Geothermal energy, an efficacious and readily available resource, has emerged as a sustainable alternative poised to satisfy escalating global energy demands. This study undertakes a comprehensive analysis of low (heat below 100℃) and medium (heat between 100℃ to 150℃) enthalpy geothermal energy through a bibliometric approach and a literature survey, with an emphasis on the environmental and economic aspects. The methodological procedure encompasses: (i) systematic information processing and configuration, (ii) bibliometric assessment of the evolution and domains of the investigated field, (iii) evaluation of environmental and economic contributions, and (iv) Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis, facilitated by a Focus Group comprising experts from the energy sector. The research on low and medium enthalpy geothermal energy has been identified as an expanding field, with five primary areas of focus: sustainability, cascade systems, heat pumps, numerical modelling, and groundwater potential in geothermal systems. Italy, the United States, and Germany have been recognized as the leading contributors in terms of scientific production. Geothermal energy, from an environmental standpoint, aids the decarbonisation process, reducing reliance on fossil fuels and other renewable energy sources. Although initial investment costs are considerable, the financial recovery period is relatively short. The promotion of geothermal energy, alongside the active involvement of academia, corporations, and governments, bolsters energy and socio-economic development, thereby contributing to the achievement of the Sustainable Development Goals (SDG).

Amid the COVID-19 pandemic, the imperative for alternative biometric attendance systems has arisen. Traditionally, fingerprint and facial recognition have been employed; however, these methods posed challenges in adherence to Standard Operational Procedures (SOPs) set during the pandemic. In response to these limitations, iris detection has been advanced as a superior alternative. This research introduces a novel machine learning approach to iris detection, tailored specifically for educational environments. Addressing the restrictions posed by COVID-19 SOPs, which permitted only 50% of student occupancy, an automated e-attendance mechanism has been proposed. The methodology comprises four distinct phases: initial registration of the student's iris, subsequent identity verification upon institutional entry, evaluation of individual attendance during examinations to assess exam eligibility, and the maintenance of a defaulter list. To validate the efficiency and accuracy of the proposed system, a series of experiments were conducted. Results indicate that the proposed system exhibits remarkable accuracy in comparison to conventional methods. Furthermore, a desktop application was developed to facilitate real-time iris detection.

In 2022, Idaho stood fourth among U.S. states in renewable electricity share, with 74% generated from renewable sources like hydro, solar, wind, and geothermal. The dominant contributor has historically been hydropower. However, due to population growth and limited potential for new dam sites, reliance on solar and wind energy has increased. This paper aims to document the evolution of Idahoan public opinion regarding renewable energy's role in electricity production over 35 years. Public surveys were conducted every five years from 1987 to 2022, each involving at least 500 respondents. The surveys reveal strong public support for enhancing Idaho's renewable energy share. Over 75% of respondents expressed pride in the state's renewable electricity generation. Support for solar and wind energy has grown from 60% in 1987 to over 80% in 2022. Geographical preferences emerged, with south-western and south-central residents favoring solar, south-eastern residents favoring wind, and northern residents divided between hydro, solar, and wind. The surveys disclose that Idahoans: (1) strongly support increased renewable electricity production, (2) endorse solar and wind energy as key contributors, and (3) desire to replace Idaho’s remaining non-renewable energy production with renewable sources within the next decade.

Anaerobic digestion (AD) is a versatile process that entails low energy consumption and has low capital costs. Unfortunately, this process is not widely commercialized due to instabilities. The instability in the system is due to variations in the feedstock, operating, and environmental conditions. Therefore, this study aimed to determine the effect of organic loading rate (OLR), temperature, and pH on the AD system. An online pH and temperature monitoring sensor calibration were also studied to adjust AD parameters. The water quality parameters that were monitored were turbidity, chemical oxygen demand (COD), and colour. Wastewater with a low and high organic loading had a 90% and 36% COD reduction respectively after 5 days. Without pH adjustment, the pH of the system was 4.1 to 4.4 and the maximum COD reduction was 56.2%. When the pH was increased to 6.8, the maximum COD reduction was 66.5%. For the unadjusted temperature (room temperature), a maximum COD reduction of 56% was achieved. When the temperature was increased to 40℃, the maximum COD reduction was 66%. The increase in pH and temperature resulted in a 10% increase in COD reduction in the AD system. From the study, online pH, and temperature sensor calibrations errors were found to be 0.5 and 0.05 respectively as compared with the manual analytical technique. One of the limitations of this study was obtaining the apparatus to control temperature and pH at the same time. Future research will involve the automation of the AD system will the determined optimum conditions. This suggests smart monitoring and control sensors of AD operational parameters can repurpose its reliability for commercial activity.

Solar energy, as the most abundant renewable energy source, has garnered significant attention for its potential in electricity generation. Photovoltaic (PV) panels, capable of converting solar energy into electricity, have been widely considered for residential applications, including on-grid and off-grid systems. This study seeks to evaluate the economic feasibility of implementing on-grid and off-grid solar PV systems in residential settings through a case study in Gemolong, Sragen. The Hybrid Optimization Model for Electric Renewable (HOMER) software is employed to simulate these systems, providing an economic analysis over a specified time period. Results indicate that the on-grid system presents a more favorable option for the Gemolong region, owing to its optimized monthly production, minimal maintenance costs, and investment potential. The total installation cost for the on-grid system is estimated to be Rp 64,985,200.00, compared to the off-grid system’s cost of Rp 745,731,208.82. Furthermore, the on-grid system demonstrates a 13.3% advantage in energy production. Based on the energy and economic analyses, the on-grid PV system is recommended for adoption in the Gemolong area.

University campuses in Iraq are substantial energy consumers, with consumption increasing significantly during periods of high temperatures, underscoring the necessity to enhance their energy performance. Energy simulation tools offer valuable insights into evaluating and improving the energy efficiency of buildings. This study focuses on simulating passive architectural design for three selected buildings at Al-Khwarizmi College of Engineering (AKCOE) to examine the effectiveness of their cooling systems. DesignBuilder software was employed, and climatic data for a year in Baghdad was collected to assess the influence of passive architectural strategies on the thermal performance of the targeted buildings. The simulations revealed that the implementation of passive architectural design in AKCOE buildings led to a decrease in energy consumption for cooling purposes. Energy savings were achieved through natural ventilation, which minimized heat gain, and by employing continuous sun protection with double-glazed windows. By adopting a passive cooling strategy in AKCOE facilities, annual energy consumption for cooling within the campus could potentially be reduced by up to 23.6 percent. In conclusion, it was found that the current glazing system utilized in Iraqi building construction significantly contributes to electrical energy consumption.

The task of interpreting multi-variable time series data, while also forecasting outcomes accurately, is an ongoing challenge within the machine learning domain. This study presents an advanced method of utilizing Long Short-Term Memory (LSTM) recurrent neural networks in the analysis of such data, with specific attention to both target and exogenous variables. The novel approach aims to extract hidden states that are unique to individual variables, thereby capturing the distinctive dynamics inherent in multi-variable time series and allowing the elucidation of each variable's contribution to predictive outcomes. A pioneering mixture attention mechanism is introduced, which, by leveraging the aforementioned variable-specific hidden states, characterizes the generative process of the target variable. The study further enhances this methodology by formulating associated training techniques that permit concurrent learning of network parameters, variable interactions, and temporal significance with respect to the target prediction. The effectiveness of this approach is empirically validated through rigorous experimentation on three real-world datasets, including the 2022 closing prices of three major stocks - Apple (AAPL), Amazon (AMZN), and Microsoft (MSFT). The results demonstrated superior predictive performance, attributable to the successful encapsulation of the diverse dynamics of different variables. Furthermore, the study provides a comprehensive evaluation of the interpretability outcomes, both qualitatively and quantitatively. The presented framework thus holds substantial promise as a comprehensive solution that not only enhances prediction accuracy but also aids in the extraction of valuable insights from complex multi-variable datasets.

In recent years, a surge in studies concerning indigenous knowledge (IK) has been observed, yet a clear definition of IK remains elusive. Discrepancies in international studies lead to fluid interpretations of the concept. The present study seeks to delineate the key elements characterizing knowledge as either indigenous or foreign to a specific community. Through a meticulous exploration of definitions surrounding indigenous knowledge, it is posited that all knowledge forms can be considered indigenous within the communities of their origination. To elucidate this argument, the impact of community demographics on the adoption of knowledge perceived as indigenous within the Chief Albert Luthuli Municipality was investigated. Data were collected using structured interviews, involving a total of 398 respondents. Analyses were conducted employing a mixed-method approach, utilizing Microsoft Excel and the Statistical Package for Social Sciences (SPSS). Findings revealed a significant relationship between variables such as commonly spoken language, cultural attributes, age, and employment level with IK practices within communities. Furthermore, the economic factors, including employment status, education levels, and household income, were examined for their association with the adoption of IK practices. It was discerned that such variables were correlated with the adoption of IK practices, especially as alternative strategies in the absence of consistent household income. Key determinants like the language proficiency of the household head, employment status, educational attainment, family size, household income level, age, and gender of the household heads were analyzed. The influence of these determinants on household adoption of indigenous practices was assessed using inferential statistical methods, specifically probability and regression analysis.

In Multiple-Input Multiple-Output (MIMO) systems, a considerable number of antennas are deployed at each base station, utilizing Time-shifted pilot contamination strategies. It was observed that Time-shifted pilot contamination can mitigate the adverse effects of pilot contamination, subsequently reducing Inter-group interference. However, constraints are introduced in the channel estimation process when pilots are time-shifted. To address the challenge of increasing mean square channel estimation errors in finite antenna massive MIMO systems, a novel approach using a Dual Quadratic Decomposition Algorithm for Interference Cancellation (DQDA-IC) is introduced. Through this methodology, data interference gets effectively canceled when base stations collaborate. Furthermore, compressive sensing techniques are employed, resulting in enhanced channel compensation and reduced pilot contamination in massive MIMO systems. Comparative experimental analysis, conducted using the MATLAB tool, pitted this method against two conventional techniques: Integer Linear Programming (ILP) and Q-Learning based Interference Control (QLIC). Results indicated that the DQDA-IC model surpassed its counterparts by achieving a 63% improvement in Signal to Noise Ratio (SNR), a 56% reduction in Bit Error Rate (BER), and a 92% enhancement in spectral efficiency, all within a 40 ms computational timeframe.

Dominant points, or control points, represent areas of high curvature on shape contours and are extensively utilized in the representation of shape outlines. Herein, we introduce a novel, descriptor-based approach for the efficient detection of these pivotal points. Each point on a shape contour is evaluated and mapped to an invariant descriptor set, accomplished through the use of point-neighborhood. These descriptors are then harnessed to discern whether a point qualifies as a dominant one. Our proposed methodology eliminates the need for costly computations typically associated with evaluating candidate dominant points. Furthermore, our algorithm significantly outperforms its predecessors in terms of speed, relying solely on integer operations and obviating the necessity for an optimization phase. Experimental outcomes, derived from the widely used MPEG7_CE-Shape-1_Part_B, denote a minimum enhancement of 2.3 times in terms of running time. This implies that the proposed methodology is particularly suitable for real-time applications or scenarios managing shapes comprising a substantial number of points.

Helical or spiral coiled heat exchangers, prevalent in industries such as power generation, heat recovery systems, the food sector, and various plant processes, exhibit potential for performance enhancement through optimal fluid selection. Notably, nanofluids, distinguished by their superior thermophysical properties, including enhanced thermal conductivity, viscosity, and convective heat transfer coefficient (HTC), are considered viable candidates. In this study, the thermo-physical attributes of helical coil heat exchangers (HCHEs), when subjected to nanofluids, were meticulously examined. During the design phase, Creo parametric design software was employed to refine the geometric configuration, subsequently enhancing fluid flow dynamics, thereby yielding a design improvement for the HCHE. Subsequent computational fluid dynamics (CFD) simulations of the heat exchanger were conducted via the ANSYS CFX program. A CuO/water nanofluid, at a 1% volume fraction, served as the basis for the CFD analysis, incorporating the Re-Normalisation Group ($k-\varepsilon$) turbulence model. From these simulations, zones exhibiting elevated temperature and pressure were discerned. It was observed that the wall HTC value for the CuO/water mixture surpassed that of pure water by 10.01%. Concurrently, the Nusselt number, when the CuO/water nanofluid was employed, escalated by 6.8% in comparison to utilizing water alone. However, it should be noted that a 5.43% increment in the pressure drop was recorded for the CuO/water nanofluid in contrast to pure water.

Robotic Process Automation (RPA), employing software robots or bots, has emerged as a pivotal technological advancement, automating repetitive, rule-based tasks within business operations. This leads to enhanced operational efficiency and substantial cost reductions. In this systematic review, information was extracted from 62 pertinent research articles on RPA published between 2016 and 2022. The findings elucidate the fundamental principles of RPA, predominant trends, and leading RPA frameworks, alongside their optimal industry applications. Moreover, the necessary procedural steps for RPA implementation in industries are delineated. The objectives of this study encompass highlighting contemporary RPA research directions and evaluating its potential in streamlining diverse business processes.