23, SCC80, SCC55, ICS95 and CCN51), which were established in the departments of Santander (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.) and Huila (931 m a.s.l.). These were established in the departments of Santander (931 m a.s.l.), Huila (885 m a.s.l.) and Arauca (204 m a.s.l.), the main cocoa-producing areas in Colombia. For the evaluation of the physical characteristics of the collected materials, 21 quantitative descriptors were used to determine the physical variability of the fruit according to clone and place of collection. The data collected were analyzed by means of Pearson’s correlation matrix and principal component analysis, it was possible to identify those descriptors that contribute most to the variability among materials (ear index, diameter length ratio, seed weight and diameter, and fruit weight and length). In addition, it was possible to verify the effect of the place of harvest on the physical characteristics of the materials, high-lighting the importance of the adaptation study prior to the planting of the cocoa material, with the objective of guaranteeing a premium, productive and quality cocoa crop for the industry, which is competitive in the market.