By 2050, according to the UN medium forecast, 68.6% of the world’s population will live in cities. This growth will place a strain on critical infrastructure distribution networks, which already operate in a state that is complex and intertwined within society. In order to create a sustainable society, there needs to be a change in both societal behaviours (for example, reducing water, energy or food waste activities) and future use of smart technologies. The main challenges are that there is a limited aggregated understanding of current waste behaviours within critical infrastructure ecosystems, and a lack of technological solutions to address this. Therefore, this article reflects on theoretical and applied works concerning waste behaviours, the reliability/availability and resilience of critical infrastructures, and the use of advanced technologies for reducing waste. Articles in the Scopus digital library are considered in the investigation, with 51 papers selected by means of a systematic literature review, from which 38 strains, 86 barriers and 87 needs are identified, along with 60 methods of analysis. The focus of the work is primarily on behaviours, barriers and needs that create an excess or wastage.

Optimizing the performance of electric vehicle (EV) batteries is essential to improve efficiency and prolong the lifespan of EVs. This research introduces a simulation model based on MATLAB to analyze and enhance lithium-ion battery performance under different driving conditions inclusive of urban, highway and mixed scenarios. The model takes into consideration battery metrics like capacity, energy density, power density and cycle life. By assessing how different driving patterns impact battery performance, energy consumption, and thermal regulation the study aims to pinpoint the driving cycle that minimizes energy consumption while maximizing battery longevity. The methodology involves using a gradient descent optimization method to determine the optimal driving cycle. The findings illustrate energy consumption levels and projected battery lifespan for each driving cycle offering insights for refining strategies related to managing EV batteries.

To employ the wind speed measurements to determine the wind power potential of sites by way of computation methods to: 1) appraise the wind data to detect outliers (that is, spurious or distrustful values) that have the potential to degrade the quality of the data; and 2) obtain generic data information utilizing statistical procedures, such as the wind rose and Weibull distribution parameters of the wind in a specific time period. The study revisits some computation methods for estimating the parameters of the Weibull distribution of the wind and thereby assess the potential for generating electricity from wind for a site. Computation methods for estimating the parameters of the Weibull Distribution namely: 1) the graphical method; 2) the linear least square method; 3) the maximum likelihood estimation; and 4) the methods of moments: are presented and discussed. A meaningful example on applying the preceding computation methods on how to compute the Weibull distribution parameters from a wind speed time series data for a specific time period is presented. The statistical computations and analysis for estimating the parameters of the Weibull Distribution were conducted using MATLAB. The boxplot was used to examine the example data for outliers (that is, unusual observations that are far removed from the mass of data).

Effective energy management is essential for sustaining grid stability. Traditional household load management methods are often inflexible. They fail to adapt to different household income levels and energy consumption patterns. This results in uneven energy savings and potential grid instability. To address this, a MATLAB model is presented to mimic how several household types—categorized as low-income, medium-income, and high-income—manage their energy consumption, employing a prioritized, staggered switch-off of appliances when predefined load limit thresholds are exceeded. The model defines load threshold limits (1.5 kW for low income, 2.5 kW for medium income, and 4.0 kW for high income) to maintain reasonable electrical energy usage. If the total power consumption exceeds these established limits, the model takes an approach to reduce the load by switching off appliances in a prioritized and staggered manner in the order of water heater, HVAC, stove, and plugs, while keeping the lights on. The simulation results demonstrated an overall peak reduction over a 24-hour period for medium-income household at 2.3kW, high-income household at 11.5kW reflecting their higher energy consumption compared to other households, while no reduction was necessary for low-income household. This study contributes an adaptable load management model that incorporates both household income and appliance usage patterns. In comparison to the traditional methods, which assume a uniform load reduction or shedding approach, the staggered switch-off mechanism offers enhanced control and ensures equitable energy savings across different income levels.

Complex networks consisting of communications, transportation and power grids have components that operate at different performance levels rather than being either on or off. This study uses multi-state components to achieve maximum performance while optimizing network dependability and quality of service (QoS). The main contribution is an integrated and practical structure that merges UI/CUI guided rules, with validation for optimal component allocation, in multi-state network systems. It also incorporates analysis and a replicable MATLAB execution to facilitate adoption, by practitioners. We introduce two utility metrics which assess individual component effects and their combined effects under different load or failure conditions through Utility Importance (UI) and Conditional Utility Importance (CUI). The heuristic uses UI/CUI-based criteria to perform an initial ranking of components before a comprehensive search begins. The verification of our methodology occurs through examination of a five-component “bridge” network which contains three states per element (0= failed, 0.5= degraded, and 1= functioning). A MATLAB script performs the following steps: 1) it counts all $5!=120$ component-to-label permutations; 2) it calculates joint state probabilities for all $3^{\wedge} 5$ combinations; 3) it assesses system performance using maximum flow across four parallel paths; and 4) it determines the best / worst assignments and an expected performance metric. The exhaustive search produces 0.78855 which represents a 7.7% improvement over random selection and a 3.8% increase above the UI/CUI heuristic while the UI/CUI heuristic results in a 3.7% improvement from 0.73220 (random baseline) to 0.75950. The article also discusses scalability to larger networks using symmetrical exploitation and importance-based pruning to control combinatorial expansion and provides visual analytics tools for evaluating assignments. We end with recommendations for integrating multi-state component allocation into reliabilitycentered maintenance, cost-benefit analysis, and future research on dynamic reconfiguration under time-varying loads.

The research evaluates different finite difference post-processing methods for extracting temperature gradients in solar collector systems through comparison. The research evaluates two types of node distribution methods which include uniform grids and Legendre-Gauss-Lobatto (LGL) nodes. The research aims to determine which method performs best for detecting thermal gradients in the vicinity of absorber boundaries. Fornberg's algorithm generates high-order derivative approximations which allow precise gradient calculations for uniform and non-uniform grid systems. The research uses MATLAB simulations to analyze different collector profiles while measuring errors through interpolation-based methods. The research evaluates accuracy through error norm calculations which include $\mathbf{L}_{2}$ and $\mathbf{L}^{\infty}$ norms for different spatial profiles. The study validates its results by using analytical temperature fields that include sinusoidal and exponential functions. The research examines both the fundamental temperature gradient differences and the effects of grid resolution on numerical stability and computational time. The research determines specific situations where adaptive grid clustering would lead to better local sensitivity. The research provides practical knowledge about implementing high-fidelity diagnostics in solar energy systems and establishes criteria for selecting differentiation schemes based on thermal behavior and control objectives. The results demonstrate that uniform grids deliver straightforward operations with good interior performance, yet LGL nodes achieve better boundary precision. The findings highlight important trade-offs which impact gradient-based diagnostics and control systems and design optimization in renewable energy applications. Future research extensions will integrate these methods into complete thermal-fluid simulation frameworks for better system evaluation.